Journal: PLOS Pathogens

Article Title: NK cell-macrophage interactions in granulomas correlate with limited tuberculosis pathology

doi: 10.1371/journal.ppat.1012980

Figure Lengend Snippet: (A) Imaging mass cytometry images overlaid with cellular identities determined in and , showing one granuloma of each lung pathology score category (low, intermediate, high). (B) Interaction analysis for the granuloma of each lung pathology category with the colour of the square representing the frequency of the interaction between the phenotype of interest and the phenotype of neighborhood cells as a percentage of the total interactions for the phenotype of interest (blue to green gradient). The statistical analysis of the significant occurrence of an interaction is represented as a dot on the interaction square, showing only positive correlations (grey to black gradient). (C) Localization and frequency of NK cell-macrophage interactions in the three granuloma shown in Fig A. (D) Frequency of cell interactions as frequency of total macrophage interactions. (E) Frequency of macrophage-NK cell (interactions in each granuloma as percentage of total interactions in the granuloma, shown across the lung pathology score categories. (F) Interaction glyphs showing an abstract representation of the four most abundant neighborhoods found in the macrophage-NK cell interactions and a table showing the proportion this neighborhood makes up of the macrophage-NK cell interactions, with the associated Z-score in brackets.

Article Snippet: All mass cytometry data was acquired on the Hyperion mass cytometry imaging system (Standard Biotools, San Fransisco, CA, USA) at the Flow cytometry Core Facility at the LUMC.

Techniques: Imaging, Mass Cytometry

Journal: Nature Communications

Article Title: Microbiota-derived H 2 S induces c-kit + cDC1 autophagic cell death and liver inflammation in metabolic dysfunction-associated steatohepatitis

doi: 10.1038/s41467-025-57574-3

Figure Lengend Snippet: a Heatmap showing the median expression values and hierarchical clustering of the identified subpopulations. n = 5/group. b Composition of the myeloid cells. Color as shown in A. n = 5/group. c , d Gating strategy and representative flow cytometry dot plots of c-kit + CD103 + and c-kit + CD103 - cDC1 in the liver. Lin includes CD3, CD19, NK1.1. n = 5/group. e Representative flow cytometry dot plots of hepatic c-kit + CD103 + cDC1 and c-kit + CD103 - cDC1, as well as their proportion in XCR1 + cDC1. n = 5/group. f Liver mIHC images stained with CD103 (red), c-kit (green), and XCR1 (white). Cell nuclei were stained with DAPI (blue). Magnification ×40, scale bar = 100 µm. Arrows indicate c-kit + CD103 + cDC1, and arrowheads indicate c-kit + CD103 - cDC1. n = 5/group and two sections were stained from each liver sample. Data were represented as mean ± SEM and analyzed using a two-tailed unpaired Student’s t -test ( b , d ). Significance levels were reported as * P < 0.05, ** P < 0.01. Source data were provided as a Source Data file.

Article Snippet: Mass cytometry data generated in this study have been deposited in the Flow Repository ( https://data.mendeley.com/datasets/vm5rkkk96d/1 , 10.17632/vm5rkkk96d.1).

Techniques: Expressing, Flow Cytometry, Staining, Two Tailed Test

Journal: Nature Communications

Article Title: Microbiota-derived H 2 S induces c-kit + cDC1 autophagic cell death and liver inflammation in metabolic dysfunction-associated steatohepatitis

doi: 10.1038/s41467-025-57574-3

Figure Lengend Snippet: a Top 10 KEGG enrichment of hepatic c-kit + cDC1 from WT mice fed with NCD and WD for 30 W. n = 3/group. b GSEA analysis of Autophagy signaling. c Flow cytometry histograms of CYTO-ID (autophagic flux). n = 5/group. d Schematic of the H 2 S delivery. e Autophagic flux analysis of hepatic XCR1 + cDC1. n = 5/group. f Correlation analysis between the concentration of H 2 S and the autophagic flux of XCR1 + cDC1 in the liver. Spearman’s rank correlation coefficient test was performed. 95% confidence interval (0.7132 to 0.9658), P < 0.0001. g TEM showing the autophagosome and autolysosome in c-kit + DC induced in vitro. n = 3/group. h Relative expression of p62 and LC3. n = 3/group. i MTT data showing the cell survival rate of c-kit + DC induced in vitro. j MTT data showing the cell survival rate of c-kit + DC in Atg5 flox/flox and Atg5 -/- mice. n = 5/group. Data were represented as mean ± SEM. P values calculated by a two-tailed unpaired Student’s t -test ( g , j ), one-way ANOVA with Tukey’s post hoc test ( c , e , i ) or two-way ANOVA with Tukey’s post hoc test ( h ). The screening criteria for KEGG were adjusted p < 0.05 and FDR value ( q value) < 0.25, and the p value correction method was Benjamini-Hochberg ( a ). Significance levels were reported as * P < 0.05, ** P < 0.01. a – c , groups sharing no common letters indicate statistically significant differences ( P < 0.05), groups sharing partial letters indicate no significant difference ( P > 0.05). 3MA 3-Methyladenine, FDR False Discovery Rate, MTT methylthiazolyl diphenyl-tetrazolium bromide. Source data were provided as a Source Data file.

Article Snippet: Mass cytometry data generated in this study have been deposited in the Flow Repository ( https://data.mendeley.com/datasets/vm5rkkk96d/1 , 10.17632/vm5rkkk96d.1).

Techniques: Flow Cytometry, Concentration Assay, In Vitro, Expressing, Two Tailed Test

Journal: Blood cancer journal

Article Title: Multi-omics reveal immune microenvironment alterations in multiple myeloma and its precursor stages.

doi: 10.1038/s41408-024-01172-x

Figure Lengend Snippet: Fig. 7 Composition alteration of MM clones in paired samples shows patient-specific heterogeneity in progressed disease. a Box plot showing plasma cell (PC) composition changes among different disease stages by CyTOF analysis. p values for each cell type were calculated using Kruskal–Wallis with Dunn’s multiple comparisons test. b Dot plot showing gene expression of plasma cells for individual patients and healthy donors. c UMAP plots of plasms cell subtypes. d UMAP plots showing plasma cell subclusters in paired samples (left). Density map on the UMAP plot showing representative marker gene expression among plasma cell subtypes (right).

Article Snippet: Mass cytometry (CyTOF) data acquisition A Helios mass cytometer (Fluidigm) was used for data acquisition.

Techniques: Clone Assay, Clinical Proteomics, Gene Expression, Marker