Biology subjects

Collins, B. C.

Publications and source records attributed to Collins, B. C..

15 recordsLinked to original sources

bioRxiv2026

Spectronaut-nf: A Nextflow Pipeline for Parallel Processing of DIA Data with Spectronaut

SummaryContemporary proteomics methods can now generate large-scale DIA datasets of thousands of files that demand substantial computational resources for efficient analysis. Spectronaut is a widely used platform for DIA data processing; however, large-scale searches are often constrained by computational performance and long execution times when run on single workstations. Here, we present Spectronaut-nf, a Nextflow-based pipeline that enables scalable and parallelized execution of Spectronaut analyses across high-performance computing (HPC) environments. The workflow divides directDIA analysis into modular stages, including spectral library generation, DIA searching, and merging results, allowing efficient distribution of tasks across multiple compute nodes. Benchmarking using 72 diaPASEF raw files using typical hardware demonstrated that Spectronaut-nf completed searches in 23.77 hours, compared with 39.09 hours on a Windows workstation and 67.04 hours on a single-node Linux HPC setup. Stress testing with 1,037 diaPASEF raw files further demonstrated the scalability and robustness of the workflow for large proteomics datasets. Across platforms, protein and peptide identifications remained consistent, with only minimal variability attributable to platform-specific differences. Overall, Spectronaut-nf provides a flexible, scalable, and efficient framework for high-throughput DIA proteomics analysis in HPC environments. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=129 SRC="FIGDIR/small/741433v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@4a7107org.highwire.dtl.DTLVardef@14287d8org.highwire.dtl.DTLVardef@e484e9org.highwire.dtl.DTLVardef@d21c07_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2026

Proteogenomic discovery of novel small proteins in clinical Mycobacterium tuberculosis strains

Even though our meta-analysis ranks Mycobacterium tuberculosis genomes among the bacterial pathogens that are most straightforward to assemble, most available assemblies relied on short-read sequencing and contain genomic blind spots that miss functionally important genes. Complete genomes are essential for functional genomics, particularly for identifying small ORF-encoded proteins (SEPs; [≤]100 amino acids), which can play critical biological roles yet are frequently missed by standard annotations. Here, we generated complete long-read assemblies for six clinical reference strains representing lineage 1 and the more pathogenic lineage 2, followed by comparative genomic and proteogenomic analyses. We additionally provide software to predict comprehensive sets of mycobacteria-specific proline-glutamic acid (PE) and PPE family genes, including lineage-specific variants. Using parallel accumulation-serial fragmentation mass spectrometry, we detected approximately two-thirds of each strains annotated proteome from unfractionated cell extracts. Extending our proteogenomic framework across related strains, and adding rigorous control of proteogenomic discovery rates using entrapment strategies, we revealed 12-24 previously unannotated proteins per strain, predominantly SEPs, 56-60 alternative translation start sites, and 9-17 expressed pseudogenes. Newly identified proteins included conserved and lineage-specific SEPs, an antitoxin, candidate antimicrobial peptides and novel proteins under purifying selection. Overall, applying this improved proteogenomics method to phylogenomically selected clinical reference strains provides a valuable approach for discovering candidate diagnostics or therapeutics, as illustrated here for a WHO-listed critical bacterial pathogen.

microbiology↗

bioRxiv2025

Complex Assembly and Activity States as Multifaceted Protein Attributes Explaining Phenotypic Variability

Cell function studies primarily focus on measuring overall molecular abundances while often overlooking critical clues--including protein modifications and molecular interaction networks--that critically determine the functional properties of the cell. In prior work, we introduced a suite of methods to reveal context-specific transcription factor-gene regulatory networks, kinase-substrate networks, and protein interaction networks and leveraged them to gain deeper insights into transcriptional regulation and signal transduction. However, the complex interdependencies between these networks are still elusive. To address this challenge, we introduce a multi-omics framework, aimed at harnessing measured or inferred protein activity in context-specific networks, which yields deeper functional insights into mechanisms underlying molecular phenotypes, compared to protein abundance alone. As proof of concept, we utilized progressively differentiated instances of HeLa CCL2 and Kyoto cell lines to explore the role of protein complexes and interactions in cell doubling time and susceptibility to Salmonella Typhimurium infection. Notably, this analysis underscores the pivotal role of protein interaction networks in linking molecular profiles to phenotypic outcomes, thus providing a highly generalizable framework for multi-omics dataset analysis.

systems biology↗

bioRxiv2025

Induced pluripotent stem cell-derived macrophages as a model for human inflammasome signaling

Macrophage models are a mainstay of inflammasome research, however current human in vitro macrophage models have significant limitations. Here we generate induced pluripotent stem cell (iPSC)-derived macrophages (iMacs) to study inflammasome signaling and benchmark them with human monocyte-derived macrophages (HMDMs). We confirm that iMacs express high levels of macrophage markers and are highly phagocytic. Whole cell proteomics analysis shows that iMacs express many inflammasome sensors and related proteins, and in functional assays iMacs respond to multiple inflammasome stimuli. The NLRP3 inflammasome is strongly activated in iMacs and we find that nigericin alone activates NLRP3. The non-canonical inflammasome does not require a priming step in iMacs as caspase-4 is constitutively expressed. High levels of NAIP/NLRC4 inflammasome activation are also observed in response to needle toxin. Finally, unlike HMDMs, iMacs activate NLRP1. Therefore, we demonstrate that iMacs are a physiologically relevant and highly tractable model to study human inflammasome signaling and regulation. MotivationiPSC-derived macrophages (iMacs) are functionally, transcriptionally, and phenotypically similar to primary human macrophages. iMacs therefore offer new opportunities to study inflammasome activity in a human macrophage model, but to date they have not been widely used. In this study, we describe a protocol to differentiate and characterize iMacs. We then describe how to activate a range of different inflammasomes within these cells and assess the inflammasome response by measuring pyroptosis, cytokine release, ASC speck formation, and processing of inflammasome-related proteins. We also benchmark iMac responses with the current gold standard primary human monocyte derived macrophage model.

immunology↗

bioRxiv2025

Spatial distribution of the proteome in human body and cancers

A comprehensive spatial distribution of the proteome in human body and cancers is fundamental for understanding human biology and diseases including cancers. Here, we present an anatomically resolved human proteome derived from 1781 benign and malignant samples from 58 major tissue types encompassing 251 specific tissues and 25 carcinomas. Based on a spectral library covering over 75% of the human protein-coding genes with 208 understudied and 82 missing proteins characterized, we quantified over 13,000 proteins in these samples using data-independent acquisition proteomics. This data resource presents the so far most comprehensive quantitative proteomic landscape of human tissues and common carcinomas. It allows systematic evaluation of tissue-specific drug responses, identification of drug candidates that may be repurposed as antineoplastics, and discovery of novel targets for anticancer therapy. This resource, available as an online knowledgebase, refines our knowledge of spatial distribution of the human proteome and tumor-specific protein modulation.

systems biology↗

bioRxiv2023

Cellular Dynamics of Skeletal Muscle Regeneration

The function of many organs, including skeletal muscle, depends on its three-dimensional structure. Muscle regeneration therefore requires not only reestablishment of myofibers, but restoration of tissue architecture. Resident muscle stem cells (SCs) are essential for regeneration, but how SCs regenerate muscle architecture is largely unknown. We address this problem using genetic labeling of SCs and whole mount imaging to reconstruct in three dimensions muscle regeneration. Unexpectedly, we found that the residual basement membrane of necrotic myofibers is critical for promoting fusion and orienting regenerated myofibers and myofibers form via two distinct phases of fusion. Furthermore, the centralized myonuclei characteristic of regenerated myofibers are associated with myofibrillogenesis and permanently mark regenerated myofibers. Finally, we elucidate a new cellular mechanism for the formation of branched myofibers, a pathology characteristic of diseased muscle. We provide a new synthesis of the cellular events of regeneration and show these differ from those used during development.

cell biology↗

bioRxiv2022

A gas phase fractionation acquisition scheme integrating ion mobility for rapid diaPASEF library generation

Data independent acquisition (DIA or DIA/SWATH) mass spectrometry has emerged as a primary measurement strategy in the field of quantitative proteomics. diaPASEF is a recent adaptation that leverages trapped ion mobility spectrometry (TIMS) to improve selectivity and increase sensitivity. The complex fragmentation spectra generated by co-isolation of peptides in DIA mode are most typically analyzed with reference to prior knowledge in the form of spectral libraries. The best-established method for generating libraries uses data dependent acquisition (DDA) mode, or DIA mode if appropriately deconvoluted, often including offline fractionation to increase depth of coverage. More recently strategies for spectral library generation based on gas phase fractionation (GPF), where a representative sample is injected serially using narrow DIA windows that cover different mass ranges of the complete precursor space, have been introduced that performed comparably to deep offline fractionation-based libraries for DIA data analysis. Here, we investigated whether an analogous GPF-based library building approach that accounts for the ion mobility (IM) dimension is useful for the analysis of diaPASEF data and can remove the need for offline fractionation. To enable a rapid library development approach for diaPASEF we designed a GPF acquisition scheme covering the majority of multiply charged precursors in the m/z vs 1/K0 space requiring 7 injections of a representative sample and compared this with libraries generated by direct deconvolution-based analysis of diaPASEF data or by deep offline fractionation and ddaPASEF. We found that library generation by IM-GPF outperformed direct deconvolution of the diaPASEF data and had performance approaching that of a deep offline fractionation library, when analysing diaPASEF data. This establishes the ion mobility integrated GPF scheme as a pragmatic approach to rapid library generation for the analysis of diaPASEF data.

systems biology↗

bioRxiv2021

Rapid profiling of protein complex re-organization in perturbed systems

Protein complexes constitute the primary functional modules of cellular activity. To respond to perturbations, complexes undergo changes in their abundance, subunit composition or state of modification. Understanding the function of biological systems requires global strategies to capture this contextual state information on protein complexes and interaction networks. Methods based on co-fractionation paired with mass spectrometry have demonstrated the capability for deep biological insight but the scope of studies using this approach has been limited by the large measurement time per biological sample and challenges with data analysis. As such, there has been little uptake of this strategy beyond a few expert labs into the broader life science community despite rich biological information content. We present a rapid integrated experimental and computational workflow to assess the re-organization of protein complexes across multiple cellular states. It enables complex experimental designs requiring increased sample/condition numbers. The workflow combines short gradient chromatography and DIA/SWATH mass spectrometry with a data analysis toolset to quantify changes in complex organization. We applied the workflow to study the global protein complex rearrangements of THP-1 cells undergoing monocyte to macrophage differentiation and a subsequent stimulation of macrophage cells with lipopolysaccharide. We observed massive proteome organization in functions related to signaling, cell adhesion, and extracellular matrix during differentiation, and less pronounced changes in processes related to innate immune response induced by the macrophage stimulation. We therefore establish our integrated differential pipeline for rapid and state-specific profiling of protein complex organization with broad utility in complex experimental designs.

systems biology↗

bioRxiv2020

Systematic detection of functional proteoform groups from bottom-up proteomic datasets

The cellular proteome, the ensemble of proteins derived from a genome, catalyzes and controls thousands of biochemical functions that are the basis of living cells. Whereas the protein coding regions of the genome of the human and many other species are well known, the complexity and composition of proteomes largely remains to be explored. This task is challenging because mechanisms including alternative splicing and post-translational modifications generally give rise to multiple distinct, but related proteins - proteoforms - per coding gene that expand the functional capacity of a cell. Bottom-up proteomics is a mass spectrometric method that infers the identity and quantity of proteins from the measurement of peptides derived from these proteins by proteolytic digestion. Whereas bottom-up proteomics has become the method of choice for the detection of translation products from essentially any gene, the inherent missing link between measured peptides and their parental proteins has so far precluded the systematic assessment of proteoforms and thus limited the resolution of proteome maps. Here we present a novel, data-driven strategy to assign peptides to unique functional proteoform groups based on peptide correlation patterns across large bottom-up proteomic datasets. Our strategy does not fully characterize specific proteoforms, as is achievable in top-down approaches. Rather, it clusters peptides into functional proteoform groups that are directly linked to the biological context of the study. This allows the detection of tens to hundreds of proteoform groups in an untargeted fashion from bottom-up proteomics experiments. We applied the strategy to two types of bottom-up proteomic datasets. The first is a protein complex co-fractionation dataset where native complexes across two different cell cycle stages were resolved and analyzed. Here, our approach enabled the systematic detection and evaluation of assembly specific proteoforms at an unprecedented scale. The second is a protein abundance vs. sample data matrix typical for bottom-up cohort studies consisting of tissue samples from the mouse BXD genetic reference panel. In either data type the method detected state-specific proteoform groups that could be linked to distinct molecular mechanisms including proteolytic cleavage, alternative splicing and phosphorylation. We envision that the presented approach lays the foundation for a systematic assessment of proteoforms and their functional implications directly from bottom-up proteomic datasets.

systems biology↗

bioRxiv2020

Network analysis identifies regulators of lineage-specific phenotypes in Mycobacterium tuberculosis

The Mycobacterium tuberculosis (Mtb) complex includes ten phylogenetically distinct human-adapted lineages with varying geographical distributions and pathogenicity. Lineage 1 (L1) is associated with low virulence, while Lineage 2 (L2) is linked to hyper-virulence, increased transmission, and drug resistance. We performed multi-layer comparative analyses integrating whole-genome sequencing with quantitative transcriptomic and proteomic profiling of a panel of L1 and L2 clinical strains, each grown under two in vitro conditions. Our data revealed variable correlations between transcript and protein levels across strains and gene categories, indicating lineage-specific post-translational regulation. Transcriptional and translational differences scaled with phylogenetic distance, with one in three SNPs on average leading to gene expression changes. A newly developed genome-scale transcriptional regulatory model identified master transcription factors--closely linked to the sigma factor network--whose targets were differentially expressed between L1 and L2. Notably, DosR proteins showed higher basal levels and a stronger nitric oxide (NO) response in L2. Time-course validation using an Rv1985c induction and wild-type H37Rv under hypoxia and reaeration confirmed the role of Rv1985c in the differentially expressed genes between L1 and L2. These findings suggest that limited genetic variation can translate into significant phenotypic differences via differential regulation of key transcriptional networks. HighlightsO_LIProteomic and transcriptomic characterization of fully sequenced diverse L1 and L2 clinical strains of Mtb. C_LIO_LIPost translational control mechanisms for regulatory and virulence genes are mitigated in Mtb L2. C_LIO_LIA genome-scale transcriptional framework identifies DosR, Rv1985c, Lsr2 and Rv0691c as master transcription factors responsible for differential target gene expression in L2 compared to L1 strains. C_LIO_LIL1 and L2 DosR proteins respond differently to nitric oxide stress, thus determining a relevant phenotype. C_LIO_LITime-course phenotypic and transcriptomic data under hypoxia and reaeration validate Rv1985c as a key transcription factor differentiating L2 and L1 strains. C_LI

systems biology↗

bioRxiv2019

SECAT: Quantifying differential protein-protein interaction states by network-centric analysis

Protein-protein interactions (PPIs) play critical functional and regulatory roles in virtually all cellular processes. They are essential for the formation of macromolecular complexes, which in turn constitute the basis for extended protein interaction networks that determine the functional state of a cell. We and others have previously shown that chromatographic fractionation of native protein complexes in combination with bottom-up mass spectrometric analysis of consecutive fractions supports the multiplexed characterization and detection of state-specific changes of protein complexes. In this study, we describe a computational approach that extends the analysis of data from the co-fractionation / mass spectrometric analysis of native complexes to the level of PPI networks, thus enabling a qualitative and quantitative comparison of the proteome organization between samples and states. The Size-Exclusion Chromatography Algorithmic Toolkit (SECAT) implements a novel, network-centric strategy for the scalable and robust differential analysis of PPI networks. SECAT and its underlying statistical framework elucidate differential quantitative abundance and stoichiometry attributes of proteins in the context of their PPIs. We validate algorithm predictions using publicly available datasets and demonstrate that SECAT represents a more scalable and effective methodology to assess protein-network state and that our approach thus obviates the need to explicitly infer individual protein complexes. Further, by differential analysis of PPI networks of HeLa cells in interphase and mitotic state, respectively, we demonstrate the ability of the algorithm to detect PPI network differences and to thus suggest molecular mechanisms that differentiate cellular states.

systems biology↗

bioRxiv2019

Parallel accumulation - serial fragmentation combined with data-independent acquisition (diaPASEF): Bottom-up proteomics with near optimal ion usage

Data independent acquisition (DIA) modes isolate and concurrently fragment populations of different precursors by cycling through segments of a predefined precursor m/z range. Although these selection windows collectively cover the entire m/z range, overall only a few percent of all incoming ions are sampled. Making use of the correlation of molecular weight and ion mobility in a trapped ion mobility device (timsTOF Pro), we here devise a novel scan mode that samples up to 100% of the peptide precursor ion current. We extend an established targeted data extraction workflow by including the ion mobility dimension for both signal extraction and scoring, thereby increasing the specificity for precursor identification. Data acquired from whole proteome digests and mixed organism samples demonstrate deep proteome coverage and a very high degree of reproducibility as well as quantitative accuracy, even from 10 ng sample amounts.

systems biology↗

bioRxiv2019

A global screen for assembly state changes of the mitotic proteome by SEC-SWATH-MS

Living systems integrate biochemical reactions that determine the functional state of each cell. Reactions are primarily mediated by proteins that have in systematic studies been treated as independent entities, disregarding their higher level organization into complexes which affects their activity and/or function and is thus of great interest for biological research. Here, we describe the implementation of an integrated technique to quantify cell state-specific changes in the physical arrangement of protein complexes, concurrently for thousands of proteins and hundreds of complexes. Applying this technique for comparison of human cells in interphase and mitosis, we provide a systematic overview of mitotic proteome reorganization. The results recall key hallmarks of mitotic complex remodeling and discover new events, such as a new model of nuclear pore complex disassembly, validated by orthogonal methods. To support the interpretation of quantitative SEC-SWATH-MS datasets, we extend the software CCprofiler and provide an interactive exploration tool, SECexplorer-cc.\n\nHighlightsO_LIQuantification of proteome assembly state changes using SEC-SWATH-MS\nC_LIO_LISystems-wide analysis of assembly state changes in the mitotic proteome\nC_LIO_LIDiscovery and validation of a novel mitotic disassembly intermediate of the nuclear pore complex\nC_LIO_LIHigher sensitivity and information content compared to thermostability-based approaches for global measurement of proteome states\nC_LIO_LISECexplorer, an online platform to browse results and investigate proteins newly implicated in cell division\nC_LI

systems biology↗

bioRxiv2018

Resource misallocation as a mediator of fitness costs in antibiotic resistance.

Antimicrobial resistance poses a threat to global health and the economy. It is widely accepted that, in the absence of antibiotics, drug resistance mutations carry a fitness cost. In the case of rifampicin resistance in fast-growing bacteria, this cost stems from a reduced transcription rate of the RNA polymerase resulting in slower ribosome biosynthesis. However, this relationship does not apply in the slow-growing Mycobacterium tuberculosis, where the true mechanism of fitness cost of rifampicin resistance as well as the impact of compensatory evolution remain unknown. Here we show, using global transcriptomic and proteomic profiling of selected M. tuberculosis mutants and clinical strains, that the fitness cost of rifampicin resistance in M. tuberculosis is the result of the physiological burden caused by aberrant gene expression. We further show that the perceived burden can be increased, effectively suppressing the emergence of drug resistance.

microbiology↗

bioRxiv2018

Estrogen Regulates the Satellite Cell Compartment in Females

Skeletal muscle mass, strength, and regenerative capacity decline with age, with many measures showing greater deterioration in females about the time estrogen levels decrease at menopause. Here we show that maintenance of muscle stem cells, satellite cells, as well as self-renewal and differentiation into muscle fibers, are severely compromised by estrogen deficiency. Mechanistically, by hormone replacement, use of a selective estrogen-receptor modulator (bazedoxifene), and conditional estrogen receptor knockout, we implicate 17{beta}-estradiol and satellite cell expression of estrogen receptor (ER) and show that estrogen signaling through this receptor is necessary to prevent apoptosis of satellite cells. Early data from a biopsy study of women who transitioned from peri-to post-menopause are consistent with the loss of satellite cells coincident with the decline in estradiol in humans. Together, these results demonstrate an important role for estrogen in satellite cell maintenance and muscle regeneration in females.

cell biology↗