Slide-tags: scalable, single-nucleus barcoding for multi-modal spatial genomics
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1022468 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR26236606.fastq.gz | Fastqsanger.gz | |||
| SRR26236607.fastq.gz | Fastqsanger.gz | |||
| SRR26236608.fastq.gz | Fastqsanger.gz | |||
| SRR26236609.fastq.gz | Fastqsanger.gz | |||
| SRR26236610.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 5 of 6 |
Similar Datasets
Project description:Recent technological innovations have enabled the high-throughput quantification of gene expression and epigenetic regulation within individual cells, transforming our understanding of how complex tissues are constructed. Missing from these measurements, however, is the ability to routinely and easily spatially localise these profiled cells. We developed a strategy, Slide-tags, in which single nuclei within an intact tissue section are ‘tagged’ with spatial barcode oligonucleotides derived from DNA-barcoded beads with known positions. These tagged nuclei can then be used as input into a wide variety of single-nucleus profiling assays. We used Slide-tags to profile two different stages of development in the mouse brain.
2023-10-03 | GSE244355 | GEO
Project description:Single-cell multiomic techniques have sparked immense interest in developing a comprehensive multi-modal map of diverse neuronal cell types and their brain-wide projections. However, investigating the complex wiring diagram, spatial organization, transcriptional, and epigenetic landscapes of brain-wide projection neurons is hampered by the lack of efficient and easily adoptable tools. Here we introduce Projection-TAGs, a retrograde AAV platform that allows multiplex tagging of projection neurons using RNA barcodes. By using Projection-TAGs, we performed multiplex projection tracing of the mouse cortex and high-throughput single-cell profiling of the transcriptional and epigenetic landscapes of the cortical projection neurons. Projection-TAGs can be leveraged to obtain a snapshot of activity-dependent recruitment of distinct projection neurons and their molecular features in the context of a specific stimulus. Given its flexibility, usability, and compatibility, we envision that Projection-TAGs can be readily applied to build a comprehensive multi-modal map of brain neuronal cell types and their projections.
2025-04-01 | GSE277718 | GEO
Project description:Projection-TAGs enable multiplex projection tracing and multi-modal profiling of projection neurons
| PRJNA1163431 | ENA
Project description:Recent technological innovations have enabled the high-throughput quantification of gene expression and epigenetic regulation within individual cells, transforming our understanding of how complex tissues are constructed1-6. However, missing from these measurements is the ability to routinely and easily spatially localize these profiled cells. We developed a strategy, Slide-tags, in which single nuclei within an intact tissue section are tagged with spatial barcode oligonucleotides derived from DNA-barcoded beads with known positions. These tagged nuclei can then be used as an input into a wide variety of single-nucleus profiling assays. Application of Slide-tags to the mouse hippocampus positioned nuclei at less than 10 μm spatial resolution and delivered whole-transcriptome data that are indistinguishable in quality from ordinary single-nucleus RNA-sequencing data. To demonstrate that Slide-tags can be applied to a wide variety of human tissues, we performed the assay on brain, tonsil and melanoma. We revealed cell-type-specific spatially varying gene expression across cortical layers and spatially contextualized receptor-ligand interactions driving B cell maturation in lymphoid tissue. A major benefit of Slide-tags is that it is easily adaptable to almost any single-cell measurement technology. As a proof of principle, we performed multiomic measurements of open chromatin, RNA and T cell receptor (TCR) sequences in the same cells from metastatic melanoma, identifying transcription factor motifs driving cancer cell state transitions in spatially distinct microenvironments. Slide-tags offers a universal platform for importing the compendium of established single-cell measurements into the spatial genomics repertoire.
| S-EPMC10764288 | biostudies-literature
Project description:[Oryza sativa Genome Oligo Set Version 1.0 was designed by Beijing Genomics Institute (BGI) and contain 60,727 70mer oligos representing both indica and japonica genomes. All oligos were designed from cDNAs, expreseed sequence tag (EST) sequences, predicted genes of BGI rice genome build and other public resources. Mapping to TIGR rice genome pseudommolecues release 2 is based on the BLAST results, if a oligo has greater than 97% identity to a gene sequence from TIGR pseudomolecules, the oligo represents that gene. Rice 30K Operon Version 1.0 -70 mer oligos. The array is in 48 pin conformation. It has 25 columns and 25 rows in each of 4 x 12 subarrays. This is the slideA of the two slide sets. Platform_catalog_number: XDEN-A Platform_coating: poly L lysine Platform_manufacture_protocol: Omnigrid 100 contact printer; TeleChem Stealth SMP3 split pins; See http://keck.med.yale.edu/dnaarrays/printing Platform_manufacturer: Keck Biotechnology Resource Lab at Yale Platform_support: glass slide Platform_technology: spotted oligonucleotide ] Series_sample_order: Sample 1-15 Slide A; Sample 16-30 Slide B Keywords: other
2005-05-24 | GSE2691 | GEO
Project description:[Oryza sativa Genome Oligo Set Version 1.0 was designed by Beijing Genomics Institute (BGI) and contain 60,727 70mer oligos representing both indica and japonica genomes. All oligos were designed from cDNAs, expreseed sequence tag (EST) sequences, predicted genes of BGI rice genome build and other public resources. Mapping to TIGR rice genome pseudommolecues release 2 is based on the BLAST results, if a oligo has greater than 97% identity to a gene sequence from TIGR pseudomolecules, the oligo represents that gene. Rice 30K Operon Version 1.0 -70 mer oligos. The array is in 48 pin conformation. It has 25 columns and 25 rows in each of 4 x 12 subarrays. This is the slideA of the two slide sets. Platform_catalog_number: XDEN-A Platform_coating: poly L lysine Platform_manufacture_protocol: Omnigrid 100 contact printer; TeleChem Stealth SMP3 split pins; See http://keck.med.yale.edu/dnaarrays/printing Platform_manufacturer: Keck Biotechnology Resource Lab at Yale Platform_support: glass slide Platform_technology: spotted oligonucleotide ] Series_sample_order: Sample 1-15 Slide A; Sample 16-30 Slide B
2010-06-10 | E-GEOD-2691 | biostudies-arrayexpress
Project description:The three-dimensional (3D) organization of cis-regulatory elements (CREs) plays a central role in transcription control. However, capturing transcriptome, epigenome, and 3D genome from the same single cells remains technically challenging. Here, we present scHiCAR (single-cell Hi-C with ATAC and RNA-seq), a combinatorial barcoding-based method that simultaneously profiles mRNA, open chromatin, and chromosome-conformation-capture from the same cells. Compared to existing single-cell 3D genome methods, scHiCAR more efficiently enriches long-range cis-interactions anchored at candidate CREs (cCREs). Applied to 1.62 million mouse brain cells and complemented with a deep learning-based loop caller, scHiCAR accurately defines cell-type-specific transcriptomes, accessible cCREs, and 5kb-resolution enhancer-promoter pairs across 22 brain cell types. scHiCAR also performs robustly in challenging tissues such as skeletal muscle, enabling tri-modal single-cell level analysis of gene regulation dynamics during muscle stem cell regeneration. scHiCAR offers a scalable, efficient, and cost-effective platform for studying gene regulatory landscapes in complex tissues at single-cell resolution.
2025-08-30 | GSE305439 | GEO
Project description:The three-dimensional (3D) organization of cis-regulatory elements (CREs) plays a central role in transcription control. However, capturing transcriptome, epigenome, and 3D genome from the same single cells remains technically challenging. Here, we present scHiCAR (single-cell Hi-C with ATAC and RNA-seq), a combinatorial barcoding-based method that simultaneously profiles mRNA, open chromatin, and chromosome-conformation-capture from the same cells. Compared to existing single-cell 3D genome methods, scHiCAR more efficiently enriches long-range cis-interactions anchored at candidate CREs (cCREs). Applied to 1.62 million mouse brain cells and complemented with a deep learning-based loop caller, scHiCAR accurately defines cell-type-specific transcriptomes, accessible cCREs, and 5kb-resolution enhancer-promoter pairs across 22 brain cell types. scHiCAR also performs robustly in challenging tissues such as skeletal muscle, enabling tri-modal single-cell level analysis of gene regulation dynamics during muscle stem cell regeneration. scHiCAR offers a scalable, efficient, and cost-effective platform for studying gene regulatory landscapes in complex tissues at single-cell resolution.
2025-08-30 | GSE304674 | GEO
Project description:The three-dimensional (3D) organization of cis-regulatory elements (CREs) plays a central role in transcription control. However, capturing transcriptome, epigenome, and 3D genome from the same single cells remains technically challenging. Here, we present scHiCAR (single-cell Hi-C with ATAC and RNA-seq), a combinatorial barcoding-based method that simultaneously profiles mRNA, open chromatin, and chromosome-conformation-capture from the same cells. Compared to existing single-cell 3D genome methods, scHiCAR more efficiently enriches long-range cis-interactions anchored at candidate CREs (cCREs). Applied to 1.62 million mouse brain cells and complemented with a deep learning-based loop caller, scHiCAR accurately defines cell-type-specific transcriptomes, accessible cCREs, and 5kb-resolution enhancer-promoter pairs across 22 brain cell types. scHiCAR also performs robustly in challenging tissues such as skeletal muscle, enabling tri-modal single-cell level analysis of gene regulation dynamics during muscle stem cell regeneration. scHiCAR offers a scalable, efficient, and cost-effective platform for studying gene regulatory landscapes in complex tissues at single-cell resolution.
2025-08-30 | GSE267126 | GEO
Project description:The three-dimensional (3D) organization of cis-regulatory elements (CREs) plays a central role in transcription control. However, capturing transcriptome, epigenome, and 3D genome from the same single cells remains technically challenging. Here, we present scHiCAR (single-cell Hi-C with ATAC and RNA-seq), a combinatorial barcoding-based method that simultaneously profiles mRNA, open chromatin, and chromosome-conformation-capture from the same cells. Compared to existing single-cell 3D genome methods, scHiCAR more efficiently enriches long-range cis-interactions anchored at candidate CREs (cCREs). Applied to 1.62 million mouse brain cells and complemented with a deep learning-based loop caller, scHiCAR accurately defines cell-type-specific transcriptomes, accessible cCREs, and 5kb-resolution enhancer-promoter pairs across 22 brain cell types. scHiCAR also performs robustly in challenging tissues such as skeletal muscle, enabling tri-modal single-cell level analysis of gene regulation dynamics during muscle stem cell regeneration. scHiCAR offers a scalable, efficient, and cost-effective platform for studying gene regulatory landscapes in complex tissues at single-cell resolution.
2025-08-30 | GSE267118 | GEO