Project description:Multi-omics integration is highly effective for the holistic characterization of biological systems. However, current bioinformatics tools face limitations when incorporating metabolomics data, particularly for handling untargeted annotation, and when providing alternatives to predefined knowledge bases. Advanced algorithms required for multi-omics integration typically demand programming skills and statistical background, restricting their use to specialized users. Here, we introduce TurbOmics, a user-friendly web-based platform that enables researchers with diverse backgrounds to analyze metabolomics, proteomics, and transcriptomics data using an integrative workflow that includes advanced algorithms for multi-omics integration, while addressing key challenges associated with metabolomics data. The platform is available at https://proteomics.cnic.es/TurboPutative/TurbOmicsApp.html.
Project description:Kidney fibrosis represents an urgent unmet clinical need due to the lack of effective therapies and inadequate understanding of the molecular pathogenesis. We have generated a comprehensive and integrated multi-omics data set (proteomics, mRNA and small RNA transcriptomics) of fibrotic kidneys that is searchable through a user-friendly web application. Two commonly used mouse models were utilized: a reversible chemical-induced injury model (folic acid (FA) induced nephropathy) and an irreversible surgically-induced fibrosis model (unilateral ureteral obstruction (UUO)). mRNA and small RNA sequencing as well as 10-plex tandem mass tag (TMT) proteomics were performed with kidney samples from different time points over the course of fibrosis development. The bioinformatics workflow used to process, technically validate, and integrate the single data sets will be described. In summary, we present temporal and integrated multi-omics data from fibrotic mouse kidneys that are accessible through an interrogation tool to provide a searchable transcriptome and proteome for kidney fibrosis researchers.
Project description:Kidney fibrosis represents an urgent unmet clinical need due to the lack of effective therapies and inadequate understanding of the molecular pathogenesis. We have generated a comprehensive and integrated multi-omics data set (proteomics, mRNA and small RNA transcriptomics) of fibrotic kidneys that is searchable through a user-friendly web application. Two commonly used mouse models were utilized: a reversible chemical-induced injury model (folic acid (FA) induced nephropathy) and an irreversible surgically-induced fibrosis model (unilateral ureteral obstruction (UUO)). mRNA and small RNA sequencing as well as 10-plex tandem mass tag (TMT) proteomics were performed with kidney samples from different time points over the course of fibrosis development. The bioinformatics workflow used to process, technically validate, and integrate the single data sets will be described. In summary, we present temporal and integrated multi-omics data from fibrotic mouse kidneys that are accessible through an interrogation tool to provide a searchable transcriptome and proteome for kidney fibrosis researchers.
Project description:In this work, a microwell-chip was prepared and modified. The microwell-chip was used for extraction of metabolites and subsequent protein digestion. Next, direct electrospray ionization mass spectrometry (ESI-MS) was adopted for metabolome identification and a data independent acquisition (DIA)-MS approach was established for simultaneous proteome profiling and phosphoproteome analysis. In particular, application of this strategy provides a multi-omics view of cellular changes.
Project description:Here we describe our unprecedented approach in proposing parsley (PAR) as a nutraceutical intervention in inflammatory bowel disease (IBD) using a mouse model of dextran sodium sulphate (DSS)-induced colitis, following a multi-integrated-omics analysis. PAR supplementation (n=7) significantly improved colon shortening and increased the disease activity index compared to the DSS group (n=7). The colonic transcriptome revealed the down-regulation of inflammatory cytokines, and the hepatic transcriptome and metabolome revealed the up-regulation of fatty acid synthesis genes, thereby improving body weight loss. Down-regulated cancer markers were observed in the hepatic transcriptome and proteome. A global plasma metabolite analysis indicated shifts in the citric cycle and urea cycle, implicating improved impaired glycolysis and oxidative stress. Our integration of three omics analyses highlighted the involvement of the methionine-recycling pathway and PARM-bM-^@M-^Ys role in decreasing the risk of IBD. This pioneering use of multi-integrated-omics in the evaluation of nutrientsM-bM-^@M-^Y effects on physiology is expected to be widely useful and informative, shaping the future of nutritional research. Here we describe our unprecedented approach in proposing parsley (PAR) as a nutraceutical intervention in inflammatory bowel disease (IBD) using a mouse model of dextran sodium sulphate (DSS)-induced colitis, following a multi-integrated-omics analysis. PAR supplementation (n=7) significantly improved colon shortening and increased the disease activity index compared to the DSS group (n=7). The colonic transcriptome revealed the down-regulation of inflammatory cytokines, and the hepatic transcriptome and metabolome revealed the up-regulation of fatty acid synthesis genes, thereby improving body weight loss. Down-regulated cancer markers were observed in the hepatic transcriptome and proteome. A global plasma metabolite analysis indicated shifts in the citric cycle and urea cycle, implicating improved impaired glycolysis and oxidative stress. Our integration of three omics analyses highlighted the involvement of the methionine-recycling pathway and PARM-bM-^@M-^Ys role in decreasing the risk of IBD. This pioneering use of multi-integrated-omics in the evaluation of nutrientsM-bM-^@M-^Y effects on physiology is expected to be widely useful and informative, shaping the future of nutritional research. Total hepatic and colonic RNA from each respective group were pooled (n=7). The microarray analysis was carried as out as described by Jia et al. 8 Mouse Genome 430 2.0 Array GeneChips (Affymetrix, Santa Clara, CA) containing over 30,000 gene probe sets were used for genome-wide expression profiling.
Project description:We use Saccharomyces cerevisiae to perform absolute quantitative multi-omics analysis to map interactions of different cellular processes during the yeast cell cycle.
Project description:We use Saccharomyces cerevisiae grown on ethanol to perform absolute quantitative multi-omics analysis to map interactions of different cellular processes during the yeast cell cycle.
Project description:Spatial multi-omics technologies enable simultaneous measurements of multiple omics modalities. Integration across spatial omics modalities followed by multi-omic spatial domain and cell-type annotation are two fundamental tasks for downstream analysis. We present Domain Invariant Representation through Adversarial Calibration (DIRAC), a geometric deep learning model that unifies both tasks by treating horizontal integration (different cells/spots, same omic modality) and vertical integration (same cells/spots, different omics modalities) under a generalized domain adaptation framework. DIRAC uses an adversarial domain discriminator to integrate multiple spatial omics modalities into a unified domain-invariant embedding space and to automate cell-type annotation by transferring labels from reference multi-omic data. DIRAC delineated more biologically meaningful spatial domains and improved clustering and cell-type annotation performance across omics modalities (histone marks, chromatin accessibility, RNA, and protein) and technology platforms (sequencing and imaging-based). We used DIRAC to build cellularly resolved spatial multi-omics atlases of mouse spleen and thymus, revealing the spatial migratory patterns of T cells in the thymus and the spatial organization of finely-resolved immune cell types in the spleen. DIRAC is substantially faster than existing multi-omic integration methods and scales to millions of cells.