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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Spatial Proteomics towards cellular Resolution

Introduction: Spatial biology is an emerging interdisciplinary field facilitating biological discoveries through the use of spatial omics technologies. Recent advancements in spatial transcriptomics, spatial genomics (e.g. genetic mutations and epigenetic marks), multiplexed immunofluorescence, and spatial metabolomics/lipidomics have enabled high-resolution spatial profiling of gene expression, genetic variation, protein expression, and metabolites/lipids profiles in tissue. These developments contribute to a deeper understanding of the spatial organization within tissue microenvironments at the molecular level. Areas covered: This report provides an overview of the untargeted, bottom-up mass spectrometry (MS)-based spatial proteomics workflow. It highlights recent progress in tissue dissection, sample processing, bioinformatics, and liquid chromatography (LC)-MS technologies that are advancing spatial proteomics toward cellular resolution. Expert opinion: The field of untargeted MS-based spatial proteomics is rapidly evolving and holds great promise. To fully realize the potential of spatial proteomics, it is critical to advance data analysis and develop automated and intelligent tissue dissection at the cellular or subcellular level, along with high-throughput LC-MS analyses of thousands of samples. In conclusion, achieving these goals will necessitate significant advancements in tissue dissection technologies, LC-MS instrumentation, and computational tools.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Untargeted Spatial Metabolomics and Spatial Proteomics on the Same Tissue Section

An increasing number of spatial multiomic workflows have been recently developed. Some of these approaches have leveraged initial mass spectrometry imaging (MSI)-based spatial metabolomics to inform region of interest (ROI) selection for downstream spatial proteomics. However, these workflows have been limited by varied substrate requirements between modalities or have required analyzing serial sections (i.e., one section per modality). To mitigate these issues, we present a novel multiomic workflow that uses desorption electrospray ionization (DESI)-MSI to identify representative spatial metabolite patterns on-tissue prior to spatial proteomic analyses on the same tissue section. Further, this workflow is demonstrated here with a model mammalian tissue (coronal rat brain section) mounted on a polyethylene naphthalate-membrane slide. Initial DESI-MSI resulted in 160 annotations (SwissLipids) within to the METASPACE platform (≤20% false discovery rate). A segmentation map from the annotated ion images informed downstream ROI selection for spatial proteomics characterization from the same sample. The unspecific substrate requirements and minimal sample disruption inherent to DESI-MSI allowed for an optimized, downstream spatial proteomics assay, resulting in 3888 ± 240 to 4717 ± 48 proteins being confidently directed per ROI (200 µm x 200 µm). Finally, we demonstrate the integration of multiomic information, where we found ceramide localization to be correlated with SMPD3 abundance (ceramide synthesis protein), and we also utilized protein abundance to resolve metabolite isomeric ambiguity. Overall, the integration of DESI-MSI into the multiomic workflow allows for complementary spatial and molecular-level information to be achieved from optimized implementations of each MS assay inherent to the workflow itself.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Open-Source and FAIR Research Software for Proteomics

Scientific discovery relies on innovative software as much as experimental methods, especially in proteomics, where computational tools are essential for mass spectrometer setup, data analysis, and interpretation. Since the introduction of SEQUEST, proteomics software has grown into a complex ecosystem of algorithms, predictive models, and workflows, but the field faces challenges, including the increasing complexity of mass spectrometry data, limited reproducibility due to proprietary software, and difficulties integrating with other omics disciplines. Closed-source, platform-specific tools exacerbate these issues by restricting innovation, creating inefficiencies, and imposing hidden costs on the community. Open-source software (OSS), aligned with the FAIR Principles (Findable, Accessible, Interoperable, Reusable), offers a solution by promoting transparency, reproducibility, and community-driven development, which fosters collaboration and continuous improvement. In this manuscript, we explore the role of OSS in computational proteomics, its alignment with FAIR principles, and its potential to address challenges related to licensing, distribution, and standardization. Drawing on lessons from other omics fields, we present a vision for a future where OSS and FAIR principles underpin a transparent, accessible, and innovative proteomics community.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Parallel measurement of transcriptomes and proteomes from same single cells using nanodroplet splitting

Single-cell multiomics provides comprehensive insights into gene regulatory networks, cellular diversity, and temporal dynamics. Here, we introduce nanoSPLITS (nanodroplet SPlitting for Linked-multimodal Investigations of Trace Samples), an integrated platform that enables global profiling of the transcriptome and proteome from same single cells via RNA sequencing and mass spectrometry-based proteomics, respectively. Benchmarking of nanoSPLITS demonstrates high measurement precision with deep proteomic and transcriptomic profiling of single-cells. We apply nanoSPLITS to cyclin-dependent kinase 1 inhibited cells and found phospho-signaling events could be quantified alongside global protein and mRNA measurements, providing insights into cell cycle regulation. We extend nanoSPLITS to primary cells isolated from human pancreatic islets, introducing an efficient approach for facile identification of unknown cell types and their protein markers by mapping transcriptomic data to existing large-scale single-cell RNA sequencing reference databases. Accordingly, we establish nanoSPLITS as a multiomic technology incorporating global proteomics and anticipate the approach will be critical to furthering our understanding of biological systems.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Proteomic insights into the physiology and metabolism of oleaginous yeasts and filamentous fungi

Fungi are vital to the bioeconomy, serving as key producers of food, beverages, biofuels, and medicines, while also acting as essential resource recyclers in ecosystem management. For nearly a century, oleaginous yeast and filamentous fungi have been explored for their proficiency in oleochemicals production and carbon storage. Lipogenesis is one of the most well-studied fungal processes, with substantial progress having been made through reductionist biochemical approaches; however, the physiology and metabolism of fungal systems operating under different conditions arise from the functions of thousands of proteins, for which very little is known outside of model yeast. In this review, we discuss how proteomics provides a valuable analytical approach to contextualize lipogenesis within a complex biological system, where lipid accumulation is fundamentally governed by changes in proteins of multiple pathways. In the past two decades, proteomics has been applied to study stress response to nutrient limitations, metabolism of various carbon and nitrogen sources, the lipid droplet hub of carbon storage, protein post-translational modifications and signaling pathways, as well as oleochemical biosynthesis, thereby advancing our understanding of the oleaginous phenotype. Over 40 studies are reviewed herein to evaluate the impact, critically assess the utility, and propose future applications of proteomics. In the coming years, large systems-level proteomics studies will lay a foundation for marrying modeling and metabolic engineering strategies to optimize oleochemicals production in oleaginous fungi.

Lipid production↗
NASA NTRS2016

Elucidating the Gravome: Quantitative Proteomic Profiling of the Response to Chronic Hypergravity in Drosophila

Altered gravity conditions, such as experienced by organisms during spaceflight, is known to cause transcriptomic and proteomic changes. We describe the proteomic changes in the whole body of adult Drosophila melanogaster (fruit fly), but focus specifically on the localized changes in the adult head in response to chronic hypergravity (3G) treatment. Canton S adult female flies (2-3 days old) were exposed to chronic hypergravity for 9 days and compared with parallel 1G controls. After hypergravity treatment, whole flies and fly heads were separated, and evaluated for quantitative comparison of the two gravity conditions using an isobaric tagging liquid chromatography-tandem mass spectrometry approach. Data revealed a total of 1948 (whole flies) and 1480 (head) proteins to be differentially present in hypergravity-treated flies. Gene Ontology analysis of head specific proteomics revealed host immune response and humoral stress proteins were significantly upregulated. Proteins related to calcium signaling, ion transport and ATPase were decreased. Enhanced expression of cuticular proteins may suggest an alteration in chitin metabolism and in chitin-based cuticle development. We therefore present a comprehensive quantitative survey of proteomic changes in response to chronic hypergravity in Drosophila, which will help elucidate the underlying molecular mechanisms associated with altered gravity environments.

Hypergravity↗
DOE OSTI2025

Proteomic characterization of Mycobacterium tuberculosis subjected to carbon starvation

ABSTRACT Mycobacterium tuberculosis(Mtb) is the causative agent of tuberculosis (TB), the leading cause of infectious disease-related deaths worldwide. TB infections present on a spectrum from active to latent disease. In the human host,Mtbfaces hostile environments, such as nutrient deprivation, hypoxia, and low pH. Under these conditions,Mtbcan enter a dormant, but viable, state characterized by a lack of cell replication and increased resistance to antibiotics. DormantMtbposes a major challenge to curing infections and eradicating TB globally. We subjectedMtbmc 2 6020 (ΔlysAand ΔpanCD), a double auxotrophic strain, to carbon starvation (CS), a culture condition that induces growth stasis and mimics environmental conditions associated with dormancyin vivo. We provide a detailed analysis of the proteome in CS compared to replicating samples. We observed extensive proteomic reprogramming, with 36% of identified proteins significantly altered in CS. Many enzymes involved in oxidative phosphorylation and lipid metabolism were retained or more abundant in CS. The cell wall biosynthetic machinery was present in CS, although numerous changes in the abundance of peptidoglycan, arabinogalactan, and mycolic acid biosynthetic enzymes likely result in pronounced remodeling of the cell wall. Many clinically approved anti-TB drugs target cell wall biosynthesis, and we found that these enzymes were largely retained in CS. Lastly, we compared our results to those of other dormancy models and propose that CS produces a physiologically distinct state of stasis compared to hypoxia inMtb. IMPORTANCE Tuberculosis is a devastating human disease that kills over 1.2 million people a year. This disease is caused by the bacterial pathogenMycobacterium tuberculosis(Mtb).Mtbexcels at surviving in the human host by entering a non-replicating, dormant state. The current work investigated the proteomic changes thatMtbundergoes in response to carbon starvation, a culture condition that models dormancy. The authors found broad effects of carbon starvation on the proteome, with the relative abundance of 37% of proteins significantly altered. Protein changes related to cell wall biosynthesis, metabolism, and drug susceptibility are discussed. Proteins associated with a carbon starvation phenotype are identified, and results are compared to other dormancy models, including hypoxia.

Microbiology↗
NASA NTRS2018

Marginal protein stability drives subcellular proteome isoelectric point

There exists a positive correlation between the pH of subcellular compartments and the median isoelectric point (pI) for the associated proteomes. Proteins in the human lysosome—a highly acidic compartment in the cell—have a median pI of ∼6.5, whereas proteins in the more basic mitochondria have a median pI of ∼8.0. Proposed mechanisms reflect potential adaptations to pH. For example, enzyme active site general acid/base residue pKs are likely evolved to match environmental pH. However, such effects would be limited to a few residues on specific proteins, and might not affect the proteome at large. A protein model that considers residue burial upon folding recapitulates the correlation between proteome pI and environmental pH. This correlation can be fully described by a neutral evolution process; no functional selection is included in the model. Proteins in acidic environments incur a lower energetic penalty for burying acidic residues than basic residues, resulting in a net accumulation of acidic residues in the protein core. The inverse is true under alkaline conditions. The pI distributions of subcellular proteomes are likely not a direct result of functional adaptations to pH, but a molecular spandrel stemming from marginal stability.

Kaiser Loell↗
DOE OSTI2025

Leveraging structure-informed machine learning for fast steric zipper propensity prediction across whole proteomes

Predicting the amyloid fold and the propensity of peptide segments to adopt amyloid-like structures remain a challenge. However, recent progress has facilitated structure-based prediction of steric zipper propensity and the use of machine learning to accelerate the calculation of predictive models across many scientific areas. Leveraging these advances, we have developed a new approach for rapid proteome-wide assessment of zipper profiles that is informed by four million steric zipper predictions collected over ten years. This collection is used to build a machine learning model capable of rapidly predicting steric zipper propensity, and allowing for the assessment of zippers at both the protein and proteome level. Our predictions show enrichment for zipper forming segments in proteins involved in cell wall reorganization in yeast, highlighting a potential category of interest for experimental characterization. Overall, our predictive model allows for the exploration of amyloid formation across the tree of life and provides a tool for assessment of both novel and designed sequences for zipper density.

Biochemistry & Molecular Biology↗
DOE OSTI2024

In-Depth Proteome Profiling of the Hippocampus of LDLR Knockout Mice Reveals Alternation in Synaptic Signaling Pathway

The low-density lipoprotein receptor (LDLR) is a major apolipoprotein receptor that regulates cholesterol homeostasis. LDLR deficiency is associated with cognitive impairment by the induction of synaptopathy in the hippocampus. Despite the close relationship between LDLR and neurodegenerative disorders, proteomics research for protein profiling in the LDLR knockout (KO) model remains insufficient. Therefore, understanding LDLR KO-mediated differential protein expression within the hippocampus is crucial for elucidating a role of LDLR in neurodegenerative disorders. In this study, we conducted first-time proteomic profiling of hippocampus tissue from LDLR KO mice using tandem mass tag (TMT)-based MS analysis. LDLR deficiency induces changes in proteins associated with the transport of diverse molecules, and activity of kinase and catalyst within the hippocampus. Additionally, significant alterations in the expression of components in the major synaptic pathways were found. Furthermore, these synaptic effects were verified using a data-independent acquisition (DIA)-based proteomic method. In conclusion, our data will serve as a valuable resource for further studies to discover the molecular function of LDLR in neurodegenerative disorders.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Single‐Cell Nanodroplet Processing Proteomics Pipeline for Analysis of Human‐Derived Microglia

Single-cell omics tools provide unique insights into heterogeneous cell populations and their responses to stimuli. For example, single-cell RNA sequencing has identified several transcriptionally distinct populations of microglia, which are resident immune cells of the central nervous system (CNS) that are responsive to CNS injury, infection, and neurodegeneration. To date, single-cell studies of microglia have focused on RNA-sequencing or cytometry by time of flight (CyTOF), which provide indirect readouts of protein abundance or quantification of a limited number of targets. Herein, we present a workflow based on FACS-assisted isolation, cryopreservation, and nanodroplet-based processing for single-cell mass spectrometry proteomics analysis of the postmortem human brain cortex-derived microglia. From a single microglial cell, 1039 proteins could be identified on average. As a proof-of-principle, we applied single-cell proteomics for exploring the heterogeneity of brain microglia at the cellular level. This pilot proteomics data partially recapitulates the prior microglia subtypes. Specifically, we determined that mitochondrial proteins, in particular members of NADH dehydrogenase (Complex I), cytochrome b-c1 (Complex III), cytochrome c oxidase (Complex IV), F1-ATPase (Complex V), and Na+/K+-ATPase complex, drive variation across microglia. This pipeline offers the potential for identifying functionally and analytically relevant protein targets for microglia in Alzheimer's disease and other neurological disorders.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Integrated lipidomic and proteomic profiling reveals metabolic network disruption by SARS-CoV-2 variants

The rapid evolution of SARS-CoV-2 has produced myriad viral strains with increasing transmissibility and capacity for immune evasion. While effective vaccination campaigns have reduced the fatalities associated with SARS-CoV-2, infections continue, and a detailed understanding of how this virus manipulates host biochemical pathways remains elusive. We asked both whether the patterns of host lipid rewiring remained consistent across variants and whether the changes in the abundance of lipid classes are related to changes in the expression of the enzymes involved in their biosynthesis. We compared global nontargeted lipidomics on A549-ACE2 cells infected with the delta variant (B.1.617.2), or the omicron (B.1.1.529) variant to our previous results of global nontargeted lipidomics on A549-ACE2 cells infected with the original WA1 strain and further performed quantitative proteomics to assess changes in the host proteome. We found that metabolic rewiring, both on the lipid and the enzymatic level, is remarkably consistent across all three variants. We further mapped changes in the expression of host metabolic enzymes, linking enzyme expression to alterations in the abundance of specific lipids during infection. This analysis identified key proteins related to virus-mediated changes in lipid abundance, including fatty acid synthase (FASN), lysosomal acid lipase (LIPA), and ORMDL, a regulator of sphingolipid biosynthesis. These integrated lipidomic and proteomic experiments shed light on the importance of the complex network of host metabolism networks that support SARS-CoV-2 infection and suggest that lipid metabolism may be a promising avenue for uncovering conserved therapeutic targets.

SARS-CoV-2↗
DOE OSTI2025

Discovery of Proteoforms Associated With Alzheimer's Disease Through Quantitative Top-Down Proteomics

The complex nature of Alzheimer's disease (AD) and its heterogenous clinical presentation has prompted numerous large-scale - omic analyses aimed at providing a global understanding of the pathophysiological processes involved. AD involves isoforms, proteolytic products, and posttranslationally modified proteins such as amyloid beta (Aβ) and microtubule-associated protein tau. Top-down proteomics directly measures these species and thus, offers a comprehensive view of pathologically relevant proteoforms that are difficult to analyze using traditional proteomic techniques. Here, we broadly explored associations between proteoforms and clinicopathological traits of AD by deploying a quantitative top-down proteomics approach across frontal cortex of 103 subjects selected from the ROS and MAP cohorts. The approach identified 1213 proteins and 11,782 proteoforms, of which 154 proteoforms had at least one significant association with a clinicopathological phenotype. One important finding included identifying Aβ C-terminal truncation state as the key property for differential association between amyloid plaques and cerebral amyloid angiopathy. Furthermore, various N-terminally truncated forms of Aβ had noticeably stronger association with amyloid plaques and global cognitive function. Additionally, we discovered six VGF neuropeptides that were positively associated with cognitive function independent of pathological burden. The database of brain cortex proteoforms provides a valuable context for functional characterization of the proteins involved in AD and other late-onset brain pathologies.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Expanded genome and proteome reallocation in a novel, robust Bacillus coagulans strain capable of utilizing pentose and hexose sugars

Bacillus coagulans, a Gram-positive thermophilic bacterium, is recognized for its probiotic properties and recent development as a microbial cell factory. Despite its importance for biotechnological applications, the current understanding of B. coagulans’ robustness is limited, especially for undomesticated strains. To fill this knowledge gap, we characterized the metabolic capability and performed functional genomics and systems analysis of a novel, robust strain, B. coagulans B-768. Genome sequencing revealed that B-768 has the largest B. coagulans genome known to date (3.94 Mbp), about 0.63 Mbp larger than the average genome of sequenced B. coagulans strains, with expanded carbohydrate metabolism and mobilome. Functional genomics identified a well-equipped genetic portfolio for utilizing a wide range of C5 (xylose, arabinose), C6 (glucose, mannose, galactose), and C12 (cellobiose) sugars present in biomass hydrolysates, which was validated experimentally. For growth on individual xylose and glucose, the dominant sugars in biomass hydrolysates, B-768 exhibited distinct phenotypes and proteome profiles. Faster growth and glucose uptake rates resulted in lactate overflow metabolism, which makes B. coagulans a lactate overproducer; however, slower growth and xylose uptake diminished overflow metabolism due to the high energy demand for sugar assimilation. Carbohydrate Transport and Metabolism (COG-G), Translation (COG-J), and Energy Conversion and Production (COG-C) made up 60%–65% of the measured proteomes but were allocated differently when growing on xylose and glucose. The trade-off in proteome reallocation, with high investment in COG-C over COG-G, explains the xylose growth phenotype with significant upregulation of xylose metabolism, pyruvate metabolism, and tricarboxylic acid (TCA) cycle. Strain B-768 tolerates and effectively utilizes inhibitory biomass hydrolysates containing mixed sugars and exhibits hierarchical sugar utilization with glucose as the preferential substrate.

carbohydrate metabolism↗
DOE OSTI2026

Single-cell proteomics of Arabidopsis leaf mesophyll reveals dynamic protein responses to water-deficit stress

Background The application of single-cell omics tools to biological systems can provide unique insights into diverse cellular populations and their heterogeneous responses to internal and external perturbations. Thus far, most single-cell studies in plant systems have been limited to RNA-sequencing approaches, which only provide indirect readouts of cellular functions. Results Here, we present a single-cell proteomics workflow for plant cells that integrates tape-sandwich protoplasting, piezoelectric cell sorting, nanoPOTS sample preparation, and ion mobility-based MS data acquisition method for label-free single-cell proteomics analysis of Arabidopsis leaf mesophyll cells. From a single leaf protoplast, over 3,000 proteins were quantified with high precision. The workflow is demonstrated to identify stress associated changes in protein abundance by analyzing 117 protoplasts from well-watered and water-deficit stressed plants. Additionally, we describe a new approach for constructing covarying protein networks at the single-cell level and demonstrate how single-cell protein covariation analysis can reveal previously unrecognized protein functions while also capturing stress-induced changes in protein–protein dynamics. Conclusions The label-free scProteomic approach presented here represents a significant advance through the demonstration of a facile protoplast isolation method combined with deep and precise proteomic coverage of Arabidopsis leaf mesophyll cell types. We believe this study will serve as an informative reference to future plant scProteomic investigations.

Arabidopsis↗
DOE OSTI2026

Data for Mitochondrial ATP Generation is More Proteome Efficient than Glycolysis

Metabolic efficiency profoundly influences organismal fitness. Heterotrophs, from yeast to mammals, derive usable energy primarily through glycolysis and respiration. While respiration is more energy-efficient, some cells favor glycolysis even when oxygen is available (aerobic glycolysis, Warburg effect). A leading explanation is that glycolysis is more efficient in terms of ATP production per unit mass of protein (i.e. faster). Through quantitative flux analysis and proteomics, we find however that mitochondrial respiration is actually more proteome-efficient than aerobic glycolysis. This is shown across yeasts, T cells, cancer cells, and tissues and tumors in vivo. Instead of aerobic glycolysis being valuable for fast ATP production, it correlates with high glycolytic protein expression, which is valuable for hypoxic growth. Aerobic glycolytic yeasts do not excel at aerobic growth, but outgrow respiratory cells in oxygen limitation. Thus, aerobic glycolysis emerges from cells maintaining a proteome conducive to both aerobic and hypoxic growth.

Metabolomics↗
DOE OSTI2024

PPI DataHub Project Data Package: High-density Lipoprotein (HDL) Structure and Function Proteomics

The purpose of this experiment was to investigate how the interactions between APOA1 and APOA2 on the surface of high-density lipoproteins (HDL) impact particle function. Interactions were investigated on HDL isolated from human blood plasma using structural proteomics tools such as chemical cross-linking and limited proteolysis (LiP). The structural proteomics data was acquired using a Q-Exactive HF-X mass spectrometer and data was processed and compiled using MaxQuant sofware (v.1.6.17.0). Processed datasets are openly accessible from the download button (~2.8 GB) and contain secondary processed LiP and global proteomic results files and supporting metadata materials. Processed data downloads include a sample naming key, processed MaxQuant results/parameters, and protein annotated relative abundance files.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Emerging protein sequencing technologies: proteomics without mass spectrometry?

Liquid chromatography-tandem mass spectrometry (LC-MS/MS) has been a leading method for proteomics for 30 years. Advantages provided by LC-MS/MS are offset by significant disadvantages, including cost. Recently, several non-mass spectrometric methods have emerged, but little information is available about their capacity to analyze the complex mixtures routine for mass spectrometry. Areas Covered: We review recent non-mass-spectrometric methods for sequencing proteins and peptides, including those using nanopores, sequencing by degradation, reverse translation, and short-epitope mapping, with comments on bioinformatics challenges, fundamental limitations, and areas where new technologies will be more or less competitive with LC-MS/MS. In addition to conventional literature searches, instrument vendor websites, patents, webinars, and preprints were also consulted to give a more up-to-date picture. Expert Opinion: Many new technologies are promising. However, demonstrations that they outperform mass spectrometry in terms of peptides and proteins identified have not yet been published, and astute observers note important disadvantages, especially relating to the dynamic range of single-molecule measurements of complex mixtures. Still, even if the performance of emerging methods proves inferior to LC-MS/MS, their low cost could create a different kind of revolution: a dramatic increase in the number of biology laboratories engaging in new forms of proteomics research.

59 BASIC BIOLOGICAL SCIENCES↗