Journal of Proteome Research最新文献

The human neuroblastoma SH-SY5Y cell line is a widely utilized model for studying neurodegenerative diseases, owing to its ability to differentiate into cells with a neuron-like phenotype. However, a comprehensive understanding of the cellular and molecular mechanisms underpinning SH-SY5Y cell differentiation and maturation is still lacking from a deep proteomics perspective. We systematically benchmarked an “in-cell proteomics” strategy against the sodium dodecyl sulfate (SDS) lysate-based processing method and showed superior performance in terms of simplicity, sensitivity, and quantitation accuracy while requiring minimal inputs. We employed the in-cell proteomics strategy to characterize SH-SY5Y cells at undifferentiated, and partially and terminally differentiated states, respectively. Among over 9000 proteins identified in total, we were able to detect marker proteins in neuronal development and integrity, and observed increases in glutamatergic synapse-related proteins and proteins previously reported in mature neurons as well as in differentiated neuroblastoma cells. Lastly, we examined proteins involved in the ubiquitin-proteasome system and found stage-specific expression of E3 ubiquitin ligases, deubiquitinases (DUBs), and proteasome subunits, revealing an important role of protein homeostasis in neuroblastoma cell differentiation. In summary, our study presented the first benchmark data set of neuroblastoma cells using an in-cell proteomics strategy and demonstrated its great potential in cataloging neuronal function and disease.

One persistent challenge in untargeted metabolomics is the identification of compounds from their mass spectrometry (MS) signal, which is necessary for biological data interpretation. This process can be facilitated by building in-house libraries of metabolite standards containing retention time (RT) information, which is orthogonal and complementary to large, published MS/MS spectra repositories. Creating such libraries can require substantial effort and is time intensive. To streamline this process, we developed metScribeR, an R package with a Shiny application to accelerate the creation of RT and m/z libraries. metScribeR provides an easy, user-friendly interface for peak finding, filtering, and comprehensive quality review of the MS data. Uniquely, metScribeR does not require MS/MS spectral information and reports an identification probability estimate for each adduct. In our benchmarking, metScribeR required approximately 10 s of computational and manual effort per standard, showed a correlation of 0.99 between manual and metScribeR-derived RTs, and appropriately filtered out poor quality peaks. The metScribeR output is a.csv file including the identity, m/z, RT, and peak quality information for standards along with MS/MS spectra retrieved from MassBank of North America (MoNA). metScribeR is open source and available for download on GitHub at https://github.com/ncats/metScribeR

Mesenchymal stromal cells (MSCs) show great promise as a clinical treatment for a variety of diseases, but their susceptibility to senescence during culture reduces the therapeutic potential and limits cell expansion. In this study, we explored how MSC lipid metabolism is altered in culture over time using ultrahigh-performance liquid chromatography mass spectrometry. The proportion of cells with senescence-associated β-galactosidase (SA-β-gal) activity was evaluated during 12 days of culture expansion of MSCs from two human donors. Lipid profiles were evaluated in parallel using exact mass and tandem mass spectrometry spectral database matching to generate 237 unique lipid annotations. Lipid abundance generally increased across most lipid classes over serial culture; however, many changes were heterogeneous between donors. Despite donor differences, 12 lipids, including 4 triglycerides (TG), provided discrimination between cultures with less than 10% SA-β-gal+, those with 10–20% SA-β-gal+, and greater than 20% SA-β-gal+ senescence proportion regardless of donor. More specifically, TG composed of long-chain, highly unsaturated fatty acids was strongly associated with higher MSC senescence. These changes in bulk lipid profiles may inform future strategies to monitor early culture senescence during the expansion of MSCs.

The development of ultrasensitive proteomic methods for detecting potential protein tumor biomarkers remains a key challenge in modern biomedicine. We integrated data from classical reference proteomic methods─both panoramic (DDA-Shotgun LC-MS/MS) and targeted (MRM)─with a novel AFM-based enrichment approach coupled to mass spectrometry (AFM-MS), providing lower detection limits. This integrated strategy enabled the compilation of an expanded list of proteins associated with ovarian cancer progression. We identified a panel of previously unreported, ovarian cancer–specific candidate markers. A total of 371 proteins were found to be potentially involved in the pathological process, with 33% detected exclusively by the ultrasensitive AFM-MS method and 26% discovered through metabolomic associations. Notably, 6% of the identified proteins correspond to previously recognized ovarian cancer–specific markers, validating our multiplatform approach. Nine potential biomarkers are proposed for the first time, including ATRN, CPN1, APOF, TGM3, and CRNN. Immunoglobulin variable region peptides were reclassified as low-specificity background signals due to their high abundance and inflammation dependence. The identified biomarkers are present in blood at concentrations ranging from 10^–12 to 10^–6 mol/L. The proposed approach overcomes the sensitivity limitations of conventional proteomic methods and may be adapted for the discovery of candidate markers in other multifactorial diseases.

Whole proteome digests are routinely used to diagnose chromatograph and mass spectrometer outputs to ensure suitability for the analysis of complex matrices, but their usage inherently fails to prove system reliability under varying “load” conditions. There is a need for a reliable, predictive tool that can explain variation in both instrument response and downstream identification results from a whole proteome analysis. We designed an experiment using a hybrid sample of standardized materials to create such an approach, which could then lead to a new system suitability test for bottom-up proteomics. The standard HeLa protein digest was combined with Promega 6 × 5 LC–MS/MS Peptide Reference Mix and diluted to create a range of sample mass loadings and reference peptide concentrations. Data were collected using data-dependent (DDA) and data-independent acquisition methods, and reference peptide peak abundances were correlated to the number of protein identifications (IDs), peptide groups (PGs), and peptide spectrum matches (PSMs) found by Proteome Discoverer. An asymptotic relationship explained decreasing IDs, PGs, and PSMs identified from the HeLa digest with decreasing 6 × 5 Peptide abundances. By linking the mass spectrometer measurement of ion abundance with downstream results obtained from a complex matrix, we successfully used the hybrid standardized sample to mathematically define new system suitability thresholds.

Expanding plasma proteome coverage increases the success in proteomic discovery of blood biomarkers. Here we report that the sequential precipitation of plasma by increasing concentrations of acetonitrile (AcN) can fractionate proteins. Combining some of these fractions with other fractions from polyethylene glycol (PEG) precipitation and albumin depletion to have the mixed sample that included these partitioned fractions: 10% whole plasma, 20% 20%-AcN-pellet, 10% 40%-AcN-pellet, 20% 50%-AcN-supernatant, 20% 10%-PEG-precipitation-albumin-depletion, and 20% 20%-PEG-precipitation-albumin-depletion, has yielded identification of 5441 proteins, remarkably larger than the 2040 proteins detected in the whole plasma directly. This study provides nearly the largest plasma proteomics data sets and recommends this fractionation and mixture strategy as an efficient approach for expanded plasma proteomics coverage.

Data-Independent Acquisition (DIA) has emerged as a powerful mass spectrometry (MS) strategy for comprehensive metabolomics. This study presents a novel short gradient (13 min) nanosensitive analytical method for human plasma analysis using DIA LC-MS/MS, focusing on in-depth optimization of MS parameters to maximize data quality and metabolite coverage. Key MS parameters, including scan speed, isolation window width, resolution, automatic gain control, and collision energy, were systematically tuned to balance the sensitivity and specificity while minimizing interferences. The optimized method enabled the detection of 2,907 features with 675 annotated compounds, leveraging recent progress in nano-LC-MS/MS for multiomics applications and showcasing the possibility of combining proteomics and metabolomics within a single chromatographic system. Ultimately, a comparison was performed between the data acquired through the DIA and DDA MS approaches in the context of untargeted metabolomics. This optimized analytical method yields more robust and reproducible results, thereby expanding the potential for meaningful discoveries across diverse biological fields.

Clostridium butyricum (C. butyricum) is a notable butyrate-producing intestinal probiotic that has been utilized for the enhancement and treatment of various intestinal and extraintestinal diseases. Butyrate and acetate, as significant metabolites of this bacterium, are short-chain fatty acids (SCFAs) that play crucial roles in maintaining intestinal stability and safeguarding host health. However, the regulatory functions of lysine butyrylation (Kbu) and acetylation (Kac), mediated by these metabolites, remain inadequately understood. In this study, we performed a comprehensive dynamic multiomic analysis encompassing protein expression, Kbu, and Kac in C. butyricum. Our multiomic analysis identified a total of 2622 proteins, 1887 Kbu sites, and 2188 Kac sites. Subsequent bioinformatic analyses revealed that biological functions such as gene expression and energy metabolism exhibited dynamic changes throughout the growth cycle at both the proteome and post-translational modification levels. Enzymatic experiments demonstrated that Kbu modified key active sites of important substrates, including alanine-tRNA ligase at K794, GMP synthase at K384, and probable butyrate kinase at K269. This study enhances the current understanding of lysine acylations in bacteria and focuses on elucidating the finely tuned regulatory mechanisms of Kbu during the growth and development of C. butyricum.

Serological screening, including immunological lateral flow assays, remains common for body fluid identification in sexual assault investigations but lacks the sensitivity and specificity of modern DNA profiling. To address this gap, alternative molecular approaches, including MS-based proteomics, have been explored. However, adoption is hindered by lengthy bottom-up workflows and reliance on research-grade instrumentation. Here, a streamlined, protease-free assay for the identification of saliva and seminal fluid in sexual assault evidence is described. Casework-type body fluid samples were extracted in a single step and analyzed by targeted DDA on a Q Exactive MS with a 25-min separation and data search using Byos software. The 96-well plate format used is amenable to higher-throughput automation. Discovery data sets included 50 saliva and 60 semen samples (including samples from 5 vasectomized males). This resulted in the identification of 7 saliva biomarkers (PRB1, PRB2, PRB4, PRH1, STATH, HTN1, and SMR3B) and 5 seminal fluid biomarkers (SEMG1, SEMG2, PSA, PAP, and PIP). Peptide standards were synthesized to confirm the discovery results and to develop a targeted assay. The method was successfully validated using 168 forensic casework-type samples, including diluted, laundered, and environmentally challenged samples on a variety of substrates.

Approximately 50% of melanoma patients carry a mutation in the BRAF gene, and over 90% of these mutations lead to the substitution of valine 600 with glutamic acid (V600E). Vemurafenib is an FDA-approved kinase inhibitor for BRAF^V600E; while the drug elicits effective remission of metastatic melanoma, relapse typically occurs within several months after therapy. Recent studies documented critical roles of reversible modifications in RNA in modulating resistance to cancer therapy. Herein we explored the contributions of epitranscriptomic alterations to vemurafenib resistance by assessing the differential expression of epitranscriptomic reader, writer and eraser (RWE) proteins in IGR37 metastatic melanoma cells and the isogenic vemurafenib-resistant cells (IGR37xp). Our results revealed altered expressions of multiple epitranscriptomic RWE proteins, including markedly elevated expressions of MTO1 and TRMU─which act sequentially to produce 5-taurinomethyl-2-thiouridine (τm⁵s²U) at the 34th position of human mitochondrial (mt) tRNA^Glu, tRNA^Gln and tRNA^Lys─in the resistant line. We also observed elevated oxidative phosphorylation in IGR37xp relative to IGR37 cells. Moreover, we found that genetic depletion of TRMU in IGR37xp cells results in diminished oxidative phosphorylation and resensitizes IGR37xp cells to vemurafenib. Together, we uncovered a role of TRMU in conferring vemurafenib resistance in melanoma through modulating oxidative phosphorylation.