Molecular & Cellular Proteomics最新文献

The development of targeted assays that monitor biomedically relevant proteins is an important step in bridging discovery experiments to large scale clinical studies. Targeted assays are currently unable to scale to hundreds or thousands of targets. We demonstrate the generation of large-scale assays using a novel hybrid nominal mass instrument. The scale of these assays is achievable with the Stellar^TM mass spectrometer through the accommodation of shifting retention times by real-time alignment, while being sensitive and fast enough to handle many concurrent targets. Assays were constructed using precursor information from gas-phase fractionated (GPF) data-independent acquisition (DIA). We demonstrate the ability to schedule methods from orbitrap and linear ion trap acquired GPF DIA library, and compare the quantification of a matrix-matched calibration curve from orbitrap DIA and linear ion trap parallel reaction monitoring (PRM). Two applications of these proposed workflows are shown with a cerebrospinal fluid (CSF) neurodegenerative disease protein PRM assay and with a Mag-Net enriched plasma extracellular vesicle (EV) protein survey PRM assay. In CSF, our assay targets proteins discovered previously to be associated with Alzheimer's disease in a small independent sample set. For the Mag-Net enriched plasma survey assay, we observe that proteins selected based on their measurement robustness are still able to capture differences in abundance across disease groups in a small sample set. These highlight the application of highly multiplex, targeted protein assays in clinical research.

Malaria transmission from humans to mosquitoes is essential to the parasite life cycle. In the human malaria parasite, Plasmodium falciparum, the rate of commitment to produce the sexual transmission stages, or gametocytes varies and is governed by genetic, epigenetic and environmental factors. The sexually committed parasite has so far remained elusive due to the lack of markers to efficiently isolate these parasites for subsequent functional studies including proteomic analysis of the isolated population. Here, we demonstrate that MSRP1 is a highly specific sexual commitment marker. Using this marker, we generated and validated reporter parasite lines for subsequent FACS-based isolation of sexually and asexually committed parasites. Proteomics of isolated parasites defined distinct protein signatures, including several merozoite surface proteins, indicating functional differences between the two parasite populations. This study provides a blueprint for systematic characterisation of the parasite stage at this crucial juncture in the life cycle.

Although the plasma membrane (PM) is among the most biologically important and therapeutically targeted cellular compartments, it is among the most challenging to faithfully capture using proteomic approaches. The quality of quantitative surfaceomics data depends heavily on the effectiveness of the cell surface enrichment used during sample preparation. Enrichment improves sensitivity for low abundance PM proteins and ensures that the changes detected reflect PM expression changes rather than whole cell changes. Cell surface biotinylation with PM-impermeable, amine-reactive reagents is a facile, accessible, and unbiased approach to enrich PM proteins. However, it results in unexpectedly high contamination with intracellular proteins, reducing its utility. We report that biotinylating human cells with amine-reactive reagents intracellularly labels a small but reproducible population of nonviable cells. Although these dead cells represent only 5 ± 2% of the total, we find that in T cell preparations the dead cells account for 90% of labelled proteins. Depleting Annexin V positive dead T cells postlabelling removes ∼99% of the intracellularly labelled cells, resulting in markedly improved PM identifications, peptide counts, and intensity-based absolute quantification intensities. Correspondingly, we found substantial depletion of intracellular proteins, particularly of nuclear origin. Overall, the cumulative intensity of PM proteins increased from 4% to 55.8% with dead cell depletion. Finally, we demonstrate that immature ER/Golgi glycoforms of CD11a and CD18 are selectively removed by dead-cell depletion. We conclude that high intracellular labelling of nonviable cells is the major source of intracellular protein contaminants in amine-reactive surface enrichment methods and can be reduced by dead-cell depletion postlabelling, improving both the sensitivity and accuracy of PM proteomics.

Post-translational modifications (PTMs) play a central role in cellular regulation and are implicated in numerous diseases. Database searching remains the standard for identifying modified peptides from tandem mass spectra but is hindered by the combinatorial expansion of modification types and sites. De novo peptide sequencing offers an attractive alternative, yet existing methods remain limited to unmodified peptides or a narrow set of PTMs. Here, we curated a large dataset of spectra from endogenous and synthetic peptides from ProteomeTools spanning 19 biologically relevant amino acid-PTM combinations, covering phosphorylation, acetylation, and ubiquitination. We used this dataset to develop Modanovo, an extension of the Casanovo transformer architecture for de novo peptide sequencing. Modanovo achieved robust performance across these amino acid-PTM combinations (median area under the precision-coverage curve 0.92), while maintaining performance on unmodified peptides (0.93), nearly identical to Casanovo (0.94). The model outperformed π-PrimeNovo-PTM and InstaNovo-P and showed increased precision and complementarity to the database search tool MSFragger. Robustness was confirmed across independent datasets, particularly at peptide lengths frequently represented in the curated dataset. Applied to a phosphoproteomics dataset from monkeypox virus-infected cells, Modanovo recovered numerous confident peptides not reported by database search, including new viral phosphosites supported by spectral evidence, thereby demonstrating its complementarity to database-driven identification approaches. These results establish Modanovo as a broadly applicable model for comprehensive de novo sequencing of both modified and unmodified peptides.

Investigating multiple protein post-translational modifications (PTMs) is critical for unraveling the complexities of protein regulation and the dynamic interplay among PTMs, a growing focus in proteomics. However, simultaneous analysis of diverse PTMs remains a significant technical challenge, as existing workflows struggle to balance throughput, sensitivity, and reproducibility, particularly when sample amounts are limited. To address these limitations, we present MoSAIC, a multi-PTM workflow integrating co-enrichment strategies, multiplexing, fractionation, hybrid data acquisition, and unified data analysis, optimized for clinically relevant biological samples. This approach targets phosphorylation, glycosylation, acetylation, and ubiquitination, enabling comprehensive interrogation of these modifications simultaneously. Compared to the traditional CPTAC workflow, MoSAIC doubles PTM coverage (4 vs. 2 PTMs) while maintaining the same instrument time (24 MS runs), achieving increased identifications of PTM-modified peptides. By leveraging fractionation and tandem mass tag (TMT) labeling, we achieved concurrent identification and quantification of PTM-specific peptides from the same sample, enhancing throughput and data consistency. This robust workflow addresses key limitations in multi-PTM proteomics, providing a cost-effective and efficient platform to advance biological and clinical research.

Post-translational modifications (PTMs) are pivotal in cellular regulations, and their crosstalk is related to various diseases such as cancer. Given the prevalence of PTM crosstalk within close amino acid ranges, identifying peptides with multiple PTMs is essential. However, this task is an NP-hard combinatorial problem with exponential complexity, posing significant challenges for existing analysis methods. Here, we introduce PIPI-C (PTM-Invariant Peptide Identification with a Combinatorial model), a novel search engine that addresses this challenge through a mixed integer linear programming (MILP) model, thereby overcoming the limitations of existing approaches that struggle with high-order PTM combinations. Rigorous validation across diverse datasets confirms PIPI-C's superior performance in detecting PTM combinations. When applied to over 72 million mass spectra of three human cancers-lung squamous cell carcinoma (LSCC), colorectal adenocarcinoma (COAD), and glioblastoma (GBM)-PIPI-C reveals significantly upregulated PTM combinations. In LSCC, 50% of 860 upregulated unique PTM site patterns (UPSPs) (when comparing cancer vs. normal samples) carried at least two PTMs, including literature-supported crosstalks such as di-methylation with trifluoroleucine substitution and amidation with proline-to-valine substitution. Similar findings in COAD and GBM highlight PIPI-C's utility in uncovering cancer-relevant PTM combination landscapes. Overall, PIPI-C provides a robust mathematical framework for decoding complex PTM patterns, advancing our understanding of PTM-driven cellular processes in diseases.

Translational errors (TEs) result in a mismatch between mRNA codons and the amino acids (AAs) of the corresponding protein. Unlike DNA mutations or RNA editing, where nucleotide sequences can be used to infer AA substitutions, TEs can only be detected at the protein level. Although high-throughput mass spectrometry (MS) proteomics offers the potential to resolve peptide sequences and could theoretically be used to identify TEs, the feasibility of current MS data analysis approaches for this application remains uncertain. Here, we utilize patient-derived xenograft proteomics data, which include both human and mouse peptides with identifiable cross-species AA variations, as a ground truth for benchmarking TE identification methods. By using high-confidence mouse peptides as surrogates for "TE-containing" peptides, we show that current open search approaches can achieve >65% overall sensitivity and >70% overall precision for high-quality samples. The intersection of different search strategies significantly enhances precision, albeit at the expense of reduced sensitivity. Notably, the evaluation metrics vary significantly across individual AA substitutions, suggesting that caution is warranted when detecting or interpreting specific AA substitutions. Moreover, closed searches targeting predefined AA changes exhibit poor precision, with post-translational modification mislocalization identified as a key bottleneck for this application. Overall, our study provides a first-of-its-kind benchmark for MS-based TE discovery and offers guidance for optimizing MS search strategies.

We present the first integrated transcriptomic and proteomic profiling of the iridocorneal region in the spontaneous murine glaucoma model DBA/2J and DBA/2J-Gpnmb⁺/Sj controls to define molecular changes associated with ocular hypertension and glaucoma. Using RNA sequencing and label-free quantitative proteomics, we identified over 20,000 transcripts and 8500 proteins, creating a comprehensive molecular atlas of glaucoma-related alterations in DBA/2J mice. Principal component and differential expression analyses revealed distinct genotype-specific molecular signatures. In DBA/2J mice, upregulated genes were enriched in pathways related to extracellular matrix remodeling, collagen organization, TGF-β signaling, and inflammation. Proteomic data confirmed increased levels of complement components, antigen presentation proteins, and autophagy markers. Integrated analyses identified 29 genes upregulated at both transcript and protein levels, primarily involved in extracellular matrix structure and immune regulation. Downregulated genes were associated with melanocyte differentiation and pigment-organelle function, including Pmel, a gene implicated in pigmentary glaucoma. Cross-referencing with human genome-wide association studies data revealed overlap with glaucoma-associated genes (LTBP2, LOXL1, COL11A1, VCAM1), alongside reduced expression of Angpt and Lmx1b, linked to ocular hypertension. Together, these findings support the existence of an immune-fibrotic feed-forward loop and implicate collagen-elastic fiber dysfunction as a central mechanism in glaucoma pathogenesis.