Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Spatial transcriptomics and single-nucleus RNA-sequencing reveal a transcriptomic atlas of human spinal cord

ABSTRACT: Spatial transcriptomics and single-nucleus RNA-sequencing reveal a transcriptomic atlas of human spinal cord

PROVIDER: PRJNA1016323 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR26055300_1.fastq.gz	Fastqsanger.gz
	SRR26055300_2.fastq.gz	Fastqsanger.gz
	SRR26055301_1.fastq.gz	Fastqsanger.gz
	SRR26055301_2.fastq.gz	Fastqsanger.gz
	SRR26055302_1.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 125

Similar Datasets

Spatial transcriptomics and single-nucleus RNA-sequencing reveal a transcriptomic atlas of human spinal cord [Spinal spatial]

Project description:Despite the recognized importance of the spinal cord in sensory processing, motor behaviors, and neural diseases, the underlying organization of neuronal clusters and their spatial location remain elusive. Recently, several studies have attempted to define the neuronal types and functional heterogeneity in the spinal cord using single-cell or single-nucleus RNA sequencing in animal models or developing humans. However, molecular evidence of cellular heterogeneity in the adult human spinal cord is limited. Here, we classified spinal cord neurons into 21 subclusters and determined their distribution from nine human donors using single-nucleus RNA sequencing and spatial transcriptomics. Moreover, we compared the human findings with previously published single-nucleus data of the mouse adult spinal cord, which revealed an overall similarity in the neuronal composition of the spinal cord between the two species while simultaneously highlighting some degree of heterogeneity. Additionally, we examined the sex differences in the spinal neuronal subclusters. Several genes, such as SCN10A and HCN1, showed sex differences in motor neurons. Finally, we classified human DRG neurons using spatial transcriptomics and explored the putative interactions between DRG and spinal cord neuronal subclusters. In summary, these results illustrate the complexity and diversity of spinal neurons in humans and provide an important resource for future research to explore the molecular mechanisms underlying spinal cord physiology and diseases.

2024-01-11 | GSE243074 | GEO

Spatial transcriptomics and single-nucleus RNA-sequencing reveal a transcriptomic atlas of human spinal cord [DRG Spatial]

2024-01-11 | GSE243073 | GEO

Spatial transcriptomics and single-nucleus RNA-sequencing reveal a transcriptomic atlas of human spinal cord [Spinal snRNA-seq]

2024-01-11 | GSE243076 | GEO

Single-nucleus transcriptomic atlas of spinal cord neuron in human

Project description:Despite the recognized importance of the spinal cord in sensory processing, motor behaviors, and/or neural diseases, the underlying organization of neuronal clusters remain elusive. Recently, several studies have attempted to define the neuronal types and functional heterogeneity in the spinal cord using single-cell and/or single-nucleus RNA-sequencing in various animal models. However, molecular evidence of neuronal heterogeneity in the human spinal cord has not yet been established. Here, we sought to classify spinal cord neurons from human donors using high-throughput single-nucleus RNA-sequencing. The functional heterogeneity among the identified cell types and signaling pathways that connect neuronal subtypes were explored. Moreover, we compared the transcriptional patterns obtained in human samples with previously published single-cell transcriptomic profiles of the mouse spinal cord. As a result, we generated the first comprehensive transcriptomic atlas of human spinal cord neurons and defined 18 neuronal clusters. In addition to identifying new and functionally distinct neuronal subtypes, our results also provide novel marker genes for previously described neuronal types. The comparison with mouse transcriptomic profiles revealed an overall similarity in the cellular composition of the spinal cord between the two species, while simultaneously highlighting some degree of heterogeneity. In summary, these results illustrate the complexity and diversity of neuronal types in the human spinal cord and provide an important resource for future research to explore the molecular mechanisms underlying spinal cord physiology and diseases.

2022-11-07 | GSE188255 | GEO

Spatial transcriptomics and single-nucleus RNA-sequencing reveal a transcriptomic atlas of human spinal cord [Spinal spatial]

Project description:Spatial transcriptomics and single-nucleus RNA-sequencing reveal a transcriptomic atlas of human spinal cord [Spinal spatial]

| PRJNA1016330 | ENA

Single-nucleus transcriptomic atlas of spinal cord neuron in human

Project description:Single-nucleus transcriptomic atlas of spinal cord neuron in human

| PRJNA778071 | ENA

Spatial transcriptomics and single-nucleus RNA-sequencing reveal a transcriptomic atlas of human spinal cord [Spinal snRNA-seq]

Project description:Spatial transcriptomics and single-nucleus RNA-sequencing reveal a transcriptomic atlas of human spinal cord [Spinal snRNA-seq]

| PRJNA1016336 | ENA

Spatial transcriptomics and single-nucleus RNA-sequencing reveal a transcriptomic atlas of human spinal cord [DRG Spatial]

Project description:Spatial transcriptomics and single-nucleus RNA-sequencing reveal a transcriptomic atlas of human spinal cord [DRG Spatial]

| PRJNA1016331 | ENA

A harmonized atlas of spinal cord cell types and their computational classification

Project description:Single cell sequencing is transforming many fields of science but the vast amount of data it creates has the potential to both illuminate and obscure underlying biology. To harness the exciting potential of single cell data for the study of the mouse spinal cord, we have created a harmonized atlas of spinal cord transcriptomic cell types that unifies six independent and disparate studies into one common analysis. With the power of this large and diverse dataset, we reveal spinal cord cell type organization, validate a combinatorial set of markers for in-tissue spatial gene expression analysis, and optimize the computational classification of spinal cord cell types based on transcriptomic data. This work provides a comprehensive resource with unprecedented resolution of spinal cord cell types and charts a path forward for how to utilize transcriptomic data to expand our knowledge of spinal cord biology.

2020-12-31 | GSE158380 | GEO

Single cell atlas of spinal cord injury in mice reveals a pro-regenerative signature in spinocerebellar neurons

Project description:After spinal cord injury, tissue distal to the lesion contains undamaged cells that could support or augment recovery. Targeting these cells requires a clearer understanding of their injury responses and capacity for repair. Here, we use single nucleus RNA sequencing to profile how each cell type in the lumbar spinal cord changes after a thoracic injury in mice. We present an atlas of these dynamic responses across dozens of cell types in the acute, subacute, and chronically injured spinal cord. Using this resource, we find rare spinal neurons that express a signature of regeneration in response to injury, including a major population that represent spinocerebellar projection neurons. We characterize these cells anatomically and observed axonal sparing, outgrowth, and remodeling in the spinal cord and cerebellum. Together, this work provides a key resource for studying cellular responses to injury and uncovers the spontaneous plasticity of spinocerebellar neurons, uncovering a potential candidate for targeted therapy.

2022-08-01 | GSE172167 | 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