DirectMS1Quant: Ultrafast Quantitative Proteomics with MS/MS-Free Mass Spectrometry

Modified Decision Tree with Custom Splitting Logic Improves Generalization across Multiple Brains' Proteomic Data Sets of Alzheimer's Disease

Many factors negatively affect a generalization of the findings in discovery proteomics. They include differentiation between patient cohorts, a variety of experimental conditions, etc. We presented a machine-learning-based workflow for proteomics data analysis, aiming at improving generalizability across multiple data sets. In particular, we customized the decision tree model by introducing a new parameter, min_groups_leaf, which regulates the presence of the samples from each data set inside the model's leaves. Further, we analyzed a trend for the feature importance's curve as a function of the novel parameter for feature selection to a list of proteins with significantly improved generalization. The developed workflow was tested using five proteomic data sets obtained for post-mortem human brain samples of Alzheimer's disease. The data sets consisted of 535 LC-MS/MS acquisition files. The results were obtained for two different pipelines of data processing: (1) MS1-only processing based on DirectMS1 search engine and (2) a standard MS/MS-based one. Using the developed workflow, we found seven proteins with expression patterns that were unique for asymptomatic Alzheimer patients. Two of them, Serotransferrin TRFE and DNA repair nuclease APEX1, may be potentially important for explaining the lack of dementia in patients with the presence of neuritic plaques and neurofibrillary tangles.

Proteogenomics offers a novel avenue in neoantigen identification for cancer immunotherapy

Cancer neoantigens are tumor-specific non-synonymous mutant peptides that activate the immune system to produce an anti-tumor response. Personalized cancer vaccines based on neoantigens are currently one of the most promising therapeutic approaches for cancer treatment. By utilizing the unique mutations within each patient's tumor, these vaccines aim to elicit a strong and specific immune response against cancer cells. However, the identification of neoantigens remains challenging due to the low accuracy of current prediction tools and the high false-positive rate of candidate neoantigens. Since the concept of "proteogenomics" emerged in 2004, it has evolved rapidly with the increased sequencing depth of next-generation sequencing technologies and the maturation of mass spectrometry-based proteomics technologies to become a more comprehensive approach to neoantigen identification, allowing the discovery of high-confidence candidate neoantigens. In this review, we summarize the reason why cancer neoantigens have become attractive targets for immunotherapy, the mechanism of cancer vaccines and the advances in cancer immunotherapy. Considerations relevant to the application emerging of proteogenomics technologies for neoantigen identification and challenges in this field are described.

Bioinformatics in Russia: history and present-day landscape

Bioinformatics has become an interdisciplinary subject due to its universal role in molecular biology research. The current status of Russia's bioinformatics research in Russia is not known. Here, we review the history of bioinformatics in Russia, present the current landscape, and highlight future directions and challenges. Bioinformatics research in Russia is driven by four major industries: information technology, pharmaceuticals, biotechnology, and agriculture. Over the past three decades, despite a delayed start, the field has gained momentum, especially in protein and nucleic acid research. Dedicated and shared centers for genomics, proteomics, and bioinformatics are active in different regions of Russia. Present-day bioinformatics in Russia is characterized by research issues related to genetics, metagenomics, OMICs, medical informatics, computational biology, environmental informatics, and structural bioinformatics. Notable developments are in the fields of software (tools, algorithms, and pipelines), use of high computation power (e.g. by the Siberian Supercomputer Center), and large-scale sequencing projects (the sequencing of 100 000 human genomes). Government funding is increasing, policies are being changed, and a National Genomic Information Database is being established. An increased focus on eukaryotic genome sequencing, the development of a common place for developers and researchers to share tools and data, and the use of biological modeling, machine learning, and biostatistics are key areas for future focus. Universities and research institutes have started to implement bioinformatics modules. A critical mass of bioinformaticians is essential to catch up with the global pace in the discipline.

Reanalysis of DIA Data Demonstrates the Capabilities of MS/MS-Free Proteomics to Reveal New Biological Insights in Disease-Related Samples

Data-independent acquisition (DIA) at the shortened data acquisition time is becoming a method of choice for quantitative proteomic applications requiring high throughput analysis of large cohorts of samples. With the advent of the combination of high resolution mass spectrometry with an asymmetric track lossless analyzer, these DIA capabilities were further extended with the recent demonstration of quantitative analyses at the speed of up to hundreds of samples per day. In particular, the proteomic data for the brain samples related to multiple system atrophy disease were acquired using 7 and 28 min chromatography gradients (Guzman et al., Nat. Biotech. 2024). In this work, we applied the recently introduced DirectMS1 method to reanalysis of these data using only MS1 spectra. Both DirectMS1 and DIA results were matched against long gradient DDA analysis from the earlier study of the same sample cohort. While the quantitation efficiency of DirectMS1 was comparable with DIA on the same data sets, we found an additional five proteins of biological significance relevant to the analyzed tissue samples. Among the findings, DirectMS1 was able to detect decreased caspase activity for Vimentin protein in the multiple system atrophy samples missed by the MS/MS-based quantitation methods. Our study suggests that DirectMS1 can be an efficient MS1-only addition to the analysis of DIA data in high-throughput quantitative proteomic studies.

Ultrafast Proteomics

Current stage of proteomic research in the field of biology, medicine, development of new drugs, population screening, or personalized approaches to therapy dictates the need to analyze large sets of samples within the reasonable experimental time. Until recently, mass spectrometry measurements in proteomics were characterized as unique in identifying and quantifying cellular protein composition, but low throughput, requiring many hours to analyze a single sample. This was in conflict with the dynamics of changes in biological systems at the whole cellular proteome level upon the influence of external and internal factors. Thus, low speed of the whole proteome analysis has become the main factor limiting developments in functional proteomics, where it is necessary to annotate intracellular processes not only in a wide range of conditions, but also over a long period of time. Enormous level of heterogeneity of tissue cells or tumors, even of the same type, dictates the need to analyze biological systems at the level of individual cells. These studies involve obtaining molecular characteristics for tens, if not hundreds of thousands of individual cells, including their whole proteome profiles. Development of mass spectrometry technologies providing high resolution and mass measurement accuracy, predictive chromatography, new methods for peptide separation by ion mobility and processing of proteomic data based on artificial intelligence algorithms have opened a way for significant, if not radical, increase in the throughput of whole proteome analysis and led to implementation of the novel concept of ultrafast proteomics. Work done just in the last few years has demonstrated the proteome-wide analysis throughput of several hundred samples per day at a depth of several thousand proteins, levels unimaginable three or four years ago. The review examines background of these developments, as well as modern methods and approaches that implement ultrafast analysis of the entire proteome.

On the utility of ultrafast MS1-only proteomics in drug target discovery studies based on thermal proteome profiling method

Advances in high-throughput high-resolution mass spectrometry and the development of thermal proteome profiling approach (TPP) have made it possible to accelerate a drug target search. Since its introduction in 2014, TPP quickly became a method of choice in chemical proteomics for identifying drug-to-protein interactions on a proteome-wide scale and mapping the pathways of these interactions, thus further elucidating the unknown mechanisms of action of a drug under study. However, the current TPP implementations based on tandem mass spectrometry (MS/MS), associated with employing lengthy peptide separation protocols and expensive labeling techniques for sample multiplexing, limit the scaling of this approach for the ever growing variety of drug-to-proteomes. A variety of ultrafast proteomics methods have been developed in the last couple of years. Among them, DirectMS1 provides MS/MS-free quantitative proteome-wide analysis in 5-min time scale, thus opening the way for sample-hungry applications, such as TPP. In this work, we demonstrate the first implementation of the TPP approach using the ultrafast proteome-wide analysis based on DirectMS1. Using a drug topotecan, which is a known topoisomerase I (TOP1) inhibitor, the feasibility of the method for identifying drug targets at the whole proteome level was demonstrated for an ovarian cancer cell line.

The One Hour Human Proteome

We describe deep analysis of the human proteome in less than 1 h. We achieve this expedited proteome characterization by leveraging state-of-the-art sample preparation, chromatographic separations, and data analysis tools, and by using the new Orbitrap Astral mass spectrometer equipped with a quadrupole mass filter, a high-field Orbitrap mass analyzer, and an asymmetric track lossless (Astral) mass analyzer. The system offers high tandem mass spectrometry acquisition speed of 200 Hz and detects hundreds of peptide sequences per second within data-independent acquisition or data-dependent acquisition modes of operation. The fast-switching capabilities of the new quadrupole complement the sensitivity and fast ion scanning of the Astral analyzer to enable narrow-bin data-independent analysis methods. Over a 30-min active chromatographic method consuming a total analysis time of 56 min, the Q-Orbitrap-Astral hybrid MS collects an average of 4319 MS1 scans and 438,062 tandem mass spectrometry scans per run, producing 235,916 peptide sequences (1% false discovery rate). On average, each 30-min analysis achieved detection of 10,411 protein groups (1% false discovery rate). We conclude, with these results and alongside other recent reports, that the 1-h human proteome is within reach.

Special Issue with Research Topics on "Recent Analysis and Applications of Mass Spectra on Biochemistry"

Analytical mass spectrometry applies irreplaceable mass spectrometric (MS) methods to analytical chemistry and chemical analysis, among other areas of analytical science [...].

Global Proteomics for Identifying the Alteration Pathway of Niemann-Pick Disease Type C Using Hepatic Cell Models

Niemann-Pick disease type C (NPC) is an autosomal recessive disorder with progressive neurodegeneration. Although the causative genes were previously identified, NPC has unclear pathophysiological aspects, and patients with NPC present various symptoms and onset ages. However, various novel biomarkers and metabolic alterations have been investigated; at present, few comprehensive proteomic alterations have been reported in relation to NPC. In this study, we aimed to elucidate proteomic alterations in NPC and perform a global proteomics analysis for NPC model cells. First, we developed two NPC cell models by knocking out NPC1 using CRISPR/Cas9 (KO1 and KO2). Second, we performed a label-free (LF) global proteomics analysis. Using the LF approach, more than 300 proteins, defined as differentially expressed proteins (DEPs), changed in the KO1 and/or KO2 cells, while the two models shared 35 DEPs. As a bioinformatics analysis, the construction of a protein-protein interaction (PPI) network and an enrichment analysis showed that common characteristic pathways such as ferroptosis and mitophagy were identified in the two model cells. There are few reports of the involvement of NPC in ferroptosis, and this study presents ferroptosis as an altered pathway in NPC. On the other hand, many other pathways and DEPs were previously suggested to be associated with NPC, supporting the link between the proteome analyzed here and NPC. Therapeutic research based on these results is expected in the future.

Ultra-Fast Mass Spectrometry in Plant Biochemistry: Response of Winter Wheat Proteomics to Pre-Sowing Treatment with Iron Compounds

In recent years, ultrafast liquid chromatography/mass spectrometry methods have been extensively developed for the use in proteome profiling in biochemical studies. These methods are intended for express monitoring of cell response to biotic stimuli and elucidation of correlation of molecular changes with biological processes and phenotypical changes. New technologies, including the use of nanomaterials, are actively introduced to increase agricultural production. However, this requires complex approbation of new fertilizers and investigation of mechanisms underlying the biotic effects on the germination, growth, and development of plants. The aim of this work was to adapt the method of ultrafast chromatography/mass spectrometry for rapid quantitative profiling of molecular changes in 7-day-old wheat seedlings in response to pre-sowing seed treatment with iron compounds. The used method allows to analyze up to 200 samples per day; its practical value lies in the possibility of express proteomic diagnostics of the biotic action of new treatments, including those intended for agricultural needs. Changes in the regulation of photosynthesis, biosynthesis of chlorophyll and porphyrin- and tetrapyrrole-containing compounds, glycolysis (in shoot tissues), and polysaccharide metabolism (in root tissues) were shown after seed treatment with suspensions containing film-forming polymers (PEG 400, Na-CMC, Na2-EDTA), iron (II, III) nanoparticles, or iron (II) sulfate. Observations at the protein levels were consistent with the results of morphometry, superoxide dismutase activity assay, and microelement analysis of 3-day-old germinated seeds and shoots and roots of 7-day-old seedlings. A characteristic molecular signature involving proteins participating in the regulation of photosynthesis and glycolytic process was suggested as a potential marker of the biotic effects of seed treatment with iron compounds, which will be confirmed in further studies.

Potential of Negative-Ion-Mode Proteomics: An MS1-Only Approach

Current proteomics approaches rely almost exclusively on using the positive ionization mode, resulting in inefficient ionization of many acidic peptides. This study investigates protein identification efficiency in the negative ionization mode using the DirectMS1 method. DirectMS1 is an ultrafast data acquisition method based on accurate peptide mass measurements and predicted retention times. Our method achieves the highest rate of protein identification in the negative ion mode to date, identifying over 1000 proteins in a human cell line at a 1% false discovery rate. This is accomplished using a single-shot 10 min separation gradient, comparable to lengthy MS/MS-based analyses. Optimizing separation and experimental conditions was achieved by utilizing mobile buffers containing 2.5 mM imidazole and 3% isopropanol. The study emphasized the complementary nature of data obtained in positive and negative ion modes. Combining the results from all replicates in both polarities increased the number of identified proteins to 1774. Additionally, we analyzed the method's efficiency using different proteases for protein digestion. Among the four studied proteases (LysC, GluC, AspN, and trypsin), trypsin and LysC demonstrated the highest protein identification yield. This suggests that digestion procedures utilized in positive-mode proteomics can be effectively applied in the negative ion mode. Data are deposited to ProteomeXchange: PXD040583.

Label-free single cell proteomics utilizing ultrafast LC and MS instrumentation: A valuable complementary technique to multiplexing

The ability to map a proteomic fingerprint to transcriptomic data would master the understanding of how gene expression translates into actual phenotype. In contrast to nucleic acid sequencing, in vitro protein amplification is impossible and no single cell proteomic workflow has been established as gold standard yet. Advances in microfluidic sample preparation, multi-dimensional sample separation, sophisticated data acquisition strategies, and intelligent data analysis algorithms have resulted in major improvements to successfully analyze such tiny sample amounts with steadily boosted performance. However, among the broad variation of published approaches, it is commonly accepted that highest possible sensitivity, robustness, and throughput are still the most urgent needs for the field. While many labs have focused on multiplexing to achieve these goals, label-free SCP is a highly promising strategy as well whenever high dynamic range and unbiased accurate quantification are needed. We here focus on recent advances in label-free single-cell mass spectrometry workflows and try to guide our readers to choose the best method or combinations of methods for their specific applications. We further highlight which techniques are most propitious in the future and which applications but also limitations we foresee for the field.