Biology subjects

Leduc, A.

Publications and source records attributed to Leduc, A..

16 recordsLinked to original sources

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↗

bioRxiv2026

Unraveling the potential of short and long read sequencing for human genome profiling

BackgroundShort read sequencing technologies have dominated the field of human whole genome sequencing in the past years in terms of cost, throughput, and accuracy. However, thanks to recent technological evolution, long read approaches have become increasingly competitive and complementary to short reads. With the gap in the cost per genome closing slowly between both approaches, long reads might replace short read sequencing in future research and clinical applications. Still, comprehensive evaluation is necessary to conclude on the performance and general advantages of each technology. ResultsIn this study, we compared the latest chemistries of major suppliers of short and long read technologies: Illumina short reads, Illumina Complete Long Reads (ICLR), Pacific Biosciences HiFi reads (PacBio), and Oxford Nanopore Technologies long reads (ONT). Using the HG002 human reference sample and established bioinformatics guidelines, we assessed their variant calling performance against the latest available truth sets at different levels of coverage. For single nucleotide variant detection, all technologies were equivalent. Despite the latest improvements in chemistry, indel calling with ONT continues to lag in accuracy behind other technologies. In contrast, long reads delivered a clear advantage in structural variant detection, surpassing short reads in both accuracy and sensitivity. The hybrid ICLR approach achieved intermediate performance, narrowing the gap between short and long read sequencing. Furthermore, long reads enhanced haplotype-phasing resolution, enabling the phasing of over 80% of the genome. ConclusionsThese findings highlight the specific strengths and limitations of recent sequencing technologies, aiding the decision-making in future research projects, technological platforms development, and clinical applications.

genomics↗

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

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

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

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↗

bioRxiv2024

The 2023 wildfire season in Quebec: an overview of extreme conditions, impacts, lessons learned and considerations for the future.

The 2023 wildfire season in Quebec set records due to extreme warm and dry conditions, burning 4.5 million hectares and indicating persistent and escalating impacts associated with climate change. The study reviews the unusual weather conditions that led to the fires, discussing their extensive impacts on the forest sector, fire management, boreal caribou habitats, and particularly the profound effects on First Nation communities. The wildfires led to significant declines in forest productivity and timber supply, overwhelming fire management resources, and necessitating widespread evacuations. First Nation territories were dramatically altered, facing severe air quality issues and disruptions. While caribou impacts were modest across the province, the broader ecological, economical, and social repercussions were considerable. To mitigate future extreme wildfire seasons, the study suggests changes in forest management practices to increase forest resilience and resistance, adapting industrial structures to new timber supplies, and enhancing fire suppression and risk management strategies. It calls for a comprehensive, unified approach to risk management that incorporates the lessons from the 2023 fire season and accounts for ongoing climate change. The study underscores the urgent need for detailed planning and proactive measures to reduce the growing risks and impacts of wildfires in a changing climate.

ecology↗

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

An adult clock component links circadian rhythms to pancreatic β-cell maturation

Circadian clocks attune metabolism to daily energy cycles, but how they regulate functional maturation of metabolic tissues is poorly understood. Here we show that DEC1, a clock transcription factor induced in adult islet {beta} cells, coordinates their glucose responsiveness by synchronizing energetic and secretory rhythms. DEC1 binds and regulates maturity-linked genes to integrate insulin exocytosis with {beta}-cell energy metabolism, and {beta}-cell-specific Dec1 ablation disrupts their transcription synchrony. Dec1-disrupted mice develop lifelong glucose intolerance and insulin deficiency, despite normal islet formation. Metabolic dysfunction upon {beta}-cell Dec1 loss stems from poor coupling of islet insulin secretion to glucose metabolism, reminiscent of fetal/neonatal islet immaturity. We link stunted maturation to a deficit in circadian bioenergetics, prompted by compromised glucose utilization, mitochondrial dynamics, and respiratory metabolism, which is rescued by increased metabolic flux. Thus, DEC1 links circadian clockwork to {beta}-cell metabolic maturation, revealing a hierarchy for how the clock programs metabolic tissue specialization.

cell biology↗

bioRxiv2023

Data-Driven Optimization of DIA Mass-Spectrometry by DO-MS

Mass spectrometry (MS) enables specific and accurate quantification of proteins with ever increasing throughput and sensitivity. Maximizing this potential of MS requires optimizing data acquisition parameters and performing efficient quality control for large datasets. To facilitate these objectives for data independent acquisition (DIA), we developed a second version of our framework for data-driven optimization of mass spectrometry methods (DO-MS). The DO-MS app v2.0 (do-ms.slavovlab.net) allows to optimize and evaluate results from both label free and multiplexed DIA (plexDIA) and supports optimizations particularly relevant for single-cell proteomics. We demonstrate multiple use cases, including optimization of duty cycle methods, peptide separation, number of survey scans per duty cycle, and quality control of single-cell plexDIA data. DO-MS allows for interactive data display and generation of extensive reports, including publication quality figures, that can be easily shared. The source code is available at: github.com/SlavovLab/DO-MS. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=68 SRC="FIGDIR/small/526809v3_ufig1.gif" ALT="Figure 1"> View larger version (13K): org.highwire.dtl.DTLVardef@1334corg.highwire.dtl.DTLVardef@cec8bcorg.highwire.dtl.DTLVardef@1dcc07eorg.highwire.dtl.DTLVardef@1e1abd2_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2022

Prioritized single-cell proteomics reveals molecular and functional polarization across primary macrophages

Major aims of single-cell proteomics include increasing the consistency, sensitivity, and depth of protein quantification, especially for proteins and modifications of biological interest. To simultaneously advance all these aims, we developed prioritized Single Cell ProtEomics (pSCoPE). pSCoPE consistently analyzes thousands of prioritized peptides across all single cells (thus increasing data completeness) while analyzing identifiable peptides at full duty-cycle, thus increasing proteome depth. These strategies increased the sensitivity, data completeness, and proteome coverage over 2-fold. The gains enabled quantifying protein variation in untreated and lipopolysaccharide-treated primary macrophages. Within each condition, proteins covaried within functional sets, including phagosome maturation and proton transport. This protein covariation within a treatment condition was similar across the treatment conditions and coupled to phenotypic variability in endocytic activity. pSCoPE also enabled quantifying proteolytic products, suggesting a gradient of cathepsin activities within a treatment condition. pSCoPE is freely available and widely applicable, especially for analyzing proteins of interest without sacrificing proteome coverage. Support for pSCoPE is available at: scp.slavovlab.net/pSCoPE O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=76 SRC="FIGDIR/small/484655v2_ufig1.gif" ALT="Figure 1"> View larger version (19K): org.highwire.dtl.DTLVardef@c44f04org.highwire.dtl.DTLVardef@1fb3e8org.highwire.dtl.DTLVardef@15c456eorg.highwire.dtl.DTLVardef@d271e0_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2021

Increasing the throughput of sensitive proteomics by plexDIA

Current mass-spectrometry methods enable high-throughput proteomics of large sample amounts, but proteomics of low sample amounts remains limited in depth and throughput. To increase the throughput of sensitive proteomics, we developed an experimental and computational framework, plexDIA, for simultaneously multiplexing the analysis of both peptides and samples. Multiplexed analysis with plexDIA increases throughput multiplicatively with the number of labels without reducing proteome coverage or quantitative accuracy. By using 3-plex nonisobaric mass tags, plexDIA enables quantifying 3-fold more protein ratios among nanogram-level samples. Using 1 hour active gradients and first-generation Q Exactive, plexDIA quantified about 8,000 proteins in each sample of labeled 3-plex sets. plexDIA also increases data completeness, reducing missing data over 2-fold across samples. We applied plexDIA to quantify proteome dynamics during the cell division cycle in cells isolated based on their DNA content; plexDIA detected many classical cell cycle proteins and discovered new ones. When applied to single human cells, plexDIA quantified about 1,000 proteins per cell and achieved 98 % data completeness within a plexDIA set while using about 5 min of active chromatography per cell. These results establish a general framework for increasing the throughput of sensitive and quantitative protein analysis. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=81 SRC="FIGDIR/small/467007v2_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@144bba6org.highwire.dtl.DTLVardef@17bbf20org.highwire.dtl.DTLVardef@140f497org.highwire.dtl.DTLVardef@179faa9_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2021

Droplet sample preparation for single-cell proteomics applied to the cell cycle

Many biological processes, such as the cell division cycle, are reflected in protein covariation across single cells. This covariation can be quantified and interpreted by single-cell mass-spectrometry (MS) with sufficiently high throughput and accuracy. Towards this goal, we developed nPOP, a method that uses piezo acoustic dispensing to isolate individual cells in 300 picoliter volumes and performs all subsequent sample preparation steps in small droplets on a fluorocarbon-coated slide. This design enabled simultaneous sample preparation of thousands of single cells, including lysing, digesting, and labeling individual cells in volumes of 8-20 nl. Protein covariation analysis identified cell-cycle dynamics that were similar across cell types and dynamics that differed between cell types, even within sub-populations of melanoma cells defined by markers for drug-resistance priming. The melanoma cells expressing these markers accumulated in the G1 phase of the cell cycle, displayed distinct protein covariation across the cell cycle, accumulated glycogen, and had lower abundance of glycolytic enzymes. The non-primed melanoma cells exhibited gradients of protein abundance and covariation, suggesting transition states. These results were validated by different MS methods. Together, they demonstrate that protein covariation across single cells may reveal functionally concerted biological differences between closely related cell states.

bioengineering↗

bioRxiv2021

Multiplexed single-cell proteomics using SCoPE2

Many biological systems are composed of diverse single cells. This diversity necessitates functional and molecular single-cell analysis. Single-cell protein analysis has long relied on affinity reagents, but emerging mass-spectrometry methods (either label-free or multiplexed) have enabled quantifying over 1,000 proteins per cell while simultaneously increasing the specificity of protein quantification. Isobaric carrier based multiplexed single-cell proteomics is a scalable, reliable, and cost-effective method that can be fully automated and implemented on widely available equipment. It uses inexpensive reagents and is applicable to any sample that can be processed to a single-cell suspension. Here we describe an automated Single Cell ProtEomics (SCoPE2) workflow that allows analyzing about 200 single cells per 24 hours using only standard commercial equipment. We emphasize experimental steps and benchmarks required for achieving quantitative protein analysis. SCoPE2 Protocol O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=40 SRC="FIGDIR/small/435034v2_ufig1.gif" ALT="Figure 1"> View larger version (13K): org.highwire.dtl.DTLVardef@b0d69dorg.highwire.dtl.DTLVardef@1da07d4org.highwire.dtl.DTLVardef@1381c5borg.highwire.dtl.DTLVardef@cdb087_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗