Data-independent acquisition: A milestone and prospect in clinical mass spectrometry-based proteomics.

Data-independent acquisition (DIA) has revolutionized the field of mass spectrometry (MS)-based proteomics over the past few years. DIA stands out for its ability to systematically sample all peptides in a given mass-to-charge range, allowing an unbiased acquisition of proteomics data. This greatly mitigates the issue of missing values and significantly enhances quantitative accuracy, precision, and reproducibility compared to many traditional methods. This review focuses on the critical role of DIA analysis software tools, primarily focusing on their capabilities and the challenges they address in proteomic research. Advances in MS technology, such as trapped ion mobility spectrometry, or high field asymmetric waveform ion mobility spectrometry require sophisticated analysis software capable of handling the increased data complexity and exploiting the full potential of DIA. We identify and critically evaluate leading software tools in the DIA landscape, discussing their unique features, and the reliability of their quantitative and qualitative outputs. We present the biological and clinical relevance of DIA-MS and discuss crucial publications that paved the way for in-depth proteomic characterization in patient-derived specimens. Furthermore, we provide a perspective on emerging trends in clinical applications and present upcoming challenges including standardization and certification of MS-based acquisition strategies in molecular diagnostics. While we emphasize the need for continuous development of software tools to keep pace with evolving technologies, we advise researchers against uncritically accepting the results from DIA software tools. Each tool may have its own biases, and some may not be as sensitive or reliable as others. Our overarching recommendation for both researchers and clinicians is to employ multiple DIA analysis tools, utilizing orthogonal analysis approaches to enhance the robustness and reliability of their findings.

Robust, Precise, and Deep Proteome Profiling Using a Small Mass Range and Narrow Window Data-Independent-Acquisition Scheme.

In recent years, a plethora of different data-independent acquisition methods have been developed for proteomics to cover a wide range of requirements. Current deep proteome profiling methods rely on fractionations, elaborate chromatography, and mass spectrometry setups or display suboptimal quantitative precision. We set out to develop an easy-to-use one shot DIA method that achieves high quantitative precision and high proteome coverage. We achieve this by focusing on a small mass range of 430-670 m/z using small isolation windows without overlap. With this new method, we were able to quantify >9200 protein groups in HEK lysates with an average coefficient of variance of 3.2%. To demonstrate the power of our newly developed narrow mass range method, we applied it to investigate the effect of PGC-1α knockout on the skeletal muscle proteome in mice. Compared to a standard data-dependent acquisition method, we could double proteome coverage and, most importantly, achieve a significantly higher quantitative precision, as compared to a previously proposed DIA method. We believe that our method will be especially helpful in quantifying low abundant proteins in samples with a high dynamic range. All raw and result files are available at massive.ucsd.edu (MSV000092186).

Deep Proteomic Investigation of Metabolic Adaptation in Mycobacteria under Different Growth Conditions.

Understanding the complex mechanisms of mycobacterial pathophysiology and adaptive responses presents challenges that can hinder drug development. However, employing physiologically relevant conditions, such as those found in human macrophages or simulating physiological growth conditions, holds promise for more effective drug screening. A valuable tool in this pursuit is proteomics, which allows for a comprehensive analysis of adaptive responses. In our study, we focused on Mycobacterium smegmatis, a model organism closely related to the pathogenic Mycobacterium tuberculosis, to investigate the impact of various carbon sources on mycobacterial growth. To facilitate this research, we developed a cost-effective, straightforward, and high-quality pipeline for proteome analysis and compared six different carbon source conditions. Additionally, we have created an online tool to present and analyze our data, making it easily accessible to the community. This user-friendly platform allows researchers and interested parties to explore and interpret the results effectively. Our findings shed light on mycobacterial adaptive physiology and present potential targets for drug development, contributing to the fight against tuberculosis.

Analysis of DIA proteomics data using MSFragger-DIA and FragPipe computational platform.

Liquid chromatography (LC) coupled with data-independent acquisition (DIA) mass spectrometry (MS) has been increasingly used in quantitative proteomics studies. Here, we present a fast and sensitive approach for direct peptide identification from DIA data, MSFragger-DIA, which leverages the unmatched speed of the fragment ion indexing-based search engine MSFragger. Different from most existing methods, MSFragger-DIA conducts a database search of the DIA tandem mass (MS/MS) spectra prior to spectral feature detection and peak tracing across the LC dimension. To streamline the analysis of DIA data and enable easy reproducibility, we integrate MSFragger-DIA into the FragPipe computational platform for seamless support of peptide identification and spectral library building from DIA, data-dependent acquisition (DDA), or both data types combined. We compare MSFragger-DIA with other DIA tools, such as DIA-Umpire based workflow in FragPipe, Spectronaut, DIA-NN library-free, and MaxDIA. We demonstrate the fast, sensitive, and accurate performance of MSFragger-DIA across a variety of sample types and data acquisition schemes, including single-cell proteomics, phosphoproteomics, and large-scale tumor proteome profiling studies.

Krüppel-like factor 7 influences translation and pathways involved in ribosomal biogenesis in breast cancer.

BACKGROUND: Ribosomal biogenesis and ribosomal proteins have attracted attention in the context of tumor biology in recent years. Instead of being mere translational machineries, ribosomes might play an active role in tumor initiation and progression. Despite its importance, regulation of ribosomal biogenesis is still not completely understood. METHODS: Using Gene Set Enrichment Analysis of RNA sequencing and proteomical mass spectrometry data in breast cancer cells expressing Krüppel-like factor 7 (KLF7), we identified processes altered by this transcription factor. In silico analyses of a cohort of breast cancer patients in The Cancer Genome Atlas confirmed our finding. We further verified the role of KLF7 the identified ribosomal processes in in vitro assays of mammary carcinoma cell lines and analyses of breast cancer patients' tissue slices. RESULTS: We identified the transcription factor Krüppel-like factor 7 (KLF7) as a regulator of ribosomal biogenesis and translation in breast cancer cells and tissue. Highly significant overlapping processes related to ribosomal biogenesis were identified in proteomics and transcriptomics data and confirmed in patients' breast cancer RNA Seq data. Further, nucleoli, the sites of ribosomal biogenesis, were morphologically altered and quantitatively increased in KLF7-expressing cells. Pre-rRNA processing was identified as one potential process affected by KLF7. In addition, an increase in global translation independent from proliferation and transcription was observed upon exogenous KLF7 expression in vitro. Importantly, in a cohort of breast cancer patients, KLF7-expression levels correlated with aggressiveness of the intrinsic breast cancer subtype and tumor grading. Moreover, KLF7 correlated with nucleolar characteristics in human breast tumor tissue, indicating a role for KLF7 in ribosomal biogenesis. CONCLUSION: In mammary carcinoma, KLF7 is involved in ribosomal biogenesis. Alterations of ribosomal biogenesis has far reaching quantitative and qualitative implications for the proteome of the cancer cells. This might influence the aggressiveness of cancer cells.

MaxQuant and MSstats in Galaxy Enable Reproducible Cloud-Based Analysis of Quantitative Proteomics Experiments for Everyone.

Quantitative mass spectrometry-based proteomics has become a high-throughput technology for the identification and quantification of thousands of proteins in complex biological samples. Two frequently used tools, MaxQuant and MSstats, allow for the analysis of raw data and finding proteins with differential abundance between conditions of interest. To enable accessible and reproducible quantitative proteomics analyses in a cloud environment, we have integrated MaxQuant (including TMTpro 16/18plex), Proteomics Quality Control (PTXQC), MSstats, and MSstatsTMT into the open-source Galaxy framework. This enables the web-based analysis of label-free and isobaric labeling proteomics experiments via Galaxy's graphical user interface on public clouds. MaxQuant and MSstats in Galaxy can be applied in conjunction with thousands of existing Galaxy tools and integrated into standardized, sharable workflows. Galaxy tracks all metadata and intermediate results in analysis histories, which can be shared privately for collaborations or publicly, allowing full reproducibility and transparency of published analysis. To further increase accessibility, we provide detailed hands-on training materials. The integration of MaxQuant and MSstats into the Galaxy framework enables their usage in a reproducible way on accessible large computational infrastructures, hence realizing the foundation for high-throughput proteomics data science for everyone.

Benchmarking of analysis strategies for data-independent acquisition proteomics using a large-scale dataset comprising inter-patient heterogeneity.

Numerous software tools exist for data-independent acquisition (DIA) analysis of clinical samples, necessitating their comprehensive benchmarking. We present a benchmark dataset comprising real-world inter-patient heterogeneity, which we use for in-depth benchmarking of DIA data analysis workflows for clinical settings. Combining spectral libraries, DIA software, sparsity reduction, normalization, and statistical tests results in 1428 distinct data analysis workflows, which we evaluate based on their ability to correctly identify differentially abundant proteins. From our dataset, we derive bootstrap datasets of varying sample sizes and use the whole range of bootstrap datasets to robustly evaluate each workflow. We find that all DIA software suites benefit from using a gas-phase fractionated spectral library, irrespective of the library refinement used. Gas-phase fractionation-based libraries perform best against two out of three reference protein lists. Among all investigated statistical tests non-parametric permutation-based statistical tests consistently perform best.

Sex-related differences of early cardiac functional and proteomic alterations in a rat model of myocardial ischemia.

BACKGROUND: Reduced cardiovascular risk in premenopausal women has been the focus of research in recent decades. Previous hypothesis-driven experiments have highlighted the role of sex hormones on distinct inflammatory responses, mitochondrial proteins, extracellular remodeling and estrogen-mediated cardioprotective signaling pathways related to post-ischemic recovery, which were associated with better cardiac functional outcomes in females. We aimed to investigate the early, sex-specific functional and proteomic changes following myocardial ischemia in an unbiased approach. METHODS: Ischemia was induced in male (M-Isch) and female (F-Isch) rats with sc. injection of isoproterenol (85 mg/kg) daily for 2 days, while controls (M-Co, F-Co) received sc. saline solution. At 48 h after the first injection pressure-volume analysis was carried out to assess left ventricular function. FFPE tissue slides were scanned and analyzed digitally, while myocardial proteins were quantified by liquid chromatography-tandem mass spectrometry (LC-MS/MS) using isobaric labeling. Concentrations of circulating steroid hormones were measured with LC-MS/MS. Feature selection (PLS and PLS-DA) was used to examine associations among functional, proteomic and hormonal datasets. RESULTS: Induction of ischemia resulted in 38% vs 17% mortality in M-Isch and F-Isch respectively. The extent of ischemic damage to surviving rats was comparable between the sexes. Systolic dysfunction was more pronounced in males, while females developed a more severe impairment of diastolic function. 2224 proteins were quantified, with 520 showing sex-specific differential regulation. Our analysis identified transcriptional, cytoskeletal, contractile, and mitochondrial proteins, molecular chaperones and the extracellular matrix as sources of disparity between the sexes. Bioinformatics highlighted possible associations of estrogens and their metabolites with early functional and proteomic alterations. CONCLUSIONS: Our study has highlighted sex-specific alterations in systolic and diastolic function shortly after ischemia, and provided a comprehensive look at the underlying proteomic changes and the influence of estrogens and their metabolites. According to our bioinformatic analysis, inflammatory, mitochondrial, chaperone, cytoskeletal, extracellular and matricellular proteins are major sources of intersex disparity, and may be promising targets for early sex-specific pharmacologic interventions.

A Systematic Evaluation of Semispecific Peptide Search Parameter Enables Identification of Previously Undescribed N-Terminal Peptides and Conserved Proteolytic Processing in Cancer Cell Lines.

Liquid chromatography-tandem mass spectrometry (LC-MS/MS) has become the most commonly used technique in explorative proteomic research. A variety of open-source tools for peptide-spectrum matching have become available. Most analyses of explorative MS data are performed using conventional settings, such as fully specific enzymatic constraints. Here we evaluated the impact of the fragment mass tolerance in combination with the enzymatic constraints on the performance of three search engines. Three open-source search engines (Myrimatch, X! Tandem, and MSGF+) were evaluated concerning the suitability in semi- and unspecific searches as well as the importance of accurate fragment mass spectra in non-specific peptide searches. We then performed a semispecific reanalysis of the published NCI-60 deep proteome data applying the most suited parameters. Semi- and unspecific LC-MS/MS data analyses particularly benefit from accurate fragment mass spectra while this effect is less pronounced for conventional, fully specific peptide-spectrum matching. Search speed differed notably between the three search engines for semi- and non-specific peptide-spectrum matching. Semispecific reanalysis of NCI-60 proteome data revealed hundreds of previously undescribed N-terminal peptides, including cases of proteolytic processing or likely alternative translation start sites, some of which were ubiquitously present in all cell lines of the reanalyzed panel. Highly accurate MS2 fragment data in combination with modern open-source search algorithms enable the confident identification of semispecific peptides from large proteomic datasets. The identification of previously undescribed N-terminal peptides in published studies highlights the potential of future reanalysis and data mining in proteomic datasets.

Impact of DJ-1 and Helix 8 on the Proteome and Degradome of Neuron-Like Cells.

DJ-1 is an abundant and ubiquitous component of cellular proteomes. DJ-1 supposedly exerts a wide variety of molecular functions, ranging from enzymatic activities as a deglycase, protease, and esterase to chaperone functions. However, a consensus perspective on its molecular function in the cellular context has not yet been reached. Structurally, the C-terminal helix 8 of DJ-1 has been proposed to constitute a propeptide whose proteolytic removal transforms a DJ-1 zymogen to an active hydrolase with potential proteolytic activity. To better understand the cell-contextual functionality of DJ-1 and the role of helix 8, we employed post-mitotically differentiated, neuron-like SH-SY5Y neuroblastoma cells with stable over-expression of full length DJ-1 or DJ-1 lacking helix 8 (ΔH8), either with a native catalytically active site (C106) or an inactive site (C106A active site mutation). Global proteome comparison of cells over-expressing DJ-1 ΔH8 with native or mutated active site cysteine indicated a strong impact on mitochondrial biology. N-terminomic profiling however did not highlight direct protease substrate candidates for DJ-1 ΔH8, but linked DJ-1 to elevated levels of activated lysosomal proteases, albeit presumably in an indirect manner. Finally, we show that DJ-1 ΔH8 loses the deglycation activity of full length DJ-1. Our study further establishes DJ-1 as deglycation enzyme. Helix 8 is essential for the deglycation activity but dispensable for the impact on lysosomal and mitochondrial biology; further illustrating the pleiotropic nature of DJ-1.

Detection and Characterization of a Mycobacterial L-Arabinofuranose ABC Transporter Identified with a Rapid Lipoproteomics Protocol.

Nutrient uptake is essential for survival of organisms, and carbohydrates serve as a crucial carbon and energy source for most microorganisms. Given the importance of mycobacteria as human pathogens a detailed knowledge of carbohydrate uptake transporters is highly desirable, but currently available information is severely limited and mainly based on in silico analyses. Moreover, there is only very little data available on the in vitro characterization of carbohydrate transporters from mycobacterial species. To overcome these significant limitations there is a strong demand for innovative approaches to experimentally match substrates to ATP-binding cassette (ABC) transporters in a straightforward manner. Our study focuses on the model organism Mycobacterium smegmatis and identifies a mycobacterial ABC transport system based on a rapid label-free mass spectrometry lipoproteomics assay with broad applicability. Further validation and X-ray structure analyses reveal a highly selective mycobacterial L-arabinose uptake system.