Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

High resolution mapping of the tumor microenvironment using integrated single-cell, spatial and in situ analysis [scRNA-seq and Visium]

ABSTRACT: Single-cell and spatial technologies that profile gene expression across a whole tissue are revolutionizing the resolution of molecular states in clinical tissue samples. Current commercially available technologies provide whole transcriptome single-cell, whole transcriptome spatial context, or high resolution in situ targeted gene expression analysis. Here, we combine these technologies to explore tissue heterogeneity in large, FFPE human breast cancer sections. In one sample, we identify three molecularly distinct tumor subtypes, enabling us to define cell neighborhoods and biomarkers in the progression towards invasive carcinoma. In another sample, we identify rare boundary cells that sit at the critical myoepithelial border confining the spread of malignant cells. We demonstrate that each technology alone provides information about molecular signatures relevant to understanding cancer heterogeneity. However, it is the integration of these technologies that leads to even deeper insights, ushering in discoveries that will progress oncology research and the development of diagnostics and therapeutics.

ORGANISM(S): Homo sapiens

PROVIDER: GSE243275 | GEO | 2023/10/09

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

High resolution mapping of the tumor microenvironment using integrated single-cell, spatial and in situ analysis [Xenium]

Project description:Single-cell and spatial technologies that profile gene expression across a whole tissue are revolutionizing the resolution of molecular states in clinical tissue samples. Current commercially available technologies provide whole transcriptome single-cell, whole transcriptome spatial context, or high resolution in situ targeted gene expression analysis. Here, we combine these technologies to explore tissue heterogeneity in large, FFPE human breast cancer sections. In one sample, we identify three molecularly distinct tumor subtypes, enabling us to define cell neighborhoods and biomarkers in the progression towards invasive carcinoma. In another sample, we identify rare boundary cells that sit at the critical myoepithelial border confining the spread of malignant cells. We demonstrate that each technology alone provides information about molecular signatures relevant to understanding cancer heterogeneity. However, it is the integration of these technologies that leads to even deeper insights, ushering in discoveries that will progress oncology research and the development of diagnostics and therapeutics.

2023-10-09 | GSE243168 | GEO

Comparing 10x Visium spatial transcriptomic technologies

Project description:Spatial transcriptomics facilitates the understanding of gene expression within complex tissue contexts. Among the array of spatial capture technologies available is 10x Genomics’ Visium which provides whole tissue section profiling, enabling whole transcriptome spatial analysis. Our dataset comprises spleen tissue from mice infected with malaria, spanning multiple experiments and sample preparation protocols for tissue preservation, either as fresh frozen at optimal cutting temperature (OCT) or formalin-fixed paraffin-embedded (FFPE). Tissue placement was also considered, comparing direct tissue placement on the slide with the use of CytAssist (CA), which expands the Visium platform’s capabilities by allowing for the pre-selection of tissue sections and genes through a set of probes. We also include a matching scRNA-seq dataset that can be integrated with the spatial data.

2024-02-26 | GSE254652 | GEO

Bulk RNAseq expression data of colon biopsies from intestinal mucosa of non-IBD controls and patients with Crohn's disease or ulcerative colitis

Project description:Ulcerative colitis and Crohn’s disease are chronic inflammatory intestinal diseases with perplexing heterogeneity in manifestations and response to treatment. While the molecular basis for this heterogeneity remains uncharacterized, single-cell technologies allow us to explore the transcriptional states within tissues at an unprecedented resolution which could further understanding of these complex diseases. Here, we apply single-cell RNA-sequencing to human inflamed intestine and show that the largest differences among patients are present within the myeloid compartment including macrophages and neutrophils. Using spatial transcriptomics in human tissue at single-cell resolution (CosMx Spatial Molecular Imaging) we spatially localized each of the macrophage and neutrophil subsets identified by single-cell RNA-sequencing and unravel further macrophage diversity based on their tissue localization. Finally, single-cell RNA-sequencing combined with single-cell spatial analysis reveals a strong communication network involving macrophages and inflammatory fibroblasts. Our data sheds light on the cellular complexity of these diseases and points towards the myeloid and stromal compartments as important cellular subsets for understanding patient-to-patient heterogeneity.

2023-06-20 | GSE235236 | GEO

Uncovering the spatial landscape of molecular interactions within the tumor microenvironment through latent spaces

Project description:Recent advances in spatial transcriptomics (ST) enable gene expression measurements from a tissue sample while retaining its spatial context. This technology enables unprecedented in situ resolution of the regulatory pathways that underlie the heterogeneity in the tumor and its microenvironment (TME). The direct characterization of cellular co-localization with spatial technologies facilities quantification of the molecular changes resulting from direct cell-cell interaction, as occurs in tumor-immune interactions. We present SpaceMarkers, a novel bioinformatics algorithm to infer molecular changes from cell-cell interaction from latent space analysis of ST data. We apply this approach to infer molecular changes from tumor-immune interactions in Visium spatial transcriptomics data of metastasis, invasive and precursor lesions, and immunotherapy treatment. Further transfer learning in matched scRNA-seq data enabled further quantification of the specific cell types in which SpaceMarkers are enriched. Altogether, SpaceMarkers can identify the location and context-specific molecular interactions within the TME from ST data.

2023-04-19 | GSE224411 | GEO

Polony gels enable amplifiable DNA stamping and spatial transcriptomics of chronic pain

Project description:Despite wide applications in tissue mapping and vastly improved imaging and omics technologies, spatial, genome-wide assays of gene expression are yet to reliably achieve the resolution and sensitivity approaching dissociative single-cell RNA sequencing. Here, we introduce Pixel-seq, a spatial barcoding method using “continuous” polony gels of ≤ 1 µm feature resolution for capturing tissue RNAs and precise aggregation of mapped transcripts into individual cells. The single-cell assay was demonstrated on mapping the most common brain structures, neuron layers and clusters, creating a three-dimensional atlas of the mouse parabrachial nucleus, and discovering neuropathic pain-associated gene regulation, glial transcriptomic dynamics, and cell-cell communication in the homeostatic adult brain. Our results highlight the necessity of the non-dissociative single-cell sequencing for mapping structural and functional heterogeneity in tissues.

2022-10-31 | GSE186097 | GEO

A comprehensive evaluation of common quantification strategies for LC-MS/MS-based spatial proteomics using nanoPOTS

Project description:A deep understanding of the tissue microenvironment can provide significant insight into the function of tissue and organs in health and disease states. Spatially resolved proteomics techniques can aid in studying spatial tissue organization and cellular communication in their native microenvironment. However, current spatial proteomics technologies using antibody-based methods or mass spectrometry imaging cannot achieve deep proteome coverage. Bottom-up or global proteomics can offer deep coverage of >5000 proteins, but the low sample amount and inefficient workflow limit both proteome coverage and spatial resolution. To overcome these limitations, we developed and evaluated two MS sample preparation and data acquisition strategies coupled with our nanoPOTS-LCM-based spatial proteomics workflow. We compared label-free sample preparation with a 3D matching approach and multiplexed isobaric labeling with improved MS/MS data acquisition. Our study demonstrates the capability of nanoPOTS-based spatial proteomics in mapping the human pancreas at near single-cell resolution, enabling the visualization of protein expression patterns and the discovery of spatially co-expressed proteins in their tissue context.

2023-03-22 | MSV000091531 | MassIVE

Ultra-high sensitivity mass spectrometry quantifies single-cell proteome changes upon perturbation

Project description:Single-cell technologies are revolutionizing biology but are today mainly limited to imaging and deep sequencing. However, proteins are the main drivers of cellular function and in-depth characterization of individual cells by mass spectrometry (MS)-based proteomics would thus be highly valuable and complementary. Here, we develop a robust workflow combining miniaturized sample preparation, very low flow-rate chromatography and a novel trapped ion mobility mass spectrometer, resulting in a more than ten-fold improved sensitivity. We precisely and robustly quantify proteomes and their changes in single, FACS-isolated cells. Arresting cells at defined stages of the cell cycle by drug treatment retrieves expected key regulators. Furthermore, it highlights potential novel ones and allows cell phase prediction. Comparing the variability in more than 430 single-cell proteomes to transcriptome data revealed a stable core proteome despite perturbation, while the transcriptome appears stochastic. Our technology can readily be applied to ultra-high sensitivity analyses of tissue material, posttranslational modifications and small molecule studies from small cell counts to gain unprecedented insights into cellular heterogeneity in health and disease.

2022-02-23 | PXD024043 | Pride

Sci-Space E14 Mouse Embryo Data

Project description:Spatial patterns of gene expression span many scales, and are shaped by both local (e.g. cell-cell interactions) and global (e.g. tissue, organ) context. However, most in situ methods for profiling gene expression either average local contexts or are restricted to limited fields of view. Here we introduce sci-Space, a scale-flexible method that retains single cell resolution while resolving spatial heterogeneity in gene expression at larger scales. As a proof-of-concept, we apply sci-Space to the developing mouse embryo (E14), capturing the approximate spatial coordinates of profiled cells from whole embryo serial sections.

2021-07-01 | GSE166692 | GEO

Multiregional sampling of primary glioblastoma specimens

Project description:Glioblastoma (GBM), the most common malignant tumor originating in the brain remains incurable with few treatment advances despite decades of investigation. Treatment failure is often attributed to intratumoral heterogeneity, which fosters tumor evolution and selection of resistant clones. However, intratumoral heterogeneity and tumor evolution remain poorly understood in GBM as studies are typically based on single tissue biopsies and lack spatial context. Here, we have utilized pre-operative MRI scans and intra-operative 3-D surgical neuronavigation for 10 primary IDH-WT GBM patients to acquire 103 tissue samples that represent maximal tumor diversity and are mapped by 3-D spatial coordinates. We combine insights from deconvolution of GBM transcriptomes and chromatin landscapes with single-cell analysis to assess intratumoral heterogeneity of each program at the cellular level and to distinguish neuronal, glial, and immune programs aberrantly active in tumor cells from their counterparts in normal cells. Collectively, these data provide unprecedented insight into GBM intratumoral heterogeneity and evolution from single-cell to whole-tumor 3D spatial resolution, redefining current understanding and providing a rich resource of targets for therapeutic investigation.

2024-01-18 | GSE226726 | GEO

All-at-once spatial proteome profiling of complex tissue context with single-cell-type resolution by proximity proteomics

Project description:Spatial proteomics enables in-depth mapping of cellular components and tissue architectures, mostly achieved by laser microdissection-mass spectrometry (LMD-MS) and antibody-based imaging. However, the trade-offs among sampling resolution, throughput, and proteome coverage still limit the applicability of these strategies, especially toward deep proteome profiling with high spatial resolution. Here, we propose PSPro (Proximity labeling for Spatial Proteomics) by leveraging precise antibody-targeted biotinylation at three-dimension scale and efficient affinity purification for MS-based cell-type-specific proteome profiling from single tissue slice. With fine-tuned labeling parameters, PSPro achieves all-at-once cell-type proteome capture with sub-micrometer resolution and shows comparable performance to flow cytometry- and LMD-based proteomic workflows. We apply PSPro to fixed tumor and spleen slices, enriching thousands of proteins containing known markers from ten cell types. For tissue microenvironments with high heterogeneity, we further incorporate region-resolved LMD into PSPro to facilitate direct comparison of cell subpopulations from the same tissue slice, revealing spatial proteome heterogeneity of cancer cells and immune cells in pancreatic tumor. Collectively, PSPro converts traditional “antibody-epitope” paradigm to “antibody-cell type proteome” for spatial biology in a user-friendly manner.

2024-02-02 | MSV000093995 | MassIVE

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

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