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.

Benchmarking commonly used software suites and analysis workflows for DIA proteomics and phosphoproteomics.

A plethora of software suites and multiple classes of spectral libraries have been developed to enhance the depth and robustness of data-independent acquisition (DIA) data processing. However, how the combination of a DIA software tool and a spectral library impacts the outcome of DIA proteomics and phosphoproteomics data analysis has been rarely investigated using benchmark data that mimics biological complexity. In this study, we create DIA benchmark data sets simulating the regulation of thousands of proteins in a complex background, which are collected on both an Orbitrap and a timsTOF instruments. We evaluate four commonly used software suites (DIA-NN, Spectronaut, MaxDIA and Skyline) combined with seven different spectral libraries in global proteome analysis. Moreover, we assess their performances in analyzing phosphopeptide standards and TNF-α-induced phosphoproteome regulation. Our study provides a practical guidance on how to construct a robust data analysis pipeline for different proteomics studies implementing the DIA technique.

DeepPhospho accelerates DIA phosphoproteome profiling through in silico library generation.

Phosphoproteomics integrating data-independent acquisition (DIA) enables deep phosphoproteome profiling with improved quantification reproducibility and accuracy compared to data-dependent acquisition (DDA)-based phosphoproteomics. DIA data mining heavily relies on a spectral library that in most cases is built on DDA analysis of the same sample. Construction of this project-specific DDA library impairs the analytical throughput, limits the proteome coverage, and increases the sample size for DIA phosphoproteomics. Herein we introduce a deep neural network, DeepPhospho, which conceptually differs from previous deep learning models to achieve accurate predictions of LC-MS/MS data for phosphopeptides. By leveraging in silico libraries generated by DeepPhospho, we establish a DIA workflow for phosphoproteome profiling which involves DIA data acquisition and data mining with DeepPhospho predicted libraries, thus circumventing the need of DDA library construction. Our DeepPhospho-empowered workflow substantially expands the phosphoproteome coverage while maintaining high quantification performance, which leads to the discovery of more signaling pathways and regulated kinases in an EGF signaling study than the DDA library-based approach. DeepPhospho is provided as a web server as well as an offline app to facilitate user access to model training, predictions and library generation.

Multiregional profiling of the brain transmembrane proteome uncovers novel regulators of depression.

Transmembrane proteins play vital roles in mediating synaptic transmission, plasticity, and homeostasis in the brain. However, these proteins, especially the G protein-coupled receptors (GPCRs), are underrepresented in most large-scale proteomic surveys. Here, we present a new proteomic approach aided by deep learning models for comprehensive profiling of transmembrane protein families in multiple mouse brain regions. Our multiregional proteome profiling highlights the considerable discrepancy between messenger RNA and protein distribution, especially for region-enriched GPCRs, and predicts an endogenous GPCR interaction network in the brain. Furthermore, our new approach reveals the transmembrane proteome remodeling landscape in the brain of a mouse depression model, which led to the identification of two previously unknown GPCR regulators of depressive-like behaviors. Our study provides an enabling technology and rich data resource to expand the understanding of transmembrane proteome organization and dynamics in the brain and accelerate the discovery of potential therapeutic targets for depression treatment.

Yesprit and Yeaseq: Applications for designing primers and browsing sequences for research using the four Schizosaccharomyces species.

The polymerase chain reaction (PCR)-based gene targeting method, which can delete a specific gene or introduce tags, has been widely utilized to study gene function in fission yeast. One of the critical steps in this method is to design primers for amplifying DNA fragments of deletion or tagging modules and for checking the integration of those DNA fragments at designated loci. Although the primer design tool Pombe PCR Primer Program (PPPP) is available for Schizosaccharomyces pombe, there is no such publicly available application for the other three fission yeast species, S. cryophilus, S. japonicus, and S. octosporus. Likewise, no application enabling DNA/protein sequence retrieval for these three fission yeast species is available either. Therefore, access to such functionality would substantially assist in retrieval of gene sequences of interest and primer design in these fission yeast species. In this report, we describe two applications for fission yeast study: Yesprit and Yeaseq. Yesprit is a primer design tool for strain construction using the PCR-based method, and Yeaseq is a sequence viewer that can acquire the DNA/protein sequences of specific genes. Both tools can be run on the Windows, macOS, and Linux platforms. We believe that the Yesprit and Yeaseq will facilitate research using the four fission yeast species.

Hybrid Spectral Library Combining DIA-MS Data and a Targeted Virtual Library Substantially Deepens the Proteome Coverage.

Data-independent acquisition mass spectrometry (DIA-MS) is a powerful technique that enables relatively deep proteomic profiling with superior quantification reproducibility. DIA data mining predominantly relies on a spectral library of sufficient proteome coverage that, in most cases, is built on data-dependent acquisition-based analysis of the same sample. To expand the proteome coverage for a pre-determined protein family, we report herein on the construction of a hybrid spectral library that supplements a DIA experiment-derived library with a protein family-targeted virtual library predicted by deep learning. Leveraging this DIA hybrid library substantially deepens the coverage of three transmembrane protein families (G protein-coupled receptors, ion channels, and transporters) in mouse brain tissues with increases in protein identification of 37%-87% and peptide identification of 58%-161%. Moreover, of the 412 novel GPCR peptides exclusively identified with the DIA hybrid library strategy, 53.6% were validated as present in mouse brain tissues based on orthogonal experimental measurement.

Evaluation of chemical cross-linkers for in-depth structural analysis of G protein-coupled receptors through cross-linking mass spectrometry.

Chemical cross-linking would conceivably cause structural disruption of a protein, but few cross-linkers have been fully evaluated in this aspect. Furthermore, integral membrane proteins may differ from soluble proteins in the selection of suitable cross-linkers, which has never been investigated. In this study, we systematically evaluated the impact of five conventional cross-linkers targeting Lys, Asp and Glu, and two Arg-reactive cross-linkers on the structural and functional integrity of two G protein-coupled receptors (GPCRs). Perturbation of the receptor structure and ligand-binding activity was observed, depending on the receptor and cross-linking conditions. In particular, our study demonstrated that the concentrations of PDH and KArGO need to be fine-tuned in order to minimize the structural and functional disturbance of specific GPCRs. A set of amenable cross-linkers was selected to acquire the most comprehensive cross-link maps for two GPCRs. Our in-depth cross-linking mass spectrometry (CXMS) analysis has revealed dynamic features of structural regions in GPCRs that are not observable in the crystal structures. Thus, CXMS analysis of GPCRs using the expanded toolkit would facilitate structural modeling of uncharacterized receptors and gain new insights into receptor-ligand interactions.