Journal: Briefings in Bioinformatics

Article Title: Probabilistic-graph-based spatial context-aware framework for interpretable spatial omics denoising and augmentation

doi: 10.1093/bib/bbaf674

Figure Lengend Snippet: Comparative analysis on Stereo-seq MOB and Slide-seqV2 Hippocampus datasets. (a) Raw MOB image with anntated regions (left) and clustering results from baseline models and CadaST (right) with the number of clusters constrained to 7. (b) Visual comparison of the identified spatial domains corresponding to 7 annotated anatomical layers (top), and heatmaps showing the pairwise Jaccard similarity of the top 10 marker genes identified by each method for each specific layer (bottom). (c) Barplots of CHAOS scores across different cluster numbers. (d) Differential expression analysis: heatmap showing DE gene scores after CadaST denoising. Gene expression differences are more distinct following CadaST processing. (e) Slide-seqV2 hippocampal data compared to the Allen Brain Atlas reference. (f) Expression state and denoised results of the representative marker genes for hippocampal regions. (g) Clustering using binarized information only (0 or 1), from CadaST’s genes’ states prediction.

Article Snippet: The public datasets used in this study can be accessed from original repositories: the human DLPFC dataset via spatialLIBD ( https://research.libd.org/spatialLIBD/ ); mouse coronal hemibrain and olfactory bulb Stereo-seq data from STOmics DB ( https://db.cngb.org/stomics ); mouse hippocampus Slide-seqV2 data through Squidpy ( https://github.com/scverse/squidpy ); Human breast cancer Visium datasets from 10 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\times $\end{document} Genomics ( https://support.10xgenomics.com/spatial-gene-expression/datasets ); The spatial ATAC-RNA-seq dataset is available at the Gene Expression Omnibus with accession code GSE205055 ( www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE205055 .

Techniques: Comparison, Marker, Quantitative Proteomics, Gene Expression, Expressing

Journal: Briefings in Bioinformatics

Article Title: Probabilistic-graph-based spatial context-aware framework for interpretable spatial omics denoising and augmentation

doi: 10.1093/bib/bbaf674

Figure Lengend Snippet: High efficiency and scalability of CadaST in embryonic development analysis. (a) Computational efficiency comparison of CadaST, STAGATE, and GraphST on Stereo-seq embryonic datasets. (b) ARI and NMI scores for CadaST, STAGATE, and GraphST on E13.5 and E14.5 sections. (c) Annotated regions of E13.5 section from the original study (left) and domains identified by CadaST and baseline models (right). (d) Comparative visualization of spatial domains in E13.5 identified by the original Stereo-seq study (top) and CadaST (bottom). (e) Magnified view of the spinal cord region segmented by CadaST showing distinct neuroanatomical domains: lateral spinal alar plate (domain 7), spinal cord basal plate (domain 16), and ventricular zone (domain 17), with corresponding marker gene expression ( Nefl, Hoxb8, andHopx ). (f) Annotated regions of E14.5 section from the original study (left) and domains identified by CadaST and baseline models (right). (g) Comparative visualization of spatial domains in E14.5 identified by the original Stereo-seq study (top) and CadaST (bottom). (h) Visualization of tissue-specific marker genes ( Krt5, Tpm2, Col1a1, and Slc25a37 ) in E14.5, showing raw expression, CadaST-predicted spatial patterns, and denoised expression data.

Article Snippet: The public datasets used in this study can be accessed from original repositories: the human DLPFC dataset via spatialLIBD ( https://research.libd.org/spatialLIBD/ ); mouse coronal hemibrain and olfactory bulb Stereo-seq data from STOmics DB ( https://db.cngb.org/stomics ); mouse hippocampus Slide-seqV2 data through Squidpy ( https://github.com/scverse/squidpy ); Human breast cancer Visium datasets from 10 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\times $\end{document} Genomics ( https://support.10xgenomics.com/spatial-gene-expression/datasets ); The spatial ATAC-RNA-seq dataset is available at the Gene Expression Omnibus with accession code GSE205055 ( www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE205055 .

Techniques: Comparison, Marker, Gene Expression, Expressing

Journal: Small Methods

Article Title: Robust Spatial Cell‐Type Deconvolution with Qualitative Reference for Spatial Transcriptomics

doi: 10.1002/smtd.202401145

Figure Lengend Snippet: Analysis of mouse olfactory bulb data. a) H&E staining of the olfactory bulb (top) and the deconvolution results of all candidate methods displayed by the spatial scatter pie plot of cell‐type composition on each spatial location. The examined cell types were granule cells (GC), olfactory sensory neurons (OSNs), periglomerular cells (PGC), mitral/tufted cells (M‐TC), and external plexiform layer interneurons (EPL‐IN) b) Manual annotation of anatomic layers (top), including the granule cell layer (GCL), the mitral cell layer (MCL), the glomerular layer (GL), and the nerve layer (ONL), and the spatial domains of different deconvolution methods visualized by spatial scatters of specific domain types. c) Performance comparison between candidate deconvolution methods, including QR‐SIDE, STdeconvolve, CARDfree, RCTD, CARD, SPOTlight, and spatialDWLS in terms of NMI (left) and ARI (right). d) UMAP plots of gene expression for Topic 1, 2, 3 identified by QR‐SIDE. The color scheme of each topic domain was the same as in (b). e) The heatmap of normalized expression level for the top 10 DE genes for topic domain 1, 2, 3. f) The correlation between DE genes of identified domains and marker genes of each cell type. g) The mean expression level of an example marker gene list for QR‐SIDE, where Tyro3 was included as the interference marker gene of cell type GC. h) Left and middle panels: The estimated spot‐separable η scores of correct marker Penk and misclassified marker Tyro3 . Right panel: The line plots of mean η of all markers across all spatial spots and the RMSE between estimated cell‐type composition by varying the inclusion of top 3‐7 marker genes of each cell type as the input gene list and the deconvolution results using high‐quality marker genes (as shown in a).

Article Snippet: The mouse olfactory bulb ST data from Spatial Transcriptomics v1.0 technology was considered.

Techniques: Staining, Comparison, Gene Expression, Expressing, Marker