Fig 1: Consistence between CSOmap prediction and IHC imaging of the tumor sample from HCC.a Spearman correlation between cell connections based on IHC images (X-axis) and the CSOmap prediction (Y-axis) after normalizing the biases introduced by uneven cell counts among different cell types. Treg: regulatory T cell (Foxp3+); Tex: exhausted T cell (PD-1+); CD8: CD8+PD-1- T cells; cDC1: CLEC9A+ dendritic cells; M: macrophages (CD68+); O: other cells. b Example IHC image showing the interaction between CD8+PD-1– T cells and macrophages. c Enlarged illustrations of the selected windows in b (from left to right in order). d Example IHC images showing the interaction between Texs and Tregs.
Fig 2: Performance of CSOmap in reconstructing the spatial organization of a liver tumor sample.a, b Tumor core cells tend to locate in the center of the pseudo-space reconstructed by CSOmap. c The CSOmap reconstruction revealed that genes encoding HSPs show spatial preference. d IHC staining of independent liver tumor samples confirmed the spatial preference of Hsp70. Scale bar, 50 µm. e Quantification based on IHC images confirmed the statistical significance of the spatial preference of Hsp70 and Hsp90 (Student’s t-test, right tailed, *P < 0.05; ***P < 0.01). f 3D plot of the tumor sample by stacking 19 IHC images together after manual rotation, in which six major cell types were discriminated by the corresponding markers. g Spearman correlation between cell connections based on IHC images (X-axis) and the CSOmap prediction (Y-axis). Treg: regulatory T cells (Foxp3+); Tex: exhausted T cell (PD-1+); CD8: CD8+PD-1- T cells; cDC1: CLEC9A+ dendritic cells; M: macrophages (CD68+); O: other cells. The median distance of the 3rd nearest neighbor of all cells was used as the cutoff to determine whether two cells were spatially connected or not. The overwhelming number of “other cells” highlights the fact that millions of cells can crowd in a compact piece of tissue, posing great challenges for staining/imaging-based analysis.
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