Enhanced Proteomic Coverage in Tissue Microenvironment by Immune Cell Subtype Library-assisted DIA-MS.

Immune cells that infiltrate the tumor microenvironment (TME) play crucial roles in shaping cancer development and influencing clinical outcomes and therapeutic responses. However, obtaining a comprehensive proteomic snapshot of tumor-infiltrating immunity in clinical specimens is often hindered by small sample amounts and a low proportion of immune infiltrating cells in the TME. To enable in-depth and highly sensitive profiling of microscale tissues, we established an immune cell-enriched library-assisted strategy for data-independent acquisition mass spectrometry (DIA-MS). Firstly, 6 immune cell subtype-specific spectral libraries were established from sorted CD8+, CD4+ T lymphocytes, B lymphocytes, natural killer cells, dendritic cells, and macrophages in murine mesenteric lymph nodes (MLNs), covering 7,815 protein groups with surface markers and immune cell-enriched proteins. The feasibility of microscale immune proteomic profiling was demonstrated on 1 µg tissue protein from the tumor of murine colorectal cancer (CRC) models using single-shot DIA; the immune cell-enriched library increased coverage to quantify 7,419 proteins compared to directDIA analysis (6,978 proteins). The enhancement enabled the mapping of 841 immune function-related proteins and exclusive identification of many low-abundant immune proteins, such as CD1D1, and CD244, demonstrating high sensitivity for immune landscape profiling. This approach was employed to characterize the MLNs in CRC models, aiming to elucidate the mechanism underlying their involvement in cancer development within the TME. Even with a low percentage of immune cell infiltration (0.25-3%) in the tumor, our results illuminate down-regulation in the adaptive immune signaling pathways (C-type lectin receptor signaling, chemokine signaling, etc.), T cell receptor signaling, and Th1/Th2/Th17 cell differentiation, suggesting an immunosuppressive status in MLNs of CRC models. The DIA approach using the immune cell-enriched libraries showcased deep coverage and high sensitivity that can facilitate illumination of the immune proteomic landscape for microscale samples.

Cerebrospinal Fluid Proteome Map Reveals Molecular Signatures of Reversible Cerebral Vasoconstriction Syndrome.

Reversible cerebral vasoconstriction syndrome (RCVS) is a complex neurovascular disorder characterized by repetitive thunderclap headaches and reversible cerebral vasoconstriction. The pathophysiological mechanism of this mysterious syndrome remains under-explored and there is no clinically available molecular biomarker. To provide insight into the pathogenesis of RCVS, this study reported the first landscape of dysregulated proteome of cerebrospinal fluid (CSF) in patients with RCVS (n = 21) compared to the age- and sex-matched controls (n = 20) using data-independent acquisition mass spectrometry (DIA-MS). Protein-protein interaction and functional enrichment analysis were employed to construct functional protein networks using the RCVS proteome. An RCVS-CSF proteome library resource of 1,054 proteins was established, which illuminated large groups of upregulated proteins enriched in the brain and blood-brain barrier (BBB). Personalized RCVS-CSF proteomic profiles from 17 RCVS patients and 20 controls reveal proteomic changes involving the complement system, adhesion molecules, and extracellular matrix, which may contribute to the disruption of BBB and dysregulation of neurovascular units. Moreover, an additional validation cohort validated a panel of biomarker candidates and a two-protein signature predicted by machine learning model to discriminate RCVS patients from controls with an area under the curve of 0.997. This study reveals the first RCVS proteome and a potential pathogenetic mechanism of BBB and neurovascular unit dysfunction. It also nominates potential biomarker candidates that are mechanistically plausible for RCVS, which may offer potential diagnostic and therapeutic opportunities beyond the clinical manifestations.

Advances in data-independent acquisition mass spectrometry towards comprehensive digital proteome landscape.

The data-independent acquisition mass spectrometry (DIA-MS) has rapidly evolved as a powerful alternative for highly reproducible proteome profiling with a unique strength of generating permanent digital maps for retrospective analysis of biological systems. Recent advancements in data analysis software tools for the complex DIA-MS/MS spectra coupled to fast MS scanning speed and high mass accuracy have greatly expanded the sensitivity and coverage of DIA-based proteomics profiling. Here, we review the evolution of the DIA-MS techniques, from earlier proof-of-principle of parallel fragmentation of all-ions or ions in selected m/z range, the sequential window acquisition of all theoretical mass spectra (SWATH-MS) to latest innovations, recent development in computation algorithms for data informatics, and auxiliary tools and advanced instrumentation to enhance the performance of DIA-MS. We further summarize recent applications of DIA-MS and experimentally-derived as well as in silico spectra library resources for large-scale profiling to facilitate biomarker discovery and drug development in human diseases with emphasis on the proteomic profiling coverage. Toward next-generation DIA-MS for clinical proteomics, we outline the challenges in processing multi-dimensional DIA data set and large-scale clinical proteomics, and continuing need in higher profiling coverage and sensitivity.

Sample Size-Comparable Spectral Library Enhances Data-Independent Acquisition-Based Proteome Coverage of Low-Input Cells.

Despite advancements of data-independent acquisition mass spectrometry (DIA-MS) to provide comprehensive and reproducible proteome profiling, its utility in very low-input samples is limited. Due to different proteome complexities and corresponding peptide ion abundances, the conventional LC-MS/MS acquisition and widely used large-scale DIA libraries may not be suitable for the micro-nanogram samples. In this study, we report a sample size-comparable library-based DIA approach to enhance the proteome coverage of low-input nanoscale samples (i.e., nanogram cells, ∼5-50 cells). By constructing sample size-comparable libraries, 2380 and 3586 protein groups were identified from as low as 0.75 (∼5 cells) and 1.5 ng (∼10 cells), respectively, highlighting one of the highest proteome coverage with good reproducibility (86%-99% in triplicate results). For the 0.75 ng sample (∼5 cells), significantly superior identification (2380 proteins) was achieved by small-size library-based DIA, compared to 1908, 1749, and 107 proteins identified from medium-size and large-size libraries and a lung cancer resource spectral library, respectively. A similar trend was observed using a different instrument and data analysis pipeline, indicating the generalized conclusion of the approach. Furthermore, the small-size library uniquely identified 518 (22%) proteins in the low-abundant region and spans over a 5-order dynamic range. Spectral similarity analysis revealed that the fragmentation ion pattern in the DIA-MS/MS spectra of the dataset and spectral library play crucial roles for mapping low abundant proteins. With these spectral libraries made freely available, the optimized library-based DIA strategy and DIA digital map will advance quantitative proteomics applications for mass-limited samples.

Application of Data-Independent Acquisition Approach to Study the Proteome Change from Early to Later Phases of Tomato Pathogenesis Responses.

Plants and pathogens are entangled in a continual arms race. Plants have evolved dynamic defence and immune mechanisms to resist infection and enhance immunity for second wave attacks from the same or different types of pathogenic species. In addition to evolutionarily and physiological changes, plant-pathogen interaction is also highly dynamic at the molecular level. Recently, an emerging quantitative mass spectrometry-based proteomics approach named data-independent acquisition (DIA), has been developed for the analysis of the proteome in a high-throughput fashion. In this study, the DIA approach was applied to quantitatively trace the change in the plant proteome from the early to the later stage of pathogenesis progression. This study revealed that at the early stage of the pathogenesis response, proteins directly related to the chaperon were regulated for the defence proteins. At the later stage, not only the defence proteins but also a set of the pathogen-associated molecular pattern-triggered immunity (PTI) and effector triggered immunity (ETI)-related proteins were highly induced. Our findings show the dynamics of the plant regulation of pathogenesis at the protein level and demonstrate the potential of using the DIA approach for tracing the dynamics of the plant proteome during pathogenesis responses.