Assessing the Relationship Between Mass Window Width and Retention Time Scheduling on Protein Coverage for Data-Independent Acquisition

MS based foodomics: An edge tool integrated metabolomics and proteomics for food science

Foodomics has become a popular methodology in food science studies. Mass spectrometry (MS) based metabolomics and proteomics analysis played indispensable roles in foodomics research. So far, several methodologies have been developed to detect the metabolites and proteins in diets and consumers, including sample preparation, MS data acquisition, annotation and interpretation. Moreover, multiomics analysis integrated metabolomics and proteomics have received considerable attentions in the field of food safety and nutrition, because of more comprehensive and deeply. In this context, we intended to review the emerging strategies and their applications in MS-based foodomics, as well as future challenges and trends. The principle and application of multiomics were also discussed, such as the optimization of data acquisition, development of analysis algorithm and exploration of systems biology.

Network-based elucidation of colon cancer drug resistance mechanisms by phosphoproteomic time-series analysis

Aberrant signaling pathway activity is a hallmark of tumorigenesis and progression, which has guided targeted inhibitor design for over 30 years. Yet, adaptive resistance mechanisms, induced by rapid, context-specific signaling network rewiring, continue to challenge therapeutic efficacy. Leveraging progress in proteomic technologies and network-based methodologies, we introduce Virtual Enrichment-based Signaling Protein-activity Analysis (VESPA)-an algorithm designed to elucidate mechanisms of cell response and adaptation to drug perturbations-and use it to analyze 7-point phosphoproteomic time series from colorectal cancer cells treated with clinically-relevant inhibitors and control media. Interrogating tumor-specific enzyme/substrate interactions accurately infers kinase and phosphatase activity, based on their substrate phosphorylation state, effectively accounting for signal crosstalk and sparse phosphoproteome coverage. The analysis elucidates time-dependent signaling pathway response to each drug perturbation and, more importantly, cell adaptive response and rewiring, experimentally confirmed by CRISPR knock-out assays, suggesting broad applicability to cancer and other diseases.

Reciprocal antagonism of PIN1-APC/CCDH1 governs mitotic protein stability and cell cycle entry

Induced oncoproteins degradation provides an attractive anti-cancer modality. Activation of anaphase-promoting complex (APC/CCDH1) prevents cell-cycle entry by targeting crucial mitotic proteins for degradation. Phosphorylation of its co-activator CDH1 modulates the E3 ligase activity, but little is known about its regulation after phosphorylation and how to effectively harness APC/CCDH1 activity to treat cancer. Peptidyl-prolyl cis-trans isomerase NIMA-interacting 1 (PIN1)-catalyzed phosphorylation-dependent cis-trans prolyl isomerization drives tumor malignancy. However, the mechanisms controlling its protein turnover remain elusive. Through proteomic screens and structural characterizations, we identify a reciprocal antagonism of PIN1-APC/CCDH1 mediated by domain-oriented phosphorylation-dependent dual interactions as a fundamental mechanism governing mitotic protein stability and cell-cycle entry. Remarkably, combined PIN1 and cyclin-dependent protein kinases (CDKs) inhibition creates a positive feedback loop of PIN1 inhibition and APC/CCDH1 activation to irreversibly degrade PIN1 and other crucial mitotic proteins, which force permanent cell-cycle exit and trigger anti-tumor immunity, translating into synergistic efficacy against triple-negative breast cancer.

EGFR targeting PhosTACs as a dual inhibitory approach reveals differential downstream signaling

We recently developed a heterobifunctional approach [phosphorylation targeting chimeras (PhosTACs)] to achieve the targeted protein dephosphorylation (TPDephos). Here, we envisioned combining the inhibitory effects of receptor tyrosine kinase inhibitors (RTKIs) and the active dephosphorylation by phosphatases to achieve dual inhibition of kinases. We report an example of tyrosine phosphatase-based TPDephos and the effective epidermal growth factor receptor (EGFR) tyrosine dephosphorylation. We also used phosphoproteomic approaches to study the signaling transductions affected by PhosTAC-related molecules at the proteome-wide level. This work demonstrated the differential signaling pathways inhibited by PhosTAC compared with the TKI, gefitinib. Moreover, a covalent PhosTAC selective for mutated EGFR was developed and showed its inhibitory potential for dysregulated EGFR. Last, EGFR PhosTACs, consistent with EGFR dephosphorylation profiles, induced apoptosis and inhibited cancer cell viability during prolonged PhosTAC treatment. PhosTACs showcased their potential of modulating RTKs activity, expanding the scope of bifunctional molecule utility.

Wnt5 controls splenic myelopoiesis and neutrophil functional ambivalency during DSS-induced colitis

Neutrophils are important innate immune cells with plasticity, heterogenicity, and functional ambivalency. While bone marrow is often regarded as the primary source of neutrophil production, the roles of extramedullary production in regulating neutrophil plasticity and heterogenicity in autoimmune diseases remain poorly understood. Here, we report that the lack of wingless-type MMTV integration site family member 5 (WNT5) unleashes anti-inflammatory protection against colitis in mice, accompanied by reduced colonic CD8+ T cell activation and enhanced splenic extramedullary myelopoiesis. In addition, colitis upregulates WNT5 expression in splenic stromal cells. The ablation of WNT5 leads to increased splenic production of hematopoietic niche factors, as well as elevated numbers of splenic neutrophils with heightened CD8+ T cell suppressive capability, in part due to elevated CD101 expression and attenuated pro-inflammatory activities. Thus, our study reveals a mechanism by which neutrophil plasticity and heterogenicity are regulated in colitis through WNT5 and highlights the role of splenic neutrophil production in shaping inflammatory outcomes.

Acquisition and Analysis of DIA-Based Proteomic Data: A Comprehensive Survey in 2023

Data-independent acquisition (DIA) mass spectrometry (MS) has emerged as a powerful technology for high-throughput, accurate, and reproducible quantitative proteomics. This review provides a comprehensive overview of recent advances in both the experimental and computational methods for DIA proteomics, from data acquisition schemes to analysis strategies and software tools. DIA acquisition schemes are categorized based on the design of precursor isolation windows, highlighting wide-window, overlapping-window, narrow-window, scanning quadrupole-based, and parallel accumulation-serial fragmentation-enhanced DIA methods. For DIA data analysis, major strategies are classified into spectrum reconstruction, sequence-based search, library-based search, de novo sequencing, and sequencing-independent approaches. A wide array of software tools implementing these strategies are reviewed, with details on their overall workflows and scoring approaches at different steps. The generation and optimization of spectral libraries, which are critical resources for DIA analysis, are also discussed. Publicly available benchmark datasets covering global proteomics and phosphoproteomics are summarized to facilitate performance evaluation of various software tools and analysis workflows. Continued advances and synergistic developments of versatile components in DIA workflows are expected to further enhance the power of DIA-based proteomics.

The CUL5 E3 ligase complex negatively regulates central signaling pathways in CD8+ T cells

CD8+ T cells play an important role in anti-tumor immunity. Better understanding of their regulation could advance cancer immunotherapies. Here we identify, via stepwise CRISPR-based screening, that CUL5 is a negative regulator of the core signaling pathways of CD8+ T cells. Knocking out CUL5 in mouse CD8+ T cells significantly improves their tumor growth inhibiting ability, with significant proteomic alterations that broadly enhance TCR and cytokine signaling and their effector functions. Chemical inhibition of neddylation required by CUL5 activation, also enhances CD8 effector activities with CUL5 validated as a major target. Mechanistically, CUL5, which is upregulated by TCR stimulation, interacts with the SOCS-box-containing protein PCMTD2 and inhibits TCR and IL2 signaling. Additionally, CTLA4 is markedly upregulated by CUL5 knockout, and its inactivation further enhances the anti-tumor effect of CUL5 KO. These results together reveal a negative regulatory mechanism for CD8+ T cells and have strong translational implications in cancer immunotherapy.

Dynamic Data-Independent Acquisition Mass Spectrometry with Real-Time Retrospective Alignment

Data-independent acquisition (DIA) mass spectrometry has grown in popularity in recent years, because of the reproducibility and quantitative rigor of a systematic tandem mass spectrometry (MS/MS) sampling method. However, traditional DIA methods may spend valuable instrument time acquiring MS/MS spectra with no usable information in them, affecting sensitivity and quantitative performance. We developed a DIA strategy that dynamically adjusts the MS/MS windows during the chromatographic separation. The method focuses MS/MS acquisition on the most relevant mass range at each point in time─increasing the quantitative sensitivity by increasing the time spent on each DIA window. We demonstrate an improved lower limit of quantification, on average, without sacrificing the number of peptides detected.

An optogenetic-phosphoproteomic study reveals dynamic Akt1 signaling profiles in endothelial cells

The serine/threonine kinase AKT is a central node in cell signaling. While aberrant AKT activation underlies the development of a variety of human diseases, how different patterns of AKT-dependent phosphorylation dictate downstream signaling and phenotypic outcomes remains largely enigmatic. Herein, we perform a systems-level analysis that integrates methodological advances in optogenetics, mass spectrometry-based phosphoproteomics, and bioinformatics to elucidate how different intensity, duration, and pattern of Akt1 stimulation lead to distinct temporal phosphorylation profiles in vascular endothelial cells. Through the analysis of ~35,000 phosphorylation sites across multiple conditions precisely controlled by light stimulation, we identify a series of signaling circuits activated downstream of Akt1 and interrogate how Akt1 signaling integrates with growth factor signaling in endothelial cells. Furthermore, our results categorize kinase substrates that are preferably activated by oscillating, transient, and sustained Akt1 signals. We validate a list of phosphorylation sites that covaried with Akt1 phosphorylation across experimental conditions as potential Akt1 substrates. Our resulting dataset provides a rich resource for future studies on AKT signaling and dynamics.

7SK methylation by METTL3 promotes transcriptional activity

A fundamental feature of cell signaling is the conversion of extracellular signals into adaptive transcriptional responses. The role of RNA modifications in this process is poorly understood. The small nuclear RNA 7SK prevents transcriptional elongation by sequestering the cyclin dependent kinase 9/cyclin T1 (CDK9/CCNT1) positive transcription elongation factor (P-TEFb) complex. We found that epidermal growth factor signaling induces phosphorylation of the enzyme methyltransferase 3 (METTL3), leading to METTL3-mediated methylation of 7SK. 7SK methylation enhanced its binding to heterogeneous nuclear ribonucleoproteins, causing the release of the HEXIM1 P-TEFb complex subunit1 (HEXIM1)/P-TEFb complex and inducing transcriptional elongation. Our findings establish the mechanism underlying 7SK activation and uncover a previously unknown function for the m6A modification in converting growth factor signaling events into a regulatory transcriptional response via an RNA methylation-dependent switch.

Network-based elucidation of colon cancer drug resistance by phosphoproteomic time-series analysis

Aberrant signaling pathway activity is a hallmark of tumorigenesis and progression, which has guided targeted inhibitor design for over 30 years. Yet, adaptive resistance mechanisms, induced by rapid, context-specific signaling network rewiring, continue to challenge therapeutic efficacy. By leveraging progress in proteomic technologies and network-based methodologies, over the past decade, we developed VESPA-an algorithm designed to elucidate mechanisms of cell response and adaptation to drug perturbations-and used it to analyze 7-point phosphoproteomic time series from colorectal cancer cells treated with clinically-relevant inhibitors and control media. Interrogation of tumor-specific enzyme/substrate interactions accurately inferred kinase and phosphatase activity, based on their inferred substrate phosphorylation state, effectively accounting for signal cross-talk and sparse phosphoproteome coverage. The analysis elucidated time-dependent signaling pathway response to each drug perturbation and, more importantly, cell adaptive response and rewiring that was experimentally confirmed by CRISPRko assays, suggesting broad applicability to cancer and other diseases.

Sensitive and Specific Spectral Library Searching with CompOmics Spectral Library Searching Tool and Percolator

Maintaining high sensitivity while limiting false positives is a key challenge in peptide identification from mass spectrometry data. Here, we investigate the effects of integrating the machine learning-based postprocessor Percolator into our spectral library searching tool COSS (CompOmics Spectral library Searching tool). To evaluate the effects of this postprocessing, we have used 40 data sets from 2 different projects and have searched these against the NIST and MassIVE spectral libraries. The searching is carried out using 2 spectral library search tools, COSS and MSPepSearch with and without Percolator postprocessing, and using sequence database search engine MS-GF+ as a baseline comparator. The addition of the Percolator rescoring step to COSS is effective and results in a substantial improvement in sensitivity and specificity of the identifications. COSS is freely available as open source under the permissive Apache2 license, and binaries and source code are found at https://github.com/compomics/COSS.

Coupling suspension trapping-based sample preparation and data-independent acquisition mass spectrometry for sensitive exosomal proteomic analysis

It has been a challenge to analyze minute amounts of proteomic samples in a facile and robust manner. Herein, we developed a quantitative proteomics workflow by integrating suspension trapping (S-Trap)-based sample preparation and label-free data-independent acquisition (DIA) mass spectrometry and then applied it for the analysis of microgram and even nanogram amounts of exosome samples. S-Trap-based sample preparation outperformed the traditional in-solution digestion-based approach and the commonly used filter-aided sample preparation (FASP)-based approach with regard to the number of proteins and peptides identified. Moreover, S-Trap-based sample preparation coupled with DIA mass spectrometry also showed the highest reproducibility for protein quantification. In addition, this approach allowed for identification and quantification of exosome proteins with low starting amounts (down to 50 ~ 200 ng). Finally, the proposed method was successfully applied to label-free quantification of exosomal proteins extracted from MDA-MB-231 breast cancer cells and MCF-10A non-tumorigenic epithelial breast cells. Prospectively, we envision the integrated S-Trap sample preparation coupled with DIA quantification strategy as a promising alternative for highly efficient and sensitive analysis of trace amounts of proteomic samples (e.g., exosomal samples).

DeepLC can predict retention times for peptides that carry as-yet unseen modifications

The inclusion of peptide retention time prediction promises to remove peptide identification ambiguity in complex liquid chromatography-mass spectrometry identification workflows. However, due to the way peptides are encoded in current prediction models, accurate retention times cannot be predicted for modified peptides. This is especially problematic for fledgling open searches, which will benefit from accurate retention time prediction for modified peptides to reduce identification ambiguity. We present DeepLC, a deep learning peptide retention time predictor using peptide encoding based on atomic composition that allows the retention time of (previously unseen) modified peptides to be predicted accurately. We show that DeepLC performs similarly to current state-of-the-art approaches for unmodified peptides and, more importantly, accurately predicts retention times for modifications not seen during training. Moreover, we show that DeepLC's ability to predict retention times for any modification enables potentially incorrect identifications to be flagged in an open search of a wide variety of proteome data.

Extensive and Accurate Benchmarking of DIA Acquisition Methods and Software Tools Using a Complex Proteomic Standard

Over the past decade, the data-independent acquisition mode has gained popularity for broad coverage of complex proteomes by LC-MS/MS and quantification of low-abundance proteins. However, there is no consensus in the literature on the best data acquisition parameters and processing tools to use for this specific application. Here, we present the most comprehensive comparison of DIA workflows on Orbitrap instruments published so far in the field of proteomics. Using a standard human 48 proteins mixture (UPS1-Sigma) at 8 different concentrations in an E. coli proteome background, we tested 36 workflows including 4 different DIA window acquisition schemes and 6 different software tools (DIA-NN, DIA-Umpire, OpenSWATH, ScaffoldDIA, Skyline, and Spectronaut) with or without the use of a DDA spectral library. On the basis of the number of proteins identified, quantification linearity and reproducibility, as well as sensitivity and specificity in 28 pairwise comparisons of different UPS1 concentrations, we summarize the major considerations and propose guidelines for choosing the DIA workflow best suited for LC-MS/MS proteomic analyses. Our 96 DIA raw files and software outputs have been deposited on ProteomeXchange for testing or developing new DIA processing tools.

BoxCarmax: A High-Selectivity Data-Independent Acquisition Mass Spectrometry Method for the Analysis of Protein Turnover and Complex Samples

The data-independent acquisition (DIA) performed in the latest high-resolution, high-speed mass spectrometers offers a powerful analytical tool for biological investigations. The DIA mass spectrometry (DIA-MS) combined with the isotopic labeling approach holds a particular promise for increasing the multiplexity of DIA-MS analysis, which could assist the relative protein quantification and the proteome-wide turnover profiling. However, the wide MS1 isolation windows employed in conventional DIA methods lead to a limited efficiency in identifying and quantifying isotope-labeled peptide pairs through peptide fragment ions. Here, we optimized a high-selectivity DIA-MS named BoxCarmax that supports the analysis of complex samples, such as those generated from Stable isotope labeling by amino acids in cell culture (SILAC) and pulse SILAC (pSILAC) experiments. BoxCarmax enables multiplexed acquisition at both MS1 and MS2 levels, through the integration of BoxCar and MSX features, as well as a gas-phase separation strategy. We found BoxCarmax significantly improved the quantitative accuracy in SILAC and pSILAC samples by mitigating the ratio suppression of isotope-peptide pairs. We further applied BoxCarmax to measure protein degradation regulation during serum starvation stress in cultured cells, revealing valuable biological insights. Our study offered an alternative and accurate approach for the MS analysis of protein turnover and complex samples.

Isoform-resolved correlation analysis between mRNA abundance regulation and protein level degradation

Profiling of biological relationships between different molecular layers dissects regulatory mechanisms that ultimately determine cellular function. To thoroughly assess the role of protein post‐translational turnover, we devised a strategy combining pulse stable isotope‐labeled amino acids in cells (pSILAC), data‐independent acquisition mass spectrometry (DIA‐MS), and a novel data analysis framework that resolves protein degradation rate on the level of mRNA alternative splicing isoforms and isoform groups. We demonstrated our approach by the genome‐wide correlation analysis between mRNA amounts and protein degradation across different strains of HeLa cells that harbor a high grade of gene dosage variation. The dataset revealed that specific biological processes, cellular organelles, spatial compartments of organelles, and individual protein isoforms of the same genes could have distinctive degradation rate. The protein degradation diversity thus dissects the corresponding buffering or concerting protein turnover control across cancer cell lines. The data further indicate that specific mRNA splicing events such as intron retention significantly impact the protein abundance levels. Our findings support the tight association between transcriptome variability and proteostasis and provide a methodological foundation for studying functional protein degradation.

Data-independent acquisition-based proteome and phosphoproteome profiling across six melanoma cell lines reveals determinants of proteotypes

Human cancer cell lines are widely used in pharmacological and systems biological studies. The rapid documentation of the steady-state gene expression landscape of the cells used in a particular experiment may help to improve the reproducibility of scientific research. Here we applied a data-independent acquisition mass spectrometry (DIA-MS) method, coupled with a peptide spectral-library-free data analysis workflow, to measure both the proteome and phosphoproteome of a melanoma cell line panel with different metastatic properties. For each cell line, the single-shot DIA-MS detected 8100 proteins and almost 40 000 phosphopeptides in the respective measurements of two hours. Benchmarking the DIA-MS data towards the RNA-seq data and tandem mass tag (TMT)-MS results from the same set of cell lines demonstrated comparable qualitative coverage and quantitative reproducibility. Our data confirmed the high but complex mRNA-protein and protein-phospsite correlations. The results successfully established DIA-MS as a strong and competitive proteotyping approach for cell lines. The data further showed that all subunits of the glycosylphosphatidylinositol (GPI)-anchor transamidase complex were overexpressed in metastatic melanoma cells and identified altered phosphoprotein modules such as the BAF complex and mRNA splicing between metastatic and primary cells. This study provides a high-quality resource for calibrating DIA-MS performance, benchmarking DIA bioinformatic algorithms, and exploring the metastatic proteotypes in melanoma cells.

FGF23 contains two distinct high-affinity binding sites enabling bivalent interactions with α-Klotho

The three members of the endocrine-fibroblast growth factor (FGF) family, FGF19, 21, and 23 are circulating hormones that regulate critical metabolic processes. FGF23 stimulates the assembly of a signaling complex composed of α-Klotho (KLA) and FGF receptor (FGFR) resulting in kinase activation, regulation of phosphate homeostasis, and vitamin D levels. Here we report that the C-terminal tail of FGF23, a region responsible for KLA binding, contains two tandem repeats, repeat 1 (R1) and repeat 2 (R2) that function as two distinct ligands for KLA. FGF23 variants with a single KLA binding site, FGF23-R1, FGF23-R2, or FGF23-wild type (WT) with both R1 and R2, bind to KLA with similar binding affinity and stimulate FGFR1 activation and MAPK response. R2 is flanked by two cysteines that form a disulfide bridge in FGF23-WT; disulfide bridge formation in FGF23-WT is dispensable for KLA binding and for cell signaling via FGFRs. We show that FGF23-WT stimulates dimerization and activation of a chimeric receptor molecule composed of the extracellular domain of KLA fused to the cytoplasmic domain of FGFR and employ total internal reflection fluorescence microscopy to visualize individual KLA molecules on the cell surface. These experiments demonstrate that FGF23-WT can act as a bivalent ligand of KLA in the cell membrane. Finally, an engineered Fc-R2 protein acts as an FGF23 antagonist offering new pharmacological intervention for treating diseases caused by excessive FGF23 abundance or activity.

Global and Site-Specific Effect of Phosphorylation on Protein Turnover

To date, the effects of specific modification types and sites on protein lifetime have not been systematically illustrated. Here, we describe a proteomic method, DeltaSILAC, to quantitatively assess the impact of site-specific phosphorylation on the turnover of thousands of proteins in live cells. Based on the accurate and reproducible mass spectrometry-based method, a pulse labeling approach using stable isotope-labeled amino acids in cells (pSILAC), phosphoproteomics, and a unique peptide-level matching strategy, our DeltaSILAC profiling revealed a global, unexpected delaying effect of many phosphosites on protein turnover. We further found that phosphorylated sites accelerating protein turnover are functionally selected for cell fitness, enriched in Cyclin-dependent kinase substrates, and evolutionarily conserved, whereas the glutamic acids surrounding phosphosites significantly delay protein turnover. Our method represents a generalizable approach and provides a rich resource for prioritizing the effects of phosphorylation sites on protein lifetime in the context of cell signaling and disease biology.

NAguideR: performing and prioritizing missing value imputations for consistent bottom-up proteomic analyses

Mass spectrometry (MS)-based quantitative proteomics experiments frequently generate data with missing values, which may profoundly affect downstream analyses. A wide variety of imputation methods have been established to deal with the missing-value issue. To date, however, there is a scarcity of efficient, systematic, and easy-to-handle tools that are tailored for proteomics community. Herein, we developed a user-friendly and powerful stand-alone software, NAguideR, to enable implementation and evaluation of different missing value methods offered by 23 widely used missing-value imputation algorithms. NAguideR further evaluates data imputation results through classic computational criteria and, unprecedentedly, proteomic empirical criteria, such as quantitative consistency between different charge-states of the same peptide, different peptides belonging to the same proteins, and individual proteins participating protein complexes and functional interactions. We applied NAguideR into three label-free proteomic datasets featuring peptide-level, protein-level, and phosphoproteomic variables respectively, all generated by data independent acquisition mass spectrometry (DIA-MS) with substantial biological replicates. The results indicate that NAguideR is able to discriminate the optimal imputation methods that are facilitating DIA-MS experiments over those sub-optimal and low-performance algorithms. NAguideR further provides downloadable tables and figures supporting flexible data analysis and interpretation. NAguideR is freely available at http://www.omicsolution.org/wukong/NAguideR/ and the source code: https://github.com/wangshisheng/NAguideR/.