PMC9510984_01
Spatial enrichment of COVID immune response signatures in biopsied HCC and CRC tissues from COVID-19-recovered patients. (A-P) Visium-based (10x Genomics) spatial transcriptomics-generated tissue heat maps show the spatial localization of COVID immune responses in HCC (A, E, I, M) and CRC (C, G, K, O) tumors and their adjacent normal tissues (B, F, J, N, D, H, L, P, respectively). Analysis was based on lymphocyte-associated COVID immune response gene signatures (Lee et al., A-D; and Ren et al., E-P): CD8+ T/NK cells-associated (A-D), B cell-associated (E-H), T cell-associated (I-L), and NK cell-associated (M-P).
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All bioinformatic analysis was performed on individual tissues using the sctransform-normalized data computed by R package Seurat (v4.0.3) (unless stated otherwise) ( Supplementary Figure 1A ). For the four lymphocyte signatures, spatial profiles were broadly consistent in all four tissues analyzed ( Figure 1 ); however, some differences in localization were observed for the SARS-CoV-2 response signatures associated with T cells and NK cells in the CRC-adjacent normal tissue ( Figures 1L, P ). Materials). Single-cell sequencing used Chromium Single Cell 5' and 3' Reagent Kits v2 and v3 (10x Genomics, San Francisco, CA, USA), while paired-end sequencing (2x150 bp) was performed using the NovaSeq 6000 platform (Illumina, Inc., San Diego, CA, USA) (Supplementary Materials; bioinformatic analysis is depicted in  Supplementary Figure 1B ). Marker genes for the B cell phenotypes were identified by comparing SARS-CoV-2-stimulated and unstimulated samples (using the FindMarkers function in Seurat;  Supplementary
Distribution of SARS-CoV-2 spike protein (SP) and immune cell types in the spatial transcriptomic (ST) niches of HCC and CRC biopsies from COVID-19-recovered patients. (A-D) From left to right: HCC, HCC-adjacent normal, CRC, and CRC-adjacent normal tissues; ST niches (clusters 1-4) were determined using the spatially-aware BayesSpace (BS) transcriptomic clustering method. (E-L) RNAscope-detected SARS-CoV-2 SP was quantified in regions of interest (marked as boxes) in the Visium-defined ST niches (E-H); see 
Supplementary Table 2
 for raw data and assignment of ROIs to ST niches. Bar charts (I-L) show the relative SP counts in the ST niches; SP counts in ROIs assigned to the same ST niche were averaged. Areas with poor staining quality (F) and smooth muscle and collagen (H, L) were omitted. (M) Distribution of immune cell abundance (estimated by a deconvolution-based microenvironment cell population-counter) in ST niches. The orange stars indicate viral-high regions (ST niches with the highest SARS-CoV-2 SP counts).
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Using the Visium ST data ( Supplementary Figure 2 ), we carried out spatial enrichment analysis (AddModuleScore function in Seurat) based on the SARS-CoV-2 immune response signatures from two public databases ( Supplementary Table 1 ). Independent of these SARS-CoV-2 immune responses, we identified four regions with distinct ST niches within each tissue using a spatially-aware clustering method (BayesSpace R package v1.2.0) ( Figures 2A-D ); the four clusters were selected arbitrarily to balance resolution and interpretability. Table 2 ;  Supplementary Materials ). The SARS-CoV-2 SP counts were spatially variable, especially in the CRC tumor tissue ( Figures 2I-L ). Both RNAscope and mIHC analyses were based on multiple high-quality regions of interest (ROIs; marked as boxes in  Figures 2E-H  and  Supplementary Figures 9A-D ) that were selected by a pathologist (JY). Figures 2I-L  and  Supplementary Figures 9E-H ). Furthermore, using a pathologist (JY)-trained machine-learning classifier in QuPath (v0.3.2; Supplementary Materials), the viral-high regions were predominantly identified as stromal or normal epithelial tissues ( Supplementary Figure 10 ). The results revealed intra-tissue heterogeneity in terms of tissue coverage (% spots) and detectability ( Figure 2M ). B cells were detected homogeneously within viral-high regions in all tissues (although they were relatively sparse in the HCC-adjacent normal tissue), while other immune cells were either sparse or dispersed throughout the tissue ( Figure 2M ,  Supplementary Figures 11 - 12 ).
Spatial colocalization of immune cell types and COVID immune response. (A-D) Immune cell types (determined by a deconvolution-based microenvironment cell population-counter) and COVID immune responses (quantified as enrichment scores for the COVID response-associated gene signatures reported in Lee et al. within the viral-high regions in HCC (A) and CRC (C) tumors and the corresponding adjacent normal tissues (B, D, respectively). Lymphocyte and myeloid-associated COVID responses are represented by triangles and circles, respectively. The vertical green dashed line delineates log-fold change (FC) = 0 (log-FC > 0 for cell types with higher COVID immune scores in Visium spots where the immune cell type was detected than in spots where it was not detected). The horizontal green dashed line represents P = 0.05; above the line, P < 0.05. Red text and annotations represent P < 0.005. P-values were computed using the Kruskal-Wallis test. CTLs, cytotoxic lymphocytes; DCs, dendritic cells.
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By contrast, myeloid-associated SARS-CoV-2 responses did not demonstrate distinctive spatial localization, except in the HCC-adjacent normal tissue ( Supplementary Figure 3 , see panels  B  and  F ). SARS-CoV-2 response was significantly positively associated with the spatial localization of B cells across HCC and CRC tissues (P < 0.005,  Figures 3A-D ); in contrast, the myeloid COVID-19 signature was only significantly associated with T cells in HCC tissues (P < 0.005,  Figures 3A, B ). Similar findings were obtained using the COVID-19 signatures from Ren et al.; the lymphocyte-specific SARS-CoV-2 responses (i.e., B, T, and NK-associated responses) were significantly associated with the spatial localization of B cells across HCC and CRC tissues (P < 0.005), but not CRC-adjacent normal tissue ( Supplementary Figure 13 ).
Spatial associations of immune cell types and immuno-predictive scores (IMPRES) in individual spatial transcriptomic niches. From left to right, BayesSpace (BS) clusters 1-4 in HCC, HCC-adjacent normal tissue, CRC, and CRC-adjacent normal tissue are shown. Each BS cluster was stratified by the presence of the immune cell of interest (determined by a deconvolution-based microenvironment cell population-counter) as follows: B lineage, T cells, CD8+ T cells, cytotoxic lymphocytes, NK cells, myeloid dendritic cells, monocytic lineage, and neutrophils (from top to bottom). Orange stars indicate viral-high regions (transcriptomic niches with the highest SARS-CoV-2 spike protein/nucleocapsid protein counts); red arrows indicate higher IMPRES scores in Visium spots analyzed for B lineage cells, T cells, and cytotoxic lymphocytes within the viral-high regions.
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Generation of uniform manifold approximation and projection (UMAP) plots was based on the top 30 principal components (RunPCA and RunUMAP functions in Seurat) of the ST niches ( Supplementary Figure 4 ). B cells showed consistently higher IMPRES scores for all four tissues ( Figure 4 ; viral-high regions are indicated by orange stars/red arrows). By contrast, spots with detection of T cells and cytotoxic lymphocytes (representing key effector cells in immunotherapy) demonstrated consistently higher IMPRES scores across the ST niches, although they were undetected in some CRC regions.