Proteostasis sustains T cell differentiation potential and tumor-infiltrating lymphocyte function.

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Published in final edited form as: Cell. 2026 Apr 29;189(10):2857–2874.e31. doi: 10.1016/j.cell.2026.02.019 Proteostasis sustains T cell differentiation potential and tumor-infiltrating lymphocyte function Nicole E Scharping 1 , Xuezhen Ge 2 , Maria Inês Matias 3 , Fulin Jiang 2 , Allison Cafferata 1 , Maximilian Heeg 1 , Alexander Monell 1 , Giovanni Galletti 1 , Kitty P Cheung 1 , Angelica Rock 1 , Nick Thao 1 , Sydnye L Shuttleworth 4 , Michael A Bauer 5 , Kennidy K Takehara 1 , Amir Ferry 1 , Sara Quon 1 , Brian Koss 4 , Samuel A Myers 3 , Eric J Bennett 2 , Ananda W Goldrath 1, 6, 7 Author information Article notes Copyright and License information 1 School of Biological Sciences, Department of Molecular Biology, University of California, San Diego, La Jolla, CA, USA. 2 School of Biological Sciences, Department of Cell and Developmental Biology, University of California, San Diego, La Jolla, CA, USA. 3 La Jolla Institute for Immunology, La Jolla, CA, USA. 4 Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, Little Rock, AR, USA 5 Department of Biomedical Informatics, University of Arkansas for Medical Science, Little Rock, AR, USA. 6 Allen Institute for Immunology, Seattle, WA, USA 7 Lead contact ✉ Contact information: Ananda W. Goldrath: agoldrath@ucsd.edu 7 Author contributions: Conceptualization: N.E.S and A.W.G. Methodology: N.E.S and A.W.G. Investigation: N.E.S., X.G., M.I.M., F.J., A.C., G.G., K.P.C., A.R., N.T., S.L.S., K.K.T., A.F., S.Q. Formal Analysis: N.E.S., X.G., M.I.M., M.H., A.M., G.G., S.L.S., M.A.B. Visualization: N.E.S., M.H., A.M., A.W.G. Data Curation: X.G., M.I.M., M.H., G.G., A.M., S.L.S. Resources: B.K., S.A.M., E.J.B., A.W.G. Writing – Original Draft: N.E.S. and A.W.G. Writing – Review & Editing: N.E.S., E.J.B., A.W.G. Supervision: B.K., S.A.M., E.J.B., A.W.G. Project Administration: B.K., S.A.M., E.J.B., A.W.G. Funding Acquisition: A.W.G. Issue date 2026 May 14. PMC Copyright notice PMCID: PMC13235417  NIHMSID: NIHMS2169433  PMID: 42061400 The publisher's version of this article is available at Cell Previous version available: This article is based on a previously available preprint posted on bioRxiv on February 10, 2026: " Proteostasis sustains T cell differentiation potential and tumor-infiltrating lymphocyte function ". Summary: Tumor-infiltrating lymphocytes (TIL) often fail to restrain tumor growth due to progressive differentiation to an ‘exhausted’ state. Tissue-resident memory T cells (T RM ) maintain protection from infection for years in healthy tissues, and patient tumors that contain TIL with T RM features are associated with better prognosis. Proteomic and transcriptomic profiling of T cell populations identified proteostasis as a significant factor distinguishing T RM and progenitor-exhausted TIL from terminally-exhausted TIL, including loss of E3 ubiquitin ligases NEURL3, RNF149, and WSB1, with accumulation of unfolded proteins in spite of functional proteasome activity. Enforced expression of these ligases by TIL preserved stem-like TCF1 + populations and improved anti-tumor function, whereas deficiency impaired TIL and altered T cell differentiation in acute infection. Sustained ligase expression rescued accumulation of unfolded proteins in TIL and improved immunotherapy outcome in preclinical models, underscoring the critical role of proteostasis in TIL function and highlighting a promising avenue for advancing cancer immunotherapy. In Brief: Exhausted T cells experience a breakdown of proteostasis, characterized by unfolded protein accumulation and rescued by E3 ubiquitin ligase activity. Targeting T cell proteostasis highlights a promising strategy for cancer immunotherapy. Graphical Abstract Introduction: Naive CD8 + T cell activation involves the integration of diverse signals to shape cell fates 1 . In response to tumor or chronic viral infection, persistent TCR stimulation drives T cell differentiation through a progressive loss of function 2 , with a spectrum of functionality from more stem-like T PEX to terminally-differentiated T EX 3 , 4 . T PEX retain self-renewal capacity via TCF1 expression and give rise to T EX . T EX are characterized by co-inhibitory receptor expression including PD-1 and TIM-3, metabolic dysfunction with high levels of reactive oxygen species production from dysfunctional mitochondria, an altered chromatin landscape associated with expression of the transcription factor TOX that contributes the T EX epigenomic state, and limited cytokine polyfunctionality 5 – 7 . The T RM population is comprised of long-lived memory T cells that are essential for surveying barrier and non-lymphoid tissues, by providing a first-line defense against reinfection 8 . A population of TIL with T RM features that are distinct from T EX and T PEX has also been identified in some patient tumors 4 , 9 . We have previously shown that enforcing adaptations consistent with tissue residency through the modulation of transcription factors or immunometabolic pathways are sufficient to improve tumor control in mice 10 – 12 . In humans, cancer patients with T RM -like TIL show increased overall survival and have improved outcomes after immunotherapeutic checkpoint blockade 9 , 13 . Thus, elucidating the relationships among T PEX , T EX , and T RM -like TIL populations is important to improve our knowledge of T cell biology and to facilitate the development of cancer immunotherapies. Protein homeostasis (proteostasis) is an underexplored facet of T cell exhaustion, despite its known function in maintaining stem-like cell states. Targeted protein degradation plays a crucial role in regulating stem cell differentiation and embryonic development via the ubiquitin-proteasome system (UPS), regulating protein turnover by appending chains of ubiquitin onto target proteins 14 – 16 . E3 ubiquitin ligases are the substrate-specifying arm of the UPS which catalyzes ubiquitin transfer onto target proteins, leading to proteasome-dependent degradation. Beyond its role in cellular differentiation, proteostasis is critical for limiting excess protein accumulation, thus decreasing cellular stress. Accumulation of unfolded proteins due to rapid proliferation or metabolic dysfunction can trigger endoplasmic reticulum (ER) stress and the unfolded protein response (UPR) 17 , 18 Furthermore, E3-dependent protein degradation is required for optimal responses to environmental stresses, such as hypoxia and increased reactive oxygen species 19 and chronic stress can result in the accumulation of unfolded proteins that drive cellular dysfunction 20 . For T cells, some aspects of proteostasis have been characterized; in naive T cells, activation increases protein synthesis to meet the energy demands of rapid proliferation and differentiation 21 – 23 . Regulation of protein turnover has been characterized in effector T cell differentiation, as proteasome function plays a role in directing cell fates and inhibition during T cell proliferation has been shown to halt cell-cycle progression 24 – 26 . Memory T cells maintain a high level of protein turnover at steady state and rely on proteasomal activity for development and survival 27 – 29 . While more than 500 genes encoding E3 ligases have been identified in the human genome 16 , the roles of specific E3 ligases in immune cells are understudied. A well-characterized example in many cell types, including T cells, is the E3 ubiquitin ligase Von Hippel-Lindau (VHL), which targets the transcription factor hypoxia-inducible factor (including HIF1α and HIF2α), allowing the cells to respond transcriptionally to local oxygen levels 30 . We have previously shown that the loss of VHL in T cells increases HIF activity, promoting T cell activity in infections and tumors 12 , 31 . 32 – 36 However, which of the hundreds of T cell-expressed ligases are required to maintain proteostasis through regulated protein degradation, and how ligase dysfunction impacts T cell function, are largely unknown. Here, we show that modulation of T RM - and T PEX -associated E3 ubiquitin ligases NEURL3, RNF149, and WSB1 alters T cell function. Ligase overexpression in TIL increases TCF1 + T cell accumulation and improves T cell function, while ligase knockout (KO) T cells show altered differentiation in the response to cancer and acute infection. We utilize low-input mass spectrometry for unbiased proteomics, enabling for the identification of thousands of proteins from ex vivo -sorted T cells, including T PEX and T EX . In characterizing T EX , we show that despite possessing functional proteasomes, T EX accumulate unfolded proteins and by enforcing upstream E3 ligase activity, it is possible to rescue this unfolded protein accumulation. In addition, we enhance proteostasis via ligase overexpression in combination with checkpoint blockade to boost the efficacy of adoptive cell therapy. Together, these data demonstrate the role of proteostasis in T cell exhaustion and identify a promising avenue for advancing immunotherapy. Results: Rescue of TIL function by sustained expression of T RM -associated E3 ubiquitin ligases NEURL3, RNF149 and WSB1 To understand the transcriptional relationships between T RM and TIL populations, we first analyzed enrichment of tissue-residency-associated genes among endogenous T PEX cells (CD8 + PD1 lo/int Tim3 − TCF1 hi TOX lo ) and T EX cells (CD8 + PD1 hi Tim3 + TCF1 lo TOX hi ) sorted from day 14 murine B16 melanoma tumors ( Figure 1A , Supplement Figure 2A , Supplemental Table 1 ) 32 , 33 . This comparison revealed almost two-thirds of the T RM -associated genes were expressed in T PEX , but downregulated or repressed in T EX . Of the T RM -associated genes lost with terminal-differentiation to T EX , 24% were associated with protein folding and degradation, including genes encoding for E3 ubiquitin ligases neuralized E3 ubiquitin protein ligase 3 ( Neurl3 , also known as Lincr ), RING finger protein 149 ( Rnf149 ), and WD repeat and suppressor of cytokine signaling (SOCS) box-containing 1 ( Wsb1 ) ( Figure 1B ). NEURL3 contains a neuralized domain, and based on sequence-structure annotation, both NEURL3 and RNF149 contain RING domains, which facilitate ubiquitin transfer from E2 ubiquitin-conjugating proteins to target proteins 34 , 35 . WSB1 contains a SOCS domain (similar to VHL) and can act as a substrate adaptor protein for the multisubunit CUL5 RING ligase (CRL5) complex to facilitate ubiquitin transfer to substrate proteins 36 , 37 . However, the functions of these three ligases in immune cells are unknown. Figure 1: E3 ubiquitin ligases NEURL3, RNF149 and WSB1 are expressed in T PEX and T RM , and sustained expression can rescue TIL function in MC38 adenocarcinoma. Open in a new tab A) Heatmap of bulk RNA-seq from endogenous CD8 + T cells sorted from B16 mouse melanoma dLN and TIL ( GSE175408 33 ). TIL sorted on PD1 lo , PD1 mid , PD1 hi , PD1 hi Tim3 + , data were filtered using the core T RM gene signature 32 and quantified via hierarchical clustering by rows and columns using one minus Pearson correlation ( Supplemental Table 1 ). B) Neurl3 , Rnf149 , or Wsb1 RNA TPM ( GSE175408 33 ). C) Neurl3 , Rnf149 , and Wsb1 RNA expression levels from scRNA-seq of CD8 + T RM cells isolated from the IEL, kidney, SG, fat and liver, and memory from the spleen and blood ( GSE182275 78 ), quantified after MAGIC imputation. D) Experimental schematic: Congenically mismatched P14 T cells were activated, transduced at 24 h with Empty Vector (EV) control or ligase-overexpressing retrovirus (Neurl3 OE , Rnf149 OE , or Wsb1 OE ). Next day, cells were sorted on ametrine reporter expression, then cultured for three additional days, mixed 50:50 (250,000 EV + Ligase OE ; 500,000 total), then transferred into d10 MC38-GP 33–41 -bearing mice; dLN and TIL were analyzed 10 days later (BioRender). E) Representative flow plots and quantification of transferred P14 T cell frequencies as in D ( Neurl3 OE n=7, Rnf149 OE n=11, Wsb1 OE n=10); three independent experiments. F) Tox + P14 T cell frequencies in tumors as in D ( Neurl3 OE n=10, Rnf149 OE n=8, Wsb1 OE n=9); three independent experiments. G) TCF1 + P14 T cell frequencies as in D ( Neurl3 OE n=10, Rnf149 OE n=8, Wsb1 OE n=6); two or more independent experiments. H) Representative flow plots and quantification of IFNɣ + TNF + P14 T cell frequencies as in D followed by 4 h of peptide restimulation plus protein transport inhibitor ( Neurl3 OE n=10, Rnf149 OE n=8, Wsb1 OE n=7); three independent experiments. I) Quantification of MC38-GP 33–41 tumor area in mice that received no T cells, EV P14 or Neurl3 OE , Rnf149 OE , or Wsb1 OE P14 T cells on d8 after tumor cell implantation. CR = complete regression; two independent experiments. J) Survival data from I. Statistical significance was calculated using paired Student’s t -tests (E-H), or log-rank (Mantel-Cox) test (J). Connecting lines indicate that cells were isolated from the same mouse. We found that antigen-specific T RM isolated from multiple tissues expressed Neurl3 , Rnf149 , and Wsb1 ( Figure 1C ). In a single-cell (sc) RNA-seq time-course of P14 T cells responding to the lymphocytic choriomeningitis virus (LCMV) Armstrong, ligase expression in the spleen and small intestine intraepithelial lymphocytes (IEL) was observed on day 5 (d5) of infection and peaked at effector to early memory timepoints ( Supplemental Figure 2B ). In vitro exhaustion also led to significantly decreased ligase expression, an observation consistent with data from ex vivo T EX ( Supplemental Figure 2C ). To determine if enforced ligase expression could improve TIL function, we overexpressed NEURL3, RNF149, or WSB1 in P14 TCR-transgenic CD8 + T cells, specific for the LCMV glycoprotein 33–41 peptide (GP 33–41 ) ( Supplemental Figure 2D ). Ligase-expressing T cells were co-adoptively transferred with an equal number of empty vector (EV) congenically-mismatched P14 T cells into MC38 adenocarcinoma-bearing mice that expressed GP 33–41 ( Figure 1D , Supplemental Figures 1A ). After 10 days, ligase-overexpressing P14 T cells were more abundant in the tumor-draining lymph node (dLN) and the tumor, had a higher frequency of TCF1-expressing cells, and were more polyfunctional following ex vivo GP 33–41 peptide restimulation compared to EV T cells ( Figure 1E – H ). However, there were no differences in the frequencies of Tim3 hi Tcf1 lo cells or TOX + cells between control and ligase-overexpressing TIL ( Figure 1F , Supplemental Figure 2E ). Ligase-overexpressing cells also showed enhanced accumulation and improved TIL function in B16-GP 33–41 melanoma-bearing mice, but only dLN P14 displayed an increased frequency of TCF1-expressing cells ( Supplemental Figure 2F – J ). This phenotype indicated that ligase overexpression supported a stem-like population in the dLN, where this phenotype was more apparent in response to a highly immunogenic tumor (MC38) compared to a poorly immunogenic tumor (B16) 38 . To examine the functional impact of ligase overexpression, we used cell therapy-sensitive MC38-GP 33–41 where EV- or ligase-transduced P14 T cells were transferred into mice after tumor implantation, and tumor growth was monitored ( Figure 1I ). All three ligase-overexpressing T cell populations either slowed tumor growth or cleared tumors more efficiently than EV P14 T cells, resulting in significantly improved mouse survival ( Figure 1J ). Overall, we found that T PEX and T RM shared proteostasis-related gene expression, and overexpression of Neurl3 , Rnf149 , or Wsb1 improved T cell function by maintaining a T PEX -like T cell fate in cancer. Rescue of T cell function by sustained expression of NEURL3, RNF149 and WSB1 in chronic viral infection We next tested how ligase overexpression impacted T cell differentiation with LCMV-clone 13, which leads to chronic viral infection 39 – 41 ( Figure 2A ). We found a higher T PEX frequency (CX3CR1 lo SLAMF6 hi ), decreased T EX frequency (CX3CR1 lo SLAMF6 lo ) in ligase overexpressing P14 T cells compared to control T cells ( Figure 2B ). This corresponded with an increased frequency of TCF1 + expression in ligase-overexpressing P14 compared to control, but no difference in frequency of TOX + expression ( Figure 2C , D ). These changes in exhaustion subset frequencies also correspond to increased functionality, with increased frequencies of ligase-overexpressing TNF + IFNɣ + P14 T cells after peptide restimulation compared to control P14 T cells ( Figure 2E ). These data highlight that ligase overexpression favors T PEX accumulation regardless of exhaustion context. Figure 2: E3 ubiquitin ligases NEURL3, RNF149 and WSB1 expression increases T PEX and decreases T EX frequency in LCMV Cl13 chronic infection. Open in a new tab A) Experimental schematic: Congenically mismatched P14 T cells were activated, transduced 24 h later with Empty Vector (EV) or ligase-overexpressing retrovirus (Neurl3 OE , Rnf149 OE , or Wsb1 OE ). Next day, cells were sorted on ametrine reporter expression, then EV or Ligase OE cells were mixed 50:50 (1,000 each; 2,000 total) and transferred into recipient mice subsequently infected with LCMV Cl13; spleens were analyzed 21 days later (BioRender). B) Representative flow plots and quantification of SLAMF6 vs CX3CR1 P14 T cell frequencies as in A. Subsets defined as T PEX (CX3CR1 lo SLAMF6 hi ), T EX -Effector (CX3CR1 hi ) and T EX (CX3CR1 lo SLAMF6 lo ). ( Neurl3 OE n=16; Rnf149 OE n=14; Wsb1 OE n=18); five independent experiments. C) Quantification of TOX + P14 T cell frequencies as in A ( Neurl3 OE n=10; Rnf149 OE n=11; Wsb1 OE n=12); two independent experiments. D) Quantification of TCF1 + P14 T cell frequencies as in A ( Neurl3 OE n=10; Rnf149 OE n=11; Wsb1 OE n=10); two independent experiments. E) Representative flow plots and quantification of TNF + IFNɣ + P14 T cell frequencies as in A followed by 4 h of peptide restimulation plus protein transport inhibitor ( Neurl3 OE n=15; Rnf149 OE n=13; Wsb1 OE n=17); four independent experiments. Statistical significance was calculated using (B-E) paired Student’s t -tests. Connecting lines indicate that cells were isolated from the same mouse. Loss of NEURL3, RNF149, or WSB1 accelerates T cell exhaustion in tumors and differentiation during acute infection To understand how loss of NEURL3, RNF149, and WSB1 impacted T cell differentiation, we used CRISPR/Cas9-mediated deletion of each of these ligases and followed the T cell response to both tumor and acute infection. P14 T cells were nucleofected with recombinant Cas9 complexed with trans-activating CRISPR RNA (tracrRNA) and ligase-specific crRNA. Equal numbers of control Thy1 KO and ligase KO P14 T cells for each ligase were transferred into B16-GP 33–41 -bearing mice ( Figure 3A , Supplemental Figure 3A ). Seven days after transfer, all three ligase KO T cell populations were less abundant, had lower TCF1 + frequency, and produced less IFN-ɣ upon ex vivo restimulation, when compared with their control counterparts in the same host ( Figure 3B – E ). Taken together, these data indicate that ligase KO T cells become dysfunctional more quickly in comparison to control T cells, with decreased TCF1 + frequency and reduced functionality. Figure 3: Loss of NEURL3, RNF149, or WSB1 expression accelerates T cell differentiation kinetics. Open in a new tab A) Experimental schematic: Congenically mismatched P14 T cells were activated then nucleofected 24 h later with Thy1 KO Control CRISPR RNP or ligase KO (Neurl3 KO , Rnf149 KO , Wsb1 KO ) plus Thy1 KO CRISPR RNP. Cells were cultured to d5–6, mixed 50:50 (1 × 10 6 each; 2 × 10 6 total), and transferred into d8 B16-GP 33–41 -bearing mice; dLN and TIL were analyzed 7 days later (BioRender). B) Quantification of transferred P14 T cell frequencies as in A ( Neurl3 KO n=13; Rnf149 KO n=12; Wsb1 KO n=12); three independent experiments. C) Quantification of Tim3 hi TCF1 lo P14 T cell frequencies as in A ( Neurl3 KO n=13; Rnf149 KO n=9; Wsb1 KO n=9); three independent experiments. D) Quantification of TCF1 + P14 T cell frequencies as in A ( Neurl3 KO n=13; Rnf149 KO n=9; Wsb1 KO n=9); three independent experiments. E) Quantification of IFNɣ + P14 T cell frequencies as in A followed by 4 h of peptide restimulation plus protein transport inhibitor ( Neurl3 KO n=10; Rnf149 KO n=11; Wsb1 KO n=12); three or more independent experiments. F) Experimental schematic: Congenically mismatched P14 T cells were activated and nucleofected 24 h later with Thy1 KO Control CRISPR RNP, or ligase KO ( Neurl3 KO , Rnf149 KO , Wsb1 KO ) plus Thy1 KO CRISPR RNP. EV and ligase KO cells were mixed 50:50 (100,000 each; 200,000 total) and transferred into recipient mice subsequently infected with LCMV Armstrong. At 5 or 7 days, circulating CD8 + T cells were IV labeled before sacrifice, and tissues were analyzed (BioRender). G) Quantification transferred P14 T cell numbers and CD69 + frequencies in the kidney, liver, and IEL at day 5 as in F ( Neurl3 KO n=7; Rnf149 KO n=6; Wsb1 KO n=6); two independent experiments. H) (Top left) Representative CD69 vs CD103 flow plot in the IEL; (Top right) CD69 + CD103 + T cell frequencies in the IEL; (Bottom left) transferred P14 T cell numbers; and (Bottom right) CD69 + P14 T cell frequencies in the kidney, liver, and IEL at day 7 as in F ( Neurl3 KO n=7; Rnf149 KO n=7; Wsb1 KO n=7); two independent experiments. I) (Left) Representative KLRG1 vs CD127 flow plot in the spleen; (Right) Splenic KLRG1 + or CD127 + P14 T cell frequencies at day 5 as in F ( Neurl3 KO n=11; Rnf149 KO n=6; Wsb1 KO n=10); two or more independent experiments. J) (Left) Representative KLRG1 vs CD127 flow plot in the spleen; (Right) splenic KLRG1 + or CD127 + P14 T cell frequencies at day 7 as in F ( Neurl3 KO n=11; Rnf149 KO n=7; Wsb1 KO n=11); two or more independent experiments. K) Representative splenic P14 CD62L vs CD127 flow plots at 21–22 days as in F. L) Frequencies and cell numbers of splenic CD127 lo CD62L lo P14 T cells at day 21–22 as in F ( Neurl3 KO n=8; Rnf149 KO n=8; Wsb1 KO n=8); two independent experiments. M) Frequencies and cell numbers of splenic CD127 hi CD62L lo P14 T cells at day 21–22 as in F ( Neurl3 KO n=8; Rnf149 KO n=8; Wsb1 KO n=8); two independent experiments. N) Frequencies and cell numbers of splenic CD127 hi CD62L hi P14 T cells at day 21–22 as in F ( Neurl3 KO n=8; Rnf149 KO n=8; Wsb1 KO n=8); two independent experiments. Statistical significance was calculated using paired Student’s t -tests (B-E, G-N). Connecting lines indicate that cells were isolated from the same mouse. To test if NEURL3, RNF149, and WSB1 deletion changed T cell differentiation in acute infection, control and ligase KO P14 T cells were transferred into mice that were then infected with LCMV Armstrong, and T cells were isolated on d5, d7, and d21–22 of infection ( Figure 3F ). While there were no differences in the number of ligase KO T cells compared to control T cells on d5, we observed increased CD69 expression by ligase KO T cells in the tissue on both d5 and d7 post-infection, including more CD69 + CD103 + , CD103 + , and CCR9 + ligase KO T cells in the IEL, suggesting that ligase loss alters differentiation kinetics early after acute infection ( Figure 3G – H , Supplemental Figure 1B , 3B , C ). In the spleen, ligase deficiency resulted in a higher frequency of CD127 + memory-precursor T cells on d5 of infection, but there was no difference in the frequency of KLRG1 + short-lived effector T cells ( Figure 3I ). By d7 post-infection, we observed a decline in the frequency of KLRG1 + T cells, which suggested that the ligase KO T cells were already beginning to contract ( Figure 3J ). At memory timepoints (days 21–22 post-infection), we found that higher frequencies of CD69 + ligase KO T cells were maintained in the tissues, and that there was an increase in the number of ligase KO T RM in IEL ( Supplemental Figure 3D ). In the spleen, we observed decreased frequencies of long-lived, terminal-effector (CD62L lo CD127 lo ) and effectormemory (CD62L lo CD127 hi ) T cell subsets, with a corresponding increase in the central-memory (CD62L hi CD127 hi ) T cell subset, revealed both by frequency and absolute cell number of ligase KO T cells compared with the control T cells, indicating that deficiency in all three ligases had a long-term impact on cell fate ( Figure 3K – N ). Finally, to directly interrogate the differentiation potential of ligase-deficient T cells, we adoptively transferred control or ligase KO P14 cells into mice that were then infected with LCMV Armstrong. On day 30 of infection, T CM from control or ligase KO P14 T cells were sorted and then co-transferred at a 50:50 ratio into new recipients followed by LCMV Armstrong infection to assess secondary memory differentiation potential. This experiment showed that ligase-deficient T cells were less abundant than control T cells at secondary memory timepoints across tissue compartments ( Supplemental Figure 3E ). Thus, we found that in response to tumors and to acute infection, loss of ligase expression led to altered T cell differentiation in circulating memory, tissue-resident memory, and TIL populations. To characterize and compare the functional impact of NEURL3, RNF149, and WSB1 on the T cell transcriptome, we performed scRNA-seq analysis of control and ligase KO P14 T cells on d5 of infection with LCMV Armstrong, when we observed early phenotypic differences. We found that ligase deficiency in either the spleen or IEL shared RNA scRNA-seq clusters with the control and analysis revealed few differentially expressed genes enriched in the ligase KO T cells, indicating that ligase loss-of-function minimally altered the transcriptome. ( Supplemental Figure 4A , B , Supplemental Table 2 ). Consistent with this observation, RNA-seq performed on EV and Neurl3 OE P14 T cells obtained from MC38-GP 33–41 tumors, revealed few significant differences in gene expression between EV and Neurl3 OE T cells in the dLN or tumor ( Supplemental Figure 4C , D ). Taken together, these data indicate that NEURL3, RNF149, and WSB1 impact T cell differentiation, but the presence or absence of these ligases minimally impacted mRNA expression. E3 ligases have been previously shown to control self-renewal and cell-fate determination in intestinal stem cells, hematopoietic stem cells, and embryonic development through targeted protein degradation 15 , 42 , and therefore NEURL3, RNF149, and WSB1 may be having a similar function in CD8 + T cell differentiation. Comparing proteostasis in ex vivo T EX , T PEX , T RM , and T EFF As NEURL3, RNF149, and WSB1 likely influence cell differentiation potential and cell fates via targeted protein degradation, we used quantitative proteomics to measure protein abundance in T cells. The extent to which TIL mRNA corresponds to protein expression has mainly been characterized on a gene-by-gene basis and relative mRNA versus protein comparisons have been made for in vitro -cultured T cells, lymphocytes at steady-state, and other non-immune cell types, but similar evaluations in ex vivo T cells upon infection or tumor formation have been undercharacterized 23 , 27 , 43 – 46 . To understand the proteome in ex vivo T cell populations, we used mass spectrometry to compare proteomes in T EX and T PEX from tumors and T RM and T EFF from acute infection ( Figure 4A ). We isolated P14 T cells from the spleen at the peak of immune response (T EFF ) and memory timepoint from IEL (T RM ) following LCMV Armstrong infection, as well as endogenous TIL (T PEX and T EX ) from B16-bearing mice. We performed scRNA CITE-seq on these populations, as well as proteomic analysis using low-input data-independent acquisition (DIA) mass spectrometry and obtained >8,000 unique proteins per replicate from 1 × 10 5 sorted cells ( Figure 4B , C , Supplemental Tables 3 , 4 ). We used TotalSeq oligonucleotide-conjugated antibodies to measure PD-1 and TIM-3 protein expression on TIL in our scRNA-seq data and compared gene-signature enrichment with previously published datasets 3 , 10 , confirming that the T cell populations identified by scRNA-seq were comparable to the bulk sorted T cells analyzed by mass spectrometry ( Supplemental Figure 4E , F ). To ensure sufficient cell numbers for analysis, we included scRNA-seq data for T RM from both endogenous CD8 + T cells and P14 T cells ( Supplemental Figure 4G ). Direct comparison of RNA and protein abundances for individual genes revealed that for some genes, RNA and protein showed a linear relationship (quadrant 1 [Q 1 ], [Q 3 ]), while other genes had greater protein abundance than RNA expression [Q 2 ], or greater RNA expression than protein abundance [Q 4 ] ( Figure 4D , Supplemental Figure 4H ). Performing pathway enrichment to identify the top three Gene Ontology Biological Processes (GO:BP) pathways in each quadrant revealed that genes with greater protein than RNA expression were enriched for metabolite pathways, while genes with greater RNA than protein expression included enrichment in cytokine pathways, consistent with observations that T RM and other effector T cell populations can maintain cytokine mRNA in a translationally repressed state 47 , 48 ( Figure 4E ). Using a distance matrix to summarize the relationships between populations categorized by RNA or protein abundance, we observed different correlation patterns: proteomic analyses revealed that T PEX and T EX were more distinct from each other than suggested by transcriptomic data alone ( Figure 4F ), highlighting how integrating proteomic analyses of ex vivo T cells with RNA expression can improve our understanding of T cell biology. Figure 4: Comparing ex vivo T EX , T PEX , T RM , and T EFF transcriptome and proteome. Open in a new tab A) Experimental schematic: Endogenous B16 TIL were sorted at d14 (T PEX sorted on CD8 + PD1 lo/int Tim3 − and T EX sorted on CD8 + PD1 hi Tim3 + ). In parallel, P14 were sorted from d7 LCMV Armstrong infection from spleen (T EFF ), and at memory timepoint from the IEL (T RM ) (BioRender). B) RNA UMAP from CITE-seq of CD8 + T cells from d7 LCMV Armstrong spleen (P14), d30 IEL (P14 + endogenous), and endogenous B16 TIL T PEX and T EX (defined by PD1 vs Tim3 CITE-seq and enrichment signature; Supplemental Figure 4E ). The sequenced cells were derived from 1 mouse for d7 spleen, pooled from 6 mice for d30 IEL, and pooled from 10 mice for TIL. C) Protein PCA (DIA-PASEF) of CD8 + T cells from d7 LCMV Armstrong spleen (P14), d14 IEL (P14), and endogenous B16 TIL T PEX and T EX . D14 T RM was used as an early memory timepoint due to cell number limitations at d30. Replicates: d7 spleen n=2, d14 IEL n=3 (6 pooled samples/replicate), T PEX n=3 (6 pooled/replicate) and T EX n=3 (6 pooled/replicate). D) RNA-protein scatter per T cell subset (non-zero RNA vs non-zero protein, one dot per gene), split into four quadrants by mean expression of total x - or y -axis. E) Dot plot of genes from either Q 1 , Q 2 , or Q 4 for each T cell subset (from D), with GO:BP pathway enrichment for the top 3 significantly pathways per subset. F) Euclidean distance heatmap of pseudobulk RNA-seq samples (in blue) and protein intensities (in green) in PCA space (darker = more similar). G) Heatmap with unsupervised hierarchical clustering of the z-score of significant differentially expressed proteins from T EFF , T RM , T PEX , and T EX , with adjusted p-value <0.05. H) Gene Ontology bubble plot of proteins defining each cluster from G. Analysis of differentially expressed proteins revealed nine unique clusters ( Figure 4G , Supplemental Table 5 ). Performing gene ontology pathway analysis of the top five pathways per cluster revealed unique biological processes associated with each cluster ( Figure 4H ). We found that T RM had increased levels of proteins associated with roles in actin organization and the catabolism of small molecule pathways (clusters 1 and 2) and shared upregulation of proteins related to lipid catabolic processes with T PEX (cluster 3). T RM -specific expression of actin-regulatory proteins may indicate a poised activation state, as the immunological synapse requires dynamic actin regulation 49 or it may be related to the motile nature of T RM , as surveying tissue requires dynamic actin regulation for movement 50 , 51 . For lipid metabolic pathways, lipid catabolism has been previously associated with tissue adaptation and function in T RM , and with improved T cell function in cancer 11 , 52 . We found that T PEX and T EFF both shared proteins with roles in T cell differentiation (cluster 9), and T EX uniquely upregulated proteins related to glycolysis (cluster 5), consistent with previous reports that T EX upregulate glycolytic activities 53 . We found T EX shared the upregulation of proteasome-mediated, ubiquitin-dependent protein catabolism pathways with T RM (cluster 7), an aspect of T EX biology that had not yet been characterized. Similar analysis of differential gene expression followed by GO BP pathway enrichment using RNA revealed the top RNA-enriched pathways were distinct from most pathways observed in the proteomic data ( Supplemental Figure 5 ). Thus, proteomic analyses revealed distinct enrichment of proteasome-mediated protein catabolism pathways in T EX and T RM and uncovered unique proteomic profiles for each of the T cell populations. T EX have functional proteasomes, yet accumulate short-lived proteins Performing GSVA on differentially expressed proteins, we found that both T EX and T RM had increased expression of proteins involved in a number of pathways related to proteasome-dependent degradation relative to T EFF and T PEX (a pattern not reflected in GSVA of differentially-expressed RNA), including protein K48-linked ubiquitination ( Figure 5A , Supplemental Figure 6A ). We cross-validated our DIA-proteomics results with tandem mass tag (TMT)-based mass spectrometry on both related experimental groups and additional populations: P14 from dLN and B16-GP 33–41 TIL to allow us to compare endogenous to antigen-specific T cells, T EM and T RM P14 post-LCMV Armstrong infection to compare multiple memory T cell populations, and endogenous T PEX and T EX TIL from B16-GP 33–41 to validate our DIA-proteomic T PEX and T EX populations ( Supplemental Figure 6B , C , Supplemental Table 6 ). While fewer proteins were identified compared to the DIA-PASEF dataset, we observed similar patterns of protein expression ( Supplemental Figure 6D ). Using GSVA for differentially expressed proteins followed by GO:BP pathway analysis, we saw similar enrichment in protein degradation and K48-linked ubiquitination in T EX and T RM compared to T PEX , but also found that antigen-specific dLN and TIL P14 had similar relative abundance of the pathway proteins to T EX , indicating that antigen-specific TIL and endogenous T EX showed a similar phenotype ( Supplemental Figure 6E ). We also found T EM had similar protein degradation pathway phenotype to T PEX rather than T RM , indicating that not all memory T cells upregulate proteasomal degradation and K48-linked ubiquitination equivalently ( Supplemental Figure 6E ). Figure 5: T EX accumulates short-lived proteins yet have functional proteasomes. Open in a new tab A) GSVA heatmaps of protein-degradation GO:BP pathways from proteomics (left, data from 4C) and mRNA (right, data from 4B). B) K48 ubiquitin MFI in CD8 + T cells from P14 IEL d30 (n=8) and P14 spleen d7 (n=9) after LCMV Armstrong infection; and endogenous dLN (n=10), T PEX (n=10) and T EX (n=10) from B16 melanoma after treatment with 10 μM MG-132 for 1 h at 37°C prior to fixation and staining. C) Chymotrypsin-, caspase-, and trypsin-like activities in sorted dLN, T PEX , and T EX incubated ± 10 μM proteasome inhibitor MG-132 (1 h, 37°C, inhibitor as negative control); 2–3 independent replicates. D) GSVA heatmaps of unfolded protein response GO:BP pathways from proteomics (left, data from 4C) and mRNA (right, data from 4B). E) Using in vitro T cell protein half-life data ( Supplemental Table 7 ), the ex vivo T EFF , T RM , T PEX , and T EX proteomics data were stratified into non-short-lived proteins (half-life between >8 h and ≤20 h; Left) and short-lived proteins (half-life ≤8 h; Right). The mean total abundance per category were quantified. F) For short-lived proteins (defined in E), rank-abundance curves by subset. G) For short-lived proteins (defined in E), composition shown as 100% stacked bars with the top 20 proteins per cell type and remaining proteins collapsed into “other”. H) For short-lived proteins (defined in E), Protein Set Enrichment Analysis across pairwise comparisons with T EX , plots show running enrichment score and short-lived protein postion. Statistical significance was calculated using (B, C, E) one-way ANOVA and empirical Bayes–moderated t-statistic (G). To compare the proteostasis status of T RM to additional circulating memory T cell populations, we performed DIA-PASEF mass spectroscopy on P14 T cells sorted on day 33 post LCMV-Armstrong infection from the spleen (CD127 hi CD62L hi T CM , CD127 hi CD62L lo T EM , CD127 lo CD62L lo LLTE) and the IEL (T RM ) ( Supplemental Figure 7A ). We obtained >6,000 unique proteins per replicate from 2 × 10 3 sorted cells ( Supplemental Figure 7B ) and integrated these data with results from Figure 4C by normalizing the datasets to their shared T RM population ( Supplemental Figure 7C ). We then compared these data to our previously identified cell-type specific protein expression from Figure 4G and found that all three circulating memory T cell populations shared substantial protein expression with T RM -enriched proteins but did not share protein expression with T PEX +T RM , T EFF +T PEX +T EX , or the T EX protein clusters ( Supplemental Figure 7D ). Using GSVA for differentially-expressed proteins followed by GO:BP pathway analysis on the combined datasets, we observed similar enrichment in protein degradation and K48-linked ubiquitination in T EX and T RM compared to T PEX , T EFF , T CM , T EM , and LLTE ( Supplemental Figure 7E ). Increased abundance of K48-linked polyubiquitin chains can be used to indicate defective protein degradation, therefore we measured K48 ubiquitin staining by flow cytometry and found significantly increased levels of K48-linked polyubiquitin in T EX and T RM ; however, K48-linked polyubiquitin was higher in T EX compared to other CD8 + T cell groups, suggesting that T EX may have increased proteostasis dysfunction compared to other T cell populations ( Figure 5B ). To determine if proteasome-mediated protein degradation was functional in the T EX population, we utilized proteasome peptidase activity assays and found that T EX possessed functional proteasomes, as assessed by increased chymotrypsin-like, caspase-like, and trypsin-like proteasome activities compared with the dLN or T PEX populations ( Figure 5C ). Interestingly, GO pathway analysis revealed that both T EX and T EFF had higher levels of proteins involved in UPR pathways, a pattern not reflected in RNA expression ( Figure 5D , Supplemental Figure 6A ). In our TMT-mass spectrometry dataset, we saw both endogenous T EX and antigen-specific P14 + TIL had similarly upregulated proteins involved in the UPR pathways, while endogenous T PEX , P14 T EM , and P14 T RM had decreased relative abundance compared to T EX ( Supplemental Figure 6F ). In comparison to circulating memory T cells, we also observed enrichment in response to unfolded protein and ER stress response (hallmarks of UPR) only in T EX and T EFF , but not in T RM , T PEX , T CM , T EM , and LLTE ( Supplemental Figure 7F ). The UPR in TIL has previously been characterized as negatively correlated with T cell function in tumors, since studies limiting UPR activation via IRE1α–XBP1 or PERK–CHOP showed improvement of TIL function 54 – 57 . To understand proteostasis dysregulation and evaluate protein turnover dynamics within TIL, we performed metabolic protein labeling using heavy isotopes of arginine ( 13 C 6 , 15 N 4 ) and lysine ( 13 C 6 , 15 N 2 ) in in vitro -cultured CD8 + T cells. This approach allowed us to identify rapidly degraded proteins in mouse CD8 + T cells (i.e., half-life ≤8 h, Supplemental Table 7 ). We then used this data to assign our ex vivo proteomics data to short-lived (half-life ≤8 h) and non-short-lived (half-life >8 h to ≤20 h) protein subsets ( Figure 5E – H ). The abundance of non-short-lived proteins was similar among T RM , T EFF , T PEX , and T EX . However, when quantifying the abundance of short-lived proteins, we observed that T EX had an enrichment for numerous short-lived proteins, despite having functional proteasomes ( Figure 5E – H ). These data indicate that protein degradation in T EX is dysfunctional either due to a proteasome-independent mechanism or that T EX proteasomal subunit composition may be suboptimal for protein degradation in vivo ( Figure 5C , E , Supplemental Figure 7G ). Unfolded protein accumulation in T EX is rescued by NEURL3, RNF149, or WSB1 expression To characterize the conformational states of the proteins in T cells, we stained for unfolded proteins using Proteostat, a dye that fluoresces upon binding to unfolded protein quaternary structures 58 . We found that T EX had significantly increased levels of unfolded proteins compared to T PEX , T RM , and T EFF cells ( Figure 6A , B ). Inhibiting protein synthesis ex vivo for 6 hr did not change unfolded protein accumulation in T EX , despite observing a decrease in Cyclin A2 (a rapidly degraded protein 59 ) during this timeframe ( Figure 6C ). These data indicate that the increased levels of unfolded proteins in T EX do not primarily arise from newly synthesized proteins or from dysfunctional proteasomes. Figure 6: T EX accumulates unfolded proteins, rescued by NEURL3, RNF149, or WSB1 expression. Open in a new tab A) ImageStream visualization of Proteostat staining of CD8 + T cells isolated from dLN or TIL (T PEX and T EX ), plus MC-132-treated CD8 + T cells (5 μM, 16hr). B) Proteostat MFI (normalized to spiked-in, congenically mismatched naive CD8 + T cells) in P14 IEL d30 (n=8) or P14 spleen d7 (n=9) after LCMV Armstrong infection, and endogenous dLN (n=10), T PEX (n=10) and T EX (n=10) TIL from B16 melanoma. C) (Left) Proteostat MFI (normalized as in B) and (Right) Cyclin A2 MFI from CD8 + T cells isolated from endogenous dLN (n=6), T PEX (n=6) and T EX (n=6) TIL from B16 melanoma, treated ± Cycloheximide (20 μg/mL, 6 h, 37°C). Representative of three independent experiments. D) Proteostat MFI (normalized as in B) from P14 T cells from dLN or tumors of mice with congenically distinct EV vs Neurl3 OE , Rnf149 OE , or Wsb1 OE 8 days after adoptive transfer into tumor-bearing mice ( Neurl3 OE n=12; Rnf149 OE n=12; Wsb1 OE n=11); three independent experiments. Statistical significance was calculated using (B) one-way ANOVA and paired Student’s t -tests (C-D). Connecting lines indicate that cells were isolated from the same mouse. Since T EX proteasomes were functional and protein synthesis did not drive unfolded protein accumulation, we instead targeted protein degradation upstream of proteasome activity by overexpressing the E3 ubiquitin ligases NEURL3, RNF149, or WSB1 in P14 TIL. We found that there was a minimal impact on dLN T cells, whereas ligase overexpression rescued the elevated unfolded protein accumulation in TIL ( Figure 6D ). Taken together, these data show that T EX experience a unique proteostasis state in which protein degradation can occur and the UPR is upregulated, yet T EX have significant accumulation of unfolded proteins. Overexpression of NEURL3, RNF149, or WSB1 in T cells rescues accumulation of unfolded proteins, indicating that unfolded proteins contribute to T EX dysfunction and that modulation of specific E3 ubiquitin ligase activity reduces proteotoxic stress. NEURL3, RNF149, or WSB1 improve immunotherapeutic responses in humans and improve cancer immunotherapy in mice We next wanted to understand the therapeutic potential of NEURL3, RNF149, and WSB1 expression in the context of anti-tumor immunity. Quantification of the scRNA-seq data from melanoma patient TIL 60 showed that RNF149 and WSB1 were expressed at higher levels in T RM -like TIL and T PEX than in other CD8 + T cell populations in the tumor ( Figure 7A ), consistent with our murine TIL data ( Figure 1B ). Quantification of RNF149 and WSB1 transcripts in patients that received checkpoint blockade showed that prior to the start of therapy, the expression levels of RNF149 and WSB1 were moderately predictive of response to therapy, while the levels of RNF149 and WSB1 expression following checkpoint blockade were highly correlated with the favorable response to checkpoint blockade ( Figure 7B ). Human scRNA-seq analysis did not detect the NEURL3 transcript, however, bulk RNA-seq from patients with metastatic melanoma who had their primary tumor resected and sequenced prior to checkpoint blockade immunotherapy 61 showed higher levels of NEURL3 , RNF149 , or WSB1 expression in patients that had improved survival following checkpoint blockade immunotherapy ( Figure 7C ). Thus, ligase expression correlates with improved responses to immunotherapy in humans. Figure 7: NEURL3, RNF149, or WSB1 expression corresponds with immunotherapeutic responses to checkpoint blockade in human melanoma and improves cancer immunotherapy in mice. Open in a new tab A) Heatmap of CD8 + T cell clusters from checkpoint blockade-treated melanoma patients ( GSE120575 60 ), annotated by top gene expression per cluster; RNF149 and WSB1 expression shown. B) Violin plots of RNF149 or WSB1 RNA expression in CD8 + TIL with patient data from A, stratified into non-responder vs responder, pre-treatment (top) and on-treatment (bottom). C) Survival of patients with melanoma (dbGaP Study Accession: phs000452.v3.p1 61 ) treated with checkpoint blockade after primary tumor resection and sequencing, stratified by gene expression (high/top vs low/bottom) in the primary tumor. The survival risk score is z -score gene effect of death risk in a Cox proportional hazards model. Analysis conducted by TIDE 79 . D) B16-GP 33–41 tumor area in mice that received no T cells, EV P14 or Neurl3 OE+ Rnf149 OE+ Wsb1 OE P14 T cells on d6 after tumor cell implantation with isotype or anti-PD1 treatment every other day; two independent experiments. E) Survival from D. Statistical significance was calculated using the (B) unpaired Student’s t -tests or (C) CoxPH test or the (E) log-rank (Mantel-Cox) test. To determine if ligase overexpression could improve T cell responses to B16-GP 33–41 mouse melanoma in a tumor resistant to checkpoint blockade, we combined cell therapy and anti-PD1 antibody treatment. P14 T cells were transduced with all three ligases and adoptively transferred into mice with established B16-GP 33–41 melanoma and treated with either isotype control or anti-PD1 therapy. We found that triple ligase OE cell therapy with checkpoint blockade improved tumor control and significantly improved survival compared to EV P14 and triple ligase OE isotype treated mice ( Figure 7D , E ). Thus, ligase-expressing CD8 + T cell therapy synergizes with checkpoint blockade and improves immunotherapeutic responses to cancer, highlighting the role of proteostasis in regulating anti-tumor T cell responses during immunotherapy targeting co-inhibitory receptors. Discussion: In this study, we sought to understand the relationship between T cell function in tumors versus tissues given the association of T RM -like TIL and improved responses to immunotherapy in patients with cancer 9 . The identification of T RM -associated genes lost with terminal differentiation to T EX introduces a tissue adaptation-centered approach to understanding T cell exhaustion, with downregulation of protein folding and degradation expression profiles suggesting that T EX experience disrupted proteostasis, while T PEX and T RM actively maintain proteostasis. Active maintenance of differentiation potential by sustained proteostasis may be an important biological difference between memory and naive T cells: proteasome activity has been shown to be crucial for generating circulating memory T cell populations, and memory T cells maintain a higher level of protein turnover than naive T cells prior to TCR stimulation, while quiescent naive T cells and quiescent stem cells both have low levels of protein synthesis and degradation 27 , 28 , 62 , 63 . Previous research supports the roles of NEURL3, RNF149, and WSB1 in maintaining differentiation potential and responding to cell stress in non-immune cells, which may also help to maintain differentiation potential 64 . NEURL3 was previously shown to target Vimentin for degradation, to inhibit the epithelial mesenchymal transition of nasopharyngeal carcinoma 65 . RNF149 targets BRAF for degradation in cancer cells, reduces reactive oxygen species in cell lines, and regulates protein quality control in the ER during cell stress 66 – 68 . WSB1 was observed to be expressed under hypoxic conditions, and has been reported to target VHL, DNA damage-responsive ataxia-telangiectasia mutated (ATM) serine/threonine kinase, and Rho-binding protein RhoGDI2 for degradation 69 – 71 . WSB1 also correlates with increased tumor cell metastasis, although it exerts a protective effect against Parkinson’s disease by targeting the leucine-rich repeat kinase 2 protein (LRRK2) in neurons 69 , 71 – 73 . It is not clear from our work what the specific targets of NEURL3, RNF149, or WSB1 are in T cells or if targets are shared across cell types. Given that these ligases do not share sequence homology, each ligase may target unique proteins, converging on a similar stem-like phenotype. This may include proteins in the TCR signaling cascade, proteins involved in RNA transcription, or proteins involved in protein translation, or additional pathways that help enforce a stem-like state. Recent publications identifying E3 ligase targets in cell lines using proximity labeling showed many E3 ligases can have hundreds of protein targets 74 , 75 , rather than one predominant target. Defining the protein targets for NEURL3, RNF149, and WSB1 will be an important topic for future work. Mass spectrometry of immune cells has been used as a tool to reveal metabolic states of in vitro and ex vivo -isolated T cells 76 , but it has previously required large cell numbers for the acquisition of in-depth protein information, making ex vivo assessment of rare populations technically challenging. DIA-PASEF mass spectroscopy and TMT mass spectrometry 45 are important technological advancements, allowing for proteomic assessments of less-abundant populations and yielding higher sensitivity and deeper proteome coverage. Our data is consistent with reports that mRNA profiles poorly correlate with protein expression patterns 23 , 27 , 44 – 46 , highlighting that comparing mRNA and protein provides complementary yet unique biological information. This may be a particularly important insight for T EX , as the RNA profiles in T EX do not clearly explain why T EX are dysfunctional or how checkpoint blockade permits some patients to respond to therapy while others do not. Proteomics may help reveal information about T EX and other immune cell populations to aid immunotherapy development. Our mass spectrometry datasets have revealed shared pathway enrichment in T RM and T EX proteasomal degradation, yet only T EX upregulate UPR, despite having functional proteasomes ( Figure 5C , D ). Complementary findings examining disrupted proteostasis by T EX were recently reported, further characterizing T EX as not only having unfolded proteins, but protein aggregates and stress granules as well 46 . Comparing T EX to cells from other unfolded protein diseases, such as neurodegenerative diseases, may help us to understand the biological consequences of unfolded protein accumulation in T EX 20 : unfolded proteins themselves or ER stress driven by genetic or environmental factors can trigger the UPR, causing cells to respond transcriptionally to the proteotoxic stress. If this response is successful, the cell will return to proteostasis, known as “adaptive UPR” 20 . Conversely, if the UPR is unable to resolve the proteotoxic stress, this “terminal UPR” state results in cell dysfunction 20 . From our data, we see that T EX upregulate the UPR and yet still have an abundance of unfolded proteins, suggesting that T EX also experience a terminal UPR state. It is unclear whether NEURL3, RNF149, or WSB1 directly reduce the accumulation of unfolded proteins resulting from targeted degradation or instead prevent this phenotype through indirect mechanisms, but since all three ligases rescue unfolded protein accumulation, it suggests that this phenotype is a broader consequence of disrupted proteostasis involving multiple proteins rather than the dysfunction of a single unfolded protein. Proteotoxic cell stress has been shown to be associated with resistance to checkpoint blockade in human cancers 77 , therefore engineering T EX -specific proteolysis-targeting chimeras (PROTACs), or ligase overexpression in chimeric antigen receptor (CAR) T cell or other cellular immunotherapies, could be used for immunotherapy in human cancer. Limitations of the study: We identify NEURL3, RNF149, and WSB1 as molecular contributors to the maintenance of CD8 + T cells differentiation potential in both cancer and chronic viral infection, but the direct targets remain undefined. Proximity-labeling studies of other E3 ubiquitin ligases have identified hundreds of potential targets, suggesting that these ligases may similarly act through a broad network. It also remains unclear whether unfolded protein accumulation in T EX is a cause or a consequence of dysfunction, therefore future studies will be required to clarify the role of unfolded protein in the overall biological state of T cell exhaustion. In addition, the analysis of human T cell exhaustion is currently limited to gene expression in melanoma. Extending these findings to other human cancer types as well as proteome profiling will be essential to assess the human TIL proteostasis state. Resource availability: Lead Contact: Requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact , Ananda Goldrath ( agoldrath@ucsd.edu ). Materials availability: Unique reagents generated in this study are available from the lead contact with a completed materials transfer agreement. Data and Code Availability: The RNA datasets generated during this study are available at NCBI GEO repository as indicated in the key resources table . The mass spectrometry proteomics datasets generated during this study are available at the ProteomeXchange Consortium via the PRIDE partner repository or MassIVE as indicated in the key resources table . This paper does not report original code. 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Antibody BioLegend Cat#123157; RRID: AB_2832437 TotalSeq ™ -C1063 anti-mouse CD45RB Antibody BioLegend Cat#103321; RRID: AB_2888760 TotalSeq ™ -C0171 anti-human/mouse/rat CD278 (ICOS) Antibody BioLegend Cat#313553; RRID: AB_2800823 TotalSeq ™ -C0378 anti-mouse CD223 (LAG-3) Antibody BioLegend Cat#125237; RRID: AB_2832450 TotalSeq ™ -C0228 anti-mouse CD183 (CXCR3) Antibody BioLegend Cat#126545; RRID: AB_2832454 TotalSeq ™ -C0130 anti-mouse Ly-6A/E (Sca-1) Antibody BioLegend Cat#108151; RRID: AB_2832370 TotalSeq ™ -C0837 anti-mouse IL-33Rα (IL1RL1, ST2) Antibody BioLegend Cat#145319; RRID: AB_2819892 TotalSeq ™ -C0193 anti-mouse CD357 (GITR) Antibody BioLegend Cat#126327; RRID: AB_2832452 TotalSeq ™ -C0834 anti-mouse CD39 Antibody BioLegend Cat#143815; RRID: AB_2819888 TotalSeq ™ -C1064 anti-mouse/rat CD81 Antibody BioLegend Cat#104915; RRID: AB_2876412 Totalseq ™ -C0214 anti-human/mouse integrin β7 Antibody BioLegend Cat#321229; RRID: AB_2810481 TotalSeq ™ -C0237 Rat IgG1, λ Isotype Ctrl Antibody BioLegend Cat#401921; RRID: AB_3097123 TotalSeq ™ -C0200 anti-mouse CD86 Antibody BioLegend Cat#105059; RRID: AB_2860608 TotalSeq ™ -C0121 anti-mouse TCR γ/δ Antibody BioLegend Cat#118141; RRID: AB_2819838 TotalSeq ™ -C0070 anti-human/mouse CD49f Antibody BioLegend Cat#313635; RRID: AB_2800825 TotalSeq ™ -C0197 anti-mouse CD69 Antibody BioLegend Cat#104551; RRID: AB_2832333 TotalSeq ™ -C0120 anti-mouse TCR β chain Antibody BioLegend Cat#109259; RRID: AB_2819820 TotalSeq ™ -C0013 anti-mouse Ly-6C Antibody BioLegend Cat#128051; RRID: AB_2832461 TotalSeq ™ -C0429 anti-mouse CD48 Antibody BioLegend Cat#103459; RRID: AB_2876409 TotalSeq ™ -C0885 anti-mouse CD270 (HVEM) Antibody BioLegend Cat#136309; RRID: AB_2876474 TotalSeq ™ -C0850 anti-mouse CD49a Antibody BioLegend Cat#142615; RRID: AB_2832511 TotalSeq ™ -C0421 anti-mouse CD49b Antibody BioLegend Cat#103527; RRID: AB_2876410 TotalSeq ™ -C0570 anti-mouse/rat CD29 Antibody BioLegend Cat#102237; RRID: AB_2890670 TotalSeq ™ -C0241 Armenian Hamster IgG Isotype Ctrl Antibody BioLegend Cat#400977; RRID: AB_2894970 TotalSeq ™ -C0916 anti-mouse CD124 (IL-4Rα) Antibody BioLegend Cat#144811; RRID: AB_2832524 TotalSeq ™ -C0073 anti-mouse/human CD44 Antibody BioLegend Cat#103063; RRID: AB_2813930 TotalSeq ™ -C0235 anti-mouse TCR Vβ8.1, 8.2 Antibody BioLegend Cat#118419; RRID: AB_2860641 TotalSeq ™ -C0983 anti-mouse TCR Vα8.3 Antibody BioLegend Cat#125711; RRID: AB_2819853 TotalSeq ™ -C0846 anti-mouse CD185 (CXCR5) Antibody BioLegend Cat#145537; RRID: AB_2814051 TotalSeq ™ -C0191 anti-mouse/rat/human CD27 Antibody BioLegend Cat#124245; RRID: AB_2832448 TotalSeq ™ -C0212 anti-mouse CD24 Antibody BioLegend Cat#101843; RRID: AB_2819784 TotalSeq ™ -C0014 anti-mouse/human CD11b Antibody BioLegend Cat#101275; RRID: AB_2832272 TotalSeq ™ -C0595 anti-mouse CD11a Antibody BioLegend Cat#101131; RRID: AB_2832271 TotalSeq ™ -C0117 anti-mouse I-A/I-E Antibody BioLegend Cat#107659; RRID: AB_2832368 TotalSeq ™ -C1062 anti-mouse CD30 Antibody BioLegend Cat#102313; RRID: AB_2888806 TotalSeq ™ -C0112 anti-mouse CD62L Antibody BioLegend Cat#104455; RRID: AB_2819800 TotalSeq ™ -C1012 anti-mouse CD178 (FasL) Antibody BioLegend Cat#106615; RRID: AB_2892272 TotalSeq ™ -C0562 anti-mouse CD83 Antibody BioLegend Cat#121521; RRID: AB_2860647 TotalSeq ™ -C0190 anti-mouse CD274 (B7-H1, PD-L1) Antibody BioLegend Cat#153610; RRID: AB_2860721 TotalSeq ™ -C0915 anti-mouse VISTA (PD-1H) Antibody BioLegend Cat#150219; RRID: AB_2860714 TotalSeq ™ -C0091 Mouse IgG2a, κ isotype Ctrl Antibody BioLegend Cat#400293; RRID: AB_2888922 TotalSeq ™ -C0090 Mouse IgG1, κ isotype Ctrl Antibody BioLegend Cat#400187; RRID: AB_2888921 TotalSeq ™ -C0092 Mouse IgG2b, κ isotype Ctrl Antibody BioLegend Cat#400381; RRID: AB_2888923 TotalSeq ™ -C0354 anti-mouse TCR Vβ5.1, 5.2 Antibody BioLegend Cat#139519; RRID: AB_2819882 TotalSeq ™ -C0813 anti-mouse CD9 Antibody BioLegend Cat#124825; RRID: AB_2876447 TotalSeq ™ -C0106 anti-mouse CD11c Antibody BioLegend Cat#117361; RRID: AB_2819834 TotalSeq ™ -C0807 anti-mouse CD200R (OX2R) Antibody BioLegend Cat#123919; RRID: AB_2876445 TotalSeq ™ -C0380 anti-rat CD90/mouse CD90.1 (Thy1.1) Antibody BioLegend Cat#202551; RRID: AB_2876578 TotalSeq ™ -C0195 anti-mouse CD134 (OX-40) Antibody BioLegend Cat#119435; RRID: AB_2832423 TotalSeq ™ -C0079 anti-mouse CD200 (OX2) Antibody BioLegend Cat#123823; RRID: AB_2860656 TotalSeq ™ -C0097 anti-mouse CD25 Antibody BioLegend Cat#102065; RRID: AB_2876399 TotalSeq ™ -C0118 anti-mouse NK-1.1 Antibody BioLegend Cat#108765; RRID: AB_2819815 TotalSeq ™ -C0078 anti-mouse CD49d Antibody BioLegend Cat#103633; RRID: AB_2860605 TotalSeq ™ -C0103 anti-mouse/human CD45R/B220 Antibody BioLegend Cat#103273; RRID: AB_2832307 TotalSeq ™ -C0116 anti-mouse Ly-6G/Ly-6C (Gr-1) Antibody BioLegend Cat#108463; RRID: AB_2832371 TotalSeq ™ -C0558 anti-mouse CD55 (DAF) Antibody BioLegend Cat#131815; RRID: AB_2876464 TotalSeq ™ -C0892 anti-mouse CD2 Antibody BioLegend Cat#100121; RRID: AB_2892259 TotalSeq ™ -C0001 anti-mouse CD4 Antibody BioLegend Cat#100571; RRID: AB_2813913 TotalSeq ™ -C0004 anti-mouse CD279 (PD-1) Antibody BioLegend Cat#109127; RRID: AB_2819819 TotalSeq ™ -C0003 anti-mouse CD366 (Tim-3) Antibody BioLegend Cat#119739; RRID: AB_2832424 TotalSeq ™ -C0236 Rat IgG1, κ Isotype Ctrl Antibody BioLegend Cat#400467; RRID: AB_2894972 TotalSeq ™ -C0238 Rat IgG2a, κ Isotype Ctrl Antibody BioLegend Cat#400577; RRID: AB_2894971 TotalSeq ™ -C0240 Rat IgG2c, κ Isotype Ctrl Antibody BioLegend Cat#400741; RRID: AB_3097108 TotalSeq ™ -C0095 Rat IgG2b, κ Isotype Ctrl Antibody BioLegend Cat#400677; RRID: AB_2894967 TotalSeq ™ -C0110 anti-mouse CD43 Antibody BioLegend Cat#143221; RRID: AB_2860697 TotalSeq ™ -C0563 anti-mouse CX3CR1 Antibody BioLegend Cat#149043; RRID: AB_2819898 TotalSeq ™ -C0909 anti-mouse CD182 (CXCR2) Antibody BioLegend Cat#149319; RRID: AB_2814053 TotalSeq ™ -C0926 anti-mouse CD186 (CXCR6) Antibody BioLegend Cat#151127; RRID: AB_2894676 TotalSeq ™ -C0426 anti-mouse CD192 (CCR2) Antibody BioLegend Cat#150631; RRID: AB_2876502 TotalSeq ™ -C0907 anti-mouse CD198 (CCR8) Antibody BioLegend Cat#150321; RRID: AB_2819900 TotalSeq ™ -C0192 anti-mouse CD20 Antibody BioLegend Cat#150427; RRID: AB_2860716 TotalSeq ™ -C0917 anti-mouse CD95 (Fas) Antibody BioLegend Cat#152616; RRID: AB_2860719 TotalSeq ™ -C0823 anti-mouse CD34 Antibody BioLegend Cat#152211; RRID: AB_2832536 TotalSeq ™ -C0203 anti-mouse CD150 (SLAM) Antibody BioLegend Cat#115947; RRID: AB_2819830 TotalSeq ™ -C0122 anti-mouse TER-119/Erythroid Cells Antibody BioLegend Cat#116259; RRID: AB_2832405 TotalSeq ™ -C1058 anti-mouse FR4 (Folate Receptor 4) Antibody BioLegend Cat#125113; RRID: AB_2876448 TotalSeq ™ -C0949 anti-mouse CD226 (DNAM-1) Antibody BioLegend Cat#133631; RRID: AB_2894682 TotalSeq ™ -C1011 anti-mouse CD155 (PVR) Antibody BioLegend Cat#131533; RRID: AB_2892286 TotalSeq ™ -C0077 anti-mouse CD73 Antibody BioLegend Cat#127237; RRID: AB_2860665 TotalSeq ™ -C0914 anti-mouse CD273 (B7-DC, PD-L2) Antibody BioLegend Cat#107229; RRID: AB_2860615 TotalSeq ™ -C0211 anti-mouse TCR Vγ2 Antibody BioLegend Cat#137713; RRID: AB_2819877 TotalSeq ™ -C0842 anti-mouse Ly-49A Antibody BioLegend Cat#116813; RRID: AB_2876431 TotalSeq ™ -C0074 anti-mouse CD54 Antibody BioLegend Cat#116137; RRID: AB_2819831 TotalSeq ™ -C0230 anti-mouse CD8b (Ly-3) Antibody BioLegend Cat#126639; RRID: AB_2892281 TotalSeq ™ -C0893 anti-mouse CD120b (TNF R Type II/p75) Antibody BioLegend Cat#113409; RRID: AB_2894663 TotalSeq ™ -C0301 anti-mouse Hashtag 1 Antibody BioLegend Cat: 155861; RRID: AB_2800693 TotalSeq ™ -C0302 anti-mouse Hashtag 2 Antibody BioLegend Cat: 155863; RRID: AB_2800694 TotalSeq ™ -C0303 anti-mouse Hashtag 3 Antibody BioLegend Cat: 155865; RRID: AB_2800695 TotalSeq ™ -C0304 anti-mouse Hashtag 4 Antibody BioLegend Cat: 155867; RRID: AB_2800696 TotalSeq ™ -C0305 anti-mouse Hashtag 5 Antibody BioLegend Cat: 155869; RRID: AB_2800697 TotalSeq ™ -C0306 anti-mouse Hashtag 6 Antibody BioLegend Cat: 155871; RRID: AB_2819910 TotalSeq ™ -C0307 anti-mouse Hashtag 7 Antibody BioLegend Cat: 155873; RRID: AB_2819911 TotalSeq ™ -C0308 anti-mouse Hashtag 8 Antibody BioLegend Cat: 155875; RRID: AB_2819912 TotalSeq ™ -C0309 anti-mouse Hashtag 9 Antibody BioLegend Cat: 155877; RRID: AB_2819913 TotalSeq ™ -C0310 anti-mouse Hashtag 10 Antibody BioLegend Cat: 155879; RRID: AB_2819914 TotalSeq ™ -C Mouse Universal Cocktail, V1.0 Biolegend Cat: 199903; RRID: AB_2924498 Bacterial and virus strains Subcloning Efficiency ™ DH5α Competent Cells Thermo Scientific Cat#18265017 LCMV-Armstrong Rafi Ahmed, Emory University NA LCMV-Cl13 Rafi Ahmed, Emory University NA Biological samples Chemicals, peptides, and recombinant proteins AccuCheck Counting Beads Thermo Scientific Cat#PCB100 eBioscience Fixable Viability Dye eFluor 780 Thermo Scientific Cat# 65-0865-14 eBioscience Fixable Viability Dye eFluor 450 Thermo Scientific Cat#65-0863-18 Brilliant II SYBR Green qPCR Master Mix Agilent Technologies Cat#600828 HyClone ™ RPMI 1640 media: Liquid Fisher Scientific Cat#SH30096FS Gibco ™ DMEM, high glucose, no glutamine Fisher Scientific Cat#11-960-069 HyClone ™ Fetal Bovine Serum Characterized HyClone Cat#SH30071.03 Gibco ™ Sodium Pyruvate (100 mM) Thermo Scientific Cat#11360070 Gibco ™ HEPES (1M) Thermo Scientific Cat#15630080 Gibco ™ MEM Non-Essential Amino Acids Solution (100X) Fisher Scientific Cat#11-140-050 Gibco ™ 2-Mercaptoethanol Thermo Scientific Cat#21985023 Gibco ™ Penicillin-Streptomycin-Glutamine (100X) Thermo Scientific Cat#10378-016 Clontech Labs 3P RETRONECTIN Takara Bio Cat#T100B STREPTAVIDIN MICROBEADS Miltenyi Biotec Cat#130-048-101 SIGMA Percoll ® pH 8.5-9.5 (25 °C) Sigma-Aldrich Cat#P1644-1L Corning ® DPBS (Dulbecco’s Phosphate-Buffered Saline) Corning Cat#20-031-CV Magnesium Chloride Hexahydrate OmniPur Cat#5980 Calcium Chloride Dihydrate (White Crystals to Powder), Fisher BioReagents ™ Fisher Scientific Cat#BP510-500 Calf Serum Iron Supplemented R&D Systems Cat#S11950H Collagenase, Type 1 Worthington Biochemical Cat# LS004196 HEPES (Fine White Crystals/Molecular Biology), Fisher BioReagents ™ Fisher Scientific Cat# BP310500 L-Glutamine ≥99% (HPLC), ReagentPlus ® Sigma-Aldrich Cat#G3126-100G Biochemical Fisher Chemical ™ Ammonium Chloride ACS Grade ≥99.5% Fisher Scientific Cat#A661-500 Potassium Bicarbonate (Powder/USP/FCC), Fisher Chemical Fisher Scientific Cat#P235-500 Apex Bioresearch Products 20-147 EDTA, Disodium Dihydrate Salt, Ultra Pure Grade Genesee Scientific Cat#Genesee20-147 Corning ™ Lymphocyte Separation Medium Fisher Scientific Cat#MT25072CV Sodium Bicarbonate (Powder/Certified ACS), Fisher Chemical ™ Fisher Scientific Cat#S233-500 Hank’s Balanced Salt Solution, 10X without sodium bicarbonate, calcium and magnesium Corning Cat#20-021-CV 1,4-Dithioerythritol ≥99.0% Sigma-Aldrich Cat# D8255-25G Fixation Buffer BioLegend Cat#420801 eBioscience ™ Foxp3 / Transcription Factor Staining Buffer Set Invitrogen Cat#00-5523-00 BD Cytofix/Cytoperm ™ Fixation/Permeabilization Kit BD Biosciences Cat#BD554714 PROTEOSTAT ® Protein aggregation assay Enzo Life Sciences Cat#ENZ-51023-KP050 MG-132, Ready Made Solution ≥90% (HPLC) Sigma-Aldrich Cat#M7449-200UL RPMI 1640 Medium for SILAC Thermo Scientific Cat#A33823 Fetal Bovine Serum, dialyzed, US origin Thermo Scientific Cat#26-400-044 L-Arginine-HCl, 13C6, 15N4 for SILAC Thermo Scientific Cat#89990 L-Lysine-2HCl, 13C6, 15N2 for SILAC Thermo Scientific Cat#88209 L-Alanine ≥98% (TLC) Sigma-Aldrich Cat#A7627-1G Recombinant Cas9-NLS purified protein UC Berkeley MacroLab NA DAPI (4’,6-Diamidino-2-Phenylindole, Dilactate) Biolegend Cat#422801 Recombinant human IL2 protein CETUS NA Mouse IL-15 Recombinant Protein PeproTech Cat# 210-15-100UG Recombinant Mouse TGF-β1 Biolegend Cat# 763104 Retinoic acid ≥98% (HPLC) powder Sigma-Aldrich Cat# R2625-50MG Sequencing grade modified trypsin Promega Cat# V5111 Lys-C protease Fujifilm Wako Cat# 129-02541 TCEP Sigma-Aldrich Cat# 580560-1GM Iodoacetamide (IAA) MP Biomedicals Cat# 100351 Dithiothreitol (DTT) Fisher Scientific Cat# BP172-25 SPRIselect reagent Beckman Coulter Cat: B23318 Cycloheximide InSolution, 100 mg/mL in DMSO, Suitable for cell culture Sigma-Aldrich Cat: 239765-1ML LCMV gp33-41 peptide AnaSpec Cat: 61296 Protein Transport Inhibitor Cocktail (500X) ebioscience Cat: 00-4980-93 Triton X-100 Sigma-Aldrich Cat#X100 UltraPure ™ SDS Solution, 10% Thermo Scientific Cat#24730020 Halt ™ Protease Inhibitor Cocktail (100X) Thermo Scientific Cat#78430 Pierce ™ Phosphatase Inhibitor Mini Tablets Thermo Scientific Cat#A32957 Water, Optima ™ LC/MS Grade Fisher Chemical Cat#W6-1 Pierce ™ Premium Grade TCEP-HCl Thermo Scientific Cat#PIPG82089 Triethylammonium Bicarbonate Buffer Honeywell Fluka Cat#60-044-973 Chloroform Fisher Chemical Cat#C297-4 Methanol, Optima ™ LC/MS Grade Fisher Chemical Cat#A456-4 Acetonitrile, Optima ™ LC/MS Grade Fisher Chemical Cat#A955-4 Formic Acid, 99.0+%, Optima ™ LC/MS Grade Fisher Chemical Cat#A117-50 Trifluoroacetic Acid, Optima ™ LC/MS Grade Fisher Chemical Cat#A116-10X1AMP Waters Sep-Pak C18 1 cc Vac Cartridge, 50 mg Sorbent per Cartridge, 55 - 105 μm Waters Cat#WAT054955 AcroPrep Advance Filter Plate (Pall) Fisher Scientific Cat#17164641 Sequencing Grade Modified Trypsin, lyophilized Promega Cat#V5111 C18 Evotips Evosep Cat#EV2013 Critical commercial assays Proteasome-Glo 3-Substrate Cell-Based Assay System Promega Cat#G1180 P3 Primary Cell 4D-Nucleofector ® X Kit S Lonza Cat#V4XP-3032 RNeasy Mini Kit (250) Qiagen Cat#74106 Applied Biosystems ™ High-Capacity cDNA Reverse Transcription Kit Fisher Scientific Cat#43-688-14 Chromium Next GEM Single Cell 5’ Kit v2 10X Genomics Cat: PN-1000263 5’ Feature Barcode Kit 10X Genomics Cat: PN-1000541 Library Construction Kit 10X Genomics Cat: PN-1000190 Dual Index Kit TT Set A 10X Genomics Cat: PN-1000215 Dual Index Kit TN Set A 10X Genomics Cat: PN-1000250 Pierce ™ BCA Protein Assay Kits Thermo Scientific Cat#23227 Deposited data Mouse B16 CD8+ T cell dLN and TIL bulk RNAseq Ford et al. 33 GEO: GSE175408 Mouse P14 circ memory and TRM scRNAseq Crowl et al. 78 GEO: GSE182275 Mouse P14 T cells isolated from IEL or spleens postinfection (naïve to d90) scRNAseq Kurd et al. 80 GEO: GSE131847 Human melanoma TIL pre/post checkpoint blockade scRNAseq Sade-Feldman et al. 60 GEO: GSE120575 Clinical outcomes to PD1 blockade in patients with metastatic melanoma bulk RNAseq Liu et al. 61 dbGaP Study Accession: phs000452.v3.p1 Exhausted Mouse CD8 T cells generated in vitro bulk RNAseq Scharping et al. 81 GEO: GSE155192 CITE-seq of d5 ligase KO P14 T cells from LCMV Armstrong This paper GEO: GSE298127 Token: gxghoosifloznup DIA PASEF ex vivo T cell mass spectrometry data (T EFF , TPEX, TEX, TRM) This paper ProteomeXchange Consortium PRIDE: PXD063865 Token: ihZ5VtMxuC4V DIA PASEF ex vivo T cell mass spectrometry data (T CM , T EM , LLTE, T RM ) This paper ProteomeXchange Consortium PRIDE: PXD070932 Token: cdDl1Hst6nOo TMT ex vivo T cell mass spectrometry data This paper ProteomeXchange Consortium MassIVE: MSV000097956 Password: Sharping2025 CITE-seq of CD3ε+ T cells from d7 LCMV Armstrong This paper GEO: GSE301271 Token: inevuaaobruthqv CITE-seq of CD3ε+ T cells from d30 LCMV Armstrong This paper GEO: GSE301960 Token: elsngwocxtwphiz CITE-seq of CD3ε+ T cells from B16-GP33 This paper GEO: currently uploading, ID available in <5 business days Protein turnover in vitro T cell mass spectrometry data This paper ProteomeXchange Consortium PRIDE: PXD067417 Token: g2FhAs25KH6N EV and Neurl3 OE P14 - dLN and TIL bulk RNAseq This paper GEO: GSE298128 token: kfenckiqrdozhqz Experimental models: Cell lines Platinum-E (Plat-E) Retroviral Packaging Cell Line Cell Biolabs Cat# RV-101 C57BL/6 mouse colon adenocarcinoma MC38, modified for GP33-41 expression Kerafast Parental line: Cat#ENH204-FP C57BL/6 mouse B16-F10 skin melanoma, modified for GP33-41 expression Gift from Dr Alain Lamarre NA Experimental models: Organisms/strains Mouse; C57BL/6J Jackson Laboratory Strain#000664; RRID: IMSR_JAX:000664 Mouse; B6.PL- Thy1 a /CyJ Jackson Laboratory Strain#000406; RRID: IMSR_JAX:000406 Mouse; P14 CD8 Transgenic (NCI background) Rafi Ahmed, Emory University N/A Mouse; B6.SJL- Ptprc a Pepc b /BoyJ Jackson Laboratory Strain#002014; RRID: IMSR_JAX:002014 Oligonucleotides Beta Actin F: GAGACCTTCAACACCCC This paper NA Beta Actin R: GTGGTGGTGAAGCTGTAGCC This paper NA Neurl3 F: TGTGTACGGGACCACGAAAG This paper NA Neurl3 R: ACTACAGCCACCTCCTCGAT This paper NA Rnf149 F: GGAGTTCATCAGCGGTCAGT This paper NA Rnf149 R: ACACATTGGACACGTTCGGT This paper NA Wsb1 F: CTGGACGGTTGCTTTTGCTC This paper NA Wsb1 R: TAACGTGCTCAGGAGGCTTG This paper NA tracr RNA (Alt-R ® CRISPR-Cas9 tracrRNA, ATTO ™ 550) IDT Cat#1077024 /AltR1/rCrC rGrCrC rArUrG rArGrA rArUrA rArCrA rCrCrA rGrUrU rUrUrA rGrArG rCrUrA rUrGrC rU/AltR2/ IDT Mm.Cas9.THY1.1.AC /AltR1/rUrA rGrArG rGrGrU rGrGrC rUrCrU rUrCrG rCrCrA rGrUrU rUrUrA rGrArG rCrUrA rUrGrC rU/AltR2/ IDT Mm.Cas9.NEURL3.1.AR /AltR1/rGrG rArGrG rArGrC rArGrA rGrUrC rCrCrA rCrGrU rGrUrU rUrUrA rGrArG rCrUrA rUrGrC rU/AltR2/ IDT Mm.Cas9.NEURL3.1.AS /AltR1/rArG rGrUrC rCrGrG rGrCrA rCrArC rGrArA rGrGrG rGrUrU rUrUrA rGrArG rCrUrA rUrGrC rU/AltR2/ IDT Mm.Cas9.NEURL3.1.AL /AltR1/rArG rUrUrU rCrCrA rCrGrG rGrArA rCrGrC rCrArC rGrUrU rUrUrA rGrArG rCrUrA rUrGrC rU/AltR2/ IDT Mm.Cas9.NEURL3.1.AJ /AltR1/rArU rGrGrU rArUrC rGrUrG rUrUrC rArGrC rCrArG rGrUrU rUrUrA rGrArG rCrUrA rUrGrC rU/AltR2/ IDT Mm.Cas9.NEURL3.1.AP /AltR1/rGrU rUrGrU rArGrA rCrCrA rCrCrA rCrGrG rCrCrG rGrUrU rUrUrA rGrArG rCrUrA rUrGrC rU/AltR2/ IDT Mm.Cas9.RNF149.1.AB /AltR1/rGrU rArGrC rUrGrA rUrCrC rUrGrU rCrCrA rArArU rGrUrU rUrUrA rGrArG rCrUrA rUrGrC rU/AltR2/ IDT Mm.Cas9.WSB1.1.AT /AltR1/rUrC rUrUrA rCrGrG rCrArC rUrGrG rGrArC rCrArC rGrUrU rUrUrA rGrArG rCrUrA rUrGrC rU/AltR2/ IDT Mm.Cas9.WSB1.1.AS /AltR1/rUrC rCrUrU rGrUrG rArCrC rArCrG rCrArA rArGrU rGrUrU rUrUrA rGrArG rCrUrA rUrGrC rU/AltR2/ IDT Mm.Cas9.WSB1.1.AP /AltR1/rCrA rGrUrC rUrArU rArArC rGrUrG rCrUrC rArGrG rGrUrU rUrUrA rGrArG rCrUrA rUrGrC rU/AltR2/ IDT Mm.Cas9.WSB1.1.AD /AltR1/rArC rArArG rGrArG rUrArA rGrCrU rCrCrC rArUrC rGrUrU rUrUrA rGrArG rCrUrA rUrGrC rU/AltR2/ IDT Mm.Cas9.WSB1.1.AG Recombinant DNA pMSCV-IRES-mAmetrine 1.1 FP Addgene Cat#52113 pMSCV-IRES Neurl3 mAmetrine This paper NA pMSCV-IRES Rnf149 mAmetrine This paper NA pMSCV-IRES Wsb1 mAmetrine This paper NA pMSCV-IRES Thy1.1 Addgene Cat#17442 pMSCV-IRES Neurl3 Thy1.1 This paper NA pMSCV-IRES Rnf149 Thy1.1 This paper NA pMSCV-IRES Wsb1 Thy1.1 This paper NA pMSCV-IRES-mCherry FP Addgene Cat#52114 pMSCV-IRES Rnf149 mCherry This paper NA Software and algorithms GraphPad Prism (v 7.0 / 9.0) GraphPad https://www.graphpad.com/ FlowJo TreeStar https://www.flowjo.com/ Trimmomatic Bolger et al. 97 https://github.com/usadellab/Trimmomatic STAR Dobin et al. 98 https://github.com/alexdobin/STAR DESeq2 Love et al. 100 https://github.com/mikelove/DESeq2 EnhancedVolcano Bioconductor https://github.com/kevinblighe/EnhancedVolcano dsb Mulè et al. 87 https://github.com/niaid/dsb Scanpy Wolf et al. 88 https://github.com/scverse/scanpy scvi-tools Gayoso et al. 90 https://github.com/scverse/scvi-tools gseapy Fang et al. 93 https://github.com/zqfang/GSEApy clusterProfiler Yu et al. 108 https://github.com/YuLabSMU/clusterProfiler GSVA Hänzelmann et al. 109 https://bioconductor.org/packages/release/bioc/html/GSVA.html limma Ritchie et al. 107 https://bioconductor.org/packages/release/bioc/html/limma.html pheatmap CRAN https://cran.r-project.org/web/packages/pheatmap/index.html MAGIC van Dijk et al. 102 https://github.com/KrishnaswamyLab/MAGIC contrastiveVI Weinberger et al. 96 https://github.com/suinleelab/contrastiveVI UMAP McInnes et al. 101 https://github.com/lmcinnes/umap nlfitr Github https://github.com/TJMurphy/nlfitr broom CRAN https://CRAN.Rproject.org/package=broom Seaborn Waskom 95 https://github.com/mwaskom/seaborn Matplotlib Hunter 94 https://matplotlib.org/ ggplot2 CRAN https://CRAN.Rproject.org/package=ggplot2 py_diAID Skowronek et al. 105 https://github.com/MannLabs/pydiaid DIA-NN 1.8.1 Demichev et al. 106 https://github.com/vdemichev/DiaNN Biorender Biorender https://www.biorender.com/ R (v4.4.3) The R Project for Statistical Computing http://www.r-project.org Spectronaut software (version 18.2 and 20.3) Biognosys https://biognosys.com/software/spectronaut/ Other LS Columns Miltenyi Biotec Cat#130-042-401 MidiMACS ™ Separator and Starting Kits Miltenyi Biotec Cat#130-042-301 4D-Nucleofector ® Core Unit Lonza Cat#AAF-1003B 4D-Nucleofector ® X Unit Lonza Cat#AAF-1003X PepMap Neo trap column Thermo Scientific Cat# 174500 PepSep reversed-phase C18 column Bruker Daltonics Cat# 1893476 Probe Sonicator Fisher Scientific Cat# FB120110 Molecular Devices SpectraMax iD3 Molecular Devices NA Resolvex A200 Tecan NA SpeedVac SPD210 Thermo Scientific Cat# SPD210-230 EvoSep One EvoSep Cat# EV1000 timsTOF Pro Bruker Daltonik NA Endurance Column (OE) – 30 SPD EvoSep Cat# EV1113 Open in a new tab STAR methods: Reagents: See key resource table . Software: See key resource table . Experimental Model and Study Participant Details Mice All mouse strains were bred and housed in specific pathogen-free conditions in accordance with the Institutional Animal Care and Use Guidelines of the University of California, San Diego (UCSD) in the temperature range of 18°–23°C in 40%–60% humidity. Male and female mice were used in the present study, between 6–12 weeks old. All the mice used had the C57BL/6J background. P14, Thy1.1 and CD45.1 congenic mice were bred in-house. All the animal studies were approved by the Institutional Animal Care and Use Committees of UCSD and performed in accordance with UC guidelines. Cell culture B16 melanoma cells and MC38 colorectal tumor cells that expressed the LCMV glycoprotein epitope amino acids 33–41 (B16-GP 33–41 ) and PLAT-E cells were cultured in Dulbecco’s modified Eagle’s medium + D-glucose supplemented with 10% fetal bovine serum (FBS), 100 U ml −1 penicillin, 100 μg ml −1 streptomycin, 292 μg ml −1 L-glutamine, 1× non-essential amino acids, 1 mM sodium pyruvate, 10 mM HEPES and 55 μM 2-mercaptoethanol. The cell lines were confirmed to be free of mycoplasma using quantitative PCR (qPCR). Enriched CD8 + T cells were maintained in RPMI supplemented with 10% FBS, 100 U ml −1 penicillin, 100 μg ml −1 streptomycin, 292 μg ml −1 L-glutamine, 1× non-essential amino acids, 10 mM HEPES and 55 μM 2-mercaptoethanol. For the protein turnover experiment, T cells were cultured in SILAC media, including SILAC RPMI supplemented with 10% dialyzed FBS, 100 U ml −1 penicillin, 100 μg ml −1 streptomycin, 292 μg ml −1 L-glutamine, 1× non-essential amino acids, 10 mM HEPES and 55 μM 2-mercaptoethanol, heavy isotopes of arginine ( 13 C 6 , 15 N 4 ) and lysine ( 13 C 6 , 15 N 2 ), and 100 μM alanine 83 . Method Details Tumor studies For the MC38-GP 33–41 TIL analysis, recipient mice were injected intradermally with 1 × 10 6 MC38-GP 33–41 adenocarcinoma cells. For co-transfers of transduced P14 cells, donor mice were sex- and age-matched to recipients or female donors, and the cells were transferred into recipients. On d5 post-activation, sorted ametrine + cells were mixed at a 1:1 ratio and a total of 5 × 10 5 P14 cells was transferred by intravenous (i.v.) injection into CD45 congenic recipients with d10 tumors. The mice were monitored three times per week, and the mice were sacrificed 10 days after the adoptive T cell transfer, for dLN and TIL analyses. For the MC38-GP 33–41 tumor growth curves, a total of 1 × 10 6 P14 cells was transferred by i.v. injection into CD45 congenic recipients with d8 tumors. The mice were monitored three times per week, and mice were removed from the study when their tumors extended 15 mm in any direction. For the B16-GP 33–41 TIL analysis, recipient mice were injected intradermally with 2.5 × 10 5 B16-GP 33–41 melanoma tumor cells. For co-transfers of transduced P14 cells, donor mice were sex- and age-matched to recipients or female donors, and the cells were transferred into recipients. On d5 post-activation, sorted ametrine + cells were mixed at a 1:1 ratio and a total of 2 × 10 6 P14 cells was transferred by i.v. injection into CD45 congenic recipients with d8 tumors. The mice were monitored three times per week, and mice were sacrificed 8 days after the adoptive T cell transfer, for dLN and TIL analyses. For the B16-GP 33–41 tumor growth curves, a total of 5 × 10 6 P14 cells was transferred by i.v. injection into CD45 congenic recipients with d6 tumors. Mice were also administered isotype or anti-PD-1 therapy, 200 μg of Armenian hamster isotype antibody or anti-PD-1 antibody intraperitoneally (i.p.) every other day. Mice were removed from the study when their tumors extended 15 mm in any direction. Infection studies Donor mice were sex- and age-matched to recipients or female donors, and the cells were transferred into recipients. For LCMV Amstrong experiments, C57BL/6J P14 CD8 + T cells congenic for CD45 or Thy1 were adoptively transferred at 2.5 × 10 4 cells per recipient mouse by i.v. injection into CD45 or Thy1 congenic recipients. The mice were then infected with 2 × 10 5 plaque-forming units (pfu) of LCMV Armstrong by i.p. injection 24 h after transfer. For co-transfers of nucleofected cells, P14 cells were mixed at a 1:1 ratio and a total of 5 × 10 5 P14 cells was transferred by i.v. injection into CD45 or Thy1 congenic recipients. Thereafter, recipient mice were infected with 2 × 10 5 pfu of LCMV by i.p. injection. For the experiment using sorted T CM followed by reinfection, control or KO T CM P14 cells mixed at a 1:1 ratio and a total of 6 × 10 3 P14 cells was transferred by i.v. injection into CD45 or Thy1 congenic recipients. Thereafter, recipient mice were infected with 2 × 10 5 pfu of LCMV by i.p. injection. For LCMV Cl13 experiments, co-transfers of transduced P14 cells cells were mixed at a 1:1 ratio of sorted, ametrine + cells, and a total of 2 × 10 3 P14 cells was transferred by i.v. injection into CD45 or Thy1 congenic recipients. Thereafter, recipient mice were infected with 2 × 10 6 pfu of LCMV by retro-orbital injection. Preparations of single-cell suspensions (tumor) Single-cell suspensions of lymphocytes were prepared by mechanical disaggregation followed by treatment with ACK (ammonium–chloride–potassium) lysing buffer. The tumor was minced into small pieces and then incubated in RPMI with 1.2 mg ml −1 of HEPES, 292 μg ml −1 L-glutamine, 1 mM MgCl 2 , 1 mM CaCl 2 , 5% FBS and 100 U ml −1 collagenase (Worthington) at 37°C for 30 min. Lymphocytes from the tumor were separated on a 44%/67% Percoll density gradient. For the B16 experiments, Percoll density gradient separation was followed by lymphocyte separation media to further purify the lymphocytes. Preparation of single-cell suspensions (other non-lymphoid tissues) To identify CD8 + T cells in the vasculature of nonlymphoid tissues (small intestine, kidney, and liver), 3 μg of anti-CD8α (clone 53–6.7) conjugated with APC-eFluor 780 were injected i.v. into the mice 5 min before sacrifice. Cells that displayed weak or no labeling with the anti-CD8α antibody were considered to be located outside the vasculature. Single-cell suspensions of splenocytes were prepared by mechanical disaggregation followed by treatment with ACK lysing buffer. Intraepithelial lymphocytes (IEL) were prepared through the removal of Peyer’s patches and the luminal contents. The small intestine was cut longitudinally and into 1-cm pieces, then incubated at 37°C for 30 min in Hanks’ balanced salt solution with 2.1 mg ml −1 sodium bicarbonate, 2.4 mg ml −1 HEPES, 8% bovine growth serum and 0.154 mg ml −1 DTE (EMD Millipore). The kidneys and liver were minced into small pieces and then incubated in RPMI with 1.2 mg ml −1 of HEPES, 292 μg ml −1 L-glutamine, 1 mM MgCl 2 , 1 mM CaCl 2 , 5% FBS and 100 U ml −1 collagenase (Worthington) at 37°C for 30 min. Lymphocytes from the small intestine, kidney, and liver were separated on a 44%/67% Percoll density gradient. Generation of retrovirus-containing supernatants and CD8 + T cell transduction To generate Neurl3 -, Rnf149 -, and Wsb1 -overexpressing vectors, gBlocks of coding sequences (IDT) were cloned into MSCV-IRES-reporter vectors via NEBuilder HiFi DNA Assembly cloning, and then transformed into DH5alpha competent Escherichia coli . Successful cloning was confirmed by whole plasmid sequencing. Retroviral particles were produced by first plating 2 million PLAT-E cells on a 10-cm tissue culture dish 1 day before transfection. On the following day, each plate was transfected with 2 μg of pCL-Eco and 6 μg of the plasmid of interest using TransIT-LT1 (Mirus). After 24 h, plat-E media was replaced with T cell culture media to collect the retrovirus. On the same day, P14 T cells were isolated from the spleen and lymph nodes and negatively enriched; these cells were the resuspended with 2 μg/ml anti-CD28 antibody (catalog no. 37.51, eBioscience) and 50 U/ml IL-2 (CETUS) and plated in a 6-well dish that was coated with 5 μg/ml anti-CD3 antibody (catalog no. 145–2C11, eBioscience) for activation. Twenty-four hours after plating the T cells, retronectin-coated, non-tissue culture-treated plates were first incubated with retroviral media, after which the T cells resuspended in retroviral medium plus IL-2 were added to retronectin plates and spinfected for 40 min at 1,600 RPM at 37°C, followed by incubation overnight in retronectin plates with retroviral medium. Twenty-four hours after transduction, all the ametrine + cells (as assessed by flow cytometry) were considered to be transduced. For the TIL analysis experiments, the cells were then sorted based on reporter expression, and expanded in vitro prior to adoptive transfer. CRISPR-mediated gene knockout P14 T cells were isolated from the spleen and lymph nodes and negatively enriched. They were then resuspended with 2 μg/ml anti-CD28 antibody (catalog no. 37.51, eBioscience) and 50 U/ml IL-2 (CETUS) and plated in a 6-well dish coated with 5 μg/ml anti-CD3 antibody (catalog no. 145–2C11, eBioscience) for activation. At 24 h post-activation, using the P3 Primary Cell 4D-Nucleofector X Kit and Unit, the T cells were electroporated with complexed tracrRNA (IDT), Cas9 (UC Berkeley), and pooled crRNA (IDT). crRNA targeting Thy1 (control) was used alone as a control and was mixed with the conditional crRNA to be used as an indicator of electroporated cells 84 , 85 . Flow cytometry and cell sorting For sorting, the cells were incubated with antibodies for 15 min at 4°C in PBS that was supplemented with 0.5% BSA and 2 mM EDTA, followed by cell collection in medium. For flow cytometry, the cells were incubated with the indicated antibodies for 15 min at 4°C in PBS that was supplemented with 2% bovine growth serum and 0.01% sodium azide. All cells were then fixed with Fixation Buffer (BioLegend) to preserve surface staining. Cytoplasmic staining was completed using the BD Cytofix/Cytoperm ™ Fixation/Permeabilization Kit (BD), and intranuclear staining was performed using the FoxP3 transcription factor staining kit (eBioscience). For assays that involved CD8 + T cell stimulation, P14 cells were incubated for 4 h in T cell culture medium at 37°C with 10 nM GP 33–41 peptide and Protein Transport Inhibitor (eBioscience), together with congenially distinct splenocytes for peptide presentation. K48 staining included 10 μM MG-132 and incubation at 37°C for 1 h prior to staining. Proteostat staining included congenically distinct, naive splenocytes added to each well for dye staining normalization, followed by surface staining, surface fixation, and cytoplasmic fixation, followed by staining with Proteostat dye (diluted 1:50,000 in perm wash). The dye was not washed out before running the samples, as according to manufacturer’s instructions. For ligase-overexpressing P14 Proteostat staining, Thy1.1 was used as a reporter, due to ametrine’s spectral overlap with Proteostat. For ImageStream visualization of Proteostat staining, the same numbers of cells per group were stained to normalize for background staining. Stained cells were analyzed using the LSRFortessa or LSRFortessa X-20 cytometers (BD), FACSDiva software, and Flowjo software (TreeStar). All sorting was performed on BD FACSAria Fusion instruments. Real-Time PCR RNA was isolated using the RNeasy Plus Mini Kit (QIAGEN). Complementary DNA was reverse-transcribed using the High Capacity cDNA Reverse Transcription Kit (Applied Biosystems). Real-Time PCR was performed with SYBR Green and primers for Neurl3 , Rnf149 , Wsb1 , and Actb ; quantification was performed using the ΔΔC t method. Proteasome activity assay Single-cell suspensions of LN and TIL were sorted as described above. After sorting, 5,000 cells per well were plated in 100 μl of medium in a white-walled, clear-bottom plate, with or without 10 μM MG-132 and incubated at 37°C for 1 h. Proteasome activity was then assessed using the Cell-Based Proteasome-Glo 3-Substrate System (Promega) according to the manufacturer’s instructions. CITE-seq of CD3ε + T cells from B16-GP 33–41 and LCMV Armstrong Experimental setup, staining, and sorting – B16-GP 33–41 Recipient mice were injected intradermally with 5 × 10 5 B16-GP 33–41 melanoma tumor cells. Congenically distinct CD45.1.2 P14 T cells and CD45.1 Pmel T cells were activated in vitro (plate-bound CD3 and CD28), expanded to day 5, then 2.5 × 10 6 of each transgenic were transferred into day 7 tumor recipients (CD45.2 B6 mice). Mice also began 200ug anti-4–1BB + 50ug anti-PD-1 checkpoint blockade immunotherapy or isotype control on the day of adoptive T cell transfer, then every other day thereafter. CD3ε+ T cells from dLN or TIL were profiled by CITE-seq on day 7 after transfer. We first sorted (unless otherwise specified) 90,000 endogenous CD3ε+ T cells or 11,250 transferred T cells from the dLN or tumor into DMEM supplemented with 2% bovine growth serum and 10 mM HEPES. Due to cell number limitations, 10,000 endogenous CD3ε+ cells, 6000 P14 T cells, and 1000 Pmel T cells were sorted from the immunotherapeutic tumor group. When enough cells could not be sorted from the original sample, unstained cells were adding to get a total 500,000 cells. Following the first sort, hash-tagged cells were pooled incubated with the ImmGenT TotalSeq-C custom mouse panel containing a custom panel of 128 antibody-derived tags (ADT; see STAR methods ) and washed thoroughly. For the second sort, 50,000 CD3ε+ T total cells from the pooled tissues were sorted. 5,000 CD3ε+ T cells from a naive spleen were also sorted to allow for batch correction with totalVI. Experimental setup, staining, and sorting – LCMV-Armstrong Prior to LCMV infection, congenically distinct 5 × 10 4 naive CD45.1 P14 T cells and 1 × 10 5 naive CD45.1.2 SMARTA T cells were transferred into naive CD45.2 B6 recipients. Then, CD3ε+ T cells from siIEL. siLPL, prostate, SG, lung, bone marrow, spleen, mesenteric lymph nodes and mediastinal lymph nodes were profiled by CITE-seq on days 7, 30, and 60 of LCMV Armstrong infection. For each timepoint, we first sorted 5×10 4 CD3ε+ T cells from the spleen, prostate, SG, bone marrow, mesenteric lymph node, siIEL, siLPL, lung and mediastinal lymph node were initially sorted (unless otherwise specified) into DMEM supplemented with 2% bovine growth serum and 10 mM HEPES. Due to cell number limitations, on day 7 4.36 ×10 3 CD3e cells were sorted from the SG and 4.32 ×10 3 CD3e cells from the prostate. On day 30, 25,106 CD3e cells from the SG, 19,277 CD3e cells from the prostate and 1,245 CD3e cells were sorted from the lung. When 5×10 4 cells could not be sorted from the original sample, unstained cells were adding to get a total 5×10 4 cells. Following the first sort, hash-tagged cells were pooled incubated with ImmGenT TotalSeq-C custom mouse panel containing a custom panel of 128 antibody-derived tags (ADT; see STAR methods ) and washed thoroughly. For the second sort, 5×10 4 CD3ε+ T total cells from the pooled tissues were sorted. For each timepoint, 5×10 3 CD3ε+ T cells from a naïve spleen were also sorted to allow for batch correction with totalVI. Single-cell RNA, TCR, and TotalSeq-C sequencing—Library preparation The collected cells were pooled and split into two technical replicates (independent libraries) for all subsequent steps. scRNA-seq was performed using 10X Genomics 5’v2 with feature barcoding for cell surface protein and immune receptor mapping following the manufacturer’s instructions. After cDNA amplification, the smaller fragments containing the TotalSeq-C–derived cDNA were purified and saved for feature barcode library construction. The larger fragments containing transcript-derived cDNA were saved for TCR and gene expression library construction. For both cDNA portions, the library size was measured with an Agilent Bioanalyzer 2100 high-sensitivity DNA assay and quantified using a Qubit dsDNA HS assay kit on a Qubit 4.0 fluorometer. TCR cDNA was amplified from the transcript-derived cDNA by nested PCR according to the manufacturer’s protocol. The TCR libraries were then subjected to enzymatic fragmentation, ligation and indexing with unique Dual Index TT set A index sequences with an index PCR. Transcript-derived cDNA was processed into RNA libraries. After enzymatic fragmentation and size selection, purified cDNA was ligated to an Illumina R2 sequence and indexed with unique Dual Index TT set A index sequences with an index PCR. TotalSeq-C–derived cDNA was processed into feature barcode libraries. The cDNA was indexed with unique Dual Index TN set A index sequences with an index PCR. The three libraries were pooled together on the basis of molarity with relative proportions of 47.5% RNA, 47.5% feature barcode, and 5% TCR. This pool was then sequenced on an Illumina NovaSeq S2, 100c. The sequencing configuration followed 10X Genomics specifications. Single-cell RNA, TCR, and TotalSeq-C sequencing—Data processing Gene and TotalSeq-C antibody (surface protein panel and hashtags) counts were obtained by aligning reads to mm10(GRCm38) and the M25(GRCm38.p6) GeneCode annotation (GRCm38.p6) of the mouse genome and the DNA barcode for the TotalSeq-C panel using CellRanger software (v7.1.0) with default parameters. Cells were distinguished from droplets with high RNA and TotalSeq-C counts using inflection points on a total count curve (barcodeRanks function in the DropletUtils package). Samples were demultiplexed using hashtag oligo (HTO) counts and the HashSolo algorithm 86 , and doublets (cells positive for multiple hashtags) were excluded. Surface protein expression was normalized using the DSB method to remove ambient background and technical noise 87 . Cells were filtered if they met any of the following criteria: expression of two isotype control antibodies, fewer than 150 RNA UMI counts, or >5% mitochondrial gene expression. RNA counts were normalized to 10,000 transcripts per cell using normalize_total() and log-transformed with log1p in Scanpy 88 . Highly variable genes were selected using the Seurat v3 method with batch correction 89 , and expression data were scaled to unit variance (max value = 10) prior to integration. To jointly model RNA and protein data, we applied totalVI from the scvi-tools framework 90 . RNA and protein modalities from 3 conditions (d7 LCMV, d30 LCMV, Tumor) were concatenated using MuData, and totalVI was trained to a shared latent space accounting for batch effects and noise. Clustering, dimensionality reduction, and subset annotation Cells of interest were first subset from the larger multimodal dataset based on protein expression of CD4 and CD8A. TotalVI-denoised protein values for CD4 and CD8A were visualized in a 2D scatter plot, and thresholds were manually defined to retain CD8-enriched and CD4-low cells, isolating CD8 + T cell populations. To further refine the dataset, P14 TCR transgenic T cells were identified by high co-expression of TCRVA2 and CD45.1 proteins, using denoised TotalVI protein layers. For RNA-based clustering, we used the totalVI-derived RNA embedding (X_pca) and computed a neighborhood graph using 10 nearest neighbors (sc.pp.neighbors). Clustering was performed using the Leiden algorithm 91 at a resolution of 0.1. UMAP was used for visualization (sc.tl.umap). Clusters were annotated post hoc based on enrichment of previously defined transcriptional signatures for T RM 10 , T PEX , T EX , and T EFF 3 subsets. Enrichment scores were calculated using scanpy.tl.score_genes() for each signature and violin plots were generated to visualize signature distribution across clusters. TIL T PEX vs T EX clusters were also confirmed using the protein modality PDCD1 and HAVCR2 expression. Differential expression and pathway analysis For each unique pairwise comparison between clusters, a Wilcoxon rank-sum test was applied using scanpy.tl.rank_genes_groups() to identify significantly differentially expressed genes (DEGs) 88 by RNA expression. Genes were considered significant if they had an adjusted p-value < 0.05 and an absolute log 2 fold-change > 1.0. DEGs across clusters was used to construct gene “signatures,” representing unique combinations of clusters in which a gene was upregulated. Each signature was stored as a tuple and linked to its associated genes, enabling identification of genes uniquely or jointly expressed across subsets. Each gene signature was subjected to enrichment analysis using the mouse GO:Biological Process 2023 gene set via Enrichr in Python through the gseapy package, with background set to genes expressed in the dataset 92 , 93 . Signatures with fewer than five genes were excluded. For each valid group the top five pathways (by adjusted p-value) were tagged, all significantly enriched pathways (FDR < 0.05) were retained, and combined pathway lists were exported, both globally and per T cell subset (T RM , T PEX , T EX , T EFF ). To visualize pathways enriched across gene signatures, a bubble plot was generated using Seaborn and Matplotlib 94 , 95 . The size of each point reflected the number of genes contributing to enrichment, while color intensity encoded statistical significance as –log 10 (FDR), capped at 10. Pathway names were plotted on the y-axis and gene signature combinations on the x-axis. GSVA was performed on normalized scRNA-seq data using defined clusters. Differentially expressed genes between clusters were first identified with Wilcoxon test, and the union of significant DEGs (|log 2 FC| > 1, FDR < 0.05) was used. GO Biological Process gene sets were retrieved with gseapy (GO_Biological_Process_2023, Mouse), mapped to mouse gene symbols, and GSVA scores were computed per cell; pathway differences across clusters were tested by one-way ANOVA with Benjamini–Hochberg FDR correction, and significant pathways were visualized as GSVA score heatmaps using seaborn and matplotlib 93 . CITE-seq of d5 ligase KO P14 T cells from LCMV Armstrong Experimental setup, staining, and sorting Congenically distinct P14 T cells were activated and electroporated with complexed tracrRNA, Cas9, and crRNA (as described above) to generate CRISPR control and KO P14 T cells, which were co-transferred into recipients then infected with LCMV Armstrong. On day 5 of infection, P14 T cells from IEL, liver, and spleen, were profiled by CITE-seq. We first sorted 5×10 4 P14 T cells from the IEL, liver, and spleen of 4 pooled mice per group (control vs NEURL3 KO , control vs RNF149 KO , and control vs WSB1 KO ) into DMEM supplemented with 2% bovine growth serum and 10 mM HEPES. Due to the number of groups, all control samples were pooled together and all KO samples were pooled together. Following the first sort, hash-tagged cells were pooled incubated with antibody-derived tags (ADT, TotalSeq ™ -C Mouse Universal Cocktail, V1.0) and washed thoroughly. For the second sort, all stained cells from the pooled tissues were sorted as well as 5×10 4 CD8+ T cells from a naive spleen were also sorted to allow for batch correction with totalVI. Single-cell RNA and TotalSeq-C sequencing—Library preparation scRNA-seq was performed using 10X Genomics 5’v2 with feature barcoding for cell surface protein and immune receptor mapping following the manufacturer’s instructions. After cDNA amplification, the smaller fragments containing the TotalSeq-C–derived cDNA were purified and saved for feature barcode library construction. The larger fragments containing transcript-derived cDNA were saved for gene expression library construction. For both cDNA portions, the library size was measured with an Agilent Bioanalyzer D5000 high-sensitivity DNA assay and quantified using a Qubit 2.0 fluorometer. Transcript-derived cDNA was processed into RNA libraries. After enzymatic fragmentation and size selection, purified cDNA was ligated to an Illumina R2 sequence and indexed with unique Dual Index TT set A index sequences with an index PCR. TotalSeq-C–derived cDNA was processed into feature barcode libraries. The cDNA was indexed with unique Dual Index TN set A index sequences with an index PCR. The two libraries were pooled together on the basis of molarity with relative proportions of 40% GEX (RNA) and 10% surface (feature barcode) for each control and KO pooled samples. This pool was then sequenced on an Illumina NovaSeq X Plus 10B. The sequencing configuration followed 10X Genomics specifications. Single-cell RNA and TotalSeq-C sequencing—Data processing Gene expression and TotalSeq-C antibody (surface protein panel and hashtags) counts were obtained using CellRanger (v7.1.0, 10x Genomics) with default parameters. Reads were aligned to the mm10 (2020) reference transcriptome and the DNA barcode for the TotalSeq-C panel. Samples were demultiplexed using hashtag oligo (HTO) counts and the HashSolo algorithm 86 , and doublets (cells positive for multiple hashtags) were excluded. Surface protein expression was normalized using the DSB method to remove ambient background and technical noise 87 . Cells were filtered if they met any of the following criteria: expression of two isotype control antibodies, fewer than 150 RNA UMI counts, or >5% mitochondrial gene expression. RNA counts were normalized to 10,000 transcripts per cell using normalize_total() and log-transformed with log1p in Scanpy 88 . Highly variable genes were selected using the Seurat v3 method with batch correction 89 , and expression data were scaled to unit variance (max value = 10) prior to integration. To jointly model RNA and protein data, we applied totalVI from the scvi-tools framework 90 . RNA and protein modalities from 2 conditions (Control and KO) were concatenated using MuData, and totalVI was trained to a shared latent space accounting for batch effects and noise. Dimensionality reduction & differential expression Dimensionality reduction was performed using contrastiveVI 96 . UMAP projections were computed using the salient representation, min_dist=1.5. Differentially expressed (DE) genes between knockout (KO) and control samples were identified using the Wilcoxon rank-sum test as implemented in Scanpy’s rank_genes_groups() function 88 , DE genes were considered significant if they had an adjusted p-value < 0.05 and an absolute log fold change > 0.1, and visualized using dot plots. EV and Neurl3 OE P14 – dLN and TIL bulk RNAseq Recipient mice were injected intradermally with 1 × 10 6 MC38-GP 33–41 adenocarcinoma cells. Congenically distinct P14 T cells were activated and transduced (as described above) to generate EV and Neurl3 OE P14 T cells, which were co-transferred into recipients on d5 post-activation cells were mixed at a 1:1 ratio and a total of 5 × 10 5 P14 cells was transferred by intravenous (i.v.) injection into CD45 congenic recipients with d6 tumors. The mice were monitored three times per week, and the mice were sacrificed 6 days after the adoptive T cell transfer. For each replicate, cells from 2–3 mice were pooled per tissue and then 5,000 P14 sorted for bulk RNA-seq. 1 × 10 3 were then resorted into 1× TCL lysis buffer + 1% 2-mercaptoethanol. Library preparation for ultra-low-input RNA-seq was performed as described online ( https://www.immgen.org/img/Protocols/ImmGenULI_RNAseq_methods.pdf ). Trimmomatic was used to remove adapters and trim low-quality reads (NexteraPE-PE.fa:2:30:10:1:TRUE LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:25) 97 . Trimmed reads were then aligned to the gencode M25 annotation of the mm10 genome using STAR with the default conditions 98 . Aligned reads were then quantified with featureCounts 99 (-t exon -g gene_id -p -B) and DEG was identified using DEseq2 100 . PCA analysis in was generated with the plotPCA function in DEseq2. Volcano Plot was generated with EnhancedVolcano. Analysis of human melanoma single-cell RNA-seq dataset Single-cell RNA-seq data from human melanoma patients treated with checkpoint blockade 60 was processed beginning with raw cell-by-gene matrices and associated metadata. Cells were filtered to retain those expressing at least 200 genes, and genes were retained if they were detected in three or more cells. Normalization was performed using Scanpy’s normalize_total and log1p functions 88 . Highly variable genes were identified and used for PCA dimensionality reduction. A k-nearest neighbors graph was constructed in PCA space, followed by UMAP embedding 101 and Leiden clustering 91 . Clusters expressing CD8 markers were identified and retained for downstream analysis, and only cells with CD8A expression above a defined threshold were kept. These CD8 + cells were re-clustered, inspected, and clusters of interest were identified based on expression profiles of canonical markers. A subset of clusters were extracted and relabeled (e.g., T PEX , T EX , T RM -like-TIL, etc.) based on gene expression in clusters. UMAP was recomputed for this subset and used for visualization. These renamed clusters were treated as categorical variables and used to stratify subsequent expression analyses. A heatmap was generated using canonical markers and the E3 ligases RNF149 and WSB1 to illustrate overlap with transcriptional T cell subsets, and expression values were scaled between 0 and 1 for comparability across genes. Excluding cells with zero expression, violin plots were then generated to visualize RNF149 and WSB1 expression across CD8 + T cells, stratified by treatment and response status, and significance was tested using unpaired t-test. Analysis of T RM infection time course and T RM across tissues For analysis of the P14 infection time course 80 and P14 T RM across tissues 78 , each were combined into a counts matrix using the merge function in Seurat. Data were log(normalized) and scaled using NormalizeData and ScaleData. Data imputation was performed using MAGIC 102 with the log(normalized expression) values and the default settings and the exact solver and plotted as violin plots. Data Independent Acquisition-parallel accumulation–serial fragmentation mass spectrometry For the T EFF , T PEX , T EX , and T RM dataset, naive P14 were adoptively transferred into mice and infected with LCMV as described above. At d7, 2 replicates of P14 from the spleen (T EFF ) and at d14, 3 replicates comprised of 6 pooled samples of P14 from the IEL (T RM ) were isolated, washed with PBS, and frozen as cell pellets. D14 was used as an early memory timepoint due to cell number limitations at d30. For endogenous TIL, mice received B16-GP 33–41 tumors as described above. Ad d14, 3 replicates comprised of 6 pooled samples each for T PEX , and 3 replicates comprised of 6 pooled samples each for T EX (T PEX sorted on CD8 + PD1 lo/int Tim3 − and T EX sorted on CD8 + PD1 hi Tim3 + ) were isolated, washed with PBS, and frozen as cell pellets. For the T CM , T EM , LLTE, and T RM dataset naive P14 were adoptively transferred into mice and infected with LCMV as described above. At d33, 4 replicates of P14 from the spleen (CD127 hi CD62L hi T CM , CD127 hi CD62L lo T EM , CD127 lo CD62L lo LLTE) and the IEL (T RM ) were isolated. The sample processing approach for low input T cell proteomics was adapted from a previous study 103 . 2,000 T cells were sorted into a PCR tube containing 2 μL of freshly prepared cell lysis buffer (0.045% n-dodecyl-β-D-maltoside, 90 mM TEAB pH 8.5) and snap frozen on dry ice. Sample preparation (T EFF , T PEX , T EX , and T RM ) Cell pellets, each containing 10 5 sorted cells, were resuspended in 50 μL of freshly prepared urea lysis buffer (8 M urea, 75 mM NaCl, 50 mM Tris-HCl, pH 8.0, 1 mM NaF, 1 mM sodium orthovanadate, 1 mM β-glycerophosphate). The suspension was sonicated for 300 s (30 s on/30 s off cycles) in a water bath sonicator (Fosmon, model #51028HOM). Protein was reduced with 10 mM TCEP for 30 min, alkylated with 15 mM freshly prepared iodoacetamide (IAA) for 45 min in the dark, and then treated with 10 mM DTT for 15 min to quench excess IAA. The samples were diluted to a final volume of 400 μL with 50 mM Tris-HCl, pH 8.0. For digestion, 200 ng LysC and 400 ng sequencing grade modified trypsin were added to the samples, which were then incubated at 37 °C with shaking overnight. The digestion was terminated by adding formic acid to achieve a final concentration of 1%. Sample preparation (T CM , T EM , LLTE, and T RM ) The samples were incubated at 95°C for 5 min and then at 4°C for 1 min in a thermal cycler (Applied Biosystems). For digestion, 1 μL of 100 ng sequencing grade modified trypsin was added to the solution, which was then incubated at 37 °C in a thermal cycler overnight. The digestion was terminated by adding 1 μL of 6% formic acid. Analysis (T EFF , T PEX , T EX , and T RM ) The digests were desalted using the Stage-Tip method. Subsequently, 2 μL of the 10 μL peptide sample was analyzed using an LC-MS/MS setup comprising a timsTOF Pro 2 mass spectrometer (Bruker Daltonics) coupled to a nanoElute 2 nano-LC system (Bruker Daltonics) via a CaptiveSpray ion source. Peptides were first loaded onto a trap column (PepMap Neo, 5 mm × 300 μm Trap) and then eluted through a reversed-phase C18 column (PepSep, 25 cm × 150 μm, 1.5 μm) maintained at 50 °C over a 50 min gradient of 5–35% solvent B (acetonitrile in 0.1% formic acid) at a flow rate of 450 nL/min. The peptides were analyzed using data-independent acquisition (dia) Parallel Accumulation-Serial Fragmentation (PASEF) mode. The isolation windows for dia-PASEF were determined based on data-dependent acquisition (dda)-PASEF data 104 and generated by implementing the py_diAID algorithm to maximize proteome coverage 105 . The settings for dda-PASEF included the following: a ramp time and an accumulation time of 75 ms each, one MS scan, and 10 PASEF MS/MS ramps per acquisition cycle. The MS survey scan covered 100–1700 m/z and 0.6–1.6 V·s/cm 2 . Precursors with up to 5 charges were selected, with an active exclusion time of 0.4 min. For dia-PASEF, 50 variable isolation windows for 25 dia-PASEF scan cycles (two windows per cycle) were designed to encompass the precursor distribution across the m/z-1/K0 plane as defined by the dda-PASEF data, ranging from 323.6 to 1221.6 m/z and 0.7 to 1.34 V·s/cm 2 , resulting in a cycle time of 2.11 s. Collision energy was decreased linearly with increasing mobility, starting from 59 eV at 1/K0 = 1.6 V·s/cm 2 and reducing to 20 eV at 1/K0 = 0.6 V·s/cm 2 . Analysis (T CM , T EM , LLTE, and T RM ) The peptides were directly loaded onto a trap column (PepMap Neo, 5 mm × 300 μm Trap), desalted online with 20 μL of solvent A (0.1% formic acid in H2O), and then eluted through a reversed-phase C18 column (PepSep, 25 cm × 150 μm, 1.5 μm) maintained at 50 °C over a 50 min gradient of 2–35% solvent B (0.1% formic acid in acetonitrile) at a flow rate of 300 nL/min. The dia-PASEF was performed as described above with the following modifications: high-sensitivity detection was enabled; ion accumulation and ramp time were set to 200 ms; each MS1 full scan was followed by 10 dia-PASEF MS2 scans (two isolation windows per scan), resulting in a cycle time of 2.2 s. Data processing (T EFF , T PEX , T EX , and T RM ) The dia-PASEF raw files were processed using a library-free search approach in DIA-NN version 1.8.1 106 . An in silico digestion of a UniProt Mus musculus reference proteome (downloaded in January 2023) was employed to predict the spectral library. The search parameters applied included the following: tryptic digestion with up to two missed cleavages, carbamidomethylation of cysteine as a static modification, and oxidation of methionine and N-terminal acetylation as variable modifications. The precursor false discovery rate (FDR) threshold was set at 1%. For quantification, the quantification strategy was set to Robust LC (high precision) and cross-run normalization was set to RT-dependent. Match-between-runs (MBR) was enabled. The resulting protein group matrix from DIA-NN, reported at a 1% FDR (global q-values for protein groups; global and run-specific q-values for precursors), was used for downstream and statistical analysis. The statistical analysis was performed using the limma package in R 107 . Data processing (T CM , T EM , LLTE, and T RM ) The data processing was performed as described above with the following modifications: DIA-NN version 2.2 was used; cysteine modification was unchecked; the quantification strategy was set to QuantUMS (high precision). Data analysis – differentially expressed proteins and pathway enrichment Principal component analysis (PCA) was performed on variance-stabilized data (vst and rlog transformations) to visualize sample clustering. Data were structured as a count matrix for differential expression analysis using the DESeq2 package 100 . Sample metadata were loaded in parallel and matched to sample columns for experimental group assignment. Differential expression analysis was performed using a negative binomial generalized linear model (GLM) framework implemented in DESeq2, with group.type used as the design formula. For each pairwise group comparison, significantly differentially expressed proteins were defined as those with an adjusted p -value < 0.05 and an absolute log 2 fold change > 0.5. Heatmaps of normalized expression values for the union of all DEGs were generated using the pheatmap package. To explore functional patterns among DEGs, hierarchical clustering was applied to the DEG genes were partitioned into 9 expression clusters. Gene Ontology (GO) enrichment analysis for Biological Process (BP) terms was performed on each cluster using the clusterProfiler package 108 , with gene symbol-to-Entrez ID mapping via org.Mm.eg.db. Top enriched mouse GO terms were visualized using a custom bubble plot showing –log 10 (adjusted p -value) and number of genes per term, with background set to proteins expressed in the dataset. Redundant GO terms were collapsed by prioritizing terms associated with shared gene sets and lower adjusted p -values. All visualizations were created using ggplot2. GSVA was used to calculate sample-wise enrichment scores using log-transformed, variance-stabilized intensity values. Mouse GO:BP gene sets were used as input, and enrichment scores were computed using the Gaussian kernel density estimation method (kcdf=“Gaussian”) in the GSVA package 109 . Differential enrichment of GSVA scores across groups was assessed using the limma package 107 , and significantly enriched pathways (FDR < 0.05) were identified via linear modeling and empirical Bayes moderation. Pathways related to proteasomal degradation and the unfolded protein response (UPR) were selected based on biological relevance and filtered to remove redundant GO terms, and heatmaps were generated via clustered cell type. Redundancy and overlap among enriched gene sets were assessed via Jaccard similarity matrices. T RM -anchored normalization and integration Proteomic data from the two DIA-PASEF datasets were processed in R: protein intensities were log 2 -transformed, and sample-median centered. Within each dataset, TRM samples were used as an internal reference to compute a per-gene TRM mean, which was subtracted to generate a TRM-centered matrix. TRM-centered matrices from both datasets were then combined on the union of genes, and genes with any non-finite values were removed for downstream analyses. PCA, heatmaps, GSVA and pathway analysis PCA was performed in R using prcomp (base R) and visualized with ggplot2. For heatmaps, TRM-anchored log 2 values were summarized as cell group mean protein expression on a TRM-anchored log 2 scale, row-z-scored, and plotted with pheatmap, using DE and LFC of 0.5-filtered gene clusters (1–5) as defined in Figure 4G . GSVA was performed with the GSVA R package on the TRM-anchored matrix using mouse GO:BP gene sets to generate pathway activity scores across T cell groups to identify significantly enriched protein degradation and unfolded protein response pathways. RNA vs protein analysis - RNA–Protein graph, quadrant-based pathway enrichment analysis, and distance matrix To assess the concordance between gene expression at the transcriptomic and proteomic levels, pseudobulk RNA-seq data and averaged protein intensities were merged by gene symbol. Scatter plots comparing RNA and protein abundances were generated, and genes were assigned to one of four quadrants based on their log-transformed expression relative to the geometric mean of RNA and protein values. For each quadrant (Q1–Q4), gene ontology enrichment analysis was performed using Enrichr via the gseapy package, and the top 3 biological process (GO:BP) pathways (filtered on ≥5 gene expression) were extracted per quadrant, with background set to genes expressed in the dataset 92 , 93 . The enrichment results were visualized using quadrant-wise bubble plots. Dot sizes encoded the number of genes per pathway and color indicated statistical significance (–log 10 p -value). To compare global transcriptome- and proteome-level similarity across subsets, principal component analysis (PCA) was conducted separately for RNA and protein data using scanpy 88 . Pairwise Euclidean distances between subsets in PCA space were computed using scipy.spatial.distance_matrix, and results were visualized as heatmaps. Tandem Mass Tag mass spectrometry For memory T cells, naive P14 were adoptively transferred into mice and infected with LCMV as described above. At d14, 3 replicates comprised of 6 pooled samples of CD127 hi CD62L lo P14 from the spleen (T EM ) and P14 from the IEL (T RM ) were isolated, washed with PBS, and frozen as cell pellets. D14 was used as an early memory timepoint due to cell number limitations at d30. For endogenous TIL, mice received B16-GP 33–41 tumors as described above. Ad d14, 3 replicates comprised of 6 pooled samples each for T PEX , and 3 replicates comprised of 6 pooled samples each for T EX (T PEX sorted on CD8 + PD1 lo/int Tim3 − and T EX sorted on CD8 + PD1 hi Tim3 + ) were isolated, washed with PBS, and frozen as cell pellets. For P14 dLN and TIL, mice received B16-GP 33–41 tumors as described above, then received d5 in vitro -activated P14 on d7 after tumor implantation. On d14, 3 replicates comprised of 10 pooled samples each for P14 dLN, 2 replicates comprised of 10 pooled samples each for P14 TIL were isolated, washed with PBS, and frozen as cell pellets. TMT-based proteomics was performed as previously described 45 . Briefly, samples with several modifications. First, TMTpro reagents were lysed, reduced, and alkylated using 8 M urea, 10 mM TCEP, and 10 mM iodoacetamide. Urea was then diluted to <2 M using 50 mM ammonium bicarbonate, and sequencing-grade trypsin was added at a 1:100 enzyme-to-substrate ratio utilized for overnight digestion at 37°C. The entire digest was passed through a Stage-tip and the on-tip labeling was performed using TMTpro reagents by adding 1 μL of reagent to 100 μL of freshly prepared. 1 uL of TMTpro was added to 50 mM phosphate buffer (pH 8.0), which was then passed before passing over the C18-bound peptides. Peptides were fractionated using Stage-tip-based SDB-RPS fractionation. Peptides were eluted stepwise using increasing concentrations of acetonitrile (5–90%) in 20 mM ammonium formate, pH 10. Fractions were vacuum-dried and stored at –80°C until LC-MS analysis. Mass spectrometry data were tip. Data was acquired usingon an Orbitrap Eclipse (Thermo Scientific) mass spectrometer. Raw data were processed using Spectrum Mill software (Agilent and Broad Institute) and searched against the UniProt Mouse database. Carbamidomethylation of cysteine and TMTpro on lysines and peptide N-termini were set as fixed modifications, with oxidation of methionine and protein N-terminal acetylation set as variable modifications. Up to three missed tryptic cleavages were allowed. Peptide and protein FDRs were controlled at <1% using a target-decoy approach. Protein quantification was performed using TMT reporter ion intensities normalized to a pooled reference channel. Proteins were reported if identified by at least two unique peptides and a protein score ≥20. Only one representative proteoform per gene was used for downstream analyses). GSVA was used to calculate sample-wise enrichment scores using log-transformed, variance-stabilized intensity values. Mouse GO:BP gene sets were used as input, and enrichment scores were computed using the Gaussian kernel density estimation method (kcdf=“Gaussian”) in the GSVA package 109 . Differential enrichment of GSVA scores across groups was assessed using the limma package 107 , and significantly enriched pathways (FDR < 0.05) were identified via linear modeling and empirical Bayes moderation. Pathways related to proteasomal degradation and the unfolded protein response (UPR) were selected based on biological relevance and filtered to remove redundant GO terms, and heatmap was generated via clustered cell type. To compare T cell proteomic signatures across the two mass spectrometry datasets, intensity values were log 2 -transformed (where applicable) and rescaled per gene to the range [–3, 3] using min–max normalization to account for differences in dynamic range across studies. Combined heatmaps were constructed by merging the scaled matrices from both studies. Rows were clustered using hierarchical clustering on row-wise imputed matrices (mean imputation for missing values), while columns were manually ordered to reflect biological groupings. Heatmaps were generated using the pheatmap package and included a top annotation bar to indicate dataset origin. Missing values were colored in gray to distinguish from scaled intensities. Protein turnover mass spectrometry Experimental design WT T cells were activated and electroporated with complexed tracrRNA, Cas9, and crRNA (as described above) to generate CRISPR control and KO T cells, which were then switched to T RM culture conditions (25 U/mL IL-2, 10 ng/mL IL-15, 10 ng/mL TGFβ and 10 nM retinoic acid) 110 1 day after nucleofection. Cells are expanded 2 more days in T RM culture conditions, then on day 5 post-activation, cells were counted, washed with PBS, and frozen as pellets (for timepoint 0 hr), then remaining cells were switched to SILAC media including heavy isotopes of arginine ( 13 C 6 , 15 N 4 ) and lysine ( 13 C 6 , 15 N 2 ) plus T RM cytokines. Cells were then sampled, counted, washed with PBS, and frozen as pellets for 2, 4, 8, 12, 16, 24, 32, 40, 48, 72 hr. Sample Preparation Cell pellets were lysed in protein (RIPA) lysis buffer (10mM Tris-Cl pH 8.0; 1.5mM EDTA; 1% Triton X-100; 0.1% SDS; 140 mM NaCl; 0.1% sodium deoxycholate; phosphatase and protease inhibitor cocktails (ThermoFisher Scientific)). Lysates were incubated on ice for 30min, sonicated for 10 seconds at 40% amplitude, and centrifuged at 14000 rcf for 10 minutes at 4°C. The supernatant was collected, and protein concentration was determined using Pierce BCA Protein Assay Kit (ThermoFisher Scientific) and a Molecular Devices SpectraMax iD3. A total of 30μg of protein was reduced with TCEP (ThermoFisher Scientific) to a concentration of 10mM and alkylated with IAA (Sigma) to a concentration of 30mM. Peptides underwent chloroform/methanol extraction (Fisher Chemical) and digested overnight at 47°C with sequencing grade modified porcine trypsin (Promega) in Triethylammonium bicarbonate (TEAB, Honeywell Fluka) at a protein:trypsin ratio of 50:1. The following day, samples were acidified to 0.5% formic acid/0.1% trifluoroacetic acid (TFA, Fisher Chemical). After plate equilibration with Buffer B, tryptic peptides were loaded onto a Sep-Pak C18 96-well plate (Waters) to be desalted using a Resolvex A200 (Tecan) automated method where samples underwent four washes with Buffer A. Peptides were eluted from the plate using 40% Buffer B. After equilibrating the plate with 70% Buffer B, samples were filtered using an AcroPrep Advance Filter Plate (Pall). Samples were lyophilized overnight (SpeedVac SPD210, ThermoFisher Scientific) and stored at −80°C. Buffer A = 0.1% formic acid, 0.5% acetonitrile in water, Buffer B = 80% acetonitrile, 20% water, 0.1% formic acid. Analysis SILAC LC-MS analysis was conducted using an EvoSep One (EvoSep) coupled to a trapped ion mobility spectrometry quadrupole time-of-flight mass spectrometer (timsTOF Pro, Bruker Daltonik) by a captive spray ion source (Bruker Daltonik). Tryptic peptides were reconstituted in Buffer A. 500 ng of sample was loaded on C18 Evotips (Evosep) according to the manufacturer’s protocol. Peptides were separated on a 15cm × 150μm ReproSil-pur C18 Endurance analytical column with 1.9 μm beads (EvoSep) at 50°C using a linear 44 min gradient 30 SPD method (Evosep). Mass spectrometry analysis was performed in data-independent (dia-PASEF) mode with 1 MS1 survey TIMS-MS and 10 PASEF MS/MS scans per acquisition cycle. The captive spray ion source capillary voltage was set to 1650 V. Both the ramp time and ion accumulation time were set to 100.0 ms. The ion mobility ranged from 1/K0 = 0.85 to 1.27 V·s/cm 2 , excluding singly or triply charged precursor ions. Full scan mass ranged from 100 to 1700 m/z. Collision energy was decreased linearly with increasing mobility, starting from 59 eV at 1/K0 = 1.6 V·s/cm 2 and reducing to 20 eV at 1/K0 = 0.6 V·s/cm 2 . Data Processing Raw files acquired from abundance LC-MS analysis were analyzed with Spectronaut software (version 18.2) using the directDIA method with MS2 scans used for protein and peptide quantification. The UniProt murine proteome reference (Mouse_UP000000589_10090) containing 21,756 protein entries was used with protein and peptide-level false discovery rates (FDR) set to 0.01. Selected variable modifications included N-terminal acetylation and methionine oxidation while cysteine carbamidomethylation was set as a fixed modification. Trypsin was selected as the digestion enzyme with a maximum of two allowed missed cleavages and a minimum peptide length of seven amino acids. A total of 90,392 peptides and 7,520 protein groups were identified. Protein MS2 exclusive intensity values were quality-assessed using ProteiNorm 111 . Data normalization was performed with Variance Stabilizing Normalization (VSN) 112 , followed by statistical analysis using proteoDA 113 . Linear Models for Microarray Data (limma) combined with empirical Bayes (eBayes) smoothing were applied to the standard errors 107 . Proteins with a false discovery rate (FDR)-adjusted p-value of less than 0.05 were considered statistically significant. Raw files acquired from SILAC LC-MS analysis were analyzed with Spectronaut software (version 20.3). Using the Pulsar search engine, a DIA-based spectral library was created using the “Generate Library from Pulsar/Search Archives” function. The UniProt murine proteome reference (Mouse_UP000000589_10090) containing 21,756 protein entries was used with protein and peptide-level false discovery rates (FDR) set to 0.01. Selected variable modifications included N-terminal acetylation and methionine oxidation while cysteine carbamidomethylation was set as a fixed modification. Trypsin was selected as the digestion enzyme with a maximum of two allowed missed cleavages and a minimum peptide length of seven amino acids. To ensure robust filtering in multiplexed SILAC DIA data, the Multi Channel Q-value Filter was set to “Channel Qvalue,” enforcing channel-specific confidence estimation. A total of 92,824 peptides and 7,604 protein groups were identified . A labeled spectral library was then generated, utilizing two label channels with Arg10 and Lys8 modifications added to the second channel. Raw files were searched using the labeled spectral library with MS2 scans used for protein and peptide quantification and half-life determination. Protein half-life determination The statistical software R (v4.4.3) was used to calculate protein half-life. Peptide-level quantification were processed to compute the percentage of light-labeled peptides and organized by protein, gene, condition, replicate. Peptides were filtered to include only those that contained at least 11 timepoints between 0 and 72 hours and excluding those with an increasing light fraction over time (suggesting incomplete labeling or biological noise). Using the fitdecay1 function from the R package nlfitr (v0.1.0) a non-linear leas squares (NLS) decay model was fit for each peptide time course, with upper and lower asymptotes fixed at 1 and 0 respectively, and weighted fitting enabled. The R package broom (v1.0.7) was used to extract the model output. Peptides with valid model fits were evaluated for goodness-of-fit by calculating pseudo and adjusted R-squared values. Only peptides with an adjusted R-squared ≥ 0.9 were retained. These peptides were averaged across proteins to compute mean percent light curves that were re-fit using the same NLS procedure to obtain protein-label kinetic parameters. The decay constant (k) was converted to half-life for each protein and condition (half-life = log2(k)). Short-Lived Protein Abundance Analysis To compare the abundance of short-lived (half-life ≤8 h) versus non-short-lived (half-life >8 h to ≤20 h) proteins across T cell populations, protein annotations for short-lived proteins were curated into a list using half-life quantification from either of the two replicates. Then protein intensity values obtained from the DIA-PASEF dataset were used to evaluate the abundance of short-lived proteins across T cell populations. Proteins were annotated as “short-lived” or “non-short-lived” based on curated list. Intensities were summed for each protein within a sample. For each biological replicate, the total abundance was computed as the sum of intensities for all detected proteins within each category. Mean abundance and standard error were calculated across replicates using the dplyr package. Visualization of group-wise means and significance was performed with ggplot2 and ggpubr, using bar plots with error bars and annotated p -values. For rank–abundance analysis, each T cell population was converted from protein intensities to within-sample proportions, averaged these across replicates, and ranked proteins by mean proportional contribution to plot faceted rank–abundance curves (implemented in R with dplyr, tidyr, and ggplot2). For stacked composition plots, the top 20 proteins per population were visualized (and the remaining collapsed as “Other”) as 100% stacked bars with ordered fills (implemented with dplyr, forcats, ggplot2, and scales). For Protein Set Enrichment Analysis, peptide-count matrices values were log2-transformed (log2[count+1]) in R. Pairwise differential ranking statistics between group.type levels were computed using limma (linear modeling with empirical Bayes moderation), and a short-lived proteins set was tested for enrichment against the ranked t-statistics using fgsea (fgseaMultilevel). GSEA-style running enrichment score plots were generated with ggplot2. Quantitative and Statistical Methods Statistical tests were performed using Prism (7.0/9.0; GraphPad), R (v.4.1), and Python (3.11.9) in VS Code software packages. Two-tailed, paired or unpaired Student’s t -tests, or one- or two-way analyses of variance (ANOVA) were used for comparisons between groups. P -values <0.05 were considered statistically significant. Values of P < 0.05 were ranked as follows: *, P < 0.05; **, P < 0.01; ***, P < 0.001; and ****, P < 0.0001. Supplementary Material 1 Figure S1: Gating Strategies for tumor and LCMV Armstrong, related to Figure 1 and 3 A) Gating strategy for EV vs ligase OE P14 co-transfer experiments into tumor-bearing mice. B) Gating strategy for control vs ligase KO P14 co-transfers into LCMV Armstrong-infected mice (d7 post-infection). NIHMS2169433-supplement-1.pdf (19.2MB, pdf) 2 Figure S2: Neurl3 , Rnf149 and Wsb1 expression in memory and exhaustion, and sustained ligase expression can improve TIL function in B16 melanoma, related to Figure 1 A) Tcf7 or Tox RNA TPM endogenous CD8 + T cells sorted from B16 melanoma dLN and TIL ( GSE175408 33 ). B) Neurl3 , Rnf149 , or Wsb1 RNA expression in P14 T cells from IEL or spleens across infection timepoints (naive-d90) scRNA-seq ( GSE131847 80 ), quantified after MAGIC imputation. C) Neurl3 , Rnf149 , or Wsb1 RNA TPM from CD8 + T cells under acute or continuous stimulation in hypoxic or normoxic environments ( GSE155192 81 ). D) qPCR validation of Neurl3 , Rnf149 , or Wsb1 overexpression in P14 T cells (cDNA fold-change vs EV, normalized to actin) prior to adoptive transfer; four independent experiments. E) Tumor Tim3 hi TCF1 lo P14 T cell frequencies of EV vs Neurl3 OE , Rnf149 OE , or Wsb1 OE 10 days after adoptive transfer into MC38-GP 33–41 mice ( Neurl3 OE n=10; Rnf149 OE n=8; Wsb1 OE n=6); two or more independent experiments. F) Experimental schematic: Congenically mismatched P14 T cells were activated and transduced 24 h later with Empty Vector (EV) or ligase-overexpressing retrovirus (Neurl3 OE , Rnf149 OE , or Wsb1 OE ). Next day, cells were sorted on ametrine reporter expression, cultured d5–6, then mixed 50:50 (1 × 10 6 each, 2 × 10 6 total) and transferred into d8 B16-GP 33–41 -bearing mice, dLN and TIL were analyzed 8 days later (BioRender). G) Transferred P14 T cell frequencies in dLN or tumors as in C ( Neurl3 OE n=12; Rnf149 OE n=13; Wsb1 OE n=11); three independent experiments. H) Tumor Tim3 hi TCF1 lo P14 T cell frequencies as in C ( Neurl3 OE n=22; Rnf149 OE n=18; Wsb1 OE n=16); five independent experiments. I) TCF1 + P14 T cell frequencies as in C ( Neurl3 OE n=22; Rnf149 OE n=18; Wsb1 OE n=16); five independent experiments. J) Representative flow plots and IFNɣ + P14 T cell frequencies as in C followed by 4 h of peptide restimulation plus protein transport inhibitor ( Neurl3 OE n=12; Rnf149 OE n=10; Wsb1 OE n=9); at least three independent experiments. Statistical significance was calculated using (C) one-way ANOVA, (D, G-J) paired Student’s t -tests. Connecting lines indicate that cells were isolated from the same mouse. NIHMS2169433-supplement-2.pdf (19.9MB, pdf) 3 Figure S3: Loss of NEURL3, RNF149, or WSB1 expression accelerates d21–22 T RM differentiation, related to Figure 3 A) qPCR validation of Neurl3 , Rnf149 , or Wsb1 KO in P14 T cells (cDNA fold-change vs control, actin-normalized) prior to transfer; at least three independent experiments. B) CD103 + P14 T cell frequencies in the IEL of EV vs Neurl3 KO , Rnf149 KO , or Wsb1 KO 5 days (left) or 7 days (right) after transfer into LCMV Armstrong-infected mice (d5: Neurl3 KO n=9; Rnf149 KO n=9; Wsb1 KO n=10; for d7: Neurl3 KO n=8; Rnf149 KO n=8; Wsb1 KO n=8); two-three independent experiments. C) CCR9 + P14 T cell frequencies in the IEL of EV vs Neurl3 KO , Rnf149 KO , or Wsb1 KO 5 days (left) or 7 days (right) after transfer into LCMV Armstrong-infected mice (d5: Neurl3 KO n=9; Rnf149 KO n=9; Wsb1 KO n=10; d7: Neurl3 KO n=8; Rnf149 KO n=8; Wsb1 KO n=8); two-three independent experiments. D) CD69 vs CD103 in IEL flow plots and quantification of CD69 + P14 T frequencies and cell numbers in kidney, liver, and IEL with EV vs Neurl3 KO , Rnf149 KO , or Wsb1 KO 21–22 days post transfer into LCMV Armstrong-infected mice ( Neurl3 KO n=8; Rnf149 KO n=8; Wsb1 KO n=8); two independent experiments. E) (Left) Serial-transfer schematic: Congenically mismatched P14 T cells were activated then nucleofected 24 h later with Thy1 KO Control CRISPR RNP, or ligase KO ( Neurl3 KO , Rnf149 KO , or Wsb1 KO ) plus Thy1 KO CRISPR RNP, mixed 50:50 (100,000 each; 200,000 total) and adoptively transferred into recipient mice that then received LCMV Armstrong. After 30 days, Ctrl and KO T CM were sorted from LN and spleens, remixed 50:50 (6,000 total) and transferred into new recipients that then received LCMV Armstrong; tissues were analyzed d30–32, after IV labeling of circulating CD8 + T cells (BioRender). (Right) P14 T cell frequencies in the spleen, LN, and IEL of EV vs Neurl3 KO , Rnf149 KO , or Wsb1 KO 30–32 days after T CM adoptive transfer into LCMV Armstrong-infected mice ( Neurl3 KO n=9; Rnf149 KO n=9; Wsb1 KO n=9); three independent experiments. Statistical significance was calculated using (B-E) paired Student’s t -tests. Connecting lines indicate that cells were isolated from the same mouse. NIHMS2169433-supplement-3.pdf (18.8MB, pdf) 4 Figure S4: Transcriptomics of NEURL3, RNF149, or WSB1 KO or overexpression, and single cell RNAseq cluster characterization, related to Figure 3 and 4 A) RNA UMAP of control or ligase KO P14 T cells isolated from the spleen or IEL 5 days after LCMV Armstrong-infection; four replicates per control-KO comparison in each tissue. B) Dot plot of splenic DE genes for control vs Neurl3 KO (top left), Rnf149 KO (top-right), and Wsb1 KO (bottom). Circle size = fraction of cells expressing gene, color = mean expression of gene. C) PCA of bulk RNA-seq from LN and tumor EV vs Neurl3 OE P14; n=2 pooled replicates per condition. D) Volcano plot from C: LN EV vs Neurl3 OE P14 (left) and tumor EV vs Neurl3 OE P14 (right). E) PD-1 (left) and Tim-3 (right) TotalSeq protein quantification across Figure 4 CITE-seq T cell groups. F) T EX , T PEX , and T EFF T cell gene enrichment signatures 82 and the T RM gene enrichment signature 32 overlaid on CITE-seq T cell clusters. G) UMAP as in Figure 4B , highlighting P14 IEL (gray) and endogenous T RM (orange). H) Dot plot of genes from Q 3 for each T cell subset (from Figure 4D ), with GO:BP pathway enrichment for the top 3 significantly pathways per subset. NIHMS2169433-supplement-4.pdf (22.1MB, pdf) 5 Figure S5: Single Cell RNAseq top unique and shared pathway enrichment, related to Figure 4 Bubble plot of GO:BP enrichment for differentially expressed RNA from Figure 4B . NIHMS2169433-supplement-5.pdf (18.8MB, pdf) 6 Figure S6: TMT mass spectrometry data of antigen-specific dLN and TIL, endogenous TIL, T RM and T EM , related to Figures 4 and 5 A) Jaccard Index similarity among GO:BP protein-degradation (top) and unfolded protein response (bottom) terms; high Jaccard similarity (>0.7) indicates likely identical genes labeled with different GO terms while low values (0–0.3) indicates low gene overlap. B) Experimental schematic: CD8 + T cells were isolated from endogenous d14 B16 TIL (T PEX sorted on CD8 + PD1 lo/int Tim3 − and T EX sorted on CD8 + PD1 hi Tim3 + ), d14 B16 tumors that received in vitro -activated and expanded P14 (P14 transferred at d7), and from LCMV Armstrong memory P14 (d14 CD127 hi CD62L lo from the spleen (T EM ) and P14 from the IEL (T RM )). (BioRender). C) TMT-MS PCA of CD8 + T cells from LCMV Armstrong memory P14 T EM spleens and T RM , B16-GP 33–41 P14 dLN and TIL from, and endogenous B16 T PEX and T EX . Replicates: IEL n=3 (6 pooled/replicate), T EM n=3 (6 pooled/replicate), P14 dLN n=3 (10 pooled/replicate), P14 TIL n=2 (10 pooled/replicate; one outlier removed from analysis), T PEX n=3 (6 pooled/replicate), and T EX n=3 (6 pooled/replicate). D) Scaled Protein Expression heatmap, of proteins shared between the DIA-PASEF mass spectrometry data ( Figure 4C ) and the TMT mass spectrometry data from B. Gray indicates the protein was not detected in the dataset. E) GSVA heatmap of protein degradation GO:BP pathways from C. F) GSVA heatmap of unfolded protein response GO:BP pathways from C. NIHMS2169433-supplement-6.pdf (18.9MB, pdf) 7 Figure S7: Circulating memory T cell proteomics, and proteasome of T EFF , T RM , T PEX , and T EX , related to Figures 4 and 5 A) Experimental schematic: naive P14 T cells were transferred and mice infected with LCMV Armstrong, obtained on d33 from the spleen (CD127 hi CD62L hi T CM , CD127 hi CD62L lo T EM , CD127 lo CD62L lo LLTE) and the IEL (T RM ) (BioRender). B) Protein PCA of d33 from the spleen (CD127 hi CD62L hi T CM , CD127 hi CD62L lo T EM , CD127 lo CD62L lo LLTE) and the IEL (T RM ), n=4 replicates (one mouse per replicate). C) PCA of the TRM-anchored, protein expression matrix combining Figure 4C data and Supplemental Figure 7B data. Protein values are log 2 -transformed and T RM -centered within each dataset. D) Heatmaps of cell-group mean T RM -anchored log 2 protein expression for proteins in clusters 1+2, 3, 4, and 5 as defined in Figure 4G . T RM -anchored values were averaged within each group, and proteins with missing values were removed. E) GSVA heatmap of protein degradation GO:BP pathways, data from Figure Fig 4C and Supplemental 7B , combined using a T RM -anchored log 2 protein expression matrix. F) GSVA heatmap of unfolded protein response GO:BP pathways, data from Figure Fig 4C and Supplemental 7B , combined using a TRM-anchored log 2 protein expression matrix. G) Heatmap of proteasomal proteins from T EFF , T RM , T PEX , and T EX , expression normalized to row. NIHMS2169433-supplement-7.pdf (18.8MB, pdf) 8 Supplementary Table 1: Core T RM gene signature gene expression from endogenous CD8 + T cells sorted from B16 mouse melanoma dLN and TIL, related to figure 1 . Bulk RNA-seq transcripts per kilobase million data derived from endogenous CD8+ T cells sorted from B16 mouse melanoma dLN and TIL ( GSE175408 ), used to generate Figure 1A . RNAseq dataset was filtered on the core TRM gene signature. NIHMS2169433-supplement-8.xlsx (31.5KB, xlsx) 9 Supplementary Table 2: Differentially expressed genes from d5 control and ligase KO CITE-seq, related to figure 3 . Pairwise RNA comparison of different T cell groups using the Wilcoxon rank-sum test. NIHMS2169433-supplement-9.xlsx (31.5KB, xlsx) 10 Supplementary Table 3: Differentially expressed genes (pairwise comparisons) from ex vivo T EFF , T RM , T PEX , and T EX CITE-seq, related to figure 4 . Pairwise RNA comparison of different T cell groups using the Wilcoxon rank-sum test. NIHMS2169433-supplement-10.xlsx (4.3MB, xlsx) 11 Supplementary Table 4: Differentially expressed proteins (pairwise comparisons) from ex vivo T EFF , T RM , T PEX , and T EX DIA-PASEF mass spectrometry, related to figure 4 . Pairwise protein comparison of different T cell groups using the Wilcoxon rank-sum test. NIHMS2169433-supplement-11.xlsx (617.8KB, xlsx) 12 Supplementary Table 5: Differentially expressed protein cluster lists from ex vivo T EFF , T RM , T PEX , and T EX DIA-PASEF mass spectrometry, related to figure 4 . Lists of proteins per cluster, from Figure 4G . NIHMS2169433-supplement-12.xlsx (55.4KB, xlsx) 13 Supplementary Table 6: Differentially expressed proteins (pairwise comparisons) from ex vivo TMT mass spectrometry, related to supplemental figure 6 . Pairwise protein comparison of different T cell groups using the Wilcoxon rank-sum test. NIHMS2169433-supplement-13.xlsx (522.5KB, xlsx) 14 Supplementary Table 7: Protein turnover list of short-lived proteins, related to figure 5 . List of genes and corresponding half-lives (in hr). NIHMS2169433-supplement-14.xlsx (267.4KB, xlsx) Highlights. T RM and T PEX share expression of proteostasis-related genes T EX experience a loss of proteostasis, including an accumulation of unfolded proteins Ex vivo T cell proteomics reveals biological pathways not reflected in transcriptomes E3 ligases rescue proteostasis and improves immunotherapeutic responses to cancer Acknowledgments: We would like to thank the Goldrath lab and other co-authors for manuscript review. We thank the La Jolla Institute for Immunology Flow Cytometry Core facility and staff for their sorting assistance. We also thank the ImmGen consortium and ImmGenT team including David Zemmour and Ian Magill for their expertise and assistance with the pan-tissue scRNA-seq experiments. This work was performed in the context of the ImmGenT project within the ImmGen consortium, supported by R24072073. This work was supported by National Institutes of Health grants K00CA222711 (N.E.S.), GM133351 (A.R.), F31AI176705 (K.K.T), DP5OD031863 (B.K.), R35GM148339 (E.J.B.), R21AI178362 (A.W.G.), R01AI179952 (A.W.G.), R37AI067545 (A.W.G.), R01AI072117 (A.W.G.), and R01AI150282 (A.W.G.). Declaration of interests: A.F. is a co-founder, CEO and board member of TCura Bioscience. A.W.G. is a co-founder of TCura Bioscience, Inc. and serves on the scientific advisory board of Foundery Biosciences and is a Senior Advisor to the Allen Institute for Immunology. Footnotes Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. 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NIHMS2169433-supplement-1.pdf (19.2MB, pdf) 2 Figure S2: Neurl3 , Rnf149 and Wsb1 expression in memory and exhaustion, and sustained ligase expression can improve TIL function in B16 melanoma, related to Figure 1 A) Tcf7 or Tox RNA TPM endogenous CD8 + T cells sorted from B16 melanoma dLN and TIL ( GSE175408 33 ). B) Neurl3 , Rnf149 , or Wsb1 RNA expression in P14 T cells from IEL or spleens across infection timepoints (naive-d90) scRNA-seq ( GSE131847 80 ), quantified after MAGIC imputation. C) Neurl3 , Rnf149 , or Wsb1 RNA TPM from CD8 + T cells under acute or continuous stimulation in hypoxic or normoxic environments ( GSE155192 81 ). D) qPCR validation of Neurl3 , Rnf149 , or Wsb1 overexpression in P14 T cells (cDNA fold-change vs EV, normalized to actin) prior to adoptive transfer; four independent experiments. E) Tumor Tim3 hi TCF1 lo P14 T cell frequencies of EV vs Neurl3 OE , Rnf149 OE , or Wsb1 OE 10 days after adoptive transfer into MC38-GP 33–41 mice ( Neurl3 OE n=10; Rnf149 OE n=8; Wsb1 OE n=6); two or more independent experiments. F) Experimental schematic: Congenically mismatched P14 T cells were activated and transduced 24 h later with Empty Vector (EV) or ligase-overexpressing retrovirus (Neurl3 OE , Rnf149 OE , or Wsb1 OE ). Next day, cells were sorted on ametrine reporter expression, cultured d5–6, then mixed 50:50 (1 × 10 6 each, 2 × 10 6 total) and transferred into d8 B16-GP 33–41 -bearing mice, dLN and TIL were analyzed 8 days later (BioRender). G) Transferred P14 T cell frequencies in dLN or tumors as in C ( Neurl3 OE n=12; Rnf149 OE n=13; Wsb1 OE n=11); three independent experiments. H) Tumor Tim3 hi TCF1 lo P14 T cell frequencies as in C ( Neurl3 OE n=22; Rnf149 OE n=18; Wsb1 OE n=16); five independent experiments. I) TCF1 + P14 T cell frequencies as in C ( Neurl3 OE n=22; Rnf149 OE n=18; Wsb1 OE n=16); five independent experiments. J) Representative flow plots and IFNɣ + P14 T cell frequencies as in C followed by 4 h of peptide restimulation plus protein transport inhibitor ( Neurl3 OE n=12; Rnf149 OE n=10; Wsb1 OE n=9); at least three independent experiments. Statistical significance was calculated using (C) one-way ANOVA, (D, G-J) paired Student’s t -tests. Connecting lines indicate that cells were isolated from the same mouse. NIHMS2169433-supplement-2.pdf (19.9MB, pdf) 3 Figure S3: Loss of NEURL3, RNF149, or WSB1 expression accelerates d21–22 T RM differentiation, related to Figure 3 A) qPCR validation of Neurl3 , Rnf149 , or Wsb1 KO in P14 T cells (cDNA fold-change vs control, actin-normalized) prior to transfer; at least three independent experiments. B) CD103 + P14 T cell frequencies in the IEL of EV vs Neurl3 KO , Rnf149 KO , or Wsb1 KO 5 days (left) or 7 days (right) after transfer into LCMV Armstrong-infected mice (d5: Neurl3 KO n=9; Rnf149 KO n=9; Wsb1 KO n=10; for d7: Neurl3 KO n=8; Rnf149 KO n=8; Wsb1 KO n=8); two-three independent experiments. C) CCR9 + P14 T cell frequencies in the IEL of EV vs Neurl3 KO , Rnf149 KO , or Wsb1 KO 5 days (left) or 7 days (right) after transfer into LCMV Armstrong-infected mice (d5: Neurl3 KO n=9; Rnf149 KO n=9; Wsb1 KO n=10; d7: Neurl3 KO n=8; Rnf149 KO n=8; Wsb1 KO n=8); two-three independent experiments. D) CD69 vs CD103 in IEL flow plots and quantification of CD69 + P14 T frequencies and cell numbers in kidney, liver, and IEL with EV vs Neurl3 KO , Rnf149 KO , or Wsb1 KO 21–22 days post transfer into LCMV Armstrong-infected mice ( Neurl3 KO n=8; Rnf149 KO n=8; Wsb1 KO n=8); two independent experiments. E) (Left) Serial-transfer schematic: Congenically mismatched P14 T cells were activated then nucleofected 24 h later with Thy1 KO Control CRISPR RNP, or ligase KO ( Neurl3 KO , Rnf149 KO , or Wsb1 KO ) plus Thy1 KO CRISPR RNP, mixed 50:50 (100,000 each; 200,000 total) and adoptively transferred into recipient mice that then received LCMV Armstrong. After 30 days, Ctrl and KO T CM were sorted from LN and spleens, remixed 50:50 (6,000 total) and transferred into new recipients that then received LCMV Armstrong; tissues were analyzed d30–32, after IV labeling of circulating CD8 + T cells (BioRender). (Right) P14 T cell frequencies in the spleen, LN, and IEL of EV vs Neurl3 KO , Rnf149 KO , or Wsb1 KO 30–32 days after T CM adoptive transfer into LCMV Armstrong-infected mice ( Neurl3 KO n=9; Rnf149 KO n=9; Wsb1 KO n=9); three independent experiments. Statistical significance was calculated using (B-E) paired Student’s t -tests. Connecting lines indicate that cells were isolated from the same mouse. NIHMS2169433-supplement-3.pdf (18.8MB, pdf) 4 Figure S4: Transcriptomics of NEURL3, RNF149, or WSB1 KO or overexpression, and single cell RNAseq cluster characterization, related to Figure 3 and 4 A) RNA UMAP of control or ligase KO P14 T cells isolated from the spleen or IEL 5 days after LCMV Armstrong-infection; four replicates per control-KO comparison in each tissue. B) Dot plot of splenic DE genes for control vs Neurl3 KO (top left), Rnf149 KO (top-right), and Wsb1 KO (bottom). Circle size = fraction of cells expressing gene, color = mean expression of gene. C) PCA of bulk RNA-seq from LN and tumor EV vs Neurl3 OE P14; n=2 pooled replicates per condition. D) Volcano plot from C: LN EV vs Neurl3 OE P14 (left) and tumor EV vs Neurl3 OE P14 (right). E) PD-1 (left) and Tim-3 (right) TotalSeq protein quantification across Figure 4 CITE-seq T cell groups. F) T EX , T PEX , and T EFF T cell gene enrichment signatures 82 and the T RM gene enrichment signature 32 overlaid on CITE-seq T cell clusters. G) UMAP as in Figure 4B , highlighting P14 IEL (gray) and endogenous T RM (orange). H) Dot plot of genes from Q 3 for each T cell subset (from Figure 4D ), with GO:BP pathway enrichment for the top 3 significantly pathways per subset. NIHMS2169433-supplement-4.pdf (22.1MB, pdf) 5 Figure S5: Single Cell RNAseq top unique and shared pathway enrichment, related to Figure 4 Bubble plot of GO:BP enrichment for differentially expressed RNA from Figure 4B . NIHMS2169433-supplement-5.pdf (18.8MB, pdf) 6 Figure S6: TMT mass spectrometry data of antigen-specific dLN and TIL, endogenous TIL, T RM and T EM , related to Figures 4 and 5 A) Jaccard Index similarity among GO:BP protein-degradation (top) and unfolded protein response (bottom) terms; high Jaccard similarity (>0.7) indicates likely identical genes labeled with different GO terms while low values (0–0.3) indicates low gene overlap. B) Experimental schematic: CD8 + T cells were isolated from endogenous d14 B16 TIL (T PEX sorted on CD8 + PD1 lo/int Tim3 − and T EX sorted on CD8 + PD1 hi Tim3 + ), d14 B16 tumors that received in vitro -activated and expanded P14 (P14 transferred at d7), and from LCMV Armstrong memory P14 (d14 CD127 hi CD62L lo from the spleen (T EM ) and P14 from the IEL (T RM )). (BioRender). C) TMT-MS PCA of CD8 + T cells from LCMV Armstrong memory P14 T EM spleens and T RM , B16-GP 33–41 P14 dLN and TIL from, and endogenous B16 T PEX and T EX . Replicates: IEL n=3 (6 pooled/replicate), T EM n=3 (6 pooled/replicate), P14 dLN n=3 (10 pooled/replicate), P14 TIL n=2 (10 pooled/replicate; one outlier removed from analysis), T PEX n=3 (6 pooled/replicate), and T EX n=3 (6 pooled/replicate). D) Scaled Protein Expression heatmap, of proteins shared between the DIA-PASEF mass spectrometry data ( Figure 4C ) and the TMT mass spectrometry data from B. Gray indicates the protein was not detected in the dataset. E) GSVA heatmap of protein degradation GO:BP pathways from C. F) GSVA heatmap of unfolded protein response GO:BP pathways from C. NIHMS2169433-supplement-6.pdf (18.9MB, pdf) 7 Figure S7: Circulating memory T cell proteomics, and proteasome of T EFF , T RM , T PEX , and T EX , related to Figures 4 and 5 A) Experimental schematic: naive P14 T cells were transferred and mice infected with LCMV Armstrong, obtained on d33 from the spleen (CD127 hi CD62L hi T CM , CD127 hi CD62L lo T EM , CD127 lo CD62L lo LLTE) and the IEL (T RM ) (BioRender). B) Protein PCA of d33 from the spleen (CD127 hi CD62L hi T CM , CD127 hi CD62L lo T EM , CD127 lo CD62L lo LLTE) and the IEL (T RM ), n=4 replicates (one mouse per replicate). C) PCA of the TRM-anchored, protein expression matrix combining Figure 4C data and Supplemental Figure 7B data. Protein values are log 2 -transformed and T RM -centered within each dataset. D) Heatmaps of cell-group mean T RM -anchored log 2 protein expression for proteins in clusters 1+2, 3, 4, and 5 as defined in Figure 4G . T RM -anchored values were averaged within each group, and proteins with missing values were removed. E) GSVA heatmap of protein degradation GO:BP pathways, data from Figure Fig 4C and Supplemental 7B , combined using a T RM -anchored log 2 protein expression matrix. F) GSVA heatmap of unfolded protein response GO:BP pathways, data from Figure Fig 4C and Supplemental 7B , combined using a TRM-anchored log 2 protein expression matrix. G) Heatmap of proteasomal proteins from T EFF , T RM , T PEX , and T EX , expression normalized to row. NIHMS2169433-supplement-7.pdf (18.8MB, pdf) 8 Supplementary Table 1: Core T RM gene signature gene expression from endogenous CD8 + T cells sorted from B16 mouse melanoma dLN and TIL, related to figure 1 . Bulk RNA-seq transcripts per kilobase million data derived from endogenous CD8+ T cells sorted from B16 mouse melanoma dLN and TIL ( GSE175408 ), used to generate Figure 1A . RNAseq dataset was filtered on the core TRM gene signature. NIHMS2169433-supplement-8.xlsx (31.5KB, xlsx) 9 Supplementary Table 2: Differentially expressed genes from d5 control and ligase KO CITE-seq, related to figure 3 . Pairwise RNA comparison of different T cell groups using the Wilcoxon rank-sum test. NIHMS2169433-supplement-9.xlsx (31.5KB, xlsx) 10 Supplementary Table 3: Differentially expressed genes (pairwise comparisons) from ex vivo T EFF , T RM , T PEX , and T EX CITE-seq, related to figure 4 . Pairwise RNA comparison of different T cell groups using the Wilcoxon rank-sum test. NIHMS2169433-supplement-10.xlsx (4.3MB, xlsx) 11 Supplementary Table 4: Differentially expressed proteins (pairwise comparisons) from ex vivo T EFF , T RM , T PEX , and T EX DIA-PASEF mass spectrometry, related to figure 4 . Pairwise protein comparison of different T cell groups using the Wilcoxon rank-sum test. NIHMS2169433-supplement-11.xlsx (617.8KB, xlsx) 12 Supplementary Table 5: Differentially expressed protein cluster lists from ex vivo T EFF , T RM , T PEX , and T EX DIA-PASEF mass spectrometry, related to figure 4 . Lists of proteins per cluster, from Figure 4G . NIHMS2169433-supplement-12.xlsx (55.4KB, xlsx) 13 Supplementary Table 6: Differentially expressed proteins (pairwise comparisons) from ex vivo TMT mass spectrometry, related to supplemental figure 6 . Pairwise protein comparison of different T cell groups using the Wilcoxon rank-sum test. NIHMS2169433-supplement-13.xlsx (522.5KB, xlsx) 14 Supplementary Table 7: Protein turnover list of short-lived proteins, related to figure 5 . List of genes and corresponding half-lives (in hr). NIHMS2169433-supplement-14.xlsx (267.4KB, xlsx) Data Availability Statement The RNA datasets generated during this study are available at NCBI GEO repository as indicated in the key resources table . The mass spectrometry proteomics datasets generated during this study are available at the ProteomeXchange Consortium via the PRIDE partner repository or MassIVE as indicated in the key resources table . This paper does not report original code. Key resources table REAGENT or RESOURCE SOURCE IDENTIFIER Antibodies Anti-mouse CD3e PE eBioscience Cat: 12-0031-83; RRID: AB_465497 anti-mouse CD45.1 APC eBioscience Cat# 17-0453-82; RRID: AB_469398 anti-mouse CD45.1 Brilliant Violet 785 Biolegend Cat# 110743; RRID: AB_2563379 anti-mouse CD45.1 FITC eBioscience Cat# 11-0453-85; RRID: AB_465059 anti-mouse CD45.1 PerCP Cy5.5 eBioscience Cat# 45-0453-82; RRID: AB_1107003 anti-mouse CD45.2 Brillant Violet 605 Biolegend Cat# 109837; RRID: AB_2561393 anti-mouse CD45.2 PerCP Cy5.5 eBioscience Cat# 45-0454-82; RRID: AB_953590 anti-mouse CD45.2 BV421 Biolegend Cat: 109831; RRID: AB_10900256 anti-mouse CD45.2 PE-Cyanine7 eBioscience Cat# 25-0454-82; RRID: AB_2573350 anti-mouse CD45.2 APC eBioscience Cat: 17-0454-82; RRID: AB_469400 anti-mouse CD8a FITC Biolegend Cat# 100705; RRID: AB_312744 anti-mouse CD8a Brilliant Violet 711 Biolegend Cat# 100747; RRID: AB_11219594 anti-mouse CD8a Brilliant Violet 785 Biolegend Cat# 100747; RRID: AB_11219594 anti-mouse CD8a APC-eFluor780 eBioscience Cat# 47-0081-82; RRID: AB_1272185 anti-mouse TCF1 Pacific Blue Cell Signaling Technology Cat# 9066S; RRID: AB_2797696 anti-mouse TCF1 A488 Cell Signaling Technology Cat# 6444S; RRID: AB_2797627 anti-mouse TOX PE Miltenyi Biotec Cat# 130-120-716; RRID: AB_2801780 anti-mouse TOX APC Miltenyi Biotec Cat# 130-118-335; RRID: AB_2751485 anti-mouse TIM3 Brilliant Violet 785 Biolegend Cat# 119725; RRID: AB_2716066 anti-mouse PD-1 PE Cy7 eBioscience Cat# 25-9985-82; RRID: AB_10853805 anti-mouse PD-1 BV421 Biolegend Cat# 135217; RRID: AB_10900085 anti-mouse TNF PE eBioscience Cat# 12-7321-82; RRID: AB_466199 anti-mouse IFNɣ APC Biolegend Cat# 505810; RRID: AB_315404 anti-mouse K48 Abcam Cat# ab140601; RRID: AB_2783797 anti-mouse CD69 APC eBioscience Cat#17-0691-82; RRID: AB_1210795 anti-mouse CD103 PE eBioscience Cat# 12-1031-82; RRID: AB_465798 anti-mouse CCR9 PE-Cy7 eBioscience Cat# 25-1991-82; RRID: AB_10854423 anti-mouse KLRG1 FITC eBioscience Cat# 11-5893-82; RRID: AB_1311265 anti-mouse CD127 PE Cy7 eBioscience Cat# 25-1271-82; RRID: AB_469649 anti-mouse CD62L Brillant Violet 605 Biolegend Cat# 104437; RRID: AB_11125577 anti-mouse THY1.1 Brillant Violet 650 Biolegend Cat# 202533; RRID: AB_2562254 anti-mouse THY1.2 Brillant Violet 510 Biolegend Cat# 140319; RRID: AB_2561395 anti-mouse SLAMF6 PE BD Bioscience Cat# 561540; RRID: AB_10713170 anti-mouse CX3CR1 A488 Biolegend Cat# 149022; RRID: AB_2565705 anti-rabbit APC Cell Signaling Technology Cat# 4414S; RRID: AB_10693544 anti-mouse MHCII biotin eBioscience Cat: 13-5321-81; RRID: AB_466661 anti-mouse B220 biotin Biolegend Cat: 103204; RRID: AB_312989 anti-mouse CD4 biotin Biolegend Cat: 100404; RRID: AB_312689 anti-mouse CD19 biotin Biolegend Cat: 115504; RRID: AB_313639 anti-mouse CD11b biotin Biolegend Cat: 101204; RRID: AB_312787 Biotin anti-mouse Ly-6G/Ly-6C (Gr-1) Antibody BioLegend Cat#108404; RRID: AB_313368 anti-mouse NK1.1 biotin ebioscience Cat: 13-5941-85; RRID: AB_466805 Alexa Fluor ® 488 AffiniPure ® Donkey Anti-Rabbit IgG (H+L) Jackson Immunoresearch Cat: 711-545-152; RRID: AB_2313584 Cyclin A2 Recombinant Rabbit Monoclonal Antibody Invitrogen Cat: MA5-54060; RRID: AB_3248531 InVivoMAb polyclonal Armenian hamster IgG Bio X Cell Cat# BE0091 InVivoMAb anti-mouse PD-1 (CD279) Bio X Cell Cat# BE0033-2 InVivoMAb anti-mouse 4-1BB (CD137) Bio X Cell Cat #BE0239 InVivoMAb rat IgG2a isotype control, anti-trinitrophenol Bio X Cell Cat #BE0089 TotalSeq ™ -C0833 anti-mouse CD194 (CCR4) Antibody BioLegend Cat#131221; RRID: AB_2876463 TotalSeq ™ -C0096 anti-mouse CD45 Antibody BioLegend Cat#103169; RRID: AB_2832302 TotalSeq ™ -C0075 anti-mouse CD90.2 (Thy1.2) Antibody BioLegend Cat#105353; RRID: AB_2832346 TotalSeq ™ -C0930 anti-mouse Ly108 Antibody BioLegend Cat#134613; RRID: AB_2890692 TotalSeq ™ -C0879 anti-mouse IL-21R Antibody BioLegend Cat#131911; RRID: AB_2876466 TotalSeq ™ -C0377 anti-mouse CD197 (CCR7) Antibody BioLegend Cat#120131; RRID: AB_2819839 TotalSeq ™ -C0836 anti-mouse DR3 (TNFRSF25) Antibody BioLegend Cat#144415; RRID: AB_2876493 TotalSeq ™ -C0002 anti-mouse CD8a Antibody BioLegend Cat#100785; RRID: AB_2819776 TotalSeq ™ -C0111 anti-mouse CD5 Antibody BioLegend Cat#100647; RRID: AB_2860586 TotalSeq ™ -C0227 anti-mouse CD122 (IL-2Rβ) Antibody BioLegend Cat#105915; RRID: AB_2860612 TotalSeq ™ -C0881 anti-mouse CD272 (BTLA) Antibody BioLegend Cat#139123; RRID: AB_2892292 TotalSeq ™ -C0093 anti-mouse CD19 Antibody BioLegend Cat#115571; RRID: AB_2832392 TotalSeq ™ -C1006 anti-mouse CD160 Antibody BioLegend Cat#143015; RRID: AB_2892297 TotalSeq ™ -C0178 anti-mouse CD45.1 Antibody BioLegend Cat#110757; RRID: AB_2819824 TotalSeq ™ -C0198 anti-mouse CD127 (IL-7Rα) Antibody BioLegend Cat#135047; RRID: AB_2819874 TotalSeq ™ -C0981 anti-mouse TCR Vα2 Antibody BioLegend Cat#127829; RRID: AB_2819866 TotalSeq ™ -C0982 anti-mouse TCR Vα8.3 Antibody BioLegend Cat#127709; RRID: AB_2819865 TotalSeq ™ -C0114 anti-mouse F4/80 Antibody BioLegend Cat#123157; RRID: AB_2832437 TotalSeq ™ -C1063 anti-mouse CD45RB Antibody BioLegend Cat#103321; RRID: AB_2888760 TotalSeq ™ -C0171 anti-human/mouse/rat CD278 (ICOS) Antibody BioLegend Cat#313553; RRID: AB_2800823 TotalSeq ™ -C0378 anti-mouse CD223 (LAG-3) Antibody BioLegend Cat#125237; RRID: AB_2832450 TotalSeq ™ -C0228 anti-mouse CD183 (CXCR3) Antibody BioLegend Cat#126545; RRID: AB_2832454 TotalSeq ™ -C0130 anti-mouse Ly-6A/E (Sca-1) Antibody BioLegend Cat#108151; RRID: AB_2832370 TotalSeq ™ -C0837 anti-mouse IL-33Rα (IL1RL1, ST2) Antibody BioLegend Cat#145319; RRID: AB_2819892 TotalSeq ™ -C0193 anti-mouse CD357 (GITR) Antibody BioLegend Cat#126327; RRID: AB_2832452 TotalSeq ™ -C0834 anti-mouse CD39 Antibody BioLegend Cat#143815; RRID: AB_2819888 TotalSeq ™ -C1064 anti-mouse/rat CD81 Antibody BioLegend Cat#104915; RRID: AB_2876412 Totalseq ™ -C0214 anti-human/mouse integrin β7 Antibody BioLegend Cat#321229; RRID: AB_2810481 TotalSeq ™ -C0237 Rat IgG1, λ Isotype Ctrl Antibody BioLegend Cat#401921; RRID: AB_3097123 TotalSeq ™ -C0200 anti-mouse CD86 Antibody BioLegend Cat#105059; RRID: AB_2860608 TotalSeq ™ -C0121 anti-mouse TCR γ/δ Antibody BioLegend Cat#118141; RRID: AB_2819838 TotalSeq ™ -C0070 anti-human/mouse CD49f Antibody BioLegend Cat#313635; RRID: AB_2800825 TotalSeq ™ -C0197 anti-mouse CD69 Antibody BioLegend Cat#104551; RRID: AB_2832333 TotalSeq ™ -C0120 anti-mouse TCR β chain Antibody BioLegend Cat#109259; RRID: AB_2819820 TotalSeq ™ -C0013 anti-mouse Ly-6C Antibody BioLegend Cat#128051; RRID: AB_2832461 TotalSeq ™ -C0429 anti-mouse CD48 Antibody BioLegend Cat#103459; RRID: AB_2876409 TotalSeq ™ -C0885 anti-mouse CD270 (HVEM) Antibody BioLegend Cat#136309; RRID: AB_2876474 TotalSeq ™ -C0850 anti-mouse CD49a Antibody BioLegend Cat#142615; RRID: AB_2832511 TotalSeq ™ -C0421 anti-mouse CD49b Antibody BioLegend Cat#103527; RRID: AB_2876410 TotalSeq ™ -C0570 anti-mouse/rat CD29 Antibody BioLegend Cat#102237; RRID: AB_2890670 TotalSeq ™ -C0241 Armenian Hamster IgG Isotype Ctrl Antibody BioLegend Cat#400977; RRID: AB_2894970 TotalSeq ™ -C0916 anti-mouse CD124 (IL-4Rα) Antibody BioLegend Cat#144811; RRID: AB_2832524 TotalSeq ™ -C0073 anti-mouse/human CD44 Antibody BioLegend Cat#103063; RRID: AB_2813930 TotalSeq ™ -C0235 anti-mouse TCR Vβ8.1, 8.2 Antibody BioLegend Cat#118419; RRID: AB_2860641 TotalSeq ™ -C0983 anti-mouse TCR Vα8.3 Antibody BioLegend Cat#125711; RRID: AB_2819853 TotalSeq ™ -C0846 anti-mouse CD185 (CXCR5) Antibody BioLegend Cat#145537; RRID: AB_2814051 TotalSeq ™ -C0191 anti-mouse/rat/human CD27 Antibody BioLegend Cat#124245; RRID: AB_2832448 TotalSeq ™ -C0212 anti-mouse CD24 Antibody BioLegend Cat#101843; RRID: AB_2819784 TotalSeq ™ -C0014 anti-mouse/human CD11b Antibody BioLegend Cat#101275; RRID: AB_2832272 TotalSeq ™ -C0595 anti-mouse CD11a Antibody BioLegend Cat#101131; RRID: AB_2832271 TotalSeq ™ -C0117 anti-mouse I-A/I-E Antibody BioLegend Cat#107659; RRID: AB_2832368 TotalSeq ™ -C1062 anti-mouse CD30 Antibody BioLegend Cat#102313; RRID: AB_2888806 TotalSeq ™ -C0112 anti-mouse CD62L Antibody BioLegend Cat#104455; RRID: AB_2819800 TotalSeq ™ -C1012 anti-mouse CD178 (FasL) Antibody BioLegend Cat#106615; RRID: AB_2892272 TotalSeq ™ -C0562 anti-mouse CD83 Antibody BioLegend Cat#121521; RRID: AB_2860647 TotalSeq ™ -C0190 anti-mouse CD274 (B7-H1, PD-L1) Antibody BioLegend Cat#153610; RRID: AB_2860721 TotalSeq ™ -C0915 anti-mouse VISTA (PD-1H) Antibody BioLegend Cat#150219; RRID: AB_2860714 TotalSeq ™ -C0091 Mouse IgG2a, κ isotype Ctrl Antibody BioLegend Cat#400293; RRID: AB_2888922 TotalSeq ™ -C0090 Mouse IgG1, κ isotype Ctrl Antibody BioLegend Cat#400187; RRID: AB_2888921 TotalSeq ™ -C0092 Mouse IgG2b, κ isotype Ctrl Antibody BioLegend Cat#400381; RRID: AB_2888923 TotalSeq ™ -C0354 anti-mouse TCR Vβ5.1, 5.2 Antibody BioLegend Cat#139519; RRID: AB_2819882 TotalSeq ™ -C0813 anti-mouse CD9 Antibody BioLegend Cat#124825; RRID: AB_2876447 TotalSeq ™ -C0106 anti-mouse CD11c Antibody BioLegend Cat#117361; RRID: AB_2819834 TotalSeq ™ -C0807 anti-mouse CD200R (OX2R) Antibody BioLegend Cat#123919; RRID: AB_2876445 TotalSeq ™ -C0380 anti-rat CD90/mouse CD90.1 (Thy1.1) Antibody BioLegend Cat#202551; RRID: AB_2876578 TotalSeq ™ -C0195 anti-mouse CD134 (OX-40) Antibody BioLegend Cat#119435; RRID: AB_2832423 TotalSeq ™ -C0079 anti-mouse CD200 (OX2) Antibody BioLegend Cat#123823; RRID: AB_2860656 TotalSeq ™ -C0097 anti-mouse CD25 Antibody BioLegend Cat#102065; RRID: AB_2876399 TotalSeq ™ -C0118 anti-mouse NK-1.1 Antibody BioLegend Cat#108765; RRID: AB_2819815 TotalSeq ™ -C0078 anti-mouse CD49d Antibody BioLegend Cat#103633; RRID: AB_2860605 TotalSeq ™ -C0103 anti-mouse/human CD45R/B220 Antibody BioLegend Cat#103273; RRID: AB_2832307 TotalSeq ™ -C0116 anti-mouse Ly-6G/Ly-6C (Gr-1) Antibody BioLegend Cat#108463; RRID: AB_2832371 TotalSeq ™ -C0558 anti-mouse CD55 (DAF) Antibody BioLegend Cat#131815; RRID: AB_2876464 TotalSeq ™ -C0892 anti-mouse CD2 Antibody BioLegend Cat#100121; RRID: AB_2892259 TotalSeq ™ -C0001 anti-mouse CD4 Antibody BioLegend Cat#100571; RRID: AB_2813913 TotalSeq ™ -C0004 anti-mouse CD279 (PD-1) Antibody BioLegend Cat#109127; RRID: AB_2819819 TotalSeq ™ -C0003 anti-mouse CD366 (Tim-3) Antibody BioLegend Cat#119739; RRID: AB_2832424 TotalSeq ™ -C0236 Rat IgG1, κ Isotype Ctrl Antibody BioLegend Cat#400467; RRID: AB_2894972 TotalSeq ™ -C0238 Rat IgG2a, κ Isotype Ctrl Antibody BioLegend Cat#400577; RRID: AB_2894971 TotalSeq ™ -C0240 Rat IgG2c, κ Isotype Ctrl Antibody BioLegend Cat#400741; RRID: AB_3097108 TotalSeq ™ -C0095 Rat IgG2b, κ Isotype Ctrl Antibody BioLegend Cat#400677; RRID: AB_2894967 TotalSeq ™ -C0110 anti-mouse CD43 Antibody BioLegend Cat#143221; RRID: AB_2860697 TotalSeq ™ -C0563 anti-mouse CX3CR1 Antibody BioLegend Cat#149043; RRID: AB_2819898 TotalSeq ™ -C0909 anti-mouse CD182 (CXCR2) Antibody BioLegend Cat#149319; RRID: AB_2814053 TotalSeq ™ -C0926 anti-mouse CD186 (CXCR6) Antibody BioLegend Cat#151127; RRID: AB_2894676 TotalSeq ™ -C0426 anti-mouse CD192 (CCR2) Antibody BioLegend Cat#150631; RRID: AB_2876502 TotalSeq ™ -C0907 anti-mouse CD198 (CCR8) Antibody BioLegend Cat#150321; RRID: AB_2819900 TotalSeq ™ -C0192 anti-mouse CD20 Antibody BioLegend Cat#150427; RRID: AB_2860716 TotalSeq ™ -C0917 anti-mouse CD95 (Fas) Antibody BioLegend Cat#152616; RRID: AB_2860719 TotalSeq ™ -C0823 anti-mouse CD34 Antibody BioLegend Cat#152211; RRID: AB_2832536 TotalSeq ™ -C0203 anti-mouse CD150 (SLAM) Antibody BioLegend Cat#115947; RRID: AB_2819830 TotalSeq ™ -C0122 anti-mouse TER-119/Erythroid Cells Antibody BioLegend Cat#116259; RRID: AB_2832405 TotalSeq ™ -C1058 anti-mouse FR4 (Folate Receptor 4) Antibody BioLegend Cat#125113; RRID: AB_2876448 TotalSeq ™ -C0949 anti-mouse CD226 (DNAM-1) Antibody BioLegend Cat#133631; RRID: AB_2894682 TotalSeq ™ -C1011 anti-mouse CD155 (PVR) Antibody BioLegend Cat#131533; RRID: AB_2892286 TotalSeq ™ -C0077 anti-mouse CD73 Antibody BioLegend Cat#127237; RRID: AB_2860665 TotalSeq ™ -C0914 anti-mouse CD273 (B7-DC, PD-L2) Antibody BioLegend Cat#107229; RRID: AB_2860615 TotalSeq ™ -C0211 anti-mouse TCR Vγ2 Antibody BioLegend Cat#137713; RRID: AB_2819877 TotalSeq ™ -C0842 anti-mouse Ly-49A Antibody BioLegend Cat#116813; RRID: AB_2876431 TotalSeq ™ -C0074 anti-mouse CD54 Antibody BioLegend Cat#116137; RRID: AB_2819831 TotalSeq ™ -C0230 anti-mouse CD8b (Ly-3) Antibody BioLegend Cat#126639; RRID: AB_2892281 TotalSeq ™ -C0893 anti-mouse CD120b (TNF R Type II/p75) Antibody BioLegend Cat#113409; RRID: AB_2894663 TotalSeq ™ -C0301 anti-mouse Hashtag 1 Antibody BioLegend Cat: 155861; RRID: AB_2800693 TotalSeq ™ -C0302 anti-mouse Hashtag 2 Antibody BioLegend Cat: 155863; RRID: AB_2800694 TotalSeq ™ -C0303 anti-mouse Hashtag 3 Antibody BioLegend Cat: 155865; RRID: AB_2800695 TotalSeq ™ -C0304 anti-mouse Hashtag 4 Antibody BioLegend Cat: 155867; RRID: AB_2800696 TotalSeq ™ -C0305 anti-mouse Hashtag 5 Antibody BioLegend Cat: 155869; RRID: AB_2800697 TotalSeq ™ -C0306 anti-mouse Hashtag 6 Antibody BioLegend Cat: 155871; RRID: AB_2819910 TotalSeq ™ -C0307 anti-mouse Hashtag 7 Antibody BioLegend Cat: 155873; RRID: AB_2819911 TotalSeq ™ -C0308 anti-mouse Hashtag 8 Antibody BioLegend Cat: 155875; RRID: AB_2819912 TotalSeq ™ -C0309 anti-mouse Hashtag 9 Antibody BioLegend Cat: 155877; RRID: AB_2819913 TotalSeq ™ -C0310 anti-mouse Hashtag 10 Antibody BioLegend Cat: 155879; RRID: AB_2819914 TotalSeq ™ -C Mouse Universal Cocktail, V1.0 Biolegend Cat: 199903; RRID: AB_2924498 Bacterial and virus strains Subcloning Efficiency ™ DH5α Competent Cells Thermo Scientific Cat#18265017 LCMV-Armstrong Rafi Ahmed, Emory University NA LCMV-Cl13 Rafi Ahmed, Emory University NA Biological samples Chemicals, peptides, and recombinant proteins AccuCheck Counting Beads Thermo Scientific Cat#PCB100 eBioscience Fixable Viability Dye eFluor 780 Thermo Scientific Cat# 65-0865-14 eBioscience Fixable Viability Dye eFluor 450 Thermo Scientific Cat#65-0863-18 Brilliant II SYBR Green qPCR Master Mix Agilent Technologies Cat#600828 HyClone ™ RPMI 1640 media: Liquid Fisher Scientific Cat#SH30096FS Gibco ™ DMEM, high glucose, no glutamine Fisher Scientific Cat#11-960-069 HyClone ™ Fetal Bovine Serum Characterized HyClone Cat#SH30071.03 Gibco ™ Sodium Pyruvate (100 mM) Thermo Scientific Cat#11360070 Gibco ™ HEPES (1M) Thermo Scientific Cat#15630080 Gibco ™ MEM Non-Essential Amino Acids Solution (100X) Fisher Scientific Cat#11-140-050 Gibco ™ 2-Mercaptoethanol Thermo Scientific Cat#21985023 Gibco ™ Penicillin-Streptomycin-Glutamine (100X) Thermo Scientific Cat#10378-016 Clontech Labs 3P RETRONECTIN Takara Bio Cat#T100B STREPTAVIDIN MICROBEADS Miltenyi Biotec Cat#130-048-101 SIGMA Percoll ® pH 8.5-9.5 (25 °C) Sigma-Aldrich Cat#P1644-1L Corning ® DPBS (Dulbecco’s Phosphate-Buffered Saline) Corning Cat#20-031-CV Magnesium Chloride Hexahydrate OmniPur Cat#5980 Calcium Chloride Dihydrate (White Crystals to Powder), Fisher BioReagents ™ Fisher Scientific Cat#BP510-500 Calf Serum Iron Supplemented R&D Systems Cat#S11950H Collagenase, Type 1 Worthington Biochemical Cat# LS004196 HEPES (Fine White Crystals/Molecular Biology), Fisher BioReagents ™ Fisher Scientific Cat# BP310500 L-Glutamine ≥99% (HPLC), ReagentPlus ® Sigma-Aldrich Cat#G3126-100G Biochemical Fisher Chemical ™ Ammonium Chloride ACS Grade ≥99.5% Fisher Scientific Cat#A661-500 Potassium Bicarbonate (Powder/USP/FCC), Fisher Chemical Fisher Scientific Cat#P235-500 Apex Bioresearch Products 20-147 EDTA, Disodium Dihydrate Salt, Ultra Pure Grade Genesee Scientific Cat#Genesee20-147 Corning ™ Lymphocyte Separation Medium Fisher Scientific Cat#MT25072CV Sodium Bicarbonate (Powder/Certified ACS), Fisher Chemical ™ Fisher Scientific Cat#S233-500 Hank’s Balanced Salt Solution, 10X without sodium bicarbonate, calcium and magnesium Corning Cat#20-021-CV 1,4-Dithioerythritol ≥99.0% Sigma-Aldrich Cat# D8255-25G Fixation Buffer BioLegend Cat#420801 eBioscience ™ Foxp3 / Transcription Factor Staining Buffer Set Invitrogen Cat#00-5523-00 BD Cytofix/Cytoperm ™ Fixation/Permeabilization Kit BD Biosciences Cat#BD554714 PROTEOSTAT ® Protein aggregation assay Enzo Life Sciences Cat#ENZ-51023-KP050 MG-132, Ready Made Solution ≥90% (HPLC) Sigma-Aldrich Cat#M7449-200UL RPMI 1640 Medium for SILAC Thermo Scientific Cat#A33823 Fetal Bovine Serum, dialyzed, US origin Thermo Scientific Cat#26-400-044 L-Arginine-HCl, 13C6, 15N4 for SILAC Thermo Scientific Cat#89990 L-Lysine-2HCl, 13C6, 15N2 for SILAC Thermo Scientific Cat#88209 L-Alanine ≥98% (TLC) Sigma-Aldrich Cat#A7627-1G Recombinant Cas9-NLS purified protein UC Berkeley MacroLab NA DAPI (4’,6-Diamidino-2-Phenylindole, Dilactate) Biolegend Cat#422801 Recombinant human IL2 protein CETUS NA Mouse IL-15 Recombinant Protein PeproTech Cat# 210-15-100UG Recombinant Mouse TGF-β1 Biolegend Cat# 763104 Retinoic acid ≥98% (HPLC) powder Sigma-Aldrich Cat# R2625-50MG Sequencing grade modified trypsin Promega Cat# V5111 Lys-C protease Fujifilm Wako Cat# 129-02541 TCEP Sigma-Aldrich Cat# 580560-1GM Iodoacetamide (IAA) MP Biomedicals Cat# 100351 Dithiothreitol (DTT) Fisher Scientific Cat# BP172-25 SPRIselect reagent Beckman Coulter Cat: B23318 Cycloheximide InSolution, 100 mg/mL in DMSO, Suitable for cell culture Sigma-Aldrich Cat: 239765-1ML LCMV gp33-41 peptide AnaSpec Cat: 61296 Protein Transport Inhibitor Cocktail (500X) ebioscience Cat: 00-4980-93 Triton X-100 Sigma-Aldrich Cat#X100 UltraPure ™ SDS Solution, 10% Thermo Scientific Cat#24730020 Halt ™ Protease Inhibitor Cocktail (100X) Thermo Scientific Cat#78430 Pierce ™ Phosphatase Inhibitor Mini Tablets Thermo Scientific Cat#A32957 Water, Optima ™ LC/MS Grade Fisher Chemical Cat#W6-1 Pierce ™ Premium Grade TCEP-HCl Thermo Scientific Cat#PIPG82089 Triethylammonium Bicarbonate Buffer Honeywell Fluka Cat#60-044-973 Chloroform Fisher Chemical Cat#C297-4 Methanol, Optima ™ LC/MS Grade Fisher Chemical Cat#A456-4 Acetonitrile, Optima ™ LC/MS Grade Fisher Chemical Cat#A955-4 Formic Acid, 99.0+%, Optima ™ LC/MS Grade Fisher Chemical Cat#A117-50 Trifluoroacetic Acid, Optima ™ LC/MS Grade Fisher Chemical Cat#A116-10X1AMP Waters Sep-Pak C18 1 cc Vac Cartridge, 50 mg Sorbent per Cartridge, 55 - 105 μm Waters Cat#WAT054955 AcroPrep Advance Filter Plate (Pall) Fisher Scientific Cat#17164641 Sequencing Grade Modified Trypsin, lyophilized Promega Cat#V5111 C18 Evotips Evosep Cat#EV2013 Critical commercial assays Proteasome-Glo 3-Substrate Cell-Based Assay System Promega Cat#G1180 P3 Primary Cell 4D-Nucleofector ® X Kit S Lonza Cat#V4XP-3032 RNeasy Mini Kit (250) Qiagen Cat#74106 Applied Biosystems ™ High-Capacity cDNA Reverse Transcription Kit Fisher Scientific Cat#43-688-14 Chromium Next GEM Single Cell 5’ Kit v2 10X Genomics Cat: PN-1000263 5’ Feature Barcode Kit 10X Genomics Cat: PN-1000541 Library Construction Kit 10X Genomics Cat: PN-1000190 Dual Index Kit TT Set A 10X Genomics Cat: PN-1000215 Dual Index Kit TN Set A 10X Genomics Cat: PN-1000250 Pierce ™ BCA Protein Assay Kits Thermo Scientific Cat#23227 Deposited data Mouse B16 CD8+ T cell dLN and TIL bulk RNAseq Ford et al. 33 GEO: GSE175408 Mouse P14 circ memory and TRM scRNAseq Crowl et al. 78 GEO: GSE182275 Mouse P14 T cells isolated from IEL or spleens postinfection (naïve to d90) scRNAseq Kurd et al. 80 GEO: GSE131847 Human melanoma TIL pre/post checkpoint blockade scRNAseq Sade-Feldman et al. 60 GEO: GSE120575 Clinical outcomes to PD1 blockade in patients with metastatic melanoma bulk RNAseq Liu et al. 61 dbGaP Study Accession: phs000452.v3.p1 Exhausted Mouse CD8 T cells generated in vitro bulk RNAseq Scharping et al. 81 GEO: GSE155192 CITE-seq of d5 ligase KO P14 T cells from LCMV Armstrong This paper GEO: GSE298127 Token: gxghoosifloznup DIA PASEF ex vivo T cell mass spectrometry data (T EFF , TPEX, TEX, TRM) This paper ProteomeXchange Consortium PRIDE: PXD063865 Token: ihZ5VtMxuC4V DIA PASEF ex vivo T cell mass spectrometry data (T CM , T EM , LLTE, T RM ) This paper ProteomeXchange Consortium PRIDE: PXD070932 Token: cdDl1Hst6nOo TMT ex vivo T cell mass spectrometry data This paper ProteomeXchange Consortium MassIVE: MSV000097956 Password: Sharping2025 CITE-seq of CD3ε+ T cells from d7 LCMV Armstrong This paper GEO: GSE301271 Token: inevuaaobruthqv CITE-seq of CD3ε+ T cells from d30 LCMV Armstrong This paper GEO: GSE301960 Token: elsngwocxtwphiz CITE-seq of CD3ε+ T cells from B16-GP33 This paper GEO: currently uploading, ID available in <5 business days Protein turnover in vitro T cell mass spectrometry data This paper ProteomeXchange Consortium PRIDE: PXD067417 Token: g2FhAs25KH6N EV and Neurl3 OE P14 - dLN and TIL bulk RNAseq This paper GEO: GSE298128 token: kfenckiqrdozhqz Experimental models: Cell lines Platinum-E (Plat-E) Retroviral Packaging Cell Line Cell Biolabs Cat# RV-101 C57BL/6 mouse colon adenocarcinoma MC38, modified for GP33-41 expression Kerafast Parental line: Cat#ENH204-FP C57BL/6 mouse B16-F10 skin melanoma, modified for GP33-41 expression Gift from Dr Alain Lamarre NA Experimental models: Organisms/strains Mouse; C57BL/6J Jackson Laboratory Strain#000664; RRID: IMSR_JAX:000664 Mouse; B6.PL- Thy1 a /CyJ Jackson Laboratory Strain#000406; RRID: IMSR_JAX:000406 Mouse; P14 CD8 Transgenic (NCI background) Rafi Ahmed, Emory University N/A Mouse; B6.SJL- Ptprc a Pepc b /BoyJ Jackson Laboratory Strain#002014; RRID: IMSR_JAX:002014 Oligonucleotides Beta Actin F: GAGACCTTCAACACCCC This paper NA Beta Actin R: GTGGTGGTGAAGCTGTAGCC This paper NA Neurl3 F: TGTGTACGGGACCACGAAAG This paper NA Neurl3 R: ACTACAGCCACCTCCTCGAT This paper NA Rnf149 F: GGAGTTCATCAGCGGTCAGT This paper NA Rnf149 R: ACACATTGGACACGTTCGGT This paper NA Wsb1 F: CTGGACGGTTGCTTTTGCTC This paper NA Wsb1 R: TAACGTGCTCAGGAGGCTTG This paper NA tracr RNA (Alt-R ® CRISPR-Cas9 tracrRNA, ATTO ™ 550) IDT Cat#1077024 /AltR1/rCrC rGrCrC rArUrG rArGrA rArUrA rArCrA rCrCrA rGrUrU rUrUrA rGrArG rCrUrA rUrGrC rU/AltR2/ IDT Mm.Cas9.THY1.1.AC /AltR1/rUrA rGrArG rGrGrU rGrGrC rUrCrU rUrCrG rCrCrA rGrUrU rUrUrA rGrArG rCrUrA rUrGrC rU/AltR2/ IDT Mm.Cas9.NEURL3.1.AR /AltR1/rGrG rArGrG rArGrC rArGrA rGrUrC rCrCrA rCrGrU rGrUrU rUrUrA rGrArG rCrUrA rUrGrC rU/AltR2/ IDT Mm.Cas9.NEURL3.1.AS /AltR1/rArG rGrUrC rCrGrG rGrCrA rCrArC rGrArA rGrGrG rGrUrU rUrUrA rGrArG rCrUrA rUrGrC rU/AltR2/ IDT Mm.Cas9.NEURL3.1.AL /AltR1/rArG rUrUrU rCrCrA rCrGrG rGrArA rCrGrC rCrArC rGrUrU rUrUrA rGrArG rCrUrA rUrGrC rU/AltR2/ IDT Mm.Cas9.NEURL3.1.AJ /AltR1/rArU rGrGrU rArUrC rGrUrG rUrUrC rArGrC rCrArG rGrUrU rUrUrA rGrArG rCrUrA rUrGrC rU/AltR2/ IDT Mm.Cas9.NEURL3.1.AP /AltR1/rGrU rUrGrU rArGrA rCrCrA rCrCrA rCrGrG rCrCrG rGrUrU rUrUrA rGrArG rCrUrA rUrGrC rU/AltR2/ IDT Mm.Cas9.RNF149.1.AB /AltR1/rGrU rArGrC rUrGrA rUrCrC rUrGrU rCrCrA rArArU rGrUrU rUrUrA rGrArG rCrUrA rUrGrC rU/AltR2/ IDT Mm.Cas9.WSB1.1.AT /AltR1/rUrC rUrUrA rCrGrG rCrArC rUrGrG rGrArC rCrArC rGrUrU rUrUrA rGrArG rCrUrA rUrGrC rU/AltR2/ IDT Mm.Cas9.WSB1.1.AS /AltR1/rUrC rCrUrU rGrUrG rArCrC rArCrG rCrArA rArGrU rGrUrU rUrUrA rGrArG rCrUrA rUrGrC rU/AltR2/ IDT Mm.Cas9.WSB1.1.AP /AltR1/rCrA rGrUrC rUrArU rArArC rGrUrG rCrUrC rArGrG rGrUrU rUrUrA rGrArG rCrUrA rUrGrC rU/AltR2/ IDT Mm.Cas9.WSB1.1.AD /AltR1/rArC rArArG rGrArG rUrArA rGrCrU rCrCrC rArUrC rGrUrU rUrUrA rGrArG rCrUrA rUrGrC rU/AltR2/ IDT Mm.Cas9.WSB1.1.AG Recombinant DNA pMSCV-IRES-mAmetrine 1.1 FP Addgene Cat#52113 pMSCV-IRES Neurl3 mAmetrine This paper NA pMSCV-IRES Rnf149 mAmetrine This paper NA pMSCV-IRES Wsb1 mAmetrine This paper NA pMSCV-IRES Thy1.1 Addgene Cat#17442 pMSCV-IRES Neurl3 Thy1.1 This paper NA pMSCV-IRES Rnf149 Thy1.1 This paper NA pMSCV-IRES Wsb1 Thy1.1 This paper NA pMSCV-IRES-mCherry FP Addgene Cat#52114 pMSCV-IRES Rnf149 mCherry This paper NA Software and algorithms GraphPad Prism (v 7.0 / 9.0) GraphPad https://www.graphpad.com/ FlowJo TreeStar https://www.flowjo.com/ Trimmomatic Bolger et al. 97 https://github.com/usadellab/Trimmomatic STAR Dobin et al. 98 https://github.com/alexdobin/STAR DESeq2 Love et al. 100 https://github.com/mikelove/DESeq2 EnhancedVolcano Bioconductor https://github.com/kevinblighe/EnhancedVolcano dsb Mulè et al. 87 https://github.com/niaid/dsb Scanpy Wolf et al. 88 https://github.com/scverse/scanpy scvi-tools Gayoso et al. 90 https://github.com/scverse/scvi-tools gseapy Fang et al. 93 https://github.com/zqfang/GSEApy clusterProfiler Yu et al. 108 https://github.com/YuLabSMU/clusterProfiler GSVA Hänzelmann et al. 109 https://bioconductor.org/packages/release/bioc/html/GSVA.html limma Ritchie et al. 107 https://bioconductor.org/packages/release/bioc/html/limma.html pheatmap CRAN https://cran.r-project.org/web/packages/pheatmap/index.html MAGIC van Dijk et al. 102 https://github.com/KrishnaswamyLab/MAGIC contrastiveVI Weinberger et al. 96 https://github.com/suinleelab/contrastiveVI UMAP McInnes et al. 101 https://github.com/lmcinnes/umap nlfitr Github https://github.com/TJMurphy/nlfitr broom CRAN https://CRAN.Rproject.org/package=broom Seaborn Waskom 95 https://github.com/mwaskom/seaborn Matplotlib Hunter 94 https://matplotlib.org/ ggplot2 CRAN https://CRAN.Rproject.org/package=ggplot2 py_diAID Skowronek et al. 105 https://github.com/MannLabs/pydiaid DIA-NN 1.8.1 Demichev et al. 106 https://github.com/vdemichev/DiaNN Biorender Biorender https://www.biorender.com/ R (v4.4.3) The R Project for Statistical Computing http://www.r-project.org Spectronaut software (version 18.2 and 20.3) Biognosys https://biognosys.com/software/spectronaut/ Other LS Columns Miltenyi Biotec Cat#130-042-401 MidiMACS ™ Separator and Starting Kits Miltenyi Biotec Cat#130-042-301 4D-Nucleofector ® Core Unit Lonza Cat#AAF-1003B 4D-Nucleofector ® X Unit Lonza Cat#AAF-1003X PepMap Neo trap column Thermo Scientific Cat# 174500 PepSep reversed-phase C18 column Bruker Daltonics Cat# 1893476 Probe Sonicator Fisher Scientific Cat# FB120110 Molecular Devices SpectraMax iD3 Molecular Devices NA Resolvex A200 Tecan NA SpeedVac SPD210 Thermo Scientific Cat# SPD210-230 EvoSep One EvoSep Cat# EV1000 timsTOF Pro Bruker Daltonik NA Endurance Column (OE) – 30 SPD EvoSep Cat# EV1113 Open in a new tab ACTIONS View on publisher site PDF (3.6 MB) Cite Collections Permalink RESOURCES Similar articles Cited by other articles Links to NCBI Databases Follow NCBI NCBI on X (formerly known as Twitter) NCBI on Facebook NCBI on LinkedIn NCBI on GitHub NCBI RSS feed Connect with NLM NLM on X (formerly known as Twitter) NLM on Facebook NLM on YouTube National Library of Medicine 8600 Rockville Pike Bethesda, MD 20894 Web Policies FOIA HHS Vulnerability Disclosure Help Accessibility Careers NLM NIH HHS USA.gov Back to Top

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