Open-ST: High-resolution spatial transcriptomics in 3D
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ABSTRACT:
PROVIDER: PRJNA1055947 | ENA |
REPOSITORIES: ENA
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Project description:Spatial transcriptomics (ST) methods unlock molecular mechanisms underlying tissue development, homeostasis, or disease. However, there is a need for easy-to-use, high-resolution, cost-efficient, and 3D-scalable methods. Here, we report Open-ST, a sequencing-based, open-source experimental and computational resource to address these challenges and to study the molecular organization of tissues in 2D and 3D. In mouse brain, Open-ST captured transcripts at subcellular resolution and reconstructed cell types. In primary head-and-neck tumors and patient-matched healthy/metastatic lymph nodes, Open-ST captured the diversity of immune, stromal, and tumor populations in space, validated by imaging-based ST. Distinct cell states were organized around cell-cell communication hotspots in the tumor but not the metastasis. Strikingly, the 3D reconstruction and multimodal analysis of the metastatic lymph node revealed spatially contiguous structures not visible in 2D and potential biomarkers precisely at the 3D tumor/lymph node boundary. All protocols and software are available at https://rajewsky-lab.github.io/openst.
2024-06-23 | GSE251926 | GEO
Project description:Spatial transcriptomics (ST) methods unlock molecular mechanisms underlying tissue development, homeostasis, or disease. However, there is a need for easy-to-use, high-resolution, cost-efficient, and 3D-scalable methods. Here, we report Open-ST, a sequencing-based, open-source experimental and computational resource to address these challenges and to study the molecular organization of tissues in 2D and 3D. In mouse brain, Open-ST captured transcripts at subcellular resolution and reconstructed cell types. In primary head-and-neck tumors and patient-matched healthy/metastatic lymph nodes, Open-ST captured the diversity of immune, stromal, and tumor populations in space, validated by imaging-based ST. Distinct cell states were organized around cell-cell communication hotspots in the tumor but not the metastasis. Strikingly, the 3D reconstruction and multimodal analysis of the metastatic lymph node revealed spatially contiguous structures not visible in 2D and potential biomarkers precisely at the 3D tumor/lymph node boundary. All protocols and software are available at https://rajewsky-lab.github.io/openst.
2024-06-23 | GSE263498 | GEO
Project description:Open-ST: High-resolution spatial transcriptomics in 3D [xenium]
| PRJNA1097649 | ENA
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 | GSE256318 | GEO
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
Project description:Nova-ST: Nano-Patterned Ultra-Dense platform for spatial transcriptomics [Nova-ST]
| PRJNA1079222 | ENA
Project description:Spatial-resolution functional proteomic profiling analysis of clinical colorectal patients
2023-11-01 | PXD046567 |
Project description:The function of mammalian cells is largely influenced by their tissue microenvironment and by inter- celllular interactions. Advances in spatial transcriptomics open the way for studying these important determinants of cellular function, by enabling a transcriptome wide evaluation of gene expression in-situ. A critical limitation of the current technologies, however, is that their resolution is limited to regions (spots) of sizes well beyond that of a single cell, thus providing measurements for cell-aggregates which may mask critical interactions between neighboring cells of different types. While joint analysis with single cell RNA-sequencing (scRNA-seq) can be leveraged to alleviate this problem, current analyzes are limited to a discrete view of cell type proportion inside every spot. This limitation becomes critical in the common case where, even within a cell type, there is a continuum of cell states, which can not be clearly demarcated and may reflect important differences in the cells’ function and interaction with their surrounding. To address this, we developed Deconvolution of Spatial Transcriptomics profiles using Variational Inference (DestVI), a probabilistic method for multi- resolution analysis for spatial transcriptomics, which explicitly models continuous variation within cell types. Using simulations, we demonstrate that DestVI is capable of providing higher resolution compared to the existing methods, and that it is also the first method to enable an estimate of gene expression by every cell type inside every spot. We then introduce an automated pipeline for analysis of a tissue, as well as comparison between tissues. We apply DestVI and this pipeline to study the response of lymph nodes to a pathogen and to explore the spatial organization of a mouse tumor model. In both cases, we demonstrate that DestVI can provide a reliable view of the organization of these tissues, and that it is capable of identifying important cell-type specific changes in gene expression - between different tissue regions or between conditions. DestVI is available as an open-source software in the scvi-tools codebase (https://scvi-tools.org).
2021-05-05 | GSE173776 | GEO
Project description:We designed a neural network-based computational method that learns transcriptome-to-space mapping and reconstructs 3D tissue organization by learning from scRNA-seq and spatial transcriptomic data.
2023-08-08 | GSE220572 | GEO
Project description:Spatial genome organization is essential to direct fundamental DNA-templated biological processes (e.g. transcription, replication, and repair), but the 3D in situ nanometer-scale structure of accessible cis-regulatory DNA elements within the crowded nuclear environment remains elusive. Here, we combined the recently developed Assay for Transposase-Accessible Chromatin with visualization (ATAC-see), PALM super-resolution imaging and lattice light-sheet microscope (a method termed 3D ATAC-PALM) to selectively image and quantitatively analyze key features of the 3D accessible genome in single cells. 3D ATAC-PALM reveals that accessible chromatin are non-homogeneously organized into spatially segregated clusters or accessible chromatin domains (ACDs). To directly link imaging and genomic data, we optimized multiplexed imaging of 3D ATAC-PALM with Oligopaint DNA-FISH, RNA-FISH and protein fluorescence. We found that ACDs colocalize with active chromatin and enclose transcribed genes. By applying these methods to analyze genetically purterbed cells, we demonstrated that genome architectural protein CTCF prevents excessive clustering of accessible chromatin and decompacts ACDs. These results highlight the 3D ATAC-PALM as a useful tool to probe the structure and organizing mechanism of the genome.
2020-01-29 | GSE126112 | GEO