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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Enhanced Spatial Proteomics and Metabolomics from a Single Tissue Section Using MALDI-MSI and LCM-microPOTS Platforms

Spatially resolved mass spectrometry (MS)-based multi-omics workflows are becoming more utilized for revealing the complex biology that occurs within tissues. However, these approaches commonly require multiple independent tissue sections to analyze the metabolite and protein compositions of these samples. This poses a significant challenge in preserving cell- or region-specific molecular fidelity, as variations between tissue sections can compromise the accurate correlation of molecular data. Here, in this study, we developed workflows for comprehensive multi-omics profiling from a single tissue section (STS) using different MS modalities. We enhanced the functionality of an electrically insulated substrate by employing metal-assisted approaches that enabled both MS-based untargeted spatial metabolomics and proteomics from STS. This allowed metabolite imaging using matrix-assisted laser desorption/ionization-MS imaging (MALDI-MSI), without compromising it for subsequent proteome profiling with laser capture microdissection (LCM)-based technology. Specifically, implementing copper tape as a backing for polyethylene naphthalate (PEN) slides enabled the detection of >140 metabolites across a poplar root tissue section using MALDI-trapped ion mobility spectrometry time of flight (timsTOF)-MS. Afterwards, we detected 6,571 unique proteins from two distinct root regions by leveraging LCM technology coupled to our microdroplet based sample preparation approach. We also developed an alternative workflow utilizing gold-coated PEN substrates for imaging with MALDI-Fourier-transform ion cyclotron resonance (FTICR)-MS, which permitted the profiling of >170 metabolites and the identification of 6,542 unique proteins across a single poplar root tissue section. These results were comparable to using each assay independently without modifications. These approaches offer new opportunities for high-resolution molecular profiling of multiple omics-levels across biological tissues.

Veličković, Marija [Pacific Northwest National Lab↗
DOE OSTI2025

Model of metabolism and gene expression predicts proteome allocation in Pseudomonas putida

Abstract The genome-scale model of metabolism and gene expression (ME-model) forPseudomonas putidaKT2440,iPpu1676-ME, provides a comprehensive representation of biosynthetic costs and proteome allocation. Compared to a metabolic-only model,iPpu1676-ME significantly expands on gene expression, macromolecular assembly, and cofactor utilization, enabling accurate growth predictions without additional constraints. Multi-omics analysis using RNA sequencing and ribosomal profiling data revealed translational prioritization inP. putida, with core pathways, such as nicotinamide biosynthesis and queuosine metabolism, exhibiting higher translational efficiency, while secondary pathways displayed lower priority. Notably, the ME-model significantly outperformed the M-model in alignment with multi-omics data, thereby validating its predictive capacity. Thus,iPpu1676-ME offers valuable insights intoP. putida’s proteome allocation and presents a powerful tool for understanding resource allocation in this industrially relevant microorganism.

Mathematical & Computational Biology↗
DOE OSTI2026

Microbial Co-Culture Control Proteomics (MC-DP1)

Co-cultured S. elongatus PCC 7942 CscB/SPS and R. toruloides IFO0880 presented as an effective photosynthesis-driven biofuel production platform. The goal of this experiment was to understand the molecular mechanism of this co-culture system at redox post-translational modification level. The redox proteome of the co-cultured strains was compared to a mono-cultured S. elongatus in light or dark conditions. Samples were processed using a resin-assisted capture (RAC) workflow with TMT labeling to enrich and quantify modified cysteines at proteome level. The datasets were generated by a Q Exactive Plus Orbitrap Mass Spectrometer coupled with a Waters nanoAcquity UPLC, then searched by MSGF+ for downstream redox PTM analysis.

Source record↗
DOE OSTI2025

Characterization of Cytokine Treatment on Human Pancreatic Islets by Top‐Down Proteomics

Type 1 diabetes (T1D) results from autoimmune-mediated destruction of insulin-producing β cells in the pancreatic islet. This process is modulated by pro-inflammatory cytokine signaling, which has been previously shown to alter protein expression in ex vivo islets. Herein, we applied top-down proteomics to globally evaluate proteoforms from human islets treated with proinflammatory cytokines (interferon-γ and interleukin-1β). We measured 1636 unique proteoforms across six donors and two time points (control and 24 h post-treatment) and observed consistent changes in abundance across the glicentin-related pancreatic polypeptide (GRPP) and major proglucagon fragment regions of glucagon, as well as the LF-19/catestatin and vasostatin-1/2 region of chromogranin-A. We also observe several proteoforms that increase after cytokine-treatment or are exclusively observed after cytokine-treatment, including forms of beta-2 microglobulin (B2M), high-mobility group N2 protein (HMGN2), and chemokine (C-X-C motif) ligands (CXCL). Together, our quantitative results provide a baseline proteoform profile for human islets and identify several proteoforms that may serve as interesting candidate markers for T1D progression or therapeutic intervention.

glucagon↗
DOE OSTI2024

Integrative Multi-PTM Proteomics Reveals Dynamic Global, Redox, Phosphorylation, and Acetylation Regulation in Cytokine-treated Pancreatic Beta Cells

Studying regulation of protein function at a systems level necessitates an understanding of the interplay among diverse post-translational modifications (PTMs). A variety of proteomics sample processing workflows are currently used to study specific PTMs but rarely characterize multiple types of PTMs from the same sample inputs. Method incompatibilities and laborious sample preparation steps complicate large-scale physiological investigations and can lead to variations in results. The single-pot, solid-phase-enhanced sample preparation (SP3) method for sample cleanup is compatible with different lysis buffers and amenable to automation, making it attractive for high-throughput multi-PTM profiling. Herein, we describe an integrative SP3 workflow for multiplexed quantification of protein abundance, cysteine thiol oxidation, phosphorylation, and acetylation. The broad applicability of this approach is demonstrated using cell and tissue samples, and its utility for studying interacting regulatory networks is highlighted in a time-course experiment of cytokine-treated ß-cells. We observed a swift response in global regulation of protein abundances consistent with rapid activation of JAK-STAT and NF-?B signaling pathways. Regulators of these pathways as well as proteins involved in their target processes displayed multi-PTM dynamics indicative of a complex cellular response stages: acute, adaptation, and chronic (prolonged stress). PARP14, a negative regulator of JAK-STAT, had multiple co-localized PTMs that may be involved in intraprotein regulatory crosstalk. Our workflow provides a high-throughput platform that can profile multi-PTMomes from the same sample set, which is valuable in unraveling the functional roles of PTMs and their co-regulation.

proteomics, PTM, automation, SP3, cysteine thiol o↗
DOE OSTI2026

Longitudinal Plasma Proteomic Profiling Reveals Divergent Immune Responses in Durably Cured and Relapsed Pulmonary Tuberculosis

Background: Predicting the risk of tuberculosis (TB) relapse is vital to improving treatment outcomes. Although clinical risk factors of relapse are well characterized, the biological mechanisms driving relapse, particularly host immune responses, remain poorly understood. Elucidating these mechanisms is necessary to better predict relapse risk. Methods: We conducted a longitudinal, global proteomic study on 60 participants with active pulmonary TB, half who were durably cured and half who relapsed. Plasma was collected at seven time-points: at treatment initiation (baseline), during therapy, and 52 weeks post-baseline. Samples were analyzed by high-resolution LC-MS/MS. Results: 2,418 proteins were identified across all samples, with 1,756 being differentially expressed relative to baseline (unadjusted p < 0.05). 956 proteins were differentially abundant between cured and relapsed participants. Relapsed participants showed heightened humoral immunity throughout treatment, as well as upregulated complement activation and HDL particles. Cured participants exhibited elevated recovery-related pathways by week 4, including downregulated epithelial invasion and upregulated oxygen transport. Conclusions: Heightened humoral and innate immune responses were associated with relapse, whereas recovery signatures were associated with durable cure. These findings advance our understanding of host responses to treatment and provide a basis for developing blood-based biomarkers to identify patients at increased risk of relapse.

LC-MS/MS↗
DOE OSTI2024

S. elongatus PCC 7942 Carbon Metabolism Proteomics (MC-DP2)

The purpose of this experiment was to examine the redox proteome of S. elongatus PCC 7942 CscB/SPS under different light conditions (light and dark) and culture conditions (dense and dilute optical density). Samples were processed using a resin-assisted capture (RAC) workflow with TMT labeling to enrich and quantify protein cysteines which were differentially oxidized under four conditions. The datasets were generated by a Q Exactive Plus Orbitrap Mass Spectrometer coupled with a Waters nanoAcquity UPLC, then searched by MSGF+ for downstream redox PTM analysis.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Proteome results.

Proteome results. Protein abundance was determined for N. aromaticivorans DSM12444 wild-type and strain JMN2 during growth with glucose, and for strain JMN2 during growth with guaiacol.

cbi↗
DOE OSTI2025

Proteome-wide characterization of PTMs reveals host cell responses to viral infection and identifies putative antiviral drug targets

Post-translational modifications (PTMs) are biochemical modifications that can significantly alter protein structure, function, stability, localization, and interactions with other molecules, thereby activating or inactivating intracellular processes. A growing body of research has begun to highlight the role of PTMs, including phosphorylation, ubiquitination, acetylation, and redox modifications, during virus-host interactions. Collectively, these PTMs regulate key steps in mounting the host immune response and control critical host pathways required for productive viral replication. This has led to the conception of antiviral therapeutics that focus on controlling host protein PTMs, potentially offering pathogen-agnostic treatment options and revolutionizing our capacity to prevent virus transmission. On the other hand, viruses can hijack the host cellular PTM machinery to modify viral proteins in promoting viral replication and evading immune surveillance. PTM regulation during virus-host interactions is complex and poorly mapped, and the development of effective PTM-targeted antiviral drugs will require a more comprehensive understanding of the cellular pathways essential for virus replication. In this review, we discuss the roles of PTMs in virus infection and how technological advances in mass spectrometry-based proteomics can capture systems-level PTM changes during viral infection. Additionally, we explore how such knowledge is leveraged to identify PTM-targeted candidates for developing antiviral drugs. Looking ahead, studies focusing on the discovery and functional elucidation of PTMs, either on the host or viral proteins, will not only deepen our understanding of molecular pathology but also pave the way for developing better drugs to fight emerging viruses.

Immunology↗
DOE OSTI2025

Challenges in spatial metabolomics and proteomics for functional tissue unit and single-cell resolution

While transcriptomics is the most broadly applied technology for global spatial and single cell measurements in healthy and diseased tissues. Transcripts are often used as a proxy for protein and even metabolite measurements, but it has become commonly accepted that extrapolating this kind of information is a poor proxy and not a substitute for direct measurement. Within the last decade advanced developments of mass spectrometry-based assays have made these direct measurements not only possible, but routine. Where mass spectrometry has become an enabling technology, and various methods can now detect hundreds of metabolites and thousands of proteins from samples. Not only can this be performed within bulk measurements, but much effort has been directed into translating these measurements to single cells and tissues at cellular resolution. The information obtained from mass spectrometry is now able to trace metabolic events and decipher feedback loops across anatomical regions, connecting genetic and metabolic networks that define phenotypes. Herein, we will broadly overview developments in the field over the past decade, leading into several case studies which highlight the direct measurement of metabolites, proteins, and proteoforms from thinly sliced tissues. Much of this work is feasible due to multidisciplinary team science, and we offer brief perspective on paths forward and the challenges that persist with adoption and application of spatial omics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

A Multiplexed Quantitative Analysis of Germline Single Amino Acid Variants by Targeted Proteomics in Nondepleted Human Plasma

Single amino acid variants (SAAVs) in protein sequences are often a direct result of single-nucleotide polymorphisms (SNPs). Certain germline SAAVs have shown biological relevance in different disease conditions but lack precise quantification in circulation, which could hinder functional investigations and progress in biomarker development. Here, we have developed a multiplexed liquid chromatography-selected reaction monitoring (LC-SRM) assay that monitors 5 wild-type and variant peptide pairs (Complement Factor B: CFB-R32Q/R32W, Clusterin: CLU-N317H, Fetuin B: FETUB-K360R, and Kininogen: KNG1-L212P) in nondepleted human plasma. The assay was optimized for imprecision, linearity, stability, and calibration assessments with CVs of under 20%. The wild-type and variant peptide pairs were characterized in a set of healthy individual plasma samples. These target identifications were also validated by SNP genotyping with more than 99% accuracy. For all protein targets, we observed significantly lower concentrations of WT species in the presence variant peptides. In CFB, the concentration of R32Q was significantly lower than its counterpart R32W variant and WT species. Furthermore, our results distinguished phenotypes of homozygosity and heterozygosity of the SAAV presence through direct concentration level characterization. These findings provide some insights into how SAAVs affect quantitative assessments of target peptides. The assay demonstrates a platform for proteogenomic analyses with potential applications in both research and clinical settings.

genetics↗
DOE OSTI2026

Human Lung Fibroblast Response to HCoV-229E Infection, Top-down Proteomics of Histones (ACS-TZ-DP7)

The purpose of this experiment was to evaluate the human host cellular response to wild type human coronavirus strain 229E (HCoV-229E) infection, specifically how histones are modified following infection. Sample data was obtained from mock-infected and HCoV-229E-infected immortalized human lung fibroblasts (MRC-5) (MOI 3). Whole cell lysates were collected at 24 hours post infection and histones from all samples were enriched and were processed for proteoform identification analysis.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Visualizing and analyzing 3D biomolecular structures using Mol* at RCSB.org: Influenza A H5N1 virus proteome case study

The easiest and often most useful way to work with experimentally determined or computationally predicted structures of biomolecules is by viewing their three-dimensional (3D) shapes using a molecular visualization tool. Mol* was collaboratively developed by RCSB Protein Data Bank (RCSB PDB, RCSB.org) and Protein Data Bank in Europe (PDBe, PDBe.org) as an open-source, web-based, 3D visualization software suite for examination and analyses of biostructures. It is capable of displaying atomic coordinates and related experimental data of biomolecular structures together with a variety of annotations, facilitating basic and applied research, training, education, and information dissemination. Across RCSB.org, the RCSB PDB research-focused web portal, Mol* has been implemented to support single-mouse-click atomic-level visualization of biomolecules (e.g., proteins, nucleic acids, carbohydrates) with bound cofactors, small-molecule ligands, ions, water molecules, or other macromolecules. RCSB.org Mol* can seamlessly display 3D structures from various sources, allowing structure interrogation, superimposition, and comparison. Using influenza A H5N1 virus as a topical case study of an important pathogen, we exemplify how Mol* has been embedded within various RCSB.org tools—allowing users to view polymer sequence and structure-based annotations integrated from trusted bioinformatics data resources, assess patterns and trends in groups of structures, and view structures of any size and compositional complexity. In addition to being linked to every experimentally determined biostructure and Computed Structure Model made available at RCSB.org, Standalone Mol* is freely available for visualizing any atomic-level or multi-scale biostructure at rcsb.org/3d-view.

3D biostructure↗
DOE OSTI2024

Quantitative proteomics reveals extensive lysine ubiquitination and transcription factor stability states in Arabidopsis

Abstract Protein activity, abundance, and stability can be regulated by post-translational modification including ubiquitination. Ubiquitination is conserved among eukaryotes and plays a central role in modulating cellular function; yet, we lack comprehensive catalogs of proteins that are modified by ubiquitin in plants. In this study, we describe an antibody-based approach to enrich ubiquitinated peptides coupled with isobaric labeling to enable quantification of up to 18-multiplexed samples. This approach identified 17,940 ubiquitinated lysine sites arising from 6,453 proteins from Arabidopsis (Arabidopsis thaliana) primary roots, seedlings, and rosette leaves. Gene ontology analysis indicated that ubiquitinated proteins are associated with numerous biological processes including hormone signaling, plant defense, protein homeostasis, and metabolism. We determined ubiquitinated lysine residues that directly regulate the stability of three transcription factors, CRYPTOCHROME-INTERACTING BASIC-HELIX-LOOP-HELIX 1 (CIB1), CIB1 LIKE PROTEIN 2 (CIL2), and SENSITIVE TO PROTON RHIZOTOXICITY1 (STOP1) using in vivo degradation assays. Furthermore, codon mutation of CIB1 to create a K166R conversion to prevent ubiquitination, via CRISPR/Cas9-derived adenosine base editing, led to an early flowering phenotype and increased expression of FLOWERING LOCUS T (FT). These comprehensive site-level ubiquitinome profiles provide a wealth of data for future functional studies related to modulation of biological processes mediated by this post-translational modification in plants.

Biochemistry & Molecular Biology↗
DOE OSTI2026

Development and implementation of high-throughput proteomic and metabolomics assays by using advanced chromatographic and mass spectrometric systems (CRADA Final Report)

The mission of this CRADA with Agilent was to couple powerful MS platforms (QQQ, IM-QTOFMS) with Agilent’s novel Ultra-High-Performance Liquid Chromatography (UHPLC) fast metabolomic workflows and perform ABF Machine Learning (ML) to generated datasets. Agilent transferred UHPLC methods to PNNL and LBNL and methods were implemented and demonstrated in both labs, achieving total acquisition times of < 10 min. Metabolites analyzed using Agilent’s shared methods included metabolites from central carbon metabolism, common across hosts, and metabolites unique to engineered strains. Standards were acquired in an UHPLC-Drift Tube Ion Mobility Mass Spectrometer (DTIMS) system for the first time within the context of ABF and methods were optimized based on Agilent’s protocols. Samples from ABF hosts Pseudomonas putida, Aspergillus pseudoterreus, Aspergillus niger and Rhodosporidium toruloides were analyzed using the UHPLC-DTIMS platform for a total of 276 runs. A data analysis workflow compatible with the Experimental Data Depot (EDD) and completely shareable was developed for the acquired UHPLC-DTIMS data. Samples were analyzed using a Data Independent Acquisition Approach (DIA), which for most of the standards provided more transitions therefore increasing detection confidence. Using the data acquired by PNNL, LBNL, and Agilent’s specifications from previous ML projects, SNL applied an ensemble ML strategy to pick the best performing model for automated LC-method selection. Finally, with the contribution of the participant labs and Agilent, SNL developed an Automated Method Selection (AMS) software tool to predict the best liquid chromatography method for analysis of any new molecules of interest. Samples with novel pathways and new metabolite targets of interest are generated at a high pace in the ABF. Overall, the project advanced rapid metabolomics by combining liquid chromatography, ion mobility spectrometry, and data-independent mass spectrometry with machine learning. This multidimensional approach uses retention time, collision cross-section, precursor mass, and fragment-ion information to distinguish chemically similar metabolites that can be difficult to resolve using conventional liquid- or gas-chromatography methods. The resulting workflow also provided automated metabolite-identification error estimates, addressing a recognized need for statistical confidence measures in metabolomics.

Petzold, Christopher [Lawrence Berkeley National L↗