Journal: Briefings in bioinformatics

Article Title: Single-cell multi-omics analysis identifies context-specific gene regulatory gates and mechanisms.

doi: 10.1093/bib/bbae180

Figure Lengend Snippet: Figure 7. ROC and PR curves are plotted for TF-gene network inference in the Spleen-10X_P7_6 sample from mouse tissue.

Article Snippet: Context specific network inference in mouse scRNA-seq datasets To further assess the performance of scGATE, we utilized 10X Genomics scRNA-seq datasets from five different mouse tissues (Spleen, Lung, Liver, Kidney and Heart) obtained from the Tabula Muris project [25].

Techniques:

Journal: Frontiers in Cell and Developmental Biology

Article Title: SARS-CoV-2 Causes Acute Kidney Injury by Directly Infecting Renal Tubules

doi: 10.3389/fcell.2021.664868

Figure Lengend Snippet: Histological location of ACE2 and SARS-CoV-2 in the human kidney. (A) The public single-cell RNA sequencing datasets based on two different platforms (10X Genomics and Microwell-seq) were analyzed. After quality control, dimension descending, and cell type identification, major cell types in the kidney were shown in the t-SNE plot. ACE2 was highly expressed in the proximal tubular cells, whereas TMPRSS2 was highly expressed in the intercalated cells. (B) ACE2 and TMPRSS2 protein levels were analyzed in normal kidney tissues (n = 3). ACE2 signature was detected mainly on the apical side of the proximal tubular cells. In contrast, TMPRSS2 was mainly localized at the distal tubular cells. (C) SARS-CoV-2 nucleoprotein was co-stained with ACE2 or TMPRSS2 by Immunofluorescence (IFC) staining in 10 COVID-19 patients’ kidney samples. SARS-CoV-2 nucleoprotein was detected in ACE2 + or TMPRSS2 + renal tubular cells. (D) A novel in situ hybridization assay (RNAscope ® Assay) targeting the SARS-CoV-2 Spike gene was positive in the kidney distal tubular cells of COVID-19 patients.

Article Snippet: In order to investigate the localization of both the ACE2 and TMPRSS2 in the human kidney, we firstly performed bioinformatic analysis on public single-cell RNA sequencing (scRNA-seq) datasets based on two different platforms (10X Genomics and Microwell-seq).

Techniques: RNA Sequencing, Control, Staining, Immunofluorescence, In Situ Hybridization, RNAscope

Journal: GigaScience

Article Title: scGraph2Vec: a deep generative model for gene embedding augmented by graph neural network and single-cell omics data

doi: 10.1093/gigascience/giae108

Figure Lengend Snippet: Performance comparison between different methods. (A) Comparison of silhouette coefficients among standard outputs of scGraph2Vec and other variants using brain and PBMC datasets. The x-axis is the number of clusters. The y-axis is the silhouette coefficients. (B, C) Comparison of silhouette coefficients among scGraph2Vec and 11 tools using brain (B) and PBMC (C) datasets. The methods scapGNN and SAUCIE were excluded as they failed to extract clear gene clusters. The x-axis is the number of clusters. The y-axis is the silhouette coefficients and DBI. (D) Comparison of gene clustering similarity across methods using the Jaccard index. (E) Comparison of the similarity between gene clusters from different methods and the MSigDB hallmark gene sets.

Article Snippet: Other scRNA-seq datasets for this article are available via the following databases: brain dataset from the human cell landscape [ ]; heart dataset from the Single Cell Portal [ ], with accession code SCP498; kidney dataset from the Kidney Cell Atlas, specifically the mature kidney dataset [ ]; lung dataset from the Human Lung Cell Atlas [ ] with Synapse ID: syn21041850; PBMC dataset from the 10X Genomics 3k PBMC supporting dataset; and LUAD dataset from EMBL-EBI database, with accession codes E-MTAB-6149 and E-MTAB-6653.

Techniques: Comparison

Journal: Nature Communications

Article Title: Model-based deep embedding for constrained clustering analysis of single cell RNA-seq data

doi: 10.1038/s41467-021-22008-3

Figure Lengend Snippet: Summary of four small scRNA-seq datasets.

Article Snippet: The scRNA-seq datasets supporting this study are available publicly: 10X PBMC dataset ( https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/pbmc4k ); mouse bladder cells ( https://figshare.com/s/865e694ad06d5857db4b ); worm neuron cells ( http://atlas.gs.washington.edu/worm-rna/docs/ ); human kidney cells ( https://github.com/xuebaliang/scziDesk/tree/master/dataset/Young ); Macosko mouse retina cells ( https://scrnaseq-public-datasets.s3.amazonaws.com/scater-objects/macosko.rds ); Shekhar mouse retina cells ( https://scrnaseq-public-datasets.s3.amazonaws.com/scater-objects/shekhar.rds ); CITE-seq dataset ( https://github.com/canzarlab/Specter/tree/master/data ); human liver dataset ( https://github.com/BaderLab/scClustViz ).

Techniques: Sequencing

Journal: Nature Communications

Article Title: Model-based deep embedding for constrained clustering analysis of single cell RNA-seq data

doi: 10.1038/s41467-021-22008-3

Figure Lengend Snippet: a 10X PBMC; b Mouse bladder cells; c Worm neuron cells; d Human kidney cells. Clustering performances of scDCC on four small scRNA-seq datasets with different numbers of pairwise constraints, measured by NMI, CA, and ARI. All experiments are repeated ten times, and the means and standard errors are displayed.

Article Snippet: The scRNA-seq datasets supporting this study are available publicly: 10X PBMC dataset ( https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/pbmc4k ); mouse bladder cells ( https://figshare.com/s/865e694ad06d5857db4b ); worm neuron cells ( http://atlas.gs.washington.edu/worm-rna/docs/ ); human kidney cells ( https://github.com/xuebaliang/scziDesk/tree/master/dataset/Young ); Macosko mouse retina cells ( https://scrnaseq-public-datasets.s3.amazonaws.com/scater-objects/macosko.rds ); Shekhar mouse retina cells ( https://scrnaseq-public-datasets.s3.amazonaws.com/scater-objects/shekhar.rds ); CITE-seq dataset ( https://github.com/canzarlab/Specter/tree/master/data ); human liver dataset ( https://github.com/BaderLab/scClustViz ).

Techniques:

Journal: Nature Communications

Article Title: Model-based deep embedding for constrained clustering analysis of single cell RNA-seq data

doi: 10.1038/s41467-021-22008-3

Figure Lengend Snippet: Comparison of 2D visualization of embedded representations of ZINB model-based autoencoder ( a , d , g , j ), scDeepCluster ( b , e , h , k ) and scDCC with pairwise constraints ( c , f , i , l ). The same instances and constraints are visualized for each dataset ( a – c , 10X PBMC; d – f Mouse bladder cells; g – i Worm neuron cells; j – l Human kidney cells). The red lines indicate cannot-link and blue lines indicate must-link. The axes are arbitrary units. Each point represents a cell. The distinct colors of the points represent the true labels, and colors are arbitrarily selected.

Article Snippet: The scRNA-seq datasets supporting this study are available publicly: 10X PBMC dataset ( https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/pbmc4k ); mouse bladder cells ( https://figshare.com/s/865e694ad06d5857db4b ); worm neuron cells ( http://atlas.gs.washington.edu/worm-rna/docs/ ); human kidney cells ( https://github.com/xuebaliang/scziDesk/tree/master/dataset/Young ); Macosko mouse retina cells ( https://scrnaseq-public-datasets.s3.amazonaws.com/scater-objects/macosko.rds ); Shekhar mouse retina cells ( https://scrnaseq-public-datasets.s3.amazonaws.com/scater-objects/shekhar.rds ); CITE-seq dataset ( https://github.com/canzarlab/Specter/tree/master/data ); human liver dataset ( https://github.com/BaderLab/scClustViz ).

Techniques: Comparison

Journal: Nature Communications

Article Title: Model-based deep embedding for constrained clustering analysis of single cell RNA-seq data

doi: 10.1038/s41467-021-22008-3

Figure Lengend Snippet: a Clustering performances of PhenoGraph and k-means on proteins, SC3, and scDCC (without and with constraints) on mRNAs of CITE-seq PBMC dataset, measured by NMI, CA, and ARI. All experiments are repeated ten times (one dot represents one experiment), and the means and standard errors are displayed. Constraints were generated from protein expression levels. b CD4 and CD8 protein expression levels in the identified CD4 and CD8 specific cells. Colors (Cyan represents CD8 cells and red represents CD4 cells) are cluster labels identified by scDCC with and without constraints on proteins. Cell labels were annotated by differential expression analysis.

Article Snippet: The scRNA-seq datasets supporting this study are available publicly: 10X PBMC dataset ( https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/pbmc4k ); mouse bladder cells ( https://figshare.com/s/865e694ad06d5857db4b ); worm neuron cells ( http://atlas.gs.washington.edu/worm-rna/docs/ ); human kidney cells ( https://github.com/xuebaliang/scziDesk/tree/master/dataset/Young ); Macosko mouse retina cells ( https://scrnaseq-public-datasets.s3.amazonaws.com/scater-objects/macosko.rds ); Shekhar mouse retina cells ( https://scrnaseq-public-datasets.s3.amazonaws.com/scater-objects/shekhar.rds ); CITE-seq dataset ( https://github.com/canzarlab/Specter/tree/master/data ); human liver dataset ( https://github.com/BaderLab/scClustViz ).

Techniques: Generated, Expressing, Quantitative Proteomics

Journal: Nature Communications

Article Title: Knowledge-graph-based cell-cell communication inference for spatially resolved transcriptomic data with SpaTalk

doi: 10.1038/s41467-022-32111-8

Figure Lengend Snippet: a Overview of SpaTalk, including the input, intermediate process of decoding spatially resolved cell–cell communications, and output. b Conceptual framework of cell-type decomposition with SpaTalk. Five different spatial technologies and datasets were selected and analyzed: spot-based ST data (Slide-seq and 10x Visium) and single-cell ST data (STARmap, MERFISH, and seqFISH+). NNLM was used to dissect the optimal proportion of cell types for the projection of cells from scRNA-seq reference data onto the spatial cells/spots, generating single-cell ST data with known cell types. c Schematic representation of SpaTalk to infer spatially resolved cell–cell communications mediated by LRIs. The inter-cellular and intracellular scores were obtained and combined from the cell–cell graph network and the LRT-knowledge graph (KG), respectively, by integrating the KNN, permutation test, and random walk algorithms. L, ligand; R, receptor; TF, transcription factor; T, target. d Visualization of spatially resolved cell–cell communications, including a heatmap, Sankey plot, and diagram of the LRI from senders to receivers in space, as well as ligand–receptor–target (LRT) signaling pathways over the reconstructed single-cell ST data.

Article Snippet: For 10x Visium, the ST data and scRNA-seq data of human SCC were downloaded from the Gene Expression Omnibus (GEO) repository: “ GSE144240 ” , and the mouse kidney ST and single-nucleus RNA-sequencing data were obtained from 10x Visium Spatial Gene Expression of “‘Adult Mouse Kidney (FFPE)’ [ https://www.10xgenomics.com/resources/datasets ]” and “ GSE119531 ” , respectively.

Techniques: Protein-Protein interactions

Journal: Nature Communications

Article Title: Knowledge-graph-based cell-cell communication inference for spatially resolved transcriptomic data with SpaTalk

doi: 10.1038/s41467-022-32111-8

Figure Lengend Snippet: a Slide-seq (v2) ST dataset of the mouse kidney involving 27044 spots and 20591 genes. CD-IC, collecting duct intercalated cells; CD-PC, collecting duct principal cells; DCT, distal convoluted tubules; Endo, endothelial cells; FB, fibroblasts; GC, granular cells; Macro, macrophages; MC, mesangial cells; PCT, proximal convoluted tubules; Pod, podocytes; TAL, thick ascending limb; vSMC, vascular smooth muscle cells. b Significantly enriched LRIs that mediate cell–cell communications among MC, Endo, and Pod inferred by SpaTalk with P < 0.05. The P -value represents the significance of spatial proximity of LRIs using the permutation test. Top 20 LRI pairs were plotted for Pod-Endo communications. c Significantly enriched Gene ontology (GO) biological processes determined with the Metascape web tool for the ligands and receptors from MC and Pod to Endo inferred by SpaTalk. d Spatial d istribution of the Vegfa-Kdr pairs between the Pod senders and Endo receivers. e Number of Vegfa-Kdr pairs from Pod to Endo in space across other slides of mouse kidney. f Mouse kidney ST dataset generated from 10x Visium involving 3124 spots and 19465 genes and the reconstructed single-cell ST data by SpaTalk. The percent of MC, Endo, and Pod as well as the expression of the corresponding known markers were plotted. PT, proximal tubule; CNT, connecting tubule; LHDL, loop of Henle descending loop; LHAL, loop of Henle ascending loop. g Significantly enriched LRIs that mediate Pod-Endo communications. Top 20 LRIs (left) and spatial distribution of the Angpt1-Tek pairs between the Pod senders and Endo receivers were plotted. h Communications of Endo-MC mediated by the Pdgfb-Pdgfrb interaction in space and the spatial distances of Pdgfb-Pdgfrb in Endo-MC and all cell–cell pairs, respectively. P -value was calculated with the one-sided t -test. The numbers of data points (minima, 25th percentile, median, 75th percentile, and maxima) for the boxplots from left to right are 2,237,963 and 405,756, respectively.

Article Snippet: For 10x Visium, the ST data and scRNA-seq data of human SCC were downloaded from the Gene Expression Omnibus (GEO) repository: “ GSE144240 ” , and the mouse kidney ST and single-nucleus RNA-sequencing data were obtained from 10x Visium Spatial Gene Expression of “‘Adult Mouse Kidney (FFPE)’ [ https://www.10xgenomics.com/resources/datasets ]” and “ GSE119531 ” , respectively.

Techniques: Generated, Expressing

Journal: Nature Communications

Article Title: Knowledge-graph-based cell-cell communication inference for spatially resolved transcriptomic data with SpaTalk

doi: 10.1038/s41467-022-32111-8

Figure Lengend Snippet: a Visium spot-based ST dataset of human skin SCC in patient 2 with the matched scRNA-seq dataset involving the main keratinocytes (KC), stromal cells, and immune cells. b Cell-type decomposition by SpaTalk. Cyc, cycling; Diff, differentiating; NK, natural killer; FB, fibroblasts; TSK, tumor-specific keratinocytes. c TSK percent and TSK score across spatial spots. The expression of known TSK markers is plotted. d Pearson’s correlation coefficient between the TSK percent and TSK score. The gray band represents the 95% confidence interval of the mean value for the fitting straight line. e Cell-type decomposition by SpaTalk at single-cell resolution for the spot-based human skin SCC ST data. f Contour plot of TSK, FB, and Endo based on the reconstructed single-cell ST atlas by SpaTalk. g TSK leading spots with a TSK score >0.8 in space. The bar chart represents the number of different cell types and the line chart represents the number of neighbors adjacent to TSKs among the TSK leading spots. h Visium spot-based ST dataset of human skin SCC in patient 10 and the cell-type decomposition by SpaTalk showing the percent of TSKs across 621 spatial spots.

Article Snippet: For 10x Visium, the ST data and scRNA-seq data of human SCC were downloaded from the Gene Expression Omnibus (GEO) repository: “ GSE144240 ” , and the mouse kidney ST and single-nucleus RNA-sequencing data were obtained from 10x Visium Spatial Gene Expression of “‘Adult Mouse Kidney (FFPE)’ [ https://www.10xgenomics.com/resources/datasets ]” and “ GSE119531 ” , respectively.

Techniques: Expressing

Journal: Cell Genomics

Article Title: Unified cross-modality integration and analysis of T cell receptors and T cell transcriptomes by low-resource-aware representation learning

doi: 10.1016/j.xgen.2024.100553

Figure Lengend Snippet:

Article Snippet: The pairwise scRNA-seq/TCR-seq data can be accessed through the following lincs: (1) Kidney dataset: GEO: GSE216763 ; (2) SCC dataset: GEO: GSE123813 ; (3) SARS-CoV2 dataset: GEO: GSE191089 ; (4) BCC dataset: GEO: GSE123813 ; (5) 10X donor1 dataset: https://www.10xgenomics.com/cn/datasets/cd-8-plus- t -cells-of-healthy-donor-1-1-standard-3-0-2 ; (6) 10X donor2 dataset: https://www.10xgenomics.com/cn/datasets/cd-8-plus- t -cells-of-healthy-donor-2-1-standard-3-0-2 ; (7) 10X donor3 dataset: https://www.10xgenomics.com/cn/datasets/cd-8-plus- t -cells-of-healthy-donor-3-1-standard-3-0-2 ; (8) 10X donor4 dataset: https://www.10xgenomics.com/cn/datasets/cd-8-plus-t-cells-of-healthy-donor-4-1-standard-3-0-2 .

Techniques: Software