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: Frontiers in Immunology

Article Title: Unveiling spatial complexity in solid tumor immune microenvironments through multiplexed imaging

doi: 10.3389/fimmu.2024.1383932

Figure Lengend Snippet: Immunophenotyping panel for multiplexed tissue imaging of cancer.

Article Snippet: CD3 , REA1151 , 50 , 130-120-267 , FITC (APC) , Miltenyi Biotec.

Techniques: Imaging

Journal: Cell Death and Differentiation

Article Title: Characterizing the regulatory Fas (CD95) epitope critical for agonist antibody targeting and CAR-T bystander function in ovarian cancer

doi: 10.1038/s41418-023-01229-7

Figure Lengend Snippet: A Schematic of various His-tagged FasL mutants described in the figure. B Binding analysis of His-tagged FasL to 96-well-immobilized Fc-tagged Fas by ELISA. C OVCAR3 cells were treated with indicated His-tagged FasL, followed by immunoprecipitation using anti-His antibody and immunoblotting using anti-Fas antibody. Total Fas levels were equal in all samples. D Schematic of FasL A-A′ and G-H beta loops with corresponding residues either WT or mutated (Red) in the loops are shown. Negatively charged residues are blue. E Cell-killing assay of OVCAR3 and Jurkat cells 36 h after treatment of indicated FasL mutants. IC 50 values are shown at the bottom. F , G Cell survival assay of human and murine PBMC-derived and CD3-positive T cells 48 h after treatment of indicated FasL mutants. Error bars in ( B ), ( F ) and ( G ) represent SEM ( n = 3+).

Article Snippet: CD3ε (CD3-12) Rat mAb , Cell Signaling Technology , 4443.

Techniques: Binding Assay, Enzyme-linked Immunosorbent Assay, Immunoprecipitation, Western Blot, Clonogenic Cell Survival Assay, Derivative Assay

Journal: Cell Death and Differentiation

Article Title: Characterizing the regulatory Fas (CD95) epitope critical for agonist antibody targeting and CAR-T bystander function in ovarian cancer

doi: 10.1038/s41418-023-01229-7

Figure Lengend Snippet: A CD3-enriched human PBMC-derived T cells were treated with either IgG1 alone or CD3/CD28 agonist alone or indicated Fas-ligand mutants (DB-FasL) alone or together for 60 min (as indicated), followed by immunoblotting of indicated T-cell scaffold proteins and kinases. B CD3-enriched human PBMC-derived T cells were treated as indicated on top (DB-FasL mutants) for 1 h, followed by BS3 crosslinker treatment to capture the membrane-clustered Fas. Lysates were run in a non-reducing and partially denaturing gel and immunoblotted for Fas. C Jurkat cell killing assay of indicated FasL APLS mutants. rFOLR1 is a negative control. IC50 values are shown. D Same as ( C ), except human PBMC-derived CD3-enriched pan-T cells were used after activation with CD3/CD28 antibodies. E Ribbon diagram showing zoomed FasL trimer interface in sky blue (1), green (2), and pink (3) colors (PDB: 4MSV). Hydrophobic residues Y192 and F190 (colored orange and represented with sticks) on the C beta-strand of three FasL monomers (C1, C2, and C3) are shown. A247, with a short sidechain, is on the F beta-strands of three FasL monomers (F1, F2, and F3) and is colored pink and represented with sticks. F Same as ( E ), except the ALPS mutant E247 (substituted with A247 of the F beta-strands of three FasL monomers) with sidechain potentially sterically clashing with Y192 (on the C beta-strands of other FasL monomers) is shown in pink sticks. G Indicated FasL mutants on top were added to Jurkat cells in culture wells. After 30 min, soup and total lysates were pelleted together, followed by SDS-PAGE on non-reducing and partially denaturing gel and immunoblotting with anti-His antibody to detect recombinant FasL trimer formation on cells. Error bars in ( D ) represent SEM ( n = 3+).

Article Snippet: CD3ε (CD3-12) Rat mAb , Cell Signaling Technology , 4443.

Techniques: Derivative Assay, Western Blot, Membrane, Negative Control, Activation Assay, Mutagenesis, SDS Page, Recombinant

Journal: Cell Death and Differentiation

Article Title: Characterizing the regulatory Fas (CD95) epitope critical for agonist antibody targeting and CAR-T bystander function in ovarian cancer

doi: 10.1038/s41418-023-01229-7

Figure Lengend Snippet: A Schematic of genetic construction of various N-terminal His-tagged FasL mutants generated in tandem right next to FasL WT . B CHO cells expressing various tandem FasL mutants (as in A ) were purified using HisTrap Ni Sepharose and were run on reducing gel along with an IgG1 for size confirmation. C Binding of indicated His-tagged tandem FasL mutants (as indicated on the right) to 96-well-immobilized Fc-tagged Fas by ELISA. D Cell survival assay of Jurkat cells after treatment with indicated His-tagged tandem FasL mutants. E Jurkat cells were treated with indicated His-tagged tandem FasL mutants on top for 1 hr, followed by low-dose BS3 treatment on ice (15 min) to capture the membrane-clustered Fas. Lysates were run in a non-reducing and partially denaturing gel, followed by Fas immunoblotting. F CD3-enriched human PBMC-derived T cells were treated with indicated His-tagged tandem FasL mutants followed by Fas clustering as described in ( E ).

Article Snippet: CD3ε (CD3-12) Rat mAb , Cell Signaling Technology , 4443.

Techniques: Generated, Expressing, Purification, Binding Assay, Enzyme-linked Immunosorbent Assay, Clonogenic Cell Survival Assay, Membrane, Western Blot, Derivative Assay

Journal: Cell Death and Differentiation

Article Title: Characterizing the regulatory Fas (CD95) epitope critical for agonist antibody targeting and CAR-T bystander function in ovarian cancer

doi: 10.1038/s41418-023-01229-7

Figure Lengend Snippet: A Cartoon schematic of chimeric Fas with human ectodomain and murine TM, ICD, and signal peptide. B Immunoblotting confirmation of murine ID8 cell clones expressing huFas chimeric clones 1 and 2. C E09 IgG1 Flow cytometry confirmation of murine ID8 cells expressing huFas ectodomain. D Confirmation of cytotoxic sensitivity of chimeric huFas stable murine ID8 cells against Fas agonist reagents. E Cartoon schematic of anti-FOLR1 (Farletuzumab) expressing CAR-T (CAR-T FOLR1 ). F Immunoblotting confirmation of CAR-T FOLR1 construct expression in T cells using CD3 zeta antibody. G Flow cytometry using goat anti-human Fab specific antibody after two rounds of CAR-T construction lentiviral infections (represents n = 3). H CAR-T FOLR1 cells were co-cultured either alone or with OVCAR3 cells or ID8 Parental cells, or ID8 huFas stable cells at an effector/target (E/T) ratio 5:1 for 4 h followed by granzyme B measurement using ELISA ( n = 4). I Schematic of CAR-T FOLR1 cells co-cultured with the 50:50 mix of GFP + ID8 huFas stable cells and OVCAR3. In additional sets, cultured plates were precoated either with recombinant rFas WT or rFas R87A (5 μg/ml). J %GFP flow cytometry signal as an indicator of FOLR1 antigen negative but GFP positive ID8 huFas stable cell lysis from the experiment described in I ( n = 3). K Same as ( J ) and ( I ), except in one additional set, a 50:50 mix of ID8 Parental (huFas - ) cells and OVCAR3 were used for indicated times. Immunoblotting analyzed the total cell lysates for cleaved caspase-8 levels. L Same as ( K ), except additional controls were included via precoating the culture plates with rFas WT or rFas R87A (5 μg/ml) ( n = 3). M Same as ( K ) and ( L ), except GFP levels (as an indicator of ID8 huFas stable cell lysis) were analyzed using immunoblotting. Error bars in ( J ) represent SEM ( n = 3+).

Article Snippet: CD3ε (CD3-12) Rat mAb , Cell Signaling Technology , 4443.

Techniques: Western Blot, Clone Assay, Expressing, Flow Cytometry, Construct, Cell Culture, Enzyme-linked Immunosorbent Assay, Recombinant, Stable Transfection, Lysis

Journal: Cell Death and Differentiation

Article Title: Characterizing the regulatory Fas (CD95) epitope critical for agonist antibody targeting and CAR-T bystander function in ovarian cancer

doi: 10.1038/s41418-023-01229-7

Figure Lengend Snippet: Details of commercial kits, antibodies, and other reagents.

Article Snippet: CD3ε (CD3-12) Rat mAb , Cell Signaling Technology , 4443.

Techniques: Virus, Recombinant, Plasmid Preparation, Proliferation Assay, Modification, Protease Inhibitor, Transfection, Generated, Software, Imaging, Chromatography

Journal: Journal for immunotherapy of cancer

Article Title: PARP inhibitors enhance antitumor immune responses by triggering pyroptosis via TNF-caspase 8-GSDMD/E axis in ovarian cancer.

doi: 10.1136/jitc-2024-009032

Figure Lengend Snippet: Figure 5 Deficiency of GSDME blunts the immune response triggered by PAPR inhibitor in vivo. (A–L) Immunocompetent C57BL/6 mice were transplanted with wild type (WT) or Gsdme-deficient ID8 cells and challenged with niraparib for about 4 weeks. Tumors were harvested and subjected to bulk T-cell receptor (TCR) sequencing and evaluation of immune status. The frequency of T-cell clonotypes in the top 25% of TCR repertoires was shown using pie charts ((A) n=4) and quantified (B). The clonal expansion was evaluated using clonality (C). The TCR diversity was calculated by normalized Shannon diversity entropy (D). The proportion of dendritic cell differentiation (CD11c+MHCIIHigh) and maturation (CD11c+MHCIIHighCD86High) in tumors (E), lymph nodes (F), and spleens (G) was determined by flow cytometry. The proportion of CD3+ T cells and NK cells among CD45+ immune cells in tumors was determined by flow cytometry (H). The production of IFN-γ in tumor-infiltrated CD4/8+ T and NK cells was examined by flow cytometry (I). The expression of granzyme B in tumor-infiltrated CD8+ T and NK cells was evaluated by mean fluorescence intensity (J). Representative images of GSDME, CD4, CD8, and GZMB IHC staining in tumor sections (K) and numbers of indicated immune cells in a ×20 field of a microscope (L). Scale bars: 200 µm. WT or Gsdme-deficient ID8 cells were intrabursally transplanted into C57BL/6 mice and received niraparib and/or anti-PD-1 treatment. Representative bioluminescent images of mice bearing WT and Gsdme-KO ID8 tumors at the endpoint (M). The tumor weight was quantified in each mouse treated with niraparib and/or anti-PD-1 antibodies ((N) n=7). Mean values±SEM. *P<0.05, **p<0.01, and ***p<0.001 by Student’s t-test in (B–J, L, and N).

Article Snippet: Subsequently, these isolated T cells were stimulated with anti- CD3 (BioXCell, BE0002) and anti- CD28 antibodies (BioXCell, BE0328) in the presence or absence of olaparib/niraparib.

Techniques: In Vivo, Sequencing, Cell Differentiation, Flow Cytometry, Expressing, Fluorescence, Immunohistochemistry, Microscopy

Journal: Cell proliferation

Article Title: Single-cell transcriptomic analysis of the senescent microenvironment in bone metastasis.

doi: 10.1111/cpr.13743

Figure Lengend Snippet: FIGURE 4 CD4Tstr cells exhibit pronounced senescent features in the bone-metastatic microenvironment. (A) UMAP plot of 45,603 subclustered T/NK cells coloured by annotated cell types. (B) Heatmap illustrating the expression of selected signatures in annotated subtypes. (C) Boxplot indicating the proportion of annotated cell types in PT and BM. (D-E) Representative immunofluorescence images (D) and boxplot (E) showing the expression level of CD4Tstr cells (positive for CD3D, CD4, and HSP70) in PT and BM. Scale bar: 20 μm (200) and 10 μm (400). (F) Kaplan–Meier plot showing the difference in survival probability (%) in patients with high versus low scores of the CD4Tstr signature according to the optimal cutoff based on the TCGA-NSCLC cohort. (G) Dot plot displaying the expression of selected genes across T-cell subclusters and sample groups. (H) Violin-boxplots displaying senescent scores in CD4Tstr cells from PT and BM. (I) Spearman correlation of the SenMayo signature with the angiogenesis signature by cells (left panel) and by samples (right panel) in CD4Tstr cells. (J) Spearman correlation of the Fridman_up signature with the angiogenesis signature by cells (left panel) and by samples (right panel) in CD4Tstr cells. PT, primary tumour; BM, bone metastases; NSCLC, non-small cell lung cancer.

Article Snippet: After deparaffinization, rehydration, and antigen retrieval by microwave, the tissue slides were incubated with primary antibodies targeting CD3D (1:200; 16669-1-AP; Proteintech, Wuhan, China), CD4 (1:200; 67786-1-Ig; Proteintech), CD8A (1:400; 66868-1-Ig; Proteintech), HSP70 (1:100; 66183-1-Ig; Proteintech), SOX18 (1:100; sc-166025; Santa Cruz Biotechnology, Dallas, TX, USA), CD31 (1:200; 66065-2-Ig; Proteintech), SERPINE1 (1:100; sc5297; Santa Cruz Biotechnology), KRT19 (1:200; 10712-1-AP; Proteintech) and SPP1 (1:100; 22952-1-AP; Proteintech).

Techniques: Expressing, Immunofluorescence