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Journal: bioRxiv
Article Title: Integrated kidney and urine proteomics define encrypted antimicrobial peptides as effectors of host defence in human pyelonephritis
doi: 10.64898/2026.04.10.717476
Figure Lengend Snippet: FFPE human kidney sections from PN ( n =3) and control patients ( n =2) were stained with DAPI (nuclei), CD14 (monocytes), CD66b (neutrophils), AQP1 (proximal tubules), NKCC2 (distal tubules), and the following AMPs: AZU1, CTSG, S100A8, and S100A12. (A) Exemplary images of immune and tubular markers as well as AMPs in kidney tissue from patients C2 (Control) and PN3 (Pyelonephritis) (B) Frequency of AMP + CD14 + CD66b - monocytes (red), AMP + CD66b + neutrophils (orange), AMP + AQP1 + stromal cells (turquoise), and AMP + NKCC2 + stromal cells (green) per mm 2 in control (top) and PN (bottom) kidney tissue.
Article Snippet: Antibodies targeted AQP1 (Sigma-Aldrich, HPA019206), NKCC2 (Atlas Antibodies, HPA014967), CD14 (Miltenyi Biotec, 130-110-576),
Techniques: Control, Staining
Journal: bioRxiv
Article Title: Integrated kidney and urine proteomics define encrypted antimicrobial peptides as effectors of host defence in human pyelonephritis
doi: 10.64898/2026.04.10.717476
Figure Lengend Snippet: Neutrophils and CD14+ monocytes were isolated from peripheral blood of healthy donors (n = 5) and incubated with 0, 4, or 16 μM Calcitermin for 18 h. Representative dot plots show side scatter characteristics, the proportion of Annexin V+ apoptotic cells, and mean fluorescence intensity (MFI) of markers associated with activation and function, including IL-8, IL-12, CD11b, CD63, CD66b, CXCR4, LAP–TGF-β1, TLR2, and TLR4. Reactive oxygen species (ROS) production was assessed using dihydrorhodamine 123 (DHR 123). Statistical significance is indicated as *p < 0.05 and **p < 0.005.
Article Snippet: Antibodies targeted AQP1 (Sigma-Aldrich, HPA019206), NKCC2 (Atlas Antibodies, HPA014967), CD14 (Miltenyi Biotec, 130-110-576),
Techniques: Isolation, Incubation, Fluorescence, Activation Assay
Journal: Nature Communications
Article Title: NCX1 reverse mode promotes calcium-dependent Neutrophil Extracellular Trap formation and lung damage in chronic obstructive pulmonary disease
doi: 10.1038/s41467-026-69636-1
Figure Lengend Snippet: a Representative immunofluorescence images of NCX1 (purple) co-localization with different immune cell-type markers (CD3 + , T cells, CD68 + , macrophages, CD66b + , neutrophils; CD11c + , neutrophils/dendritic cells) in distal airways and bronchioles and adjacent alveolar regions from Mixed CBE patients ( n = 5 patients). Blue, DAPI-stained nuclei. Light blue indicates colocalization of NCX1 with neutrophils. Scale bar = 30 µm. b RT-qPCR analyzes mRNA levels of NCX1 in human lung tissue from Control patients ( n = 20), emphysema patients ( n = 10), and Mixed CBE patients ( n = 20). c Western blot analysis of NCX1 protein levels in lung tissues from Control patients and Mixed CBE patients ( n = 10), normalized to β-actin and displayed relative to controls. Uncropped blots in Source Data. d , e RT-qPCR analyzes mRNA levels of MPO and NE in lung tissues from Control individuals ( n = 20), emphysema patients ( n = 10), and Mixed CBE patients ( n = 20). f–h Western blot analysis of MPO and NE expression in lung tissues from Control patients and Mixed CBE patients ( n = 10), normalized to β-actin and displayed relative to controls. Uncropped blots in Source Data. i , j Spearman’s correlation analyzes the correlation of NCX1 expression with MPO and NE levels. 95% CI: 0.5281 to 0.8724 for ( i ), 0.7029 to 1.007 for ( j ). k Representative immunofluorescence images show expression and localization of CD66b (green) and NCX1 (purple) in lung tissues of Control and Mixed CBE patients. Scale bar = 50 µm. Blue, DAPI-stained nuclei. l–n Flow cytometry identifies neutrophil numbers ( m ) and NCX1 mean fluorescence intensity ( n ) on neutrophils in human BALF ( n = 5 individuals). o Representative immunofluorescence images show expression of NCX1 in neutrophils from human BALF of Control and Mixed CBE patients. Scale bar = 2 µm. Blue, DAPI-stained nuclei. Data point represents one biologically independent replicate with three technical replicates ( b , d , e ), one biologically independent replicate with two technical replicates ( c , g , h ). Data points represent biologically independent replicates ( m , n ). Quantitative data are presented as min to max with all points mean ( b , d , e ), and as Mean ± SD ( c , g , h , m , n ). Two-sided t -test ( g , h , m , n ) and one-way ANOVA with Tukey’s multiple comparison test ( b , d , e ) were used to calculate the p- values. At least 3 times, each experiment was independently repeated with similar results. Source data are provided as a Source Data file. CBE chronic bronchitis and emphysema; MPO myeloperoxidase; NE neutrophil elastase; BALF bronchoalveolar lavage fluid; SCC-A forward scatter-area; FSC-H forward scatter-height; FSC-A side scatter-area; ns no significance.
Article Snippet: The samples were then incubated overnight at 4 °C with primary antibodies: CD11c (Servicebio, GB11059, 1:200), CD68 (Proteintech, 25747-1, 1:200), CD3 (Servicebio, GB11014, 1:100), Ly-6G (Invitrogen, 14-5931-82, 1:50), αSMA (Invitrogen, 50976082, 1:200),
Techniques: Immunofluorescence, Staining, Quantitative RT-PCR, Control, Western Blot, Expressing, Flow Cytometry, Fluorescence, Comparison
Journal: Cancer cell
Article Title: CCL3 is produced by aged neutrophils across cancers and promotes tumor growth
doi: 10.1016/j.ccell.2026.01.006
Figure Lengend Snippet: (A) Cell type probability-based scRNAseq classifier. From left to right: classifier trained on human blood and tumor scRNAseq data from Zilionis et al. (reference map shows uniform manifold approximation and projection (UMAP)); logistic regression used to assign each event a probability of belonging to a given cell type; example showing events classified as neutrophils (black dots) with their gene counts (500 gene cutoff in red); classifier-based identification of multiple cell types in tumor data from Bill et al. with corresponding gene counts (500 gene cutoff in red). (B) Original (left) and automated classifier (middle) annotations of neutrophil-enriched data from Wang et al., with a direct comparison of the two approaches (right). (C) CD66b immunohistochemistry for neutrophil detection in HNSCC (left: two examples shown) and correlation between neutrophil content measured by immunohistochemistry and by the classifier on matched scRNAseq data (right, n = 22). Pearson’s correlation is shown. (D) Classifier-based detection of neutrophils and other cells in 11 HNSCC samples (left) and identification of CD66b mAb signal on the same cells (right). (E) Total neutrophil counts detected by the classifier (pink) versus standard gene-count filtering (gray) in three public datasets. – (F) Same analysis as in (e), shown for individual tumors. (G) Kaplan-Meier plots of Neutro hi vs. Neutro lo tumors, defined using either a reference neutrophil signature (left) or a revised neutrophil signature based on our neutrophil abundance predictor (right), and applied to bulk RNAseq TCGA data ( n = 8,305) from multiple cancer types. (H) Univariate (black) and multivariate (red) Cox regression analyses of four signatures: tumor proliferation rate, CS ratio, reference neutrophil signature, and our revised neutrophil signature. (I) Kaplan-Meier plot of TCGA data ( n = 8,305) showing four patient groups defined by CS status (hi/lo) and neutrophil abundance (hi/lo).
Article Snippet:
Techniques: Comparison, Immunohistochemistry, RNA sequencing