Journal: Nature Communications

Article Title: STIE: Single-cell level deconvolution, convolution, and clustering in in situ capturing-based spatial transcriptomics

doi: 10.1038/s41467-024-51728-5

Figure Lengend Snippet: a Computational resolution enhancement cannot achieve single-cell level. Illustration of the spot layout on the mouse brain hippocampus 10X Visium FFPE spatial transcriptome (left), the original gene expression summary at the spot level (middle), the BayesSpace-imputed gene expression summary at the subspot level along with the enlarged subspots on the H&E image (right). In the enlarged area, the white circle represents the original spot, and the red circle represents the enhanced subspot. b–e Systematic evaluation of spatial relationship between single cells and spots via simulation of high-resolution spots from real 10X Visium spatial transcriptomics FFPE data. b Nuclear morphological feature distribution of mouse kidney, mouse brain and human breast cancer. c Examples of simulated high-resolution spots on the real nuclear segmentation. The brown circle represents the high-resolution spot with 5 μm in diameter, and the black circle represents the nucleus in the real tissue. d The frequency of spots covering different numbers of cells, where the x-axis is the cell count covered by one single spot, and the y-axis represents the spot frequency. Only the spot that covers cells was considered. e The distribution of the cell area fraction covered by the spots in different diameters. In the box plots ( b , e ), center line represents median, lower and upper hinges represent first and third quartiles, whiskers extend from hinge to ±1.5 × IQR. The above distributions are drawn from 172,835 nuclei in mouse kidney, 31,546 nuclei in mouse brain, and 44,218 nuclei in human breast cancer, respectively. Source data are provided as a Source Data file.

Article Snippet: The data were acquired from the following websites or accession numbers: (1) 10X Visium adult mouse brain FFPE ; (2) 10X Visium adult mouse kidney FFPE ; (3) 10X Visium human breast cancer FFPE ; (4) 10X Visium frozen postmortem human dorsolateral prefrontal cortex (the Globus endpoint ‘jhpce#HumanPilot10x’); (5) 10X CytAssist Spatial Gene Expression (V2 Chemistry) mouse brain coronal section1 FFPE and section 2 FFPE ; (6) Human breast cancer spatial transcriptomics ( https://www.spatialresearch.org/resources-published-datasets/doi-10-1126science-aaf2403/ ); (7) H&E images for primary breast cancer frozen tissue section in HDST ; (8) Single nucleus RNA-seq dataset of 1,402 cells in the adult mouse hippocampus ( https://singlecell.broadinstitute.org/single_cell/study/SCP1/-single-nucleus-rna-seq-of-cell-diversity-in-the-adult-mouse-hippocampus-snuc-seq#study-download ); (9) Single cell RNA-seq dataset of 14,249 adult mouse cortical cell taxonomy from the Allen Institute ( https://www.dropbox.com/s/cuowvm4vrf65pvq/allen_cortex.rds?dl=1 ); (10) Single cell RNA-seq of 26 primary tumors from three major clinical subtypes of human breast cancer (GSE176078). (11) The marker genes of 31 preliminary cell clusters across 7 broad cell types on the single nucleus RNA-seq data from DLPFC using 5,231 nuclei ( https://github.com/LieberInstitute/spatialLIBD ). are provided with this paper.

Techniques: Gene Expression, Cell Counting

Journal: Nature Communications

Article Title: STIE: Single-cell level deconvolution, convolution, and clustering in in situ capturing-based spatial transcriptomics

doi: 10.1038/s41467-024-51728-5

Figure Lengend Snippet: a–j Mouse brain hippocampus 10X Visium FFPE spatial transcriptome. a Spot-level cell-type deconvolution using SPOTlight. In the enlarged area, each pie chart represents the proportion of cell types for the corresponding spot. b Subspot-level cell type deconvolution using BayesSpace followed by SPOTlight. In the enlarged area, each pie chart represents the proportion of cell types for the corresponding subspot. c Single-cell level deconvolution by STIE (left panel), which is the aggregation of cells captured by spots (middle panel) and cells missed by spots but recovered by STIE (right panel). In the enlarged area, the circle is the cell contour, with the color representing its cell type. Spot-level cell-type deconvolution using DWLS ( d ), Stereoscope ( e ), RCTD ( f ), Tangram ( g ), and BayesPrism ( h ). i Ground truth of mouse brain hippocampus cell types by In Situ Hybridization (ISH): CA1 ( Mpped1 ), CA2 ( Map3k15 ), CA3 ( Cdh24 ) and DG ( Prox1 ). The arrowhead indicates high expression. The figure is reproduced from Fig. in ref. . j Nuclear morphological feature distributions of cell types learned by STIE (422 CA1, 39 CA2, 115 CA3, 818 DG, and 872 Glia). k Single-cell level deconvolution by STIE on 10X Visium V2 Chemistry CytAssist FFPE spatial transcriptomics of two consecutive mouse brain hippocampus sections: section 1 (up panel) and section 2 (bottom panel). l–n Human breast cancer 10X Visium FFPE spatial transcriptome. l Single-cell level deconvolution by STIE. m Nuclear morphological feature distributions for cell types (2910 Bcells, 12,269 CAFs, 11,957 CancerEpithelial, 3120 Myeloid, 6997 Plasmablasts, 1920 PVL, and 4584 Tcells). Center line represents median, lower and upper hinges represent first and third quartiles, whiskers extend from hinge to ±1.5× IQR. n Manually annotated human breast cancer pathological regions. Single-cell convolution for the simulated high-resolution spatial transcriptomics data of the mouse brain hippocampus ( o ) and human breast cancer ( p ) using spots with 5 μm in diameter. Source data are provided as a Source Data file.

Article Snippet: The data were acquired from the following websites or accession numbers: (1) 10X Visium adult mouse brain FFPE ; (2) 10X Visium adult mouse kidney FFPE ; (3) 10X Visium human breast cancer FFPE ; (4) 10X Visium frozen postmortem human dorsolateral prefrontal cortex (the Globus endpoint ‘jhpce#HumanPilot10x’); (5) 10X CytAssist Spatial Gene Expression (V2 Chemistry) mouse brain coronal section1 FFPE and section 2 FFPE ; (6) Human breast cancer spatial transcriptomics ( https://www.spatialresearch.org/resources-published-datasets/doi-10-1126science-aaf2403/ ); (7) H&E images for primary breast cancer frozen tissue section in HDST ; (8) Single nucleus RNA-seq dataset of 1,402 cells in the adult mouse hippocampus ( https://singlecell.broadinstitute.org/single_cell/study/SCP1/-single-nucleus-rna-seq-of-cell-diversity-in-the-adult-mouse-hippocampus-snuc-seq#study-download ); (9) Single cell RNA-seq dataset of 14,249 adult mouse cortical cell taxonomy from the Allen Institute ( https://www.dropbox.com/s/cuowvm4vrf65pvq/allen_cortex.rds?dl=1 ); (10) Single cell RNA-seq of 26 primary tumors from three major clinical subtypes of human breast cancer (GSE176078). (11) The marker genes of 31 preliminary cell clusters across 7 broad cell types on the single nucleus RNA-seq data from DLPFC using 5,231 nuclei ( https://github.com/LieberInstitute/spatialLIBD ). are provided with this paper.

Techniques: In Situ Hybridization, Expressing

Journal: Nature Communications

Article Title: STIE: Single-cell level deconvolution, convolution, and clustering in in situ capturing-based spatial transcriptomics

doi: 10.1038/s41467-024-51728-5

Figure Lengend Snippet: Cell type specific transcriptomic signature learning from 10X Visium mouse brain hippocampus FFPE ( a ) and 10X Visium human breast cancer FFPE ( b ). Spot-level clustering by K-means, SpaGCN, MUSE, subspot-level clustering by BayesSpace and single-cell-level clustering by STIE on 10X Visium FFPE mouse brain hippocampus ( c ), mouse brain cortex ( e ) and human breast cancer ( g ). Cell type deconvolution of spot-, subspot-, and single-cell-level clustering-derived CAGE in the mouse brain hippocampus ( d ), mouse brain cortex ( f ), and human breast cancer ( h ). For the mouse brain cortex, the cell types in the transcriptomic signature, which are not cortex layers and have small proportions, are not shown in the barplot. The box plot ( h ) represents the deconvoluted proportion of 9 cell types, where center line represents median, lower and upper hinges represent first and third quartiles, and whiskers extend from hinge to ±1.5 × IQR. The p-value was calculated based on one-sided Wilcoxon signed-rank test without adjustment for multiple comparisons. i The UMAP plot of human breast cancer scRNA-seq data from 26 primary tumors . The top panel is the original cell typing of 10,060 single cells, and the bottom panel is the subset of cells that are mapped to the six STIE clusters. Spot-level clustering by K-means (left), SpaGCN (middle), and single-cell-level clustering by STIE (right) on the simulated high-resolution spot spatial transcriptome data of the mouse brain hippocampus ( j ) and human breast cancer ( m ). Cell type deconvolution of spot- and single-cell-level clustering-derived CAGE in the mouse brain hippocampus ( k ) and human breast cancer ( n ). l , o The consistency table of single-cell clusters between the simulated high-resolution spot-based STIE clustering and the original low-resolution spot-based STIE clustering as ground truth of the mouse brain hippocampus ( c ) and human breast cancer ( g ). Source data are provided as a Source Data file.

Article Snippet: The data were acquired from the following websites or accession numbers: (1) 10X Visium adult mouse brain FFPE ; (2) 10X Visium adult mouse kidney FFPE ; (3) 10X Visium human breast cancer FFPE ; (4) 10X Visium frozen postmortem human dorsolateral prefrontal cortex (the Globus endpoint ‘jhpce#HumanPilot10x’); (5) 10X CytAssist Spatial Gene Expression (V2 Chemistry) mouse brain coronal section1 FFPE and section 2 FFPE ; (6) Human breast cancer spatial transcriptomics ( https://www.spatialresearch.org/resources-published-datasets/doi-10-1126science-aaf2403/ ); (7) H&E images for primary breast cancer frozen tissue section in HDST ; (8) Single nucleus RNA-seq dataset of 1,402 cells in the adult mouse hippocampus ( https://singlecell.broadinstitute.org/single_cell/study/SCP1/-single-nucleus-rna-seq-of-cell-diversity-in-the-adult-mouse-hippocampus-snuc-seq#study-download ); (9) Single cell RNA-seq dataset of 14,249 adult mouse cortical cell taxonomy from the Allen Institute ( https://www.dropbox.com/s/cuowvm4vrf65pvq/allen_cortex.rds?dl=1 ); (10) Single cell RNA-seq of 26 primary tumors from three major clinical subtypes of human breast cancer (GSE176078). (11) The marker genes of 31 preliminary cell clusters across 7 broad cell types on the single nucleus RNA-seq data from DLPFC using 5,231 nuclei ( https://github.com/LieberInstitute/spatialLIBD ). are provided with this paper.

Techniques: Derivative Assay

Journal: Nature Communications

Article Title: STIE: Single-cell level deconvolution, convolution, and clustering in in situ capturing-based spatial transcriptomics

doi: 10.1038/s41467-024-51728-5

Figure Lengend Snippet: Identification of the bona fide area captured by spots in the mouse brain hippocampus ( a ) and human breast cancer ( b ). The x-axis represents the putative size of the bona fide area measured by the spot in the unit of a regular 10X Visium spot size (55 μm). The top panel represents the chart of bona fide spot size in the real tissue. The middle panel represents the cell count (y-axis) in the spot area (x-axis); the bottom panel represents the RMSE by fitting the STIE model using the cells within the corresponding area indicated by the x-axis. The distributions are drawn from 80 to 151 spots in mouse brain hippocampus and 1568–2508 spots in human breast cancer, respectively. c Identification of the bona fide area captured by spots in the 10X Visium V2 Chemistry CytAssist mouse brain hippocampus. The distributions are drawn from 120 to 230 spots in section 1 and 121–245 spots in section 2, respectively. The evaluation of image contribution to the cell type deconvolution in human breast cancer ( d ) and mouse brain hippocampus ( e ). The top panel represents the difference between cell-type proportions estimated from the spot gene expression and the nuclear morphological features; the bottom panel represents the RMSE of gene expression fitting. The x-axis represents the value of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\lambda$$\end{document} λ in Formula (7). The distributions are drawn from 151 spots in mouse brain hippocampus and 2,451 spots in human breast cancer, respectively, which are presented as mean values ±SEM. f Heatmap of the correlation between the cell type proportion within spots by SPOTlight, DWLS, Stereoscope, RCTD, Tangram, BayesPrism, and STIE. g The association between transcriptomic signature similarity and cell-type colocalization by SPOTlight, DWLS, Stereoscope, RCTD, Tangram, BayesPrism, and STIE. The two-sided p values are calculated for the Pearson’s correlation coefficients ( n = 36) without adjustment for multiple comparisons. h–i High-resolution spots along with STIE holds the premise to distinguish nuanced cell types. h Random assignments of Memory Bcell or Naïve Bcell to the Bcell; CD8+ Tcell, CD4+ Tcell, NK cells, Cycling Tcell, or NKT cell, to the Tcell; and Macrophage, Monocyte, Cycling Myeloid, or DCs to the Myeloid. i The barplot represents the concordance of STIE deconvoluted/convoluted single cells with the simulation ground truth ( h ). The x-axis represents the simulated spot diameter, and the y-axis represents the concordance. The color refers to the cell type in the legend. Source data are provided as a Source Data file.

Article Snippet: The data were acquired from the following websites or accession numbers: (1) 10X Visium adult mouse brain FFPE ; (2) 10X Visium adult mouse kidney FFPE ; (3) 10X Visium human breast cancer FFPE ; (4) 10X Visium frozen postmortem human dorsolateral prefrontal cortex (the Globus endpoint ‘jhpce#HumanPilot10x’); (5) 10X CytAssist Spatial Gene Expression (V2 Chemistry) mouse brain coronal section1 FFPE and section 2 FFPE ; (6) Human breast cancer spatial transcriptomics ( https://www.spatialresearch.org/resources-published-datasets/doi-10-1126science-aaf2403/ ); (7) H&E images for primary breast cancer frozen tissue section in HDST ; (8) Single nucleus RNA-seq dataset of 1,402 cells in the adult mouse hippocampus ( https://singlecell.broadinstitute.org/single_cell/study/SCP1/-single-nucleus-rna-seq-of-cell-diversity-in-the-adult-mouse-hippocampus-snuc-seq#study-download ); (9) Single cell RNA-seq dataset of 14,249 adult mouse cortical cell taxonomy from the Allen Institute ( https://www.dropbox.com/s/cuowvm4vrf65pvq/allen_cortex.rds?dl=1 ); (10) Single cell RNA-seq of 26 primary tumors from three major clinical subtypes of human breast cancer (GSE176078). (11) The marker genes of 31 preliminary cell clusters across 7 broad cell types on the single nucleus RNA-seq data from DLPFC using 5,231 nuclei ( https://github.com/LieberInstitute/spatialLIBD ). are provided with this paper.

Techniques: Cell Counting, Gene Expression

Journal: International Journal of Surgery (London, England)

Article Title: Integrative analysis identifies molecular features of fibroblast and the significance of fibrosis on neoadjuvant chemotherapy response in breast cancer

doi: 10.1097/JS9.0000000000001360

Figure Lengend Snippet: Identifying npCR-associated fibroblasts by integrating proteomics and single-cell sequencing data. (A) Volcano plot of differentially expressed proteins between the pCR and npCR groups. (B) Upregulated pathways in the npCR group based on GO term enrichment analysis of differentially expressed proteins. (C–D) Uniform Manifold Approximation and Projection (UMAP) of major cell populations in our study, different colored dots represent different cell types. (E–F) The pCR and npCR phenotypes were mapped from proteomics to scRNA-seq data. (G) Pseudotime trajectories of fibroblast. (H–I) The GO/KEGG enrichment based on the GSVA algorithm. (J) Transcription factor regulation of npCR-related fibroblast compared with other fibroblast. (K–L) The intercellular cross-talk analysis between fibroblast and other cells in TME.

Article Snippet: The spatial transcription dataset (Fresh Frozen Visium on CytAssist: HumanBreast Cancer, Probe-Based Whole Transcriptome Profiling) was retrieved from 10X Genomics ( https://www.10xgenomics.com/ ).

Techniques: Sequencing

Journal: Bioinformatics

Article Title: Deciphering high-order structures in spatial transcriptomes with graph-guided Tucker decomposition

doi: 10.1093/bioinformatics/btae245

Figure Lengend Snippet: Summary of datasets.

Article Snippet: The MBSA, MBSP, and BRCA1 datasets were downloaded from 10x Genomics website https://www.10xgenomics.com/products/spatial-gene-express .

Techniques: Blocking Assay

Journal: Bioinformatics

Article Title: Deciphering high-order structures in spatial transcriptomes with graph-guided Tucker decomposition

doi: 10.1093/bioinformatics/btae245

Figure Lengend Snippet: Spotwise cross-validation results across three Visium datasets. ^a

Article Snippet: The MBSA, MBSP, and BRCA1 datasets were downloaded from 10x Genomics website https://www.10xgenomics.com/products/spatial-gene-express .

Techniques:

Journal: Bioinformatics

Article Title: Deciphering high-order structures in spatial transcriptomes with graph-guided Tucker decomposition

doi: 10.1093/bioinformatics/btae245

Figure Lengend Snippet: Comparison of eight annotated regions from human breast cancer dataset (BRCA1) with matched spatial components. Area Under the receiving operator Curve (AUC) and Euclidean Distance (ED) between spatial component and region annotation are listed above each component. The clustering shown for GraphTucker used all core tensor entries, and the spatial components shown used only the top 5 % of entries. Best scores are marked in bold.

Article Snippet: The MBSA, MBSP, and BRCA1 datasets were downloaded from 10x Genomics website https://www.10xgenomics.com/products/spatial-gene-express .

Techniques: Comparison

Journal: Bioinformatics

Article Title: Deciphering high-order structures in spatial transcriptomes with graph-guided Tucker decomposition

doi: 10.1093/bioinformatics/btae245

Figure Lengend Snippet: Region-spatial component matching. ^a

Article Snippet: The MBSA, MBSP, and BRCA1 datasets were downloaded from 10x Genomics website https://www.10xgenomics.com/products/spatial-gene-express .

Techniques: