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27ded41a-6f97-4b96-9552-a172ddc92b34 - samples

ABSTRACT: The dataset includes spatially-resolved and single-cell antigen receptor, as well as gene expression, data from two different HER2+ breast cancer patients. The tumor piece obtained during surgery from each patient was divided into several regions and tissue sections were used for spatial transcriptomics (Visium, 10x genomics). As indicated, some tissue sections were analyzed by a new method (Spatial VDJ) to spatially resolve antigen receptor sequences (target capture), which was developed in our publication. In parallel, tissue pieces from the same tumor were dissociated for single-cell gene expression analysis (10x genomics GEX, VDJ, and feature barcoding/Hash Tag Oligonucleotide). The deposited data is in the form of fastq files. All processed data, metadata, micrographs of the tissue sections (of those used for spatial transcriptomics), and scripts used for the analysis are publicly available at Zenodo (DOI: 10.5281/zenodo.7961605). Final libraries were sequenced on NextSeq2000 (Illumina) or NovaSeq6000 (Illumina) and analyzed with Cell Ranger, Seurat, Space Ranger, and STutility pipelines.

PROVIDER: EGAD00001011061 | EGA |

REPOSITORIES: EGA

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d650622b-999b-4153-bafc-d96f070f8374 - samples

Project description:The dataset includes spatially-resolved gene expression and antigen receptor data from two Tonsil samples (1 and 2). Tissue sections from the tonsil samples were used for spatial transcriptomics (Visium, 10x genomics). Tonsil 2 tissue sections were analyzed by a new method (Spatial VDJ) to spatially resolve antigen receptor sequences (target capture), which was developed in our publication. Nearby or adjacent tissue sections (from Tonsil2) were also analyzed by a bulk antigen receptor sequencing approach (amplicon sequencing), by a method also newly developed by us in the same publication (Bulk SS3 VDJ). For Visium, the data were anonymized (all SNPs removed) using Bamboozle (Ziegenhain and Sandberg, Nature Communications 2021). The deposited data is in the form of fastq files. All remaining data, metadata, micrographs of the tissue sections (of those used for spatial transcriptomics), and scripts used for the analysis are available at Zenodo (DOI: 10.5281/zenodo.7961605). Final libraries were sequenced on NextSeq2000 (Illumina) or NovaSeq6000 (Illumina) and analyzed with Seurat, Space Ranger, and STutility pipelines.

| EGAD00001011062 | EGA

Tonsil Sequencing dataset

Project description:Human tonsil tissue sequencing datasets, which include spatial antigen receptor repertoire profiling and spatial transcriptomics (Visium). This study includes two samples.

| EGAS00001007337 | EGA

A spatial multi-omics atlas of the human lung reveals a novel immune cell survival niche - Visium

Project description:Multiple distinct cell types of the human lung and airways have been defined by single cell RNA sequencing (scRNAseq). Here we present a multi-omics spatial lung atlas to define novel cell types which we map back into the macro- and micro-anatomical tissue context to define functional tissue microenvironments. Firstly, we have generated single cell and nuclei RNA sequencing, VDJ-sequencing and Visium Spatial Transcriptomics data sets from 5 different locations of the human lung and airways. Secondly, we define additional cell types/states, as well as spatially map novel and known human airway cell types, such as adult lung chondrocytes, submucosal gland (SMG) duct cells, distinct pericyte and smooth muscle subtypes, immune-recruiting fibroblasts, peribronchial and perichondrial fibroblasts, peripheral nerve associated fibroblasts and Schwann cells. Finally, we define a survival niche for IgA-secreting plasma cells at the SMG, comprising the newly defined epithelial SMG-Duct cells, and B and T lineage immune cells. Using our transcriptomic data for cell-cell interaction analysis, we propose a signalling circuit that establishes and supports this niche. Overall, we provide a transcriptional and spatial lung atlas with multiple novel cell types that allows for the study of specific tissue microenvironments such as the newly defined gland-associated lymphoid niche (GALN).

2022-01-06 | E-MTAB-11640 | biostudies-arrayexpress

Nova-ST: Nano-Patterned Ultra-Dense platform for spatial transcriptomics [Stereo-Seq]

Project description:Spatial transcriptomics workflows using barcoded capture arrays are commonly used for resolving gene expression in tissues. However, existing techniques are either limited by capture array density or are cost prohibitive for large scale atlasing. We present Nova-ST, a dense nano-patterned spatial transcriptomics technique derived from randomly barcoded Illumina sequencing flow cells. Nova-ST enables customized, low cost, flexible, and high-resolution spatial profiling of large tissue sections. Benchmarking on mouse brain sections demonstrates significantly higher sensitivity compared to existing methods, at reduced cost.

2024-02-22 | GSE256319 | GEO

Nova-ST: Nano-Patterned Ultra-Dense platform for spatial transcriptomics [Nova-ST]

2024-02-22 | GSE256318 | GEO

Genome-wide Spatial Expression Profiling in Formalin-fixed Tissues

Project description:Method to perform spatial analysis of mRNA in FFPE and PFA fixed tissue sections using spatially barcoded slides and based on oligo(dT) mRNA capture.

2021-10-15 | GSE185715 | GEO

High-density spatial transcriptomics arrays for in situ tissue profiling

Project description:Tissue function relies on the precise spatial organization of cells characterized by distinct molecular profiles. Single-cell RNA-Seq captures molecular profiles but not spatial organization. Conversely, spatial profiling assays to date have lacked global transcriptome information, throughput or single-cell resolution. Here, we develop High-Density Spatial Transcriptomics (HDST), a method for RNA-Seq at high spatial resolution. Spatially barcoded reverse transcription oligonucleotides are coupled to beads that are randomly deposited into tightly packed individual microsized wells on a slide. The position of each bead is decoded with sequential hybridization using complementary oligonucleotides providing a unique bead-specific spatial address. We then capture, and spatially in situ barcode, RNA from the histological tissue sections placed on the HDST array. HDST recovers hundreds of thousands of transcript-coupled spatial barcodes per experiment at 2 μm resolution. We demonstrate HDST in the mouse brain, use it to resolve spatial expression patterns and cell types, and show how to combine it with histological stains to relate expression patterns to tissue architecture and anatomy. HDST opens the way to spatial analysis of tissues at high resolution.

2019-09-09 | GSE130682 | GEO

Spatial Transcriptomics correlated Electron Microscopy [scRNA-Seq]

Project description:Current spatial transcriptomics methods identify cell types and states in a spatial context but lack morphological information. Electron microscopy, in contrast, provides structural details at nanometer resolution without decoding the diverse cellular states and identity. STEM address this limitation by correlating multiplexed error-robust FISH with electron microscopy from adjacent tissue sections. Using STEM to characterize demyelinated lesions in mice, we were able to bridge spatially resolved transcriptional data with morphological information on cell identities. This approach allowed us to link the morphology of foamy microglia and interferon-response microglia with their transcriptional signatures.

2023-06-21 | GSE202636 | GEO

Spatially resolved transcriptomic profiling of degraded and challenging fresh frozen samples

Project description:Spatially resolved transcriptomics has enabled precise genome-wide mRNA expression profiling within tissue sections. The performance of unbiased SRT methods targeting the polyA tail of mRNA, relies on the availability of specimens with high RNA quality. Moreover, the high cost of currently available SRT assays requires a careful sample screening process to increase the chance of obtaining high-quality data. Indeed, the upfront analysis of RNA quality can show considerable variability due to sample handling, storage, and/or intrinsic factors. We present RNA-Rescue Spatial Transcriptomics (RRST), an SRT workflow designed to improve mRNA recovery from fresh frozen specimens with moderate to low RNA quality. First, we provide a benchmark of RRST against the standard Visium spatial gene expression protocol on high RNA quality samples represented by mouse brain and prostate cancer samples. Then, we demonstrate the RRST protocol on tissue sections collected from five challenging tissue types, including: human lung, colon, small intestine, pediatric brain tumor, and mouse bone/cartilage. In total, we analyzed 52 tissue sections and our results demonstrate that RRST is a versatile, powerful, and reproducible protocol for FF specimens of different qualities and origins.

2022-12-25 | GSE221571 | GEO

Spatial profiling of chromatin accessibility in mouse and human tissues

Project description:We present spatially resolved chromatin accessibility profiling of tissue sections via next-generation sequencing by combining in situ ATAC-seq chemistry and microfluidic deterministic barcoding. Mouse and human tissues were profiled by spatial-ATAC-seq.

2022-05-20 | GSE171943 | GEO

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