human breast cancer hbc 10x visium data  (10X Genomics)

Journal: Bioinformatics

Article Title: BISON: bi-clustering of spatial omics data with feature selection

doi: 10.1093/bioinformatics/btaf495

Figure Lengend Snippet: HBC 10x Visium data: (a) The spatial domains identified by BISON and competing methods. (b) Heatmap of gene groups identified by BISON. Pattern 0 represents the non-DGs.

Article Snippet: The mouse olfactory bulb (MOB) spatial transcriptomics (ST) data were obtained from the public domain through the Spatial Research Lab ( https://www.spatialresearch.org/resources-published-datasets/doi-10-1126science-aaf2403/ ), and the human breast cancer (HBC) 10x Visium data were obtained from the public domain through 10x Genomics ( https://support.10xgenomics.com/spatial-gene-expression/datasets ).

Techniques:

Journal: Bioinformatics

Article Title: SuperSpot: coarse graining spatial transcriptomics data into metaspots

doi: 10.1093/bioinformatics/btae734

Figure Lengend Snippet: SuperSpot overview and applications. (A) Illustration of the SuperSpot pipeline to build metaspots in spatial transcriptomic data. (B) Splitting process of metaspots to guarantee both purity according to some annotation and spatial coherence. (C) 10x Visium mouse cortex dataset at the spot and metaspot ( γ = 4) level. The spots and the metaspots are colored based on the layers of the cortex and white matter. The “Unknown” label corresponds to unannotated spots. (D) ARI scores of different spatial clustering methods with respect to the brain layer annotation at spot (red) and metaspot (blue) levels. The ARI score is computed as the mean over 10 runs with different seeds. (E) ARI scores between the clusters computed at the spot and metaspot levels by each clustering method (blue dots) and the clusters computed by different clustering methods at spot level (red dots). The ARI score is computed as the mean over 10 runs with different seeds. (F) Right slide of Nanos-tring CosMx human pancreas dataset at the metaspot ( γ = 3.07) level. The spots and the metaspots are colored based on the cell types. (G) ARI scores of CellCharter with respect to the cell type annotation at spot (red) and metaspot (blue) levels on Nanostring CosMx human pancreas dataset. The ARI score is computed as the mean over 10 runs with different seeds. (H) VisiumHD Human Colorectal Cancer dataset. Colors show clusters obtained in transcriptomic analysis of metaspots and projected on the spatial coordinates of metaspots ( γ = 64). Frames corresponds to the regions of interest for panel I. (I) Illustrations of clusters computed in 16 μm bins and in metaspots ( γ = 64), compared to the H&E staining image. (J) Peak memory (GB) and elapsed time needed for computing normalization and spatially variable features in metaspots as a function of γ for the Visium Mouse Brain (3639 spots/33 538 genes, left), the Nanostring CosMx Human Pancreas (48 944 segmented cells/18 946 genes, center) and a 9·104x zoom of the VisiumHD Human Colorectal Cancer (H. CRC) (28 358 bins out of 8 731 400 2 μm bins/18 085 genes, right). Blue dots represent the final γ of 3.07 for the CosMx Human Pancreas after splitting impure metaspots. (K) Boxplot of the number of detected genes per spot (red) and metaspot (blue) for Visium Mouse Brain ( γ = 4, left), Nanostring CosMx Human Pancreas ( γ = 3.07, center), and VisiumHD Human Colorectal Cancer ( γ = 64, right). Percentages above each boxplot correspond to the number of nonzero entries within the gene expressions matrix.

Article Snippet: The 10x Visium Human Breast Cancer data is available from 10x’s website ( https://www.10xgenomics.com/datasets/human-breast-cancer-block-a-section-1-1-standard-1-1-0 ).

Techniques: Staining

Journal: Nature Communications

Article Title: SPACEL: deep learning-based characterization of spatial transcriptome architectures

doi: 10.1038/s41467-023-43220-3

Figure Lengend Snippet: a Spatial domains identified by Splane in slice S2 and S5 from Wu et al. dataset, slice S10 from Zhao et al. dataset, and slice S11 released by 10X Genomics. b , c Spatial distribution of chromosome 1q&8q copy number gains ( b ) and 1p copy number losses ( c ) of ST spots in slices S11, calculated by inferCNV. Dashed lines represent the tumor domain. d , e CNVs of chromosome 1q & 8q ( d ) and chromosome 1p ( e ) in each spatial domain calculated by inferCNV. CNVs, copy number variations; center line, median value; box limits, upper and lower quartiles; whiskers, 1.5× interquartile range; n = 11 slices. f From left to right: Splane predicted spatial domains in slice S5, distribution of Splane predicted immune domains D7/D8/D9, distribution of Spoint predicted immune cells, and distribution of H&E staining marked immune spots. g Percentage of H&E staining marked immune spots in each domain of slice S1, S2, S5, and S6. The four slices were H&E stained in the original study. Bar height, mean value; whiskers, mean values ± 95% confidence intervals; n = 4 slices. h From left to right: Splane predicted spatial domains in slice S10, distribution of Splane predicted immune domains D7, D8, and D9, distribution of Spoint predicted immune cells, and distribution of CD3 + immunofluorescence (IF) staining marked immune spots. i Percentage of CD3 + IF staining marked immune spots in each domain of slice S10. Source data are provided as a Source Data file.

Article Snippet: The raw data of 11 ST datasets and five paired single-cell/nucleus RNA sequence datasets are available from the following studies: (1) 12 slices of human DLPFC 10X Visium data at http://research.libd.org/spatialLIBD/ ; (2) six slices of human breast cancer 10X Visium data at 10.5281/zenodo.4739739 ; (3) four slices of human breast cancer 10X Visium data: Parent_Visium_Human_BreastCancer, V1_Breast_Cancer_Block_A_Section_1, V1_Breast_Cancer_Block_A_Section_2 and Visium_FFPE_Human_Breast_Cancer at https://support.10xgenomics.com/spatial-gene-expression/datasets ; (4) one slice of human breast cancer 10X Visium data: Invasive Ductal Carcinoma Stained With Fluorescent CD3 Antibody at https://support.10xgenomics.com/spatial-gene-expression/datasets ; (5) Mouse brain STARmap data at https://www.starmapresources.org/data ; (6) 33 slices of Mouse MOp MERFISH data at https://doi.brainimagelibrary.org/doi/10.35077/g.21 ; (7) one slice of mouse E16.5 embryo brain Stereo-seq data, one slice of mouse brain Stereo-seq data, and 13 slices of mouse E16.5 whole embryo Stereo-seq data at https://db.cngb.org/stomics/mosta/download/ ; (8) ten slice of human brain MERFISH data at https://datadryad.org/stash/dataset/doi:10.5061/dryad.x3ffbg7mw ; (9) 75 slice of mouse whole brain Spatial Transcriptomics data are available in the GEO database under accession number GSE147747 ; (10) single-nucleus transcriptomics data across multiple human cortical areas at https://portal.brain-map.org/atlases-and-data/rnaseq/human-multiple-cortical-areas-smart-seq ; (11) single-cell transcriptomics data of human breast cancer data at https://singlecell.broadinstitute.org/single_cell/study/SCP1039 ; (12) single-cell transcriptomics data of mouse embryo brain at http://mousebrain.org/development/downloads.html ; (13) single-cell transcriptomics data of mouse whole cortex and hippocampus at https://portal.brain-map.org/atlases-and-data/rnaseq/mouse-whole-cortex-and-hippocampus-10x ; (14) single-cell transcriptomics data of mouse whole brain at mousebrain.org/adolescent/downloads.html .

Techniques: Staining, Immunofluorescence