Biology subjects

Kelly, R. T.

Publications and source records attributed to Kelly, R. T..

14 recordsLinked to original sources

bioRxiv2025

The ten dollar proteome: low-cost, deep and quantitative proteome profiling of limited sample amounts using the Orbitrap Astral and timsTOF Ultra 2 mass spectrometers

Mass spectrometry (MS)-based proteomics remains technically demanding and prohibitively expensive for many large-scale or routine applications, with per-sample costs of hundreds of dollars or more. To democratize access to proteomics and facilitate its integration into more high-throughput multi-omic studies, we present a robust analytical framework for achieving in-depth, quantitative proteome profiling at a cost of approximately $10 per sample, termed the "$10 proteome." Using the Thermo Fisher Orbitrap Astral and Bruker timsTOF Ultra 2 mass spectrometers, we evaluated performance across sample inputs ranging from 200 pg to 100 ng and active gradient lengths from 5 to 60 minutes. Proteome coverage saturated within the low-nanogram input range, with [~]8000 protein groups quantified from as little as 10 ng of input and nearly 6000 protein groups from 200 pg. With already demonstrated low-cost one-pot sample preparation workflows that are appropriate for this sample input range, standardized MS acquisition settings, and high-throughput nanoLC operated at [~]10 min per sample, the $10 proteome becomes feasible. This study establishes a practical, scalable, and cost-effective foundation for global proteome profiling, paving the way for routine, large-scale applications in systems biology, clinical research and beyond.

systems biology↗

bioRxiv2025

CMG2 interaction with actin is required for growth factor-induced chemotaxis in endothelial cells.

Capillary morphogenesis gene 2 (CMG2/ANTXR2) is a cell surface receptor that contributes to corneal angiogenesis and is essential for orienting endothelial cell chemotaxis in growth factor gradients. However, the mechanism by which CMG2 transmits signals to orient endothelial cells is unknown. Here, we use affinity proteomics to identify proteins that interact with CMG2. This led us to investigate CMG2 interaction with F-actin in the context of serum and individual growth factors (bFGF/PDGF/VEGF) in both primary and immortalized endothelial cells. We also measured different matrix-protein-mediated changes in chemotaxis. We find that CMG2 function is matrix dependent, and that CMG2-actin interaction is required for serum- and growth-factor-induced chemotaxis. Inhibiting chemotaxis with CMG2 antagonists (collagen 6, penta-galloyl-glucose, or anthrax toxin protective antigen; PA) leads to the release of actin cytoskeleton from CMG2. Furthermore, CMG2 co-localizes with actin at the leading edge during live migration. This colocalization is disrupted by PA, which also disrupts directional movement. Finally, we demonstrate that the conserved actin binding domain of CMG2 directly binds to F-actin in vitro where it bundles F-actin, suggesting a mechanism by which it enables directional migration.

molecular biology↗

bioRxiv2025

Oocyte mitochondria link maternal environment to offspring phenotype

During oogenesis and maturation oocytes undergo a recently discovered mitochondrial electron transport chain (ETC) remodeling in flies1, frogs1, and humans2. This conserved oocyte ETC remodeling is regulated by maternal insulin signaling, but its role in biology remains unclear. In the model animal Caenorhabditis elegans, we previously found that insulin signaling to oocytes regulates offsprings ability to adapt to future osmotic stress by altering offspring metabolism. However, the molecular mechanisms that function in oocytes to mediate this intergenerational stress response are similarly unknown. Here, we developed a low-input oocyte proteomics workflow and combined it with our C. elegans intergenerational stress response model to find that both a mothers environment and maternal insulin signaling regulate the abundance of ETC proteins in oocytes - particularly the abundance of proteins involved in the transfer of electrons from QH2 to cytochrome C by ETC Complex III. Using genetic perturbations of ETC function we further found that promoting ETC Complex III function in oocytes was both necessary and sufficient to link a mothers environment to adaptive changes in offspring metabolism. Lastly, we found that the effects of Complex III dysfunction in oocytes on offspring were mediated via an AMP-kinase (AAK-2) dependent mechanism and that AAK-2 functions in offspring to promote ATP preservation and glycerol metabolism in response to stress. Collectively, our data suggest that the role of oocyte ETC remodeling in biology includes linking maternal environments to changes in offspring metabolism that promote offspring survival in the environment experienced by their mother.

genetics↗

bioRxiv2024

Boosting the Sensitivity of Quantitative Single-Cell Proteomics with Activated Ion-Tandem Mass Tags (AI-TMT)

Single-cell proteomics is a powerful approach to precisely profile protein landscapes within individual cells toward a comprehensive understanding of proteomic functions and tissue and cellular states. The inherent challenges associated with limited starting material in single-cell analyses demands heightened analytical sensitivity. Just as advances in sample preparation maximize the amount of material that makes it from the cell to the mass spectrometer, we strive to maximize the number of ions that make it from ion source to the detector. In isobaric tagging experiments, limited reporter ion generation limits quantitative accuracy and precision. The combination of infrared photoactivation and ion parking circumvents the m/z dependence inherent in HCD, maximizing reporter generation and avoiding unintended degradation of TMT reporter molecules in a method we term activated ion-tandem mass tags (AI-TMT). The method was applied to single-cell human proteomes using 18-plex TMTpro, resulting in a 4-5-fold increase in reporter ion signal on average compared to conventional SPS-MS3 approaches. AI-TMT enables faster duty cycles, higher throughput, and increased peptide identification and quantification. Comparative experiments showcase 4-5-fold lower injection times for AI-TMT, providing superior sensitivity without compromising accuracy. In all, AI-TMT enhances the sensitivity and dynamic range of proteomic experiments and is compatible with other techniques, including gas-phase fractionation and real-time searching, promising increased gains in the study of cellular heterogeneity and disease mechanisms.

biochemistry↗

bioRxiv2023

TDP-43-stratified single-cell proteomic profiling of postmortem human spinal motor neurons reveals protein dynamics in amyotrophic lateral sclerosis

Unbiased proteomics has been employed to interrogate central nervous system (CNS) tissues (brain, spinal cord) and fluid matrices (CSF, plasma) from amyotrophic lateral sclerosis (ALS) patients; yet, a limitation of conventional bulk tissue studies is that motor neuron (MN) proteome signals may be confounded by admixed non-MN proteins. Recent advances in trace sample proteomics have enabled quantitative protein abundance datasets from single human MNs (Cong et al., 2020b). In this study, we leveraged laser capture microdissection (LCM) and nanoPOTS (Zhu et al., 2018c) single-cell mass spectrometry (MS)-based proteomics to query changes in protein expression in single MNs from postmortem ALS and control donor spinal cord tissues, leading to the identification of 2515 proteins across MNs samples (>900 per single MN) and quantitative comparison of 1870 proteins between disease groups. Furthermore, we studied the impact of enriching/stratifying MN proteome samples based on the presence and extent of immunoreactive, cytoplasmic TDP-43 inclusions, allowing identification of 3368 proteins across MNs samples and profiling of 2238 proteins across TDP-43 strata. We found extensive overlap in differential protein abundance profiles between MNs with or without obvious TDP-43 cytoplasmic inclusions that together point to early and sustained dysregulation of oxidative phosphorylation, mRNA splicing and translation, and retromer-mediated vesicular transport in ALS. Our data are the first unbiased quantification of single MN protein abundance changes associated with TDP-43 proteinopathy and begin to demonstrate the utility of pathology-stratified trace sample proteomics for understanding single-cell protein abundance changes in human neurologic diseases.

pathology↗

bioRxiv2022

Data-Dependent Acquisition with Precursor Coisolation Improves Proteome Coverage and Measurement Throughput for Label-Free Single-Cell Proteomics

The sensitivity of single-cell proteomics (SCP) has increased dramatically in recent years due to advances in experimental design, sample preparation, separations and mass spectrometry instrumentation. Further increasing the sensitivity of SCP methods and instrumentation will enable the study of proteins within single cells that are expressed at copy numbers too small to be measured by current methods. Here we combine efficient nanoPOTS sample preparation and ultra-low-flow liquid chromatography with a newly developed data acquisition and analysis scheme termed wide window acquisition (WWA) to quantify >3,000 proteins from single cells in fast label-free analyses. WWA is based on data-dependent acquisition (DDA) but employs larger precursor isolation windows to intentionally co-isolate and co-fragment additional precursors along with the selected precursor. The resulting chimeric MS2 spectra are then resolved using the CHIMERYS search engine within Proteome Discoverer 3.0. Compared to standard DDA workflows, WWA employing isolation windows of 8-12 Th increases peptide and proteome coverage by ~28% and ~39%, respectively. For a 40-min LC gradient operated at ~15 nL/min, we identified an average of 2,150 proteins per single-cell-sized aliquots of protein digest directly from MS2 spectra, which increased to an average of 3,524 proteins including proteins identified with MS1-level feature matching. Reducing the active gradient to 20 min resulted in a modest 10% decrease in proteome coverage. We also compared the performance of WWA with DIA. DIA underperformed WWA in terms of proteome coverage, especially with faster separations. Average proteome coverage for single HeLa and K562 cells was respectively 1,758 and 1,642 based on MS2 identifications with 1% false discovery rate and 3042 and 2891 with MS1 feature matching. As such, WWA combined with efficient sample preparation and rapid separations extends the depths of the proteome that can be studied at the single-cell level.

cell biology↗

bioRxiv2021

Features of peptide fragmentation spectra in single cell proteomics

The goal of proteomics is to identify and quantify the complete set of proteins in a biological sample. Single cell proteomics specializes in identification and quantitation of proteins for individual cells, often used to elucidate cellular heterogeneity. The significant reduction in ions introduced into the mass spectrometer for single cell samples could impact the features of MS2 fragmentation spectra. As all peptide identification software tools have been developed on spectra from bulk samples and the associated ion rich spectra, the potential for spectral features to change is of great interest. We characterize the differences between single cell spectra and bulk spectra by examining three fundamental spectral features that are likely to affect peptide identification performance. All features show significant changes in single cell spectra, including loss of annotated fragment ions, blurring signal and background peaks due to diminishing ion intensity and distinct fragmentation pattern compared to bulk spectra. As each of these features is a foundational part of peptide identification algorithms, it is critical to adjust algorithms to compensate for these losses.

bioinformatics↗

bioRxiv2021

Benchmarking PSM identification tools for single cell proteomics

Single cell proteomics is an emerging sub-field within proteomics with the potential to revolutionize our understanding of cellular heterogeneity and interactions. Recent efforts have largely focused on technological advancements in sample preparation, chromatography and instrumentation to enable measuring proteins present in these ultra-limited samples. Although advancements in data acquisition have rapidly improved our ability to analyze single cells, the software pipelines used in data analysis were originally written for traditional bulk samples and their performance on single cell data has not been investigated. We benchmarked five popular peptide identification tools on single cell proteomics data. We found that MetaMorpheus achieved the greatest number of peptide spectrum matches at a 1% false discovery rate. Depending on the tool, we also find that post processing machine learning can improve spectrum identification results by up to [~]40%. Although rescoring leads to a greater number of peptide spectrum matches, these new results typically are generated by 3rd party tools and have no way of being utilized by the primary pipeline for quantification. Exploration of novel metrics for machine learning algorithms will continue to improve performance.

bioinformatics↗

bioRxiv2021

Temporal variation in lymphocyte proteomics

Chronic Lymphocytic Leukemia (CLL) is a slow progressing disease, characterized by a long asymptomatic stage followed by a symptomatic stage during which patients receive treatment. While proteomic studies have discovered differential pathways in CLL, the proteomic evolution of CLL during the asymptomatic stage has not been studied. In this pilot study, we show that by using small sample sizes comprising ~145 cells, we can detect important features of CLL necessary for studying tumor evolution. Our small samples are collected at two time points and reveal large proteomic changes in healthy individuals over time. A meta-analysis of two CLL proteomic papers showed little commonality in differentially expressed proteins and demonstrates the need for larger control populations sampled over time. To account for proteomic variability between time points and individuals, large control populations sampled at multiple time points are necessary for understanding CLL progression. Data is available via ProteomeXchange with identifier PXD027429.

bioinformatics↗

bioRxiv2021

Robust, sensitive, and quantitative single-cell proteomics based on ion mobility filtering

Unbiased single-cell proteomics (scProteomics) promises to advance our understanding of cell functions within complex biological systems. However, a major challenge for current methods is their ability to identify and provide accurate quantitative information for low abundance proteins. Herein, we describe an ion mobility-enhanced mass spectrometry acquisition and peptide identification method, TIFF (Transferring Identification based on FAIMS Filtering), designed to improve the sensitivity and accuracy of label-free scProteomics. TIFF significantly extends the ion accumulation times for peptide ions by filtering out singly charged background ions. The peptide identities are then assigned by a 3-dimensional MS1 feature matching approach (retention time, accurate mass, and FAIMS compensation voltage). The TIFF method enabled unbiased proteome analysis to a depth of >1,700 proteins in single HeLa cells with >1,100 proteins consistently quantified. As a demonstration, we applied the TIFF method to obtain temporal proteome profiles of >150 single murine macrophage cells during a lipopolysaccharide stimulation experiment and identified time-dependent proteome profiles.

systems biology↗

bioRxiv2020

Calculating sample size requirements for temporal dynamics in single cell proteomics

Single cell measurements are uniquely capable of characterizing cell-to-cell heterogeneity, and have been used to explore the large diversity of cell types and physiological functions present in tissues and other complex cell assemblies. An intriguing application of single cell proteomics is the characterization of proteome dynamics during biological transitions, like cellular differentiation or disease progression. Time course experiments, which regularly take measurements during state transitions, rely on the ability to detect dynamic trajectories in a data series. However, in a single cell proteomics experiment, cell-to-cell heterogeneity complicates the confident identification of proteome dynamics as measurement variability may be higher than expected. Therefore, a critical question for these experiments is how many data points need to be acquired during the time course to enable robust statistical analysis. We present here an analysis of the most important variables that affect statistical confidence in the detection of proteome dynamics: fold-change, measurement variability, and the number of cells measured during the time course. Importantly, we show that datasets with less than 16 measurements across the time domain suffer from low accuracy and also have a high false-positive rate. We also demonstrate how to balance competing demands in experimental design to achieve a desired result.

bioinformatics↗

bioRxiv2020

Ultrasensitive single-cell proteomics workflow identifies >1000 protein groups per mammalian cell

We report on the combination of nanodroplet sample preparation, ultra-low-flow nanoLC, high-field asymmetric ion mobility spectrometry (FAIMS), and the latest-generation Orbitrap Eclipse Tribrid mass spectrometer for greatly improved single-cell proteome profiling. FAIMS effectively filtered out singly charged ions for more effective MS analysis of multiply charged peptides, resulting in an average of 1056 protein groups identified from single HeLa cells without MS1-level feature matching. This is 2.3 times more identifications than without FAIMS and a far greater level of proteome coverage for single mammalian cells than has been previously reported for a label-free study. Differential analysis of single microdissected motor neurons and interneurons from human spinal tissue indicated a similar level of proteome coverage, and the two subpopulations of cells were readily differentiated based on single-cell label-free quantification.

systems biology↗

bioRxiv2019

Single-cell proteomics reveals downregulation of TMSB4X to drive actin release for stereocilia assembly

Hearing and balance rely on small sensory hair cells that reside in the inner ear. To explore dynamic changes in the abundant proteins present in differentiating hair cells, we used nanoliter-scale shotgun mass spectrometry of single cells, each [~]1 picoliter, from utricles of embryonic day 15 chickens. We identified unique constellations of proteins or protein groups from presumptive hair cells and from progenitor cells. The single-cell proteomes enabled the de novo reconstruction of a developmental trajectory. Inference of protein expression dynamics revealed that the actin monomer binding protein thymosin {beta}4 (TMSB4X) was present in progenitors but dropped precipitously during hair-cell differentiation. Complementary single-cell transcriptome profiling showed downregulation of TMSB4X mRNA during maturation of hair cells. We propose that most actin is sequestered by TMSB4X in progenitor cells, but upon differentiation to hair cells, actin is released to build the sensory hair bundle.

cell biology↗

bioRxiv2017

Conformational locking of Ufm1 upon binding to the Ufm1-interacting sequence of Uba5

Ubiquitin fold modifier 1 (Ufm1) is a ubiquitin-like protein (UBL) found in eukaryotic organisms which plays a crucial role in ER stress management and signal transduction. The crystal structure of UFM1 and its E1 (Uba5) in complex shows that Ufm1 binds to the adenylation domain of UBA5 and interacts with a separate Ufm1-interacting sequence (UIS) in the C-terminus of UBA5. The UIS interacts with Ufm1 on the opposite side of Ufm1 protein from the adenylation domain of Uba5 and the reason for this second interaction site is unclear. We analyzed Ufm1 bound to the UIS sequence through molecular dynamics simulations in order to identify additional functions for this interaction. We found that the residues in the adenylation interaction site of Ufm1 have less movement when the UIS peptide was bound to Ufm1 and formed a structure that aligns well with Ufm1 bound to the Uba5 adenylation domain. We further identified an amino acid that connects the UIS to the adenylation domain interacting site. Mutation of this amino acid decreases charging activity and shifts the Ufm1 conformation population toward the unlocked configuration even in the presence of the UIS peptide. These data suggest a role for the Uba5 UIS in stimulating activation of Ufm1.

biochemistry↗