Journal: NAR Genomics and Bioinformatics

Article Title: SpNeigh: spatial neighborhood and differential expression analysis for high-resolution spatial transcriptomics

doi: 10.1093/nargab/lqag039

Figure Lengend Snippet: Overview of the SpNeigh workflow. ( a ) Input includes a spatial coordinate data frame ( x, y , cell, cluster) and a normalized expression matrix. Data can originate from platforms such as Xenium, Visium HD, MERFISH, or others. ( b ) Spatial boundary detection and neighborhood extraction. Left: Cluster boundaries are identified after removing spatial outliers based on local k-nearest neighbor density. Right: Ring regions are constructed by buffering outward from the cluster boundaries. Black lines denote cluster boundaries; blue lines indicate outer ring boundaries. ( c ) Spatial weight computation. Cells are assigned weights based on their distance to either the boundary (left) or the centroid (right) of the cluster using inverse distance decay. Weights range from 0 (far) to 1 (close), reflecting proximity. ( d ) Neighborhood composition and interaction analysis. Top: Pie chart showing the proportion of neighboring cell types within the rings. Bottom: Heatmap of spatial interaction scores between focal and neighboring clusters. ( e ) Downstream analyses enabled by SpNeigh. Left: Differential expression analysis between cells of the same cluster in the inner region versus the ring. Middle: Spatial differential expression analysis using smooth functions of distance-based weights. Right: Spatial enrichment analysis quantifying expression bias relative to spatial proximity.

Article Snippet: Mouse brain Visium HD dataset: https://www.10xgenomics.com/datasets/visium-hd-cytassist-gene-expression-mouse-brain-fresh-frozen .

Techniques: Expressing, Extraction, Construct, Quantitative Proteomics

Journal: Nucleic Acids Research

Article Title: Benchmarking sketching methods on spatial transcriptomics data

doi: 10.1093/nar/gkag434

Figure Lengend Snippet: Overall sketching performance for 0.10 sampling fraction across datasets. ( A ) Spatial scatter plots of real datasets colored by cell type or cluster label. ( B ) Heatmap of rank-sums for each method aggregated by metric across all real world datasets. Low rank indicates best performance for that metric. ( C ) Spatial scatter plots of simulated Visium HD-like and Xenium like datasets. ( D ) Heatmap of rank-sums for each method aggregated by metric across all simulated datasets. Low rank indicates best performance for that metric.

Article Snippet: Mouse embryo: We downloaded the whole mouse embryo Visium HD dataset from the publicly available datasets on the 10x website https://www.10xgenomics.com/datasets/visium-hd-three-prime-mouse-embryo-fresh-frozen (last accessed date: 12 February 2025).

Techniques: Sampling

Journal: Nucleic Acids Research

Article Title: Benchmarking sketching methods on spatial transcriptomics data

doi: 10.1093/nar/gkag434

Figure Lengend Snippet: Retained cell type/cluster label proportions at 0.10 sketching fraction for ( A ) Merfish mouse ovary; ( B ) Merfish sagittal mouse brain; ( C ) Xenium human breast cancer; ( D ) Xenium human lung; ( E ) Xenium whole mouse pup; ( F ) Visium HD coronal mouse brain; ( G ) Visium HD mouse embryo; ( H ) Visium HD ovarian cancer.

Article Snippet: Mouse embryo: We downloaded the whole mouse embryo Visium HD dataset from the publicly available datasets on the 10x website https://www.10xgenomics.com/datasets/visium-hd-three-prime-mouse-embryo-fresh-frozen (last accessed date: 12 February 2025).

Techniques:

Journal: Nucleic Acids Research

Article Title: Benchmarking sketching methods on spatial transcriptomics data

doi: 10.1093/nar/gkag434

Figure Lengend Snippet: Quantification of transcriptomic and coordinate Hausdorff distance at 0.10 sampling fraction for real datasets. ( A ) Quantification of imaging based (Merfish, Xenium) dataset’s Hausdorff distances. ( B ) Quntification of sequencing/spot based (Visium HD) dataset’s Hausdorff distances. Each boxplot represents one sketching method, with individual points corresponding to results from 10 independent runs with different random seeds.

Article Snippet: Mouse embryo: We downloaded the whole mouse embryo Visium HD dataset from the publicly available datasets on the 10x website https://www.10xgenomics.com/datasets/visium-hd-three-prime-mouse-embryo-fresh-frozen (last accessed date: 12 February 2025).

Techniques: Sampling, Imaging, Sequencing

Journal: Nucleic Acids Research

Article Title: Benchmarking sketching methods on spatial transcriptomics data

doi: 10.1093/nar/gkag434

Figure Lengend Snippet: Overall sketching performance for 0.10 sampling fraction across datasets. ( A ) Spatial scatter plots of real datasets colored by cell type or cluster label. ( B ) Heatmap of rank-sums for each method aggregated by metric across all real world datasets. Low rank indicates best performance for that metric. ( C ) Spatial scatter plots of simulated Visium HD-like and Xenium like datasets. ( D ) Heatmap of rank-sums for each method aggregated by metric across all simulated datasets. Low rank indicates best performance for that metric.

Article Snippet: Human ovarian cancer: We downloaded the human ovarian cancer Visium HD dataset from the publicly avaiable datasets on the 10x website https://www.10xgenomics.com/datasets/visium-hd-three-prime-ovarian-cancer-discovery-fresh-frozen (last accessed date: 12 February 2025).

Techniques: Sampling

Journal: Nucleic Acids Research

Article Title: Benchmarking sketching methods on spatial transcriptomics data

doi: 10.1093/nar/gkag434

Figure Lengend Snippet: Retained cell type/cluster label proportions at 0.10 sketching fraction for ( A ) Merfish mouse ovary; ( B ) Merfish sagittal mouse brain; ( C ) Xenium human breast cancer; ( D ) Xenium human lung; ( E ) Xenium whole mouse pup; ( F ) Visium HD coronal mouse brain; ( G ) Visium HD mouse embryo; ( H ) Visium HD ovarian cancer.

Article Snippet: Human ovarian cancer: We downloaded the human ovarian cancer Visium HD dataset from the publicly avaiable datasets on the 10x website https://www.10xgenomics.com/datasets/visium-hd-three-prime-ovarian-cancer-discovery-fresh-frozen (last accessed date: 12 February 2025).

Techniques:

Journal: Nucleic Acids Research

Article Title: Benchmarking sketching methods on spatial transcriptomics data

doi: 10.1093/nar/gkag434

Figure Lengend Snippet: Quantification of transcriptomic and coordinate Hausdorff distance at 0.10 sampling fraction for real datasets. ( A ) Quantification of imaging based (Merfish, Xenium) dataset’s Hausdorff distances. ( B ) Quntification of sequencing/spot based (Visium HD) dataset’s Hausdorff distances. Each boxplot represents one sketching method, with individual points corresponding to results from 10 independent runs with different random seeds.

Article Snippet: Human ovarian cancer: We downloaded the human ovarian cancer Visium HD dataset from the publicly avaiable datasets on the 10x website https://www.10xgenomics.com/datasets/visium-hd-three-prime-ovarian-cancer-discovery-fresh-frozen (last accessed date: 12 February 2025).

Techniques: Sampling, Imaging, Sequencing

Journal: bioRxiv

Article Title: MilliMap: interactive closed-loop analysis for spatial omics

doi: 10.64898/2026.05.01.722104

Figure Lengend Snippet: (a) sST: Visium HD mouse brain, grid expression over H&E. (b) iST: Xenium human breast cancer; DAPI/IF morphology (left) and cluster-colored centroids (right). (c) SP: CODEX human intestine with protein-defined clusters. (d) scRNA-seq: honey bee brain, 3D UMAP. (e, f) Lasso-defined inner (e) and large (f) Kenyon cell (KC) ROIs (left); linked embedding confirms molecular coherence (right). (g) Differential expression between inner and large KCs (left: Dop3 -colored spatial view; right: DEG heatmap). (h) Spatially varying gene CHIT1 expression: whole tissue (left), ROI1 (middle), ROI2 (right). (i) Same layout as h, CD83 . (j) Spatially resolved ROI1 cell-type clusters (left) and TAMs (cluster 11) sub-clusters (right). (k) Spatially resolved ROI2 cell-type clusters. (l) Cell type composition of ROI1 and ROI2. (m) Volcano of ROI1-core-specific TAMs (11.1) vs other TAMs (11.0 and 11.2).

Article Snippet: The Visium HD Mouse Brain dataset (FFPE; C57BL/6; Space Ranger v3.0.0) is available from 10x Genomics at https://www.10xgenomics.com/datasets/visium-hd-cytassist-gene-expression-libraries-of-mouse-brain-he , licensed under CC BY 4.0.

Techniques: Expressing, Quantitative Proteomics

Journal: Nature Communications

Article Title: Charting spatial ligand-target activity using Renoir

doi: 10.1038/s41467-026-72388-7

Figure Lengend Snippet: a Preparation of FFPE fetal liver tissue. Created in BioRender. Zafar, H. (2026) ( https://BioRender.com/gmj8e7g ) b Spatial communication domains inferred by Renoir based on ligand-target neighborhood scores obtained from the 10x Genomics Visium HD dataset (sample D1). Black scale bar, 1 mm. c Distribution of UMI count across the tissue section. d Spatial map of communication domains and neighborhood activity scores for differentially active ligand-target pairs ( COL18A1:BCL6 , PLG:MARCO , and MDK:KLF6 ). e Ranking of ligands based on their cumulative activities over target genes expressed by major cell types in domain 0. f UMAP plot of latent embedding for hepatocyte population in scRNA-seq data, cells are colored as either PLG + or PLG − . g Volcano plot depicting differentially expressed genes between PLG + and PLG − Hepatocytes calculated using non-parametric Wilcoxon rank sum test ( − log 10 P threshold = 50 and log 2 foldchange threshold = 0.3). h Spatial feature plot of PLG expression and cell type abundances of Hepatocytes ( PLG + and PLG − ) and FOLR2 + Macrophages overlaid onto the spots in ST data. i Spatial similarity measures between PLG:MARCO neighborhood activity scores and cell type abundances of PLG + and PLG − Hepatocytes across neighborhoods containing FOLR2 + Macrophages ( n = 39651 spots). Source data are provided as a Source Data file.

Article Snippet: Zafar, H. (2026) ( https://BioRender.com/gmj8e7g ) b Spatial communication domains inferred by Renoir based on ligand-target neighborhood scores obtained from the 10x Genomics Visium HD dataset (sample D1).

Techniques: Activity Assay, Expressing

Journal: Briefings in Bioinformatics

Article Title: Cross-modality representation and multi-sample integration of spatially resolved omics data

doi: 10.1093/bib/bbag214

Figure Lengend Snippet: PRESENT facilitates accurate spatial domain identification in spatial RNA-ADT data. (a) spatial visualization of the 10x Genomics Visium RNA-protein human lymph node sample colored by ground truth domain labels. (b) Quantitative comparison of spatial domain identification performance between PRESENT and other baseline methods, shown as a bar plot for the human lymph node dataset. (c) Quantitative comparison between PRESENT utilizing both RNA and ADT data (RNA & ADT) and PRESENT using only RNA (RNA-only) or ADT data (ADT-only), shown as a radar plot in the human lymph node dataset. (d) UMAP visualization of latent embeddings from different methods, colored by ground truth domain labels in the human lymph node dataset. (e) UMAP visualization of latent embeddings, colored by cluster labels in the human lymph node dataset. (f) Spatial visualization of spots colored by cluster labels in the human lymph node dataset. The cluster labels in e and f were derived from latent embeddings of different methods using the Leiden algorithm. (g) Histology image of the SPOTS mouse spleen dataset and spatial visualization of spots colored by cluster labels in the SPOTS mouse spleen dataset. The cluster labels were derived from latent embeddings of PRESENT using Leiden algorithm. (h) Differentially expressed proteins of each spatial domain through one-versus-all Wilcoxon rank-sum test, shown as a dot plot. (i) DEGs of all the spatial domains through one-versus-all Wilcoxon rank-sum test, shown as a dot plot. (j) Volcano plot showing the DEGs of Mac1-enriched domain and Mac2-enriched domain through Mac1-versus-Mac2 Wilcoxon rank-sum test, where the x axis denotes the log(fold-change) (log(FC)), while the y axis denotes the significance measured by -log10(false discovery rate) (−log10(FDR)). The vertical dashed line represents the threshold for log(FC)= \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\pm$\end{document} 0.2, while the horizontal dashed line denotes the threshold for -log10(FDR) = 0.05. (k) Chord plot demonstrating the linkage of DEGs in the Mac1-enriched domain and the corresponding enriched pathways. The left semicircle represents DEGs while the right semicircle denotes the enriched biological processes. Bar plot is employed to demonstrate the significance of each pathway (x axis, −log10(FDR)). (l) The linkage of DEGs in the Mac2-enriched domain and corresponding pathways as well as the significance of each enriched pathway.

Article Snippet: The 10x Visium mouse brain datasets, including a sagittal anterior section and a sagittal posterior section, are accessible at the 10x Genomics websites https://www.10xgenomics.com/datasets/mouse-brain-serial-section-2-sagittal-anterior-1-standard and https://www.10xgenomics.com/datasets/mouse-brain-serial-section-2-sagittal-posterior-1-standard , respectively.

Techniques: Comparison, Derivative Assay

Journal: Briefings in Bioinformatics

Article Title: Cross-modality representation and multi-sample integration of spatially resolved omics data

doi: 10.1093/bib/bbag214

Figure Lengend Snippet: PRESENT integrates single-omics samples of multiple developmental stages or dissected areas. (a) The quantitative evaluation of different integration methods on the three spatial ATAC mouse embryo samples using 14 metrics divided into two categories, namely batch effect removal and biological variance conservation. The category scores of these two aspects were calculated by averaging the metrics within each category. An overall score for each integration method was computed using a 40/60 weighted mean of the category scores for batch effect removal and biological variance conservation. (b) The spatial visualization of spots across the three spatial ATAC mouse embryo samples colored by ground truth spatial domains. (c) The spatial visualization of spots across the three spatial ATAC mouse embryo samples colored by the spatial clusters identified based on different integration methods. The first, second and third row of b and c denotes the samples from E12.5, E13.5 and E15.5 stages, respectively. (d) The anatomic annotation of the sagittal region in P56 mouse brain provided by Allen Reference Atlas . (e) The joint spatial clustering results based on the latent embeddings obtained by STAligner on the two horizontal mouse brain sagittal samples generated by the 10x Genomics Visium platform. (f) The joint spatial clustering results based on the latent embeddings obtained by GraphST on the two horizontal mouse brain sagittal samples generated by the 10x Genomics Visium platform. (g) The joint spatial clustering results based on the latent embeddings obtained by PRESENT on the two horizontal mouse brain sagittal samples generated by the 10x Genomics Visium platform.

Article Snippet: The 10x Visium mouse brain datasets, including a sagittal anterior section and a sagittal posterior section, are accessible at the 10x Genomics websites https://www.10xgenomics.com/datasets/mouse-brain-serial-section-2-sagittal-anterior-1-standard and https://www.10xgenomics.com/datasets/mouse-brain-serial-section-2-sagittal-posterior-1-standard , respectively.

Techniques: Generated

Journal: eLife

Article Title: Evidence of off-target probe binding affecting 10x Genomics Xenium gene panels compromise accuracy of spatial transcriptomic profiling

doi: 10.7554/eLife.107070

Figure Lengend Snippet: ( A ) Overlap regions between Visium (orange outline) and Xenium (blue outline) data, shown on the Xenium histological image and the Visium histological image, respectively. ( B ) Log transformed aggregated total gene counts for spots (~55 μm × 55 μm) in both Xenium and Visium datasets, overlaid on their corresponding histological image.

Article Snippet: The Visium CytAssist dataset, collected from a breast cancer tissue block utilized in Janesick et al., was also downloaded from the 10x Genomics website ( https://www.10xgenomics.com/products/xenium-in-situ/preview-dataset-human-breast ).

Techniques: Transformation Assay

Journal: eLife

Article Title: Evidence of off-target probe binding affecting 10x Genomics Xenium gene panels compromise accuracy of spatial transcriptomic profiling

doi: 10.7554/eLife.107070

Figure Lengend Snippet: ( A ) Spatial gene expression of MS4A1 overlaid on the corresponding histological images for Xenium and Visium, accompanied by a density plot comparing Xenium vs. Visium MS4A1 expression. The dotted line indicates the identity line ( X = Y ), and the solid line represents the line of best fit. ( B ) Gene expression patterns for APOBEC3B: Xenium expression, Visium expression, the aggregated Visium expression combining APOBEC3B and its predicted off-target gene’s expression APOBEC3D and APOBEC3F, and Visium expression of APOBEC3B’s predicted off-targets APOBEC3D and APOBEC3F. Two density plots are shown: one comparing Xenium vs. Visium for APOBEC3B alone, and one comparing Xenium vs. the aggregated Visium expression of APOBEC3B with all off-targets. The dotted line indicates the identity line ( X = Y ), and the solid line represents the line of best fit. ( C ) Scatterplot of log-transformed total expression counts (with a pseudocount) for 307 genes comparing Visium and Xenium data. The dotted line indicates the identity line ( X = Y ), and points (genes) are colored by probe information.

Article Snippet: The Visium CytAssist dataset, collected from a breast cancer tissue block utilized in Janesick et al., was also downloaded from the 10x Genomics website ( https://www.10xgenomics.com/products/xenium-in-situ/preview-dataset-human-breast ).

Techniques: Gene Expression, Expressing, Transformation Assay

Journal: eLife

Article Title: Evidence of off-target probe binding affecting 10x Genomics Xenium gene panels compromise accuracy of spatial transcriptomic profiling

doi: 10.7554/eLife.107070

Figure Lengend Snippet: ( A ) Harmonized Uniform Manifold Approximation and Projection (UMAP) visualization of MS4A1 expression for Xenium and scRNA-seq data, accompanied by a scatterplot comparing Xenium vs. scRNA-seq MS4A1 cluster expression. The dotted line indicates the identity line ( X = Y ), and the solid line represents the line of best fit. ( B ) Comparison of APOBEC3B expression patterns on harmonized UMAP: Xenium expression, scRNA-seq expression, an aggregated scRNA-seq profile combining APOBEC3B and its predicted off-target genes’ expression APOBEC3D and APOBEC3F, and scRNA-seq expression of APOBEC3B’s predicted off-targets APOBEC3D and APOBEC3F. Two scatterplots are shown: one comparing Xenium vs. scRNA-seq for APOBEC3B cluster expression alone, and one comparing Xenium vs. the aggregated scRNA-seq cluster expression of APOBEC3B and its predicted off-targets. The dotted line indicates the identity line ( X = Y ), and the solid line represents the line of best fit. ( C ) Scatterplot of log-transformed total expression counts (with a pseudocount) for 313 genes between Visium and scRNA-seq data. The dotted line indicates the identity line ( X = Y ), and points (genes) are colored by probe information.

Article Snippet: The Visium CytAssist dataset, collected from a breast cancer tissue block utilized in Janesick et al., was also downloaded from the 10x Genomics website ( https://www.10xgenomics.com/products/xenium-in-situ/preview-dataset-human-breast ).

Techniques: Expressing, Comparison, Transformation Assay

Journal: eLife

Article Title: Evidence of off-target probe binding affecting 10x Genomics Xenium gene panels compromise accuracy of spatial transcriptomic profiling

doi: 10.7554/eLife.107070

Figure Lengend Snippet: ( A ) Gene expression patterns for ACTG2 : Xenium expression, Visium expression, the aggregated Visium expression combining ACTG2 and its predicted off-target gene’s expression ACTA1 , ACTB , and POTEM , and Visium expression of ACTG2 ’s predicted off-targets ACTA1 , ACTB , and POTEM . Two density plots are shown: one comparing Xenium vs. Visium for ACTG2 alone, and one comparing Xenium vs. the aggregated Visium expression. The dotted line indicates the identity line ( X = Y ), and the solid line represents the line of best fit. ( B ) Gene expression patterns for TUBB2B : Xenium expression, Visium expression, the aggregated Visium expression combining TUBB2B and its predicted off-target gene’s expression TUBB2B and TUBB2A , and Visium expression of TUBB2B ’s predicted off-target TUBB2A . Two density plots are shown: one comparing Xenium vs. Visium for TUBB2B alone, and one comparing Xenium vs. the aggregated Visium expression. The dotted line indicates the identity line ( X = Y ), and the solid line represents the line of best fit.

Article Snippet: The Visium CytAssist dataset, collected from a breast cancer tissue block utilized in Janesick et al., was also downloaded from the 10x Genomics website ( https://www.10xgenomics.com/products/xenium-in-situ/preview-dataset-human-breast ).

Techniques: Gene Expression, Expressing

Journal: eLife

Article Title: Evidence of off-target probe binding affecting 10x Genomics Xenium gene panels compromise accuracy of spatial transcriptomic profiling

doi: 10.7554/eLife.107070

Figure Lengend Snippet: ( A ) Gene expression patterns for ADH1B : Xenium expression, Visium expression, the aggregated Visium expression combining ADH1B and its predicted off-target gene’s expression ADH1A and ADH1C , and Visium expression of ADH1B ’s predicted off-targets ADH1A and ADH1C . Two density plots are shown: one comparing Xenium vs. Visium for ADH1B alone, and one comparing Xenium vs. the aggregated Visium expression. The dotted line indicates the identity line ( X = Y ), and the solid line represents the line of best fit. ( B ) Comparison of ADH1B expression patterns on harmonized Uniform Manifold Approximation and Projection (UMAP): Xenium expression, single-cell RNA sequencing (scRNA-seq) expression, an aggregated scRNA-seq profile combining ADH1B and its predicted off-target gene’s expression ADH1A and ADH1C , and scRNA-seq expression of ADH1B ’s potential off-targets ADH1A and ADH1C . Two scatterplots are shown: one comparing Xenium vs. scRNA-seq for ADH1B cluster expression alone, and one comparing Xenium vs. the aggregated scRNA-seq cluster expression. The dotted line indicates the identity line ( X = Y ), and the solid line represents the line of best fit.

Article Snippet: The Visium CytAssist dataset, collected from a breast cancer tissue block utilized in Janesick et al., was also downloaded from the 10x Genomics website ( https://www.10xgenomics.com/products/xenium-in-situ/preview-dataset-human-breast ).

Techniques: Gene Expression, Expressing, Comparison, Single Cell, RNA Sequencing

Journal: eLife

Article Title: Evidence of off-target probe binding affecting 10x Genomics Xenium gene panels compromise accuracy of spatial transcriptomic profiling

doi: 10.7554/eLife.107070

Figure Lengend Snippet: ( A ) Spatial gene expression of HDC overlaid on the corresponding histological images for Xenium and Visium. ( B ) Harmonized Uniform Manifold Approximation and Projection (UMAP) visualization of HDC expression for Xenium and single-cell RNA sequencing (scRNA-seq) data.

Article Snippet: The Visium CytAssist dataset, collected from a breast cancer tissue block utilized in Janesick et al., was also downloaded from the 10x Genomics website ( https://www.10xgenomics.com/products/xenium-in-situ/preview-dataset-human-breast ).

Techniques: Gene Expression, Expressing, Single Cell, RNA Sequencing

Journal: bioRxiv

Article Title: MatriSpace: Identification and visualization of spatially resolved ECM gene expression patterns in health and disease

doi: 10.64898/2026.04.26.720198

Figure Lengend Snippet: We illustrate the functionalities of MatriSpace in to using the following BrK dataset from https://cf.10xgenomics.com/samples/spatial-exp/2.0.0/CytAssist_FFPE_Human_Breast_Cancer/CytAssist_FFPE_Human_Breast_Cancer_web_summary.html . A. Hematoxylin and eosin (H&E) staining conducted pre-CytAssist is shown alongside the Visium spatial plot with default Seurat-derived cell type clusters. B. Spatial visualization of matrisome signature expression, illustrated here by ECM glycoproteins, is shown as either a spatial distribution ( left ) or a hotspot map ( right ). Users can switch to an interactive plot and/or adjust spot size. Within the interactive plot, users can zoom into regions of interest, select specific areas, or download the image using the interactive panel. Hovering over individual spots displays expression value and annotated cell type.

Article Snippet: The preloaded dataset collection comprises 198 10x Genomics Visium datasets obtained from public repositories, including 10x Genomics ( https://www.10xgenomics.com/datasets ), the National Cancer Institute’s Human Tumor Atlas Network ( https://humantumoratlas.org/ ; ( )), the Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo ; ( )), and Zenodo ( https://zenodo.org/ ) ( Supplemental Table S1 ).

Techniques: Staining, Derivative Assay, Expressing