leedsomics

[leedsomics]

Single-cell transcriptomics reveals evolutionary reconfiguration of embryonic cell fate specification in the sea urchin Heliocidaris erythrogramma

Altered regulatory interactions during development likely underlie a large fraction of phenotypic diversity within and between species, yet identifying specific evolutionary changes remains challenging. Analysis of single-cell developmental transcriptomes from multiple species provides a powerful framework for unbiased identification of evolutionary changes in developmental mechanisms. Here, we leverage a "natural experiment" in developmental evolution in sea urchins, where a major life history switch recently evolved in the lineage leading to Heliocidaris erythrogramma, precipitating extensive changes in early development. Comparative analyses of scRNA-seq developmental time courses from H. erythrogramma and Lytechinus variegatus (representing the derived and ancestral states respectively) reveals numerous evolutionary changes in embryonic patterning. The earliest cell fate specification events, and the primary signaling center are co-localized in the ancestral dGRN but remarkably, in H. erythrogramma they are spatially and temporally separate. Fate specification and differentiation are delayed in most embryonic cell lineages, although in some cases, these processes are conserved or even accelerated. Comparative analysis of regulator-target gene co-expression is consistent with many specific interactions being preserved but delayed in H. erythrogramma, while some otherwise widely conserved interactions have likely been lost. Finally, specific patterning events are directly correlated with evolutionary changes in larval morphology, suggesting that they are directly tied to the life history shift. Together, these findings demonstrate that comparative scRNA-seq developmental time courses can reveal a diverse set of evolutionary changes in embryonic patterning and provide an efficient way to identify likely candidate regulatory interactions for subsequent experimental validation. http://dlvr.it/T6JHNq

[leedsomics]

Aerobic Adaptation and Metabolic Dynamics of Propionibacterium freudenreichii DSM 20271: Insights from Comparative Transcriptomics and Surfaceome Analysis

Propionibacterium freudenreichii (PFR) DSM 20271 is a bacterium known for its ability to thrive in diverse environments and to produce vitamin B12. Despite its anaerobic preference, recent studies have elucidated its ability to prosper in the presence of oxygen, prompting a deeper exploration of its physiology under aerobic conditions. Here, we investigated the response of DSM 20271 to aerobic growth by employing comparative transcriptomic and surfaceome analyses alongside metabolite profiling. Cultivation under controlled partial pressure of oxygen (pO2) conditions revealed significant increases in biomass formation and altered metabolite production, notably of B12 vitamin, pseudovitamin-B12, propionate and acetate, under aerobic conditions. Transcriptomic analysis identified differential expression of genes involved in lactate metabolism, TCA cycle, and electron transport chain, suggesting metabolic adjustments to aerobic environments. Moreover, surfaceome analysis unveiled growth environment-dependent changes in surface protein abundance, with implications for sensing and adaptation to atmospheric conditions. Supplementation experiments with key compounds highlighted the potential for enhancing aerobic growth, emphasizing the importance of iron and -ketoglutarate availability. Furthermore, in liquid culture, FeSO4 supplementation led to increased heme production and reduced vitamin B12 production, highlighting the impact of oxygen and iron availability on the metabolic pathways. These findings deepen our understanding of PFR's physiological responses to oxygen availability and offer insights for optimizing its growth in industrial applications. http://dlvr.it/T6G2bm

[leedsomics]

Evaluation of extraction solvents for untargeted metabolomics to decipher the DOM of Antarctic cryoconite holes

Cryoconite holes (CHs) are biological hotspots with a high biogeochemical turnover rate, contributing significantly to the glacial ecosystem's overall carbon cycles and net fluxes. There is limited information regarding the composition of low molecular weight (LMW) molecules formed through the metabolic processes of cryoconite-dwelling microbes. These molecules constitute a substantial portion of the dissolved organic matter (DOM) within cryoconite holes. Here, we evaluated different solvents to extract low molecular weight (LMW) compounds for untargeted metabolomics using reverse phase liquid chromatography (RP-LC) coupled with high-resolution tandem mass spectrometry in positive- and negative-ionization modes. We prepared single, binary, and ternary mixtures of highly polar to relatively non-polar solvents like water, methanol, and acetonitrile to extract intra- and extracellular metabolites from CHs sediment. The biological replicates (n=4) of each identical solvent showed high reproducibility in metabolite diversity, while substantial differences were observed among different solvent types. Among the single solvents, organic-rich 70:30 MeOH: water and in a parallel 2-single solvent combination of 70:30 MeOH: Water and 40:40:20 Acetonitrile: Methanol: Water provided increased the number and chemical diversity of extracted metabolites. Combining RP with the hydrophilic interaction liquid chromatography (HILIC) technique provided the highest number of unique metabolites. HILIC and RP detected polar and mid- to non-polar molecules at high intensity, respectively. This dual-LC and ionization polarity combination increased the detection of metabolic features by 46.96% and 24.52% in single- and two-solvent combinations compared to RP alone. This study developed a simple untargeted metabolomics workflow that is highly sensitive and robust, detecting and potentially identifying a large number of broad chemically diverse molecules present in the DOM (extracellular) and microbes (intracellular) from the CH's environment. This method can better characterize DOM's chemical composition and, after integrating with other 'omics' approaches, can be used to examine the link between metabolic pathways and microbial communities in global CHs or other similar ecosystems, revealing how these earthy systems and their microbial flora control carbon or nutrient storage or release in response to global climate change. http://dlvr.it/T6FrD5

[leedsomics]

Single-Cell Omics for Transcriptome CHaracterization (SCOTCH): isoform-level characterization of gene expression through long-read single-cell RNA sequencing

The advent of long-read single-cell transcriptome sequencing (lr-scRNA-Seq) represents a significant leap forward in single-cell genomics. With the recent introduction of R10 flowcells by Oxford Nanopore, we propose that previous computational methods designed to handle high sequencing error rates are no longer relevant, and that the prevailing approach using short reads to compile "barcode space" (candidate barcode list) to de-multiplex long reads are no longer necessary. Instead, computational methods should now shift focus on harnessing the unique benefits of long reads to analyze transcriptome complexity. In this context, we introduce a comprehensive suite of computational methods named Single-Cell Omics for Transcriptome CHaracterization (SCOTCH). Our method is compatible with the single-cell library preparation platform from both 10X Genomics and Parse Biosciences, facilitating the analysis of special cell populations, such as neurons, hepatocytes and developing cardiomyocytes. We specifically re-formulated the transcript mapping problem with a compatibility matrix and addressed the multiple-mapping issue using probabilistic inference, which allows the discovery of novel isoforms as well as the detection of differential isoform usage between cell populations. We evaluated SCOTCH through analysis of real data across different combinations of single-cell libraries and sequencing technologies (10X + Illumina, Parse + Illumina, 10X + Nanopore_R9, 10X + Nanopore_R10, Parse + Nanopore_R10), and showed its ability to infer novel biological insights on cell type-specific isoform expression. These datasets enhance the availability of publicly available data for continued development of computational approaches. In summary, SCOTCH allows extraction of more biological insights from the new advancements in single-cell library construction and sequencing technologies, facilitating the examination of transcriptome complexity at the single-cell level. http://dlvr.it/T6DpF9

[leedsomics]

Comparative genomics of the world's smallest mammals reveals links to echolocation, metabolism, and body size plasticity

Originating 30 million years ago, shrews (Soricidae) have diversified into around 400 species worldwide. Shrews display a wide array-array of adaptations, with some species having developed distinctive traits such as echolocation, underwater diving, and venomous saliva. Accordingly, these tiny insectivores are ideal to study the genomic mechanisms of evolution and adaptation. We conducted a comparative genomic analysis of four shrew species and 16 other mammals to identify genomic variations unique to shrews. Using two existing shrew genomes and de novo assemblies for the maritime (Sorex maritimensis) and smoky shrew (S. fumeus), we identified mutations in conserved regions of the genomes, also known as accelerated regions, gene families undergoing significant expansion, and positively selected genes. Our analyses unveiled shrew-specific genomic variants in genes associated with the nervous, metabolic, and auditory systems, which can be linked to unique traits in shrews. Notably, genes suggested to be under convergent evolution in echolocating mammals exhibited accelerated regions in shrews, and pathways linked to putative body size plasticity were detected. These findings provide insight into the evolutionary mechanisms shaping shrew species, shedding light on their adaptation and divergence over time. http://dlvr.it/T6CyTW

[leedsomics]

Comparative genomics of the extremophile Cryomyces antarcticus and other psychrophilic Dothideomycetes

Cryomyces antarcticus is an endolithic fungus that inhabits rock outcrops in Antarctica. It survives extremes of cold, humidity and solar radiation in one of the least habitable environments on Earth. This fungus is unusual because it produces heavily melanized, meristematic growth and is thought to be haploid and asexual. Due to its growth in the most extreme environment, it has been suggested as an organism that could survive on Mars. However, the mechanisms it uses to achieve its extremophilic nature are not known. Over a billion years of fungal evolution has enabled representatives of this kingdom to populate almost all parts of planet Earth and to adapt to some of its most uninhabitable environments including extremes of temperature, salinity, pH, water, light, or other sources of radiation. Comparative genomics can provide clues to the processes underlying biological diversity, evolution, and adaptation. This effort has been greatly facilitated by the 1000 Fungal Genomes project and the JGI MycoCosm portal where sequenced genomes have been assembled into phylogenetic and ecological groups representing different projects, lifestyles, ecologies, and evolutionary histories. Comparative genomics within and between these groups provides insights into fungal adaptations, for example to extreme environmental conditions. Here, we analyze two Cryomyces genomes in the context of additional psychrophilic fungi, as well as non-psychrophilic fungi with diverse lifestyles selected from the MycoCosm database. This analysis identifies families of genes that are expanded and contracted in Cryomyces and other psychrophiles and may explain their extremophilic lifestyle. Higher GC contents of genes and of bases in the third positions of codons may help to stabilize DNA under extreme conditions. Numerous smaller contigs in C. antarcticus suggest the presence of an alternative haplotype that could indicate that the sequenced isolate is diploid or dikaryotic. These analyses provide a first step to unraveling the secrets of the extreme lifestyle of C. antarcticus. http://dlvr.it/T6Cgd4

[leedsomics]

Single-Cell Transcriptomics Reveals the Molecular Logic Underlying Ca2+ Signaling Diversity in Human and Mouse Brain

The calcium ion (Ca2+) is a ubiquitous intracellular signaling molecule that plays a critical role in the adult and developing brain. However, the principles governing the specificity of Ca2+ signaling remain unresolved. In this work, we comprehensively analyzed the Ca2+ signaling transcriptome in the adult mouse brain and developing human brain. We found that neurons form non-stochastic Ca2+-states that are reflective of their cell types and functionality, with evidence suggesting that the diversity is driven by lineage-specific developmental changes. Focusing on the neocortical development, we reveal that an unprecedented number of Ca2+ genes are tightly regulated and evolutionarily conserved, capturing functionally driven differences within radial glia and neuronal progenitors. In summary, our study provides an in-depth understanding of the cellular and temporal diversity of Ca2+ signaling and suggests that Ca2+ signaling is dynamically tailored to specific cell states. http://dlvr.it/T67Bv4

[leedsomics]

An integrated single-nucleus and spatial transcriptomics atlas reveals the molecular landscape of the human hippocampus

The hippocampus contains many unique cell types, which serve the structure's specialized functions, including learning, memory and cognition. These cells have distinct spatial topography, morphology, physiology, and connectivity, highlighting the need for transcriptome-wide profiling strategies that retain cytoarchitectural organization. Here, we generated spatially-resolved transcriptomics (SRT) and single-nucleus RNA-sequencing (snRNA-seq) data from adjacent tissue sections of the anterior human hippocampus across ten adult neurotypical donors. We defined molecular profiles for hippocampal cell types and spatial domains. Using non-negative matrix factorization and transfer learning, we integrated these data to define gene expression patterns within the snRNA-seq data and infer the expression of these patterns in the SRT data. Using this approach, we leveraged existing rodent datasets that feature information on circuit connectivity and neural activity induction to make predictions about axonal projection targets and likelihood of ensemble recruitment in spatially-defined cellular populations of the human hippocampus. Finally, we integrated genome-wide association studies with transcriptomic data to identify enrichment of genetic components for neurodevelopmental, neuropsychiatric, and neurodegenerative disorders across cell types, spatial domains, and gene expression patterns of the human hippocampus. To make this comprehensive molecular atlas accessible to the scientific community, both raw and processed data are freely available, including through interactive web applications. http://dlvr.it/T671M2

[leedsomics]

Functional genomics screens reveal a role for TBC1D24 and SV2B in antibody-dependent enhancement of dengue virus infection

Dengue virus (DENV) can hijack non-neutralizing IgG antibodies to facilitate its uptake into target cells expressing Fc gamma receptors (FcgR) - a process known as antibody-dependent enhancement (ADE) of infection. Beyond a requirement for FcgR, host dependency factors for this non-canonical infection route remain unknown. To identify cellular factors exclusively required for ADE, here, we performed CRISPR knockout screens in an in vitro system permissive to infection only in the presence of IgG antibodies. Validating our approach, a top hit was FcgRIIa, which facilitates binding and internalization of IgG-bound DENV but is not required for canonical infection. Additionally, we identified host factors with no previously described role in DENV infection, including TBC1D24 and SV2B, both of which have known functions in regulated secretion. Using genetic knockout and trans-complemented cells, we validated a functional requirement for these host factors in ADE assays performed with monoclonal antibodies and polyclonal sera in multiple cell lines and using all four DENV serotypes. We show that knockout of TBC1D24 or SV2B impaired binding of IgG-DENV complexes to cells without affecting FcgRIIa expression levels. Thus, we identify cellular factors beyond FcgR that are required for ADE of DENV infection. Our findings represent a first step towards advancing fundamental knowledge behind the biology of ADE that can ultimately be exploited to inform vaccination and therapeutic approaches. http://dlvr.it/T66rTh

[leedsomics]

An inflection point in high-throughput proteomics with Orbitrap Astral: analysis of biofluids, cells, and tissues

This technical note presents a comprehensive proteomics workflow for the new combination of Orbitrap and Astral mass analyzers across biofluids, cells, and tissues. Central to our workflow is the integration of Adaptive Focused Acoustics (AFA) technology for cells and tissue lysis, to ensure robust and reproducible sample preparation in a high-throughput manner. Furthermore, we automated the detergent-compatible single-pot, solid-phase-enhanced sample Preparation (SP3) method for protein digestion, a technique that streamlines the process by combining purification and digestion steps, thereby reducing sample loss and improving efficiency. The synergy of these advanced methodologies facilitates a robust and high-throughput approach for cells and tissue analysis, an important consideration in translational research. This work disseminates our platform workflow, analyzes the effectiveness, demonstrates reproducibility of the results, and highlights the potential of these technologies in biomarker discovery and disease pathology. For cells and tissues (heart, liver, lung, and intestine) proteomics analysis by data-independent acquisition mode, identifications exceeding 10,000 proteins can be achieved with a 24-minute active gradient. In 200ng injections of HeLa digest across multiple gradients, an average of more than 80% of proteins have a CV less than 20%, and a 45-minute run covers ~90% of the expressed proteome. In plasma samples including naive, depleted, perchloric acid precipitated, and Seer nanoparticle captured, all with a 24-minute gradient length, we identified 87, 108, 96 and 137 out of 216 FDA approved circulating protein biomarkers, respectively. This complete workflow allows for large swaths of the proteome to be identified and is compatible across diverse sample types. http://dlvr.it/T66glc

[leedsomics]

Ultra-deep proteomics by Thin-diaPASEF with a 60-cm long column system

Recent advances have allowed for the detection of 10,000 proteins from cultured human cell samples, such as HeLa and HEK293T cells in a single-shot proteome analysis. However, deeper analysis remains challenging. Therefore, in this study, we aimed to perform a deep proteomic single-shot analysis using timsTOF HT. To achieve deep proteomics, we developed Thin-diaPASEF, a parallel accumulation-serial fragmentation (PASEF) technology featuring a thinly divided m/z axis only in regions of high ion density. Furthermore, using a 60-cm long C18 column with a particle size of 1.7 m, an average of 11,698, 11,615 and 11,019 unique proteins were successfully detected from 500 ng of HEK293T, HeLa and K562 cell digests, respectively, with a 100 min active gradient. The same system was used to analyze Lycopersicon esculentum lectin (LEL) enriched plasma and serum. The LEL method identified an average of 8,613 and 4,078 unique proteins, in plasma and serum, respectively. Our ultra-deep proteomic analysis system will be helpful for the in-depth comparison of proteins in medical and biological research because it enables the analysis of highly proteome coverage in a single-shot. http://dlvr.it/T64yg8

[leedsomics]

Multiple Omics Find New Cecal Microbial Features Associated with Feed Efficiency in Ducks

As the global population continues to grow exponentially, the competition for resources between livestock and humans has become increasingly intense. Breeding efficient animal breeds, fully utilizing feed resources, and reducing environmental damage are major challenges facing the livestock industry. Thus, we used metagenomics, transcriptomics, and metabolomics to explore how to improve the feed utilization to solve these problems. Our metagenomic analysis revealed a significant up-regulation of Elusimicrobiota at the phylum level within the high residual feed intake (HRFI) group, in comparison to the low residual feed intake (LRFI) group. Additionally, functional analysis using Clusters of Orthologous Groups of proteins (COG) indicated prominent disparities in the category of secondary metabolites biosynthesis, transport, and catabolism between the HRFI and LRFI groups. Furthermore, our metabolomics investigation identified an upregulated expression of the secondary metabolite 15-deoxy-delta12,14-prostaglandin J2 (15d-PGJ2) in the HRFI group compared to the LRFI group. Liver transcriptome analysis identified prostaglandin-endoperoxide synthase 2 (PTGS2) as a key hub gene, exerting significant regulatory influence within the arachidonic acid pathway. Notably, the metabolite 15d-PGJ2 is a terminal product in the metabolic pathway of arachidonic acid. The correlation analysis between the cecal microbiota and differential metabolites revealed a significant negative correlation between Elusimicrobiota and the metabolite 15d-PGJ2. In summary, we assumed that the intestinal microbiome Elusimicrobiota regulates the expression of the PTGS2 gene, consequently inducing variations in PTGS2 efficiency between the HRFI and LRFI groups, ultimately leading to diverse residual feed intake levels in ducks. http://dlvr.it/T64nnD

[leedsomics]

Unraveling cell-cell communication with NicheNet by inferring active ligands from transcriptomics data

arXiv:2404.16358v1 Announce Type: new Abstract: Ligand-receptor interactions constitute a fundamental mechanism of cell-cell communication and signaling. NicheNet is a well-established computational tool that infers ligand-receptor interactions that potentially regulate gene expression changes in receiver cell populations. Whereas the original publication delves into the algorithm and validation, this paper describes a best practices workflow cultivated over four years of experience and user feedback. Starting from the input single-cell expression matrix, we describe a "sender-agnostic" approach which considers ligands from the entire microenvironment, and a "sender-focused" approach which only considers ligands from cell populations of interest. As output, users will obtain a list of prioritized ligands and their potential target genes, along with multiple visualizations. In NicheNet v2, we have updated the data sources and implemented a downstream procedure for prioritizing cell-type-specific ligand-receptor pairs. Although a standard NicheNet analysis takes less than 10 minutes to run, users often invest additional time in making decisions about the approach and parameters that best suit their biological question. This paper serves to aid in this decision-making process by describing the most appropriate workflow for common experimental designs like case-control and cell differentiation studies. Finally, in addition to the step-by-step description of the code, we also provide wrapper functions that enable the analysis to be run in one line of code, thus tailoring the workflow to users at all levels of computational proficiency. http://dlvr.it/T62Gz1

[leedsomics]

Quinolone resistance genes qnr, aac(6')-Ib-cr, oqxAB, and qepA in environmental Escherichia coli: insights into their genetic contexts from comparative genomics

Previous studies have reported the occurrence of transferable quinolone resistance determinants in environmental Escherichia coli. However, little is known about their vectors and genetic contexts. To gain insights into these genetic characteristics, we analyzed the complete genomes of 53 environmental E. coli isolates, including 20 sequenced in this study and 33 sourced from RefSeq. The following transferable quinolone resistance determinants were detected: qnrS1 (n = 33), aac(6')-Ib-cr (n = 12), qnrS2 (n = 5), oqxAB (n = 4), qnrB4 (n = 3), qnrD1 (n = 3), qnrB7 (n = 2), qnrB19 (n = 2), qepA1 (n = 1), and qnrA1 (n = 1). These resistance genes were detected on plasmids of diverse replicon types; however, aac(6')-Ib-cr, qnrS1, and qnrS2 were also detected on the chromosome. The genetic contexts surrounding these genes included not only those previously reported in clinical isolates but also novel contexts, such as qnrD1 embedded within a composite transposon-like structure bounded by Tn3-derived inverted-repeat miniature elements (TIMEs). This study provides deep insights into mobile genetic elements associated with transferable quinolone resistance determinants, highlighting the importance of genomic surveillance of antimicrobial resistant bacteria in the environment. http://dlvr.it/T609tp

[leedsomics]

The Single-cell Pediatric Cancer Atlas: Data portal and open-source tools for single-cell transcriptomics of pediatric tumors

The Single-cell Pediatric Cancer Atlas (ScPCA) Portal (https://scpca.alexslemonade.org/) is a data resource for uniformly processed single-cell and single-nuclei RNA sequencing (RNA-seq) data and de-identified metadata from pediatric tumor samples. Originally comprised of data from 10 projects funded by Alex's Lemonade Stand Foundation, the Portal currently contains summarized gene expression data for over 500 samples from over 50 types of cancers from ALSF-funded and community-contributed datasets. In addition to gene expression data from single-cell and single-nuclei RNA-seq, the Portal holds data obtained from bulk RNA-seq, spatial transcriptomics, and feature barcoding methods, such as CITE-seq and cell hashing. ScPCA data are available for download as SingleCellExperiment or AnnData objects and are ready for downstream analyses. Objects include raw counts and normalized gene expression data, PCA and UMAP coordinates, and automated cell type annotations. Additionally, all downloads include two summary reports for each library: a quality control report summarizing sample statistics and displaying visualizations of cell metrics and a cell type annotation report with comparisons among cell type annotation methods and diagnostic plots to assess annotation quality. Merged SingleCellExperiment and AnnData objects containing all gene expression data and metadata for all samples in an ScPCA project are also available for download. These objects are useful when performing analysis on multiple samples simultaneously. Comprehensive documentation about data processing and the contents of files on the Portal, including a guide to getting started working with an ScPCA dataset, can be found at http://scpca.readthedocs.io. All data on the Portal were uniformly processed using scpca-nf, an open-source and efficient Nextflow workflow that uses alevin-fry to quantify all single-cell and single-nuclei RNA-seq data, any associated CITE-seq or cell hash data, spatial transcriptomics data, and bulk RNA-seq. Any pediatric cancer-relevant data sets processed with scpca-nf are eligible for inclusion on the ScPCA Portal, enabling continuous growth of the ScPCA Portal to help pediatric cancer researchers spend less time finding and processing data and more time answering their pressing research questions. http://dlvr.it/T5yP2F

[leedsomics]

A Systematic Overview of Single-Cell Transcriptomics Databases, their Use cases, and Limitations

arXiv:2404.10545v1 Announce Type: new Abstract: Rapid advancements in high-throughput single-cell RNA-seq (scRNA-seq) technologies and experimental protocols have led to the generation of vast amounts of genomic data that populates several online databases and repositories. Here, we systematically examined large-scale scRNA-seq databases, categorizing them based on their scope and purpose such as general, tissue-specific databases, disease-specific databases, cancer-focused databases, and cell type-focused databases. Next, we discuss the technical and methodological challenges associated with curating large-scale scRNA-seq databases, along with current computational solutions. We argue that understanding scRNA-seq databases, including their limitations and assumptions, is crucial for effectively utilizing this data to make robust discoveries and identify novel biological insights. Furthermore, we propose that bridging the gap between computational and wet lab scientists through user-friendly web-based platforms is needed for democratizing access to single-cell data. These platforms would facilitate interdisciplinary research, enabling researchers from various disciplines to collaborate effectively. This review underscores the importance of leveraging computational approaches to unravel the complexities of single-cell data and offers a promising direction for future research in the field. http://dlvr.it/T5x37y

[leedsomics]

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

Microbial proteomics and metaproteomic studies rely heavily on mass spectrometry. Standard liquid chromatography/tandem mass spectrometry-based methods for protein identification and quantitation exhibit lengthy analysis time. Here, we report on the implementation of the previously developed DirectMS1 approach for metaproteomics studies that provides a unique combination of accurate protein quantitation and rapid analysis time. We validated our method using a series of proteome-wide analyses of bacterial samples including strain isolates, mock microbiomes composed of bacteria spiked at known concentrations and human fecal microbiomes. We demonstrated that the developed two-stage search algorithm identifies the bacterial isolate 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 microbiome components of 12%. Pearson coefficient of up to 0.97 was achieved in estimates of strain biomass change in mock microbiome samples. By the example of Rhodococcus biodegradation of n-alkanes, phenols and its derivatives, we showed that our approach effectively characterizes changes in the activity of metabolic pathways of strain isolates. For the benchmarking, the changes in biodegradation activity were also assessed using the standard label-free and TMT DDA approaches. http://dlvr.it/T5Z4SB