Biology subjects

Slavov, N.

Publications and source records attributed to Slavov, N..

At least 19 recordsLinked to original sources

bioRxiv2026

Scalable proteomics with multiDIA resolves proteoforms in neurodegenerative disease

Deep proteome characterization has traditionally required multidimensional chromatographic fractionation, long acquisition times and large sample amounts, thus limiting scalability. Here, we introduce multiDIA, which combines independent digestions with multiple proteases and data-independent acquisition (DIA) to increase sequence coverage without extensive chromatographic fractionation. By generating complementary and overlapping peptide representations of proteins, multiDIA identifies about 270,000 distinct peptides per sample, including 3,649 phosphosites. This is more than twice the coverage of unfractionated workflows and exceeds the coverage of multidimensional fractionation workflows that use 10-fold more material and time. We demonstrate the approach in Parkinson's disease (PD) brain samples, showing that multiDIA substantially expands sequence coverage while retaining the sensitivity, reproducibility, and scalability of DIA. This enabled the identification of disease-associated proteoform changes undetectable by single-protease DIA and the quantification of many disease-relevant post-translational modifications. MultiDIA thus enables deep, high-confidence proteome characterization from limited sample quantities without the throughput and sensitivity constraints of multidimensional fractionation.

systems biology↗

bioRxiv2026

Single-cell proteomics reveals cell-type-specific functional coordination in PBMCs

The coordination of molecular networks defines cellular functions. This is reflected in molecular covariation within a cell type, which is more subtle than the differences separating cell types and has therefore been difficult to quantify. To achieve the depth, consistency and accuracy required to resolve such covariation, we leveraged single-cell proteomics (plexDIA) and transcriptomics (Smart-seq3xpress) to analyze the proteomes and transcriptomes of thousands of single peripheral blood mononuclear cells (PBMCs). plexDIA quantified more protein-coding gene products per cell with higher data completeness while Smart-seq3xpress quantified more gene products across all cells. The protein measurements revealed cell-type-specific proteome architecture -- shaped in part by protein stability, and complex coordination -- that was undetectable in our mRNA data. Specifically, protein covariation within a cell type suggests cell-type-specific protein-protein interactions and functional rewiring of biological pathways independent of previously characterized abundance differences. Covariation analysis within cell types resolves a B cell-specific axis of translational states inversely covarying with GDF6 cytokine abundance. Our framework captures cell-type-specific functional coordination representing a distinct information layer accessible via single-cell proteomics.

systems biology↗

bioRxiv2025

Principles of protein abundance regulation across single cells in a mammalian tissue

Protein synthesis and clearance are major regulatory steps of gene expression, but their in vivo regulatory roles across the cells comprising complex tissues remains unexplored. Here, we systematically quantify protein synthesis and clearance across over 4,200 cells from a primary tissue. Through integration with single-cell transcriptomics, we report the first quantitative analysis of how individual cell types regulate their proteomes across the continuum of gene expression. Our analysis quantifies the relative contributions of RNA abundance, translation, and protein clearance to the abundance variation of thousands of proteins. These results reveal a putative organizing principle: The contributions of both translation and protein clearance are linearly dependent on the cell growth rate. Further, we find that some proteins are primarily regulated by one mechanism (RNA abundance, translation, or clearance) across all cell types while the dominant regulation of other proteins is cell-type specific. Age related changes in protein abundance are cell-type specific and correlated to changes in protein clearance. Our reliable multimodal measurements enabled quantifying and functionally interpreting molecular variation across single cells from the same cell type. The protein-protein correlations are substantially stronger than the mRNA-mRNA ones both for directly interacting proteins and for functional protein sets. This difference is mediated by protein clearance regulation. Further, the protein correlations allow identifying cell-type specific functional clusters. These clusters vary across cell types, revealing differences in metabolic processes coordination, partially regulated by protein degradation. Our approach reveals organizing principles determining the relative contributions of translation and protein clearance and provides a scalable framework for investigating protein regulation in mammalian tissues.

systems biology↗

bioRxiv2025

Ubiquitin-Proteasome System Dysregulation in Alzheimer's Disease Impacts Protein Abundance

Alzheimers disease (AD) is a relentlessly progressive, fatal neurodegenerative disorder that results in widespread protein dysfunctions. However, the full extent of aberrant proteomic changes in AD and their impact remains unknown, in part, because of the challenges of comprehensively measuring the proteome. Here, we used plexDIA, an approach that provides deep proteomic coverage and high throughput by parallelizing the acquisition of peptides and samples, to characterize proteomic changes in AD. Using human dorsolateral prefrontal cortex tissue, we identified 281 differentially abundant proteins in AD. By systematically analyzing compartment and protein complex-specific shifts in protein abundance, we identified an AD-specific decrease in levels of the 20S proteasome, the catalytic core of the cells primary protein degradation pathway. This alteration was accompanied by widespread decreases in proteasome subunit stoichiometries. Many proteasome substrate proteins were negatively correlated with 20S levels and increased in AD, suggesting that reduced 20S levels leads to abnormal protein accumulation. By analyzing proteins increased in AD, we identify key properties of such proteins: They have fast degradation rates, they contain signal sequences that allow them to be targeted for proteasomal degradation, and they are targeted by quality control pathways that recognize mislocalized proteins. Changes in these gene products at the protein and mRNA levels were highly discordant, providing additional evidence for increased protein abundance driven by impaired clearance. We also identified coherent sets of ubiquitin system enzymes, proteins that target substrates for proteasomal degradation, whose levels robustly discriminate AD from non-AD samples. One subset exhibited consistent increases in AD, while another, which contained the tau E3 ligase Cul5, exhibited consistent decreases, revealing complex changes in the ubiquitin system in AD. Taken together, our results suggest that decreased ubiquitin-proteasome system capacity and impaired clearance of short-lived and mislocalized proteins contribute substantially to proteopathic burden in AD.

neuroscience↗

bioRxiv2025

PSMtags improve peptide sequencing and throughput in sensitive proteomics

Mass spectrometry-based proteomics enables comprehensive characterization of protein abundance, function, and interactions. Label-free approaches are simple to implement but challenging to scale to thousands of samples per day. Multiplexed techniques, such as plexDIA, can address these limitations but remain restricted by the lack of mass tags optimized for data-independent acquisition (DIA) workflows. Here, we present a systematic approach screening a library of 576 compounds that identifies several small molecules that, when conjugated to peptides, improve their detection and sequence identification by mass spectrometry. The lead molecule, PSMtag, substantially increases the detection of fragment b-ions, which increases the confidence of sequence identification and enhances de novo sequencing. PSMtags allow 9-plexDIA, using only stable isotopes of carbon, oxygen and nitrogen. As a result, it allows simultaneously increasing proteome coverage and sample throughput for plexDIA workflows without compromising quantitative accuracy. We demonstrate 240 samples-per-day with 9-plexDIA, while acquiring 28,359 protein data points in the same time label-free methods acquire 4,340. Our approach constitutes an expandable framework for designing mass tags to overcome existing limitations in multiplexed proteomics and provides plexDIA reagents capable of analyzing over 1,000 samples per day when using 10 minute runs. By facilitating higher throughput and improved identification, this innovation holds significant potential for accelerating proteomic studies across diverse biological and clinical applications.

bioengineering↗

bioRxiv2025

JMod: Joint modeling of mass spectra for empowering multiplexed DIA proteomics

Parallelization of data acquisition substantially increases the throughput of mass spectrometry-based proteomics. However, parallelization also increases the density of mass spectra and consequently the overlap between ions, frustrating their analysis. To improve sequence identification and quantification from such spectra, we developed an open-source software for Joint Modeling of mass spectra (JMod). JMod models overlapping peaks as linear superpositions of their components in both MS1 and MS2 space, which permits multiplexed DIA with smaller mass offsets to increase the multiplexing capacity and thus proteomics throughput for a given plexDIA tag. This enables 9-plexDIA using 2 Da offset PSMtags, increasing throughput 9-fold while preserving quantitative accuracy and coverage depth. Furthermore, we use JMod to deconvolve simultaneous labeling by mass tags and heavy amino acids, thus increasing the throughput of metabolic pulse experiments measuring protein synthesis and degradation rates in single cells from mouse liver. By supporting enhanced decoding of highly multiplexed DIA spectra, JMod provides an open and flexible software that increases the throughput of sensitive proteomics.

bioinformatics↗

bioRxiv2025

Increasing mass spectrometry throughput using time-encoded sample multiplexing

Liquid chromatography-mass spectrometry (LC-MS) can enable precise and accurate quantification of analytes at high-sensitivity, but the rate at which samples can be analyzed remains limiting. Throughput can be increased by multiplexing samples in the mass domain with plexDIA, yet multiplexing along one dimension will only linearly scale throughput with plex. To enable combinatorial-scaling of proteomics throughput, we developed a complementary multiplexing strategy in the time domain, termed timePlex. timePlex staggers and overlaps the separation periods of individual samples. This strategy is orthogonal to isotopic multiplexing, which enables combinatorial multiplexing in mass and time domains when paired together, and thus multiplicatively increased throughput. We demonstrate this with 3-timePlex and 3-plexDIA, enabling the multiplexing of 9 samples per LC-MS run, and 3-timePlex and 9-plexDIA exceeding 500 samples / day with a combinatorial 27-plex. Crucially, timePlex supports sensitive analyses, including of single cells. These results establish timePlex as a methodology for label-free multiplexing and combinatorial scaling of the throughput of LC-MS proteomics. We project this combined approach will eventually enable an increase in throughput exceeding 1,000 samples / day. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=97 SRC="FIGDIR/small/655515v1_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@1c1d799org.highwire.dtl.DTLVardef@1319339org.highwire.dtl.DTLVardef@1b8bf4borg.highwire.dtl.DTLVardef@16d9e9_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2025

Composition and RNA binding specificity of metazoan RNase MRP

Ribonuclease (RNase) MRP is a conserved RNA-based enzyme that is essential for maturation of ribosomal RNA (rRNA) in eukaryotes. However, the composition and RNA substrate specificity of this multisubunit ribonucleoprotein complex in higher eukaryotes remain a mystery. Here, we identify NEPRO and C18ORF21 as constitutive subunits of metazoan RNase MRP. Both proteins are specific to RNase MRP and are the only ones distinguishing this enzyme from the closely related RNase P, which selectively cleaves transfer RNA-like substrates. We find that NEPRO and C18ORF21 each form a complex with all other subunits of RNase MRP, stabilize its catalytic RNA, and are required for rRNA maturation and cell proliferation. We harness our discovery to identify a full suite of in vivo RNA targets of each enzyme, including positions of potential cleavage sites at nucleotide resolution. These findings resolve the general composition of metazoan RNase MRP, illuminate its RNA binding specificity, and provide valuable assets for functional exploration of this essential eukaryotic enzyme.

molecular biology↗

bioRxiv2025

Protein degradation and growth dependent dilution substantially shape mammalian proteomes

Cellular protein abundances are controlled by rates of synthesis and clearance. Since the latter includes protein degradation and dilution due to cell growth, the growth rate may influence the mechanisms controlling variation in protein abundances. To quantify this influence, we analyzed the growth-dependent effects of protein degradation within a cell type (between activated and resting human B-cells), across human cell types and mouse tissues. This analysis benefited from deep and accurate quantification of over 12,000 proteins across all four primary tissues using plexDIA. The results indicate that growth-dependent dilution accounts for 40 % of protein abundance changes across conditions. Furthermore, the variation in protein clearance rates is sufficient to explain up to 50 % of the protein abundance differences within slowly growing cells as contrasted with 7 % in growing cells. Remarkably, clearance rates also regulate the abundance differences of proteoforms encoded by the same gene, such those arising from alternative splicing or alternate RNA decoding. Overall, our results demonstrate substantially larger than previously appreciated contributions of protein degradation to protein variation at slow growth, both across proteoforms and tissue types.

systems biology↗

bioRxiv2024

Inferring post-transcriptional regulation within and across cell types in human testis

Single-cell tissue atlases commonly use RNA abundances as surrogates for protein abundances. Yet, protein abundance also depends on the regulation of protein synthesis and degradation rates. To estimate the contributions of such post transcriptional regulation, we quantified the proteomes of 5,883 single cells from human testis using 3 distinct mass spectrometry methods (SCoPE2, pSCoPE, and plexDIA). To distinguish between biological and technical factors contributing to differences between protein and RNA levels, we developed BayesPG, a Bayesian model of transcript and protein abundance that systematically accounts for technical variation and infers biological differences. We use BayesPG to jointly model RNA and protein data collected from 29,709 single cells across different methods and datasets. BayesPG estimated consensus mRNA and protein levels for 3,861 gene products and quantified the relative protein-to-mRNA ratio (rPTR) for each gene across six distinct cell types in samples from human testis. About 28% of the gene products exhibited significant differences at protein and RNA levels and contributed to about 1, 500 significant GO groups. We observe that specialized and context specific functions, such as those related to spermatogenesis are regulated after transcription. Among hundreds of detected post translationally modified peptides, many show significant abundance differences across cell types. Furthermore, some phosphorylated peptides covary with kinases in a cell-type dependent manner, suggesting cell-type specific regulation. Our results demonstrate the potential of inferring protein regulation in from single-cell proteogenomic data and provide a generalizable model, BayesPG, for performing such analyses.

systems biology↗

bioRxiv2024

Alternate RNA decoding results in stable and abundant proteins in mammals

Amino acid substitutions may substantially alter protein stability and function, but the contribution of substitutions arising from alternate translation (deviations from the genetic code) is unknown. To explore it, we analyzed deep proteomic and transcriptomic data from over 1,000 human samples, including 6 cancer types and 26 healthy human tissues. This global analysis identified 60,803 fragmentation spectra corresponding to 8,801 unique substitution sites in proteins derived from 1,782 genes, including 2,000 confidently localized sites. Some substitutions are shared across samples, while others exhibit strong tissue-type and cancer specificity. Surprisingly, products of alternate translation are more abundant than their canonical counterparts for hundreds of proteins, suggesting sense codon recoding. Recoded proteins include transcription factors, proteases, signaling proteins, and proteins associated with neurodegeneration. Mechanisms contributing to substitution abundance include protein stability, codon frequency, codon-anticodon mismatches, and RNA modifications. We characterize how alternatively translated proteoform ratios vary across protein domains, tissue types and cancers. The substitution ratios are positively associated with intrinsically disordered regions and genetic polymorphisms in gnomAD, though the polymorphisms cannot account for the substitutions. The sequence, relative abundance, and the tissue-specificity of alternatively translated proteins are conserved between human and mouse. These results demonstrate the contribution of alternate translation to diversifying mammalian proteomes, and its association with protein stability, tissue-specific proteomes, and diseases.

molecular biology↗

bioRxiv2024

Proteome asymmetry in mouse and human embryos before fate specification

Pre-patterning of the embryo, driven by spatially localized factors, is a common feature across several non-mammalian species1-4. However, mammals display regulative development and thus it was thought that blastomeres of the embryo do not show such pre-patterning, contributing randomly to the three lineages of the blastocyst: the epiblast, primitive endoderm and trophectoderm that will generate the new organism, the yolk sac and placenta respectively 4-6. Unexpectedly, early blastomeres of mouse and human embryos have been reported to have distinct developmental fates, potential and heterogeneous abundance of certain transcripts7-12. Nevertheless, the extent of the earliest intra-embryo differences remains unclear and controversial. Here, by utilizing multiplexed and label-free single-cell proteomics by mass-spectrometry13, we show that 2-cell mouse and human embryos contain an alpha and a beta blastomere as defined by differential abundance of hundreds of proteins exhibiting strong functional enrichment for protein synthesis, transport, and degradation. Such asymmetrically distributed proteins include Gps1 and Nedd8, depletion or overexpression of which in one blastomere of the 2-cell embryo impacts lineage segregation. These protein asymmetries increase at 4-cell stage. Intriguingly, halved mouse zygotes display asymmetric protein abundance that resembles alpha and beta blastomeres, suggesting differential proteome localization already within zygotes. We find that beta blastomeres give rise to a blastocyst with a higher proportion of epiblast cells than alpha blastomeres and that vegetal blastomeres, which are known to have a reduced developmental potential, are more likely to be alpha. Human 2-cell blastomeres also partition into two clusters sharing strong concordance with clusters found in mouse, in terms of differentially abundant proteins and functional enrichment. To our knowledge, this is the first demonstration of intra-zygotic and inter-blastomere proteomic asymmetry in mammals that has a role in lineage segregation.

developmental biology↗

bioRxiv2024

Limiting the impact of protein leakage in single-cell proteomics

Limiting artifacts during sample preparation can significantly increase data quality in single-cell proteomics experiments. Towards this goal, we characterize the impact of protein leakage by analyzing thousands of primary single cells that were prepared either fresh immediately after dissociation or cryopreserved and prepared at a later date. We directly identify permeabilized cells and use the data to define a signature for protein leakage. We use this signature to build a classifier for identifying damaged cells that performs accurately across cell types and species.

molecular biology↗

bioRxiv2024

Post-transcriptional cross- and auto-regulation buffer expression of the human RNA helicases DDX3X and DDX3Y

The Y-linked gene DDX3Y and its X-linked homolog DDX3X survived the evolution of the human sex chromosomes from ordinary autosomes. DDX3X encodes a multi-functional RNA helicase, with mutations causing developmental disorders and cancers. We find that, among X-linked genes with surviving Y homologs, DDX3X is extraordinarily dosage-sensitive. Studying cells of individuals with sex chromosome aneuploidy, we observe that when the number of Y chromosomes increases, DDX3X transcript levels fall; conversely, when the number of X chromosomes increases, DDX3Y transcript levels fall. In 46,XY cells, CRISPRi knockdown of either DDX3X or DDX3Y causes transcript levels of the homologous gene to rise. In 46,XX cells, chemical inhibition of DDX3X protein activity elicits an increase in DDX3X transcript levels. Thus, perturbation of either DDX3X or DDX3Y expression is buffered - by negative cross-regulation of DDX3X and DDX3Y in 46,XY cells, and by negative auto-regulation of DDX3X in 46,XX cells. DDX3X-DDX3Y cross-regulation is mediated through mRNA destabilization - as shown by metabolic labeling of newly transcribed RNA - and buffers total levels of DDX3X and DDX3Y protein in human cells. We infer that post-transcriptional auto-regulation of the ancestral (autosomal) DDX3 gene transmuted into auto- and cross-regulation of DDX3X and DDX3Y as these sex-linked genes evolved from ordinary alleles of their autosomal precursor.

genetics↗

bioRxiv2024

Single-nucleus proteomics identifies regulators of protein transport

The physiological response of a cell to stimulation depends on its proteome configuration. Therefore, the abundance variation of regulatory proteins across unstimulated single cells can be associatively linked with their response to stimulation. Here we developed an approach that leverages this association across individual cells and nuclei to systematically identify potential regulators of biological processes, followed by targeted validation. Specifically, we applied this approach to identify regulators of nucleocytoplasmic protein transport in macrophages stimulated with lipopolysaccharide (LPS). To this end, we quantified the proteomes of 3,412 individual nuclei, sampling the dynamic response to LPS treatment, and linking functional variability to proteomic variability. Minutes after the stimulation, the protein transport in individual nuclei correlated strongly with the abundance of known protein transport regulators, thus revealing the impact of natural protein variability on functional cellular response. We found that simple biophysical constraints, such as the quantity of nuclear pores, partially explain the variability in LPS-induced nucleocytoplasmic transport. Among the many proteins newly identified to be associated with the response, we selected 16 for targeted validation by knockdown. The knockdown phenotypes confirmed the inferences derived from natural protein and functional variation of single nuclei, thus demonstrating the potential of (sub-)single-cell proteomics to infer functional regulation. We expect this approach to generalize to broad applications and enhance the functional interpretability of single-cell omics data.

systems biology↗

bioRxiv2023

Dynamics of single-cell protein covariation during epithelial-mesenchymal transition

Physiological processes, such as epithelial-mesenchymal transition (EMT), are mediated by changes in protein interactions. These changes may be better reflected in protein covariation within cellular cluster than in the temporal dynamics of cluster-average protein abundance. To explore this possibility, we quantified proteins in single human cells undergoing EMT. Covariation analysis of the data revealed that functionally coherent protein clusters dynamically changed their protein-protein correlations without concomitant changes in cluster-average protein abundance. These dynamics of protein-protein correlations were monotonic in time and delineated protein modules functioning in actin cytoskeleton organization, energy metabolism and protein transport. These protein modules are defined by protein covariation within the same time point and cluster and thus reflect biological regulation masked by the cluster-average protein dynamics. Thus, protein correlation dynamics across single cells offer a window into protein regulation during physiological transitions. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=132 SRC="FIGDIR/small/572913v2_ufig1.gif" ALT="Figure 1"> View larger version (78K): org.highwire.dtl.DTLVardef@84ba15org.highwire.dtl.DTLVardef@1d4c753org.highwire.dtl.DTLVardef@6a4210org.highwire.dtl.DTLVardef@174fa1_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2023

Massively parallel sample preparation for multiplexed single-cell proteomics using nPOP

Single-cell proteomics by mass spectrometry (MS) allows quantifying proteins with high specificity and sensitivity. To increase its throughput, we developed nPOP, a method for parallel preparation of thousands of single cells in nanoliter volume droplets deposited on glass slides. Here, we describe its protocol with emphasis on its flexibility to prepare samples for different multiplexed MS methods. An implementation with plexDIA demonstrates accurate quantification of about 3,000 - 3,700 proteins per human cell. The protocol is implemented on the CellenONE instrument and uses readily available consumables, which should facilitate broad adoption. nPOP can be applied to all samples that can be processed to a single-cell suspension. It takes 1 or 2 days to prepare over 3,000 single cells. We provide metrics and software for quality control that can support the robust scaling of nPOP to higher plex reagents for achieving reliable high-throughput single-cell protein analysis. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=48 SRC="FIGDIR/small/568927v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@c82330org.highwire.dtl.DTLVardef@17e5f2forg.highwire.dtl.DTLVardef@19b200borg.highwire.dtl.DTLVardef@154e336_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2023

Controlling pericellular oxygen tension in cell culture reveals distinct breast cancer responses to low oxygen tensions

Oxygen (O2) tension plays a key role in tissue function and pathophysiology. O2-controlled cell culture, in which the O2 concentration in an incubators gas phase is controlled, is an indispensable tool to study the role of O2 in vivo. For this technique, it is presumed that the incubator setpoint is equal to the O2 tension that cells experience (i.e., pericellular O2). We discovered that physioxic (5% O2) and hypoxic (1% O2) setpoints regularly induce anoxic (0.0% O2) pericellular tensions in both adherent and suspension cell cultures. Electron transport chain inhibition ablates this effect, indicating that cellular O2 consumption is the driving factor. RNA-seq revealed that primary human hepatocytes cultured in physioxia experience ischemia-reperfusion injury due to anoxic exposure followed by rapid reoxygenation. To better understand the relationship between incubator gas phase and pericellular O2 tensions, we developed a reaction-diffusion model that predicts pericellular O2 tension a priori. This model revealed that the effect of cellular O2 consumption is greatest in smaller volume culture vessels (e.g., 96-well plate). By controlling pericellular O2 tension in cell culture, we discovered that MCF7 cells have stronger glycolytic and glutamine metabolism responses in anoxia vs. hypoxia. MCF7 also expressed higher levels of HIF2A, CD73, NDUFA4L2, etc. and lower levels of HIF1A, CA9, VEGFA, etc. in response to hypoxia vs. anoxia. Proteomics revealed that 4T1 cells had an upregulated epithelial-to-mesenchymal transition (EMT) response and downregulated reactive oxygen species (ROS) management, glycolysis, and fatty acid metabolism pathways in hypoxia vs. anoxia. Collectively, these results reveal that breast cancer cells respond non-monotonically to low O2, suggesting that anoxic cell culture is not suitable to model hypoxia. We demonstrate that controlling atmospheric O2 tension in cell culture incubators is insufficient to control O2 in cell culture and introduce the concept of pericellular O2-controlled cell culture.

bioengineering↗