QuantPipe: A User-Friendly Pipeline Software Tool for DIA Data Analysis Based on the OpenSWATH-PyProphet-TRIC Workflow

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.

Apical anchorage and stabilization of subpellicular microtubules by apical polar ring ensures Plasmodium ookinete infection in mosquito

Morphogenesis of many protozoans depends on a polarized establishment of cortical cytoskeleton containing the subpellicular microtubules (SPMTs), which are apically nucleated and anchored by the apical polar ring (APR). In malaria parasite Plasmodium, APR emerges in the host-invading stages, including the ookinete for mosquito infection. So far, the fine structure and molecular components of APR as well as the underlying mechanism of APR-mediated apical positioning of SPMTs are largely unknown. Here, we resolve an unprecedented APR structure composed of a top ring plus approximate 60 radiating spines. We report an APR-localizing and SPMT-binding protein APR2. APR2 disruption impairs ookinete morphogenesis and gliding motility, leading to Plasmodium transmission failure in mosquitoes. The APR2-deficient ookinetes display defective apical anchorage of APR and SPMT due to the impaired integrity of APR. Using protein proximity labeling, we obtain a Plasmodium ookinete APR proteome and validate ten undescribed APR proteins. Among them, APRp2 and APRp4 directly interact with APR2 and also mediate the apical anchorage of SPMTs. This study sheds light on the molecular basis of APR in the organization of Plasmodium ookinete SPMTs.

Inner membrane complex proteomics reveals a palmitoylation regulation critical for intraerythrocytic development of malaria parasite

Malaria is caused by infection of the erythrocytes by the parasites Plasmodium. Inside the erythrocytes, the parasites multiply via schizogony, an unconventional cell division mode. The inner membrane complex (IMC), an organelle located beneath the parasite plasma membrane, serving as the platform for protein anchorage, is essential for schizogony. So far, the complete repertoire of IMC proteins and their localization determinants remain unclear. Here we used biotin ligase (TurboID)-based proximity labeling to compile the proteome of the schizont IMC of the rodent malaria parasite Plasmodium yoelii. In total, 300 TurboID-interacting proteins were identified. 18 of 21 selected candidates were confirmed to localize in the IMC, indicating good reliability. In light of the existing palmitome of Plasmodium falciparum, 83 proteins of the P. yoelii IMC proteome are potentially palmitoylated. We further identified DHHC2 as the major resident palmitoyl-acyl-transferase of the IMC. Depletion of DHHC2 led to defective schizont segmentation and growth arrest both in vitro and in vivo. DHHC2 was found to palmitoylate two critical IMC proteins CDPK1 and GAP45 for their IMC localization. In summary, this study reports an inventory of new IMC proteins and demonstrates a central role of DHHC2 in governing the IMC localization of proteins during the schizont development.

StackZDPD: a novel encoding scheme for mass spectrometry data optimized for speed and compression ratio

As the pervasive, standardized format for interchange and deposition of raw mass spectrometry (MS) proteomics and metabolomics data, text-based mzML is inefficiently utilized on various analysis platforms due to its sheer volume of samples and limited read/write speed. Most research on compression algorithms rarely provides flexible random file reading scheme. Database-developed solution guarantees the efficiency of random file reading, but nevertheless the efforts in compression and third-party software support are insufficient. Under the premise of ensuring the efficiency of decompression, we propose an encoding scheme “Stack-ZDPD” that is optimized for storage of raw MS data, designed for the format “Aird”, a computation-oriented format with fast accessing and decoding time, where the core compression algorithm is “ZDPD”. Stack-ZDPD reduces the volume of data stored in mzML format by around 80% or more, depending on the data acquisition pattern, and the compression ratio is approximately 30% compared to ZDPD for data generated using Time of Flight technology. Our approach is available on AirdPro, for file conversion and the Java-API Aird-SDK, for data parsing.

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.

Aird: a computation-oriented mass spectrometry data format enables a higher compression ratio and less decoding time

Background

With the precision of the mass spectrometry (MS) going higher, the MS file size increases rapidly. Beyond the widely-used open format mzML, near-lossless or lossless compression algorithms and formats emerged in scenarios with different precision requirements. The data precision is often related to the instrument and subsequent processing algorithms. Unlike storage-oriented formats, which focus more on lossless compression rate, computation-oriented formats concentrate as much on decoding speed as the compression rate.

Results

Here we introduce “Aird”, an opensource and computation-oriented format with controllable precision, flexible indexing strategies, and high compression rate. Aird provides a novel compressor called Zlib-Diff-PforDelta (ZDPD) for m/z data. Compared with Zlib only, m/z data size is about 55% lower in Aird average. With the high-speed decoding and encoding performance of the single instruction multiple data technology used in the ZDPD, Aird merely takes 33% decoding time compared with Zlib. We have downloaded seven datasets from ProteomeXchange and Metabolights. They are from different SCIEX, Thermo, and Agilent instruments. Then we convert the raw data into mzML, mgf, and mz5 file formats by MSConvert and compare them with Aird format. Aird uses JavaScript Object Notation for metadata storage. Aird-SDK is written in Java, and AirdPro is a GUI client for vendor file converting written in C#. They are freely available at https://github.com/CSi-Studio/Aird-SDK and https://github.com/CSi-Studio/AirdPro.

Conclusions

With the innovation of MS acquisition mode, MS data characteristics are also constantly changing. New data features can bring more effective compression methods and new index modes to achieve high search performance. The MS data storage mode will also become professional and customized. ZDPD uses multiple MS digital features, and researchers also can use it in other formats like mzML. Aird is designed to become a computing-oriented data format with high scalability, compression rate, and fast decoding speed.

Recent advances in mass-spectrometry based proteomics software, tools and databases

The field of proteomics immensely depends on data generation and data analysis which are thoroughly supported by software and databases. There has been a massive advancement in mass spectrometry-based proteomics over the last 10 years which has compelled the scientific community to upgrade or develop algorithms, tools, and repository databases in the field of proteomics. Several standalone software, and comprehensive databases have aided the establishment of integrated omics pipeline and meta-analysis workflow which has contributed to understand the disease pathobiology, biomarker discovery and predicting new therapeutic modalities. For shotgun proteomics where Data Dependent Acquisition is performed, several user-friendly software are developed that can analyse the pre-processed data to provide mechanistic insights of the disease. Likewise, in Data Independent Acquisition, pipelines are emerged which can accomplish the task from building the spectral library to identify the therapeutic targets. Furthermore, in the age of big data analysis the implications of machine learning and cloud computing are appending robustness, rapidness and in-depth proteomics data analysis. The current review talks about the recent advancement, and development of software, tools, and database in the field of mass-spectrometry based proteomics.

Deep representation features from DreamDIAXMBD improve the analysis of data-independent acquisition proteomics

We developed DreamDIA^XMBD (denoted as DreamDIA), a software suite based on a deep representation model for data-independent acquisition (DIA) data analysis. DreamDIA adopts a data-driven strategy to capture comprehensive information from elution patterns of peptides in DIA data and achieves considerable improvements on both identification and quantification performance compared with other state-of-the-art methods such as OpenSWATH, Skyline and DIA-NN. Specifically, in contrast to existing methods which use only 6 to 10 selected fragment ions from spectral libraries, DreamDIA extracts additional features from hundreds of theoretical elution profiles originated from different ions of each precursor using a deep representation network. To achieve higher coverage of target peptides without sacrificing specificity, the extracted features are further processed by nonlinear discriminative models under the framework of positive-unlabeled learning with decoy peptides as affirmative negative controls. DreamDIA is publicly available at https://github.com/xmuyulab/DreamDIA-XMBD for high coverage and accuracy DIA data analysis.

DIAproteomics: A Multifunctional Data Analysis Pipeline for Data-Independent Acquisition Proteomics and Peptidomics

Data-independent acquisition (DIA) is becoming a leading analysis method in biomedical mass spectrometry. The main advantages include greater reproducibility and sensitivity and a greater dynamic range compared with data-dependent acquisition (DDA). However, the data analysis is complex and often requires expert knowledge when dealing with large-scale data sets. Here we present DIAproteomics, a multifunctional, automated, high-throughput pipeline implemented in the Nextflow workflow management system that allows one to easily process proteomics and peptidomics DIA data sets on diverse compute infrastructures. The central components are well-established tools such as the OpenSwathWorkflow for the DIA spectral library search and PyProphet for the false discovery rate assessment. In addition, it provides options to generate spectral libraries from existing DDA data and to carry out the retention time and chromatogram alignment. The output includes annotated tables and diagnostic visualizations from the statistical postprocessing and computation of fold-changes across pairwise conditions, predefined in an experimental design. DIAproteomics is well documented open-source software and is available under a permissive license to the scientific community at https://www.openms.de/diaproteomics/.

SCASP: A Simple and Robust SDS-Aided Sample Preparation Method for Proteomic Research

SDS is widely used in sample preparation for proteomic research. However, SDS is incompatible with LC and electrospray ionization. SDS depletion is therefore required ahead of LC-MS analysis. Most of current SDS removal strategies are time consuming, laborious, and have low reproducibility. Here, we describe a method, SDS-cyclodextrin (CD)-assisted sample preparation, by which CD can bind to SDS and form CD-SDS complexes in solutions, allowing for direct tryptic digestion. We demonstrate that SDS-CD-assisted sample preparation is a simple, fast, and robust SDS-based sample preparation method for proteomics application.