Biology subjects

Bubis, J. A.

Publications and source records attributed to Bubis, J. A..

9 recordsLinked to original sources

bioRxiv2024

Quantitative assessment of strain isolates and microbiomes using fast MS/MS-free metaproteomics

BACKGROUNDMicrobial communities play a crucial role in human health and environmental regulation, but present an especial challenge for the analytical science due to their diversity and dynamic range. Tandem mass spectrometry provides functional insights on microorganisms life cycle, but still lacks throughput and sensitivity. MALDI TOF is widely used for ultrafast identification of species, but does not assess their functional activity. Development of ultrafast mass spectrometry methods and bioinformatic approaches applicable for both accurate identification and functional assessment of microbial communities based on their protein content is of high interest. RESULTSWe show for the first time that both identity and functional activity of microorganisms and their communities can be accurately determined in experiments as short as 7 minutes per sample, using the basic Orbitrap MS configuration without peptide fragmentation. The approach was validated using strain isolates, mock microbiomes composed of bacteria spiked at known concentrations and human fecal microbiomes. Our new bioinformatic algorithm identifies the bacterial species with an accuracy of 95 %, when no prior information on the sample is available. Microbiome composition was resolved at the genus level with the mean difference between the actual and identified components of 12 %. For mock microbiomes, Pearson coefficient of up to 0.97 was achieved in estimates of strain biomass change. By the example of Rhodococcus biodegradation of n-alkanes, phenols and its derivatives, we showed the accurate assessment of functional activity of strain isolates, compared with the standard label-free and label-based approaches. SIGNIFICANCEOur approach makes microbial proteomics fast, functional and insightful using the Orbitrap instruments even without employing peptide fragmentation technology. The approach can be applied to any microorganisms and can take a niche in routine functional assessment of microbial pathogens and consortiums in clinical diagnostics together with MALDI TOF MS and 16S rRNA gene sequencing.

microbiology↗

bioRxiv2024

Challenging the Astral mass analyzer - going beyond 5200 proteins per single-cell at unseen quantitative accuracy to study cellular heterogeneity.

A detailed proteome map is crucial for understanding molecular pathways and protein functions. Despite significant advancements in sample preparation, instrumentation, and data analysis, single-cell proteomics is currently limited by proteomic depth and quantitative performance. We combine a zero dead-end volume chromatographic column running at high throughput with the Thermo Scientific Orbitrap Astral mass spectrometer running in DIA mode. We demonstrate unprecedented depth of proteome coverage as well as accuracy and precision for quantification of ultra-low input amounts. Using a tailored library, we identify up to 7400 protein groups from as little as 250 pg HeLa at a throughput of 50 samples per day (SPD). We benchmark multiple data analysis strategies, estimate their influence on FDR and show that FDR on protein level can easily be maintained at 1 %. Using a two-proteome mix, we check for optimal parameters of quantification and show that fold change differences of 2 can still be successfully determined at single-cell level inputs. Eventually, we apply our workflow to A549 cells yielding a proteome coverage of up to 5300 protein groups from a single cell, which allows the observation of heterogeneity in a cellular population and studying dependencies between cell size and cell-cycle phase. Additionally, our work-flow enables us to distinguish between in vitro analogs of two human blastocyst lineages: naive human pluripotent stem cells (epiblast) and trophectoderm (TE)-like cells. Gene Ontology analysis of enriched proteins in TE-like cells harmoniously aligns with transcriptomic data, indicating that single-cell proteomics possesses the capability to identify biologically relevant differences between these two lineages within the blastocyst.

biochemistry↗

bioRxiv2023

IQMMA: an efficient MS1 intensity extraction using multiple feature detection algorithms for DDA proteomics

One of the key steps in data dependent acquisition (DDA) proteomics is detection of peptide isotopic clusters, also called < >, in MS1 spectra and matching them to MS/MS-based peptide identifications. A number of peptide feature detection tools became available in recent years each relying on its own matching algorithm. Here, we provide an integrated solution, Intensity-based Quantitative Mix and Match Approach (IQMMA), which integrates a number of peptide feature detection algorithms and returns the most probable intensity values for the MS/MS-based identifications. IQMMA was tested using available proteomic data acquired for both well-characterized (ground truth) and real-world biological samples, including a mix of E. Coli digest spiked at different concentrations into the HeLa digest used as a background and a set of glioblastoma cell lines. Three open-source feature detection algorithms were integrated: Dinosaur, biosaur2 and OpenMS FeatureFinder. Neither of them was found optimal when applied individually to all the datasets employed in this work, however, their combined use in IQMMA improved efficiency of subsequent protein quantitation. The software implementing IQMMA is freely available at https://github.com/PostoenkoVI/IQMMA under Apache 2.0 license.

bioinformatics↗

bioRxiv2022

Massive proteogenomic reanalysis of publicly available proteomic datasets of human tissues in search for protein recoding via adenosine-to-inosine RNA editing

The proteogenomic search pipeline developed in this work has been applied for re-analysis of 40 publicly available shotgun proteomic datasets from various human tissues comprising more than 8,000 individual LC-MS/MS runs, of which 5442 .raw data files were processed in total. The scope of this re-analysis was focused on searching for ADAR-mediated RNA editing events, their clustering across samples of different origin, and classification. In total, 33 recoded protein sites were identified in 21 datasets. Of those, 18 sites were detected in at least two datasets representing the core human protein editome. In agreement with prior art works, neural and cancer tissues were found being enriched with recoded proteins. Quantitative analysis indicated that recoding of specific sites did not directly depend on the levels of ADAR enzymes or targeted proteins themselves, rather it was provided by differential and yet undescribed regulation of interaction of enzymes with mRNA. Nine recoding sites conservative between human and rodents were validated by targeted proteomics using stable isotope standards in murine brain cortex and cerebellum, and an additional one was validated in human cerebrospinal fluid. In addition to previous data of the same type from cancer proteomes, we provide a comprehensive catalog of recoding events caused by ADAR RNA editing in the human proteome.

bioinformatics↗

bioRxiv2022

DirectMS1Quant: ultrafast quantitative proteomics with MS/MS-free mass spectrometry

Recently, we presented the DirectMS1 method of ultrafast proteome-wide analysis based on minute-long LC gradients and MS1-only mass spectra acquisition. Currently, the method provides the depth of human cell proteome coverage of 2500 proteins at 1% false discovery rate (FDR) when using 5-min LC gradients and 7.3 min runtime in total. While the standard MS/MS approaches provide 4000 to 5000 protein identifications within a couple of hours of instrumentation time, we advocate here that the higher number of identified proteins does not always translate into better quantitation quality of the proteome analysis. To further elaborate on this issue we performed one-by-one comparison of quantitation results obtained using DirectMS1 with three popular MS/MS-based quantitation methods: label-free quantification (LFQ), tandem mass tag (TMT), both based on data dependent acquisition (DDA), and data independent acquisition (DIA). For the comparison we performed a series of proteome-wide analysis of well-characterized (ground truth) and biological relevant samples, including a mix of UPS1 proteins spiked at different concentrations into E. coli digest used as a background and a set of glioblastoma cell lines. MS1-only data was analyzed using a novel quantitation workflow called DirectMS1Quant developed in this work. The results obtained in this study demonstrated comparable quantitation efficiency of 5 min DirectMS1 with both TMT and DIA methods utilizing 10 to 20-fold longer instrumentation time.

bioinformatics↗

bioRxiv2022

A comprehensive evaluation of consensus spectrum generation methods in proteomics

Spectrum clustering is a powerful strategy to minimize redundant mass spectral data by grouping highly similar mass spectra corresponding to repeatedly measured analytes. Based on spectrum similarity, near-identical spectra are grouped in clusters, after which each cluster can be represented by its so-called consensus spectrum for downstream processing. Although several algorithms for spectrum clustering have been adequately benchmarked and tested, the influence of the consensus spectrum generation step is rarely evaluated. Here, we present an implementation and benchmark of common consensus spectrum algorithms, including spectrum averaging, spectrum binning, the most similar spectrum, and the best-identified spectrum. We have analyzed diverse public datasets using two different clustering algorithms (spectra-cluster and MaRaCluster) to evaluate how the consensus spectrum generation procedure influences downstream peptide identification. The BEST and BIN methods were found the most reliable methods for consensus spectrum generation, including for datasets with post-translational modifications (PTM) such as phosphorylation. All source code and data of the present study are freely available on GitHub at https://github.com/statisticalbiotechnology/representative-spectra-benchmark.

bioinformatics↗

bioRxiv2020

Boosting the MS1-only proteomics with machine learning allows 2000 protein identifications in 5-minute proteome analysis

Proteome-wide analyses most often rely on tandem mass spectrometry imposing considerable instrumental time consumption that is one of the main obstacles in a broader acceptance of proteomics in biomedical and clinical research. Recently, we presented a fast proteomic method termed DirectMS1 based on MS1-only mass spectra acquisition and data processing. The method allowed significant squeezing of the proteome-wide analysis to a few minute time frame at the depth of quantitative proteome coverage of 1000 proteins at 1% FDR. In this work, to further increase the capabilities of the DirectMS1 method, we explored the opportunities presented by the recent progress in the machine learning area and applied the LightGBM tree-based learning algorithm into the scoring of peptide-feature matches when processing MS1 spectra. Further, we integrated the peptide feature identification algorithm of DirectMS1 with the recently introduced peptide retention time prediction utility, DeepLC. Additional approaches to improve performance of the DirectMS1 method are discussed and demonstrated, such as FAIMS coupled to the Orbitrap mass analyzer. As a result of all improvements to DirectMS1, we succeeded in identifying more than 2000 proteins at 1% FDR from the HeLa cell line in a 5 minute LC-MS1 analysis.

bioinformatics↗

bioRxiv2020

AA_stat enables extensive characterization of artefact and post-translational modifications in bottom-up proteomics

Characterization of post-translational modifications is among the most challenging tasks in tandem mass spectrometry-based proteomics which has yet to find an efficient solution. The ultra-tolerant (open) database search attempts to meet this challenge. However, interpretation of the mass shifts observed in open search still requires an effective and automated solution. We have previously introduced the AA_stat tool for analysis of amino acid frequencies at different mass shifts and generation of hypotheses on unaccounted in vitro modifications. Here, we report on the new version of AA_stat, which now complements amino acid frequency statistics with a number of new features: (1) MS/MS-based localization of mass shifts and localization scoring, including shifts which are the sum of modifications; (2) inferring fixed modifications to increase method sensitivity; (3) inferring monoisotopic peak assignment errors and variable modifications based on abundant mass shift localizations to increase the yield of closed search; (4) new mass calibration algorithm to account for partial systematic shifts; (5) interactive integration of all results and a rated list of possible mass shift interpretations. With these options, we improve interpretation of open search results and demonstrate the utility of AA_stat for profiling of abundant and rare amino acid modifications. AA_stat is implemented in Python as an open-source command-line tool available at https://github.com/SimpleNumber/aa_stat.

bioinformatics↗

bioRxiv2019

DirectMS1: MS/MS-free identification of 1000 proteins of cellular proteomes in 5 minutes

Proteome characterization relies heavily on tandem mass spectrometry (MS/MS) and is thus associated with instrumentation complexity, lengthy analysis time, and limited duty-cycle. It was always tempting to implement approaches which do not require MS/MS, yet, they were constantly failing in achieving meaningful depth of quantitative proteome coverage within short experimental times, which is particular important for clinical or biomarker discovery applications. Here, we report on the first successful attempt to develop a truly MS/MS-free and label-free method for bottom-up proteomics. We demonstrate identification of 1000 protein groups for a standard HeLa cell line digest using 5-minute LC gradients. The amount of loaded sample was varied in a range from 1 ng to 500 ng, and the method demonstrated 10-fold higher sensitivity compared with the standard MS/MS-based approach. Due to significantly higher sequence coverage obtained by the developed method, it outperforms all popular MS/MS-based label-free quantitation approaches.

biochemistry↗