Journal: Science immunology

Article Title: Spatial transcriptomics stratifies psoriatic disease severity by emergent cellular ecosystems

doi: 10.1126/sciimmunol.abq7991

Figure Lengend Snippet: (A) Schematic of spatial transcriptomics study workflow. Table S1 contains metadata for each sample. (B) Schematic of skin, representative hematoxylin-eosin (H&E) image and corresponding ST plot (left-to-right). Scale bar = 440μm (C) UMAP visualization of 3,815 spots colored by cluster obtained from healthy skin samples (N=3, n=5). (D) Composition plots displaying relative abundance of each cluster by sample. Note up to two samples (labeled S) were collected from each Healthy Volunteer (HV). Replicate arrays are labeled “R” along the X axis. (E) Integration with a publicly-sourced single cell RNA-seq data set (dataset 1) with a representative ST spatial feature plot. See Figure S4 for UMAP of annotated cell type clusters. SMC=smooth muscle cell. Scale bar = 520μm (F) Multimodal intersection analysis (MIA) of overlap between data from datasets 1 and 2 and our ST-generated clusters. A sample hypergeometric distribution of keratinocyte cluster from dataset 1 and our epidermis cluster (cluster 6). MIA enrichment heatmaps of non-immune cell types in dataset 1 (G) and dataset 2 (H) and ST clusters from healthy skin. The X axis denotes the scRNA seq-identified cell types while the Y axis represents the ST-generated clusters. Differentiated keratinocytes (Diff KC) lymphatic endothelium (LE), proliferating keratinocytes (Prolif KC), vascular endothelium (VE), keratinocyte (KC). (I) MIA heatmap showing the enrichment of scRNA-seq-identified adipose- cell types from Hildreth et al. within pooled healthy skin ST clusters. (J) KEGG pathway analysis of the adipose cluster (cluster 2).

Article Snippet: Spatial Transcriptomics (ST) faithfully maps gene expression in healthy human skin We developed a human skin specific protocol for 10X Genomics Visium platform and performed ST on 25 skin samples collected from 3 healthy controls and 11 patients with PsO/PsA ( , figs. S1, A to C , S14A and table S1 ).

Techniques: Labeling, RNA Sequencing, Generated

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: These include the mouse olfactory bulb ST data from Spatial Transcriptomics v1.0 ( https://www.spatialresearch.org ), the four human hepatocellular carcinoma Visium datasets ( https://www.ncbi.nlm.nih.gov/sra?linkname=bioproject_sra_all&from_uid=858545 ), mouse anterior brain 10x Visium data ( https://support.10xgenomics.com/spatial‐gene‐expression/datasets/1.0.0/V1_Mouse_Brain_Sagittal_Anterior ), and mouse posterior brain 10x Visium data ( https://support.10xgenomics.com/spatial‐gene‐expression/datasets/1.0.0/V1_Mouse_Brain_Sagittal_Posterior ).

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

Journal: Nature Communications

Article Title: SpaIM: single-cell spatial transcriptomics imputation via style transfer

doi: 10.1038/s41467-025-63185-9

Figure Lengend Snippet: SpaIM comprises an ST autoencoder and an ST generator. Both the ST autoencoder and the ST generator are built on the multilayer recursive style transfer (ReST) layers.

Article Snippet: Spatial transcriptomics (ST) autoencoder The ST autoencoder (Fig. ) comprises multilayer ReST encoders.

Techniques:

Journal: Nature Communications

Article Title: SpaIM: single-cell spatial transcriptomics imputation via style transfer

doi: 10.1038/s41467-025-63185-9

Figure Lengend Snippet: a Benchmarking results on the NanoString CosMx spatial transcriptomics dataset (Lung5–rep3), using evaluation metrics including structural similarity index measure (SSIM) and Jaccard similarity (JS). Data are presented as mean values ± 95% confidence intervals across predicted genes ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$n$$\end{document} n = 2,038). b Spatial visualization of cell types in the whole slide. c Spatial visualization of cell types in specific field of views (FOVs).

Article Snippet: Spatial transcriptomics (ST) autoencoder The ST autoencoder (Fig. ) comprises multilayer ReST encoders.

Techniques:

Journal: Nature Communications

Article Title: SpaIM: single-cell spatial transcriptomics imputation via style transfer

doi: 10.1038/s41467-025-63185-9

Figure Lengend Snippet: a Benchmarking results on the NanoString CosMx spatial transcriptomics dataset (Lung5–rep3), using evaluation metrics including structural similarity index measure (SSIM) and Jaccard similarity (JS). Data are presented as mean values ± 95% confidence intervals across predicted genes ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$n$$\end{document} n = 2,038). b Spatial visualization of cell types in the whole slide. c Spatial visualization of cell types in specific field of views (FOVs).

Article Snippet: Spatial transcriptomics (ST) generator A similar architecture (Fig. ) is used to generate ST data from SC data.

Techniques: