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C3PO applied to spatial transcriptomics - 1D and 2D gridmode sequence data

ABSTRACT: C3PO applied to spatial transcriptomics - 1D and 2D gridmode sequence data

PROVIDER: PRJEB86840 | ENA |

REPOSITORIES: ENA

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C3PO applied to spatial transcriptomics - 1D and 2D gridmode sequence data

Project description:Raw sequencing data for the proof-of-principal 1D and 2D gridmode transcriptomics performed after C3PO in the following article: https://www.biorxiv.org/content/10.1101/2024.03.12.584578v1.full. The 1D data is found in the limb68 samples here (limb #3 in the article) and the 2 replicates of the 2D data is found in the L127 samples (limb #4 in the article) and L128 samples (limb #5 in the article). For the 2D data the P# value in the filename indicates the corresponding FACS gate.

2025-03-10 | E-MTAB-14936 | biostudies-arrayexpress

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Gordonia sp. 1D strain:1D

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Genome-wide expression analysis of bri1-5 and its suppressors brs1-1D, bak1-1D, bri1-1D

Project description:bri1-5 is a weak mutant of Brassinosteroid Insensitive 1 (BRI1). Suppressors by activation tagging bri1-1D, brs1-1D and bak1-1D can recover bri1-5 phenotype. We use microarray to investigate which pathways or functional categories have been transcriptionally regulated by bri1-1D, brs1-1D and bak1-1D. Whole seedlings from wild-type (WS2), bri1-5, bri1-5/brs1-1D, bri1-5/bak1-1D, bri1-5/bri1-1D. Three biological replicates for each genotype.

2015-07-14 | E-GEOD-70843 | biostudies-arrayexpress

Single-Cell and Spatial Transcriptomics Analysis of Human Adrenal Aging (spatial transcriptomics)

Project description:These data were used in the spatial transcriptomics analysis of the article titled \\"Single-Cell and Spatial Transcriptomics Analysis of Human Adrenal Aging\\".

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Project description:To reveal the spatial distribution and the difference gene expression pattern of cancer cells in colorectal cancer, Visium spatial transcriptomics of four CRC patients was applied

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Project description:We developed an analysis pipeline that can extract microbial sequences from Spatial Transcriptomic (ST) data and assign taxonomic labels, generating a spatial microbial abundance matrix in addition to the default host expression matrix, enabling simultaneous analysis of host expression and microbial distribution. We called the pipeline Spatial Meta-transcriptome (SMT) and applied it on both human and murine intestinal sections and validated the spatial microbial abundance information with alternative assays. Biological insights were gained from this novel data that that demonstrated host-microbe interaction at various spatial scales. Finally, we tested experimental modification that can increase microbial capture while preserving host spatial expression quality and, by use of positive controls, quantitatively demonstrated the capture efficiency and recall of our methods. This proof of concept work demonstrates the feasibility of Spatial Meta-transcriptomic analysis, and paves the way for further experimental optimization and application.

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