Journal: bioRxiv
Article Title: Unbiased metastatic niche-labeling identifies estrogen receptor-positive macrophages as a barrier of T cell infiltration during bone colonization
doi: 10.1101/2024.05.07.593016
Figure Lengend Snippet: (A) Cell types, tissue origin, and biotin groups in SAMENT single-cell dataset. (B) GSVA analysis comparing biotin-positive NK cells to biotin-negative NK cells highlights potential regulations exerted by NK cells on macrophages. Biotin-positive NK cells appear to modulate the tumor microenvironment by enhancing chemotaxis and inhibiting both aging and apoptosis in macrophages. This suggests that upon encountering tumors, reprogrammed NK cells indirectly contribute to chronic inflammation and promote the survival of tumor-associated macrophages. (C) Top enriched pathways in biotin-positive immature B cells in bones compared with other tissues. (D) Top enriched pathways in monocyte and neutrophil precursors in lungs compared with other tissues. (E) Top enriched pathways in biotin-positive granulocyte-macrophage progenitors (GMPs) in bone compared with biotin-negative populations. (F) Validation of published tissue-specific macrophage signatures was conducted in the SAMENT dataset. Reported Microglia signatures include Tmem119 + , Trem2 + , Fcrls + , Slc2a5 + , P2ry12 + , and Cx3cr1 + . Alveolars signatures consist of Cd163 − , Cd11b − , Cd206 int , Spp1 + , SiglecF + and Cd11c + ; Osteoclasts signatures are characterized by Arg1 + , Spp1 + , Mmp9 + , and vacuolar [H+]-ATPase + ; Kupffers cell signatures include CD11b low , F4/80 high , and Clec4F + ; Monocyte-derived macrophage signatures , encompass CD11b + , F4/80 int , Ly6C + and CSF1R + . In our dataset, Mφ_c10 corresponds to Microglia, Mφ_c7 corresponds Alveolar macrophages, and Mφ_c5 corresponds to osteoclasts. Monocyte-derived macrophages are distributed across all macrophage subclusters, while we did not identify specific clusters for Kupffer cells. These findings support the observations depicted in .
Article Snippet: The antibodies used in this study included: Immune cell panel, CD45-VF450 (Cytek® Biosciences,75–0451), CD11b-APC/Cy7 (Cytek® Biosciences, 25–0112), Ly6G-Percp/Cy5.5 (Cytek® Biosciences, 65–1276), Ly6C-PE/Cy7 (BioLegend,128018), F4/80-BV510 (BioLegend,123135), B220-APC/Cy7 (Cytek® Biosciences, 25-0452), CD3e-Percp/Cy5.5 (Cytek® Biosciences,65-0031), CD4-PE/Cy7 (Cytek® Biosciences,60-0041), CD8a-BV711 (BD Biosciences,752634), PD-1-BV605 (BioLegend,135219), Biotin-APC (Miltenyi Biotec,130-113-288) or Biotin-PE(Miltenyi Biotec,130-113-291); Stromal cell panel, CD45-BV605 (BioLegend,103140), Ter119-BV605 (BioLegend,116239), CD31-APC/Cy7 (BioLegend,102440), Sca-1-Percp/Cy5.5 (eBioscience,45–5981-82), CD51-BV421 (BD Biosciences, 740062), CD140α-PE/Cy7 (BioLegend, 135912), Biotin-APC (Miltenyi Biotec,130-113-288) or Biotin-PE(Miltenyi Biotec,130-113-291) Flow cytometry analysis was conducted using a BD LSRFortessa flow cytometer and data were further analyzed with FlowJo software.
Techniques: Chemotaxis Assay, Biomarker Discovery, Derivative Assay