Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Spatially resolved m6A profiling using m6A-ARTR-DBiT [spatial]

ABSTRACT: N6-methyladenosine (m6A) on RNA plays diverse regulatory roles, yet its spatial distribution within tissues remains largely unexplored. Here we introduce m6A-ARTR-DBiT, a spatial m6A profiling assay that leverages reverse-transcription-based detection and deterministic barcoding in tissue to map transcriptome-wide m6A distribution while preserving native tissue context. Applying m6A-ARTR-DBiT to mouse embryonic tissues and adult brains generates spatially resolved m6A landscapes and reveals region-associated m6A features across different functional domains. Pairwise comparison of spatial m6A profiles with spatial transcriptomes uncovers positive correlations between m6A levels and the expression of its methyltransferases and binding proteins, which also enables systematic identification of tissue-region-specific epitranscriptomic regulation. In the mouse hippocampus, m6A-ARTR-DBiT allows for high-resolution mapping of m6A organization within fine-scale tissue structures. Together, m6A-ARTR-DBiT provides a platform for interrogating RNA modification distribution within intact tissue sections, offering insights into the link between spatially patterned m6A deposition and gene regulation.

ORGANISM(S): Mus musculus

PROVIDER: GSE285304 | GEO | 2026/01/14

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Spatially resolved m6A profiling using m6A-ARTR-DBiT

Project description:N6-methyladenosine (m6A) on RNA plays diverse regulatory roles, yet its spatial distribution within tissues remains largely unexplored. Here we introduce m6A-ARTR-DBiT, a spatial m6A profiling assay that leverages reverse-transcription-based detection and deterministic barcoding in tissue to map transcriptome-wide m6A distribution while preserving native tissue context. Applying m6A-ARTR-DBiT to mouse embryonic tissues and adult brains generates spatially resolved m6A landscapes and reveals region-associated m6A features across different functional domains. Pairwise comparison of spatial m6A profiles with spatial transcriptomes uncovers positive correlations between m6A levels and the expression of its methyltransferases and binding proteins, which also enables systematic identification of tissue-region-specific epitranscriptomic regulation. In the mouse hippocampus, m6A-ARTR-DBiT allows for high-resolution mapping of m6A organization within fine-scale tissue structures. Together, m6A-ARTR-DBiT provides a platform for interrogating RNA modification distribution within intact tissue sections, offering insights into the link between spatially patterned m6A deposition and gene regulation.

2026-01-14 | GSE285303 | GEO

Spatially resolved m6A profiling using m6A-ARTR-DBiT [spatial]

Project description:Spatially resolved m6A profiling using m6A-ARTR-DBiT [spatial]

| PRJNA1202196 | ENA

Spatially resolved m6A profiling using m6A-ARTR-DBiT

Project description:Spatially resolved m6A profiling using m6A-ARTR-DBiT

| PRJNA1202190 | ENA

Spatially Resolved Panoramic in vivo CRISPR Screen via Perturb-DBiT

Project description:Spatially resolved in vivo CRISPR screening integrates gene editing with spatial transcriptomics to examine how genetic perturbations alter gene expression within native tissue environments. However, current methods are mostly for small perturbation panels and the detection of a subset of protein-coding RNAs. We present Perturb-DBiT, a versatile approach for the simultaneous co-sequencing of spatial total RNA whole-transcriptome and single-guide RNAs (sgRNAs), base-by-base, on the same tissue section. This method enables unbiased discovery of how genetic perturbations influence RNA regulation, cellular dynamics, and tissue architecture in situ. Applying Perturb-DBiT to a human cancer metastatic colonization model, we mapped large panels of sgRNAs across tumor colonies in multiple consecutive tissue sections alongside their corresponding total RNA transcriptomes, providing insights into how perturbations affect long non-coding RNA (lncRNA) co-variation, microRNA–mRNA interactions, and distinct amino acid-specific tRNA alterations linked to tumor migration and growth. By integrating transcriptional pseudotime trajectories, we further observed the impact of perturbations on clonal dynamics and cooperation. In an immune-competent syngeneic mouse model, Perturb-DBiT enabled investigation of genetic perturbations within the tumor immune microenvironment, potentially revealing distinct and synergistic effects on immune infiltration and suppression. Perturb-DBiT provides a spatially resolved comprehensive view of how genetic knockouts influence diverse molecular and cellular responses including small and large RNA regulation, tumor proliferation, migration, metastasis, and immune interactions, offering a panoramic perspective on perturbation responses in complex tissues.

2026-03-17 | GSE319123 | GEO

Spatially Resolved Panoramic in vivo CRISPR Screen via Perturb-DBiT [Spatial Transcriptomics]

2026-03-17 | GSE319277 | GEO

Spatially Resolved Panoramic in vivo CRISPR Screen via Perturb-DBiT [Spatial Transcriptomics]

Project description:Spatially Resolved Panoramic in vivo CRISPR Screen via Perturb-DBiT [Spatial Transcriptomics]

| PRJNA1422738 | ENA

Spatial transcriptome of FFPE sample by DBiT-seq

Project description:Spatial transcriptome of FFPE sample by DBiT-seq

| PRJNA659282 | ENA

Spatially Resolved Panoramic in vivo CRISPR Screen via Perturb-DBiT

Project description:Spatially Resolved Panoramic in vivo CRISPR Screen via Perturb-DBiT

| PRJNA1422140 | ENA

Spatially exploring RNA biology in archival formalin-fixed paraffin-embedded tissues

Project description:The capability to spatially explore RNA biology in formalin-fixed paraffin-embedded (FFPE) tissues holds transformative potential for histopathology research. Here, we present pathology-compatible deterministic barcoding in tissue (Patho-DBiT) by combining in situ polyadenylation and computational innovation for spatial whole transcriptome sequencing, tailored to probe the diverse RNA species in clinically archived FFPE samples. It permits spatial co-profiling of gene expression and RNA processing, unveiling region-specific splicing isoforms, and high-sensitivity transcriptomic mapping of clinical tumor FFPE tissues stored for five years. Furthermore, genome-wide single nucleotide RNA variants can be captured to distinguish malignant subclones from non-malignant cells in human lymphomas. Patho-DBiT also maps microRNA regulatory networks and RNA splicing dynamics, decoding their roles in spatial tumorigenesis. Cellular-level Patho-DBiT dissects the spatiotemporal cellular dynamics driving tumor clonal architecture and progression. Patho-DBiT stands poised as a valuable platform to unravel rich RNA biology in FFPE tissues to aid in clinical pathology evaluation.

2024-08-12 | GSE274641 | GEO

Spatial epigenome-transcriptome co-profiling of mammalian tissues [Human RNA-Seq]

Project description:We present spatially resolved high-spatial-resolution genome-wide co-mapping of epigenome and transcriptome by simultaneously profiling of chromatin accessibility and gene expression (spatial-ATAC-RNA-seq), as well as histone modification and gene expression (spatial-CUT&Tag-RNA-seq) on the same tissue section at cellular level by combining the microfluidic deterministic barcoding strategy in DBiT-seq and the chemistry used in ATAC-seq/CUT&Tag.

2023-01-29 | GSE205181 | GEO

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