Integrated Spatial Transcriptomic and Proteomic Analysis of Fresh Frozen Tissue Based on Stereo-seq

Liao, Sha; Heng, Yang; Liu, Weiqing; Xiang, Jinqiong; Ma, Yan; Li-gen, Chen; Xu, Feng; Jia, Dongmei; Liang, Diyan; Huang, Chongan; Zhang, Jiajun; Jian, Min; Su, Kui; Li, Mei; Loh, Yuin-Han; Chen, Ao; Xu, Xun

doi:10.1101/2023.04.28.538364

Cited by 12 publications

(9 citation statements)

References 33 publications

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“…Stereo-CITE-seq spatial multi-omics by Liao et al 6 . The acquired data consisted of 4,697 spots with 23,622 genes and 51 proteins.…”

Section: Stereo-cite-seq Mouse Thymus Dataset Murine Thymus Tissue Sa...mentioning

confidence: 99%

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Deciphering spatial domains from spatial multi-omics with SpatialGlue

Long

Ang

Liao

et al. 2023

Preprint

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Integration of multiple data modalities in a spatially informed manner remains an unmet need for exploiting spatial multi-omics data. Here, we introduce SpatialGlue, a novel graph neural network with dual-attention mechanism, to decipher spatial domains by capturing the significance of each modality and neighbor graph in cross-omics and intra-omics integration. We demonstrate that SpatialGlue can accurately aggregate cell types into spatial domains at a higher resolution across different tissue types and technology platforms, as well as gain biological insights into cross-modality spatial correlations.

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“…Stereo-CITE-seq spatial multi-omics by Liao et al 6 . The acquired data consisted of 4,697 spots with 23,622 genes and 51 proteins.…”

Section: Stereo-cite-seq Mouse Thymus Dataset Murine Thymus Tissue Sa...mentioning

confidence: 99%

“…We next tested SpatialGlue on a mouse thymus dataset acquired with Stereo-CITE-seq spatial multi-omics 6 that measures mRNA and protein at sub-cellular resolution. The thymus is a small gland surrounded by a capsule of fibers and collagen (Figure 2a, b).…”

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confidence: 99%

Deciphering spatial domains from spatial multi-omics with SpatialGlue

Long

Ang

Liao

et al. 2023

Preprint

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“…Hematoxylin-eosin 4 / 18 (H&E) and single strand DNA fluorescence (ssDNA) nuclei staining images are also commonly used and readily obtainable data. The updated Stereo-seq technology implement a procedure based on simultaneous cell membrane/wall and cell nuclei staining by adding multiplex immunofluorescence (mIF) and calcofluor white (CFW) staining [9,10], which can automatically acquire more accurate cell boundary information and thereby obtain more reliable single-cell spatial gene expression profiles.…”

Section: Statement Of Needmentioning

confidence: 99%

Generating single-cell gene expression profiles for high-resolution spatial transcriptomics based on cell boundary images

Zhang,

Li,

Kang

et al. 2023

Preprint

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Stereo-seq is a cutting-edge technique for spatially resolved transcriptomics that combines subcellular resolution with centimeter-level field-of-view, serving as a technical foundation for analyzing large tissues at the single-cell level. Our previous work presents the first one-stop software that utilizes cell nuclei staining images and statistical methods to generate high-confidence single-cell spatial gene expression profiles for Stereo-seq data. With recent advancements in Stereo-seq technology, it is possible to acquire cell boundary information, such as cell membrane/wall staining images. To take advantage of this progress, we updated our software to a new version, named STCellbin, which utilizes the cell nuclei staining images as a bridge to align cell membrane/wall staining images with spatial gene expression maps. By employing an advanced cell segmentation technique, accurate cell boundaries can be obtained, leading to more reliable single-cell spatial gene expression profiles. Experimental results verify the application of STCellbin on mouse liver (cell membranes) andArabidopsisseed (cell walls) datasets. The improved capability of capturing single cell gene expression profiles by this update results in a deeper understanding of the contribution of single cell phenotypes to tissue biology.Availability & ImplementationThe source code of STCellbin is available athttps://github.com/STOmics/STCellbin.

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“…These technologies can be roughly divided into two categories, sequencing based and imaging based. Sequencing-based techniques include DBiT-seq 1 , spatial-CITE-seq 2 , spatial assay for transposase-accessible chromatin and RNA using sequencing (spatial ATAC-RNA-seq) and CUT&Tag-RNA-seq 3 , SPOTS 4 , SM-Omics 5 , Stereo-CITE-seq 6 , spatial RNA-TCR-seq 7 and 10x Genomics Xenium 8 , while imaging-based techniques include DNA seqFISH+ 9 , DNA-MERFISH-based DNA and RNA profiling 10 , MERSCOPE 11 and Nanostring CosMx 12 . With these technologies, we can now acquire multiple complementary views of each cell within their spatial context.…”

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confidence: 99%

Deciphering spatial domains from spatial multi-omics with SpatialGlue

Long,

Ang,

Sethi

et al. 2024

Nat Methods

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Advances in spatial omics technologies now allow multiple types of data to be acquired from the same tissue slice. To realize the full potential of such data, we need spatially informed methods for data integration. Here, we introduce SpatialGlue, a graph neural network model with a dual-attention mechanism that deciphers spatial domains by intra-omics integration of spatial location and omics measurement followed by cross-omics integration. We demonstrated SpatialGlue on data acquired from different tissue types using different technologies, including spatial epigenome–transcriptome and transcriptome–proteome modalities. Compared to other methods, SpatialGlue captured more anatomical details and more accurately resolved spatial domains such as the cortex layers of the brain. Our method also identified cell types like spleen macrophage subsets located at three different zones that were not available in the original data annotations. SpatialGlue scales well with data size and can be used to integrate three modalities. Our spatial multi-omics analysis tool combines the information from complementary omics modalities to obtain a holistic view of cellular and tissue properties.

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Integrated Spatial Transcriptomic and Proteomic Analysis of Fresh Frozen Tissue Based on Stereo-seq

Cited by 12 publications

References 33 publications

Deciphering spatial domains from spatial multi-omics with SpatialGlue

Deciphering spatial domains from spatial multi-omics with SpatialGlue

Generating single-cell gene expression profiles for high-resolution spatial transcriptomics based on cell boundary images

Deciphering spatial domains from spatial multi-omics with SpatialGlue

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