Protein Contaminants Matter: Building Universal Protein Contaminant Libraries for DDA and DIA Proteomics

Endogenous Protein-Protein Interaction Network of the NPC Cholesterol Transporter 1 in the Cerebral Cortex

NPC intracellular cholesterol transporter 1 (NPC1) is a multipass, transmembrane glycoprotein mostly recognized for its key role in facilitating cholesterol efflux. Mutations in the NPC1 gene result in Niemann-Pick disease, type C (NPC), a fatal, lysosomal storage disease. Due to the progressively expanding implications of NPC1-related disorders, we investigated endogenous NPC1 protein-protein interactions in the mouse cortex and human-derived iPSCs neuronal models of the disease through coimmunoprecipitation-coupled with LC-MS based proteomics. The current study investigated protein-protein interactions specific to the wild-type and the most prevalent NPC1 mutation (NPC1I1061T) while filtering out any protein interactor identified in the Npc1-/- mouse model. Additionally, the results were matched across the two species to map the parallel interactome of wild-type and mutant NPC1I1061T. Most of the identified wild-type NPC1 interactors were related to cytoskeleton organization, synaptic vesicle activity, and translation. We found many putative NPC1 interactors not previously reported, including two SCAR/WAVE complex proteins that regulate ARP 2/3 complex actin nucleation and multiple membrane proteins important for neuronal activity at synapse. Moreover, we identified proteins important in trafficking specific to wild-type and mutant NPC1I1061T. Together, the findings are essential for a comprehensive understanding of NPC1 biological functions in addition to its classical role in sterol efflux.

OmicScope unravels systems-level insights from quantitative proteomics data

Shotgun proteomics analysis presents multifaceted challenges, demanding diverse tool integration for insights. Addressing this complexity, OmicScope emerges as an innovative solution for quantitative proteomics data analysis. Engineered to handle various data formats, it performs data pre-processing - including joining replicates, normalization, data imputation - and conducts differential proteomics analysis for both static and longitudinal experimental designs. Empowered by Enrichr with over 224 databases, OmicScope performs Over Representation Analysis (ORA) and Gene Set Enrichment Analysis (GSEA). Additionally, its Nebula module facilitates meta-analysis from independent datasets, providing a systems biology approach for enriched insights. Complete with a data visualization toolkit and accessible as Python package and a web application, OmicScope democratizes proteomics analysis, offering an efficient and high-quality pipeline for researchers.

Multiomics Evaluation of Human iPSCs and iPSC-Derived Neurons

Human induced pluripotent stem cells (iPSCs) can be differentiated into neurons, providing living human neurons to model brain diseases. However, it is unclear how different types of molecules work together to regulate stem cell and neuron biology in healthy and disease states. In this study, we conducted integrated proteomics, lipidomics, and metabolomics analyses with confident identification, accurate quantification, and reproducible measurements to compare the molecular profiles of human iPSCs and iPSC-derived neurons. Proteins, lipids, and metabolites related to mitosis, DNA replication, pluripotency, glycosphingolipids, and energy metabolism were highly enriched in iPSCs, whereas synaptic proteins, neurotransmitters, polyunsaturated fatty acids, cardiolipins, and axon guidance pathways were highly enriched in neurons. Mutations in the GRN gene lead to the deficiency of the progranulin (PGRN) protein, which has been associated with various neurodegenerative diseases. Using this multiomics platform, we evaluated the impact of PGRN deficiency on iPSCs and neurons at the whole-cell level. Proteomics, lipidomics, and metabolomics analyses implicated PGRN's roles in neuroinflammation, purine metabolism, and neurite outgrowth, revealing commonly altered pathways related to neuron projection, synaptic dysfunction, and brain metabolism. Multiomics data sets also pointed toward the same hypothesis that neurons seem to be more susceptible to PGRN loss compared to iPSCs, consistent with the neurological symptoms and cognitive impairment from patients carrying inherited GRN mutations.

Common viral infections inhibit egg laying in honey bee queens and are linked to premature supersedure

With their long lives and extreme reproductive output, social insect queens have escaped the classic trade-off between fecundity and lifespan, but evidence for a trade-off between fecundity and immunity has been inconclusive. This is in part because pathogenic effects are seldom decoupled from effects of immune induction. We conducted parallel, blind virus infection experiments in the laboratory and in the field to interrogate the idea of a reproductive immunity trade-off in honey bee (Apis mellifera) queens and to better understand how these ubiquitous stressors affect honey bee queen health. We found that queens injected with infectious virus had smaller ovaries and were less likely to recommence egg-laying than controls, while queens injected with UV-inactivated virus displayed an intermediate phenotype. In the field, heavily infected queens had smaller ovaries and infection was a meaningful predictor of whether supersedure cells were observed in the colony. Immune responses in queens receiving live virus were similar to queens receiving inactivated virus, and several of the same immune proteins were negatively associated with ovary mass in the field. This work supports the hypothesized relationship between virus infection and symptoms associated with queen failure and suggests that a reproductive-immunity trade-off is partially, but not wholly responsible for these effects.

High-resolution electron cryomicroscopy of V-ATPase in native synaptic vesicles

Intercellular communication in the nervous system occurs through the release of neurotransmitters into the synaptic cleft between neurons. In the presynaptic neuron, the proton pumping vesicular- or vacuolar-type ATPase (V-ATPase) powers neurotransmitter loading into synaptic vesicles (SVs), with the V1 complex dissociating from the membrane region of the enzyme before exocytosis. We isolated SVs from rat brain using SidK, a V-ATPase-binding bacterial effector protein. Single-particle electron cryomicroscopy allowed high-resolution structure determination of V-ATPase within the native SV membrane. In the structure, regularly spaced cholesterol molecules decorate the enzyme's rotor and the abundant SV protein synaptophysin binds the complex stoichiometrically. ATP hydrolysis during vesicle loading results in a loss of the V1 region of V-ATPase from the SV membrane, suggesting that loading is sufficient to induce dissociation of the enzyme.

Benchmarking and Automating the Biotinylation Proteomics Workflow

Protein biotinylation has been widely used in biotechnology with various labeling and enrichment strategies. However, different enrichment strategies have not been systematically evaluated due to the lack of a benchmarking model for fair comparison. Most biotinylation proteomics workflows suffer from lengthy experimental steps, non-specific bindings, limited throughput, and experimental variability. To address these challenges, we designed a two-proteome model, where biotinylated yeast proteins were spiked in unlabeled human proteins, allowing us to distinguish true enrichment from non-specific bindings. Using this benchmarking model, we compared common biotinylation proteomics methods and provided practical selection guidelines. We significantly optimized and shortened sample preparation from 3 days to 9 hours, enabling fully-automated 96-well plate sample processing. Next, we applied this optimized and automated workflow for proximity labeling to investigate the intricate interplay between mitochondria and lysosomes in living cells under both healthy state and mitochondrial damage. Our results suggested a time-dependent proteome remodeling and dynamic translocation within mitochondria and between mitochondria and lysosomes upon mitochondrial damage. This newly established benchmarking model and the fully-automated 9-hour workflow can be readily applied to the broad fields of protein biotinylation to study protein interaction and organelle dynamics.

Supramolecular self-assembly strategies of natural-based β-lactoglobulin modulating bitter perception of goat milk-derived bioactive peptides

Complete self-assembly and reassembly behavior of bitter peptide-protein necessitates multilevel theories that encompass phenomena ranging from the self-assembly of recombinant complex to atomic trajectories. An extension to the level of mechanism method was put forth, involves limited enzymatic digestion and bottom-up proteomics to dissect inherent heterogeneity within β-LG and β-LG-PPGLPDKY complex and uncover conformational and dynamic alterations occurring in specific local regions of the model protein. Bitter peptide PPGLPDKY spontaneously bound to IIAEKTK, IDALNENK, and YLLFCMENSAEPEQSLACQCLVR regions of β-LG in a 1:1 stoichiometric ratio to mask bitterness perception. Molecular dynamic simulation and free energy calculation provided time-varying atomic trajectories of the recombinant complex and found that a peptide was stabilized in the upper region of the hydrophobic cavity with the binding free energy of -30.56 kJ mol-1 through 4 hydrogen bonds (Glu74, Glu55, Lys69, and Ser116) and hydrophobic interactions (Asn88, Asn90, and Glu112). Current research aims to provide valuable physical insights into the macroscopic self-assembly behavior between proteins and bitter peptides, and the meticulous design of highly acceptable taste characteristics in goat milk products.

Capturing acyl-enzyme intermediates with genetically encoded 2,3-diaminopropionic acid for hydrolase substrate identification

Catalytic mechanism-based, light-activated traps have recently been developed to identify the substrates of cysteine or serine hydrolases. These traps are hydrolase mutants whose catalytic cysteine or serine are replaced with genetically encoded 2,3-diaminopropionic acid (DAP). DAP-containing hydrolases specifically capture the transient thioester- or ester-linked acyl-enzyme intermediates resulting from the first step of the proteolytic reaction as their stable amide analogs. The trapped substrate fragments allow the downstream identification of hydrolase substrates by mass spectrometry and immunoblotting. In this protocol, we provide a detailed step-by-step guide for substrate capture and identification of the peptidase domain of the large tegument protein deneddylase (UL36USP) from human herpesvirus 1, both in mammalian cell lysate and live mammalian cells. Four procedures are included: Procedure 1, DAP-mediated substrate trapping in mammalian cell lysate (~8 d); Procedure 2, DAP-mediated substrate trapping in adherent mammalian cells (~6 d); Procedure 3, DAP-mediated substrate trapping in suspension mammalian cells (~5 d); and Procedure 4, substrate identification and validation (~12-13 d). Basic skills to perform protein expression in bacteria or mammalian cells, affinity enrichment and proteomic analysis are required to implement the protocol. This protocol will be a practical guide for identifying substrates of serine or cysteine hydrolases either in a complex mixture, where genetic manipulation is challenging, or in live cells such as bacteria, yeasts and mammalian cells.

Quantitative proteomics reveals the Sox system's role in sulphur and arsenic metabolism of phototroph Halorhodospira halophila

The metabolic process of purple sulphur bacteria's anoxygenic photosynthesis has been primarily studied in Allochromatium vinosum, a member of the Chromatiaceae family. However, the metabolic processes of purple sulphur bacteria from the Ectothiorhodospiraceae and Halorhodospiraceae families remain unexplored. We have analysed the proteome of Halorhodospira halophila, a member of the Halorhodospiraceae family, which was cultivated with various sulphur compounds. This analysis allowed us to reconstruct the first comprehensive sulphur-oxidative photosynthetic network for this family. Some members of the Ectothiorhodospiraceae family have been shown to use arsenite as a photosynthetic electron donor. Therefore, we analysed the proteome response of Halorhodospira halophila when grown under arsenite and sulphide conditions. Our analyses using ion chromatography-inductively coupled plasma mass spectrometry showed that thioarsenates are chemically formed under these conditions. However, they are more extensively generated and converted in the presence of bacteria, suggesting a biological process. Our quantitative proteomics revealed that the SoxAXYZB system, typically dedicated to thiosulphate oxidation, is overproduced under these growth conditions. Additionally, two electron carriers, cytochrome c551/c5 and HiPIP III, are also overproduced. Electron paramagnetic resonance spectroscopy suggested that these transporters participate in the reduction of the photosynthetic Reaction Centre. These results support the idea of a chemically and biologically formed thioarsenate being oxidized by the Sox system, with cytochrome c551/c5 and HiPIP III directing electrons towards the Reaction Centre.

Extracellular Vesicles from Highly Metastatic Osteosarcoma Cells Induce Pro-Tumorigenic Macrophage Phenotypes

Metastasis is the principal factor in poor prognosis for individuals with osteosarcoma (OS). Understanding the events that lead to metastasis is critical to develop better interventions for this disease. Alveolar macrophages are potentially involved in priming the lung microenvironment for OS metastasis, yet the mechanisms involved in this process remain unclear. Since extracellular vesicles (EVs) are a known actor in primary tumor development, their potential role in OS metastagenesis through macrophage modulation is explored here. The interaction of EVs isolated from highly metastatic (K7M2) and less metastatic (K12) osteosarcoma cell lines is compared with a peritoneal macrophage cell line. An EV concentration that reproducibly induced macrophage migration is identified first, then used for later experiments. By confocal microscopy, both EV types associated with M0 or M1 macrophages; however, only K7M2-EVs are associated with M2 macrophages, an interaction that is abrogated by EV pre-treatment with anti-CD47 antibody. Interestingly, all interactions appeared to be surface binding, not internalized. In functional studies, K7M2-EVs polarized fewer macrophages to M1. Together, these data suggest that K7M2-EVs have unique interactions with macrophages that can contribute to the production of a higher proportion of pro-tumor type macrophages, thereby accelerating metastasis.

Proteomic Analysis Reveals Trilaciclib-Induced Senescence

Trilaciclib, a cyclin-dependent kinase 4/6 inhibitor, was approved as a myeloprotective agent for protecting bone marrow from chemotherapy-induced damage in extensive-stage small cell lung cancer. This is achieved through the induction of a temporary halt in the cell cycle of bone marrow cells. While it has been studied in various cancer types, its potential in hematological cancers remains unexplored. This research aimed to investigate the efficacy of trilaciclib in hematological cancers. Utilizing mass spectrometry-based proteomics, we examined the alterations induced by trilaciclib in the chronic myeloid leukemia cell line, K562. Interestingly, trilaciclib promoted senescence in these cells rather than cell death, as observed in acute myeloid leukemia, acute lymphoblastic leukemia, and myeloma cells. In K562 cells, trilaciclib hindered cell cycle progression and proliferation by stabilizing cyclin-dependent kinase 4/6 and downregulating cell cycle-related proteins, along with the concomitant activation of autophagy pathways. Additionally, trilaciclib-induced senescence was also observed in the nonsmall cell lung carcinoma cell line, A549. These findings highlight trilaciclib's potential as a therapeutic option for hematological cancers and underscore the need to carefully balance senescence induction and autophagy modulation in chronic myeloid leukemia treatment, as well as in nonsmall cell lung carcinoma cell line.

Environment modulates protein heterogeneity through transcriptional and translational stop codon readthrough

Stop codon readthrough events give rise to longer proteins, which may alter the protein's function, thereby generating short-lasting phenotypic variability from a single gene. In order to systematically assess the frequency and origin of stop codon readthrough events, we designed a library of reporters. We introduced premature stop codons into mScarlet, which enabled high-throughput quantification of protein synthesis termination errors in E. coli using fluorescent microscopy. We found that under stress conditions, stop codon readthrough may occur at rates as high as 80%, depending on the nucleotide context, suggesting that evolution frequently samples stop codon readthrough events. The analysis of selected reporters by mass spectrometry and RNA-seq showed that not only translation but also transcription errors contribute to stop codon readthrough. The RNA polymerase was more likely to misincorporate a nucleotide at premature stop codons. Proteome-wide detection of stop codon readthrough by mass spectrometry revealed that temperature regulated the expression of cryptic sequences generated by stop codon readthrough in E. coli. Overall, our findings suggest that the environment affects the accuracy of protein production, which increases protein heterogeneity when the organisms need to adapt to new conditions.

Wheat-Based Glues in Conservation and Cultural Heritage: (Dis)solving the Proteome of Flour and Starch Pastes and Their Adhering Properties

Plant-based adhesives, such as those made from wheat, have been prominently used for books and paper-based objects and are also used as conservation adhesives. Starch paste originates from starch granules, whereas flour paste encompasses the entire wheat endosperm proteome, offering strong adhesive properties due to gluten proteins. From a conservation perspective, understanding the precise nature of the adhesive is vital as the longevity, resilience, and reaction to environmental changes can differ substantially between starch- and flour-based pastes. We devised a proteomics method to discern the protein content of these pastes. Protocols involved extracting soluble proteins using 0.5 M NaCl and 30 mM Tris-HCl solutions and then targeting insoluble proteins, such as gliadins and glutenins, with a buffer containing 7 M urea, 2 M thiourea, 4% CHAPS, 40 mM Tris, and 75 mM DTT. Flour paste's proteome is diverse (1942 proteins across 759 groups), contrasting with starch paste's predominant starch-associated protein makeup (218 proteins in 58 groups). Transformation into pastes reduces proteomes' complexity. Testing on historical bookbindings confirmed the use of flour-based glue, which is rich in gluten and serpins. High levels of deamidation were detected, particularly for glutamine residues, which can impact the solubility and stability of the glue over time. The mass spectrometry proteomics data have been deposited to the ProteomeXchange, Consortium (http://proteomecentral.proteomexchange.org) via the MassIVE partner repository with the data set identifier MSV000093372 (ftp://MSV000093372@massive.ucsd.edu).

An Automated and Fast Sample Preparation Workflow for Laser Microdissection Guided Ultrasensitive Proteomics

Spatial tissue proteomics integrating whole-slide imaging, laser microdissection, and ultrasensitive mass spectrometry is a powerful approach to link cellular phenotypes to functional proteome states in (patho)physiology. To be applicable to large patient cohorts and low sample input amounts, including single-cell applications, loss-minimized and streamlined end-to-end workflows are key. We here introduce an automated sample preparation protocol for laser microdissected samples utilizing the cellenONE robotic system, which has the capacity to process 192 samples in 3 h. Following laser microdissection collection directly into the proteoCHIP LF 48 or EVO 96 chip, our optimized protocol facilitates lysis, formalin de-crosslinking, and tryptic digest of low-input archival tissue samples. The seamless integration with the Evosep ONE LC system by centrifugation allows 'on-the-fly' sample clean-up, particularly pertinent for laser microdissection workflows. We validate our method in human tonsil archival tissue, where we profile proteomes of spatially-defined B-cell, T-cell, and epithelial microregions of 4000 μm2 to a depth of ∼2000 proteins and with high cell type specificity. We finally provide detailed equipment templates and experimental guidelines for broad accessibility.

Advancing Sustainable Malting Practices: Aquaporins as Potential Breeding Targets for Improved Water Uptake during Controlled Germination of Barley (Hordeum vulgare L.)

The conversion of raw barley (Hordeum vulgare L.) to malt requires a process of controlled germination, where the grain is submerged in water to raise the moisture content to >40%. The transmembrane proteins, aquaporins, influence water uptake during the initial stage of controlled germination, yet little is known of their involvement in malting. With the current focus on sustainability, understanding the mechanisms of water uptake and usage during the initial stages of malting has become vital in improving efficient malting practices. In this study, we used quantitative proteomics analysis of two malting barley genotypes demonstrating differing water-uptake phenotypes in the initial stages of malting. Our study quantified 19 transmembrane proteins from nine families, including seven distinct aquaporin isoforms, including the plasma intrinsic proteins (PIPs) PIP1;1, PIP2;1, and PIP2;4 and the tonoplast intrinsic proteins (TIPs) TIP1;1, TIP2;3, TIP3;1, and TIP3;2. Our findings suggest that the presence of TIP1;1, TIP3;1, and TIP3;2 in the mature barley grain proteome is essential for facilitating water uptake, influencing cell turgor and the formation of large central lytic vacuoles aiding storage reserve hydrolysis and endosperm modification efficiency. This study proposes that TIP3s mediate water uptake in malting barley grain, offering potential breeding targets for improving sustainable malting practices.

Pharmacogene expression during progression of metabolic dysfunction-associated steatotic liver disease: Studies on mRNA and protein levels and their relevance to drug treatment

Metabolic dysfunction-associated steatotic liver disease (MASLD) is common worldwide. Genes and proteins contributing to drug disposition may show altered expression as MASLD progresses. To assess this further, we undertook transcriptomic and proteomic analysis of 137 pharmacogenes in liver biopsies from a large MASLD cohort. We performed sequencing on RNA from 216 liver biopsies (206 MASLD and 10 controls). Untargeted mass spectrometry proteomics was performed on a 103 biopsy subgroup. Selected RNA sequencing signals were replicated with an additional 187 biopsies. Comparison of advanced MASLD (fibrosis score 3/4) with milder disease (fibrosis score 0-2) by RNA sequencing showed significant alterations in expression of certain phase I, phase II and ABC transporters. For cytochromes P450, CYP2C19 showed the most significant decreased expression (30 % of that in mild disease) but significant decreased expression of other CYPs (including CYP2C8 and CYP2E1) also occurred. CYP2C19 also showed a significant decrease comparing the inflammatory form of MASLD (MASH) with non-MASH biopsies. Findings for CYP2C19 were confirmed in the replication cohort. Proteomics on the original discovery cohort confirmed decreased levels of several CYPs as MASLD advanced but this decrease was greatest for CYP2C19 where levels fell to 40 % control. This decrease may result in decreased CYP2C19 activity that could be problematic for prescription of drugs activated or metabolized by CYP2C19 as MASLD advances. More limited decreases for other P450s suggest fewer issues with non-CYP2C19 drug substrates. Negative correlations at RNA level between CYP2C19 and several cytokine genes provided initial insights into the mechanism underlying decreased expression.

Proteomics-The State of the Field: The Definition and Analysis of Proteomes Should Be Based in Reality, Not Convenience

With growing recognition and acknowledgement of the genuine complexity of proteomes, we are finally entering the post-proteogenomic era. Routine assessment of proteomes as inferred correlates of gene sequences (i.e., canonical 'proteins') cannot provide the necessary critical analysis of systems-level biology that is needed to understand underlying molecular mechanisms and pathways or identify the most selective biomarkers and therapeutic targets. These critical requirements demand the analysis of proteomes at the level of proteoforms/protein species, the actual active molecular players. Currently, only highly refined integrated or integrative top-down proteomics (iTDP) enables the analytical depth necessary to provide routine, comprehensive, and quantitative proteome assessments across the widest range of proteoforms inherent to native systems. Here we provide a broad perspective of the field, taking in historical and current realities, to establish a more balanced understanding of where the field has come from (in particular during the ten years since Proteomes was launched), current issues, and how things likely need to proceed if necessary deep proteome analyses are to succeed. We base this in our firm belief that the best proteomic analyses reflect, as closely as possible, the native sample at the moment of sampling. We also seek to emphasise that this and future analytical approaches are likely best based on the broad recognition and exploitation of the complementarity of currently successful approaches. This also emphasises the need to continuously evaluate and further optimize established approaches, to avoid complacency in thinking and expectations but also to promote the critical and careful development and introduction of new approaches, most notably those that address proteoforms. Above all, we wish to emphasise that a rigorous focus on analytical quality must override current thinking that largely values analytical speed; the latter would certainly be nice, if only proteoforms could thus be effectively, routinely, and quantitatively assessed. Alas, proteomes are composed of proteoforms, not molecular species that can be amplified or that directly mirror genes (i.e., 'canonical'). The problem is hard, and we must accept and address it as such, but the payoff in playing this longer game of rigorous deep proteome analyses is the promise of far more selective biomarkers, drug targets, and truly personalised or even individualised medicine.

Universal Pretreatment Development for Low-input Proteomics Using Lauryl Maltose Neopentyl Glycol

In recent years, there has been a growing demand for low-input proteomics, particularly in the context of single-cell proteomics (SCP). In this study, we have developed a lauryl maltose neopentyl glycol (LMNG)-assisted sample preparation (LASP) method. This method effectively reduces protein and peptide loss in samples by incorporating LMNG, a surfactant, into the digestion solution and subsequently removing the LMNG simply via reversed phase solid-phase extraction. The advantage of removing LMNG during sample preparation for general proteomic analysis is the prevention of mass spectrometry (MS) contamination. When we applied the LASP method to the low-input SP3 method and on-bead digestion in coimmunoprecipitation-MS, we observed a significant improvement in the recovery of the digested peptides. Furthermore, we have established a simple and easy sample preparation method for SCP based on the LASP method and identified a median of 1175 proteins from a single HEK239F cell using liquid chromatography (LC)-MS/MS with a throughput of 80 samples per day.

Identification of MIMAS, a multifunctional mega-assembly integrating metabolic and respiratory biogenesis factors of mitochondria

The mitochondrial inner membrane plays central roles in bioenergetics and metabolism and contains several established membrane protein complexes. Here, we report the identification of a mega-complex of the inner membrane, termed mitochondrial multifunctional assembly (MIMAS). Its large size of 3 MDa explains why MIMAS has escaped detection in the analysis of mitochondria so far. MIMAS combines proteins of diverse functions from respiratory chain assembly to metabolite transport, dehydrogenases, and lipid biosynthesis but not the large established supercomplexes of the respiratory chain, ATP synthase, or prohibitin scaffold. MIMAS integrity depends on the non-bilayer phospholipid phosphatidylethanolamine, in contrast to respiratory supercomplexes whose stability depends on cardiolipin. Our findings suggest that MIMAS forms a protein-lipid mega-assembly in the mitochondrial inner membrane that integrates respiratory biogenesis and metabolic processes in a multifunctional platform.

Contaminant Spot Check and Removal Assay (ContamSPOT) for Mass Spectrometry Analysis

Mass spectrometry (MS) analysis is often challenged by contaminations from detergents, salts, and polymers that compromise data quality and can damage the chromatography and MS instruments. However, researchers often discover contamination issues only after they acquire the data. There is no existing contaminant assay that is sensitive enough to detect trace amounts of contaminants from a few microliters of samples prior to MS analysis. To address this crucial need in the field, we developed a sensitive, rapid, and cost-effective contaminant spot check and removal assay (ContamSPOT) to detect and quantify trace amounts of contaminants, such as detergents, salts, and other chemicals commonly used in the MS sample preparation workflow. Only 1 μL of the sample was used prior to MS injection to quantify contaminants by ContamSPOT colorimetric or fluorometric assay on a thin layer chromatography (TLC) plate. We also optimized contaminant removal methods to salvage samples with minimal loss when ContamSPOT showed a positive result. ContamSPOT was then successfully applied to evaluate commonly used bottom-up proteomic methods regarding the effectiveness of removing detergent, peptide recovery, reproducibility, and proteome coverage. We expect ContamSPOT to be widely adopted by MS laboratories as a last-step quality checkpoint prior to MS injection. We provided a practical decision tree and a step-by-step protocol with a troubleshooting guide to facilitate the use of ContamSPOT by other researchers. ContamSPOT can also provide a unique readout of sample cleanliness for developing new MS-based sample preparation methods in the future.

Investigating trade-offs between ovary activation and immune protein expression in bumble bee (Bombus impatiens) workers and queens

Evidence for a trade-off between reproduction and immunity has manifested in many animal species, including social insects. However, investigations in social insect queens present a conundrum: new gynes of many social hymenopterans, such as bumble bees and ants, must first mate, then transition from being solitary to social as they establish their nests, thus experiencing confounding shifts in environmental conditions. Worker bumble bees offer an opportunity to investigate patterns of immune protein expression associated with ovary activation while minimizing extraneous environmental factors and genetic differences. Here, we use proteomics to interrogate the patterns of immune protein expression of female bumble bees (Bombus impatiens) by (i) sampling queens at different stages of their life cycle, then (ii) by sampling workers with different degrees of ovary activation. Patterns of immune protein expression in the haemolymph of queens are consistent with a reproduction-immunity trade-off, but equivalent samples from workers are not. This brings into question whether queen bumble bees really experience a reproduction-immunity trade-off, or if patterns of immune protein expression may actually be due to the selective pressure of the different environmental conditions they are exposed to during their life cycle.

Proteomic insights into mental health status: plasma markers in young adults

Global emphasis on enhancing prevention and treatment strategies necessitates an increased understanding of the biological mechanisms of psychopathology. Plasma proteomics is a powerful tool that has been applied in the context of specific mental disorders for biomarker identification. The p-factor, also known as the "general psychopathology factor", is a concept in psychopathology suggesting that there is a common underlying factor that contributes to the development of various forms of mental disorders. It has been proposed that the p-factor can be used to understand the overall mental health status of an individual. Here, we aimed to discover plasma proteins associated with the p-factor in 775 young adults in the FinnTwin12 cohort. Using liquid chromatography-tandem mass spectrometry, 13 proteins with a significant connection with the p-factor were identified, 8 of which were linked to epidermal growth factor receptor (EGFR) signaling. This exploratory study provides new insight into biological alterations associated with mental health status in young adults.

Detection of the Arabidopsis Proteome and Its Post-translational Modifications and the Nature of the Unobserved (Dark) Proteome in PeptideAtlas

This study describes a new release of the Arabidopsis thaliana PeptideAtlas proteomics resource (build 2023-10) providing protein sequence coverage, matched mass spectrometry (MS) spectra, selected post-translational modifications (PTMs), and metadata. 70 million MS/MS spectra were matched to the Araport11 annotation, identifying ∼0.6 million unique peptides and 18,267 proteins at the highest confidence level and 3396 lower confidence proteins, together representing 78.6% of the predicted proteome. Additional identified proteins not predicted in Araport11 should be considered for the next Arabidopsis genome annotation. This release identified 5198 phosphorylated proteins, 668 ubiquitinated proteins, 3050 N-terminally acetylated proteins, and 864 lysine-acetylated proteins and mapped their PTM sites. MS support was lacking for 21.4% (5896 proteins) of the predicted Araport11 proteome: the "dark" proteome. This dark proteome is highly enriched for E3 ligases, transcription factors, and for certain (e.g., CLE, IDA, PSY) but not other (e.g., THIONIN, CAP) signaling peptides families. A machine learning model trained on RNA expression data and protein properties predicts the probability that proteins will be detected. The model aids in discovery of proteins with short half-life (e.g., SIG1,3 and ERF-VII TFs) and for developing strategies to identify the missing proteins. PeptideAtlas is linked to TAIR, tracks in JBrowse, and several other community proteomics resources.

Functionally redundant formate dehydrogenases enable formate-dependent growth in Methanococcus maripaludis

Methanogens are essential for the complete remineralization of organic matter in anoxic environments. Most cultured methanogens are hydrogenotrophic, using H2 as an electron donor to reduce CO2 to CH4, but in the absence of H2 many can also use formate. Formate dehydrogenase (Fdh) is essential for formate oxidation, where it transfers electrons for the reduction of coenzyme F420 or to a flavin-based electron bifurcating reaction catalyzed by heterodisulfide reductase (Hdr), the terminal reaction of methanogenesis. Furthermore, methanogens that use formate encode at least two isoforms of Fdh in their genomes, but how these different isoforms participate in methanogenesis is unknown. Using Methanococcus maripaludis, we undertook a biochemical characterization of both Fdh isoforms involved in methanogenesis. Both Fdh1 and Fdh2 interacted with Hdr to catalyze the flavin-based electron bifurcating reaction, and both reduced F420 at similar rates. F420 reduction preceded flavin-based electron bifurcation activity for both enzymes. In a Δfdh1 mutant background, a suppressor mutation was required for Fdh2 activity. Genome sequencing revealed that this mutation resulted in the loss of a specific molybdopterin transferase (moeA), allowing for Fdh2-dependent growth, and the metal content of the proteins suggested that isoforms are dependent on either molybdenum or tungsten for activity. These data suggest that both isoforms of Fdh are functionally redundant, but their activities in vivo may be limited by gene regulation or metal availability under different growth conditions. Together these results expand our understanding of formate oxidation and the role of Fdh in methanogenesis.

Ganglioglioma cells potentiate neuronal network synchronicity and elicit burst discharges via released factors

Gangliogliomas (GGs) represent the most frequent glioneuronal tumor entity associated with chronic recurrent seizures; rare anaplastic GGs variants retain the glioneuronal character. So far, key mechanisms triggering chronic hyperexcitability in the peritumoral area are unresolved. Based on a recent mouse model for anaplastic GG (BRAFV600E, mTOR activation and Trp53KO) we here assessed the influence of GG-secreted factors on non-neoplastic cells in-vitro. We generated conditioned medium (CM) from primary GG cell cultures to developing primary cortical neurons cultured on multielectrode-arrays and assessed their electrical activity in comparison to neurons incubated with naïve and neuronal CMs. Our results showed that the GG CM, while not affecting the mean firing rates of networks, strongly accelerated the formation of functional networks as indicated increased synchrony of firing and burst activity. Washing out the GG CM did not reverse these effects indicating an irreversible effect on the neuronal network. Mass spectrometry analysis of GG CM detected several enriched proteins associated with neurogenesis as well as gliogenesis, including Gap43, App, Apoe, S100a8, Tnc and Sod1. Concomitantly, immunocytochemical analysis of the neuronal cultures exposed to GG CM revealed abundant astrocytes suggesting that the GG-secreted factors induce astroglial proliferation. Pharmacological inhibition of astrocyte proliferation only partially reversed the accelerated network maturation in neuronal cultures exposed to GG CM indicating that the GG CM exerts a direct effect on the neuronal component. Taken together, we demonstrate that GG-derived paracrine signaling alone is sufficient to induce accelerated neuronal network development accompanied by astrocytic proliferation. Perspectively, a deeper understanding of factors involved may serve as the basis for future therapeutic approaches.

An integrated workflow for quantitative analysis of the newly synthesized proteome

The analysis of proteins that are newly synthesized upon a cellular perturbation can provide detailed insight into the proteomic response that is elicited by specific cues. This can be investigated by pulse-labeling of cells with clickable and stable-isotope-coded amino acids for the enrichment and mass spectrometric characterization of newly synthesized proteins (NSPs), however convoluted protocols prohibit their routine application. Here we report the optimization of multiple steps in sample preparation, mass spectrometry and data analysis, and we integrate them into a semi-automated workflow for the quantitative analysis of the newly synthesized proteome (QuaNPA). Reduced input requirements and data-independent acquisition (DIA) enable the analysis of triple-SILAC-labeled NSP samples, with enhanced throughput while featuring high quantitative accuracy. We apply QuaNPA to investigate the time-resolved cellular response to interferon-gamma (IFNg), observing rapid induction of targets 2 h after IFNg treatment. QuaNPA provides a powerful approach for large-scale investigation of NSPs to gain insight into complex cellular processes.

Proteomic exploration reveals a metabolic rerouting due to low oxygen during controlled germination of malting barley (Hordeum vulgare L.)

Barley (Hordeum vulgare L.) is used in malt production for brewing applications. Barley malting involves a process of controlled germination that modifies the grain by activating enzymes to solubilize starch and proteins for brewing. Initially, the grain is submerged in water to raise grain moisture, requiring large volumes of water. Achieving grain modification at reduced moisture levels can contribute to the sustainability of malting practices. This study combined proteomics, bioinformatics, and biochemical phenotypic analysis of two malting barley genotypes with observed differences in water uptake and modification efficiency. We sought to reveal the molecular mechanisms at play during controlled germination and explore the roles of protein groups at 24 h intervals across the first 72 h. Overall, 3,485 protein groups were identified with 793 significant differentially abundant (DAP) within and between genotypes, involved in various biological processes, including protein synthesis, carbohydrate metabolism, and hydrolysis. Functional integration into metabolic pathways, such as glycolysis, pyruvate, starch and sucrose metabolism, revealed a metabolic rerouting due to low oxygen enforced by submergence during controlled germination. This SWATH-MS study provides a comprehensive proteome reference, delivering new insights into the molecular mechanisms underlying the impacts of low oxygen during controlled germination. It is concluded that continued efficient modification of malting barley subjected to submergence is largely due to the capacity to reroute energy to maintain vital processes, particularly protein synthesis.

Cylicins are a structural component of the sperm calyx being indispensable for male fertility in mice and human

Cylicins are testis-specific proteins, which are exclusively expressed during spermiogenesis. In mice and humans, two Cylicins, the gonosomal X-linked Cylicin 1 (Cylc1/CYLC1) and the autosomal Cylicin 2 (Cylc2/CYLC2) genes, have been identified. Cylicins are cytoskeletal proteins with an overall positive charge due to lysine-rich repeats. While Cylicins have been localized in the acrosomal region of round spermatids, they resemble a major component of the calyx within the perinuclear theca at the posterior part of mature sperm nuclei. However, the role of Cylicins during spermiogenesis has not yet been investigated. Here, we applied CRISPR/Cas9-mediated gene editing in zygotes to establish Cylc1- and Cylc2-deficient mouse lines as a model to study the function of these proteins. Cylc1 deficiency resulted in male subfertility, whereas Cylc2-/-, Cylc1-/yCylc2+/-, and Cylc1-/yCylc2-/- males were infertile. Phenotypical characterization revealed that loss of Cylicins prevents proper calyx assembly during spermiogenesis. This results in decreased epididymal sperm counts, impaired shedding of excess cytoplasm, and severe structural malformations, ultimately resulting in impaired sperm motility. Furthermore, exome sequencing identified an infertile man with a hemizygous variant in CYLC1 and a heterozygous variant in CYLC2, displaying morphological abnormalities of the sperm including the absence of the acrosome. Thus, our study highlights the relevance and importance of Cylicins for spermiogenic remodeling and male fertility in human and mouse, and provides the basis for further studies on unraveling the complex molecular interactions between perinuclear theca proteins required during spermiogenesis.

Disclosing the molecular profile of the human amniotic mesenchymal stromal cell secretome by filter-aided sample preparation proteomic characterization

BACKGROUND

The secretome of mesenchymal stromal cells isolated from the amniotic membrane (hAMSCs) has been extensively studied for its in vitro immunomodulatory activity as well as for the treatment of several preclinical models of immune-related disorders. The bioactive molecules within the hAMSCs secretome are capable of modulating the immune response and thus contribute to stimulating regenerative processes. At present, only a few studies have attempted to define the composition of the secretome, and several approaches, including multi-omics, are underway in an attempt to precisely define its composition and possibly identify key factors responsible for the therapeutic effect.

METHODS

In this study, we characterized the protein composition of the hAMSCs secretome by a filter-aided sample preparation (FASP) digestion and liquid chromatography-high resolution mass spectrometry (LC-MS) approach. Data were processed for gene ontology classification and functional protein interaction analysis by bioinformatics tools.

RESULTS

Proteomic analysis of the hAMSCs secretome resulted in the identification of 1521 total proteins, including 662 unique elements. A number of 157 elements, corresponding to 23.7%, were found as repeatedly characterizing the hAMSCs secretome, and those that resulted as significantly over-represented were involved in immunomodulation, hemostasis, development and remodeling of the extracellular matrix molecular pathways.

CONCLUSIONS

Overall, our characterization enriches the landscape of hAMSCs with new information that could enable a better understanding of the mechanisms of action underlying the therapeutic efficacy of the hAMSCs secretome while also providing a basis for its therapeutic translation.

Multi-modal proteomic characterization of lysosomal function and proteostasis in progranulin-deficient neurons

BACKGROUND

Progranulin (PGRN) is a lysosomal glycoprotein implicated in various neurodegenerative diseases, including frontotemporal dementia and neuronal ceroid lipofuscinosis. Over 70 mutations discovered in the GRN gene all result in reduced expression of the PGRN protein. Genetic and functional studies point toward a regulatory role for PGRN in lysosome functions. However, the detailed molecular function of PGRN within lysosomes and the impact of PGRN deficiency on lysosomes remain unclear.

METHODS

We developed multifaceted proteomic techniques to characterize the dynamic lysosomal biology in living human neurons and fixed mouse brain tissues. Using lysosome proximity labeling and immuno-purification of intact lysosomes, we characterized lysosome compositions and interactome in both human induced pluripotent stem cell (iPSC)-derived glutamatergic neurons (i3Neurons) and mouse brains. Using dynamic stable isotope labeling by amino acids in cell culture (dSILAC) proteomics, we measured global protein half-lives in human i3Neurons for the first time.

RESULTS

Leveraging the multi-modal proteomics and live-cell imaging techniques, we comprehensively characterized how PGRN deficiency changes the molecular and functional landscape of neuronal lysosomes. We found that PGRN loss impairs the lysosome's degradative capacity with increased levels of v-ATPase subunits on the lysosome membrane, increased hydrolases within the lysosome, altered protein regulations related to lysosomal transport, and elevated lysosomal pH. Consistent with impairments in lysosomal function, GRN-null i3Neurons and frontotemporal dementia patient-derived i3Neurons carrying GRN mutation showed pronounced alterations in protein turnover, such as cathepsins and proteins related to supramolecular polymerization and inherited neurodegenerative diseases.

CONCLUSION

This study suggested PGRN as a critical regulator of lysosomal pH and degradative capacity, which influences global proteostasis in neurons. Beyond the study of progranulin deficiency, these newly developed proteomic methods in neurons and brain tissues provided useful tools and data resources for the field to study the highly dynamic neuronal lysosome biology.

A framework for ultra-low-input spatial tissue proteomics

Spatial proteomics combining microscopy-based cell phenotyping with ultrasensitive mass-spectrometry-based proteomics is an emerging and powerful concept to study cell function and heterogeneity in (patho)physiology. However, optimized workflows that preserve morphological information for phenotype discovery and maximize proteome coverage of few or even single cells from laser microdissected tissue are currently lacking. Here, we report a robust and scalable workflow for the proteomic analysis of ultra-low-input archival material. Benchmarking in murine liver resulted in up to 2,000 quantified proteins from single hepatocyte contours and nearly 5,000 proteins from 50-cell regions. Applied to human tonsil, we profiled 146 microregions including T and B lymphocyte niches and quantified cell-type-specific markers, cytokines, and transcription factors. These data also highlighted proteome dynamics within activated germinal centers, illuminating sites undergoing B cell proliferation and somatic hypermutation. This approach has broad implications in biomedicine, including early disease profiling and drug target and biomarker discovery. A record of this paper's transparent peer review process is included in the supplemental information.

Sage: An Open-Source Tool for Fast Proteomics Searching and Quantification at Scale

The growing complexity and volume of proteomics data necessitate the development of efficient software tools for peptide identification and quantification from mass spectra. Given their central role in proteomics, it is imperative that these tools are auditable and extensible─requirements that are best fulfilled by open-source and permissively licensed software. This work presents Sage, a high-performance, open-source, and freely available proteomics pipeline. Scalable and cloud-ready, Sage matches the performance of state-of-the-art software tools while running an order of magnitude faster.

Integration of data-independent acquisition (DIA) with co-fractionation mass spectrometry (CF-MS) to enhance interactome mapping capabilities

Proteomics technologies are continually advancing, providing opportunities to develop stronger and more robust protein interaction networks (PINs). In part, this is due to the ever-growing number of high-throughput proteomics methods that are available. This review discusses how data-independent acquisition (DIA) and co-fractionation mass spectrometry (CF-MS) can be integrated to enhance interactome mapping abilities. Furthermore, integrating these two techniques can improve data quality and network generation through extended protein coverage, less missing data, and reduced noise. CF-DIA-MS shows promise in expanding our knowledge of interactomes, notably for non-model organisms (NMOs). CF-MS is a valuable technique on its own, but upon the integration of DIA, the potential to develop robust PINs increases, offering a unique approach for researchers to gain an in-depth understanding into the dynamics of numerous biological processes.

Calredoxin regulates the chloroplast NADPH-dependent thioredoxin reductase in Chlamydomonas reinhardtii

Calredoxin (CRX) is a calcium (Ca2+)-dependent thioredoxin (TRX) in the chloroplast of Chlamydomonas (Chlamydomonas reinhardtii) with a largely unclear physiological role. We elucidated the CRX functionality by performing in-depth quantitative proteomics of wild-type cells compared with a crx insertional mutant (IMcrx), two CRISPR/Cas9 KO mutants, and CRX rescues. These analyses revealed that the chloroplast NADPH-dependent TRX reductase (NTRC) is co-regulated with CRX. Electron transfer measurements revealed that CRX inhibits NADPH-dependent reduction of oxidized chloroplast 2-Cys peroxiredoxin (PRX1) via NTRC and that the function of the NADPH-NTRC complex is under strict control of CRX. Via non-reducing SDS-PAGE assays and mass spectrometry, our data also demonstrated that PRX1 is more oxidized under high light (HL) conditions in the absence of CRX. The redox tuning of PRX1 and control of the NADPH-NTRC complex via CRX interconnect redox control with active photosynthetic electron transport and metabolism, as well as Ca2+ signaling. In this way, an economic use of NADPH for PRX1 reduction is ensured. The finding that the absence of CRX under HL conditions severely inhibited light-driven CO2 fixation underpins the importance of CRX for redox tuning, as well as for efficient photosynthesis.

A Bioconductor workflow for processing, evaluating, and interpreting expression proteomics data

Background: Expression proteomics involves the global evaluation of protein abundances within a system. In turn, differential expression analysis can be used to investigate changes in protein abundance upon perturbation to such a system. Methods: Here, we provide a workflow for the processing, analysis and interpretation of quantitative mass spectrometry-based expression proteomics data. This workflow utilizes open-source R software packages from the Bioconductor project and guides users end-to-end and step-by-step through every stage of the analyses. As a use-case we generated expression proteomics data from HEK293 cells with and without a treatment. Of note, the experiment included cellular proteins labelled using tandem mass tag (TMT) technology and secreted proteins quantified using label-free quantitation (LFQ). Results: The workflow explains the software infrastructure before focusing on data import, pre-processing and quality control. This is done individually for TMT and LFQ datasets. The application of statistical differential expression analysis is demonstrated, followed by interpretation via gene ontology enrichment analysis. Conclusions: A comprehensive workflow for the processing, analysis and interpretation of expression proteomics is presented. The workflow is a valuable resource for the proteomics community and specifically beginners who are at least familiar with R who wish to understand and make data-driven decisions with regards to their analyses.

Optimizing metaproteomics database construction: lessons from a study of the vaginal microbiome

Metaproteomics, a method for untargeted, high-throughput identification of proteins in complex samples, provides functional information about microbial communities and can tie functions to specific taxa. Metaproteomics often generates less data than other omics techniques, but analytical workflows can be improved to increase usable data in metaproteomic outputs. Identification of peptides in the metaproteomic analysis is performed by comparing mass spectra of sample peptides to a reference database of protein sequences. Although these protein databases are an integral part of the metaproteomic analysis, few studies have explored how database composition impacts peptide identification. Here, we used cervicovaginal lavage (CVL) samples from a study of bacterial vaginosis (BV) to compare the performance of databases built using six different strategies. We evaluated broad versus sample-matched databases, as well as databases populated with proteins translated from metagenomic sequencing of the same samples versus sequences from public repositories. Smaller sample-matched databases performed significantly better, driven by the statistical constraints on large databases. Additionally, large databases attributed up to 34% of significant bacterial hits to taxa absent from the sample, as determined orthogonally by 16S rRNA gene sequencing. We also tested a set of hybrid databases which included bacterial proteins from NCBI RefSeq and translated bacterial genes from the samples. These hybrid databases had the best overall performance, identifying 1,068 unique human and 1,418 unique bacterial proteins, ~30% more than a database populated with proteins from typical vaginal bacteria and fungi. Our findings can help guide the optimal identification of proteins while maintaining statistical power for reaching biological conclusions. IMPORTANCE Metaproteomic analysis can provide valuable insights into the functions of microbial and cellular communities by identifying a broad, untargeted set of proteins. The databases used in the analysis of metaproteomic data influence results by defining what proteins can be identified. Moreover, the size of the database impacts the number of identifications after accounting for false discovery rates (FDRs). Few studies have tested the performance of different strategies for building a protein database to identify proteins from metaproteomic data and those that have largely focused on highly diverse microbial communities. We tested a range of databases on CVL samples and found that a hybrid sample-matched approach, using publicly available proteins from organisms present in the samples, as well as proteins translated from metagenomic sequencing of the samples, had the best performance. However, our results also suggest that public sequence databases will continue to improve as more bacterial genomes are published.

AlphaPeptStats: an open-source Python package for automated and scalable statistical analysis of mass spectrometry-based proteomics

SUMMARY

The widespread application of mass spectrometry (MS)-based proteomics in biomedical research increasingly requires robust, transparent, and streamlined solutions to extract statistically reliable insights. We have designed and implemented AlphaPeptStats, an inclusive Python package with currently with broad functionalities for normalization, imputation, visualization, and statistical analysis of label-free proteomics data. It modularly builds on the established stack of Python scientific libraries and is accompanied by a rigorous testing framework with 98% test coverage. It imports the output of a range of popular search engines. Data can be filtered and normalized according to user specifications. At its heart, AlphaPeptStats provides a wide range of robust statistical algorithms such as t-tests, analysis of variance, principal component analysis, hierarchical clustering, and multiple covariate analysis-all in an automatable manner. Data visualization capabilities include heat maps, volcano plots, and scatter plots in publication-ready format. AlphaPeptStats advances proteomic research through its robust tools that enable researchers to manually or automatically explore complex datasets to identify interesting patterns and outliers.

AVAILABILITY AND IMPLEMENTATION

AlphaPeptStats is implemented in Python and part of the AlphaPept framework. It is released under a permissive Apache license. The source code and one-click installers are freely available and on GitHub at https://github.com/MannLabs/alphapeptstats.

Mapping the Arabidopsis thaliana proteome in PeptideAtlas and the nature of the unobserved (dark) proteome; strategies towards a complete proteome

This study describes a new release of the Arabidopsis thaliana PeptideAtlas proteomics resource providing protein sequence coverage, matched mass spectrometry (MS) spectra, selected PTMs, and metadata. 70 million MS/MS spectra were matched to the Araport11 annotation, identifying ∼0.6 million unique peptides and 18267 proteins at the highest confidence level and 3396 lower confidence proteins, together representing 78.6% of the predicted proteome. Additional identified proteins not predicted in Araport11 should be considered for building the next Arabidopsis genome annotation. This release identified 5198 phosphorylated proteins, 668 ubiquitinated proteins, 3050 N-terminally acetylated proteins and 864 lysine-acetylated proteins and mapped their PTM sites. MS support was lacking for 21.4% (5896 proteins) of the predicted Araport11 proteome - the 'dark' proteome. This dark proteome is highly enriched for certain ( e.g. CLE, CEP, IDA, PSY) but not other ( e.g. THIONIN, CAP,) signaling peptides families, E3 ligases, TFs, and other proteins with unfavorable physicochemical properties. A machine learning model trained on RNA expression data and protein properties predicts the probability for proteins to be detected. The model aids in discovery of proteins with short-half life ( e.g. SIG1,3 and ERF-VII TFs) and completing the proteome. PeptideAtlas is linked to TAIR, JBrowse, PPDB, SUBA, UniProtKB and Plant PTM Viewer.

pepDESC: A method for the detection of differentially expressed proteins for mass spectrometry-based single-cell proteomics using peptide-level information

Single-cell proteomics as an emerging field has exhibited potential in revealing cellular heterogeneity at the functional level. However, accurate interpretation of single-cell proteomics data suffers from challenges such as measurement noise, internal heterogeneity, and the limited sample size of label-free quantitative mass spectrometry. Herein, the author describes pepDESC, a method for detecting differentially expressed proteins using peptide-level information designed for label-free quantitative mass spectrometry-based single-cell proteomics. While in this study, the author focuses on the heterogeneity among limited number of samples, pepDESC is also applicable to regular-size proteomics data. By balancing proteome coverage and quantification accuracy using peptide quantification, pepDESC is demonstrated to be effective in real-world single-cell and spike -in benchmark datasets. By applying pepDESC to a published single-mouse macrophages data, the author found a large fraction of differentially expressed proteins among three types of cells, which remarkably revealed distinct dynamics of different cellular functions responding to lipopolysaccharide stimulation.

Extending the Range of SLIM-Labeling Applications: From Human Cell Lines in Culture to Caenorhabditis elegans Whole-Organism Labeling

The simple light isotope metabolic-labeling technique relies on the in vivo biosynthesis of amino acids from U-[¹²C]-labeled molecules provided as the sole carbon source. The incorporation of the resulting U-[¹²C]-amino acids into proteins presents several key advantages for mass-spectrometry-based proteomics analysis, as it results in more intense monoisotopic ions, with a better signal-to-noise ratio in bottom-up analysis. In our initial studies, we developed the simple light isotope metabolic (SLIM)-labeling strategy using prototrophic eukaryotic microorganisms, the yeasts Candida albicans and Saccharomyces cerevisiae, as well as strains with genetic markers that lead to amino-acid auxotrophy. To extend the range of SLIM-labeling applications, we evaluated (i) the incorporation of U-[¹²C]-glucose into proteins of human cells grown in a complex RPMI-based medium containing the labeled molecule, considering that human cell lines require a large number of essential amino-acids to support their growth, and (ii) an indirect labeling strategy in which the nematode Caenorhabditis elegans grown on plates was fed U-[¹²C]-labeled bacteria (Escherichia coli) and the worm proteome analyzed for ¹²C incorporation into proteins. In both cases, we were able to demonstrate efficient incorporation of ¹²C into the newly synthesized proteins, opening the way for original approaches in quantitative proteomics.

Multi-modal Proteomic Characterization of Lysosomal Function and Proteostasis in Progranulin-Deficient Neurons

Progranulin (PGRN) is a lysosomal protein implicated in various neurodegenerative diseases. Over 70 mutations discovered in the GRN gene all result in reduced expression of PGRN protein. However, the detailed molecular function of PGRN within lysosomes and the impact of PGRN deficiency on lysosomal biology remain unclear. Here we leveraged multifaceted proteomic techniques to comprehensively characterize how PGRN deficiency changes the molecular and functional landscape of neuronal lysosomes. Using lysosome proximity labeling and immuno-purification of intact lysosomes, we characterized lysosome compositions and interactomes in both human induced pluripotent stem cell (iPSC)-derived glutamatergic neurons (i3Neurons) and mouse brains. Using dynamic stable isotope labeling by amino acids in cell culture (dSILAC) proteomics, we measured global protein half-lives in i3Neurons for the first time and characterized the impact of progranulin deficiency on neuronal proteostasis. Together, this study indicated that PGRN loss impairs the lysosome's degradative capacity with increased levels of v-ATPase subunits on the lysosome membrane, increased catabolic enzymes within the lysosome, elevated lysosomal pH, and pronounced alterations in neuron protein turnover. Collectively, these results suggested PGRN as a critical regulator of lysosomal pH and degradative capacity, which in turn influences global proteostasis in neurons. The multi-modal techniques developed here also provided useful data resources and tools to study the highly dynamic lysosome biology in neurons.

MS Ana: Improving Sensitivity in Peptide Identification with Spectral Library Search

Spectral library search can enable more sensitive peptide identification in tandem mass spectrometry experiments. However, its drawbacks are the limited availability of high-quality libraries and the added difficulty of creating decoy spectra for result validation. We describe MS Ana, a new spectral library search engine that enables high sensitivity peptide identification using either curated or predicted spectral libraries as well as robust false discovery control through its own decoy library generation algorithm. MS Ana identifies on average 36% more spectrum matches and 4% more proteins than database search in a benchmark test on single-shot human cell-line data. Further, we demonstrate the quality of the result validation with tests on synthetic peptide pools and show the importance of library selection through a comparison of library search performance with different configurations of publicly available human spectral libraries.