Molecular & Cellular Proteomics

Late maturity alpha-amylase (LMA) is a wheat genetic defect causing the synthesis of high isoelectric point (pI) alpha-amylase in the aleurone as a result of a temperature shock during mid-grain development or prolonged cold throughout grain development leading to an unacceptable low falling numbers (FN) at harvest or during storage. High pI alpha-amylase is normally not synthesized until after maturity in seeds when they may sprout in response to rain or germinate following sowing the next seasons crop. Whilst the physiology is well understood, the biochemical mechanisms involved in grain LMA response remain unclear. We have employed high-throughput proteomics to analyse thousands of wheat flours displaying a range of LMA values. We have applied an array of statistical analyses to select LMA-responsive biomarkers and we have mined them using a suite of tools applicable to wheat proteins. To our knowledge, this is not only the first proteomics study tackling the wheat LMA issue, but also the largest plant-based proteomics study published to date. Logistics, technicalities, requirements, and bottlenecks of such an ambitious large-scale high-throughput proteomics experiment along with the challenges associated with big data analyses are discussed. We observed that stored LMA-affected grains activated their primary metabolisms such as glycolysis and gluconeogenesis, TCA cycle, along with DNA- and RNA binding mechanisms, as well as protein translation. This logically transitioned to protein folding activities driven by chaperones and protein disulfide isomerase, as wellas protein assembly via dimerisation and complexing. The secondary metabolism was also mobilised with the up-regulation of phytohormones, chemical and defense responses. LMA further invoked cellular structures among which ribosomes, microtubules, and chromatin. Finally, and unsurprisingly, LMA expression greatly impacted grain starch and other carbohydrates with the up-regulation of alpha-gliadins and starch metabolism, whereas LMW glutenin, stachyose, sucrose, UDP-galactose and UDP-glucose were down-regulated. This work demonstrates that proteomics deserves to be part of the wheat LMA molecular toolkit and should be adopted by LMA scientists and breeders in the future.

Acute kidney injury (AKI) manifests as a major health concern, particularly for the elderly. Understanding AKI-related proteome changes is critical for prevention and development of novel therapeutics to recover kidney function and to mitigate the susceptibility for recurrent AKI or development of chronic kidney disease. In this study, mouse kidneys were subjected to ischemia-reperfusion injury, and the contralateral kidneys remained uninjured to enable comparison and assess injury-induced changes in the kidney proteome. A fast-acquisition rate ZenoTOF 7600 mass spectrometer was introduced for data-independent acquisition (DIA) for comprehensive protein identification and quantification. Short microflow gradients and the generation of a deep kidney-specific spectral library allowed for high-throughput, comprehensive protein quantification. Upon AKI, the kidney proteome was completely remodeled, and over half of the 3,945 quantified protein groups changed significantly. Downregulated proteins in the injured kidney were involved in energy production, including numerous peroxisomal matrix proteins that function in fatty acid oxidation, such as ACOX1, CAT, EHHADH, ACOT4, ACOT8, and Scp2. Injured mice exhibited severely declined health. The comprehensive and sensitive kidney-specific DIA assays highlighted here feature high-throughput analytical capabilities to achieve deep coverage of the kidney proteome and will serve as useful tools for developing novel therapeutics to remediate kidney function.

Global high-throughput profiling of oncogenic signaling pathways by phosphoproteomics is increasingly being applied to cancer specimens. Such quantitative unbiased phosphoproteomic profiling of cancer cells identifies oncogenic signaling cascades that drive disease initiation and progression; pathways that are often invisible to genomics sequencing strategies. Therefore, phosphoproteomic profiling has immense potential for informing individualized anti-cancer treatments. However, complicated and extensive sample preparation protocols, coupled with intricate chromatographic separation techniques that are necessary to achieve adequate phosphoproteomic depth, limits the clinical utility of these techniques. Traditionally, phosphoproteomics is performed using isobaric tagged based quantitation coupled with TiO2 enrichment and offline prefractionation prior to nLC-MS/MS. However, the use of isobaric tags and offline HPLC limits the applicability of phosphoproteomics for the analysis of individual patient samples in real-time. To address these limitations, here we have optimized a new protocol, phospho-Heavy-labeled-spiketide FAIMS Stepped-CV DDA (pHASED). pHASED maintained phosphoproteomic coverage yet decreased sample preparation time and complexity by eliminating the variability associated with offline prefractionation. pHASED employed online phosphoproteome deconvolution using high-field asymmetric waveform ion mobility spectrometry (FAIMS) and internal phosphopeptide standards to provide accurate label-free quantitation data. Compared with our traditional tandem mass tag (TMT) phosphoproteomics workflow and optimized using isogenic FLT3-mutant acute myeloid leukemia (AML) cell line models (n=18/workflow), pHASED halved total sample preparation, and running time (TMT=10 days, pHASED=5 days) and doubled the depth of phosphoproteomic coverage in real-time (phosphopeptides = 7,694 pHASED, 3,861 TMT). pHASED coupled with bioinformatic analysis predicted differential activation of the DNA damage and repair ATM signaling pathway in sorafenib-resistant AML cell line models, uncovering a potential therapeutic opportunity that was validated using cytotoxicity assays. Herein, we optimized a rapid, reproducible, and flexible protocol for the characterization of complex cancer phosphoproteomes in real-time, highlighting the potential for phosphoproteomics to aid in the improvement of clinical treatment strategies.

While altered protein glycosylation is regarded a trait of oral squamous cell carcinoma (OSCC), its heterogeneous glycoproteome and dynamics with disease progression remain unmapped. To this end, we here employ an integrated multi-omics approach comprising unbiased and quantitative glycomics and glycoproteomics applied to a valuable cohort of resected tumour tissues from OSCC patients with (n = 19) and without (n = 12) lymph node metastasis. While all tumour tissues displayed uniform N-glycome profiles suggesting relatively stable global N-glycosylation during lymph node metastasis, glycoproteomics and advanced correlation analysis notably uncovered altered site-specific N-glycosylation and previously unknown associations with several key clinicopathological features. Importantly, focused analyses of the multi-omics data unveiled two N-glycans and three N-glycopeptides that were closely associated with patient survival. This study provides novel insight into the complex OSCC tissue N-glycoproteome forming an important resource to further explore the underpinning disease mechanisms and uncover new prognostic glyco-markers for OSCC. TeaserDeep survey of the dynamic landscape of complex sugars in oral tumours paves a way for new prognostic disease markers.

Intercellular communication between T cells and cancer cells plays a pivotal role in determining cancer cell survival or death. Yet, our understanding of this interaction remains incomplete. Methods to study heterotypic cell interactions are either limited to targeted studies relying on predefined set of proteins, or require cell separation, thus disrupting the native environment. Stable isotope labeling by amino acids in cell culture (SILAC) enables proteome distinction in heterologous co-cultures without the need for physical separation. But dynamic studies remain constrained by the need for numerous mass spectrometry (MS) runs, the challenges in detecting low-abundant proteins, particularly in immune cells and the limited data completeness due to the use of data-dependent MS1-based precursor quantification. To overcome these limitations, we evaluate the integration of SILAC with tandem mass tag (TMT) multiplexing and SILAC-directed real-time search (RTS). TMT labeling enables simultaneous analysis of multiple samples, while RTS-MS3 acquisition using SILAC-induced mass shifts as fixed modifications triggers MS3 scans for specific proteome populations within a mixed cell system, improving quantitative accuracy and proteome coverage for target protein populations. We benchmarked our acquisition methods using SILAC-labeled samples mixed at defined ratios and validated the approach in biologically relevant co-culture experiments. Additionally, we introduced a carrier channel to enhance detection of lower-abundant T cell proteins, while maintaining acceptable quantitative precision. Our results demonstrate that the combined SILAC-TMT-RTS strategy dramatically improves proteome depth, temporal resolution, and cell-type specificity for short-term co-culture interaction proteomics studies. In co-culture samples of T cells with non-small cell lung cancer cell lines that were either sensitive or resistant to T cell killing, our method revealed candidate mechanisms underlying their differential sensitivity. Our integrated approach combining SILAC, TMT and RTS to resolve cell-specific proteome dynamics in co-culture represents a novel and powerful advance.

Although current mass spectrometry (MS)-based proteomics identifies and quantifies thousands of proteins and (modified) peptides, only a minority of them are subjected to in-depth downstream analysis. With the advent of automated processing workflows, biologically or clinically important results within a study are rarely validated by visualization of the underlying raw information. Current tools are often not integrated into the overall analysis nor readily extendable with new approaches. To remedy this, we developed AlphaViz, an open-source Python package to superimpose output from common analysis workflows on the raw data for easy visualization and validation of protein and peptide identifications. AlphaViz takes advantage of recent breakthroughs in the deep learning-assisted prediction of experimental peptide properties to allow manual assessment of the expected versus measured peptide result. We focused on the visualization of the 4-dimensional data cuboid provided by Bruker TimsTOF instruments, where the ion mobility dimension, besides intensity and retention time, can be predicted and used for verification. We illustrate how AlphaViz can quickly validate or invalidate peptide identifications regardless of the score given to them by automated workflows. Furthermore, we provide a predict mode that can locate peptides present in the raw data but not reported by the search engine. This is illustrated the recovery of missing values from experimental replicates. Applied to phosphoproteomics, we show how key signaling nodes can be validated to enhance confidence for downstream interpretation or follow-up experiments. AlphaViz follows standards for open-source software development and features an easy-to-install graphical user interface for end-users and a modular Python package for bioinformaticians. Validation of critical proteomics results should now become a standard feature in MS-based proteomics.

Proteins are the main drivers of cell function and disease, making their analysis a powerful technique to characterize determinants of cell identity and to identify biomarkers. Current proteomic technology has the breadth to profile thousands of proteins and even the sensitivity to access single cells, however limitations in throughput restrict its application, e.g. not allowing classification of samples according to biological or clinical status in large sample cohorts. Therefore, we developed a deep learning-based approach for the analysis of mass spectrometric (MS) data, assigning proteomic profiles to sample identity. Specifically, we designed an architecture referred to as Proformer, and show that it is superior to convolutional neural network-driven architectures, is explainable, and demonstrates robustness towards batch-effects. Based on its tabular approach, we highlight the integration of all four dimensions of proteomic measurements (retention time, mass-to-charge, intensity and ion mobility), and demonstrate enhanced sample discrimination involving a treatment with IFN-{gamma}, despite its subtle effect on the cells proteome. In addition, the Proformer is not restricted to proteomic depth, and can classify cells by cell type and their differentiation status even using single-cell proteomic data. Collectively, this work presents a novel deep learning-based model for rapid classification of proteomic data, with important future implications to enhance patient stratification, early detection and single-cell analysis.

N-glycosylation plays essential roles in the folding, trafficking, and maturation of proteins in the secretory pathways, but how individual protein and residue glycosylation rewires under endoplasmic reticulum (ER) stress is unknown. Particularly, intact glycopeptide data that retain the connectivity between glycosylation sites and the attached glycans are needed to reveal the micro- and macro- heterogeneity of N-glycosylation sites and their permutations in stressed cells. Here, we developed an optimized magnetic polyethyleneimine boronic acid-containing scaffold (mPBA) enrichment workflow to achieve sensitive and broad enrichment of intact glycoproteins for mass spectrometry analysis, quantifying 13759 unique protein-, site-, and glycoform combinations, termed glycopeptidoforms, in normal and stressed cells while requiring only 0.1 to 0.5 mg total peptide input. The data reveals a systems-level shift in the fate of hundreds of glycoproteins. N-glycosylation changes are highly dynamic, with magnitude far exceeding protein expression changes, and showing complex protein-, site-, and glycan-specific granularity. Individual glycoform reconfigurations can be observed that suggest lesions within specific steps in protein maturation and trafficking pathways. Mannose trimming is disrupted across multiple proteins and cell states, suggesting a central feature of ER stress mediated glycoproteome remodeling. Together, these results reveal molecular details into the remodeling of protein secretory pathways upon ER stress and highlight the utility of mPBA for sensitive N-glycoproteomics studies.

A major limitation when undertaking quantitative proteomic time-course experimentation is the tradeoff between depth-of-analysis and speed-of-analysis. In high complexity and high dynamic range sample types, such as plant extracts, balance between resolution and time is especially apparent. To address this, we evaluate multiple composition voltage (CV) High Field Asymetric Waveform Ion Mobility Spectrometry (FAIMSpro) settings using the latest label-free single-shot Orbitrap-based DIA acquisition workflows for their ability to deeply-quantify the Arabidopsis thaliana seedling proteome. Using a BoxCarDIA acquisition workflow with a -30 -50 -70 CV FAIMSpro setting we are able to consistently quantify >5000 Arabidopsis seedling proteins over a 21-minute gradient, facilitating the analysis of ~42 samples per day. Utilizing this acquisition approach, we then quantified proteome-level changes occurring in Arabidopsis seedling shoots and roots over 24 h of salt and osmotic stress, to identify early and late stress response proteins and reveal stress response overlaps. Here, we successfully quantify >6400 shoot and >8500 root protein groups, respectively, quantifying nearly ~9700 unique protein groups in total across the study. Collectively, we pioneer a short gradient, multi-CV FAIMSpro BoxCarDIA acquisition workflow that represents an exciting new analysis approach for undertaking quantitative proteomic time-course experimentation in plants.

Fully automated proteomic pipelines have the potential to achieve deep coverage of cellular proteomes with high throughput and scalability. However, it is important to evaluate performance, including both reproducibility and ability to provide meaningful levels of biological insight. Here, we present an approach combining high field asymmetric waveform ion mobility spectrometer (FAIMS) interface and data independent acquisition (DIA) proteomics approach developed as part of the induced pluripotent stem cell (iPSC) Neurodegenerative Disease Initiative (iNDI), a large-scale effort to understand how inherited diseases may manifest in neuronal cells. Our FAIMS-DIA approach identified more than 8000 proteins per mass spectrometry (MS) acquisition as well as superior total identification, reproducibility, and accuracy compared to other existing DIA methods. Next, we applied this approach to perform a longitudinal proteomic profiling of the differentiation of iPSC-derived neurons from the KOLF2.1J parental line used in iNDI. This analysis demonstrated a steady increase in expression of mature cortical neuron markers over the course of neuron differentiation. We validated the performance of our proteomics pipeline by comparing it to single cell RNA-Seq datasets obtained in parallel, confirming expression of key markers and cell type annotations. An interactive webapp of this temporal data is available for aligned-UMAP visualization and data browsing (https://share.streamlit.io/anant-droid/singlecellumap). In summary, we report an extensively optimized and validated proteomic pipeline that will be suitable for large-scale studies such as iNDI.

On-bead single-pot solid-phase enhanced sample preparation, SP3, also known as Protein Aggregation Capture (PAC), is a robust, high-throughput, and widely utilized approach for proteomic sample preparation. Recent studies have highlighted PAC/SP3 as an ideal platform for chemoproteomics, allowing chemical labelling by minimizing sample loss and improving recovery of derivatized peptides. In this work, we establish an on-bead PAC/SP3 protein-level amine and carboxyl derivatization approach to facilitate C-terminal focused proteomics. We demonstrate that on-bead protein derivatization of carboxyl groups can be achieved using ethanolamine, (2-aminoethyl)trimethylammonium (AETMA), and (carboxymethyl)trimethylammonium (Girards reagent T, GT) via EDC/HOBt coupling, enabling the labelling of protein C-termini. Using a prokaryotic model system, Acinetobacter baumannii, we demonstrate that AETMA and ethanolamine labelling each enables the identification of unique protein C-terminal peptides, with AETMA improving the identification of C-terminal peptides lacking basic residues. Finally, we apply this approach to interrogate both N- and C-termini in response to etoposide-induced apoptosis within Jurkat cells, demonstrating that combined N- and C-terminomics is achievable using on-bead derivatization, yet provides modest coverage of the C-terminome in its current form. Overall, this work establishes bead-based carboxyl group derivatization as a viable platform to enable future C-terminomics method development.

Imaging of proteoforms in human tissues is hindered by low molecular specificity and limited proteome coverage. Here, we introduce proteoform imaging mass spectrometry (PiMS), which increases the size limit for proteoform detection and identification by 4-fold compared to reported methods, and reveals tissue localization of proteoforms at <80 m spatial resolution. PiMS advances proteoform imaging by combining ambient nanospray desorption electrospray ionization (nano-DESI) with ion detection using individual ion mass spectrometry (I2MS). We demonstrate the first proteoform imaging of human kidney, identifying 169 of 400 proteoforms <70 kDa using top-down mass spectrometry and database lookup from the human proteoform atlas, including dozens of key enzymes in primary metabolism. PiMS images reveal distinct spatial localizations of proteoforms to both anatomical structures and cellular neighborhoods in the vasculature, medulla, and cortex regions of the human kidney. The benefits of PiMS are poised to increase proteome coverage for label-free protein imaging of tissues. TeaserNano-DESI combined with individual ion mass spectrometry generates images of proteoforms up to 70 kDa.

Mass spectrometry (MS)-based immunopeptidomics is a powerful approach for untargeted discovery of peptides presented on major histocompatibility complex (MHC) molecules, which can guide the selection of vaccine antigens and immunotherapy targets. First-generation immunopeptidomics workflows require processing of hundreds of millions of cells using lengthy, manual procedures. More recent approaches focus on increasing either sensitivity or throughput, but rarely combine both aspects. Here, we describe a semi-automated immunopeptidomics platform that combines high sensitivity with high throughput by implementing highly optimized conditions for immunoprecipitation, elution and purification of MHC class I and II peptides on a 96-well positive-pressure device. Upon analysis of 25% of the eluate from 16 million cells, our workflow identified over 13,500 MHC I and 6,000 MHC II peptides on a timsTOF SCP mass spectrometer, operating in DDA-PASEF mode. Exploring the sensitivity limits of our platform, we identified over 1,000 MHC I peptides from as few as 20,000 JY cells. Validating the platforms performance for quantitative biological discovery, we report the identification of known and novel bacterial immunopeptides from U937 macrophages infected with Listeria monocytogenes or Bacillus Calmette-Guerin (BCG). Together, our optimized immunopeptidomics platform enables robust immunopeptide detection from lower-input samples in a high-throughput fashion, enabling its use for biological applications where sample amounts are limiting.

Proteome analysis by data-independent acquisition (DIA) has become a powerful approach to obtain deep proteome coverage, and has gained recent traction for label-free analysis of single cells. However, optimal experimental design for DIA-based single-cell proteomics has not been fully explored, and performance metrics of subsequent data analysis tools remain to be evaluated. Therefore, we here present DIA-ME, a data analysis strategy that exploits the co-analysis of low-input samples with a so-called matching enhancer (ME) of higher input, to increase sensitivity, proteome coverage, and data completeness. We evaluate the matching specificity of DIA-ME by a two-proteome model, and demonstrate that false discovery and false transfer are maintained at low levels when using DIA-NN software, while preserving quantification accuracy. We apply DIA-ME to investigate the proteome response of U-2 OS cells to interferon gamma (IFN-{gamma}) in single cells, and recapitulate the time-resolved induction of IFN-{gamma} response proteins as observed in bulk material. Moreover, we observe co- and anti-correlating patterns of protein expression within the same cell, indicating mutually exclusive protein modules and the co-existence of different cell states. Collectively our data show that DIA-ME is a powerful, scalable, and easy-to- implement strategy for single-cell proteomics.

Oral leukoplakias (OLs) are premalignant lesions that can progress into oral squamous cell carcinoma (OSCC). This study hypothesized that tear fluid, as a noninvasive biofluid, reflects proteomic alterations associated with malignant transformation. The tear proteome of 44 individuals, including healthy controls, OL/PVL (proliferative verrucous leukoplakia), and OSCC patients, was deeply profiled, revealing 828 protein groups clustered according to histopathological alterations. N-glycoproteome analysis identified immune-related proteins, while public RNA-seq integration indicated immune imbalance marked by increased B-cell and decreased macrophage signatures during disease progression. Several immune-associated proteins and epithelial markers, including desmoplakin, KRT14, and DSC1, emerged as potential indicators of malignant transformation. These findings demonstrate that tear fluid reflects oral carcinogenic processes, thereby serving as a noninvasive liquid biopsy for early detection and clinical monitoring.

It is increasingly recognized that the omic analysis of whole blood has applications for precision medicine and disease phenotyping. Despite this realization, whole blood is generally viewed as a challenging analytical matrix in comparison to plasma or serum. Moreover, proteomic analyses of whole blood proteomics have almost exclusively focused on (non)targeted analyses of protein abundances and much less on post-translational modifications (PTMs). Here, we developed a streamlined workflow for processing twenty microliters of venous blood collected by volumetric absorptive microsampling that incorporates serial trypsinization, N-glycopeptide and phosphopeptide enrichment and avoids laborious sample dry-down or cleanup steps. Up to 10,000 analytes (reported as protein groups, glycopeptidoforms and phosphosites) were quantified by liquid chromatography-tandem mass spectrometry (LC-MS/MS) in approximately 2 h of MS acquisition time. Using these methods, we explored the stability of "dried" and "wet" blood proteomes, as well as effects of ex vivo inflammatory stimulus or phosphatase inhibition. Multi-omics factor analysis enabled facile identification of analytes that contributed to inter-individual variability of the blood proteomes, including N-glycopeptides that distinguish immunoglobulin heavy constant alpha 2 allotypes. Collectively, our results help to establish feasibility and best practices for the integrated MS-based quantification of proteins and PTMs from dried blood.

Chimeric antigen receptors (CARs) are synthetic biomolecules comprised of an extracellular antigen recognition domain and intracellular signaling domains. When expressed in immune cells, CARs direct their host cells to kill diseased cells expressing the antigen recognized by the CAR. Although signaling pathways downstream of CAR activation control the cytotoxic function of CAR-expressing cells, phospho-proteomic studies of CAR signaling have been limited. Most approaches have used antibodies or soluble ligands, rather than cell-displayed antigens, to activate CAR signaling. Here, we demonstrate an efficient and cost-effective label-free phospho-proteomic approach to analyze CAR signaling in immune cells stimulated with antigen-presenting cancer cells. Following co-culture of CAR-T cells with cancer cells, we first preserve phospho-signaling by cross-linking proteins with formalin. Then, we use magnet-activated cell sorting (MACS) to isolate CAR-T cells from the co-culture. Validation experiments demonstrated that formalin fixation did not alter the phospho-proteome and that MACS achieved >90% CAR-T cell purity. Next, we compared the phospho-proteome in CAR-T cells stimulated with either CD19-expressing or non-CD19-expressing SKOV3 ovarian cancer cells. This analysis revealed that CAR signaling activated known pathways including the mitogen- activated protein kinases (MAPKs) ERK1/2. Bioinformatic approaches further showed that CAR activation induced other signaling pathways including the MAPK p38, protein kinase A, and checkpoint kinase 1 (CHK1). Taken together, this work presents an easy and inexpensive method to better understand CAR immunotherapy by label-free phospho-proteomic analysis of CAR signaling in immune cells stimulated by antigen- presenting cancer cells.

Protein secretion is a fundamental mechanism for cellular coordination and signalling, with its dysregulation leading to widespread physiological dysfunction and disease. Immunoassay formats that utilise secondary antibody readouts are the current gold standard for measuring secreted proteins, offering high specificity and sensitivity, but relying on predefined protein panels that constrain the discovery of novel biology. We present a scalable mass spectrometry-based workflow that combines data-independent acquisition with ion mobility and parallel fragmentation to deliver rapid, global profiling of the secretome. Using a translationally relevant human iPSC-derived macrophage model, our approach identified over 1200 proteins in under 15 minutes of acquisition time, delivering exceptional reproducibility across a large sample set. We applied this approach to profile pro-inflammatory phenotypes, confirming robust identification of key cytokines and chemokines whilst revealing non-canonical immune responses absent from both targeted panels and the intracellular proteome. In particular, we identified a unique cholesterol efflux signature, marked by the secretion of APOA1 and PON1, in response to Mycobacterium Tuberculosis, consistent with the metabolic reprogramming that takes place during infection. Furthermore, temporal profiling of macrophage responses to lipopolysaccharide over 24 hours resolved dynamic secretion trajectories that distinguish between acute and chronic inflammatory states. The extended time period facilitated the observation of distinct cytokine-dependent secretion phenotypes, with early secretion of TNF and IL6 initiating downstream signalling cascades that resulted in the delayed secretion of chemokines such as CXCL10 and CCL8. Collectively, these findings establish a robust, scalable platform for global characterisation of secretory networks. Beyond macrophage biology, this workflow offers broad utility for biomarker discovery, mechanistic studies of disease progression and evaluation of new therapeutic interventions, providing a powerful tool for advancing precision medicine.

Single-cell proteomics has emerged as a powerful method to characterize cellular phenotypic heterogeneity and the cell-specific functional networks underlying biological processes. However, significant challenges remain in single-cell proteomics for the analysis of proteoforms arising from genetic mutations, alternative splicing, and post-translational modifications. Herein, we have developed a highly sensitive functionally integrated top-down proteomics method for the comprehensive analysis of proteoforms from single cells. We applied this method to single muscle fibers (SMFs) to resolve their heterogeneous functional and proteomic properties at the single cell level. Notably, we have detected single-cell heterogeneity in large proteoforms (>200 kDa) from the SMFs. Using SMFs obtained from three functionally distinct muscles, we found fiber-to-fiber heterogeneity among the sarcomeric proteoforms which can be related to the functional heterogeneity. Importantly, we reproducibly detected multiple isoforms of myosin heavy chain (~223 kDa), a motor protein that drives muscle contraction, with high mass accuracy to enable the classification of individual fiber types. This study represents the first "single-cell" top-down proteomics analysis that captures single muscle cell heterogeneity in large proteoforms and establishes a direct relationship between sarcomeric proteoforms and muscle fiber types, highlighting the potential of top-down proteomics for uncovering the molecular underpinnings of cell-to-cell variation in complex systems. Significance StatementSingle-cell technologies are revolutionizing biology and molecular medicine by allowing direct investigation of the biological variability among individual cells. Top-down proteomics is uniquely capable of dissecting biological heterogeneity at the intact protein level. Herein, we develop a highly sensitive single-cell top-down proteomics method to reveal diverse molecular variations in large proteins (>200 kDa) among individual single muscle cells. Our results both reveal and characterize the differences in protein post-translational modifications and isoform expression possible between individual muscle cells. We further integrate functional properties with proteomics and accurately measure myosin isoforms for individual muscle fiber type classification. Our study highlights the potential of top-down proteomics for understanding how single-cell protein heterogeneity contributes to cellular functions.

The growing volume of public proteomics datasets and the advent of novel machine learning (ML)-based methods create unprecedented opportunities for discovery through large-scale reanalysis. However, traditional desktop tools are increasingly insufficient for processing and integrating data at this scale. To address this challenge, we present a novel package, quantms-rescoring, that extends the cloud-native quantms workflow with a machine learning-based rescoring module. Unlike prior tools that rescore single-engine outputs, quantms-rescoring seamlessly integrates multiple search engines (SAGE, COMET, and MSGF+), performs automatic model selection, model fine-tuning, and scales reproducibly on cloud infrastructures. In quantms-rescoring, we rely on multiple fragment-ion intensity (AlphaPeptDeep and MS2PIP) and retention-time prediction (DeepLC) methods to improve results from multiple peptide database search engines. It features automatic model selection, fine-tuning, and retraining for MS/MS intensity and retention time prediction to select the best model for a given dataset. We applied the novel workflow to five representative datasets spanning DDA label-free quantification, TMT 10-plex isobaric labelling of tumor proteomics data, immunopeptidomics, phospho-proteomics, and unseen lysine malonylation experiments. We achieved a 16-22.8% increase in identified spectra, along with the quantification of 2191 additional phosphorylated peptides and 1337 phosphosites. In the tandem mass tag (TMT)-labeled clear cell renal cell carcinoma dataset, 76 novel differentially expressed multiple search engines identified proteins with quantms-rescoring. Additionally, novel 11,688 HLA-II potential binders were detected in the immunopeptidomics dataset by multiple search engines with quantms-rescoring. For unseen malonylation data, we reported more than 58.8% malonylation PSMs and 30.5% modification sites than COMET alone. Together, these results show that integrating multi-engine searches with machine learning-derived features can be combined in a scalable workflow that enhances identification, PTM localization, and quantification performance.