Browse
Submit Data
Databases
API
Help

Dataset Information

11 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Enabling automated and reproducible spatially resolved transcriptomics at scale.

ABSTRACT: Spatial information of tissues is an essential component to reach a holistic overview of gene expression mechanisms. The sequencing-based Spatial transcriptomics approach allows to spatially barcode the whole transcriptome of tissue sections using microarray glass slides. However, manual preparation of high-quality tissue sequencing libraries is time-consuming and subjected to technical variability. Here, we present an automated adaptation of the 10x Genomics Visium library construction on the widely used Agilent Bravo Liquid Handling Platform. Compared to the manual Visium library preparation, our automated approach reduces hands-on time by over 80% and provides higher throughput and robustness. Our automated Visium library preparation protocol provides a new strategy to standardize spatially resolved transcriptomics analysis of tissues at scale.

SUBMITTER: Stenbeck L

PROVIDER: S-EPMC9213715 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Spatially resolved transcriptomics reveals plant host responses to pathogens.

Project description: Not available

| S-EPMC6785889 | biostudies-literature

Spatially resolved transcriptomics reveals the architecture of the tumor-microenvironment interface

Project description: Not available

| S-EPMC8560802 | biostudies-literature

Spatially resolved transcriptomics of high-grade serous ovarian carcinoma

Project description:Bulk and single-cell RNA sequencing do not provide full characterization of tissue spatial diversity in cancer samples, and currently available in situ techniques (multiplex immunohistochemistry, imaging mass cytometry) allow for only limited analysis of a small number of targets. The current study represents the first comprehensive approach to spatial transcriptomics of high-grade serous ovarian carcinoma using intact tumor tissue. We selected a small cohort of patients with highly annotated high-grade serous ovarian carcinoma, categorized them by response to neoadjuvant chemotherapy (poor or excellent), and analyzed pre-treatment tumor tissue specimens. Our study uncovered extensive differences in tumor composition between the poor responders and excellent responders to chemotherapy, related to cell cluster organization and localization. This in-depth characterization of high-grade serous ovarian carcinoma tumor tissue from poor and excellent responders showed that spatial interactions between cell clusters may influence chemo-responsiveness more than cluster composition alone.

2021-12-02 | GSE189843 | GEO

Spatially resolved detection of a nanometer-scale gap by scanning electrochemical microscopy.

Project description: Not available

| S-EPMC2702147 | biostudies-literature

Automated analysis of filopodial length and spatially resolved protein concentration via adaptive shape tracking.

Project description: Not available

| S-EPMC5221593 | biostudies-literature

Hybridization-based in situ sequencing (HybISS) for spatially resolved transcriptomics in human and mouse brain tissue.

Project description: Not available

| S-EPMC7641728 | biostudies-literature

BLINKER: Automated Extraction of Ocular Indices from EEG Enabling Large-Scale Analysis.

Project description: Not available

| S-EPMC5289990 | biostudies-other

Spatially Resolved Proteome Mapping of Laser Capture Microdissected Tissue with Automated Sample Transfer to Nanodroplets.

Project description: Not available

| S-EPMC6126383 | biostudies-literature

Automated Image Processing for Spatially Resolved Analysis of Lipid Droplets in Cultured 3T3-L1 Adipocytes.

Project description: Not available

| S-EPMC4442582 | biostudies-literature

Spatially resolved transcriptomics reveals the architecture of the tumor-microenvironment interface

Project description:Cancer cells interact with a wide variety of other cell types, but our understanding of microenvironmental heterogeneity and how it influences tumor phenotypes is limited. While single-cell RNA-seq (scRNA-seq) has helped define these TME cell types, it provides limited information on the mechanisms that define how individual tumor cells interact with TME. Here, we integrate spatial transcriptomics with scRNA-seq to define the architecture and nature of nascent tumor and surrounding microenvironment cells as they come into contact through the process of invasion. Using a well-defined transgenic zebrafish model of BRAFV600E-driven melanoma, we identify a transcriptionally unique “interface” cluster localized at the boundary between tumor cells and surrounding tissues. Using an unbiased, data-driven approach, we identify spatially-patterned gene modules specific to the interface and show that the interface is a distinct transcriptional entity that histologically resembles the microenvironment but transcriptionally resembles the tumor. By complementing ST with scRNA-seq, we demonstrate that the interface is composed of specialized tumor and microenvironment cells. Both cell types in the interface upregulate a common set of cilia genes, and we find enrichment of cilia proteins only where the tumor meets the TME. Cilia gene expression is regulated by ETS-family transcription factors, which normally act to suppress their expression outside of this region. This unique ETS-driven interface transcriptional state is conserved across ten different human patient samples, suggesting this is a conserved feature of human melanoma. Taken together, our results demonstrate the power of spatial and single-cell transcriptomics techniques in uncovering novel biological mechanisms that drive tumor invasion into new tissues.

2021-09-21 | GSE159709 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data