Unraveling Posttranslational Modification Complexity: Advances in Quantitative Histone Proteoform Mass Spectrometry.

Unravelling Post-translational Modification (PTM) Complexity: Advances in Quantitative Histone Proteoform Mass Spectrometry - PMC Skip to main content An official website of the United States government Here's how you know Here's how you know Search Log in Primary site navigation Search Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health. Learn more: PMC Disclaimer | PMC Copyright Notice Mass Spectrom Rev . Author manuscript; available in PMC: 2026 Jun 1. Published in final edited form as: Mass Spectrom Rev. 2025 Jun 1;45(4):769–799. doi: 10.1002/mas.21941 Unravelling Post-translational Modification (PTM) Complexity: Advances in Quantitative Histone Proteoform Mass Spectrometry Karl F Poncha 1, * , Alyssa T Paparella 1, 2, * , Nicolas L Young 1, + Author information Article notes Copyright and License information 1 Verna & Marrs McLean Department of Biochemistry and Molecular Pharmacology, Baylor College of Medicine, Houston, Texas, USA 2 Center for Precision Environmental Health, Baylor College of Medicine, Houston, Texas, USA * These authors contributed to this work equally. + To whom correspondence should be addressed. Correspondence & Address: nicolas.young@bcm.edu , Address: NICOLAS L. YOUNG, Ph.D., Associate Professor, Verna and Marrs McLean Department of Biochemistry and Molecular Pharmacology, One Baylor Plaza, T-338, MS: BCM125, Houston, TX 77030-3411., Fax: 713-796-9438 Issue date 2026 Jul-Aug. PMC Copyright notice PMCID: PMC12974565  NIHMSID: NIHMS2135458  PMID: 40452177 The publisher's version of this article is available at Mass Spectrom Rev Abstract Histone proteins and their post-translational modifications are central to chromatin structure and function. These modifications often occur in combinations, generating a diverse array of histone proteoforms that contribute to the dynamic regulation of chromatin architecture. Advancements in mass spectrometry-based proteomics, particularly top-down and middle-down approaches, have significantly enhanced our ability to characterize these proteoforms and elucidate PTM crosstalk. This review provides an analysis of the epigenetic machinery involved in the addition, recognition, and removal of histone PTMs, emphasizing the complexity introduced by histone variants and combinatorial PTM patterns. We examine the challenges and limitations of traditional antibody-based methods for PTM analysis and highlight the advantages of mass spectrometry techniques in providing comprehensive and quantitative insights into histone proteoforms. Key considerations in experimental design, sample preparation, chromatographic separation, and data analysis are outlined for the effective application of mass spectrometry for histone proteoform studies. By integrating these technological advancements on the side of sample preparation, instrumentation, and data processing a deeper understanding of chromatin regulation through PTM crosstalk is achieved, paving the way for mass spectrometry-based proteomics to spearhead the discovery of novel therapeutic strategies with proteoform level specificity. Keywords: Histone, Epigenetics, PTM crosstalk, Top-down Mass Spectrometry, Proteoform Introduction Protein post-translational modifications (PTMs) are essential for the functional complexity of multicellular eukaryotes, allowing a limited number of genes to generate diverse protein functions and enabling precise regulation of biological processes 1 , 2 . The study of PTMs has significantly advanced with the development of high-resolution mass spectrometry (MS), which has enhanced our ability to identify and quantitate these modifications 3 . Similarly, top- and middle-down proteomics has uncovered that PTMs regularly exist and function in combination, increasing the complexity of these fundamental signals 4 , 5 . Early work on glycoproteins in the 1960s and 1970s first hinted at this combinatorial complexity 6 – 11 . These studies uncovered a network of elaborate carbohydrate protein post-translational modifications crucial for protein stability, cell signaling, and immune responses. The complexity observed in glycoprotein modification writing and reading provided a conceptual framework essential for understanding other PTM systems 6 – 11 . These discoveries challenged the static view of the proteome that prevailed following Crick’s Central Dogma, highlighting the dynamic and modifiable nature of the proteome 12 , 13 . Chromatin biology and specifically the PTMs of histone proteins that regulate most genetic processes emerged as one of the most complex and important systems where combinations of PTMs play a central role. The ‘Histone Code’ hypothesis, which gained traction in the early 2000s, posits that specific histone PTM patterns act like a structured language, dictating DNA accessibility and the transcriptional landscape of the cell. This concept has since become central to understanding how chromatin is regulated by these modifications 14 – 18 . In eukaryotes, chromatin, a complex of DNA and proteins, plays a vital role in organizing and regulating genetic material. Central to chromatin’s structure are histones, a highly conserved family of proteins that form nucleosomes, the basic units of chromatin. Each nucleosome consists of two copies each of histones H2A, H2B, H3, and H4, around which 147 base pairs of DNA are wrapped. The linker histone H1 binds to the DNA between nucleosomes, facilitating the formation of higher-order chromatin structures 19 – 23 . Histones undergo a variety of PTMs, such as methylation, acetylation, phosphorylation, and ubiquitination, which play a critical role in regulating chromatin structure and function. These modifications are mediated by specific enzymes that respond to environmental signals such as stress, signaling molecules, or metabolic changes. This allows cells to dynamically adjust chromatin accessibility and gene expression in response to changing conditions 24 – 26 . In addition to these modifications, the composition of nucleosomes is further diversified by the presence of histone variants (a table of currently identified variants is available in Supplemental File 1 ) 27 . While canonical histones are primarily synthesized and incorporated during the S phase of the cell cycle, other histone variants are integrated into nucleosomes independently of the cell cycle. The incorporation of histone variants into chromatin is usually facilitated by specialized chaperone proteins. The incorporation of these variants can significantly alter the interactions between histones and DNA, as well as the patterns of PTMs, thereby influencing chromatin dynamics and function 28 . Together, the combination of histone modifications and the incorporation of histone variants creates a highly responsive and adaptable chromatin landscape, essential for regulating gene expression, DNA repair, and other critical cellular processes 29 – 32 . In this review we first introduce the epigenetic machinery orchestrating histone PTM crosstalk. Next, we detail advancements and recommendations in mass spectrometry-based proteomics, covering sample preparation, instrumentation evolution, data acquisition, and novel data analysis methods. These enhancements have significantly improved our ability to accurately and sensitively quantify PTM crosstalk, offering insights into the dynamic interplay of histone modifications that govern cellular function. The recent conceptual framing of multiply modified histones as ‘histone proteoforms’ has enabled a more complete understanding of chromatin regulation. A proteoform is a specific form of a protein from a single gene, including variations from genetic changes, alternative RNA splicing, and PTMs. This concept is particularly enlightening as it recognizes a histone’s capacity to exist in multiple modified states and creates a vast array of distinct proteoforms 33 . This perspective emphasizes the unique interactions each proteoform can have with DNA and chromatin-associated proteins. Understanding chromatin dynamics requires examining the histone proteoform repertoire. Top- and middle-down proteomics provides a means of accessing this crucial information 4 , 5 , 34 – 36 .Top-down (TD) proteomics is considered the gold standard for identifying and quantifying histone proteoforms and understanding their crosstalk. This approach analyzes intact proteins, preserving the full array of post-translational modifications (PTMs) and their interrelationships, enabling a comprehensive study of PTM crosstalk ( Figure 1C ) 34 , 35 . In contrast, bottom-up (BU) proteomics involves enzymatically digesting proteins into smaller peptides, which disrupts the connectivity between PTMs. This disruption often leads to the loss of variant-specific information and limits the ability to analyze co-existing PTMs ( Figure 1A ) 37 , 38 . Figure 1. Open in a new tab Schematic overview of mass spectrometry-based approaches for the characterization of histone proteoforms. (A) The bottom-up approach involves the digestion of derivatized histones into smaller peptides, followed by separation using C18 RP-HPLC. This approach primarily supports generalized PTM analysis and the calculation of percentage relative occupancy of specific PTMs. (B) The middle-down approach generates long peptidoforms, such as those generated by GluC digestion of histone H3 and includes separation via C3/C18 RP-HPLC or WCX/HILIC. MS1 and MS2 level analyses provide insights into proteoform-specific PTMs and PTM combinations, allowing for absolute proteoform quantification. (C) The top-down approach involves the direct analysis of intact histone proteins, such as histone H4, through separation techniques (C3/C4/C18 RP-HPLC, HILIC) or infusion. This approach enables the absolute quantification of specific proteoforms and their post-translational modification (PTM) combinations. Created in BioRender: BioRender.com/e07p179 . Bottom-up analysis of histones also differs from traditional bottom-up proteomic approaches. Derivatization with reagents such as propionic anhydride is necessary to ensure accurate MS analysis and reliable relative quantitation. This is because trypsin, the enzyme commonly used in bottom-up proteomics, cleaves at K/R-X bonds. Histones contain many arginine (R) and lysine (K) residues especially in their N-terminal region where most of their PTMs occur. As a result, tryptic digests of histones without derivatization produces extremely small peptides that are difficult to retain on a reverse-phase chromatography column and analyze by MS. Furthermore, lysine PTMs, such as Kac, Kme2, and Kme3 are not cleaved by trypsin. Derivatization with propionic anhydride modifies unmodified (Kun) and monomethylated (Kme1) lysine residues by adding a propionyl group, preventing trypsin from cleaving at these sites and effectively converting the tryptic digestion process into a pseudo-Arg-C digestion, where enzymatic digestion occurs only at the C-terminal side of arginine residues. This approach generates a uniform pool of longer peptides that are more amenable to MS analysis 37 . Middle-down proteomics offers a compromise between top-down and bottom-up approaches. It generates longer peptides or ‘peptidoforms’, particularly from the unstructured N-terminal tails of histones where most modifications occur. This method preserves a significant portion of PTM connectivity therefore providing more detailed information than bottom-up proteomics, while overcoming the top-down analytical challenge presented by particularly highly modified histones, such as histone H3 ( Figure 1B ) 36 , 39 . See Table 1 for a comparison of the general characteristics, strengths, and limitations inherent to each of these three proteomics strategies. This table provides a basic guide; however, there are substantial nuances that are not readily tabulated. For example, the sensitivity of the different approaches is highly dependent on the biology being probed and the question being asked. Very low abundance discrete PTMs that are randomly distributed with other PTMs into proteoforms are most sensitively addressed with bottom-up proteomics because it collapses potentially thousands of proteoforms into one quantitation bin by disposal of PTM connectivity. Meanwhile top-down proteomics can be achieved with a similar material input and can often rival or even exceed bottomup proteomics even for quantitation of discrete PTMs when the patterns of PTMs are naturally constrained to a limited number of proteoforms. Table 1. Key Qualitative and quantitative differences between Bottom-Up, Middle-Down, and Top-Down Proteomics. Feature Bottom-Up (BU) Proteomics Middle-Down (MD) Proteomics Top-Down (TD) Proteomics Analyte Measured Small peptides (from full digest) Large peptidoforms (from limited digest) Intact proteins (no digest) Primary Goal Protein inference, Quantification, PTM site ID PTM linkages on protein domains, Bridge between BU/TD Full Proteoform Characterization Proteoform Info Lost Mostly retained on peptidoforms Directly observed (Intact molecule) Sensitivity Highest Moderate Moderate Throughput Highest Moderate Moderate MS Resolution Req. (@200 m/z) Medium-high (15–60K) High (60–120K) Highest (120–500K) Fragmentation CID/HCD ETD, HCD ETD, EThcD, ECD, EAD, UVPD Data Analysis Mature, Standardized pipelines Intermediate Complexity (BU pipelines adaptable) Complex, Specialized Key Hurdle(s) Loss of proteoform context Workflow complexity, Sensitivity vs. BU Sensitivity, Throughput, Technical demands Ease of Use Easiest / Most Standard Commercial kits and consumables readily available Intermediate Most Complex / Specialized sample preparation and instrumentation and instrumentation control software required Open in a new tab Finally, it is important to note that bottom-up analyses of peptides are significantly more sensitive to PTM-dependent shifts in electrospray ionization efficiency; a single phosphorylation, for instance, has been shown to decrease or increase the signal of otherwise identical peptides by 60%< depending on sequence context, introducing systematic quantitative bias and necessitating computational approaches like robust linear regression modelling of multiple timepoint acquisitions 40 , 41 . However, for intact proteins or large histone segments analyzed by top-down or middle-down MS, the relative contribution of any single modification to global charge distribution and hydrophobicity is much smaller. This was elegantly shown by Pesavento et al. using mixtures of multiply modified histone H4, and tracking fragment-ion intensities to the abundances of equimolar H4 proteoforms to <5% relative error 42 . Furthermore, quantitation of discrete PTMs is derived by marginalization (summation) from multiple proteoforms and spectra. Thus, derived discrete PTMs abundances are extensively averaged over both random variances in ionization and any small systematic ionization biases of individual proteoforms. Since both middle-down and top-down approaches maintain the connectivity between PTM sites, they enable precise quantitation of PTM crosstalk by measuring the stoichiometric ratios of all possible combinatorial modification states across two or more sites. This includes determining the relative proportions of modification patterns. Comparing the observed abundance of PTM combinations to their expected abundance allows for the evaluation of how particular modifications influence one another 43 – 48 . PTM crosstalk occurs at various levels of organization, adding not only complexity to biological regulation but also altering nucleosome stability and organization. Positive PTM crosstalk occurs when one or more PTMs potentiate the presence of other PTMs, whereas negative PTM crosstalk occurs when one or more PTMs preclude the presence of other PTMs ( Figure 2D ) 49 – 53 . Examples of positive and negative crosstalk between histone PTMs that drive functional outcomes are presented in Figures 2E and 2F . PTM crosstalk can be further classified based on whether the PTMs are on the same or different histone molecules 54 , 55 . PTM crosstalk is termed as cis when the two PTMs are on the same histone (relatively short-range interactions). Trans PTM crosstalk involves PTMs on different histones within the same nucleosome or entirely different nucleosomes ( Figure 2G ) 43 . Figure 2. Open in a new tab Histone proteoform complexity: theoretical predictions, empirical observations, and examples of PTM crosstalk. (A) Schematic illustrating the theoretical increase in complexity with the addition of elements across different functions. The green curve represents the rapid combinatorial explosion of histone PTM combinations, which escalates to an intractable level compared to exponential (blue curve) and linear (red curve) growth functions, with just a simple unit increase in the number of elements. (B) The formula used to calculate the number of proteoforms based on a protein’s modifiable residues, which yields a theoretical number of histone H3 proteoforms is several orders of magnitude higher than the empirically observed number. (C) Histogram and Kernel Density Estimate (KDE) plot illustrating the frequency distribution of identified histone proteoforms versus their intensity. The histogram represents the raw frequency of proteoforms at varying intensities, while the blue KDE curve provides a smoothed estimate of the data distribution. The teal curve represents a fitted log-normal distribution, showing a characteristic trend where the frequency of proteoforms decreases as intensity increases. This trend emphasizes the concept of information content, where lower intensity proteoforms, despite being less abundant, carry significant structured biological information. (D) Illustration of PTM crosstalk, which can be classified into two broad types: positive and negative crosstalk through the ensemble action of chromatin machinery. Positive crosstalk potentiates the addition of another PTM, while negative crosstalk inhibits the addition of another PTMs. Both mechanisms drive histone crosstalk act in concert to regulate chromatin activity. (E) Physiological example of positive PTM crosstalk: The activation of p21 transcription is contingent on the presence of dual marks: H3 serine 10 phosphorylation (H3S10ph) and K14 acetylation (H3K14ac). The phosphorylation of H3S10 serves as a critical priming event that facilitates the recruitment of acetyltransferases responsible for writing the H3K14ac mark. The presence of both modifications is necessary to promote cell cycle via transcriptional activation of p21. (F) Physiological example of negative PTM crosstalk: Histone H3 lysine 36 trimethylation (H3K36me3) antagonizes PRC2, the complex responsible for adding the K27me3 mark through its EZH2 subunit. (G) Additional levels of crosstalk can be seen through cis and trans mechanisms. Cis PTM Crosstalk refers to interactions that occur on the same histone molecule (relatively shorter-range interactions), while trans PTM Crosstalk refers to interactions that occur on different histone molecules within the same nucleosome or histones on different nucleosomes (relatively longer-range interactions). Here, we comprehensively illustrate the multitude of catalogued histone crosstalk relationships both in cis and in trans, to highlight the complex interconnectedness between histone marks that drive biological function. Created in BioRender: BioRender.com/t430537 . Understanding and quantifying PTM crosstalk is essential for assessing the interplay between proteoforms and the epigenetic machinery. Advances in MS-based proteomics will enhance our ability to measure PTM crosstalk across diverse biological contexts, setting the stage for “druggable crosstalk” as a promising therapeutic modality. The Epigenetic Machinery The epigenetic machinery consists of “readers,” “writers,” and “erasers,” which recognize, add, or remove post-translational modifications (PTMs) on histone proteoforms, thereby regulating chromatin structure and gene expression 54 – 56 . Less commonly discussed but equally relevant are “clippers,” enzymes that proteolytically cleave histone tails permanently removing epigenetic information and contributing to further histone proteoform diversity, though their classification remains less defined 59 . Key functionally and evolutionary conserved domains of these proteins are summarized in Table 2 . Table 2. Examples of Epigenetic reader, writer, and eraser domains and their functions. Domain Name Function Description Reader Bromodomain Recognizes acetylated lysines Found in proteins like p300/CBP and BRD4, involved in transcription regulation by recognizing acetyl-lysines. Chromodomain Binds methylated lysines Found in proteins like HP1 and Polycomb, involved in chromatin compaction and gene silencing. PHD Finger Binds unmodified and methylated lysines Found in proteins like BPTF and ING proteins, often involved in recognizing trimethylated lysine residues (H3K4me3). PWWP Domain Binds methylated lysines or arginines Consists of a Pro-Tryp-Tryp-Pro motif and found within DNMT3B and WHSC1. WD40 Binds methylated lysines Found in WRD5 with the MLL complex. ADD (ATRX-DNMT3-DNMT3L) Binds methylated lysines Found in DNMT3L. BAH (Bromo Adjacent Homology) Binds methylated lysines Consists of proteins such as BAHD1 that reads H3K27me3 and aids in gene silencing. Ankyrin Repeats Binds methylated lysines Found in proteins like G9a and GLP proteins and binds mono- and di-methylated lysines on H3 MBT (Malignant Brain Tumor) Domain Bind methylated lysines Binds mono and di methylated histones, such as in case of L3MBTL1. Tudor Domain Binds methylated lysines/arginines Found in proteins like KDM4A (JMJD2A) and 53BP1, recognizes methylated arginine/lysine residues. Writer HAT (Histone Acetyltransferase) Adds acetyl groups to lysines Includes enzymes like p300/CBP, responsible for acetylating histones, facilitating transcription activation. HMT (Histone Methyltransferase) Adds methyl groups to lysines/arginines Includes enzymes like SETD2 and EZH2, responsible for methylating histones, involved in marking gene activation and gene silencing respectively. PRMT (Protein Arginine Methyltransferase) Adds methyl groups to arginines Includes enzymes like PRMT1, involved in various cellular processes including transcriptional regulation. Kinases Adds phosphorylation to serines or threonines Includes enzymes such as MSKs (Mitogen- and Stress- Activated Kinases), CDKs (Cyclin Dependent Kinases) and Aurora Kinases. Aurora Kinase phosphorylate H3 at serine 10 during cell division to aid in chromosomal condensation. Ubiquitin Ligases Adds ubiquitin to lysine residues Can serve to activate or repress transcription. An example of repression is seen at PRC1 through H2A mono ubiquitination at K119 to result in gene silencing. Eraser HDAC (Histone Deacetylase) Removes acetyl groups from lysines Includes enzymes like HDAC1 and HDAC2, involved in transcriptional repression by deacetylating histones. KDM (Lysine Demethylase) Removes methyl groups from lysines Includes enzymes like LSD1 and JMJD3, responsible for demethylating histones, often involved in gene activation. Open in a new tab Proteins within this machinery frequently contain multiple epigenetic ‘reader’ domains that act in coordination, enabling even a single protein to recognize complex PTM patterns. For example, a PHD-bromodomain pairing can detect lysine methylation and acetylation simultaneously, providing a more comprehensive readout of histone modifications ( Figure 3 ). This combinatorial functionality is further enhanced when these proteins form larger complexes, increasing their specificity for particular histone proteoforms and enabling the nuanced and facile regulation of chromatin states ( Figure 4 ) 60 – 63 . Recent structural studies have shed light on how such protein complexes interact with histone proteoforms, showing that the presence of certain modifications influences the affinity, activity, and constituents of these complexes 64 . Overall, the epigenetic machinery lies at the core of both decoding and re-coding histone proteoforms to ultimately form and regulate the epigenome 65 – 68 . Figure 3. Open in a new tab Occurrences and combinations of tandem epigenetic domains in the human proteome. (A) Epigenetics domains: Frequency of epigenetic domains and their occurrences when found in combination with other domain structures. PHD domains appear most frequently, underscoring their prominent role in multi-domain assemblies involved in chromatin regulation. (B) Frequency of homo- or heterodomain combinations, focusing on domains that are either adjacent in protein primary structure or have structural evidence for cooperative interaction. Data queried from Structural Genomics Consortium’s Chromohub v1.0. Figure 4. Open in a new tab Epigenetic Machinery and Its Combinatorial Actions: Illustrative examples of histone-modifying enzymes, showcasing their individual functions in reading, writing, erasing, and clipping histone marks. These enzymes work in tandem within protein complexes to regulate chromatin dynamics and gene expression by functioning on histone proteoforms. (A) HP1 (Heterochromatin Protein 1) represents a reader that binds to di- and tri-methylated H3K9 (H3K9me2/3), a modification associated with transcriptional repression. (B) SETD2 (SET Domain Containing 2), a writer, is the only known methyltransferase that catalyzes the trimethylation of H3K36 (H3K36me3). (C) SIRT6 (Sirtuin 6) is an eraser, specifically deacetylating H3K9 (H3K9ac). (D) Cathepsin L is a clipper enzyme that cleaves the N-terminal tail of histone H3, particularly during embryonic stem cell differentiation. This clipping action is pivotal in altering chromatin structure and regulating gene expression during development. (E) The p300 complex exemplifies a protein with a combined read-write function. This complex possesses both a KAT (lysine acetyltransferase) domain that acetylates histones like H2A.Z at lysines 4, 7, and 11, and a BRD (bromodomain) that recognizes acetylated lysine residues on histone H4 (H4K5ac). This trans-histone crosstalk demonstrates how acetylation marks on one histone influence modifications on another, promoting chromatin remodeling and transcriptional activation. (F) PHF8 (PHD Finger Protein 8) is an example a reader-eraser protein, which reads the H3K4me3 mark and erases the H3K9me1/2 mark. This dual activity links gene activation and repression, representing a negative crosstalk cis-regulatory mechanism. (G) BPTF (Bromodomain PHD Finger Transcription Factor) is a key component of the NURF (Nucleosome Remodeling Factor) complex, exemplifying a reader-reader tandem. BPTF recognizes both H3K4me2/3 and H4K16ac marks, facilitating nucleosome sliding and chromatin remodeling. This trans-histone crosstalk underscores the complexity of chromatin regulation via multiple reader domains acting on different histones within the same nucleosome. (H) The writer-eraser complex involving Gfi1, G9a, and HDAC1 shows how a transcriptional regulator recruits both a methyltransferase (G9a) and a deacetylase (HDAC1) to repress gene expression. This example illustrates how coordinated histone modification (methylation and deacetylation) leads to chromatin compaction and transcriptional repression. (I) The writer-writer complex depicted here involves PRC2 and G9a, linked by PALI (PRC2 associated LCOR isoform 1). This interaction results in the simultaneous methylation of H3K9 and H3K27, key marks associated with gene silencing. (J) The CoREST complex, representing eraser-eraser activity, consists of LSD1 (demethylase) and HDAC1 (deacetylase), working together to remove both dimethylation and acetylation marks from histones. This dual erasure is crucial for the regulation of gene expression, often leading to transcriptional repression. Created in BioRender: BioRender.com/o02i904 . Pitfalls and Challenges Associated with Traditional Methods of PTM Analysis Though single PTM-specific antibody-based methods such as Western Blotting and ChIP-Seq continue to remain mainstays for interrogating histone modifications in biological systems, their effectiveness is critically hampered by the fact that histones naturally exist as highly modified proteoforms. Consequently, these antibody-based approaches suffer from several significant limitations, such as: Off-target recognition Epitope occlusion by neighboring PTMs Poor distinction between chemically similar modifications (e.g., di- and tri-methylation on lysine) Limited antibody repertoire that is dependent on a priori knowledge, restricting the discovery of novel marks Challenges in detecting multiple histone modifications in a single assay Lack quantitative capacity to stoichiometrically compare between PTMs on the same histone tail These limitations are compounded by the methods’ inability to accurately assess the true localization of histone marks to specific variants within the histone octamer – a crucial aspect when determining cis or trans mechanisms of PTM crosstalk. This underscores the need to integrate antibody-based methods with mass spectrometry, which adeptly overcomes these challenges and offers a more comprehensive and precise understanding of histone proteoform biology 69 , 70 . For the remainder of this review, we focus on notable advancements in, and recommendations for mass spectrometry-based proteoform quantitation. Experimental Design and Practical Considerations for the Analysis of Histone Proteoforms by Mass Spectrometry Experimental Design Prior to initiating experiments, it is crucial to determine the necessary number of samples to detect biologically meaningful and reproducible changes 71 – 73 . Histone proteoform analysis, which captures the dynamics of highly specific PTM crosstalk in different conditions, has substantial advantages over broader proteomics methods that measure changes in inferred protein groups from surrogate peptides 74 , 75 . These proteoform-specific changes are typically driven by targeted and fine-tuned epigenetic regulators/machinery, resulting in lower measurement variability 45 , 46 , 76 , 77 . Such precision not only allows for the detection of subtle differences between experimental conditions but also enhances the reliability and robustness of findings. Previous work from our lab established that with current methods a sample size of n=5 per condition is optimal for detecting percentage point (p.p.) changes of 1% and greater, achieving an 80% power level and a 5% significance level (alpha) 78 . This sample size readily accommodates the typical variability observed in proteoform analysis, ensuring effective detection of changes. Including technical replicates is essential for assessing the consistency and reliability of chromatographic and mass spectrometry performance. Technical replicates ensure that observed changes are biological by distinguishing them from technical variability. Interspersing technical replicates with other samples minimizes batch effects 46 , 79 – 83 . Proteoforms not consistently detected in at least two out of three technical replicates are typically excluded from the final analysis to maintain data quality and robustness 46 , 83 . Another notable aspect prior to initiating experiments is the careful selection of samples and the temporal range of treatments. The highly specific nature of histone proteoform changes, whether at the abundance level or in their unique set of PTMs, necessitates precise timing and conditions. For instance, using sodium butyrate, a pan-histone deacetylase inhibitor, it was established that site-specific changes in acetylation occur within 10 minutes primarily on H4K20me2 containing proteoforms. Cells also showed recovery from treatment within 10–30 minutes, highlighting the epigenome’s plasticity and raising questions about optimal drug treatment durations 77 . Prolonged exposure to epigenetic drugs can lead to compensatory pathway upregulation, obscuring the proteoform specific mechanism of action 45 , 84 , 85 . Therefore, carefully selecting an appropriate range of time-points is necessary to capture fine-tuned proteoform dynamics. It is crucial to consider the cell lines and model organism strains used in experiments. Top-down proteomics has revealed distinct proteoform signatures in different mouse strains and sexes. Davis et al. demonstrated unique proteoform signatures in the mouse brain across various strains, indicating strain-specific genotype and phenotype effects on development, function, and disease progression. Knock-out mouse strains also exhibit unique proteoform signatures, highlighting differences between strains and sexes. This underscores the importance of assessing model organisms at the proteoform level, followed by selection, as these choices significantly impact biological outcomes and have clinical implications, particularly in drug development 86 , 87 . Histone proteoforms also vary between cell lines and the media used to grow cells 45 , 75 , 78 , 88 – 90 . Sample Preparation Analysis of histone proteoforms by mass spectrometry requires pure histone extracts (>85%). These can be obtained by either acid extraction or high-salt extraction from cells or tissues. The majority of histone extraction work is based on their solubility in dilute HCl or H 2 SO 4 conditions (other nuclear proteins and nucleic acids precipitate under these conditions). HCl is strongly recommended for top-down proteomics applications. This is because the sulfate anion has been found to form adducts with histone H4 (and possibly other histones). We find that this phenomenon even persists after removing H 2 SO 4 from the sample processing workflow. Trace H 2 SO 4 may be effectively removed from tubing and source components with urea. It is common practice in our laboratory to first clean mass spectrometry components with 1M urea, followed by the standard cleaning procedure 91 , 92 . Recently, identifying acid-extraction based sample preparation as the main bottleneck for truly high-throughput quantitative top-down proteomics, our lab introduced a rapid protocol that: 1) decreased processing time seventeen-fold, 2) micro-scaled down starting material requirements to one million cells and ~38 mg of tissue (e.g., sectioned mouse brain), and 3) increased total yield across histones and their variants, thus boosting data quality and reproducibility 83 . While acid extraction is the preferred method for mammalian samples, it must be noted that when processing samples from plant cells, slime mold, and yeast, acid-extracted histones are contaminated with significant amounts of carbohydrates and polyphenols that readily cross-link and oxidize histones, which introduces artifacts into subsequently acquired MS data 93 . Yeast histones can be extracted by acid extraction but requires precision to achieve a crude histone extract, free form major contaminants 94 . Recently, Homsi et al. adapted a filter-aided sample preparation approach using acidified ethanol that simplified the extraction of yeast histones while simultaneously ensuring scalability and reducing artifacts 95 . Further considerations about histone extraction methods must be kept in mind when the goals include novel PTM discovery or analysis of acid-labile PTMs such as histidine phosphorylation. In such scenarios, a high-salt extraction offers some advantages: 1) native pH, 2) isolation of non-chromatin bound ‘free’ histones in addition to the chromatin-bound histones, and 3) differential extraction of the H2A-H2B dimers from the H3-H4 tetramers at different salt concentrations 96 . Other extraction techniques that generate subsets of histone proteoforms of interest are: 1) Perchloric acid extraction, which enriches for H1 variants 97 , and 2) Gradient-seq, which isolates sonication-resistant heterochromatin fragments via a sucrose gradient to study H3K9me3 containing heterochromatin domains 98 . Chromatographic Separation For complex proteoform mixtures, separation is necessary prior to mass spectrometry (MS) analysis. This can be achieved through either one-dimensional (1D) or two-dimensional (2D) high performance liquid chromatography (HPLC). In 1D-HPLC, proteoforms are separated in a single step, which is often simpler and faster but may not resolve highly complex mixtures effectively. In contrast, 2D-HPLC provides enhanced separation by using two distinct chromatographic steps. Typically, the first step is done offline, where bulk acid extracted histones are separated, histone variant fractions identified via Ultraviolet-visible (UV-Vis) spectroscopy at 214nm and collected. Each fraction is then further separated in the second dimension – online, and the eluted proteoforms analyzed by MS/MS 83 . Prefractionation via offline HPLC allows for greater separation and thus depth, where direct online HPLC is a more ‘one-pot’ approach that streamlines the process, minimizing sample handling and increasing efficiency 76 , 99 . The use of either approach is a context-dependent crucial choice for the characterization of complex histone proteoform mixtures. Offline LC Separation of Bulk Histones Reverse-phase HPLC (RP-HPLC) is one of the most popular methods used as the first dimension (offline) of separation. Pioneering work by Gurley et al. evaluated columns containing octadecylsilane (C18), cyanopropylsilane (CN), phenylethylsilane (Phenyl), and aminopropylsilane (NH2), as well as a non-silica-based polystyrene-divinylbenzene resin (PRP-18) 100 , 101 . Critical parameters identified were silylation, or endcapping, of the residual silanol groups on the matrix and 0.1–0.3% TFA in the mobile phases. They found that the Radial-Pak μBondapak C18 cartridge with 0.3% TFA provided the best resolution between histone families (mainly based on separation by the histone fold domain), effectively separating histone fractions H2A.1, H2A.2, and H4. Importantly, they noted that modified histones (phosphorylated and acetylated) did not separate from their unmodified forms. Based on this, C18-based offline separation of histones coupled to UV-Vis spectroscopy has been further optimized, and continues to be the most popular choice for histone separation and purification into histone families ( Table 3 ). Other stationary phases that have been used include porous graphitic carbon, C4-packed microbore, C4 Nucleosil 300–5, and C4 Jupiter columns ( Figure 5A ) 99 – 101 . Table 3. Parameters for the optimized separation of histone variants by C18 RP-HPLC. Parameter Value Linear Gradient Program (Flow rate 0.250 ml/min, UV λ = 214nm) Sample Injection Volume 80 μL Time % Buffer B Autosampler temperature 4 °C -10 min (equilibration) 25% Column 150×2.1mm Vydac 3-μm C18 column (218TP3215) 0 min 25% Column Oven Temperature 27.0 °C 55 min 60% Buffer A composition 5% ACN, 0.2% TFA 65 min 100% Buffer B composition 95% ACN, 0.188% TFA 75 min 100% Open in a new tab Figure 5. Open in a new tab Chromatographic Separation and Quantitative Estimation of Core Histones. (A) Chromatograms obtained using C4 and C18 reversed-phase high-performance liquid chromatography (RP-HPLC) columns. The C4 column (top) and the C18 column (bottom panel) are used to separate histones, with UV-Vis detection at 214 nm. The chromatograms reveal distinct peaks corresponding to histones: C4 broadly separates the different histones whereas C18 separates outs the histones into their variants. (B) Representative standard curve for quantifying histone content based on the area under the curve (AUC) at 214 nm, using commercially purchased synthetic H4 as the standard. The relationship between AUC and histone mass is shown with a linear regression equation, indicating a strong linear correlation over a wide amount of sample input. (C) Generalized formula for estimating the mass of other core histones from the H4 standard curve using their respective AUC values based on the assumption that extracted histones are in an equimolar ratio as would be in the nucleosomes. An example calculation for histone H3 is provided, demonstrating the use of the standard curve and the AUC ratio to determine the histone quantity in the sample. Created in BioRender: BioRender.com/a53j988 . Quantitation and Accurate Loading of Histones Prior to Online LC-MS/MS Absolute and stoichiometric quantitation of histone proteoforms is achievable through highly reproducible offline separation 46 , 83 . Histone and histone variant specific standard curves can be generated by purchasing commercially available synthetic histones ( Figure 5B ) and determining the area under the curve (AUC) values for known quantities of histone. While it is useful to have standard curves generated for each histone variant that is separable via offline HPLC, the amount of other histone and variants can be reasonably estimated from the standard curve quantitation of just one histone e.g.: H3 from H4 ( Figure 5C ). Quantitation of fractions enables accurate and consistent loading of precise amounts of histone (e.g.: 200 ng of H4 per replicate and 1 μg per H3 replicate) for online chromatography and to determine absolute proteoform amounts detected. While not achieving the same accuracy as standard curve-based quantitation coupled to offline separation, if proceeding with direct online HPLC, the Bradford assay can be used to achieve consistent loading of bulk histones 105 . The commonly used BCA assay is not suited for histone quantitation as histones lack sufficient amounts of Cysteine, Tyrosine, and Tryptophan residues that the BCA reagent uses to produce a colorimetric readout. Thus, the BCA tends to underestimate the concentration of histones. The Bradford assay, on the other hand, detects proteins based on the binding of Coomassie dye to basic (K and R) and aromatic (H) residues provides a more reliable (albeit slightly overestimated) histone concentration for consistent loading prior to LC-MS/MS 106 . Online LC Separation of Histone Proteoforms Due to its compatibility with electrospray ionization MS, RP-HPLC is one of the most widely used liquid chromatography methods with mass spectrometry. In RP-HPLC, analytes separate based on hydrophobic interactions between the analytes and the stationary phase. Common phases used for histone proteoform separation include C18, C8, C4, and C3 45 , 46 , 76 , 77 , 83 , 103 , 107 , 108 . While typical C18 phases used in bottom-up proteomics result in highly mixed precursor ion tandem mass spectra, recently Holt et. al. demonstrated the robustness of C3 RP-HPLC for high-throughput quantitative top-down proteomics. They showed that C3 RP-HPLC leverages modifications not used in previous approaches to resolve individual intact H4 proteoforms with a near-Gaussian peak shape reproducibly for hundreds of runs 46 . Hydrophilic interaction liquid chromatography (HILIC) is another valuable method for the analysis of histone proteoforms. Garcia et al. demonstrated isomeric separation of middle-down H3 proteoforms based on their hydrophilicity 109 . One disadvantage of HILIC was the increasing salt concentration gradient, which renders traditional HILIC MS-incompatible. Subsequently, HILIC was made compatible with MS by replacing salt gradient elution with a ‘saltless’ pH gradient organic buffer system: solvent A consisted of 75% acetonitrile, 20 mM propionic acid (pH 6.0), and solvent B was 25% acetonitrile (pH 2.5) on a PolyCAT A resin 110 , 111 . More recently, High-Performance Capillary Zone Electrophoresis (HPCZE), which utilizes the differential electrophoretic mobility of highly modified proteoforms (relating to their charge to size ratio) and a comparatively larger number of theoretical plates, has emerged as an alternative, offering high-resolution separation and increased sensitivity at a native pH 112 . Chromatographic separation is an essential step for the effective analysis of histone proteoforms by MS. Online LC methods, such as RP-HPLC, HILIC, and HPCZE, each offer unique advantages for the analysis of histone proteoforms. The choice of method depends on the specific requirements of the study, including the complexity of the proteoform mixture and the desired resolution 93 , 113 . Ion Mobility Separation Gas-phase separation by ion mobility separation (IMS) is a rapidly advancing complement to liquid chromatography. IMS readily separates gas-phase molecular ions on millisecond timescales based on their 3D structure and thus is well suited for the separation of isomeric species such as proteoforms that differ from one-another by minor structural variations that correspond to collisional cross section differences of less than a few percentages 114 – 116 . Below we detail how linear (trapped ion mobility separation - TIMS) and non-linear IMS (field asymmetric waveform ion mobility separation – FAIMS and cyclic ion mobility spectroscopy – cIMS) have demonstrated promising potential as an additional separation dimension based on the 3D structure of histone proteoforms ( Table 4 ). Table 4. Summary of IMS type and their separation capacity on different types of histone proteoform analytes. IMS type Study Reported resolving power Analyte and PTM positions CCS difference resolved FAIMS Shliaha et al., 2017 - Synthetic histone (H3K4/9/K23me1, H3K4/9/23/27/36me3, H3K9/14/18/27/36ac, H3T3/6S10/28Y41ph) ~2% FAIMS Wang et al., 2023 - Commercial histone derived from calf thymus (e.g. H2B1KS6phK15me1R86/92cit, ac-H4R3me2K5/8/12ac) 1–2% TIMS Berthias et al., 2022 Up to 350 Synthetic histone (H4K5/8/12/16/20ac) 1–3% cIMS Berthias et al., 2023 Up to 476 Synthetic histone (H3K4/9/23me1, H3K4/9/23me3) <1% Open in a new tab FAIMS: Shliaha et al. utilized FAIMS, combined with electron transfer dissociation (ETD) to analyze histone tails with various post-translational modifications (PTMs). They achieved high resolution for singly-modified (me1, me3, ac, or ph) H3 histone tail proteoforms, demonstrating the ability to separate isomeric proteoforms based on differences in their collisional cross-section (CCS) values. FAIMS resolved histone proteoforms with CCS differences of ~2% 117 . Wang et al. demonstrated the first example of coupling capillary zone electrophoresis (CZE) with FAIMS for the top-down characterization of histone proteoforms using a commercially purchased physiological substrate – histone derived from calf thymus. The integration of FAIMS improved the number of identified histone proteoforms by about threefold compared to CZE-MS/MS alone. Specifically, they identified 366 histone proteoforms with ProSight PD and 602 with TopPIC. FAIMS was able to resolve histone proteoforms with CCS differences as small as 1–2%, significantly enhancing proteoform coverage. This enhanced resolution and sensitivity allowed for the detection of low abundance proteoforms that were not detectable with CZE alone 112 . TIMS: Berthias et al. investigated the use of TIMS for separating synthetic positional isomers of monoacetylated histone H4 using a timsTOF Pro2 hybrid Q-TOF MS. They achieved a resolving power of up to 350, fully resolving several pairs of isomeric H4 species with single acetylation sites 118 . The study demonstrated the ability of the TIMS to distinguish proteoforms with CCS differences of 1–3% significantly improved the separation of co-eluting isomeric proteoforms from liquid chromatography, ultimately allowing for more precise proteoform analysis. cIMS: Berthias et al. applied cyclic IMS (cIMS) to achieve high-resolution separation and sequencing of isomeric H3 proteoforms in the middle-down mass range (5.4 Da) 119 . The cyclic nature of cIMS allowed ions to pass through the mobility cell multiple times, enhancing the resolution with each pass and achieving a resolving power of up to 476 for monomethylated histone H3. This high resolution enabled the separation of histone proteoforms with CCS differences of less than 1% with a high per-pass ion transmission rate of 95–98%. Given that the majority of histone proteoforms from physiological sources have three or more modifications, further investigation remains necessary to fully understand how increased PTM complexity affects CCS values and IMS performance 110 , 120 – 123 . Despite these substantial improvements in structural and quantitative analysis, IMS techniques must carefully balance gains in resolution with potential ion losses that may reduce quantitative reproducibility, particularly when chasing higher resolving power to distinguish similar molecular species. In cIMS, each successive pass through the mobility cell decreases ion abundance. Although with a reasonable number of passes losses may be minimal and therefore acceptable, excessive passes result in compounding ion losses, disproportionately affecting quantitation of low abundance proteoforms 124 , 125 . Likewise, TIMS and FAIMS require optimization of operational parameters, such as ion trapping durations or waveform conditions, to minimize ion transmission losses and maintain sensitivity 126 – 128 . Emerging traveling-wave IMS approaches, particularly Structures for Lossless Ion Manipulation (SLIM), promise to mitigate these limitations by routing radially confined ions (using RF and DC electrodes) through multiple passes along long (~13.5 m) serpentine confinement paths, delivering ultra-high resolving power without significant ion losses 125 . Consequently, integrating these gas-phase separation technologies before tandem MS can greatly enhance quantitative proteoform characterization through increased peak capacity and separation efficiency. Mass Spectrometry Measurement of Histone Proteoforms Analytical Challenge Quantifying histone proteoforms presents significant analytical hurdles, particularly when distinguishing positional isomers (regioisomers) of modified histone tails that exhibit identical masses and physiochemical properties, leading to co-elution during chromatographic separation. Monoacetylation regioisomers, such as histone H4 acetylated at lysines K8 versus K12, exemplify this challenge, as they yield nearly indistinguishable fragments, resulting in composite MS2 spectra characterized by overlapping fragment ions that obscure accurate quantification ( Figure 6A ). To precisely resolve the resulting mixture of monoacetylated species, advanced computational methodologies are often necessary 110 . Mixed linear optimization methods have been used to find the optimal abundance of proteoforms to explain chimeric spectra relative to a physiochemically meaningful objective function with linear constraints 110 , 129 . More recently, ion mobility spectrometry has emerged as another solution to this problem by providing a crucial orthogonal separation of monoacetylated H4 regioisomers based on subtle differences in collisional cross-sections. This separation prior to mass analysis results in more pure fragment ion spectra 130 . It is notable that the analytical complexity posed by triacetylated (3ac) regioisomers parallels that of monoacetylated species, as both scenarios involve composite spectra with precisely four unknown positional variables, making their resolution equally amenable to similar analyses. Figure 6. Open in a new tab Analytical challenge faced with different kinds of positional isomers (regioisomers) of modified histone tails (e.g.: acetylated H4 tails). (A) Challenges in quantitating isobaric positional isomers of H4 with one acetylation. (B) Challenges in quantitating isobaric positional isomers of H4 with two acetylations. (C) Partial separation of isobaric histone proteoform regioisomers using a lower-peak-capacity LC method (C3 RP-HPLC). Created in BioRender: BioRender.com/k40j029 . In contrast, diacetylated regioisomers present an elevated analytical challenge due to the increased combinatorial possibilities. For histone H4 diacetylated at lysines K5, K8, K12, and K16, there exist six potential positional isomers, yet standard MS2 fragmentation typically generates only four independent fragment ion intensity ratios ( Figure 6B ). This results in an underdetermined equation system, insufficient for resolving all positional isomers solely through standard computational deconvolution methods. To effectively address this complexity, MS3 fragmentation has proven highly advantageous; specific diagnostic fragment ions selected from MS2 spectra are subjected to an additional round of fragmentation, yielding further informative fragment ions that significantly enhance positional discrimination capabilities 131 . Another strategy employs partial chromatographic resolution using C3 reverse-phase HPLC columns. This approach effectively reduces the complexity of co-eluting isobaric species to four or fewer positional isomers per MS2 spectrum, thereby facilitating robust mathematical mixture modeling and accurate proteoform quantitation 46 , 110 , 132 , 133 . Lastly, albeit underutilized, is the strategy of incorporating Bayesian priors derived from biological and chemical constraints into computational workflows 134 . These include assumptions regarding site-specific enzyme activity, known modification hierarchies, steric hindrance that preclude particular modification combinations, or empirical knowledge of proteoform abundances under specific physiological or experimental contexts 135 , 136 . By systematically encoding this information into frameworks, the theoretical number of potential positional isomers to consider is parsimoniously reduced, simplifying the analytical task and enhancing the accuracy and interpretability of proteoform quantitation; however, at the cost of possibly missing unexpected events. Together, these strategies represent powerful and tailored solutions to histone proteoform analysis. Mass Spectrometry Data Acquisition From the analytical challenge posed by histone proteoforms, it is clear that while high peak capacity LC methods may seem to enhance overall chromatography, they are not always ideal for resolving isobaric species. Higher peak capacity separation methods often exhibit unsuitable proteoform selectivity and merge closely related isobaric species into a single peak, making it impossible to distinguish between them. Therefore, a method with lower peak capacity with better selectivity of isobaric species offers more interpretable fragment ion scans for data analysis ( Figure 6C ) 46 , 137 – 140 . To capitalize on this partial separation, it is essential to carefully balance MS1 survey scans with MS2 scans across eluting peaks. In practice, current methods employ high-resolution MS1 scans, typically at 60k resolution or higher (at 200 m/z ), followed by a TopN method where the top 10–20 most intense ions from the MS1 scan are subjected to fragmentation, using a quadrupole isolation window of 1 m/z 55 , 55 , 90 , 141 . The instrument cadence achieved with this method allows for 20 or more MS2 scans to be acquired for each isolated window allowing repeated and rapid resampling of precursor masses as quickly as 1 Hz, ensuring that proteoform are tracked, identified, and accurately quantified across their entire elution window. Notably this cadence of resampling is much higher than in typical bottom-up experiments. Insufficient instrument cadence limits the quantitative reproducibility by relying on fewer data points for both precursor signal and how mixtures of isomeric proteoforms change across chromatographic peaks 142 . If dynamic exclusion is used at all, it is generally applied for 1 or 2 seconds (in contrast to traditional 30s exclusion times used in bottom-up proteomic DDA methods) 143 . Fragmentation of proteoforms for MS2 acquisition Collision-based methods commonly applied in bottom-up proteomics, such as Collision-Induced Dissociation (CID) and Higher-energy Collisional Dissociation (HCD), are often unsuitable for the analysis of histone proteoforms. A major downside of these methods is the limited fragmentation of the peptide backbone due to their tendency to preferentially break the most labile bonds in protein ions. Intact histones, with molecular weights ranging from 11 kDa to 15 kDa and charge state envelopes between +7 to +46, present a challenge for these methods. Therefore, electron-driven dissociation methods: Electron Transfer Dissociation (ETD) and Electron Capture Dissociation (ECD) are usually employed. Both ETD and ECD are methods that induce fragmentation of the peptide backbone through electron transfer/capture processes 144 – 147 . In ETD, an electron is transferred from a radical anion to a protonated peptide, while in ECD, an electron is captured directly by the multiply charged cation. These processes cause cleavage of the Cα-N peptide backbone bond, creating complementary c and z-type ions (instead of the typical b and y-type ions observed in CID and HCD). Although the precise mechanisms of ECD and ETD are debated, both methods also preserve labile PTMs that are often lost in CID and HCD. As a result, ETD and ECD are better suited for highly charged intact protein ions and generate higher sequence coverage, making them ideal for detailed proteoform analysis 148 – 150 . Some limitations of ETD and ECD are their strong dependence on precursor ion charge state. This dependence requires iterative optimization, careful control of reaction times, and the careful selection of precursor charge states to minimize interfering peaks from other charge states. By selecting charge states with minimal overlap, the resulting fragmentation data can be more accurately interpreted ( Table 5 ) 151 . Table 5. Summary of optimized ETD reaction kinetics across histones, their charge states, and instrument types Analyte Charge state Analyte AGC target ETD reagent Target Reaction Time Instrument type Study Histone H4 (intact) z = +15 5.0e5 5.0e5 14 ms Orbitrap Fusion Lumos PMID: 29880017, 36754872 GluC digested H4 (E52, E53, E63) z = +7, +7, +9 1.0e6 5.0e6 50 ms, 50 ms, 30 ms Orbitrap Fusion Lumos PMID: 31147891 GluC digested H3 (E50) z = +9 5.0e5 5.0e5 18 ms Orbitrap Fusion Lumos PMID: 37204295, 38798640 GluC digested H3 (E50) z = +8 2.0e5 2.0e5 20 ms Orbitrap Fusion PMID: 32070774, 33818074 Histone H4 (intact) z = +18 3.0e5 2.0e5 50 ms LTQ-Orbitrap PMID: 20133344 Histone H2B1F (intact) z = +22 3.0e5 2.0e5 50 ms LTQ-Orbitrap PMID: 20133344 GluC digested H3 (E50) z = +8, +9 1.5e5 1.5e5 20 ms, 15 ms Orbitrap Fusion PMID: 28934504 GluC digested H3 (E50) z = +10, +11 2.0e5 2.0e5 15 ms, 12 ms Orbitrap Fusion PMID: 28934504 Histone H3 (intact) z = +18 1.0e6 7.0e5 1 to 7 ms Orbitrap Fusion Lumos PMID: 26272979 GluC digested H3 (E50) z = +9 3.0e4 1.0e6 80 ms LTQ-Orbitrap XL PMID: 19654425, 23889513 Open in a new tab This level of optimization limits the ability of electron-based dissociation techniques to provide extensive fragmentation and sequence information on all analytes. To address these limitations and achieve near-complete sequence coverage across a wide variety of analytes, alternative methods such as infrared photo-activation concurrent with ETD reactions (activated ion ETD, AI-ETD) and Ultraviolet Photodissociation (UVPD) have been developed. AI-ETD mitigates non-dissociative electron transfer (ETnoD) by using IR photons to disrupt non-covalent interactions and partially unfold precursors during ETD, thereby promoting the formation of diverse sequence-informative fragment ion products 148 , 152 , 153 . UVPD at 193nm, on the other hand, directly fragments proteins through high-energy UV photons, offering extensive backbone cleavage 154 . Pioneering work from the Brodbelt lab combined UVPD with proton transfer charge reduction (PTCR) to obtain 91% sequence coverage of histone H4 from calf thymus 155 , 156 . However, it is important to note that extensive fragmentation can be a ‘double-edged sword’, and more (fragmentation) is not always better. For instance, controlling the false discovery rate (FDR) in software that uses traditional target-decoy database searching becomes challenging due to the extremely variable and large number of fragment ions produced by UVPD (a/x, b/y, and c/z) 155 , 157 . This, combined with highly chimeric spectra from the co-elution and co-isolation of isobaric combinatorial histone PTMs, leads to an increased likelihood of false positive identifications, as the sheer abundance of product ions complicates the accurate differentiation between true and false hits 158 – 160 . Data Pre-processing and Analysis Software MS1 and MS2 spectra from peptidoforms and proteoforms are complex due to overlapping signals. To simplify and speed up data processing, most software requires pre-processing steps such as peak picking, smoothing, baseline correction, and alignment. Deconvolution, which simplifies spectra by aggregating features, is a key step but presents challenges 161 – 164 . Variability among deconvolution tools like Xtract, TopFD, and FlashDeconv has resulted in inconsistent precursor mass assignments, with studies indicating that only 49% of precursors are consistently assigned the same mass across deconvolution methods 165 . This inconsistency significantly affects the identification of proteoforms. Deconvolution of fragment ion spectra from ETD introduces additional challenges. Hydrogen rearrangements that may occur during electron-driven dissociation generate product ions with mass shifts of ±1 Da, often resulting in overlapping isotope patterns 166 – 169 . This overlap complicates the deconvolution process, as these mass differences can be mistaken by software for isotopologues, leading to errors in both precursor and fragment identification. These issues are particularly prominent when analyzing histone proteoforms, where overlapping signals are more prevalent due to the complexity of PTM patterns. Careful consideration is needed when deciding whether to apply deconvolution, as there are trade-offs between the enhanced signal-to-noise ratio and the loss of isotopic and charge distribution information, which ultimately impacts identification accuracy. Non-deconvoluted spectra retain this information but demand greater computational resources 170 – 175 . Advances in machine learning algorithms and isotope distribution models are being explored to mitigate these errors and improve proteoform characterization without heavily relying on deconvolution. The amount of software for top-down proteomics is rapidly increasing, however, software specifically tailored to the analytical challenge encountered in histone proteoform identification and quantitation are limited. MS2 spectra are often mixtures of isomeric or near isobaric proteoforms. Thus, accurate estimation of the contribution to the precursor signal of each component is ideal. Broadly, the software used for the analysis of histone proteoforms falls into four different algorithmic approaches: 1) target-decoy proteoform database searching with Bayesian models for Proteoform Spectral Matching or PTM level scoring (ProSightPC, TopPIC) 176 , 177 , 2) mass-graph models (TopMG, MS-Align-E) 178 , 179 , 3) rescoring of bottom-up proteomics software identifications (HistoneCoder, IsoScale) 39 , 4) Optimization: i) mixed-integer linear programming (Young Lab Algorithm – TDMS), EpiProfile 2.0, MSModDetector, modformPRO) 133 , 180 – 182 ii) least-squares optimization (FAVA) 183 . The primary data of interest generated from each mass spectrometry file acquired, following data analysis is: 1) proteoform identification, and 2) their quantitation. Proteoforms are identified via various algorithms as described in the analysis software section and represented in extended Brno proteoform nomenclature or more comprehensive ProForma 2.0 nomenclature 184 – 186 . Quantitation of proteoforms is calculated as area under the curve (AUC) or simply peak area, apportioned to multiple proteoforms as necessary. In LC-MS AUC has become the major quantitation strategy due to its high reproducibility, large linear dynamic range, and multiplexed nature (i.e. thousands of targets can be monitored in a single LC-MS/MS run) 187 , 188 . Data post-processing Analogously to traditional bottom-up proteomics, in histone proteoform data analysis there is often a large emphasis placed on sample preparation and spectral deconvolution/peak picking for proteoform spectral matching by data analysis software. As a result of this, post-processing of proteoform and PTM data – which includes normalization, marginalization of PTM combinations, statistical analysis, PTM crosstalk scoring, and data visualization is usually performed post hoc 189 . Below we discuss post-processing strategies for histone proteoform data to ensure robust and reproducible analyses ( Figure 7 ). Figure 7. Open in a new tab Schematic of data post processing workflow. Created in BioRender: BioRender.com/v50r835 . Normalization of proteoform data and marginalization of PTM combination abundances Normalization refers to a broad variety of techniques across omics data but lacks a precise definition. In regards to mass spectrometry, the objective of normalization is to remove any unwanted variation in the intensity derived from the spectrometric signal which cannot be externally controlled for or removed in any way such as modifying the experiment 190 . Normalizing proteoform data is an essential step that broadly corrects for differences in sample preparation, batch effects, and general instrument/signal drift. The most common normalization method for histone proteoform data is the percent of total proteoform family (or single proteoform sequence) 191 , 33 , 76 , 78 , 83 , 90 , 99 , 110 , 180 , 192 . In this type of normalization, the intensity of each proteoform is divided by the sum of all proteoforms within the same proteoform family. This yields a normalized value where the sum of all proteoforms of that proteoform family totals to 100%. This approach enables useful stoichiometric comparisons. Once normalization is complete, it is useful to calculate the percentage abundance of combinations of PTMs. This is achieved through a process termed marginalization. This procedure sums (traditionally in the margin of a table) the abundance of all proteoforms containing the selected combination of interest 193 . This readily provides a stoichiometric abundance of combinations. Marginalized PTM combinations are particularly useful when examining how different patterns of modifications change upon perturbation/experimental conditions without having to examine entire proteoform sequences. Depending on the number of possible modification sites and PTMs analyzed, these PTM combinations can be calculated to different levels such as discrete, binary, ternary, and so forth. Below we provide a set theory description of the PTM combination marginalization process. Definition of notation and variables: Let 𝒫 be the set of all possible proteoforms. Each proteoform 𝒫 in 𝒫 can be represented as a subset of the set of all possible PTMs, denoted as M . I 𝒫 denotes the intensity of a proteoform 𝒫 . I 𝒫 denotes the total intensity of all proteoforms, i.e., I 𝒫 = ∑ p ∈ 𝒫 I p . The percentage abundance of a specific PTM combination { m 1 , m 2 , … , m k } is given by: P e r c e n t a g e o f { m 1 , m 2 , … , m k } = ∑ p ∈ 𝒫 { m 1 , m 2 , … , m k } ⊆ p I p ∑ p ∈ 𝒫 I p × 100 For example, to determine the percentage abundance of the binary PTM combination of PTMs { m 1 , m 2 } : P e r c e n t a g e o f { m 1 , m 2 } = ∑ p ∈ 𝒫 { m 1 , m 2 } ⊆ p I p ∑ p ∈ 𝒫 I p × 100 This method is readily generalizable for any combination of PTMs such as ternary, quaternary, etc., by appropriately defining the sets and summing over relevant normalized proteoform percentages. For a quaternary combination { K 4 m e 3 K 9 a c K 18 a c K 23 a c } 194 , 195 : P e r c e n t a g e o f { K 4 m e 3 K 9 a c K 18 a c K 23 a c } = ∑ p ∈ 𝒫 { K 4 m e 3 K 9 a c K 18 a c K 23 a c } ⊆ p I p ∑ p ∈ 𝒫 I p × 100 While less utilized, other normalizations also include normalizing to the entire protein family pool. This ‘Relative Abundance’ measure quantitates the Proportion (%) of all H3 (H3.1, H3.2, and H3.3) N-terminal tails carrying a given PTM and can be useful in determining the modification extent of the protein family as a whole 143 . Overall, normalized proteoform abundances and the percentages of PTM combinations are not only fundamental for downstream statistical hypothesis testing but also essential for ensuring data quality and integrity. Effective quality control (QC) practices, such as correlation matrices, PCA plots, and heat maps with hierarchical clustering, allow for rapid visual inspection of data, facilitating the detection of outliers and ensuring consistency across experimental groups. QC, normalization, and marginalization are necessary steps to maintain the validity of the biological insights derived from proteoform data, as they help to confirm that the data accurately reflect the underlying biological phenomena 46 , 196 , 197 . Statistical analysis and hypothesis testing When analyzing proteoform data common practice is to calculate the log 2 fold-change for each proteoform and perform Student’s or Welch’s t-test to determine statistical significance. However, as proteoform data increasingly approaches a systems-level of analyses, there remains a need for more sophisticated approaches to measure biologically meaningful changes 78 , 198 . The major issue is the problem of multiple comparisons. Without correcting for multiple comparisons, the number of false positives generated with a t-test at p < 0.05 inflates rapidly ( Figure 8A ). Figure 8. Open in a new tab Multiple Comparison Corrections in statistical hypothesis testing of proteoform data. (A) The false positive rates (FPR) are plotted across increasing numbers of conditions, demonstrating the impact of different multiple comparison corrections, including uncorrected comparisons, Bonferroni correction, and Benjamini-Hochberg (BH) correction. The gray curve represents the FPR for all uncorrected comparisons, while the brown, blue, and green curves represent a single control comparison, Bonferroni correction, and BH correction, respectively. (B) Heatmaps of p-values displaying the results of pairwise comparisons using Student’s t-test and Tukey’s HSD test after performing ANOVA across conditions. Darker blue colors (Tukey’s HSD after ANOVA) demonstrate the decreased FPR compared to uncorrected Student’s t-test for multiple comparisons. Created in BioRender: BioRender.com/n38y442 . The multiple comparisons problem has been tackled in several ways in traditional proteomics. For instance, the recently released MSStatsPTM framework requires an experimental design that incorporates both modified (enriched or unenriched) and unmodified peptides from the same protein to distinguish between PTM-specific effects and overall protein changes 199 . However, these algorithms tailored towards big data sets are too stringent for the medium to small sized datasets generated during histone proteoform analysis. Medium-sized histone proteoform datasets typically include hundreds to the low thousands of identified analytes. Applying very conservative family-wise procedures (e.g.: Bonferroni) divides the global α by the total number of tests, producing an extremely low per-test threshold. This can be so low that true, modest fold-changes fall below detection, inflating type II error and masking biologically significant changes/differences 200 , 201 . Moreover, FDR methods like Benjamini-Hochberg (BH) guarantee control when tests are independent (or positively dependent). Dense and complex dependence, as seen in the reader-writer/eraser interactions amongst PTMs on proteoforms can make the BH threshold overly conservative, further reducing power and producing false negatives “by design” 202 , 203 . Analysis of Variance (ANOVAs) are standard albeit underutilized (for proteoform data) statistical models that operate by finding significant differences in group means and can be performed as one-way or two-way ANOVAs. One-way ANOVAs apply to experiments with one factor or independent variable (e.g.: effect of animal housing temperature on proteoform signature), while two-way ANOVAs apply to experiments with two factors (e.g.: effect of animal housing temperature and organ location on proteoform signature). We compared the ANOVA and Tukey’s HSD to uncorrected Student’s t-test using previously published data. Although correcting for multiple comparisons reduces the number of “significant” PTM combinations or proteoforms the adjustment constrained results to higher-quality and more biologically relevant results that were subsequently followed up with other experiments such as bisulfite sequencing for DNA methylation analysis and RNA-Seq ( Figure 8B ) 78 , 204 – 206 . When designs include repeated measures (e.g.: several organs from the same mouse, time-course sampling, technical replicates), a mixed-effects ANOVA (akin to a linear mixed-effects model) partitions variability into fixed factors (organ, time-point, temperature) and random factors (subject, batch, replicate). This preserves the ANOVA logic while accounting for non-independence, thereby controlling type I error more effectively, and typically even recovering power that would otherwise be lost to overcorrection 207 – 210 . Because random-effect shrinkage borrows strength across levels, mixed effects models also stabilize variance estimates in the small n regimes typical of histone proteoform studies. This ensures that post hoc Tukey or planned contrasts are more reliable without experimenters having to resort to aggressive α adjustments 211 . While ANOVA and Tukey’s HSD improve upon a simple Student’s t-test, they are not the only appropriate tests. Some studies have applied multiple corrections, such as LIMMA with Bonferroni or FDR, to proteoform data but found these methods often too stringent 143 . Although the ebayes moderation in LIMMA attempts to mitigate this by shrinking variances toward pooled estimates, even moderated statistics can be overpowered by harsh multiplicity corrections in focused experiments. This underscores the need to weigh the trade-off between false negatives and false positives on a per study basis. Histone proteoform quantitation can be used both for unbiased discovery and for testing specific, focused hypotheses. Multiple hypothesis correction is appropriate when testing many hypotheses, as in discovery experiments. For targeted testing of narrowly defined proteoform-based hypotheses, such corrections may be unnecessary. Acquisition of large-scale unbiased data does not preclude testing only one hypothesis 188 . Of course, a specific hypothesis is usually based in part on previous discovery or a priori knowledge and thus prior probabilities are inherent. Notably, foregoing global adjustment is defensible provided that the hypothesis was prespecified and number of biological replicates is adequate (this is because the nominal α here already reflects the experiment-wise error, and any further correction would just erode sensitivity without improving interpretability) 204 . Overall, while conservative multiple-testing corrections may be applied with the goal of guarding against false positives, in the small and medium sized datasets generated by histone proteoform studies, they suppress true biological signals. Thus, judicious relaxation and omission of these corrections is justified. Mixed-effects ANOVA extends the familiar framework to clustered or repeated measure designs, stabilizing variance estimates and maintaining correct error rates without sacrificing the interpretations from T-tests and post hoc contrasts. Well-established statistical methods are invaluable in understanding how proteoforms, PTM combinations (binary, ternary, etc.), and discrete PTMs change between experimental conditions. However, these scores are generated for each individual proteoform or PTM combination without a reduction in data complexity (they do not link different PTM data layers), making inferring the context specific Histone Code challenging. In response to this data complexity challenge, the Jensen and Schwämmle labs introduced two PTM Crosstalk scores – ‘Interplay’ and ‘Abundance Corrected Interplay’ to quantify PTM crosstalk 54 , 198 , 198 . These two scores are designed to provide insights into the cooperative or antagonistic relationships between different PTMs by evaluating whether a particular PTM or PTM combination occurs more frequently than expected under the assumption of independence. The Interplay score quantifies the degree of association between PTM pairs, asking whether two modifications are more likely to co-occur than would be expected if their occurrences were independent. The Abundance Corrected Interplay score further refines this analysis by normalizing for the overall abundance of each PTM, thus controlling for biases introduced by highly prevalent modifications. Poncha et al. recently introduced a bounded Normalized Interplay (NI) score and an asymmetric Directional Interplay (ΔI) score, together enabling scale-independent and directional hierarchy-specific assessment of PTM crosstalk 212 , 213 . By utilizing these scores, researchers can map PTM interactions and infer patterns that may have functional implications for chromatin structure and gene regulation. These methods simplify the interpretation of the Histone Code by uncovering non-random PTM interactions that could influence biological outcomes. Nevertheless, challenges remain, particularly in integrating these crosstalk scores with other omics data to fully capture the proteoforms/PTM combinations and their interactome 43 . Insights from Proteoform Analyses: Case Studies Proteoform-resolved MS studies have shown that integrated acquisition and analytical pipelines as outlined above can reveal mechanistic layers of histone-PTM regulation that remain opaque to peptide-level approaches; the following sections summarize representative studies and their key insights. Study 1: In Polycomb deficient mouse embryonic stem cells, middle-down MS of 50-residue N-terminal H3 tails quantified more than two hundred proteoforms; interplay matrices of observed versus expected co-occurrence revealed a pronounced architecture in which every neighboring monomethyl–acetyl pair displays significant positive crosstalk, whereas all seven di- and trimethylated lysines are almost universally mutually exclusive. This same dataset from Schwämmle et al. reported previously unrecognized couplings involving methylation states at H3R2, H3R8, and H3K37 and, when integrated with matched RNA-seq profiles, showed that only certain monomethyl marks and K9 methylation states track writer-enzyme abundance. This implies that higher methyl states are controlled by factors beyond enzyme copy number or S-adenosyl methionine availability (Mat2a was up-regulated, yet higher degrees of methylation at these residues were not concomitant with this increase). Because intact tails were measured, the workflow could distinguish positive from negative crosstalk directly, a level of granularity that had not previously been achieved 198 . Study 2: A series of enzymatic assays on defined nucleosomes illuminated the functional consequences of PTM interdependencies: acetylation at H3K18 and H3K23 increases MLL1- and MLL4-catalyzed turnover at H3K4 by nearly an order of magnitude, whereas methyltransferases targeting H3K9, H3K27, H3K36, the nucleosome core, or Haspin kinase remain insensitive to the same marks. In the same study, Fox et al. used middle-down quantification in HDAC-inhibited cells to orthogonally confirm that H3K4me3 is essentially dependent on, and confined to proteoforms bearing dual K18acK23ac, explaining why methylation on H3K4 is almost always observed alongside histone acetylation at active promoters. These data also support the previously hypothesized feed-forward loop in which p300/CBP (or MOZ/MORF) deposit K18acK23ac, thereby licensing MLL activity; PHD readers then recruit additional acetyltransferase and methyltransferase complexes that reinforce an open chromatin state 194 , 195 . Study 3: Tvardovskiy et al. conducted longitudinal, variant-resolved middle-down analysis in mouse liver, kidney, and brain. These results show that the replication-independent variant H3.3 progressively replaces canonical H3 and reaches ≈99 % of total H3 by 18 months, whereas heart attains only ≈76 % by 24 months. Variant turnover is accompanied by coordinated gains in K27me2K36me2 and reciprocal losses of K9me2 and K27me3, and several marks, most notably K36me2 remain stably enriched on H3.3 throughout life. These findings argue that variant specific PTM crosstalk, rather than enzyme abundance, are the primary drivers of age-dependent epigenetic remodeling (a hallmark of aging) and further show that the marked depletion of H3.3 in cancers (e.g.: hepatocarcinoma tissue) can serve as a quantitative biomarker of abnormal proliferation 54 , 143 . Study 4: A comprehensive, life-cycle stage-resolved analysis of Plasmodium falciparum chromatin by Grüning et al. mapped seventy-seven PTMs on H3, thirty-four of which were previously unreported. Histone PTM interplay networks distinguished the euchromatic landscape of asexual parasites which is dominated by independent H3K9me3 and limited combinations from the comparatively much more PTM interplay rich gametocyte code, where the ternary combination H3R17me2K18acK23ac is uniquely enriched. Proteoform immunoprecipitations identified a SAGA-like complex containing the transcription factor AP2-G2 that strongly recognizes this ternary PTM combination, directly tying higher-order Histone Code combinations to stage-specific gene activation in the disease transmission form of the parasite. Notably, arginine methylation is as prevalent as lysine modification in P. falciparum , and marks such as H3R40me1 operate independently of neighboring PTMs, underscoring the evolutionary breadth of combinatorial regulation revealed only by intact-tail measurements 214 . Taken together, these exemplary studies demonstrate that proteoform-level acquisition combined with PTM crosstalk scoring enables: (1) dissection of cooperative and antagonistic PTM layers yielding an unbiased map of context-dependent regulatory circuits that govern chromatin dynamics at single-molecule resolution; (2) pinpointing of enzyme-licensing switches concealed in composite marks which unmask rate-limiting nodes that can be selectively inhibited or activated to modulate epigenetic outcomes; (3) quantification of histone variant-driven remodeling of the genome: exposing age- and tissue-specific trajectories; and (4) linkage of higher-order histone codes to stage-restricted effector complexes in pathogens, which are key molecular fingerprints that demarcate developmental states and identify parasite-specific vulnerabilities for therapeutic intervention. By illuminating these multivalent “control points”, proteoform-resolved studies convert descriptive PTM landscapes into mechanistic hypotheses and prioritize actionable proteoform targets for drug discovery, diagnostics, and epigenetic drug targeting, underscoring the need for a concerted shift toward proteoform-forward approaches, because the “devil is in the details (of combinatorial PTM patterns)”. Perspectives and Conclusion Histone Proteoforms & the Basic Science of Chromatin and Epigenetics The role of histones in biology have been pondered since their discovery in the 1880’s as one of the two major components of eukaryotic nuclei that may explain inheritance 215 , 216 . The role of histone PTMs in gene regulation have been investigated since their discovery almost 70 years ago 217 . The Histone Code hypothesis was posited over a quarter century ago. In the last 25 years it has been established that individual discrete histone PTMs mostly do not have an injective functional output. The few discrete histone PTMs that are strongly correlated to functional outputs are near universally found in complex proteoforms, where the other PTMs often serve as prerequisites. These observations have led to an increasing awareness that combinations of histone PTMs or proteoforms likely are a key functional unit in genome biology. It is only through rigorously quantitative proteoform-centric approaches that we will fully understand their role in genome regulation and maintenance. Top- and middle-down mass spectrometry have played an essential role in establishing the importance of histone proteoforms and will continue to make essential contributions. Over the past 20 or so years of MS-based histone proteoforms analysis instrumentation, data analysis, and, importantly, the sophistication of our framing of the question has matured dramatically. Today these approaches regularly contribute to a much more complete understanding of chromatin biology. As these individual mechanisms are revealed in detail, we have come to appreciate the full depth of possibilities, and the scope of investigation has grown. For example, we now appreciate better that PTM modulated structural changes to the mostly intrinsically disordered regions are important to function. Also, proteolysis or clipping events add another layer of combinatorial complexity. While these types of realizations are crucial to progress toward understanding the Histone Code, they also continually make a complete understanding increasingly distant. The contributions of MS-based histone proteoforms analysis are only set to accelerate and become increasingly integrated with complementary approaches that are emerging from the questions this field has established. Non-MS-based alternative technologies are emerging that will compete with MS-based approaches but also provide complementary data. Histone proteoforms, packaged into nucleosomes are commercially available and are now regularly used for activity assays. Innovative reagents and novel technologies will also contribute to these efforts. Genomic localization of histone proteoforms is limited if not impossible by MS-based methods. Novel reagents that can detect the genomic location of histone proteoforms will ultimately bridge between ChIP-seq/CUT&RUN discrete PTM data and proteoform data. These future advances will enable a more complete understanding of basic science but will also enable translational applications. Moving Proteoforms towards more Translational Science Since the 1960s, proteoforms have been integral to clinical diagnostics, highlighted by the discovery and characterization of Hemoglobin A1c through glycoprotein synthesis and mass spectrometry, which is a discovery that remains central to diabetes diagnosis and management today 218 – 221 . Similarly, proteoforms serve as critical biomarkers in neurodegeneration (through the detection of amyloid β proteoforms), amyloidosis, and monoclonal gammopathies 222 – 224 . Yet the full therapeutic potential of proteoform science will only be realized when we move from merely detecting disease-associated proteoforms to directly targeting them as druggable entities. This is challenging – to the best of our knowledge there are no drugs that are proteoform specific inhibitors or degraders. As a consequence of this, therapies that exploit PTM crosstalk networks to alter the pool of pathogenic proteoforms have emerged – commonly known as combination therapies ( Table 6 ). It is, however, important to note that these combination therapies have largely been identified by methodologies other than top-down mass spectrometry. Currently, there are few, if any, clinical studies that directly explore histone proteoforms in patient-derived samples. This highlights a critical gap and an opportunity for top-down proteomics to significantly impact clinical and translational research. Table 6. Targeting histone modification crosstalk in cancer therapy. Epigenetic Machinery Targeted Drug Pairs/Combinations Cancers PMID Summary of Mechanism LSD1 and HDACs HCI-2509 and Sulforaphane Triple-negative breast cancer 29633255 Sulforaphane inhibits HDAC5 transcription via USF1 blockade and promotes LSD1 degradation, disrupting the HDAC5-LSD1 axis. This suppresses cancer-related gene expression, driving TNBC growth inhibition and tumor suppression without affecting normal breast epithelial cells. LSD1 and HDACs Bix-01294 and TSA Hepatocellular carcinoma 31432592 Inhibition disrupts the Snail2-driven complex, restoring E-cadherin expression, reversing EMT, reducing migration and invasion, and extending survival. LSD1 and HDACs Tranylcypromine and Vorinostat Glioblastoma 25795306 Combined inhibition reduces GSC viability and induces apoptosis by altering the expression of apoptosis-related genes, particularly TP53 and TP73, both in vitro and in vivo. EZH2 and HDACs Tazemetostat and Quisinostat Small cell carcinoma of the ovary 30232145 Drug synergy enhances H3K27 acetylation, reactivating silenced genes that drive apoptosis and suppress tumor growth. EZH2 and BRD4 Tazemetostat and JQ1 NUT Carcinoma 37747726 Combined inhibition reverses H3K27me3-mediated gene silencing, restoring tumor suppressor gene expression (e.g., CDKN2A), while BET inhibitors block BRD4-NUT-driven oncogenic gene activation. This combined action targets opposing chromatin states (H3K27me3 and H3K27ac) to synergistically suppress cell proliferation, promote differentiation, and achieve more robust tumor growth arrest and prolonged survival in NUT carcinoma xenograft models. EZH2 and HDACs 3-Deazaneplanocin A (DZNep, C-c3Ado) and Vorinostat (SAHA) Non-small cell lung cancer 27116120 Drug synergy decreases H3K27 trimethylation, enhances histone acetylation, depletes PRC2 components, and suppresses EGFR signaling, leading to reactivation of silenced pathways, p27Kip1 accumulation, apoptosis, and tumor growth suppression. EZH2 and HDACs GSK126 and Romidepsin Lymphoma 30979734 Drug synergy disrupts the PRC2 complex by acetylating RbAP46/48, leading to reduced H3K27 methylation, increased H3K27 acetylation, and transcriptional reactivation of chromatin remodeling genes, driving apoptosis and tumor suppression in EZH2-dysregulated lymphomas. LSD1 and HDACs 5-aza-2’-deoxycytidine (DAC) and Panobinostat or Valproic Acid Acute Myeloid Leukemia 30470836 Combined inhibition induces gene body DNA demethylation and alters H3K9 and K27 acetylation levels, synergistically downregulating oncogenes (MYC) and epigenetic modifiers (KDM2B, SUV39H1) overexpressed in AML. Open in a new tab It is thus our view that top-down proteomics is poised for a more central role in both identifying the full spectrum of relevant proteoforms and tracking how therapeutic interventions alter their abundance and modification patterns. With improvements in resolution, throughput, and data processing, top-down approaches should be embedded earlier in discovery pipelines. Such exploratory studies in human clinical samples are urgently need to pin-point critical PTM combinations, define disease-specific proteoform signatures, and uncover novel therapeutic targets. As top-down proteomics becomes more accessible and robust, the field stands to benefit from a deeper understanding of proteoform complexity in multiple disease contexts (e.g.: cancer and neurodegeneration). This includes mapping pathogenic proteoforms to their precise genomic locations. This could be accomplished by combining recent and sophisticated techniques that isolate chromatin segments (PICh) with comprehensive nucleoform defining techniques like Nuc-MS ultimately linking proteoform abundance, inter- and intra-nucleosomal crosstalk to phenotype (clinical outcomes), and providing a real-time readout of the epigenetic landscape 225 , 226 . Synergy of Mass Spectrometry with Next-generation Proteomics Rather than replacing mass spectrometry, upcoming PTM and proteoform detection platforms should be strategically integrated with it, combining the speed and biological context of affinity or single molecule assays with the universal molecular yard-stick and ultimate confirmation that only MS can provide. High-plex affinity panels now set the magnitude and pace for population-scale discovery. Assays like SomaLogic’s 11K SomaScan quantify now quantify 10700 unique human proteins from just 55uL of plasma/serum in a single run, whereas OLink’s Proximity Extension Assay reports 5416 protein targets from 2uL of similar plasma/serum input 227 – 229 . This same chemistry has already been deployed to measure plasma proteins in >50000 UK BioBank participants (with additional expansions to 600000 samples in the works) 230 , 231 . Because both platforms yield relative units (RFU/NPX) and can misestimate heavily modified and truncated proteoforms, the best practice still remains to validate hits into targeted and top-down proteomics workflows to achieve absolute stoichiometry and unambiguous PTM site localization. Single-molecule approaches push this same hand-off to mass spectrometry one layer deeper. Nautilus Biotech’s PrISM arrays successively probe individual intact proteins with hundreds of short-epitope reagents. Molecules with unexpected binding patterns are automatically flagged and directly eluted for intact mass spectrometry characterization. Pilot experiments on tau proteoforms demonstrated femtomole sensitivity for combinatorial PTMs in this two-step setup 232 , 233 . Quantum Si’s Platinum Pro sequencer reads successive N-terminal residues via kinetic recognizers. Recent studies showed that merging their traces with I2MS doubled interleukin 6 proteoform coverage 234 . Finally, prototype nanopore devices can deliver >300 aa label free reads that discriminate single phosphorylations after multi-pore-pass consensus reads. They also still lean on MS characterized calibration peptides and PTM libraries to decode ion current signatures accurately 235 – 237 . Taken together, the emerging proteomics workflow is likely to be “discover wide, confirm deep”: parallel affinity and single-molecule platforms screen millions of molecules per week, but the decisive next step is rapid, next-generation mass spectrometry that can measure intact proteoforms at the individual-ion level, fragment them with advanced (electron-driven) dissociation techniques, and feed the resulting data back into the discovery layer shortly after. In short, the advancements in top-down proteomics are not merely incremental enhancements of existing proteomics protocols; they represent a paradigm shift in our ability to interrogate the proteome at a new and physiologically relevant level of detail and complexity. These capabilities will be critical in refining combination therapies, guiding rational drug design, and ultimately moving the biomedicine closer to proteoform-focused precision medicine. Supplementary Material Supplementary File 1 NIHMS2135458-supplement-Supplementary_File_1.xlsx (21.3KB, xlsx) Supporting Information Supplemental File 1: Table listing all identified human histone variants (UniProt release 2024) that lists, for every human histone variant, the UniProt ID, UniProt entry name, recommended protein name, HGNC gene symbol, protein length (# of aa residues), and PubMed identifiers of key reference articles. Acknowledgement: We thank members of the Young lab for helpful discussions and suggestions. Funding: A.T.P is supported by HHMI grant GT16569. N.L.Y is supported by NIH grants R01GM139295, P01AG066606, R01CA193235, R01AG074540, R01CA276663, R01AG085751, and R01NS136375. Acronyms 1D-HPLC One-dimensional high performance liquid chromatography 2D-HPLC Two-dimensional high performance liquid chromatography AGC Automatic Gain Control AI-ETD Activated ion electron transfer dissociation ANOVA Analysis of variance AUC Area under the curve BH Benjamini-Hochberg BU Bottom-up proteomics CCS Collisional cross-section ChIP-seq Chromatin immunoprecipitation sequencing CID Collision-induced dissociation cIMS Cyclic ion mobility spectroscopy CN Cyanopropylsilane CZE Capillary zone electrophoresis C18 Octadecylsilane DDA Data-dependent acquisition DIA Data independent acquisition DNA Deoxyribose nucleic acid ECD Electron capture dissociation ESI Electrospray ionization ETD Electron transfer dissociation EThcD Electron-transfer/higher-energy collisional dissociation ETnoD Non-dissociative electron transfer FAIMS Field asymmetric waveform ion mobility separation FDR False discovery rate FPR False positive rate HAT Histone Acetyltransferase HbA1c Hemoglobin A1c HCD Higher-energy collisional dissociation HDAC Histone Deacetylase HILIC Hydrophilic interaction liquid chromatography HMT Histone Methyltransferase HPCZE High Performance Capillary Zone Electrophoresis HPLC High-performance liquid chromatography IMS Ion mobility separation IR Infrared Radiation KDE Kernel Density Estimate KDM Lysine Demethylase KO Knock-out LC Liquid chromatography LCMS Liquid chromatography mass spectrometry MD Middle-down proteomics MS Mass spectrometry MS/MS Tandem mass spectrometry NH2 Aminopropylsilane NuC-MS Nucleosome Mass Spectrometry p.p. Percentage point PCA Principal component analysis Phenyl Phenylethylsilane PiCh Proteomics of isolated chromatin segments PRMT Protein Arginine Methyltransferase PRP-18 Polystyrene-divinylbenzene PTCR Proton transfer charge reduction PTM Post-translational modification QC Quality control RP-HPLC Reverse-phase high performance liquid chromatography SLIM Structures for Lossless Ion Manipulation TDP Top-down proteomics TFA Trifluoroacetic acid TIMS Trapped ion mobility separation Tukey’s HSD Tukey’s honest significance test UVPD Ultraviolet Photodissociation Footnotes Conflict of Interest: All authors declare that they have no known conflict of interest. CRediT author statement: Karl F Poncha: Conceptualization, Data Curation, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing - Original Draft, Writing - Review & Editing, Project administration. Alyssa T Paparella: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing - Original Draft, Writing - Review & Editing, Project administration. Nicolas L. Young: Conceptualization, Funding acquisition, Investigation, Resources, Supervision, Validation, Visualization, Writing - Original Draft, Writing - Review & Editing, Project administration. Ethics Approval: Not applicable Data Availability: Not applicable References (1). Hunter T The Age of Crosstalk: Phosphorylation, Ubiquitination, and Beyond. Mol Cell 2007, 28 (5), 730–738. 10.1016/j.molcel.2007.11.019. [ DOI ] [ PubMed ] [ Google Scholar ] (2). Thomson M; Gunawardena J The Rationality Theorem for Multisite Post-Translational Modification Systems. J Theor Biol 2009, 261 (4), 626–636. 10.1016/j.jtbi.2009.09.003. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (3). Larsen MR; Trelle MB; Thingholm TE; Jensen ON Analysis of Posttranslational Modifications of Proteins by Tandem Mass Spectrometry: Mass Spectrometry For Proteomics Analysis. BioTechniques 2006, 40 (6), 790–798. 10.2144/000112201. [ DOI ] [ PubMed ] [ Google Scholar ] (4). Toby TK; Fornelli L; Kelleher NL Progress in Top-Down Proteomics and the Analysis of Proteoforms. Annu Rev Anal Chem (Palo Alto Calif) 2016, 9 (1), 499–519. 10.1146/annurev-anchem-071015-041550. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (5). Tran JC; Zamdborg L; Ahlf DR; Lee JE; Catherman AD; Durbin KR; Tipton JD; Vellaichamy A; Kellie JF; Li M; Wu C; Sweet SMM; Early BP; Siuti N; LeDuc RD; Compton PD; Thomas PM; Kelleher NL Mapping Intact Protein Isoforms in Discovery Mode Using Top Down Proteomics. Nature 2011, 480 (7376), 254–258. 10.1038/nature10575. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (6). Tissot B; North SJ; Ceroni A; Pang P-C; Panico M; Rosati F; Capone A; Haslam SM; Dell A; Morris HR Glycoproteomics: Past, Present and Future. FEBS Lett 2009, 583 (11), 1728–1735. 10.1016/j.febslet.2009.03.049. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (7). Hakomori S A RAPID PERMETHYLATION OF GLYCOLIPID, AND POLYSACCHARIDE CATALYZED BY METHYLSULFINYL CARBANION IN DIMETHYL SULFOXIDE. J Biochem 1964, 55, 205–208. [ PubMed ] [ Google Scholar ] (8). Geddes AJ; Graham GN; Morris HR; Lucas F; Barber M; Wolstenholme WA Mass-Spectrometric Determination of the Amino Acid Sequences in Peptides Isolated from Protein Silk Fibroin of Bombyx Mori. Biochem J 1969, 114 (4), 695–702. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (9). Das R; Muralidharan M; Mitra G; Bhat V; Mathew B; Pal D; Ross C; Mandal AK Mass Spectrometry Based Characterization of Hb Beckman Variant in a Falsely Elevated HbA(1c) Sample. Anal Biochem 2015, 489, 53–58. 10.1016/j.ab.2015.07.010. [ DOI ] [ PubMed ] [ Google Scholar ] (10). Morris HR Biomolecular Mass Spectrometry. International Journal of Mass Spectrometry and Ion Physics 1982, 45, 331–342. 10.1016/0020-7381(82)80120-4. [ DOI ] [ Google Scholar ] (11). John WG; Mosca A; Weykamp C; Goodall I HbA1c Standardisation: History, Science and Politics. Clin Biochem Rev 2007, 28 (4), 163–168. [ PMC free article ] [ PubMed ] [ Google Scholar ] (12). Ille AM; Lamont H; Mathews MB The Central Dogma Revisited: Insights from Protein Synthesis, CRISPR, and Beyond. WIREs RNA 2022, 13 (5), e1718. 10.1002/wrna.1718. [ DOI ] [ PubMed ] [ Google Scholar ] (13). Wilkins MR; Gasteiger E; Gooley AA; Herbert BR; Molloy MP; Binz PA; Ou K; Sanchez JC; Bairoch A; Williams KL; Hochstrasser DF High-Throughput Mass Spectrometric Discovery of Protein Post-Translational Modifications. J Mol Biol 1999, 289 (3), 645–657. 10.1006/jmbi.1999.2794. [ DOI ] [ PubMed ] [ Google Scholar ] (14). Strahl BD; Allis CD The Language of Covalent Histone Modifications. Nature 2000, 403 (6765), 41–45. 10.1038/47412. [ DOI ] [ PubMed ] [ Google Scholar ] (15). Fischle W; Wang Y; David Allis C Binary Switches and Modification Cassettes in Histone Biology and Beyond. Nature 2003, 425 (6957), 475–479. 10.1038/nature02017. [ DOI ] [ PubMed ] [ Google Scholar ] (16). Lo WS; Trievel RC; Rojas JR; Duggan L; Hsu JY; Allis CD; Marmorstein R; Berger SL Phosphorylation of Serine 10 in Histone H3 Is Functionally Linked in Vitro and in Vivo to Gcn5-Mediated Acetylation at Lysine 14. Mol Cell 2000, 5 (6), 917–926. 10.1016/s1097-2765(00)80257-9. [ DOI ] [ PubMed ] [ Google Scholar ] (17). Chi P; Allis CD; Wang GG Covalent Histone Modifications: Miswritten, Misinterpreted, and Miserased in Human Cancers. Nat Rev Cancer 2010, 10 (7), 457–469. 10.1038/nrc2876. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (18). Mizzen CA; Yang X-J; Kokubo T; Brownell JE; Bannister AJ; Owen-Hughes T; Workman J; Wang L; Berger SL; Kouzarides T; Nakatani Y; Allis CD The TAFII250 Subunit of TFIID Has Histone Acetyltransferase Activity. Cell 1996, 87 (7), 1261–1270. 10.1016/S0092-8674(00)81821-8. [ DOI ] [ PubMed ] [ Google Scholar ] (19). Richmond TJ; Finch JT; Klug A Studies of Nucleosome Structure. Cold Spring Harb Symp Quant Biol 1983, 47, 493–501. 10.1101/SQB.1983.047.01.059. [ DOI ] [ PubMed ] [ Google Scholar ] (20). Luger K; Mäder AW; Richmond RK; Sargent DF; Richmond TJ Crystal Structure of the Nucleosome Core Particle at 2.8 Å Resolution. Nature 1997, 389 (6648), 251–260. 10.1038/38444. [ DOI ] [ PubMed ] [ Google Scholar ] (21). Luger K; Dechassa ML; Tremethick DJ New Insights into Nucleosome and Chromatin Structure: An Ordered State or a Disordered Affair? Nat Rev Mol Cell Biol 2012, 13 (7), 436–447. 10.1038/nrm3382. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (22). Egelhofer TA; Minoda A; Klugman S; Lee K; Kolasinska-Zwierz P; Alekseyenko AA; Cheung M-S; Day DS; Gadel S; Gorchakov AA; Gu T; Kharchenko PV; Kuan S; Latorre I; Linder-Basso D; Luu Y; Ngo Q; Perry M; Rechtsteiner A; Riddle NC; Schwartz YB; Shanower GA; Vielle A; Ahringer J; Elgin SCR; Kuroda MI; Pirrotta V; Ren B; Strome S; Park PJ; Karpen G; Hawkins RD; Lieb JD An Assessment of Histone-Modification Antibody Quality. Nat Struct Mol Biol 2011, 18 (1), 91–93. 10.1038/nsmb.1972. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (23). Kornberg RD; Klug A The Nucleosome. Scientific American 1981, 244 (2), 52–65. [ DOI ] [ PubMed ] [ Google Scholar ] (24). Bannister AJ; Kouzarides T Regulation of Chromatin by Histone Modifications. Cell Res 2011, 21 (3), 381–395. 10.1038/cr.2011.22. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (25). Hattori T; Taft JM; Swist KM; Luo H; Witt H; Slattery M; Koide A; Ruthenburg AJ; Krajewski K; Strahl BD; White KP; Farnham PJ; Zhao Y; Koide S Renewable, Recombinant Antibodies to Histone Post-Translational Modifications. Nat Methods 2013, 10 (10), 992–995. 10.1038/nmeth.2605. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (26). Zhao Y; Garcia BA Comprehensive Catalog of Currently Documented Histone Modifications. Cold Spring Harb Perspect Biol 2015, 7 (9), a025064. 10.1101/cshperspect.a025064. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (27). Talbert PB; Henikoff S Histone Variants at a Glance. J Cell Sci 2021, 134 (6), jcs244749. 10.1242/jcs.244749. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (28). Bae WK; Hennighausen L Canonical and Non-Canonical Roles of the Histone Methyltransferase EZH2 in Mammary Development and Cancer. Mol Cell Endocrinol 2014, 382 (1), 10.1016/j.mce.2013.05.002. https://doi.org/10.1016/j.mce.2013.05.002 . [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (29). Ferrand J; Rondinelli B; Polo SE Histone Variants: Guardians of Genome Integrity. Cells 2020, 9 (11), 2424. 10.3390/cells9112424. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (30). Podhorecka M; Skladanowski A; Bozko P H2AX Phosphorylation: Its Role in DNA Damage Response and Cancer Therapy. J Nucleic Acids 2010, 2010, 920161. 10.4061/2010/920161. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (31). Phillips EON; Gunjan A Histone Variants: The Unsung Guardians of the Genome. DNA Repair (Amst) 2022, 112, 103301. 10.1016/j.dnarep.2022.103301. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (32). Ren M; Greenberg MM; Zhou C Participation of Histones in DNA Damage and Repair within Nucleosome Core Particles: Mechanism and Applications. Acc Chem Res 2022, 55 (7), 1059–1073. 10.1021/acs.accounts.2c00041. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (33). Smith LM; Kelleher NL Proteoform: A Single Term Describing Protein Complexity. Nat Methods 2013, 10 (3), 186–187. 10.1038/nmeth.2369. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (34). Roberts DS; Loo JA; Tsybin YO; Liu X; Wu S; Chamot-Rooke J; Agar JN; Paša-Tolić L; Smith LM; Ge Y Top-down Proteomics. Nat Rev Methods Primers 2024, 4 (1), 1–23. 10.1038/s43586-024-00318-2. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (35). Donnelly DP; Rawlins CM; DeHart CJ; Fornelli L; Schachner LF; Lin Z; Lippens JL; Aluri KC; Sarin R; Chen B; Lantz C; Jung W; Johnson KR; Koller A; Wolff JJ; Campuzano IDG; Auclair JR; Ivanov AR; Whitelegge JP; Paša-Tolić L; Chamot-Rooke J; Danis PO; Smith LM; Tsybin YO; Loo JA; Ge Y; Kelleher NL; Agar JN Best Practices and Benchmarks for Intact Protein Analysis for Top-down Mass Spectrometry. Nat Methods 2019, 16 (7), 587–594. 10.1038/s41592-019-0457-0. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (36). One-Pot Quantitative Top- and Middle-Down Analysis of GluC-Digested Histone H4 | Journal of the American Society for Mass Spectrometry. https://pubs.acs.org/doi/abs/10.1007/s13361-019-02219-1 (accessed 2024-09-27). [ DOI ] [ PubMed ] [ Google Scholar ] (37). Garcia BA; Mollah S; Ueberheide BM; Busby SA; Muratore TL; Shabanowitz J; Hunt DF Chemical Derivatization of Histones for Facilitated Analysis by Mass Spectrometry. Nat Protoc 2007, 2 (4), 933–938. 10.1038/nprot.2007.106. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (38). Zhang Y; Fonslow BR; Shan B; Baek M-C; John R Yates III Protein Analysis by Shotgun/Bottom-up Proteomics. Chemical reviews 2013, 113 (4), 2343. 10.1021/cr3003533. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (39). Sidoli S; Schwämmle V; Ruminowicz C; Hansen TA; Wu X; Helin K; Jensen ON Middle-down Hybrid Chromatography/Tandem Mass Spectrometry Workflow for Characterization of Combinatorial Post-Translational Modifications in Histones. PROTEOMICS 2014, 14 (19), 2200–2211. 10.1002/pmic.201400084. [ DOI ] [ PubMed ] [ Google Scholar ] (40). Gao Y; Wang Y A Method to Determine the Ionization Efficiency Change of Peptides Caused by Phosphorylation. J Am Soc Mass Spectrom 2007, 18 (11), 1973–1976. 10.1016/j.jasms.2007.08.010. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (41). Schlaffner CN; Kahnert K; Muntel J; Chauhan R; Renard BY; Steen JA; Steen H FLEXIQuant-LF to Quantify Protein Modification Extent in Label-Free Proteomics Data. eLife 2020, 9, e58783. 10.7554/eLife.58783. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (42). Pesavento JJ; Mizzen CA; Kelleher NL Quantitative Analysis of Modified Proteins and Their Positional Isomers by Tandem Mass Spectrometry: Human Histone H4. Anal Chem 2006, 78 (13), 4271–4280. 10.1021/ac0600050. [ DOI ] [ PubMed ] [ Google Scholar ] (43). Leutert M; Entwisle SW; Villén J Decoding Post-Translational Modification Crosstalk With Proteomics. Molecular & Cellular Proteomics 2021, 20. 10.1016/j.mcpro.2021.100129. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (44). Greer SM; Brodbelt JS Top-Down Characterization of Heavily Modified Histones Using 193 Nm Ultraviolet Photodissociation Mass Spectrometry. J. Proteome Res. 2018, 17 (3), 1138–1145. 10.1021/acs.jproteome.7b00801. [ DOI ] [ PubMed ] [ Google Scholar ] (45). Wang T; Holt MV; Young NL The Histone H4 Proteoform Dynamics in Response to SUV4–20 Inhibition Reveals Single Molecule Mechanisms of Inhibitor Resistance. Epigenetics & Chromatin 2018, 11 (1), 29. 10.1186/s13072-018-0198-9. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (46). Holt MV; Wang T; Young NL High-Throughput Quantitative Top-Down Proteomics: Histone H4. J Am Soc Mass Spectrom 2019, 30 (12), 2548–2560. 10.1007/s13361-019-02350-z. [ DOI ] [ PubMed ] [ Google Scholar ] (47). Zhang C; Gao S; Molascon AJ; Wang Z; Gorovsky MA; Liu Y; Andrews PC Bioinformatic and Proteomic Analysis of Bulk Histones Reveals PTM Crosstalk and Chromatin Features. J. Proteome Res. 2014, 13 (7), 3330–3337. 10.1021/pr5001829. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (48). Smith LE; Rogowska-Wrzesinska A The Challenge of Detecting Modifications on Proteins. Essays in Biochemistry 2020, 64 (1), 135–153. 10.1042/EBC20190055. [ DOI ] [ PubMed ] [ Google Scholar ] (49). Bu J; Chen A; Yan X; He F; Dong Y; Zhou Y; He J; Hayashi Y; Zhan D; Sun Y; Zhang Y; Xiao Z; Grimes L; Wang Q; Huang G Global Crosstalk between Two Histone Modifications That Control Transcriptional Elongation in MLL Leukemia. Blood 2017, 130 (Supplement 1), 2495. 10.1182/blood.V130.Suppl_1.2495.2495. [ DOI ] [ Google Scholar ] (50). Hosey AM; Chaturvedi C-P; Brand M Crosstalk between Histone Modifications Maintains the Developmental Pattern of Gene Expression on a Tissue-Specific Locus. Epigenetics 2010, 5 (4), 273–281. 10.4161/epi.5.4.11522. [ DOI ] [ PubMed ] [ Google Scholar ] (51). Howe FS Crosstalk between Histone Modifications in Saccharomyces Cerevisiae. http://purl.org/dc/dcmitype/Text , Oxford University, UK, 2012. https://www.ora.ox.ac.uk/objects/uuid:1e2e128e-1ec3-4d41-8ab5-b27e5930a654 (accessed 2022-02-02). [ Google Scholar ] (52). Mozzetta C; Pontis J; Ait-Si-Ali S Functional Crosstalk Between Lysine Methyltransferases on Histone Substrates: The Case of G9A/GLP and Polycomb Repressive Complex 2. Antioxidants & Redox Signaling 2015, 22 (16), 1365–1381. 10.1089/ars.2014.6116. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (53). Nagarajan S; Johnsen SA Chapter 12 - Crosstalk Between Histone Modifications Integrates Various Signaling Inputs to Fine-Tune Transcriptional Output. In Chromatin Signaling and Diseases; Binda O, Fernandez-Zapico ME, Eds.; Academic Press: Boston, 2016; pp 217–239. 10.1016/B978-0-12-802389-1.00012-5. [ DOI ] [ Google Scholar ] (54). Kirsch R; Jensen ON; Schwämmle V Visualization of the Dynamics of Histone Modifications and Their Crosstalk Using PTM-CrossTalkMapper. Methods 2020, 184, 78–85. 10.1016/j.ymeth.2020.01.012. [ DOI ] [ PubMed ] [ Google Scholar ] (55). Schwämmle V; Aspalter C-M; Sidoli S; Jensen ON Large Scale Analysis of Co-Existing Post-Translational Modifications in Histone Tails Reveals Global Fine Structure of Cross-Talk. Mol Cell Proteomics 2014, 13 (7), 1855–1865. 10.1074/mcp.O113.036335. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (56). Inoue A; Fujimoto D Enzymatic Deacetylation of Histone. Biochem Biophys Res Commun 1969, 36 (1), 146–150. 10.1016/0006-291x(69)90661-5. [ DOI ] [ PubMed ] [ Google Scholar ] (57). Dhalluin C; Carlson JE; Zeng L; He C; Aggarwal AK; Zhou MM Structure and Ligand of a Histone Acetyltransferase Bromodomain. Nature 1999, 399 (6735), 491–496. 10.1038/20974. [ DOI ] [ PubMed ] [ Google Scholar ] (58). Musselman CA; Lalonde M-E; Côté J; Kutateladze TG Perceiving the Epigenetic Landscape through Histone Readers. Nat Struct Mol Biol 2012, 19 (12), 1218–1227. 10.1038/nsmb.2436. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (59). Duncan EM; Muratore-Schroeder TL; Cook RG; Garcia BA; Shabanowitz J; Hunt DF; Allis CD Cathepsin L Proteolytically Processes Histone H3 during Mouse Embryonic Stem Cell Differentiation. Cell 2008, 135 (2), 284–294. 10.1016/j.cell.2008.09.055. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (60). Measures A Probing Tandem Epigenetic Reader Domains. http://purl.org/dc/dcmitype/Text , University of Oxford, 2016. https://ora.ox.ac.uk/objects/uuid:6a6b1161-2c7b-40f8-8a34-86d00bf342f8 (accessed 2024-09-27). [ Google Scholar ] (61). Zhang T; Cooper S; Brockdorff N The Interplay of Histone Modifications - Writers That Read. EMBO Rep 2015, 16 (11), 1467–1481. 10.15252/embr.201540945. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (62). BRPF1 bromodomain and PHD finger containing 1 [Homo sapiens (human)] - Gene - NCBI. https://www.ncbi.nlm.nih.gov/gene/7862 (accessed 2024-09-27). (63). Cheng CL-H; Tsang FH-C; Wei L; Chen M; Chin DW-C; Shen J; Law C-T; Lee D; Wong CC-L; Ng IO-L; Wong C-M Bromodomain-Containing Protein BRPF1 Is a Therapeutic Target for Liver Cancer. Commun Biol 2021, 4 (1), 1–14. 10.1038/s42003-021-02405-6. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (64). Lee K; Barone M; Waterbury AL; Jiang H; Nam E; DuBois-Coyne SE; Whedon SD; Wang ZA; Caroli J; Neal K; Ibeabuchi B; Dhoondia Z; Kuroda MI; Liau BB; Beck S; Mattevi A; Cole PA Uncoupling Histone Modification Crosstalk by Engineering Lysine Demethylase LSD1. Nat Chem Biol 2024. 10.1038/s41589-024-01671-9. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (65). Raz A; Pontarotti G; Weitzman JB Epigenetic Metaphors: An Interdisciplinary Translation of Encoding and Decoding. New Genetics and Society 2019, 38 (3), 264–288. 10.1080/14636778.2019.1601009. [ DOI ] [ Google Scholar ] (66). Borrelli E; Nestler EJ; Allis CD; Sassone-Corsi P Decoding the Epigenetic Language of Neuronal Plasticity. Neuron 2008, 60 (6), 961–974. 10.1016/j.neuron.2008.10.012. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (67). Lu Y; Brommer B; Tian X; Krishnan A; Meer M; Wang C; Vera DL; Zeng Q; Yu D; Bonkowski MS; Yang J-H; Zhou S; Hoffmann EM; Karg MM; Schultz MB; Kane AE; Davidsohn N; Korobkina E; Chwalek K; Rajman LA; Church GM; Hochedlinger K; Gladyshev VN; Horvath S; Levine ME; Gregory-Ksander MS; Ksander BR; He Z; Sinclair DA Reprogramming to Recover Youthful Epigenetic Information and Restore Vision. Nature 2020, 588 (7836), 124–129. 10.1038/s41586-020-2975-4. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (68). Yan R; Gu C; You D; Huang Z; Qian J; Yang Q; Cheng X; Zhang L; Wang H; Wang P; Guo F Decoding Dynamic Epigenetic Landscapes in Human Oocytes Using Single-Cell Multi-Omics Sequencing. Cell Stem Cell 2021, 28 (9), 1641–1656.e7. 10.1016/j.stem.2021.04.012. [ DOI ] [ PubMed ] [ Google Scholar ] (69). Rothbart SB; Dickson BM; Raab JR; Grzybowski AT; Krajewski K; Guo AH; Shanle EK; Josefowicz SZ; Fuchs SM; Allis CD; Magnuson TR; Ruthenburg AJ; Strahl BD An Interactive Database for the Assessment of Histone Antibody Specificity. Mol Cell 2015, 59 (3), 502–511. 10.1016/j.molcel.2015.06.022. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (70). Mehta D; Ahkami AH; Walley J; Xu S-L; Uhrig RG The Incongruity of Validating Quantitative Proteomics Using Western Blots. Nat. Plants 2022, 8 (12), 1320–1321. 10.1038/s41477-022-01314-8. [ DOI ] [ PubMed ] [ Google Scholar ] (71). Levin Y The Role of Statistical Power Analysis in Quantitative Proteomics. Proteomics 2011, 11 (12), 2565–2567. 10.1002/pmic.201100033. [ DOI ] [ PubMed ] [ Google Scholar ] (72). Burzykowski T; Claesen J; Valkenborg D Experimental Design in Quantitative Proteomics. Methods Mol Biol 2019, 1977, 181–197. 10.1007/978-1-4939-9232-4_12. [ DOI ] [ PubMed ] [ Google Scholar ] (73). Eriksson J; Fenyö D Modeling Experimental Design for Proteomics. Methods Mol Biol 2010, 673, 223–230. 10.1007/978-1-60761-842-3_14. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (74). Plubell DL; Käll L; Webb-Robertson B-J; Bramer LM; Ives A; Kelleher NL; Smith LM; Montine TJ; Wu CC; MacCoss MJ Putting Humpty Dumpty Back Together Again: What Does Protein Quantification Mean in Bottom-Up Proteomics? J Proteome Res 2022, 21 (4), 891–898. 10.1021/acs.jproteome.1c00894. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (75). Drown BS; Jooß K; Melani RD; Lloyd-Jones C; Camarillo JM; Kelleher NL Mapping the Proteoform Landscape of Five Human Tissues. J. Proteome Res. 2022, 21 (5), 1299–1310. 10.1021/acs.jproteome.2c00034. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (76). Holt MV; Wang T; Young NL One-Pot Quantitative Top- and Middle-Down Analysis of GluC-Digested Histone H4. J Am Soc Mass Spectrom 2019, 30 (12), 2514–2525. 10.1007/s13361-019-02219-1. [ DOI ] [ PubMed ] [ Google Scholar ] (77). Wang T; Holt MV; Young NL Early Butyrate Induced Acetylation of Histone H4 Is Proteoform Specific and Linked to Methylation State. Epigenetics 2018, 13 (5), 519–535. 10.1080/15592294.2018.1475979. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (78). Taylor BC; Steinthal LH; Dias M; Yalamanchili HK; Ochsner SA; Zapata GE; Mehta NR; McKenna NJ; Young NL; Nuotio-Antar AM Histone Proteoform Analysis Reveals Epigenetic Changes in Adult Mouse Brown Adipose Tissue in Response to Cold Stress. Epigenetics Chromatin 2024, 17, 12. 10.1186/s13072-024-00536-8. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (79). Chen W; Ding Z; Zang Y; Liu X Characterization of Proteoform Post-Translational Modifications by Top-Down and Bottom-Up Mass Spectrometry in Conjunction with Annotations. Journal of Proteome Research 2023, 22 (10), 3178. 10.1021/acs.jproteome.3c00207. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (80). Poulos RC; Hains PG; Shah R; Lucas N; Xavier D; Manda SS; Anees A; Koh JMS; Mahboob S; Wittman M; Williams SG; Sykes EK; Hecker M; Dausmann M; Wouters MA; Ashman K; Yang J; Wild PJ; deFazio A; Balleine RL; Tully B; Aebersold R; Speed TP; Liu Y; Reddel RR; Robinson PJ; Zhong Q Strategies to Enable Large-Scale Proteomics for Reproducible Research. Nat Commun 2020, 11 (1), 3793. 10.1038/s41467-020-17641-3. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (81). Diz AP; Truebano M; Skibinski DOF The Consequences of Sample Pooling in Proteomics: An Empirical Study. ELECTROPHORESIS 2009, 30 (17), 2967–2975. 10.1002/elps.200900210. [ DOI ] [ PubMed ] [ Google Scholar ] (82). Basharat AR; Zang Y; Sun L; Liu X TopFD: A Proteoform Feature Detection Tool for Top–Down Proteomics. Anal. Chem. 2023, 95 (21), 8189–8196. 10.1021/acs.analchem.2c05244. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (83). Holt MV; Wang T; Young NL Expeditious Extraction of Histones from Limited Cells or Tissue Samples and Quantitative Top-Down Proteomic Analysis. Curr Protoc 2021, 1 (2), e26. 10.1002/cpz1.26. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (84). Yamagishi M; Kuze Y; Kobayashi S; Nakashima M; Morishima S; Kawamata T; Makiyama J; Suzuki K; Seki M; Abe K; Imamura K; Watanabe E; Tsuchiya K; Yasumatsu I; Takayama G; Hizukuri Y; Ito K; Taira Y; Nannya Y; Tojo A; Watanabe T; Tsutsumi S; Suzuki Y; Uchimaru K Mechanisms of Action and Resistance in Histone Methylation-Targeted Therapy. Nature 2024, 627 (8002), 221–228. 10.1038/s41586-024-07103-x. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (85). Issa ME; Takhsha FS; Chirumamilla CS; Perez-Novo C; Vanden Berghe W; Cuendet M Epigenetic Strategies to Reverse Drug Resistance in Heterogeneous Multiple Myeloma. Clinical Epigenetics 2017, 9 (1), 17. 10.1186/s13148-017-0319-5. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (86). Davis RG; Park H-M; Kim K; Greer JB; Fellers RT; LeDuc RD; Romanova EV; Rubakhin SS; Zombeck JA; Wu C; Yau PM; Gao P; van Nispen AJ; Patrie SM; Thomas PM; Sweedler JV; Rhodes JS; Kelleher NL Top-Down Proteomics Enables Comparative Analysis of Brain Proteoforms Between Mouse Strains. Anal Chem 2018, 90 (6), 3802–3810. 10.1021/acs.analchem.7b04108. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (87). Distler U; Schumann S; Kesseler H-G; Pielot R; Smalla K-H; Sielaff M; Schmeisser MJ; Tenzer S Proteomic Analysis of Brain Region and Sex-Specific Synaptic Protein Expression in the Adult Mouse Brain. Cells 2020, 9 (2), 313. 10.3390/cells9020313. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (88). Provez L; Van Puyvelde B; Corveleyn L; Demeulemeester N; Verhelst S; Lintermans B; Daled S; Roels J; Clement L; Martens L; Deforce D; Van Vlierberghe P; Dhaenens M An Interactive Mass Spectrometry Atlas of Histone Posttranslational Modifications in T-Cell Acute Leukemia. Sci Data 2022, 9 (1), 626. 10.1038/s41597-022-01736-1. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (89). Kelleher NL A Cell-Based Approach to the Human Proteome Project. J. Am. Soc. Mass Spectrom. 2012, 23 (10), 1617–1624. 10.1007/s13361-012-0469-9. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (90). Taylor BC; Young NL Histone H4 Proteoforms and Post-Translational Modifications in the Mus Musculus Brain with Quantitative Comparison of Ages and Brain Regions. Anal Bioanal Chem 2023, 415 (9), 1627–1639. 10.1007/s00216-023-04555-4. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (91). Kang Y; Schneider BB; Covey TR On the Nature of Mass Spectrometer Analyzer Contamination. J. Am. Soc. Mass Spectrom. 2017, 28 (11), 2384–2392. 10.1007/s13361-017-1747-3. [ DOI ] [ PubMed ] [ Google Scholar ] (92). MS Tip: Mass Spectrometer Source Cleaning Procedures. https://www.sisweb.com/referenc/tips/msclean.htm (accessed 2024-10-22). (93). Shechter D; Dormann HL; Allis CD; Hake SB Extraction, Purification and Analysis of Histones. Nat Protoc 2007, 2 (6), 1445–1457. 10.1038/nprot.2007.202. [ DOI ] [ PubMed ] [ Google Scholar ] (94). Kizer KO; Xiao T; Strahl BD Accelerated Nuclei Preparation and Methods for Analysis of Histone Modifications in Yeast. Methods 2006, 40 (4), 296–302. 10.1016/j.ymeth.2006.06.022. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (95). Homsi C; Rajan RE; Minati R; St-Hilaire E; Bonneil E; Dufresne SF; Wurtele H; Verreault A; Thibault P A Rapid and Efficient Method for the Extraction of Histone Proteins. J. Proteome Res. 2023, 22 (8), 2765–2773. 10.1021/acs.jproteome.3c00266. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (96). Rodriguez-Collazo P; Leuba SH; Zlatanova J Robust Methods for Purification of Histones from Cultured Mammalian Cells with the Preservation of Their Native Modifications. Nucleic Acids Res 2009, 37 (11), e81. 10.1093/nar/gkp273. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (97). Zougman A; Wiśniewski JR Beyond Linker Histones and High Mobility Group Proteins: Global Profiling of Perchloric Acid Soluble Proteins. J Proteome Res 2006, 5 (4), 925–934. 10.1021/pr050415p. [ DOI ] [ PubMed ] [ Google Scholar ] (98). Becker JS; McCarthy RL; Sidoli S; Donahue G; Kaeding KE; He Z; Lin S; Garcia BA; Zaret KS Genomic and Proteomic Resolution of Heterochromatin and Its Restriction of Alternate Fate Genes. Mol Cell 2017, 68 (6), 1023–1037.e15. 10.1016/j.molcel.2017.11.030. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (99). Plazas-Mayorca MD; Zee BM; Young NL; Fingerman IM; LeRoy G; Briggs SD; Garcia BA One-Pot Shotgun Quantitative Mass Spectrometry Characterization of Histones. J Proteome Res 2009, 8 (11), 5367–5374. 10.1021/pr900777e. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (100). Gurley LR; Prentice DA; Valdez JG; Spall WD High-Performance Liquid Chromatography of Chromatin Histones. Journal of Chromatography A 1983, 266, 609–627. 10.1016/S0021-9673(01)90931-8. [ DOI ] [ PubMed ] [ Google Scholar ] (101). Gurley LR; Prentice DA; Valdez JG; Spall WD Histone Fractionation by High-Performance Liquid Chromatography on Cyanoalkylsilane (CN) Reverse-Phase Columns. Analytical Biochemistry 1983, 131 (2), 465–477. 10.1016/0003-2697(83)90200-2. [ DOI ] [ PubMed ] [ Google Scholar ] (102). Lindner H; Helliger W; Puschendorf B Histone Separation by High-Performance Liquid Chromatography on C4 Reverse-Phase Columns. Analytical Biochemistry 1986, 158 (2), 424–430. 10.1016/0003-2697(86)90570-1. [ DOI ] [ PubMed ] [ Google Scholar ] (103). Su X; Ren C; Freitas MA Mass Spectrometry-Based Strategies for Characterization of Histones and Their Post-Translational Modifications. Expert Rev Proteomics 2007, 4 (2), 211–225. 10.1586/14789450.4.2.211. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (104). Zhang K; Tang H Analysis of Core Histones by Liquid Chromatography–Mass Spectrometry and Peptide Mapping. Journal of Chromatography B 2003, 783 (1), 173–179. 10.1016/S1570-0232(02)00631-1. [ DOI ] [ PubMed ] [ Google Scholar ] (105). Sidoli S; Garcia BA Characterization of Individual Histone Post-Translational Modifications and Their Combinatorial Patterns by Mass Spectrometry-Based Proteomics Strategies. Methods Mol Biol 2017, 1528, 121–148. 10.1007/978-1-4939-6630-1_8. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (106). Scheid R; Dowell JA; Sanders D; Jiang J; Denu JM; Zhong X Histone Acid Extraction and High Throughput Mass Spectrometry to Profile Histone Modifications in Arabidopsis Thaliana. Current protocols 2022, 2 (8), e527. 10.1002/cpz1.527. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (107). Zheng Y; Fornelli L; Compton PD; Sharma S; Canterbury J; Mullen C; Zabrouskov V; Fellers RT; Thomas PM; Licht JD; Senko MW; Kelleher NL Unabridged Analysis of Human Histone H3 by Differential Top-Down Mass Spectrometry Reveals Hypermethylated Proteoforms from MMSET/NSD2 Overexpression. Mol Cell Proteomics 2016, 15 (3), 776–790. 10.1074/mcp.M115.053819. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (108). Strahl BD; Ohba R; Cook RG; Allis CD Methylation of Histone H3 at Lysine 4 Is Highly Conserved and Correlates with Transcriptionally Active Nuclei in Tetrahymena. Proceedings of the National Academy of Sciences 1999, 96 (26), 14967–14972. 10.1073/pnas.96.26.14967. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (109). Garcia BA; Pesavento JJ; Mizzen CA; Kelleher NL Pervasive Combinatorial Modification of Histone H3 in Human Cells. Nat Methods 2007, 4 (6), 487–489. 10.1038/nmeth1052. [ DOI ] [ PubMed ] [ Google Scholar ] (110). Young NL; DiMaggio PA; Plazas-Mayorca MD; Baliban RC; Floudas CA; Garcia BA High Throughput Characterization of Combinatorial Histone Codes. Mol Cell Proteomics 2009, 8 (10), 2266–2284. 10.1074/mcp.M900238-MCP200. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (111). Papazyan R; Taverna SD Separation and Purification of Multiply Acetylated Proteins Using Cation-Exchange Chromatography. Methods in molecular biology (Clifton, N.J.) 2013, 981, 103. 10.1007/978-1-62703-305-3_8. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (112). Wang Q; Fang F; Wang Q; Sun L Capillary Zone Electrophoresis-High Field Asymmetric Ion Mobility Spectrometry-Tandem Mass Spectrometry for Top-down Characterization of Histone Proteoforms. Proteomics 2024, 24 (3–4), e2200389. 10.1002/pmic.202200389. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (113). Minshull TC; Cole J; Dockrell DH; Read RC; Dickman MJ Analysis of Histone Post Translational Modifications in Primary Monocyte Derived Macrophages Using Reverse Phase × Reverse Phase Chromatography in Conjunction with Porous Graphitic Carbon Stationary Phase. J Chromatogr A 2016, 1453, 43–53. 10.1016/j.chroma.2016.05.025. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (114). Herbert H Hill J; Siems WF; Louis RHS. Ion mobility spectrometry. ACS Publications. 10.1021/ac00222a001. [ DOI ] [ Google Scholar ] (115). Celma A; Alygizakis N; Belova L; Bijlsma L; Fabregat-Safont D; Menger F; Gil-Solsona R Ion Mobility Separation Coupled to High-Resolution Mass Spectrometry in Environmental Analysis – Current State and Future Potential. Trends in Environmental Analytical Chemistry 2024, 43, e00239. 10.1016/j.teac.2024.e00239. [ DOI ] [ Google Scholar ] (116). Developments in Trapped Ion Mobility Mass Spectrometry to Probe the Early Stages of Peptide Aggregation | Journal of the American Society for Mass Spectrometry. https://pubs.acs.org/doi/10.1021/jasms.2c00253 (accessed 2024-10-22). [ DOI ] [ PMC free article ] [ PubMed ] (117). Shliaha PV; Baird MA; Nielsen MM; Gorshkov V; Bowman AP; Kaszycki JL; Jensen ON; Shvartsburg AA Characterization of Complete Histone Tail Proteoforms Using Differential Ion Mobility Spectrometry. Anal. Chem. 2017, 89 (10), 5461–5466. 10.1021/acs.analchem.7b00379. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (118). Berthias F; Bilgin N; Mecinović J; Jensen ON Top-down Ion Mobility/Mass Spectrometry Reveals Enzyme Specificity: Separation and Sequencing of Isomeric Proteoforms. PROTEOMICS 2024, 24 (3–4), 2200471. 10.1002/pmic.202200471. [ DOI ] [ PubMed ] [ Google Scholar ] (119). Berthias F; Cooper-Shepherd DA; Holck FHV; Langridge JI; Jensen ON Full Separation and Sequencing of Isomeric Proteoforms in the Middle-Down Mass Range Using Cyclic Ion Mobility and Electron Capture Dissociation. Anal Chem 2023, 95 (29), 11141–11148. 10.1021/acs.analchem.3c02120. [ DOI ] [ PubMed ] [ Google Scholar ] (120). Sealy L; Chalkley R DNA Associated with Hyperacetylated Histone Is Preferentially Digested by DNase I. Nucleic Acids Research 1978, 5 (6), 1863. 10.1093/nar/5.6.1863. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (121). Soldi M; Cuomo A; Bonaldi T Improved Bottom-up Strategy to Efficiently Separate Hypermodified Histone Peptides through Ultra-HPLC Separation on a Bench Top Orbitrap Instrument. Proteomics 2014, 14 (19), 2212–2225. 10.1002/pmic.201400075. [ DOI ] [ PubMed ] [ Google Scholar ] (122). Harrison JA; Pruška A; Bittner P; Muck A; Cooper-Shepherd DA; Zenobi R Advancing Cyclic Ion Mobility Mass Spectrometry Methods for Studying Biomolecules: Toward the Conformational Dynamics of Mega Dalton Protein Aggregates. Anal. Chem. 2022, 94 (36), 12435–12443. 10.1021/acs.analchem.2c02406. [ DOI ] [ PubMed ] [ Google Scholar ] (123). Müller MM; Muir TW Histones: At the Crossroads of Peptide and Protein Chemistry. Chemical Reviews 2014, 115 (6), 2296. 10.1021/cr5003529. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (124). Makey DM; Gadkari VV; Kennedy RT; Ruotolo BT Cyclic Ion Mobility-Mass Spectrometry and Tandem Collision Induced Unfolding for Quantification of Elusive Protein Biomarkers. Anal Chem 2024, 96 (15), 6021–6029. 10.1021/acs.analchem.4c00477. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (125). Lamont L; Hadavi D; Bowman AP; Flinders B; Cooper-Shepherd D; Palmer M; Jordens J; Mengerink Y; Honing M; Langridge J; Porta Siegel T; Vreeken RJ; Heeren RMA High-Resolution Ion Mobility Spectrometry–Mass Spectrometry for Isomeric Separation of Prostanoids after Girard’s Reagent T Derivatization. Rapid Communications in Mass Spectrometry 2023, 37 (5), e9439. 10.1002/rcm.9439. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (126). Fang P; Ji Y; Silbern I; Viner R; Oellerich T; Pan K-T; Urlaub H Evaluation and Optimization of High-Field Asymmetric Waveform Ion-Mobility Spectrometry for Multiplexed Quantitative Site-Specific N-Glycoproteomics. Anal. Chem. 2021, 93 (25), 8846–8855. 10.1021/acs.analchem.1c00802. [ DOI ] [ PubMed ] [ Google Scholar ] (127). Webb IK; Garimella SVB; Tolmachev AV; Chen T-C; Zhang X; Norheim RV; Prost SA; LaMarche B; Anderson GA; Ibrahim YM; Smith RD Experimental Evaluation and Optimization of Structures for Lossless Ion Manipulations for Ion Mobility Spectrometry with Time-of-Flight Mass Spectrometry. Anal. Chem. 2014, 86 (18), 9169–9176. 10.1021/ac502055e. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (128). Mun D-G; A. Bhat F; Ding H; J. Madden B; Natesampillai S; D. Badley A; L. Johnson K; T. Kelly R; Pandey A. Optimizing Single Cell Proteomics Using Trapped Ion Mobility Spectrometry for Label-Free Experiments. Analyst 2023, 148 (15), 3466–3475. 10.1039/D3AN00080J. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (129). Pesavento JJ; Bullock CR; LeDuc RD; Mizzen CA; Kelleher NL Combinatorial Modification of Human Histone H4 Quantitated by Two-Dimensional Liquid Chromatography Coupled with Top down Mass Spectrometry. J Biol Chem 2008, 283 (22), 14927–14937. 10.1074/jbc.M709796200. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (130). Berthias F; Thurman HA; Wijegunawardena G; Wu H; Shvartsburg AA; Jensen ON Top-Down Ion Mobility Separations of Isomeric Proteoforms. Anal Chem 2023, 95 (2), 784–791. 10.1021/acs.analchem.2c02948. [ DOI ] [ PubMed ] [ Google Scholar ] (131). Feller C; Forné I; Imhof A; Becker PB Global and Specific Responses of the Histone Acetylome to Systematic Perturbation. Mol Cell 2015, 57 (3), 559–571. 10.1016/j.molcel.2014.12.008. [ DOI ] [ PubMed ] [ Google Scholar ] (132). Abshiru N; Caron-Lizotte O; Rajan RE; Jamai A; Pomies C; Verreault A; Thibault P Discovery of Protein Acetylation Patterns by Deconvolution of Peptide Isomer Mass Spectra. Nat Commun 2015, 6, 8648. 10.1038/ncomms9648. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (133). Yuan Z-F; Sidoli S; Marchione DM; Simithy J; Janssen KA; Szurgot MR; Garcia BA EpiProfile 2.0: A Computational Platform for Processing Epi-Proteomics Mass Spectrometry Data. Journal of proteome research 2018, 17 (7), 2533. 10.1021/acs.jproteome.8b00133. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (134). Crook OM; Chung C; Deane CM Challenges and Opportunities for Bayesian Statistics in Proteomics. J. Proteome Res. 2022, 21 (4), 849–864. 10.1021/acs.jproteome.1c00859. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (135). Willems S; Dhaenens M; Govaert E; De Clerck L; Meert P; Van Neste C; Van Nieuwerburgh F; Deforce D Flagging False Positives Following Untargeted LC-MS Characterization of Histone Post-Translational Modification Combinations. J Proteome Res 2017, 16 (2), 655–664. 10.1021/acs.jproteome.6b00724. [ DOI ] [ PubMed ] [ Google Scholar ] (136). Tweedie-Cullen RY; Brunner AM; Grossmann J; Mohanna S; Sichau D; Nanni P; Panse C; Mansuy IM Identification of Combinatorial Patterns of Post-Translational Modifications on Individual Histones in the Mouse Brain. PLOS ONE 2012, 7 (5), e36980. 10.1371/journal.pone.0036980. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (137). Gargano AFG; Shaw JB; Zhou M; Wilkins CS; Fillmore TL; Moore RJ; Somsen GW; Paša-Tolić L Increasing the Separation Capacity of Intact Histone Proteoforms Chromatography Coupling Online Weak Cation Exchange-HILIC to Reversed Phase LC UVPD-HRMS. J. Proteome Res. 2018, 17 (11), 3791–3800. 10.1021/acs.jproteome.8b00458. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (138). Chen D; Yang Z; Shen X; Sun L Capillary Zone Electrophoresis-Tandem Mass Spectrometry As an Alternative to Liquid Chromatography-Tandem Mass Spectrometry for Top-down Proteomics of Histones. Anal Chem 2021, 93 (10), 4417–4424. 10.1021/acs.analchem.0c04237. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (139). Fouque KJD; Kaplan D; Voinov VG; Holck FHV; Jensen ON; Fernandez-Lima F Proteoform Differentiation Using Tandem Trapped Ion Mobility, Electron Capture Dissociation, and ToF Mass Spectrometry. Analytical chemistry 2021, 93 (27), 9575. 10.1021/acs.analchem.1c01735. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (140). Yang L; Tu S; Ren C; Bulloch EMM; Liao C-L; Tsai M-D; Freitas MA Unambiguous Determination of Isobaric Histone Modifications by Reversed-Phase Retention Time and High-Mass Accuracy. Analytical Biochemistry 2010, 396 (1), 13–22. 10.1016/j.ab.2009.08.027. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (141). Coradin M; Mendoza MR; Sidoli S; Alpert AJ; Lu C; Garcia BA Bullet Points to Evaluate the Performance of the Middle-down Proteomics Workflow for Histone Modification Analysis. Methods 2020, 184, 86–92. 10.1016/j.ymeth.2020.01.013. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (142). Hodge K; Have ST; Hutton L; Lamond AI Cleaning up the Masses: Exclusion Lists to Reduce Contamination with HPLC-MS/MS. Journal of Proteomics 2013, 88, 92–103. 10.1016/j.jprot.2013.02.023. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (143). Tvardovskiy A; Schwämmle V; Kempf SJ; Rogowska-Wrzesinska A; Jensen ON Accumulation of Histone Variant H3.3 with Age Is Associated with Profound Changes in the Histone Methylation Landscape. Nucleic Acids Res 2017, 45 (16), 9272–9289. 10.1093/nar/gkx696. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (144). Anderson LC; Karch KR; Ugrin SA; Coradin M; English AM; Sidoli S; Shabanowitz J; Garcia BA; Hunt DF Analyses of Histone Proteoforms Using Front-End Electron Transfer Dissociation-Enabled Orbitrap Instruments *. Molecular & Cellular Proteomics 2016, 15 (3), 975–988. 10.1074/mcp.O115.053843. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (145). Tipton JD; Tran JC; Catherman AD; Ahlf DR; Durbin KR; Kelleher NL Analysis of Intact Protein Isoforms by Mass Spectrometry *. Journal of Biological Chemistry 2011, 286 (29), 25451–25458. 10.1074/jbc.R111.239442. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (146). Kruger NA; Zubarev RA; Horn DM; McLafferty FW Electron Capture Dissociation of Multiply Charged Peptide Cations1. International Journal of Mass Spectrometry 1999, 185–187, 787–793. 10.1016/S1387-3806(98)14215-X. [ DOI ] [ Google Scholar ] (147). Syka JEP; Coon JJ; Schroeder MJ; Shabanowitz J; Hunt DF Peptide and Protein Sequence Analysis by Electron Transfer Dissociation Mass Spectrometry. Proceedings of the National Academy of Sciences 2004, 101 (26), 9528–9533. 10.1073/pnas.0402700101. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (148). Riley NM; Sikora JW; Seckler HS; Greer JB; Fellers RT; LeDuc RD; Westphall MS; Thomas PM; Kelleher NL; Coon JJ The Value of Activated Ion Electron Transfer Dissociation for High-Throughput Top-Down Characterization of Intact Proteins. Analytical chemistry 2018, 90 (14), 8553. 10.1021/acs.analchem.8b01638. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (149). Riley NM; Mullen C; Weisbrod CR; Sharma S; Senko MW; Zabrouskov V; Westphall MS; Syka JEP; Coon JJ Enhanced Dissociation of Intact Proteins with High Capacity Electron Transfer Dissociation. J Am Soc Mass Spectrom 2016, 27 (3), 520–531. 10.1007/s13361-015-1306-8. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (150). Yassaghi G; Kukačka Z; Fiala J; Kavan D; Halada P; Volný M; Novák P Top-Down Detection of Oxidative Protein Footprinting by Collision-Induced Dissociation, Electron-Transfer Dissociation, and Electron-Capture Dissociation. Anal Chem 2022, 94 (28), 9993–10002. 10.1021/acs.analchem.1c05476. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (151). Compton PD; Strukl JV; Bai DL; Shabanowitz J; Hunt DF Optimization of Electron Transfer Dissociation via Informed Selection of Reagents and Operating Parameters. Analytical Chemistry 2012, 84 (3), 1781. 10.1021/ac202807h. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (152). Riley NM; Westphall MS; Hebert AS; Coon JJ Implementation of Activated Ion Electron Transfer Dissociation on a Quadrupole-Orbitrap-Linear Ion Trap Hybrid Mass Spectrometer. Anal Chem 2017, 89 (12), 6358–6366. 10.1021/acs.analchem.7b00213. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (153). Riley NM; Westphall MS; Coon JJ Activated Ion-Electron Transfer Dissociation Enables Comprehensive Top-Down Protein Fragmentation. J Proteome Res 2017, 16 (7), 2653–2659. 10.1021/acs.jproteome.7b00249. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (154). Brodbelt JS; Morrison LJ; Santos I Ultraviolet Photodissociation Mass Spectrometry for Analysis of Biological Molecules. Chemical reviews 2019, 120 (7), 3328. 10.1021/acs.chemrev.9b00440. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (155). Greer SM; Sidoli S; Coradin M; Jespersen MS; Schwämmle V; Jensen ON; Garcia BA; Brodbelt JS Extensive Characterization of Heavily Modified Histone Tails by 193 Nm Ultraviolet Photodissociation Mass Spectrometry via a Middle-Down Strategy. Analytical chemistry 2018, 90 (17), 10425. 10.1021/acs.analchem.8b02320. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (156). Walker JN; Lam R; Brodbelt JS Enhanced Characterization of Histones Using 193 Nm Ultraviolet Photodissociation and Proton Transfer Charge Reduction. Anal Chem 2023, 95 (14), 5985–5993. 10.1021/acs.analchem.2c05765. [ DOI ] [ PubMed ] [ Google Scholar ] (157). Dunham SD; Wei B; Lantz C; Loo JA; Brodbelt JS Impact of Internal Fragments on Top-Down Analysis of Intact Proteins by 193 Nm UVPD. J Proteome Res 2023, 22 (1), 170–181. 10.1021/acs.jproteome.2c00583. [ DOI ] [ PubMed ] [ Google Scholar ] (158). Gupta N; Bandeira N; Keich U; Pevzner PA Target-Decoy Approach and False Discovery Rate: When Things May Go Wrong. Journal of the American Society for Mass Spectrometry 2011, 22 (7), 1111. 10.1007/s13361-011-0139-3. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (159). Kim H; Lee S; Park H Target-Small Decoy Search Strategy for False Discovery Rate Estimation. BMC Bioinformatics 2019, 20, 438. 10.1186/s12859-019-3034-8. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (160). Lee S; Park H; Kim H Comparison of False-Discovery Rates of Various Decoy Databases. Proteome Sci 2021, 19 (1), 11. 10.1186/s12953-021-00179-7. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (161). Liu X; Inbar Y; Dorrestein PC; Wynne C; Edwards N; Souda P; Whitelegge JP; Bafna V; Pevzner PA Deconvolution and Database Search of Complex Tandem Mass Spectra of Intact Proteins: A Combinatorial Approach. Mol Cell Proteomics 2010, 9 (12), 2772–2782. 10.1074/mcp.M110.002766. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (162). Goldfarb D; Lafferty MJ; Herring LE; Wang W; Major MB Approximating Isotope Distributions of Biomolecule Fragments. ACS Omega 2018, 3 (9), 11383. 10.1021/acsomega.8b01649. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (163). Vyatkina K; Wu S; Dekker LJM; VanDuijn MM; Liu X; Tolić N; Dvorkin M; Alexandrova S; Luider TM; Paša-Tolić L; Pevzner PA De Novo Sequencing of Peptides from Top-Down Tandem Mass Spectra. J. Proteome Res. 2015, 14 (11), 4450–4462. 10.1021/pr501244v. [ DOI ] [ PubMed ] [ Google Scholar ] (164). Tyshuk K; Vyatkina K Application of Top-Down Deconvolution Tools to Bottom-Up Mass Spectrometry Data: Advantages and Limitations. In International Youth Conference on Electronics, Telecommunications and Information Technologies; Velichko E, Kapralova V, Karaseov P, Zavjalov S, Angueira P, Andreev S, Eds.; Springer International Publishing: Cham, 2022; pp 119–128. 10.1007/978-3-030-81119-8_13. [ DOI ] [ Google Scholar ] (165). Tabb DL; Jeong K; Druart K; Gant MS; Brown KA; Nicora C; Zhou M; Couvillion S; Nakayasu E; Williams JE; Peterson HK; McGuire MK; McGuire MA; Metz TO; Chamot-Rooke J Comparing Top-Down Proteoform Identification: Deconvolution, PrSM Overlap, and PTM Detection. J Proteome Res 2023, 22 (7), 2199–2217. 10.1021/acs.jproteome.2c00673. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (166). Tsugawa H; Kind T; Nakabayashi R; Yukihira D; Tanaka W; Cajka T; Saito K; Fiehn O; Arita M Hydrogen Rearrangement Rules: Computational MS/MS Fragmentation and Structure Elucidation Using MS-FINDER Software. Analytical chemistry 2016, 88 (16), 7946. 10.1021/acs.analchem.6b00770. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (167). Peters-Clarke TM; Riley NM; Westphall MS; Coon JJ Practical Effects of Intramolecular Hydrogen Rearrangement in Electron Transfer Dissociation-Based Proteomics. Journal of the American Society for Mass Spectrometry 2021, 33 (1), 100. 10.1021/jasms.1c00284. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (168). Savitski MM; Kjeldsen F; Nielsen ML; Zubarev RA Hydrogen Rearrangement to and from Radical z Fragments in Electron Capture Dissociation of Peptides. Journal of the American Society for Mass Spectrometry 2007, 18 (1), 113–120. 10.1016/j.jasms.2006.09.008. [ DOI ] [ PubMed ] [ Google Scholar ] (169). Martens J; Grzetic J; Berden G; Oomens J Structural Identification of Electron Transfer Dissociation Products in Mass Spectrometry Using Infrared Ion Spectroscopy. Nat Commun 2016, 7 (1), 11754. 10.1038/ncomms11754. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (170). Clasen MA; Kurt LU; Santos MDM; Lima DB; Liu F; Gozzo FC; Barbosa VC; Carvalho PC Increasing Confidence in Proteomic Spectral Deconvolution through Mass Defect. Bioinformatics 2022, 38 (22), 5119–5120. 10.1093/bioinformatics/btac638. [ DOI ] [ PubMed ] [ Google Scholar ] (171). McIlwain SJ; Wu Z; Wetzel M; Belongia D; Jin Y; Wenger K; Ong IM; Ge Y Enhancing Top-Down Proteomics Data Analysis by Combining Deconvolution Results through a Machine Learning Strategy. J. Am. Soc. Mass Spectrom. 2020, 31 (5), 1104–1113. 10.1021/jasms.0c00035. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (172). Jeong K; Kim J; Gaikwad M; Hidayah SN; Heikaus L; Schlüter H; Kohlbacher O FLASHDeconv: Ultrafast, High-Quality Feature Deconvolution for Top-Down Proteomics. Cell Syst 2020, 10 (2), 213–218.e6. 10.1016/j.cels.2020.01.003. [ DOI ] [ PubMed ] [ Google Scholar ] (173). Cristobal A; Marino F; Post H; van den Toorn HWP; Mohammed S; Heck AJR Toward an Optimized Workflow for Middle-Down Proteomics. Anal Chem 2017, 89 (6), 3318–3325. 10.1021/acs.analchem.6b03756. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (174). Marty MT A Universal Score for Deconvolution of Intact Protein and Native Electrospray Mass Spectra. Anal Chem 2020, 92 (6), 4395–4401. 10.1021/acs.analchem.9b05272. [ DOI ] [ PubMed ] [ Google Scholar ] (175). Lazear MR Sage: An Open-Source Tool for Fast Proteomics Searching and Quantification at Scale. J Proteome Res 2023, 22 (11), 3652–3659. 10.1021/acs.jproteome.3c00486. [ DOI ] [ PubMed ] [ Google Scholar ] (176). Fellers RT; Greer JB; Early BP; Yu X; LeDuc RD; Kelleher NL; Thomas PM ProSight Lite: Graphical Software to Analyze Top-Down Mass Spectrometry Data. Proteomics 2015, 15 (7), 1235. 10.1002/pmic.201570050. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (177). Kou Q; Xun L; Liu X TopPIC: A Software Tool for Top-down Mass Spectrometry-Based Proteoform Identification and Characterization. Bioinformatics 2016, 32 (22), 3495–3497. 10.1093/bioinformatics/btw398. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (178). Kou Q; Wu S; Tolić N; Paša-Tolić L; Liu Y; Liu X A Mass Graph-Based Approach for the Identification of Modified Proteoforms Using Top-down Tandem Mass Spectra. Bioinformatics 2017, 33 (9), 1309–1316. 10.1093/bioinformatics/btw806. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (179). Liu X; Hengel S; Wu S; Tolić N; Pasa-Tolić L; Pevzner PA Identification of Ultramodified Proteins Using Top-down Tandem Mass Spectra. Journal of proteome research 2013, 12 (12), 5830. 10.1021/pr400849y. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (180). Peter A DiMaggio J; Young NL; Baliban RC; Garcia BA; Floudas CA A Mixed Integer Linear Optimization Framework for the Identification and Quantification of Targeted Post-Translational Modifications of Highly Modified Proteins Using Multiplexed Electron Transfer Dissociation Tandem Mass Spectrometry. Molecular & Cellular Proteomics : MCP 2009, 8 (11), 2527. 10.1074/mcp.M900144-MCP200. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (181). Faizi M; Fellers RT; Lu D; Drown BS; Jambhekar A; Lahav G; Kelleher NL; Gunawardena J MSModDetector: A Tool for Detecting Mass Shifts and Post-Translational Modifications in Individual Ion Mass Spectrometry Data. Bioinformatics 2024, 40 (6), btae335. 10.1093/bioinformatics/btae335. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (182). Compton PD; Kelleher NL; Gunawardena J Estimating the Distribution of Protein Post-Translational Modification States by Mass Spectrometry. J. Proteome Res. 2018, 17 (8), 2727–2734. 10.1021/acs.jproteome.8b00150. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (183). Guan S; Burlingame AL Data Processing Algorithms for Analysis of High Resolution MSMS Spectra of Peptides with Complex Patterns of Posttranslational Modifications*. Molecular & Cellular Proteomics 2010, 9 (5), 804–810. 10.1074/mcp.M900431-MCP200. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (184). Talbert PB; Ahmad K; Almouzni G; Ausió J; Berger F; Bhalla PL; Bonner WM; Cande WZ; Chadwick BP; Chan SWL; Cross GAM; Cui L; Dimitrov SI; Doenecke D; Eirin-López JM; Gorovsky MA; Hake SB; Hamkalo BA; Holec S; Jacobsen SE; Kamieniarz K; Khochbin S; Ladurner AG; Landsman D; Latham JA; Loppin B; Malik HS; Marzluff WF; Pehrson JR; Postberg J; Schneider R; Singh MB; Smith MM; Thompson E; Torres-Padilla M-E; Tremethick DJ; Turner BM; Waterborg JH; Wollmann H; Yelagandula R; Zhu B; Henikoff S A Unified Phylogeny-Based Nomenclature for Histone Variants. Epigenetics & Chromatin 2012, 5 (1), 7. 10.1186/1756-8935-5-7. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (185). Turner BM Reading Signals on the Nucleosome with a New Nomenclature for Modified Histones. Nat Struct Mol Biol 2005, 12 (2), 110–112. 10.1038/nsmb0205-110. [ DOI ] [ PubMed ] [ Google Scholar ] (186). LeDuc RD; Deutsch EW; Binz P-A; Fellers RT; Cesnik AJ; Klein JA; Van Den Bossche T; Gabriels R; Yalavarthi A; Perez-Riverol Y; Carver J; Bittremieux W; Kawano S; Pullman B; Bandeira N; Kelleher NL; Thomas PM; Vizcaíno JA Proteomics Standards Initiative’s ProForma 2.0: Unifying the Encoding of Proteoforms and Peptidoforms. J Proteome Res 2022, 21 (4), 1189–1195. 10.1021/acs.jproteome.1c00771. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (187). Zheng Y; Huang X; Kelleher NL Epiproteomics: Quantitative Analysis of Histone Marks and Codes by Mass Spectrometry. Curr Opin Chem Biol 2016, 33, 142–150. 10.1016/j.cbpa.2016.06.007. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (188). Gao J; Liao R; Yu Y; Zhai H; Wang Y; Sack R; Peters AHFM; Chen J; Wu H; Huang Z; Hu M; Qi W; Lu C; Atadja P; Oyang C; Li E; Yi W; Zhou S Absolute Quantification of Histone PTM Marks by MRM-Based LC-MS/MS. Anal. Chem. 2014, 86 (19), 9679–9686. 10.1021/ac502333a. [ DOI ] [ PubMed ] [ Google Scholar ] (189). Révész Á; Milley MG; Nagy K; Szabó D; Kalló G; Csősz É; Vékey K; Drahos L Tailoring to Search Engines: Bottom-Up Proteomics with Collision Energies Optimized for Identification Confidence. J. Proteome Res. 2021, 20 (1), 474–484. 10.1021/acs.jproteome.0c00518. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (190). Välikangas T; Suomi T; Elo LL A Systematic Evaluation of Normalization Methods in Quantitative Label-Free Proteomics. Brief Bioinform 2018, 19 (1), 1–11. 10.1093/bib/bbw095. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (191). Smith LM Proteoforms and Proteoform Families: Past, Present, and Future. Methods Mol Biol 2022, 2500, 1–4. 10.1007/978-1-0716-2325-1_1. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (192). Shortreed MR; Frey BL; Scalf M; Knoener RA; Cesnik AJ; Smith LM Elucidating Proteoform Families from Proteoform Intact-Mass and Lysine-Count Measurements. J Proteome Res 2016, 15 (4), 1213–1221. 10.1021/acs.jproteome.5b01090. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (193). Marginal Distribution. Wikipedia; 2023. [ Google Scholar ] (194). Fox GC; Poncha KF; Smith BR; van der Maas LN; Robbins NN; Graham B; Dowen JM; Strahl BD; Young NL; Jain K Histone H3K18 & H3K23 Acetylation Directs Establishment of MLL-Mediated H3K4 Methylation. J Biol Chem 2024, 300 (8), 107527. 10.1016/j.jbc.2024.107527. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (195). Jain K; Marunde MR; Burg JM; Gloor SL; Joseph FM; Poncha KF; Gillespie ZB; Rodriguez KL; Popova IK; Hall NW; Vaidya A; Howard SA; Taylor HF; Mukhsinova L; Onuoha UC; Patteson EF; Cooke SW; Taylor BC; Weinzapfel EN; Cheek MA; Meiners MJ; Fox GC; Namitz KE; Cowles MW; Krajewski K; Sun Z-W; Cosgrove MS; Young NL; Keogh M-C; Strahl BD An Acetylation-Mediated Chromatin Switch Governs H3K4 Methylation Read-Write Capability. eLife 2023, 12, e82596. 10.7554/eLife.82596. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (196). Thomas SP; Haws SA; Borth LE; Denu JM A Practical Guide for Analysis of Histone Post-Translational Modifications by Mass Spectrometry: Best Practices and Pitfalls. Methods 2020, 184, 53–60. 10.1016/j.ymeth.2019.12.001. [ DOI ] [ PubMed ] [ Google Scholar ] (197). Holt MV; Wang T; Young NL Recent Advances in Understanding Histone Modification Events. Curr Mol Bio Rep 2017, 3 (1), 11–17. 10.1007/s40610-017-0050-1. [ DOI ] [ Google Scholar ] (198). Schwämmle V; Sidoli S; Ruminowicz C; Wu X; Lee C-F; Helin K; Jensen ON Systems Level Analysis of Histone H3 Post-Translational Modifications (PTMs) Reveals Features of PTM Crosstalk in Chromatin Regulation. Mol Cell Proteomics 2016, 15 (8), 2715–2729. 10.1074/mcp.M115.054460. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (199). Kohler D; Tsai T-H; Verschueren E; Huang T; Hinkle T; Phu L; Choi M; Vitek O MSstatsPTM: Statistical Relative Quantification of Posttranslational Modifications in Bottom-Up Mass Spectrometry-Based Proteomics. Mol Cell Proteomics 2023, 22 (1), 100477. 10.1016/j.mcpro.2022.100477. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (200). Shuken SR; McNerney MW Costs and Benefits of Popular P-Value Correction Methods in Three Models of Quantitative Omic Experiments. Anal Chem 2023, 95 (5), 2732–2740. 10.1021/acs.analchem.2c03719. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (201). Perneger TV What’s Wrong with Bonferroni Adjustments. BMJ 1998, 316 (7139), 1236–1238. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (202). Laha N; Koner S; Sarkar ND False Discovery Rate Control: Moving beyond the Benjamini–Hochberg Method. bioRxiv February 15, 2025, p 2024.01.13.575531. 10.1101/2024.01.13.575531. [ DOI ] [ Google Scholar ] (203). Hochberg procedure: controlling false discoveries in experiments. https://www.statsig.com/perspectives/hochberg-procedure-false-discoveries (accessed 2025-04-28). (204). Diz AP; Carvajal-Rodríguez A; Skibinski DOF Multiple Hypothesis Testing in Proteomics: A Strategy for Experimental Work. Molecular & Cellular Proteomics : MCP 2010, 10 (3), M110.004374. 10.1074/mcp.M110.004374. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (205). Pascovici D; Handler DCL; Wu JX; Haynes PA Multiple Testing Corrections in Quantitative Proteomics: A Useful but Blunt Tool. Proteomics 2016, 16 (18), 2448–2453. 10.1002/pmic.201600044. [ DOI ] [ PubMed ] [ Google Scholar ] (206). Karp NA; McCormick PS; Russell MR; Lilley KS Experimental and Statistical Considerations to Avoid False Conclusions in Proteomics Studies Using Differential In-Gel Electrophoresis*. Molecular & Cellular Proteomics 2007, 6 (8), 1354–1364. 10.1074/mcp.M600274-MCP200. [ DOI ] [ PubMed ] [ Google Scholar ] (207). Litière S; Alonso A; Molenberghs G Type I and Type II Error under Random-Effects Misspecification in Generalized Linear Mixed Models. Biometrics 2007, 63 (4), 1038–1044. 10.1111/j.1541-0420.2007.00782.x. [ DOI ] [ PubMed ] [ Google Scholar ] (208). Huang T; Staniak M; da Veiga Leprevost F; Figueroa-Navedo AM; Ivanov AR; Nesvizhskii AI; Choi M; Vitek O Statistical Detection of Differentially Abundant Proteins in Experiments with Repeated Measures Designs and Isobaric Labeling. J Proteome Res 2023, 22 (8), 2641–2659. 10.1021/acs.jproteome.3c00155. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (209). Carnoli AJ; Lohuis P. oude; Buydens LMC; Tinnevelt GH; Jansen JJ. Linear Mixed-Effects Models in Chemistry: A Tutorial. Analytica Chimica Acta 2024, 1304, 342444. 10.1016/j.aca.2024.342444. [ DOI ] [ PubMed ] [ Google Scholar ] (210). Bafumi J; Gelman A Fitting Multilevel Models When Predictors and Group Effects Correlate. Social Science Research Network: Rochester, NY: September 3, 2007. 10.2139/ssrn.1010095. [ DOI ] [ Google Scholar ] (211). Ritchie ME; Phipson B; Wu D; Hu Y; Law CW; Shi W; Smyth GK Limma Powers Differential Expression Analyses for RNA-Sequencing and Microarray Studies. Nucleic Acids Res 2015, 43 (7), e47. 10.1093/nar/gkv007. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (212). Poncha KF; Paparella AT; Young NL Normalized and Directional Interplay Scoring for the Interrogation of Proteoform Data. bioRxiv 2024, 2024.11.18.624157. 10.1101/2024.11.18.624157. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (213). Poncha KF; Paparella AT; Young NL Normalized and Directional Interplay Scoring for the Interrogation of Proteoform Data. J Proteome Res 2025. 10.1021/acs.jproteome.4c00877. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (214). Grüning H. von; Coradin M; Mendoza MR; Reader J; Sidoli S; Garcia BA; Birkholtz L-M. A Dynamic and Combinatorial Histone Code Drives Malaria Parasite Asexual and Sexual Development. Molecular & Cellular Proteomics 2022, 0 (0). 10.1016/j.mcpro.2022.100199. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (215). Kossel A Ueber Einen Peptonartigen Bestandtheil Des Zellkerns. 1884, 8 (6), 511–515. 10.1515/bchm1.1884.8.6.511. [ DOI ] [ Google Scholar ] (216). Kossel A. (Albrecht); Thorpe WV. The Protamines and Histones; Monographs on biochemistry; Longmans, Green, 1928. [ Google Scholar ] (217). Allfrey VG; Faulkner R; Mirsky AE ACETYLATION AND METHYLATION OF HISTONES AND THEIR POSSIBLE ROLE IN THE REGULATION OF RNA SYNTHESIS*. Proc Natl Acad Sci U S A 1964, 51 (5), 786–794. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (218). Forgrave LM; Wang M; Yang D; DeMarco ML Proteoforms and Their Expanding Role in Laboratory Medicine. Pract Lab Med 2021, 28, e00260. 10.1016/j.plabm.2021.e00260. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (219). Allen DW; Schroeder WA; Balog J Observations on the Chromatographic Heterogeneity of Normal Adult and Fetal Human Hemoglobin: A Study of the Effects of Crystallization and Chromatography on the Heterogeneity and Isoleucine Content. J. Am. Chem. Soc. 1958, 80 (7), 1628–1634. 10.1021/ja01540a030. [ DOI ] [ Google Scholar ] (220). Bookchin RM; Gallop PM Structure of Hemoglobin AIc: Nature of the N-Terminal Beta Chain Blocking Group. Biochem Biophys Res Commun 1968, 32 (1), 86–93. 10.1016/0006-291x(68)90430-0. [ DOI ] [ PubMed ] [ Google Scholar ] (221). Rahbar S; Blumenfeld O; Ranney HM Studies of an Unusual Hemoglobin in Patients with Diabetes Mellitus. Biochem Biophys Res Commun 1969, 36 (5), 838–843. 10.1016/0006-291x(69)90685-8. [ DOI ] [ PubMed ] [ Google Scholar ] (222). Formichi P; Battisti C; Radi E; Federico A Cerebrospinal Fluid Tau, A Beta, and Phosphorylated Tau Protein for the Diagnosis of Alzheimer’s Disease. J Cell Physiol 2006, 208 (1), 39–46. 10.1002/jcp.20602. [ DOI ] [ PubMed ] [ Google Scholar ] (223). Sipe JD; Benson MD; Buxbaum JN; Ikeda S-I; Merlini G; Saraiva MJM; Westermark P Amyloid Fibril Proteins and Amyloidosis: Chemical Identification and Clinical Classification International Society of Amyloidosis 2016 Nomenclature Guidelines. Amyloid 2016, 23 (4), 209–213. 10.1080/13506129.2016.1257986. [ DOI ] [ PubMed ] [ Google Scholar ] (224). Ríos-Tamayo R; Paiva B; Lahuerta JJ; López JM; Duarte RF Monoclonal Gammopathies of Clinical Significance: A Critical Appraisal. Cancers (Basel) 2022, 14 (21), 5247. 10.3390/cancers14215247. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (225). Déjardin J; Kingston RE Purification of Proteins Associated with Specific Genomic Loci. Cell 2009, 136 (1), 175–186. 10.1016/j.cell.2008.11.045. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (226). Schachner LF; Jooβ K; Morgan MA; Piunti A; Meiners MJ; Kafader JO; Lee AS; Iwanaszko M; Cheek MA; Burg JM; Howard SA; Keogh M-C; Shilatifard A; Kelleher NL Decoding the Protein Composition of Whole Nucleosomes with Nuc-MS. Nat Methods 2021, 18 (3), 303–308. 10.1038/s41592-020-01052-9. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (227). Candia J; Fantoni G; Delgado-Peraza F; Shehadeh N; Tanaka T; Moaddel R; Walker KA; Ferrucci L Variability of 7K and 11K SomaScan Plasma Proteomics Assays. J Proteome Res 2024, 23 (12), 5531–5539. 10.1021/acs.jproteome.4c00667. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (228). Degnes M-HL; Westerberg AC; Andresen IJ; Henriksen T; Roland MCP; Zucknick M; Michelsen TM Protein Biomarker Signatures of Preeclampsia - a Longitudinal 5000-Multiplex Proteomics Study. Sci Rep 2024, 14 (1), 23654. 10.1038/s41598-024-73796-9. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (229). Scientific publications. Olink®. https://olink.com/knowledge/publications (accessed 2025-04-28). [ Google Scholar ] (230). Thermo Fisher Scientific’s Olink Platform Selected for World’s Largest Human Proteome Study. https://ir.thermofisher.com/investors/news-events/news/news-details/2025/Thermo-Fisher-Scientifics-Olink-Platform-Selected-for-Worlds-Largest-Human-Proteome-Study/default.aspx (accessed 2025-04-28). (231). Eldjarn GH; Ferkingstad E; Lund SH; Helgason H; Magnusson OT; Gunnarsdottir K; Olafsdottir TA; Halldorsson BV; Olason PI; Zink F; Gudjonsson SA; Sveinbjornsson G; Magnusson MI; Helgason A; Oddsson A; Halldorsson GH; Magnusson MK; Saevarsdottir S; Eiriksdottir T; Masson G; Stefansson H; Jonsdottir I; Holm H; Rafnar T; Melsted P; Saemundsdottir J; Norddahl GL; Thorleifsson G; Ulfarsson MO; Gudbjartsson DF; Thorsteinsdottir U; Sulem P; Stefansson K Large-Scale Plasma Proteomics Comparisons through Genetics and Disease Associations. Nature 2023, 622 (7982), 348–358. 10.1038/s41586-023-06563-x. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (232). tylerf@nautilus.bio. Tau proteoforms as potential drivers of cognitive decline. Nautilus Biotechnology. https://www.nautilus.bio/blog/tau-proteoforms-as-potential-drivers-of-cognitive-decline/ (accessed 2025-04-28). [ Google Scholar ] (233). Mallick P Single-Molecule Detection of Isoform-Specific Tau Phosphorylation. Alzheimer’s & Dementia 2022, 18 (S5), e065032. 10.1002/alz.065032. [ DOI ] [ Google Scholar ] (234). Skinner KA; Fisher TD; Lee A; Su T; Forte E; Sanchez A; Caldwell MA; Kelleher NL Next-Generation Protein Sequencing and Individual Ion Mass Spectrometry Enable Complementary Analysis of Interleukin-6. bioRxiv 2025, 2025.02.07.637157. 10.1101/2025.02.07.637157. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (235). Ouldali H; Sarthak K; Ensslen T; Piguet F; Manivet P; Pelta J; Behrends JC; Aksimentiev A; Oukhaled A Electrical Recognition of the Twenty Proteinogenic Amino Acids Using an Aerolysin Nanopore. Nat Biotechnol 2020, 38 (2), 176–181. 10.1038/s41587-019-0345-2. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (236). Sauciuc A; Morozzo della Rocca B; Tadema MJ; Chinappi M; Maglia G Translocation of Linearized Full-Length Proteins through an Engineered Nanopore under Opposing Electrophoretic Force. Nat Biotechnol 2024, 42 (8), 1275–1281. 10.1038/s41587-023-01954-x. [ DOI ] [ PubMed ] [ Google Scholar ] (237). Wang K; Zhang S; Zhou X; Yang X; Li X; Wang Y; Fan P; Xiao Y; Sun W; Zhang P; Li W; Huang S Unambiguous Discrimination of All 20 Proteinogenic Amino Acids and Their Modifications by Nanopore. Nat Methods 2024, 21 (1), 92–101. 10.1038/s41592-023-02021-8. [ DOI ] [ PubMed ] [ Google Scholar ] Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Supplementary Materials Supplementary File 1 NIHMS2135458-supplement-Supplementary_File_1.xlsx (21.3KB, xlsx) Data Availability Statement Not applicable ACTIONS View on publisher site PDF (2.6 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

Related documents

• Post‐translational Modifications in Proteins: Prediction Methods, Biological Functions, and Diseases.#4364
• MS_Lumos_2020_September#1025325
• Extending the Limits of Quantitative Proteome Profiling with Data-Independent Acquisition and Application to Acetaminophen-Treated Three-Dimensional Liver Microtissues#403259
• Discovery of New Amphipathic Helix Antibacterial Peptides with Proteomic Analysis using LC-HRMS and Virtual Screening#194512
• Processive DNA helicase activity of the minichromosome maintenance proteins 4, 6, and 7 complex requires forked DNA structures#646561