Journal: Briefings in Bioinformatics

Article Title: A universal framework for single-cell multi-omics data integration with graph convolutional networks

doi: 10.1093/bib/bbad081

Figure Lengend Snippet: Overview of the GCN-SC framework on single-cell multi-omics data integration. GCN-SC can take single-cell multi-omics data as input, including data of gene expression (scRNA-seq), chromatin accessibility (scATAC-seq) and protein expression (CITE-seq). Each omics data was pre-processed to a uniform count matrix, in which each row represents one feature and each column represents one cell. As gene expression has much higher frequency of dropout compared to other omics data, scImpute was conducted on the matrix to reduce the sparsity of the matrix. Next, one scRNA-seq data were used as the reference, while the rest were considered as the query. By using MNN algorithm, cell-pairs with the closest Euclidean distance were identified, and then a mixed graph was constructed based on cell–cell relationships, which includes both inter-dataset edges and intra-dataset edges. A GCN that includes four layers further took this mixed graph to adjust matrices from the query dataset. Finally, NMF was implemented for dimension reduction on the adjusted matrices from the query and the matrix from the reference.

Article Snippet: Integrating these two is not only able to reveal gene regulatory relationships associated with cell heterogeneity but can also increase the power for classification of subcellular population comparing to using only single omics data [ , ].

Techniques: Biomarker Discovery, Gene Expression, Expressing, Construct

Journal: Briefings in Bioinformatics

Article Title: A universal framework for single-cell multi-omics data integration with graph convolutional networks

doi: 10.1093/bib/bbad081

Figure Lengend Snippet: Integration of single-cell multi-omics data. ( A ) The UMAP visualization of the scRNA-seq and scATAC-seq data from the same group of cells (dataset 5). ( B ) The UMAP visualization of the integrated data processed by GCN-SC. The UMAP visualization of the integration data by Seurat ( C ), GLUER ( D ) and GCNSC ( E ). ( F ) The alignment score map of the three integration algorithms Seurat, GLUER and GCN-SC on dataset 5.

Article Snippet: Integrating these two is not only able to reveal gene regulatory relationships associated with cell heterogeneity but can also increase the power for classification of subcellular population comparing to using only single omics data [ , ].

Techniques: Biomarker Discovery

Journal: bioRxiv

Article Title: A rule-based data-informed cellular consensus map of the human mononuclear phagocyte cell space

doi: 10.1101/658179

Figure Lengend Snippet: (A) Workflow to generate a new consensus map of the human mononuclear myeloid cell compartment. (B) Visualization of ∼1.4 mio. live CD45 + Lin(CD3, CD19, CD20, CD56) - cells after UMAP dimensionality reduction of the flow cytometry panel introduced in A (left panel), mononuclear myeloid cell compartment (second panel), overlay of index-sorted cells (third panel), UMAP topology of the index-sorted cells based on the single-cell transcriptome data (most right panel, see also ). Grey areas in the third panel represent the CD45 + Lin - cell space. (C) Phenograph clustering of the flow cytometry data projected onto the FACS-based UMAP topology. (D) Color-coded visualization of markers used to define the mononuclear myeloid cell compartment. (E) Overlay of the cell gating strategies according to maps 1 and 2 . See also .

Article Snippet: Subsequently, we integrated the original monocyte map 1 single-cell transcriptome data (mono 1-4) into an external dataset of 33,148 PBMCs (short: 33k-PBMC dataset, https://support.10xgenomics.com/single-cell-gene-expression/datasets/1.1.0/pbmc33k ) and performed dimensionality reduction of the corresponding monocyte and NK-cell-related cellular spaces using UMAP ( ).

Techniques: Flow Cytometry

Journal: bioRxiv

Article Title: A rule-based data-informed cellular consensus map of the human mononuclear phagocyte cell space

doi: 10.1101/658179

Figure Lengend Snippet: (A) Visualization of cell surface markers detected by index sorting on the 2,509 cells which were used to define the UMAP topology of the index-sorted cells based on the single cell transcriptome data. (B) Visualization of gene-level expression of the respective cell surface markers within the 2.509 cells which were used to define the UMAP topology of the index-sorted cells based on the single cell transcriptome data.

Article Snippet: Subsequently, we integrated the original monocyte map 1 single-cell transcriptome data (mono 1-4) into an external dataset of 33,148 PBMCs (short: 33k-PBMC dataset, https://support.10xgenomics.com/single-cell-gene-expression/datasets/1.1.0/pbmc33k ) and performed dimensionality reduction of the corresponding monocyte and NK-cell-related cellular spaces using UMAP ( ).

Techniques: Expressing

Journal: bioRxiv

Article Title: A rule-based data-informed cellular consensus map of the human mononuclear phagocyte cell space

doi: 10.1101/658179

Figure Lengend Snippet: ( A ) Overlay of signatures derived from map 2 DCs onto the new scRNA-seq-based UMAP topology consensus map. ( B ) UMAP topology based on map 1 single-cell transcriptomes of map 1 DC1-6 cells and overlay of signatures derived from map 2 DCs. ( C ) Overlay of signatures derived from map 1 DC1-6 cells onto the new scRNA-seq-based UMAP topology consensus map. UMAP topology based on flow cytometry data derived from ∼1.4 mio live CD45 + Lin(CD3, CD19, CD20, CD56) - cells (see ) and separate overlays of cluster 26 defined by Phenograph (see ), map 1 DC5, map 2 pre-DC, and scRNA-seq-based cluster seven representing transcriptomic progenitor DC signatures (see ). (E) Enrichment of map 2 defined pDC, cDC1, cDC2 and pre-DC signatures in the map 1 DC1-6 subsets. (F) Heatmap of the average expression values of hallmark genes defined for map 1 DC1-6 subsets in both map 1 DC1-6 as well as map 2 DCs subsets.

Article Snippet: Subsequently, we integrated the original monocyte map 1 single-cell transcriptome data (mono 1-4) into an external dataset of 33,148 PBMCs (short: 33k-PBMC dataset, https://support.10xgenomics.com/single-cell-gene-expression/datasets/1.1.0/pbmc33k ) and performed dimensionality reduction of the corresponding monocyte and NK-cell-related cellular spaces using UMAP ( ).

Techniques: Derivative Assay, Flow Cytometry, Expressing

Journal: bioRxiv

Article Title: A rule-based data-informed cellular consensus map of the human mononuclear phagocyte cell space

doi: 10.1101/658179

Figure Lengend Snippet: (A) Annotation of cell types within the combined dataset (33-K PBMC and new scRNA-seq dataset). (B) Graphs in the left panel predict cell labels from the 33-K PBMC dataset by using the transcriptome information from the new scRNA-seq dataset. Graphs in the right panel show the visualization of the cells from the new scRNA-seq dataset after integration with the 33-k PBMC dataset. (C) UMAP dimensionality reduction of around 260.000 human cord blood cells and cell annotation based on markers obtained from the unrelated 33k-PBMC dataset . (D) Reduction of the HCA dataset to cells, which were found within clusters associated with monocytes or dendritic cells. (E) Graphs within the left panel show the prediction scores calculated for the respective cell types of the new scRNA-seq data. Graphs in the right panel show the visualization of the cells from the new scRNA-seq data after “anchoring” together with the HCA dataset.

Article Snippet: Subsequently, we integrated the original monocyte map 1 single-cell transcriptome data (mono 1-4) into an external dataset of 33,148 PBMCs (short: 33k-PBMC dataset, https://support.10xgenomics.com/single-cell-gene-expression/datasets/1.1.0/pbmc33k ) and performed dimensionality reduction of the corresponding monocyte and NK-cell-related cellular spaces using UMAP ( ).

Techniques:

Journal: Frontiers in Immunology

Article Title: Single-Cell Profiling to Explore Immunological Heterogeneity of Tumor Microenvironment in Breast Cancer

doi: 10.3389/fimmu.2021.643692

Figure Lengend Snippet: Summary table for the hallmark breast cancer studies using single-cell technologies.

Article Snippet: Spatial mapping of single-cell RNA-seq data , Spatial Transcriptomics (in-house) , Tumor tissue sections from BRCA patients diagnosed with HER2+ subtype , Demonstration of the heterogeneous nature of tumor-immune interactions and reveal interpatient differences in immune cell infiltration patterns , Potential for an improved stratification and description of the tumor-immune interplay, which is likely to be essential in treatment decisions , ( , ) .

Techniques: Biomarker Discovery, Sequencing, Activation Assay, Mass Cytometry, Clinical Proteomics, Imaging, Single-cell Analysis

Journal: Life Science Alliance

Article Title: PD-L1 regulates inflammatory programs of macrophages from human pluripotent stem cells

doi: 10.26508/lsa.202302461

Figure Lengend Snippet: (A) Schematic overview of the generation of human yolk sac organoids and macrophages. Human pluripotent stem cells (hPSCs) were differentiated into yolk sac organoids by commercial media (STEMdiff), and then, macrophages were induced with CSF-1 (the upper graph). The CD274 +/+ or CD274 -/- hPSCs were differentiated into macrophages treated with IFNγ and LPS, and then, the stimulated macrophages were used in the following single-cell RNA sequencing (the bottom graph). (B) Bright-field images of representative cellular morphology from hPSC differentiation into human yolk sac organoids and macrophages. EB: embryoid body; HPC: hematopoietic progenitor cell. (C) Flow cytometry analysis of CD45, CD11B, PD-1, and PD-L1 on macrophages in macrophage basal medium (Mφ medium), Mφ medium plus IFNγ and LPS, and Mφ medium plus IL-4. (D) Flow cytometry analysis of PD-L1 expression in hPSCs, human yolk sac organoids (STEMdiff A and STEMdiff B), and macrophages. Representative flow cytometry data are shown here. Data are shown as the mean ± SEM. N = 5 measurements from two independent experiments performed with 2∼3 technical replicates.

Article Snippet: Single-cell transcriptomic sequencing data of WT and CD274 -/- samples from 10X Genomics were processed with CellRanger software (v7.0.0) and mapped to the GRCh38 human reference genome.

Techniques: RNA Sequencing, Flow Cytometry, Expressing

Journal: Life Science Alliance

Article Title: PD-L1 regulates inflammatory programs of macrophages from human pluripotent stem cells

doi: 10.26508/lsa.202302461

Figure Lengend Snippet: (A) PD-L1 genetic knockout protocol in the hPSC stage. (B) Genetic knockout strategy. The sgRNA was designed on WT exon 2 (upper panel), and the PD-L1 locus after targeting is shown (lower panel). Sanger sequencing result reveals the four base-pair deletions on KO exon 2. (C) Pluripotency marker expression in WT and KO lines. (D) Sanger sequencing of two PD-L1 knockouts. The sgRNA is designed to target exon 2. Two KO clones (KO1: A7-1; and KO2: B2-3) are endowed with deletions or insertions validated by sequencing. (E, F) Only stimulated WT macrophages highly express PD-L1. We did PD-L1 mRNA expression of the WT and two PD-L1 knockouts from PSCs, monocytes, unstimulated macrophages to stimulated macrophages by qRT-PCR (the left figure) and FACS (the right figure). Data are shown as the mean ± SEM. N = 3∼4 measurements from one independent experiment performed with two technical replicates. **** P < 0.0001. Unsti, unstimulated; Sti, stimulated.

Article Snippet: Single-cell transcriptomic sequencing data of WT and CD274 -/- samples from 10X Genomics were processed with CellRanger software (v7.0.0) and mapped to the GRCh38 human reference genome.

Techniques: Knock-Out, Sequencing, Marker, Expressing, Clone Assay, Quantitative RT-PCR

Journal: Life Science Alliance

Article Title: PD-L1 regulates inflammatory programs of macrophages from human pluripotent stem cells

doi: 10.26508/lsa.202302461

Figure Lengend Snippet: (A) Flow cytometry analysis of PD-L1 expression of the WT and two PD-L1 knockouts at different stages (from PSCs, monocytes, unstimulated macrophages to stimulated macrophages). IgG isotype is used as a control for gate-positive population. Unsti: unstimulated; Sti: stimulated. (B) Immunofluorescence of PD-L1 expression on WT and CD274 -/- macrophages in Mφ medium plus IFNγ and LPS. Scale bar: 50 μm. (C, D) Flow cytometry analysis of macrophage (CD45 + CD11B + ) (C) and percentage (D) of WT and CD274 -/- macrophages in Mφ medium plus IFNγ and LPS. Statistical results shown here were from five independent experiments (n = 8–9). ** P < 0.01. (E) PD-L1 inhibitor BMS-1166 reduced macrophage development in human yolk sac organoids. Flow cytometry analysis of CD45 and CD11B on macrophages after 500 nM or 1 µM BMS-1166 treatment (below). Representative flow cytometry data are shown here. (F) Uniform Manifold Approximation and Projection visualization of single cells from yolk sac organoids in WT ( n = 9,181 , in blue) and PD-L1 KO ( n = 16,258 , in red). Each dot represents a single cell. In total, 19 cell types were annotated and are displayed in different colors (see ). The right panel shows the expression of macrophages and monocyte-specific marker genes. (G) Dot-line chart displaying the decrease in monocyte/macrophage (MC/Mϕ) proportion upon PD-L1 KO. Statistical significance ( P -value) was calculated by the likelihood-ratio test and adjusted by the Benjamini–Hochberg method using the R package DCATS.

Article Snippet: Single-cell transcriptomic sequencing data of WT and CD274 -/- samples from 10X Genomics were processed with CellRanger software (v7.0.0) and mapped to the GRCh38 human reference genome.

Techniques: Flow Cytometry, Expressing, Control, Immunofluorescence, Marker

Journal: Life Science Alliance

Article Title: PD-L1 regulates inflammatory programs of macrophages from human pluripotent stem cells

doi: 10.26508/lsa.202302461

Figure Lengend Snippet: (A) Quality control of the single-cell RNA-sequencing dataset. Violin plots show the total RNA counts, total feature (gene) counts, percent mitochondrial gene expression, and relative proportions of mitochondrial and ribosomal gene expression of each cell within both WT and PD-L1 KO samples. (B) Detection of doublets and singlets in two samples using the DoubletFinder R package. (C) UMAP visualization of single cells from organoids in WT and PD-L1 KO ( CD274 -/- ) organoids. Each dot represents a single cell. In total, 19 cell types were annotated and are displayed in different colors. (D) Dot plot showing the expression of marker genes used for cell-type annotation. The dot size represents the percentage of cells with the corresponding gene expression, and the color intensity indicates the average expression of each gene. (E) Stacked bar plot showing the change in the cell-type proportion between WT and KO samples. The statistical significance test (LRT_ P -value) is similarly used in .

Article Snippet: Single-cell transcriptomic sequencing data of WT and CD274 -/- samples from 10X Genomics were processed with CellRanger software (v7.0.0) and mapped to the GRCh38 human reference genome.

Techniques: Control, RNA Sequencing, Gene Expression, Expressing, Marker

Journal: Life Science Alliance

Article Title: PD-L1 regulates inflammatory programs of macrophages from human pluripotent stem cells

doi: 10.26508/lsa.202302461

Figure Lengend Snippet: (A) UMAP visualization of monocyte and macrophage (MC/Mϕ) populations colored by genotype (WT, PD-L1 KO). (B) Box plot showing the decreased expression of selected macrophage-determining transcription factors within the MC/Mϕ cluster upon PD-L1 KO. ( P -value was calculated using the “bimod” test in the Seurat R package and corrected by the “Benjamini–Hochberg” method.) (C) Selected KEGG pathways enriched by gene set enrichment analysis of differentially expressed genes between PD-L1 KO and WT cells within the MC/Mϕ cluster. NES, normalized enrichment score. Differentially expressed genes were calculated using the same method described in . (D) Bar graphs showing the ranking of major outgoing (upper panel) and incoming (lower panel) signals of MC/Mϕ upon PD-L1 KO compared with WT. The rank of signals was based on differences in the overall information flow of each group. (E) Gene set enrichment analysis shows the enrichment of differentially expressed down-regulated genes upon PD-L1 KO in MC/Mϕ cells in the interferon-gamma response. (F) Box plot and dot plot showing the reduction in both the expression and percentage of cells expressing interferon-gamma receptor IFNGR1 and interferon-alpha receptor IFNAR1 , respectively, within the MC/Mϕ cluster upon PD-L1 KO. ( P -value was calculated using the “bimod” test in the Seurat R package and corrected by the “Benjamini–Hochberg” method).

Article Snippet: Single-cell transcriptomic sequencing data of WT and CD274 -/- samples from 10X Genomics were processed with CellRanger software (v7.0.0) and mapped to the GRCh38 human reference genome.

Techniques: Expressing

Journal: Life Science Alliance

Article Title: PD-L1 regulates inflammatory programs of macrophages from human pluripotent stem cells

doi: 10.26508/lsa.202302461

Figure Lengend Snippet: Volcano plot showing the differentially expressed gene (DEGs) calculated by the Seurat R package built-in “bimod” method, between macrophages in WT and PD-L1 KO ( CD274 -/- ). DEGs (in blue/red color) are defined by q-value (calculated by the “Benjamini–Hochberg” method in default) less than 0.05, and log 2 fold change in an absolute value larger than 0.2. Gene symbols labeled are DEGs with log 2 fold change in an absolute value larger than 1. DEGs in blue or red color are genes that are down-regulated or up-regulated upon PD-L1 KO, separately.

Article Snippet: Single-cell transcriptomic sequencing data of WT and CD274 -/- samples from 10X Genomics were processed with CellRanger software (v7.0.0) and mapped to the GRCh38 human reference genome.

Techniques: Labeling

Journal: Life Science Alliance

Article Title: PD-L1 regulates inflammatory programs of macrophages from human pluripotent stem cells

doi: 10.26508/lsa.202302461

Figure Lengend Snippet: (A) Luminex profiling of secreted IFN/TNF from MC/Mϕs upon PD-L1 KO compared with WT (WT, PD-L1 KO). Data are shown as the mean ± SEM. N = 3∼4 measurements from one independent experiment performed with two technical replicates.* P < 0.05, ** P < 0.01, *** P < 0.001, and **** P < 0.0001. (B) Luminex profiling of secreted interleukins from MC/Mϕ upon PD-L1 KO compared with WT (WT, PD-L1 KO). Data are shown as the mean ± SEM. N = 3∼4 measurements from one independent experiment performed with two technical replicates. * P < 0.05, ** P < 0.01, *** P < 0.001, and **** P < 0.0001. (C) qRT-PCR analysis of transcription factors (SPI1/MAFB) and interferon receptors (IFNAR1/IFNGR1) between WT and PD-L1 KO. The relative expression is normalized by the GAPDH expression level. Data are shown as the mean ± SEM. N = 3∼4 measurements from one independent experiment performed with two technical replicates. ** P < 0.01, *** P < 0.001, and **** P < 0.0001. (D) Luminex profiling of interferon-related cytokines (IFN/TNF) from MC/Mϕs upon the PD-L1 chemical inhibitor group (WT with drug) compared with WT. Data are shown as the mean ± SEM. N = 3∼4 measurements from one independent experiment performed with two technical replicates.* P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001, and ns: no significance. (E) Luminex profiling of interleukins (IL-1/IL-10/IL-12/IL-13) from MC/Mϕs upon the PD-L1 chemical inhibitor group (WT with drug) compared with WT. Data are shown as the mean ± SEM. N = 3∼4 measurements from one independent experiment performed with two technical replicates.* P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001, and ns: no significance. (F) qRT-PCR analysis of transcription factors (SPI1/MAFB) and interferon receptors (IFNAR1/IFNGR1) between WT and the PD-L1 chemical inhibitor group (WT with drug). The relative expression is normalized by the GAPDH expression level. Data are shown as the mean ± SEM. N = 3∼4 measurements from one independent experiment performed with two technical replicates. ** P < 0.01 and *** P < 0.001. Source data are available for this figure.

Article Snippet: Single-cell transcriptomic sequencing data of WT and CD274 -/- samples from 10X Genomics were processed with CellRanger software (v7.0.0) and mapped to the GRCh38 human reference genome.

Techniques: Luminex, Quantitative RT-PCR, Expressing

Journal: Life Science Alliance

Article Title: PD-L1 regulates inflammatory programs of macrophages from human pluripotent stem cells

doi: 10.26508/lsa.202302461

Figure Lengend Snippet: Scatter plot visualizing the differential outgoing and incoming signaling associated with the monocyte/macrophage group between the WT and PD-L1 KO groups. Positive values indicate an increase in the KO group, whereas negative values indicate an increase in the WT dataset. Analysis was performed with the CellChat R package. Dot plot showing the relative significant interactions (ligand–receptor pair) of MC/Mϕs for GDF and THBS signaling pathways based on their average expression. The dot color and size represent the communication probability and P -values calculated by CellChat, respectively.

Article Snippet: Single-cell transcriptomic sequencing data of WT and CD274 -/- samples from 10X Genomics were processed with CellRanger software (v7.0.0) and mapped to the GRCh38 human reference genome.

Techniques: Protein-Protein interactions, Expressing

Journal: Life Science Alliance

Article Title: PD-L1 regulates inflammatory programs of macrophages from human pluripotent stem cells

doi: 10.26508/lsa.202302461

Figure Lengend Snippet: Transcriptomic data were used for correlation analysis between human in vivo fetal yolk sac (YS) hematopoietic clusters (YS_EC, endothelial cells; YS_Mac, macrophages; and YS_YSMP1,2, myeloid-biased progenitors) and PD-L1 WT hematopoietic cell populations (PD-L1_EC, endothelial cells; MC/Mϕs, monocyte/macrophages; CMPs, common myeloid progenitors; and granulocytes).

Article Snippet: Single-cell transcriptomic sequencing data of WT and CD274 -/- samples from 10X Genomics were processed with CellRanger software (v7.0.0) and mapped to the GRCh38 human reference genome.

Techniques: In Vivo