This record contains raw data related to article “Definition of a multi-omics signature for Esophageal Adenocarcinoma prognosis prediction "

Abstract: Esophageal cancer is a highly lethal malignancy that accounts for 5% of all cancer deaths. The two main sub-types of the disease are esophageal squamous-cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC). To date, most studies focused on analysing the transcriptional profile in ESCC only a few studies analysed EAC for transcriptional signatures that might be associated with diagnosis and/or prognosis. In this work we performed a single-cell RNA sequencing (scRNAseq) analysis of the CD45+ cells enriched from from tumor and matched non-tumor tissues obtained from 3 therapy-naïve patients to identify all the types of immune cells present in the tumor's immune infiltrate and their transcriptomic profiles, moreover we have analysed the whole transcriptome in a cohort of 23 patients from whom tissue biopsies were taken from tumor and matched non-tumor tissues. The transcriptional signatures derived from both types of analyses were then used to stratify a larger cohort of TCGA EAC patients showing a strong association with their prognosis. The transcriptional signatures here described have therefore proved capable of being able to predict the clinical outcome of patients and could be used to better define the prognosis in EAC after surgery and to direct patients towards effective therapies.

Abstract:

Biobanking of tissue from clinically obtained kidney biopsies for later use with multi-omic and imaging techniques is an inevitable step to overcome the need of disease model systems and towards translational medicine. Hence, collection protocols ensuring integration into daily clinical routines using preservation media not requiring liquid nitrogen but instantly preserving kidney tissue for clinical and scientific analyses are of paramount importance. Thus, we modified a robust single nucleus dissociation protocol for kidney tissue stored snap frozen or in the preservation media RNAlater and CellCover. Using porcine kidney tissue as surrogate for human kidney tissue, we conducted single nucleus RNA sequencing with the Chromium 10X Genomics platform. The resulting data sets from each storage condition were analyzed to identify any potential variations in transcriptomic profiles. Furthermore, we assessed the suitability of the preservation media for additional analysis techniques (proteomics, metabolomics) and the preservation of tissue architecture for histopathological examination including immunofluorescence staining.

In this study, we show that in daily clinical routines the RNAlater facilitates the collection of highly preserved kidney biopsies and enables further analysis with cutting-edge techniques like single nucleus RNA sequencing, proteomics, and histopathological evaluation. Only metabolome analysis is currently restricted to snap frozen tissue. This work will contribute to build tissue biobanks with well-defined cohorts of the respective kidney disease that can be deeply molecularly characterized, opening new horizons for the identification of unique cells, pathways and biomarkers for the prevention, early identification, and targeted therapy of kidney diseases.

In aquatic environments, the production and consumption of organic compounds is directly tied to the metabolic potential of the in situ microbial community. The community’s metabolic potential can be assessed using metatranscriptomics, which is a measure of gene expression in the environment. More recently, advances in metabolomics deliver details on specific organic compounds found in the environment. While the integration of these two datasets is desirable, the vast data streams can hinder investigations into biological and chemical processes. Here, we use data in the Kyoto Encyclopedia of Genes and Genomes (KEGG) combined with K means clustering to jointly interrogate metabolomics and metatranscriptomics data collected from a coastal marine environment. Using KEGG allowed us to focus our analysis on genes and compounds connected by a defined biochemical reaction. The K means clustering provided an unbiased means to place metabolites and genes into groups based on their temporal variability. Each of the groups defined by the K means clustering contained both transcripts and metabolites, which emphasized the interconnected nature of these two datasets. While conceptually simple, this analysis allowed us to explore a tractable number of biochemical pathways within our data. Continued development of computational tools to analyze meta-omics data is a pressing need. The application of tools such as described here is an exciting step towards integrated multi-omics data analysis that can be used to address broad questions in biogeochemical cycling.

Numerous techniques have been employed to deconstruct the heterogeneity observed in normal and diseased cellular populations, including single cell RNA sequencing, in situ hybridization, and flow cytometry. While these approaches have revolutionized our understanding of heterogeneity, in isolation they cannot correlate phenotypic information within a physiologically relevant live-cell state with molecular profiles. This inability to integrate a live-cell phenotype—such as invasiveness, cell:cell interactions, and changes in spatial positioning—with multi-omic data creates a gap in understanding cellular heterogeneity. We sought to address this gap by employing lab technologies to design a detailed protocol, termed Spatiotemporal Genomic and Cellular Analysis (SaGA), for the precise imaging-based selection, isolation, and expansion of phenotypically distinct live cells. This protocol requires cells expressing a photoconvertible fluorescent protein and employs live cell confocal microscopy to photoconvert a user-defined single cell or set of cells displaying a phenotype of interest. The total population is then extracted from its microenvironment, and the optically highlighted cells are isolated using fluorescence activated cell sorting. SaGA-isolated cells can then be subjected to multi-omics analysis or cellular propagation for in vitro or in vivo studies. This protocol can be applied to a variety of conditions, creating protocol flexibility for user-specific research interests. The SaGA technique can be accomplished in one workday by non-specialists and results in a phenotypically defined cellular subpopulations for integration with multi-omics techniques. We envision this approach providing multi-dimensional datasets exploring the relationship between live cell phenotypes and multi-omic heterogeneity within normal and diseased cellular populations.

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