Democratizing data-independent acquisition proteomics analysis on public cloud infrastructures via the Galaxy framework

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 m/z 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.

nf-encyclopedia: A Cloud-Ready Pipeline for Chromatogram Library Data-Independent Acquisition Proteomics Workflows

Data-independent acquisition (DIA) mass spectrometry methods provide systematic and comprehensive quantification of the proteome; yet, relatively few open-source tools are available to analyze DIA proteomics experiments. Fewer still are tools that can leverage gas phase fractionated (GPF) chromatogram libraries to enhance the detection and quantification of peptides in these experiments. Here, we present nf-encyclopedia, an open-source NextFlow pipeline that connects three open-source tools, MSConvert, EncyclopeDIA, and MSstats, to analyze DIA proteomics experiments with or without chromatogram libraries. We demonstrate that nf-encyclopedia is reproducible when run on either a cloud platform or a local workstation and provides robust peptide and protein quantification. Additionally, we found that MSstats enhances protein-level quantitative performance over EncyclopeDIA alone. Finally, we benchmarked the ability of nf-encyclopedia to scale to large experiments in the cloud by leveraging the parallelization of compute resources. The nf-encyclopedia pipeline is available under a permissive Apache 2.0 license; run it on your desktop, cluster, or in the cloud: https://github.com/TalusBio/nf-encyclopedia.

A Galaxy of informatics resources for MS-based proteomics

INTRODUCTION

Continuous advances in mass spectrometry (MS) technologies have enabled deeper and more reproducible proteome characterization and a better understanding of biological systems when integrated with other 'omics data. Bioinformatic resources meeting the analysis requirements of increasingly complex MS-based proteomic data and associated multi-omic data are critically needed. These requirements included availability of software that would span diverse types of analyses, scalability for large-scale, compute-intensive applications, and mechanisms to ease adoption of the software.

AREAS COVERED

The Galaxy ecosystem meets these requirements by offering a multitude of open-source tools for MS-based proteomics analyses and applications, all in an adaptable, scalable, and accessible computing environment. A thriving global community maintains these software and associated training resources to empower researcher-driven analyses.

EXPERT OPINION

The community-supported Galaxy ecosystem remains a crucial contributor to basic biological and clinical studies using MS-based proteomics. In addition to the current status of Galaxy-based resources, we describe ongoing developments for meeting emerging challenges in MS-based proteomic informatics. We hope this review will catalyze increased use of Galaxy by researchers employing MS-based proteomics and inspire software developers to join the community and implement new tools, workflows, and associated training content that will add further value to this already rich ecosystem.

Quantitative proteomics reveals reduction in central carbon and energy metabolisms contributes to gentamicin resistance in Staphylococcus aureus

The emergence of antibiotic resistance greatly increases the difficulty of treating bacterial infections. In order to develop effective treatments, the underlying mechanisms of antibiotic resistance must be understood. In this study, Staphylococcus aureus ATCC6538 strain was passaged in medium with and without gentamicin and obtained lab-evolved gentamicin-resistant S. aureus (R_GEN) and gentamicin-sensitive S. aureus (S_GEN) strains, respectively. Data-Independent Acquisition (DIA)-based proteomics approach was applied to compare the two strains. A total of 1426 proteins were identified, of which 462 were significantly different: 126 were upregulated and 336 were downregulated in R_GEN compared to S_GEN. Further analysis found that reduced protein biosynthesis was a characteristic feature in R_GEN, related to metabolic suppression. The most differentially expressed proteins were involved in metabolic pathways. In R_GEN, central carbon metabolism was dysregulated and energy metabolism decreased. After verification, it was found that the levels of NADH, ATP, and reactive oxygen species (ROS) decreased, and superoxide dismutase and catalase activities increased. These findings suggest that inhibition of central carbon and energy metabolic pathways may play an important role in the resistance of S. aureus to gentamicin, and that gentamicin resistance is associated with oxidative stress. Significance: The overuse and misuse of antibiotics have led to bacterial antibiotic resistance, which is a serious threat to human health. Understanding the mechanisms of antibiotic resistance will help better control these antibiotic-resistant pathogens in the future. The present study characterized the differential proteome of gentamicin-resistant Staphylococcus aureus using the most advanced DIA-based proteomics technology. Many of the differential expressed proteins were related to metabolism, specifically, reduced central carbon and energy metabolism. Lower levels of NADH, ROS, and ATP were detected as a consequence of the reduced metabolism. These results reveal that downregulation of protein expression affecting central carbon and energy metabolisms may play an important role in the resistance of S. aureus to gentamicin.

Characterization of serum protein expression profiles in the early sarcopenia older adults with low grip strength: a cross-sectional study

Background

Sarcopenia refers to the progressive loss of skeletal muscle mass and muscle function, which seriously threatens the quality of life of the older adults. Therefore, early diagnosis is urgently needed. This study aimed to explore the changes of serum protein profiles in sarcopenia patients through a cross-sectional study, and to provide the reference for clinical diagnosis.

Methods

This study was a cross-sectional study carried out in the Tianjin institute of physical education teaching experiment training center from December 2019 to December 2020. Ten older adults were recruited, including 5 sarcopenia and 5 healthy older adults. After a detailed diagnostic evaluation, blood samples were collected to prepare serum for proteomic analysis using the HPLC System Easy nLC method. The differentially expressed proteins (DEPs) were screened by the limma package of R software (version 4.1.0).

Results

A total of 114 DEPs were identified between the patients and healthy older adults, including 48 up-regulated proteins and 66 down-regulated proteins. The functional enrichment analysis showed that the 114 DEPs were significantly enriched in 153 GO terms, which mainly involved in low-density lipoprotein particle remodeling, and negative regulation of immune response,etc. The PPI network further suggested that the cholesteryl ester transfer protein and Apolipoprotein A2 could serve as biomarkers to facilitate diagnosis of sarcopenia.

Conclusions

This study provided a serum proteomic profile of sarcopenia patients, and identified two proteins with diagnostic value, which might help to improve the diagnostic accuracy of sarcopenia.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12891-022-05844-2.

Proteomic repository data submission, dissemination, and reuse: key messages

INTRODUCTION

The creation of ProteomeXchange data workflows in 2012 transformed the field of proteomics, consisting of the standardization of data submission and dissemination and enabling the widespread reanalysis of public MS proteomics data worldwide. ProteomeXchange has triggered a growing trend toward public dissemination of proteomics data, facilitating the assessment, reuse, comparative analyses, and extraction of new findings from public datasets. By 2022, the consortium is integrated by PRIDE, PeptideAtlas, MassIVE, jPOST, iProX, and Panorama Public.

AREAS COVERED

Here, we review and discuss the current ecosystem of resources, guidelines, and file formats for proteomics data dissemination and reanalysis. Special attention is drawn to new exciting quantitative and post-translational modification-oriented resources. The challenges and future directions on data depositions including the lack of metadata and cloud-based and high-performance software solutions for fast and reproducible reanalysis of the available data are discussed.

EXPERT OPINION

The success of ProteomeXchange and the amount of proteomics data available in the public domain have triggered the creation and/or growth of other protein knowledgebase resources. Data reuse is a leading, active, and evolving field; supporting the creation of new formats, tools, and workflows to rediscover and reshape the public proteomics data.

Implementing the reuse of public DIA proteomics datasets: from the PRIDE database to Expression Atlas

The number of mass spectrometry (MS)-based proteomics datasets in the public domain keeps increasing, particularly those generated by Data Independent Acquisition (DIA) approaches such as SWATH-MS. Unlike Data Dependent Acquisition datasets, the re-use of DIA datasets has been rather limited to date, despite its high potential, due to the technical challenges involved. We introduce a (re-)analysis pipeline for public SWATH-MS datasets which includes a combination of metadata annotation protocols, automated workflows for MS data analysis, statistical analysis, and the integration of the results into the Expression Atlas resource. Automation is orchestrated with Nextflow, using containerised open analysis software tools, rendering the pipeline readily available and reproducible. To demonstrate its utility, we reanalysed 10 public DIA datasets from the PRIDE database, comprising 1,278 SWATH-MS runs. The robustness of the analysis was evaluated, and the results compared to those obtained in the original publications. The final expression values were integrated into Expression Atlas, making SWATH-MS experiments more widely available and combining them with expression data originating from other proteomics and transcriptomics datasets.

Data-Independent Acquisition-Based Mass Spectrometry (DIA-MS) for Quantitative Analysis of Human Intestinal Ischemia/Reperfusion

Intestinal ischemia-reperfusion (II/R) injury is a complex pathologic process, which is of great significance to unravel the underlying mechanisms and pathophysiology. Our study represented a comprehensive proteomic analysis in the human intestine with ischemia-reperfusion injury. The proteomics analysis measured a total of 5,230 proteins, and 417 differently expressed proteins (DEPs) were identified between II/R and control samples. GO and KEGG analysis demonstrated that the 290 upregulated DEPs in II/R were significantly involved in immune-related biological process and tight junction, focal adhesion, and cAMP signaling pathway, whereas the 127 downregulated DEPs in II/R were enriched in lipid metabolic process and metabolic pathway. Furthermore, we screened out 20 hub proteins from the protein-protein interaction (PPI) network according to the degree of connectivity, and six clusters were identified. Combined with the result of KEGG analysis, 6 from the 20 hub proteins, ACTB, CAV1, FLNA, MYLK, ACTN1, and MYL9, were identified as the key proteins in the progress of II/R injury. According to the previous studies, FLNA and MYL9 were selected as the novel disease-related proteins for the first time. In conclusion, this study extended our understanding of the alteration in the human intestine during ischemia and reperfusion and highlighted the potential role of FLNA and MYL9 in the progress of II/R injury, which need to be further studied.

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.

Data independent acquisition (DIA) has been gaining momentum in clinical proteomics. Here, the authors create a benchmark dataset comprising inter-patient heterogeneity to compare popular DIA data analysis workflows for identifying differentially abundant proteins.

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.

Democratizing data-independent acquisition proteomics analysis on public cloud infrastructures via the Galaxy framework

BACKGROUND

Data-independent acquisition (DIA) has become an important approach in global, mass spectrometric proteomic studies because it provides in-depth insights into the molecular variety of biological systems. However, DIA data analysis remains challenging owing to the high complexity and large data and sample size, which require specialized software and vast computing infrastructures. Most available open-source DIA software necessitates basic programming skills and covers only a fraction of a complete DIA data analysis. In consequence, DIA data analysis often requires usage of multiple software tools and compatibility thereof, severely limiting the usability and reproducibility.

FINDINGS

To overcome this hurdle, we have integrated a suite of open-source DIA tools in the Galaxy framework for reproducible and version-controlled data processing. The DIA suite includes OpenSwath, PyProphet, diapysef, and swath2stats. We have compiled functional Galaxy pipelines for DIA processing, which provide a web-based graphical user interface to these pre-installed and pre-configured tools for their use on freely accessible, powerful computational resources of the Galaxy framework. This approach also enables seamless sharing workflows with full configuration in addition to sharing raw data and results. We demonstrate the usability of an all-in-one DIA pipeline in Galaxy by the analysis of a spike-in case study dataset. Additionally, extensive training material is provided to further increase access for the proteomics community.

CONCLUSION

The integration of an open-source DIA analysis suite in the web-based and user-friendly Galaxy framework in combination with extensive training material empowers a broad community of researches to perform reproducible and transparent DIA data analysis.