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

Journal: Scientific Reports

Article Title: Evolution from adherent to suspension: systems biology of HEK293 cell line development

doi: 10.1038/s41598-020-76137-8

Figure Lengend Snippet: Comparisons of genomic and transcriptomic profiles of HEK293 cells showed taxonomic divergence between parental HEK293 and progeny cell lines. ( a ) A schematic overview of the lineage relationship of the six HEK293 cell lines used in this study. Blue dots represent adherent cells whereas grey dots represent suspension cell lines. ( b ) Genomic comparison between HEK293 cell lines based on Spearman correlation coefficients of read counts. Darker blue color indicates higher correlation. ( c ) Sample-to-sample comparison between transcriptomes illustrated by a heatmap and hierarchical clustering of taxonomical divergence between samples. Darker blue color indicates shorter Euclidean distance between samples and more similarity. ( d ) PCA plot showing the separation in expression pattern between samples. ( e ) RNA expression levels (in DESeq2 median of ratios) and standard deviations (n = 3) of stably integrated viral genes (EBNA-1, Large T, E1A and E1B) in HEK293 cell lineages determined by RNA sequencing.

Article Snippet: Figure 6 Gene expression validation of the 38 previously identified differentially expressed genes in 63 human cell lines, identified nine significantly differentially expressed genes between suspension and adherent cell lines. ( a ) PCA transcriptomic data of 63 human cell-lines from the Human Protein Atlas shows a clear separation of suspension and adherent cell lines from different tissues. ( b ) Range of normalized counts in HPA cell lines for each of the previously identified 38 genes, differentially expressed between all adherent and suspension HEK293 cell lines.

Techniques: Suspension, Comparison, Expressing, RNA Expression, Stable Transfection, RNA Sequencing

Journal: Scientific Reports

Article Title: Evolution from adherent to suspension: systems biology of HEK293 cell line development

doi: 10.1038/s41598-020-76137-8

Figure Lengend Snippet: Gene expression validation of the 38 previously identified differentially expressed genes in 63 human cell lines, identified nine significantly differentially expressed genes between suspension and adherent cell lines. ( a ) PCA transcriptomic data of 63 human cell-lines from the Human Protein Atlas shows a clear separation of suspension and adherent cell lines from different tissues. ( b ) Range of normalized counts in HPA cell lines for each of the previously identified 38 genes, differentially expressed between all adherent and suspension HEK293 cell lines. The black line in each box shows median of normalized counts for the gene. ( c ) Genes that are differentially expressed between adherent and suspension cells using a Mann–Whitney U-test, with p-values < 0.01, are highlighted in purple, where length of bars shows logarithmic fold change of median between two groups and the color of bars denotes degree of significance of p-value. Non-significant genes have gray bars.

Article Snippet: Figure 6 Gene expression validation of the 38 previously identified differentially expressed genes in 63 human cell lines, identified nine significantly differentially expressed genes between suspension and adherent cell lines. ( a ) PCA transcriptomic data of 63 human cell-lines from the Human Protein Atlas shows a clear separation of suspension and adherent cell lines from different tissues. ( b ) Range of normalized counts in HPA cell lines for each of the previously identified 38 genes, differentially expressed between all adherent and suspension HEK293 cell lines.

Techniques: Gene Expression, Biomarker Discovery, Suspension, MANN-WHITNEY

Journal: Cell Reports

Article Title: Humanized mice reveal a macrophage-enriched gene signature defining human lung tissue protection during SARS-CoV-2 infection

doi: 10.1016/j.celrep.2022.110714

Figure Lengend Snippet: HNFL mice recapitulate a potent and balanced human antiviral response against SARS-CoV-2 infection (A) Cluster heatmap representing the top 33 proteins significantly (p ≤ 0.05) up- ( Z > 0) and downregulated ( Z < 0) in HNFL-LX at 2 DPI (10 6 PFU, n = 4) in comparison with naive (n = 4) HNFL-LX. (B) Relative differential expression of the set of 33 selected proteins in HNFL-LX (n = 4) and NRGL-LX (n = 4) at 2 DPI (10 6 PFU) represented through a semi-cluster heatmap. Proteins significantly (p ≤ 0.05) up- ( Z > 0) and downregulated ( Z < 0) are labeled in red. (C and D) Differentially expressed proteins in HNFL-LX (C) or NRGL-LX (D) at 2 DPI. Proteins with p ≤ 0.05 (horizontal dashed line) and with logFC ≥ 1 or ≤ −1 (vertical dashed lines) are considered significantly up- or downregulated, respectively. Naive n = 4; 2 DPI n = 4. (E and F) Differentially phosphorylated proteins at 2 DPI in HNFL-LX (E) and NRGL-LX (F). Proteins with p ≤ 0.05 (horizontal dashed line) and with logFC ≥ 1 or ≤ −1 (vertical dashed lines) are considered significantly up- or downregulated, respectively. Naive n = 4; 2 DPI n = 4. (G and H) Significantly (p ≤ 0.05) differentially expressed genes (upregulated, red; downregulated, blue) in HNFL-LX at 2 (G) and 7 DPI (H) following infection (10 6 PFU) in comparison with naive HNFL-LX. Fold changes were computed using MAST (Model-based Analysis of Single-Cell Transcriptomics) from pooled scRNA-seq clusters. Transcripts with p ≤ 10 −200 (horizontal dotted line) and with log2 fold change ≥ 0.2 or ≤ −0.2 (vertical dotted lines) are highlighted. Naive n = 2; 2 DPI n = 3. (I) List of PDGs found to be upregulated by both proteomics and transcriptomic approaches in inoculated HNFL-LX (YES) or solely via the proteomic approach (NO). Only PDGs found to be upregulated through both approaches were considered as definitive PDGs. (J–L) Differentially expressed transcripts in inoculated (J, 2 DPI; K, 7 DPI; 10 6 PFU) and contralateral non-inoculated NRGL-LX (L, 7 DPI) in comparison with naive NRGL-LX. Transcripts with p adj ≤ 0.05 and with log2 fold change ≥ 2 are considered significantly up- (red) or downregulated (blue). Naive n = 3; 2 DPI n = 4; 7 DPI n = 6; CL/contralateral n = 3. (M) Number of differentially up- (red) or downregulated (blue) genes per time point (2 and 7 DPI) and infection settings (inoculated/CL) in NRGL-LX. Naive n = 3; 2 DPI n = 4; 7 DP n = 6; CL/contralateral n = 3. See also Figure S6 ; Tables S2 , , and .

Article Snippet: • Transcriptomic data from single cell RNA sequencing are available through the National Center for Biotechnology Information Gene Expression Omnibus (GEO) under series accession no. GSE180063 .

Techniques: Infection, Comparison, Quantitative Proteomics, Labeling, Single-cell Transcriptomics

Journal: Cell & Bioscience

Article Title: Transcriptionally imprinted glycomic signatures of acute myeloid leukemia

doi: 10.1186/s13578-023-00981-0

Figure Lengend Snippet: Overview of the conducted study. AML glycosylation was explored on the level of glycomics (GPST datasets) and transcriptomics (GSE and DepMap datasets). Based on the depicted datasets originating from cell lines and primary cells we sought to explore cellular glycosylation, involved GSTs, and responsible TFs

Article Snippet: AML cell line transcriptomics data was obtained from the depmap portal (Expression 22Q2 Public; Broad Institute, Cambridge, MA, USA) [ ].

Techniques: Glycoproteomics

Journal: Cell & Bioscience

Article Title: Transcriptionally imprinted glycomic signatures of acute myeloid leukemia

doi: 10.1186/s13578-023-00981-0

Figure Lengend Snippet: Glycomic overview of various AML cell lines. a PCA of glycosylation features derived from glycomics data of 19 AML cell lines. Individual cell lines are annotated and colored by their FAB classifications as assigned earlier . b The associated score plot depicts considered glycan features, which are linked to their respective glycan class ( N -, O -, and GSL) by color (purple, orange, and green) and symbol (triangle, square, and circle). In addition, arrows indicate features that are linked to a specific type of fucosylation. c Radar plots are showing the differences in glycosylation features between AML classes M5 and M6. Again, these features are subdivided into their respective classes based on color and symbols. Data on all AML cell lines were z-transformed prior to visualizing differences between FAB classes in these radar plots. d Spearman correlation of selected glycosylation features between the different glycan classes. Thick connective lines indicate a good correlation whereas thin connective lines show less correlation. Correlation values are depicted

Article Snippet: AML cell line transcriptomics data was obtained from the depmap portal (Expression 22Q2 Public; Broad Institute, Cambridge, MA, USA) [ ].

Techniques: Glycoproteomics, Derivative Assay, Transformation Assay

Journal: Cell & Bioscience

Article Title: Transcriptionally imprinted glycomic signatures of acute myeloid leukemia

doi: 10.1186/s13578-023-00981-0

Figure Lengend Snippet: Correlation of glycosylation features of N -, O -, and GSL-glycans with the expression of selected TFs in AML cell lines. Correlation coefficients were obtained by Spearman analysis and are indicated by color as indicated in the legend. Of note, due to rather weak correlations of ST6GALs and glycomics data, we did not include these GSTs in our overview. Significant values are marked with * (p ≤ 0.05), ** (p ≤ 0.01,) and *** (p ≤ 0.001). Correlation coefficients and p-values are listed in the Additional file : Table S9

Article Snippet: AML cell line transcriptomics data was obtained from the depmap portal (Expression 22Q2 Public; Broad Institute, Cambridge, MA, USA) [ ].

Techniques: Glycoproteomics, Expressing

Journal: Cell & Bioscience

Article Title: Transcriptionally imprinted glycomic signatures of acute myeloid leukemia

doi: 10.1186/s13578-023-00981-0

Figure Lengend Snippet: Differences in glycan signatures of M5 and M6 AML cell lines as well as corresponding GST and TF expression. M5 and M6 classes are presented as grey and brown rectangles, respectively. GSTs displayed in the figure present a positive correlation with the corresponding glycosylation feature. The underlined TFs correlate with the glycosylation features. The underlined TFs colored in red are positively correlated with GSTs

Article Snippet: AML cell line transcriptomics data was obtained from the depmap portal (Expression 22Q2 Public; Broad Institute, Cambridge, MA, USA) [ ].

Techniques: Glycoproteomics, Expressing

Journal: Cell & Bioscience

Article Title: Transcriptionally imprinted glycomic signatures of acute myeloid leukemia

doi: 10.1186/s13578-023-00981-0

Figure Lengend Snippet: GST and TF expression in primary AML cells. a Determination of the matrix correlation coefficient RV2 (0.49) between expression patterns observed in cell lines and primary samples. b Spearman correlation of selected GSTs with TFs in AML cell lines (left) and primary AML cells (right). c Comparison of the expression of selected GSTs and TFs in primary AML cells grouped by FAB classification. Significances were assessed by one-way ANOVA followed by a Tukey post-hoc test. Significant values are marked with * (p ≤ 0.05), ** (p ≤ 0.01), *** (p ≤ 0.001), and **** (p ≤ 0.0001)

Article Snippet: AML cell line transcriptomics data was obtained from the depmap portal (Expression 22Q2 Public; Broad Institute, Cambridge, MA, USA) [ ].

Techniques: Expressing, Comparison

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