Journal: Nature Communications

Article Title: Predicting bacterial infection outcomes using single cell RNA-sequencing analysis of human immune cells

doi: 10.1038/s41467-019-11257-y

Figure Lengend Snippet: scRNA-seq analysis of human PBMCs before and after ex vivo Salmonella infection. a Overview of the scRNA-seq experiment: PBMCs were isolated from a blood sample of a healthy individual and were infected ex vivo with Salmonella (exposed), or remained unexposed (naïve). Overall ~7000 cells were sequenced using 10x genomics Chromium. b Visualization of the scRNA-seq data using forced layout on a two-dimensional space by k-nearest neighbor (KNN)-graph ( k = 20; naive cells (gray) and exposed cells (black)). K-means clustering of the cells revealed the seven main cell types: NK cells (red), CD8 T cells (orange), CD4 T cells (yellow), NKT cells (brown), B cells (green), monocytes (purple), and dendritic cells (DC; pink), as inferred using cluster-specific genes and marker genes expression (see Supplementary Data 1 and Supplementary Fig. ). Colored contours represent cells which belong to the same cell type in each sample (see also Supplementary Fig. for complete KNN-graph with edges and clusters). c Expression levels of representative genes from the infection signature (see methods and Supplementary Fig. ). Top: general infection genes which are upregulated following Salmonella infection in all exposed cells, and bottom: cell-type specific infection genes. Gene expression is shown using the same layout as in b , with the nodes colored by the indicated gene expression in each cell (see colorbar). d KNN-graph ( k = 20) of the scRNA-seq data after removal of the global infection signature eliminated the separation between naïve and exposed cells for all cell types, except for the monocytes, which contain intracellular bacteria. Colors and contours are the same as in b (see also Supplementary Fig. )

Article Snippet: For NKT enrichment, CD3 + CD56 + NKT Cell Isolation Kit (Miltenyi Biotec, 130-093-064) was used according to the manufacturer’s manual.

Techniques: Ex Vivo, Infection, Isolation, Marker, Expressing, Gene Expression, Bacteria

Journal: Nature Communications

Article Title: Predicting bacterial infection outcomes using single cell RNA-sequencing analysis of human immune cells

doi: 10.1038/s41467-019-11257-y

Figure Lengend Snippet: scRNA-seq based dynamic deconvolution to infer cell-type composition and infection-induced states. a Illustration of the dynamic deconvolution approach: transformation of the scRNA-seq data into two properties that can be inferred from bulk measurements - immune cell-type composition and infection-induced cell state. Cell-type composition is represented as a one-dimensional vector, where k j is the number of cells from a specific cell type j . The infection-induced cell state ( S j ) is represented as the induction of cell-type specific genes following infection. Using our deconvolution algorithm (equations at the bottom, see methods) we infer robust estimators for the relative abundance ( K j ) and infection-induced state ( S j ) of each cell type across individuals from bulk RNA-seq measurements, as illustrated on the right. b and c Reduction of the scRNA-seq data into two sets of genes which represent intrinsic cell-type properties ( b ) and cell-type specific infection-induced states ( c ). Cells are ordered by their cell type (color-coded at the bottom) and cell origin (white for naïve and black for exposed cells); see colorbar for expression levels. d Validation of our deconvolution algorithm using FACS experiment. Comparison between the percentages of each cell type as measured by FACS ( x -axis) to the relative abundance by our deconvolution ( y -axis). There is a high concordance between the deconvolution prediction and the cellular composition as determined by FACS. Each dot is the mean of 3–4 replicates for the FACS and bulk RNA-seq. Presented also are the standard error (SEM) for the replicates. e Validation of the infection-induced signatures in sorted populations. Presented are the expression levels of the intrinsic cell types (from b ) and infection-induced marker genes (from c ) in bulk measurements of sorted naïve and exposed NKT cells and monocytes. The NKT infection-induced state is upregulated following infection solely in the exposed NKT cells (left). Similarly, the monocytes cell-type signature is expressed exclusively in naïve and exposed monocytes, and the monocytes infection-induced signature is upregulated following infection exclusively in the exposed monocytes (right). Each sample is the mean of 2–4 technical replicates; cell-type signatures are color-coded ( n denotes the number of genes in each signature)

Article Snippet: For NKT enrichment, CD3 + CD56 + NKT Cell Isolation Kit (Miltenyi Biotec, 130-093-064) was used according to the manufacturer’s manual.

Techniques: Infection, Transformation Assay, Plasmid Preparation, RNA Sequencing, Expressing, Biomarker Discovery, Comparison, Marker

Journal: Nature Communications

Article Title: Predicting bacterial infection outcomes using single cell RNA-sequencing analysis of human immune cells

doi: 10.1038/s41467-019-11257-y

Figure Lengend Snippet: Dynamic deconvolution of immune cell states reveals differences between WT and TLR10 individuals. a Overview of the bulk RNA-seq experiment: isolated PBMCs from blood of eight healthy individuals: WT (green) and TLR10 (purple), were infected ex vivo with Salmonella and bulk RNA-seq was measured before infection ( t = 0), 4 ( t = 4), and 8 ( t = 8) hours post-infection in triplicates. b Box-plots of the relative abundance or infection-induced state of each cell type before and 4 or 8 h post-infection in WT vs. TLR10 uncover significant difference in NKT infection-induced states following infection. The box represents the median and 25–75th percentile, whiskers encompass all data points. * p -value < 0.05, two sample t -test. Values are inferred from bulk measurements using our deconvolution algorithm; estimators of cell-type index are in arbitrary units (au). c Unique molecular identifier (UMI) counts of IFNγ from each cell by scRNA-seq data revealed production of IFNγ exclusively from NKT cells 4 h post-infection; color-coded cell types are indicated at the bottom. d Gene Set Enrichment Analysis (GSEA) of the ‘monocytes infection-induced genes’ in the genes that are higher in WT relative to TLR10 individuals 8 h post-infection (see methods) reveals partition of the gene signature into two sets which imply differences in sub-types activation following infection. Red to blue bar at the bottom represents the gene expression fold change between WT and TLR10 individuals (see also colorbar to the right); the black bars below indicate positions of the ‘monocytes infection-induced genes’ in the ordered list of genes. p -value is calculated by the maximal Enrichment Score (ES), which also defines the group of enriched genes (all genes to the left of the maximal ES position, i.e. the dashed line). e Expression matrix (scRNA-seq data) of the set of ‘monocytes infection-induced genes’ that were enriched in the genes that are higher in WT relative to TLR10 (genes to the left of the dashed line in d ). Presented is the mean expression of these genes from each cell sub-type of the naive and exposed monocytes. The left bar represents the mean expression of these genes in each sub-types; monocytes sub-types color-coded as in Fig.

Article Snippet: For NKT enrichment, CD3 + CD56 + NKT Cell Isolation Kit (Miltenyi Biotec, 130-093-064) was used according to the manufacturer’s manual.

Techniques: RNA Sequencing, Isolation, Infection, Ex Vivo, Activation Assay, Gene Expression, Expressing

Journal: Nature Communications

Article Title: Predicting bacterial infection outcomes using single cell RNA-sequencing analysis of human immune cells

doi: 10.1038/s41467-019-11257-y

Figure Lengend Snippet: Differences in cell–cell signaling is associated with different bacterial control in WT and TLR10 individuals. a Isolated PBMCs from a WT individual were infected ex vivo with Salmonella in the presence of isotype control or anti-IFNγ neutralizing antibodies. Intracellular bacterial growth was determined by CFU 8 h post-infection. Data are presented as bar chart with the average of three independent experiments with four replicates and SEM, all data points are presented by dots. Blocking IFNγ in WT individual increases bacterial load; statistical significance was determined using Friedman’s test, p -value is indicated. b Intracellular bacteria number was determined by CFU 8 h after ex vivo Salmonella infection of monocytes alone or co-culture of monocytes and NKT cells. Data are presented as bar chart with mean and SEM of eight replicates; data points are presented by dots. Co-culture of monocytes with NKT cells provided better control of intracellular bacterial infection relative to monocytes alone; statistical significance was determined using the unpaired Mann–Whitney U test, p -value is indicated in the figure. c Secreted IFNγ levels from monocytes alone or co-culture of monocytes and NKT cells were measured before and after ex vivo Salmonella infection. Data are presented as bar chart with mean and SEM of eight replicates; data points are presented by dots. Co-culture of monocytes and NKT cells secreted significantly higher levels of IFNγ relative to monocytes alone; statistical significance was determined using the unpaired Mann–Whitney U test, p -value is indicated. d Isolated PBMCs from eight individuals (WT in green and TLR10 in purple) were infected ex vivo with Salmonella . Intracellular bacterial growth was determined by CFU 8 h post-infection. Data are presented as mean and SEM of three replicates. TLR10 individuals exhibit higher bacterial load than WT individuals; statistical significance was determined using the unpaired Mann–Whitney U test, p -value is indicated

Article Snippet: For NKT enrichment, CD3 + CD56 + NKT Cell Isolation Kit (Miltenyi Biotec, 130-093-064) was used according to the manufacturer’s manual.

Techniques: Control, Isolation, Infection, Ex Vivo, Blocking Assay, Bacteria, Co-Culture Assay, MANN-WHITNEY

Journal: Cancer Control : Journal of the Moffitt Cancer Center

Article Title: Clinical Characteristics and Prognosis of Small Cell Carcinoma in the Nasopharynx: A Population-Based Study

doi: 10.1177/10732748221087075

Figure Lengend Snippet: ( A ) Hematoxylin and eosin (H&E) staining of the nasopharyngeal tumor specimen under ×100 magnification. ( B ) Hematoxylin and eosin (H&E) staining of the nasopharyngeal tumor specimen under ×200 magnification. The tumor mass is immunopositive for ( C ) CD56, ( D ) creatine kinase (CK), ( E ) Ki67, and ( F ) synaptophysin (SYN; ×200 magnification); it is immunonegative for ( F ) chromogranin A (CgA), ( G ) EBER, ( H ) p40, and ( I ) p63.

Article Snippet: Hematoxylin and eosin (H&E) staining was performed on tumor tissue samples, followed by immunohistochemical staining with Ki67 rabbit monoclonal antibody (1:400, #9027, Cell Signaling Technology, Boston, the United States), CD56 mouse monoclonal antibody (1:800, #3576, Cell Signaling Technology, Boston, the United States), pan-keratin mouse monoclonal antibody (1:200, #67306, Cell Signaling Technology, Boston, the United States), synaptophysin (SYN) rabbit monoclonal antibody (1:200, #36406, Cell Signaling Technology, Boston, the United States), chromogranin A (CgA) rabbit monoclonal antibody (MAB-0707, MXB Biotechnologies, Fuzhou, China), p40 monoclonal antibody (RMA-0815, MXB Biotechnologies, Fuzhou, China), and p63 rabbit monoclonal antibody (1:450, #39692, Cell Signaling Technology, Boston, the United States).

Techniques: Staining

Journal: International immunopharmacology

Article Title: Compromised adenosine-A2AR axis contributes to recurrent spontaneous abortion by promoting proinflammatory macrophage polarization.

doi: 10.1016/j.intimp.2025.114838

Figure Lengend Snippet: Fig. 1. RSA patients had lower levels of CD39 and adenosine. (A) Flow cytometry gating strategy for the analysis of live+ CD45+ dNK cells in decidual tissues isolated from patients. (B) Flow cytometry staining of CD39 on dNK cells in decidual tissues from normal pregnancies (HC) (n = 12) and RSA patients (n = 10). (C) Immunofluorescence staining for CD39 (green), CD56 (red), and DAPI (blue) in decidual tissues. Yellow arrows indicate CD56+ dNK cells with high expression of CD39. (D) Quantification of CD56+ CD39+ positive cells per field (n = 4). Scale bars: 50 μm. (E) dNK cells were isolated from decidual tissues of normal pregnant women (n = 12) and RSA patients (n = 10), co-cultured with HTR-8/SVneo cells in 24-well plates (dNK: HTR-8/SVneo cells = 2:1) for 24 h and adenosine levels in the media were measured by ELISA. Data are expressed as mean ± SD. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Article Snippet: The cryostat sections were fixed with 4 % PFA and incubated in the blocking buffer at room temperature for 1 h. Sections were incubated overnight at 4 ◦C with primary antibodies: mouse anti-human CD39 (Abcam, cat: ab97552, dilution 1:100) and rabbit anti-human CD56 (CST, cat: 99746, dilution 1:100), followed by 2 h incubation with secondary antibodies: FITC-conjugated goat anti-mouse IgG (H + L) (ZSGB-BIO, cat: ZF0312, dilution 1:50) and Rhodamine-conjugated goat anti-rabbit IgG (ZSGB-BIO, cat: ZF0316, dilution 1:50).

Techniques: Flow Cytometry, Isolation, Staining, Immunofluorescence, Expressing, Cell Culture, Enzyme-linked Immunosorbent Assay

Journal: Discovery Medicine

Article Title: Development of FAP-Targeted Chimeric Antigen Receptor NK-92 Cells for Non-Small Cell Lung Cancer

doi: 10.24976/discov.med.202335176.41

Figure Lengend Snippet: Fig. 5. hFAP-CAR-NK-92 cell therapy inhibited tumor growth in NOD-SCID mice. (A) The schematic of tumor models and cell therapy. (B) Tumor morphology and volume were observed and analyzed (n = 3). (C) The expression of CD56 and Ki-67 in tumor tissue

Article Snippet: FAP-CAR expression in NK-92 cells was assessed via biotinylated antibody (#9417, Cell Signaling Technology, Danvers, MA, USA) and anti-mouse IgG (H+L), then added with streptavidin-labelled PE combined with APCconjugated anti-CD56 antibody (#73521, #51997, Cell Signaling Technology, Danvers, MA, USA).

Techniques: Expressing