Biology subjects

Meyer, J. G.

Publications and source records attributed to Meyer, J. G..

At least 19 recordsLinked to original sources

bioRxiv2026

Temporal Proteomics Links the APP London Mutation to Early Cholesterol Remodeling Cell Programs in Human Neurons

Familial Alzheimer's disease (AD) mutations in amyloid precursor protein (APP) alter amyloid-beta secretion profiles, but their effects on neuronal protein and lipid homeostasis remain incompletely understood. To identify early, cell-autonomous phenotypes associated with AD, we used isogenic KOLF2.1J iPSC-derived NGN2 neurons (iNeurons) carrying a heterozygous or homozygous APP-London mutation (V717I) and conducted a longitudinal proteomic study. Results revealed that maturation was the dominant source of proteomic variation, while genotype-dependent effects were pronounced in homozygous APP-London iNeurons. At four weeks, homozygous APP-London iNeurons showed increased APP abundance by proteomics and immunoblot, and increased alpha-CTF signal by immunoblotting. However, alpha-CTF/FL-APP and alpha-CTF/FL-APP ratios from immunoblot did not differ significantly from revertant controls, leaving preferential CTF accumulation unresolved and instead indicating increased steady-state abundance of APP-derived species. Proteomics remodeling in APP-London mutants converged on synaptic, endosomal, and calcium signaling pathways. Notably, homozygous APP-London iNeurons showed a distinct accumulation of cholesterol-biosynthetic enzymes and altered abundance of cholesterol transporters, an increased free/total cellular cholesterol without corresponding induction of SREBP2 target transcripts Pharmacological perturbation of cholesterol metabolism altered A beta 40 and Abeta 42 secretion in a compound-specific manner and produced minimal changes in bulk cellular cholesterol, indicating that A beta secretion was not simply determined by total cholesterol abundance. Finally, we identified SYT4 as a potential gamma-secretase target or associated factor that warrants further validation. These findings identify cholesterol dyshomeostasis as an early metabolic phenotype of iNeurons carrying the APP-London mutation, extending the molecular phenotype beyond altered A beta secretion.

neuroscience↗

bioRxiv2026

The p53-p21-Cyclin D2 regulatory axis drives metabolic reprogramming and a distinct senescent macrophage senotype during aging and MASLD.

Aging drives chronic disease in part through senescent cells, including macrophages, which fuel inflammation. Senescent macrophages are functionally heterogeneous: canonical p16-high macrophages promote tumorigenesis or, in other contexts, disease tolerance, whereas we previously identified a distinct p21-high, p16-low senotype that drives metabolic dysfunction-associated steatotic liver disease (MASLD). The molecular basis of this senotype has remained undefined. Using genetic and multi-omic approaches, we show that a p53-p21-dependent program actively represses p16 and is required for senescent macrophage viability. We identify Cyclin D2 as a non-canonical downstream effector that redistributes from the nucleus to mitochondria and lipid droplets, where it partners with MIC60 to drive metabolic reprogramming and modulates AKT1-mTORC1 signaling that sustains the SASP. Cyclin D2-p21 double-positive macrophages accumulate with aging and MASLD in mice and in human liver cirrhosis, and can be selectively depleted by senolytic treatment. Together, these findings define a druggable p53-p21-Cyclin D2 axis that specifies macrophage senotype.

immunology↗

bioRxiv2026

Robotic perturbation proteomics and AI agents enable scalable drug mechanism discovery

Large-scale mass spectrometry-based proteomic screening could reveal cellular mechanisms of drug action at systems resolution but remains limited by experimental complexity and the difficulty of extracting insight from high-dimensional datasets. Here, we describe an end-to-end platform that combines semi-automated sample preparation, rapid LC-MS/MS, and AI agent-based data analysis to enable scalable proteomic screening. In a screen of 172 compounds in HepG2 cells, we generated 1,232 proteomes with more than 8,700 quantified proteins in approximately three weeks. Agentic AI reduced data analysis and interpretation time to less than one day while translating proteomic measurements into structured mechanism-oriented summaries and experimentally testable hypotheses. Guided by this framework, we validated: (1) a cholesterol-lowering effect of methylene blue in vitro and (2) an association between loratadine exposure and increased circulating iron in matched electronic health record analyses. This work establishes a scalable platform for generating proteomic drug perturbation data and automatically converting that data into mechanistic insights and candidate translational hypotheses using AI.

systems biology↗

bioRxiv2026

Synergistic retinal UCHL1 dysregulation and synaptic vulnerability reflect Alzheimer's disease severity

Synaptic failure predicts cognitive decline in Alzheimers disease (AD), yet its impact and molecular drivers in the human retina remain unclear. Leveraging the retina as an accessible central nervous system (CNS) proxy, we integrated spatially resolved histopathology of retinal cross-sections with ultrastructural, proteomic, and biochemical profiling across independent postmortem cohorts spanning normal cognition, mild cognitive impairment due to AD (MCI), and AD dementia. We uncover early, progressive degeneration of excitatory glutamatergic synapses, evidenced by losses of presynaptic vesicular glutamate transporter 1 (VGLUT1) and synaptophysin, and postsynaptic density protein 95 (PSD95) and N-methyl-D-aspartate receptor subunit 2A (NMDAR2A), accompanied by disruption of synaptic ultrastructure. Retinal synaptic loss tightly associates with local accumulation of amyloid-{beta} 42 (A{beta}42) and immature tau species, heightened oxidative stress, and upregulation of the A{beta}-binding death receptor p75 neurotrophin receptor (p75NTR). Notably, the synapse-enriched deubiquitinase ubiquitin C-terminal hydrolase L1 (UCHL1) is profoundly dysregulated, correlates with synaptic integrity and cognition, and emerges as the strongest retinal predictor of Braak stage and cognitive status in multivariable machine-learning models. Together, these findings position retinal A{beta}/p75NTR-mediated UCHL1 imbalance as a proteostasis-synapse mechanistic hub and candidate biomarker reflecting AD severity.

neuroscience↗

bioRxiv2025

Identification of Chlamydia pneumoniae and NLRP3 inflammasome activation in Alzheimer's disease retina

Chlamydia pneumoniae (Cp), an obligate intracellular bacterium, has been implicated in Alzheimers disease (AD), yet its role in retinal pathology remains unexplored. We analyzed postmortem tissues from 95 human donors and found 2.9-4.1-fold increases in Cp inclusions in AD retinas and brains, with no significant elevation in mild cognitive impairment (MCI). Proteomics revealed dysregulation of retinal and brain bacterial infection-related proteins and NLRP3 inflammasome pathways. NLRP3 expression was markedly elevated in MCI and AD retinas, and its activation was evident by increased N-terminal gasdermin D (NGSDMD) and mature interleukin-1{beta}. Retinal Cp strongly correlated with A{beta}42 and NLRP3 inflammasome components, which tightly linked to cleaved caspase-3-apoptotic and NGSDMD-pyroptotic cell death. Although retinal microgliosis was elevated in AD, Cp-associated microglia were reduced by 62%, suggesting impaired Cp phagocytosis. Higher retinal Cp burden correlated with APOE{varepsilon}4, Braak stage, and cognitive deficit. Machine learning identified retinal Cp or NLRP3 combined with A{beta}42 as strong predictors of AD diagnosis, staging, and cognitive impairment. Our findings suggest that Cp infection contributes to AD dementia but not initiating pathology, whereas early NLRP3 activation may promote disease development, warranting studies on Cps role in AD pathogenesis and early antibiotic or inflammasome-targeted therapies.

neuroscience↗

bioRxiv2025

Antibiotics Rewire Core Metabolic and Ribosomal Programs in Mammalian Cells

Antibiotics are routinely added to mammalian cell culture media to prevent bacterial growth. However, the use of antibiotics in cell culture can confound downstream experimental results. While genomic and transcriptomic differences between cell cultures treated with and without antibiotics are well-documented, far fewer, if any, comprehensive proteomic comparisons on the use of antibiotics in cell culture have been performed. Here, we present a study on the proteome-wide differences of culturing HepG2 cells in antibiotic (i.e., penicillin/streptomycin) and non-antibiotic-containing media. Using a longitudinal and crossover treatment study design, we analyzed 119 samples across nine passages and four conditions. On average, 9,374 proteins were detected per sample, and we identified 383 proteins that were differentially abundant between conditions. These changes included ribosomal and mitochondrial proteins, demonstrating that off-target effects of antibiotics on mammalian cells occur at the protein level. Linear mixed-effects modeling suggested that the proteomic impact of antibiotic treatment is strongest in the first passage after treatment and stabilizes after approximately three passages. Furthermore, initiating antibiotic treatment induced a greater number of differentially abundant proteins than discontinuing treatment. Lastly, we compared our results to existing literature on the use of common antibiotics in mammalian cell culture. We identified proteins and pathways conserved across studies, -omics layers, and cell types. We hope that this detailed proteomic survey will aid researchers in comparing cross-study or cross-condition results from antibiotic-treated mammalian cells and inform appropriate experimental designs for the use of antibiotics in cell culture. O_FIG O_LINKSMALLFIG WIDTH=199 HEIGHT=200 SRC="FIGDIR/small/660816v1_ufig1.gif" ALT="Figure 1"> View larger version (90K): org.highwire.dtl.DTLVardef@123d592org.highwire.dtl.DTLVardef@19a8399org.highwire.dtl.DTLVardef@135f134org.highwire.dtl.DTLVardef@6ebcbb_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2025

Extended NGN2 Expression in iPSCs Dramatically Enhances Purity of Neuronal Cultures

iPSC-derived neuronal cultures can provide valuable insights into the pathogenesis of neurological disease. However, single-cell iPSC clones expressing NGN2 and mCherry exhibit spontaneous loss of mCherry fluorescence, raising questions about the homogeneity of neurons derived from what appear to be heterogeneous iPSCs. We find that mCherry silencing does not influence iNeurons with two lines of evidence. First, using single-cell proteomics, we found that spontaneous mCherry silencing does not drive heterogeneity in iPSCs. Second, bulk proteomics and immunofluorescence analysis indicated that iNeurons from iPSCs expressing or lacking mCherry both resemble cortical glutamatergic neurons. The primary confounding factor in iNeuron generation was that suboptimal neuronal conversion led to cell aggregates comprised of actively proliferating neuronal progenitor cells and astrocytes as the culture developed. Our results indicate that extended NGN2 dosage substantially improves neuron purity. HighlightsO_LIEF1-mCherry undergoes spontaneous silencing at the AAVS1 locus C_LIO_LISingle-cell proteomics reveals heterogeneity in edited iPSCs independent of mCherry silencing C_LIO_LImCherry silencing has minimal impact on NGN2-mediated neuronal differentiation. C_LIO_LIExtended NGN2 induction produces more homogenous neuronal cultures C_LI eTOC blurbExpression of a mCherry reporter under the EF1 promoter and integrated into the AAVS1 locus is silenced in undifferentiated iPSCs, whereas CAG-driven rtTA3G remains active for NGN2-induced neuronal differentiation. Optimizing NGN2 induction in iPSC cultures is crucial for generating homogeneous neuronal cultures, as suboptimal conditions result in heterogeneous populations enriched with neural progenitor cells (NPCs) and astrocytes, and a disrupted neuronal organization.

biochemistry↗

bioRxiv2025

Single-Cell Trajectory Inference for Detecting Transient Events in Biological Processes

Transient surges in gene or protein expression often mark the key regulatory checkpoints that propel cells from one functional state to the next, yet they are easy to miss in sparse, noisy single-cell omics data. We introduce scTransient, a trajectory-inference pipeline integrated into our cloud-based single-cell analysis platform PSCS. scTransient transforms single-cell expression profiles into continuous pseudotime signals and couples them with wavelet-based signal processing to isolate short-lived but biologically meaningful bursts of activity. After ordering cells with unsupervised graph trajectories or supervised psupertime, scTransient windows expression values along pseudotime, applies a continuous wavelet transform, and assigns every gene a Transient-Event Score (TES) that rewards sharp, isolated coefficients while penalizing background fluctuations. Synthetic benchmarks show TES robustly recovers transient events across a wide range of cell numbers, signal-to-noise ratios, and event widths. Applying scTransient to three public datasets-- hematopoietic differentiation, monocyte-to-macrophage maturation, and single-cell proteomic cell-cycle progression--uncovers previously unreported, process-specific expression spikes. These include erythropoiesis regulators (e.g., Nfe2), membrane-raft remodeling proteins during macrophage differentiation, and S-phase DNA-replication factors in A549 cells. Functional enrichment confirms that top-scoring genes cluster into pathways directly pertinent to each transition. By extending trajectory inference from descriptive ordering to quantitative detection of fleeting regulatory programs, scTransient--now readily accessible via the PSCS web interface--offers researchers a practical route to uncovering transient molecular events that drive development, differentiation, and disease.

bioinformatics↗

bioRxiv2025

Single Cell Proteomics Reveals Novel Cell Phenotypes in Marfan Mouse Aneurysm

BackgroundSingle-cell omics technology is a powerful tool in biomedical research. However, single cell proteomics has lagged due to an inability to amplify peptides in a similar fashion to nucleotide strings. Single cell proteomics is important because proteins are the main functional unit in cells, and they often poorly correlate with mRNA quantities. In this paper we describe the first single cell proteomic analysis of complex tissue, comparing aneurysmal and normal mouse aorta from males and females. We also compare and integrate our single cell proteomic profiles with a matching single cell transcriptomics dataset. MethodsWe compared single cell proteomes between male and female, wild-type and Fbn1C1041G/+ Marfan mice (N=3 per group). Individual cells from mouse aortic root single cell suspensions were deposited in 384 well plates and subjected to ultra-sensitive nanoflow liquid chromatography-ion mobility-time of flight-mass spectrometry. The data were analyzed with leiden clustering to identify cell types. Statistical analyses were performed to detect differential proteins within cell types and multi-omics analysis integrated single cell proteomics with published single cell RNA-seq. ResultsWe identified all major aortic cell types including 7 distinct smooth muscle cell subtypes. The proportion of these cells varied based on sex and the Fbn1C1041G/+ genotype. Differentially expressed proteins between male and female in addition to wild-type and Marfan samples uncovered enhanced endothelial to mesenchymal transition patterns in endothelial cells from male Marfan mice. Comparisons between single cell RNA and single cell proteomic profiles showed similarities in major subtypes but not smooth muscle cell subtypes. Multi-omics analysis of these two single cell platforms demonstrated a potential novel role for smooth muscle cell derived angiotensin signaling in the Marfan phenotype. ConclusionsSingle cell proteomics identified new subpopulations of vascular smooth muscles cells and novel cell type specific protein signatures related to sex differences and aneurysm formation. AbbreviationsNext generation sequencing (NGS), Mass spectrometer (MS), Single cell proteomics by Mass Spectrometry (ScOPE-MS), Marfans syndrome (MFS), Fibrillin 1 (FBN1), Transforming growth factor {beta} (TGF{beta}), Smooth muscle cell (SMC), Single cell proteomic (scProteomic), Differentially expressed proteins (DEPs), Wild-type (WT), Hanks balanced salt solution (HBSS), Fetal bovine serum (FBS), Dulbeccos Modified Eagle Medium (DMEM), Data-independent acquisition parallel accumulation-serial fragmentation (DIA-PASEF), Magnetic assisted cell sorted (MACS), Single Cell Analysis in Python (Scanpy), Kyoto Encyclopedia of Genes and Genomes (KEGG), Principal component analysis (PCA), Uniform manifold projection (UMAP), Single cell transcriptomic (scTranscriptomic), Smoothelin (Smtn), Transgelin (Tagln), Myosin heavy chain 11 (Myh11), Platelet endothelial cell adhesion molecule 1 (Pecam1), Dipeptidase 1 (Dpep1), Uncoupling protein 1 (Ucp1), Low-density lipoprotein receptor-related protein (Lrp1), DNA ligase 3 (Lig3), Capsaicin channel transient receptor potential vanilloid 1 (Trpv1), Endothelial to mesenchymal transition (endMT), Intercellular adhesion molecule 1 (Icam1), Intercellular adhesion molecule 2 (Icam2), Endothelial cell-selective adhesion molecule (Esam), Calponin 1 (Cnn1), Vimentin (Vim), Zinc finger E-box-binding homeobox 1 (Zeb1), Snail family transcriptional repressor 1 (Snai1), Tropomyosin alpha-4 chain (Tpm4), Angiotensin converting enzyme (Ace)

systems biology↗

bioRxiv2024

Deep Learning Predicts Non-Normal Peptide FAIMS Mobility Distributions Directly from Sequence

Peptide ion mobility adds an extra dimension of separation to mass spectrometry-based proteomics. The ability to accurately predict peptide ion mobility would be useful to expedite assay development and to discriminate true answers in database search. There are methods to accurately predict peptide ion mobility through drift tube devices, but methods to predict mobility through high-field asymmetric waveform ion mobility (FAIMS) are underexplored. Here, we successfully model peptide ions FAIMS mobility using a multi-label multi-output classification scheme to account for non-normal transmission distributions. We trained two models from over 100,000 human peptide precursors: a random forest and a long-term short-term memory (LSTM) neural network. Both models had different strengths, and the ensemble average of model predictions produced higher F2 score than either model alone. Finally, we explore cases where the models make mistakes and demonstrate predictive performance of F2=0.66 (AUROC=0.928) on a new test dataset of nearly 40,000 different E. coli peptide ions. The deep learning model is easily accessible via https://faims.xods.org.

bioinformatics↗

bioRxiv2024

Distributed Collaboration for Data, Analysis Pipelines, and Results in Single-Cell Omics

Single-cell omics data analysis pipelines are complicated to design and difficult to share or reproduce. We describe a web platform that enables no-code analysis pipeline design, simple computing via the Open Science Grid, and sharing of entire data analysis pipelines, their input data, and interactive results. We expect this platform to increase the accessibility and reproducibility of single-cell omics.

cell biology↗

bioRxiv2024

Cell Storage Conditions Impact Single-Cell Proteomic Landscapes

Single-cell transcriptomics (SCT) has revolutionized our understanding of cellular heterogeneity, yet the emergence of single-cell proteomics (SCP) promises a more functional view of cellular dynamics. A challenge is that not all mass spectrometry facilities can perform SCP, and not all labs have access to cell sorting equipment required for SCP, which together motivate an interest in sending bulk cell samples through the mail for sorting and SCP analysis. Shipping requires cell storage, which has an unknown impact on SCP results. This study investigates the impact of cell storage conditions on the proteomic landscape at the single-cell level utilizing a Data-Independent Acquisition (DIA) coupled with Parallel Accumulation Serial Fragmentation (diaPASEF). Three storage conditions were compared in 293T cells: (1) 37{degrees}C (control), (2) 4{degrees}C overnight, and (3) - 80{degrees}C storage followed by liquid nitrogen preservation. Either cold or frozen storage induced significant alterations in cell diameter, elongation, and proteome composition. By elucidating how cell storage conditions alter cellular morphology and proteome profiles, this study contributes foundational technical information about SCP sample preparation and data quality.

cell biology↗

bioRxiv2024

Multi-dataset Integration and Residual Connections Improve Proteome Prediction from Transcriptomes using Deep Learning

Proteomes are well known to poorly correlate with transcriptomes measured from the same sample. While connected, the complex processes that impact the relationships between transcript and protein quantities remains an open research topic. Many studies have attempted to predict proteomes from transcriptomes with limited success. Here we use publicly available data from the Clinical Proteomics Tumor Analysis Consortium to show that deep learning models designed by neural architecture search (NAS) achieve improved prediction accuracy of proteome quantities from transcriptomics. We find that this benefit is largely due to including a residual connection in the architecture that allows input information to be remembered near the end of the network. Finally, we explore which groups of transcripts are functionally important for protein prediction using model interpretation with SHAP.

systems biology↗

bioRxiv2024

A haplotype-resolved view of human gene regulation

Diploid human cells contain two non-identical genomes, and differences in their regulation underlie human development and disease. We present Fiber-seq Inferred Regulatory Elements (FIRE) and show that FIRE provides a more comprehensive and quantitative snapshot of the accessible chromatin landscape across the 6 Gbp diploid human genome, overcoming previously unrecognized biases in existing regulatory element catalogs. FIRE enables comprehensive detection of haplotype-selective chromatin accessibility (HSCA), exposing novel imprinted elements lacking underlying parent-of-origin CpG methylation differences, and gene regulatory modules that permit genes to escape X chromosome inactivation. We uncover that the human leukocyte antigen (HLA) locus harbors the most HSCA in immune cells, where we resolve specific transcription factor (TF) binding events disrupted by disease-associated variants. Finally, we demonstrate that the regulatory landscape of a cell is littered with autosomal somatic chromatin epimutations that are propagated by clonal expansions to create mitotically stable and non-genetically deterministic chromatin alterations.

genomics↗

bioRxiv2024

1.4 min Plasma Proteome Profiling via Nanoparticle Protein Corona and Direct Infusion Mass Spectrometry

Non-invasive detection of protein biomarkers in plasma is crucial for clinical purposes. Liquid chromatography mass spectrometry (LC-MS) is the gold standard technique for plasma proteome analysis, but despite recent advances, it remains limited by throughput, cost, and coverage. Here, we introduce a new hybrid method, which integrates direct infusion shotgun proteome analysis (DISPA) with nanoparticle (NP) protein coronas enrichment for high throughput and efficient plasma proteomic profiling. We realized over 280 protein identifications in 1.4 minutes collection time, which enables a potential throughput of approximately 1,000 samples daily. The identified proteins are involved in valuable pathways and 44 of the proteins are FDA approved biomarkers. The robustness and quantitative accuracy of this method were evaluated across multiple NPs and concentrations with a mean coefficient of variation at 17%. Moreover, different protein corona profiles were observed among various nanoparticles based on their distinct surface modifications, and all NP protein profiles exhibited deeper coverage and better quantification than neat plasma. Our streamlined workflow merges coverage and throughput with precise quantification, leveraging both DISPA and NP protein corona enrichments. This underscores the significant potential of DISPA when paired with NP sample preparation techniques for plasma proteome studies.

systems biology↗

bioRxiv2023

Simultaneous Multi-Omics Analysis by Direct Infusion Mass Spectrometry (SMAD-MS)

Combined multi-omics analysis of proteomics, metabolomics, and lipidomics requires separate liquid chromatography-mass spectrometry (LC-MS), which limits throughput and increases costs, hindering the application of mass spectrometry-based multi-omics to large-scale analyses. Here, we present single-injection multi-omics analysis by direct infusion (SMAD), an integrated platform leveraging ion mobility mass spectrometry and self-developed software tools to enable single injection multi-omics analysis without liquid chromatography. SMAD allows quantification of over 9,000 metabolite m/z features and over 1,300 proteins from the same sample in less than five minutes. We validated the efficiency and reliability of SMAD with three case studies. (1) mouse macrophages after M1/M2 polarization and senescence, (2) a pilot drug screen in human cells, and (3) large-scale high-throughput drug screening of mammalian cells in 96-well plates. Finally, relationships between proteomic and metabolomic data are discovered by machine learning and validated.

systems biology↗

bioRxiv2023

Machine Learning Identifies Plasma Proteomic Signatures of Descending Thoracic Aortic Disease

BackgroundDescending thoracic aortic aneurysms and dissections can go undetected until severe and catastrophic, and few clinical indices exist to screen for aneurysms or predict risk of dissection. MethodsThis study generated a plasma proteomic dataset from 75 patients with descending type B dissection (Type B) and 62 patients with descending thoracic aortic aneurysm (DTAA). Standard statistical approaches were compared to supervised machine learning (ML) algorithms to distinguish Type B from DTAA cases. Quantitatively similar proteins were clustered based on linkage distance from hierarchical clustering and ML models were trained with uncorrelated protein lists across various linkage distances with hyperparameter optimization using 5-fold cross validation. Permutation importance (PI) was used for ranking the most important predictor proteins of ML classification between disease states and the proteins among the top 10 PI protein groups were submitted for pathway analysis. ResultsOf the 1,549 peptides and 198 proteins used in this study, no peptides and only one protein, hemopexin (HPX), were significantly different at an adjusted p-value <0.01 between Type B and DTAA cases. The highest performing model on the training set (Support Vector Classifier) and its corresponding linkage distance (0.5) were used for evaluation of the test set, yielding a precision-recall area under the curve of 0.7 to classify between Type B from DTAA cases. The five proteins with the highest PI scores were immunoglobulin heavy variable 6-1 (IGHV6-1), lecithin-cholesterol acyltransferase (LCAT), coagulation factor 12 (F12), HPX, and immunoglobulin heavy variable 4-4 (IGHV4-4). All proteins from the top 10 most important correlated groups generated the following significantly enriched pathways in the plasma of Type B versus DTAA patients: complement activation, humoral immune response, and blood coagulation. ConclusionsWe conclude that ML may be useful in differentiating the plasma proteome of highly similar disease states that would otherwise not be distinguishable using statistics, and, in such cases, ML may enable prioritizing important proteins for model prediction.

systems biology↗

bioRxiv2023

Complete Workflow for High Throughput Human Single Skeletal Muscle Fiber Proteomics

Skeletal muscle is a major regulatory tissue of whole-body metabolism and is composed of a diverse mixture of cell (fiber) types. Aging and several diseases differentially affect the various fiber types, and therefore, investigating the changes in the proteome in a fiber-type specific manner is essential. Recent breakthroughs in isolated single muscle fiber proteomics have started to reveal heterogeneity among fibers. However, existing procedures are slow and laborious requiring two hours of mass spectrometry time per single muscle fiber; 50 fibers would take approximately four days to analyze. Thus, to capture the high variability in fibers both within and between individuals requires advancements in high throughput single muscle fiber proteomics. Here we use a single cell proteomics method to enable quantification of single muscle fiber proteomes in 15 minutes total instrument time. As proof of concept, we present data from 53 isolated skeletal muscle fibers obtained from two healthy individuals analyzed in 13.25 hours. Adapting single cell data analysis techniques to integrate the data, we can reliably separate type 1 and 2A fibers. Sixty-five proteins were statistically different between clusters indicating alteration of proteins involved in fatty acid oxidation, muscle structure and regulation. Our results indicate that this method is significantly faster than prior single fiber methods in both data collection and sample preparation while maintaining sufficient proteome depth. We anticipate this assay will enable future studies of single muscle fibers across hundreds of individuals, which has not been possible previously due to limitations in throughput.

biochemistry↗