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626 Dissecting the spatial heterogeneity of SARS-CoV-2-infected tumour microenvironment reveals a lymphocyte-dominant immune response in a HBV-associated HCC patient with COVID-19 history
  1. Benedict Tan1,
  2. Yi Yang2,
  3. Chun Chau Lawrence Cheung2,
  4. Denise Goh1,
  5. Mai Chan Lau1,
  6. Xinru Lim1,
  7. Jeffrey Lim1,
  8. Li Wen Justina Nadia Lee1,
  9. Tracy Tien1,
  10. Shirin Kalimuddin2,
  11. Wai Meng David Tai3,
  12. Jenny Low2,
  13. Cedric Chuan Young Ng3,
  14. Wei Qiang Leow4,
  15. Thuan Tong Tan4,
  16. Tony Lim4,
  17. Jin Liu5 and
  18. Joe Yeong1
  1. 1IMCB, A*STAR, Singapore, Singapore
  2. 2Duke-NUS Medical School, Singapore, Singapore
  3. 3National Cancer Centre Singapore, Singapore, Singapore
  4. 4Singapore General Hospital, Singapore, Singapore
  5. 5Duke-NUS, Singapore, Singapore


Background We previously reported the presence of SARS-CoV-2 RNA in the hepatic tissues of recovered patients1 but the spatial immune profile of SARS-CoV-2 infection remains poorly understood. To address this, here we performed deep spatial profiling in tumour-adjacent normal hepatic tissue from a HBV-associated hepatocellular carcinoma (HCC) patient with history of COVID-19.

Methods We obtained tissue from curative resection of a HCC patient 85 days post-recovery from COVID-19. Spatial immune profiling was performed by multiplex immunohistochemistry (mIHC)2 and more deeply using the Visium spatial transcriptomics platform complemented with signatures derived from single-cell RNA sequencing (scRNA-seq) and published signatures.

Results SARS-CoV-2 nucleocapsid and spike proteins were detected in a tumour-adjacent normal hepatic section in a spatially-restricted pattern (figure 1A and B) and higher abundance of lymphocytes but not macrophages were observed in regions with virus detection (figure 1C).We employed spatial transcriptomics and scRNA-seq to further characterize the immune microenvironment of SARS-CoV-2 post-infection. Unsupervised clustering and automatic annotation3 of Visium spots revealed that the distribution of SARS-CoV-2 viral proteins partially coincided with a memory T-cell signature (figure 1D). Quantification of Visium transcriptomic spots using an independent transcriptomic signature based on genes differentially upregulated in immune cells in SARS-CoV-2 infection4 (figure 1E) resulted in an enrichment pattern similar to the SARS-CoV-2 protein distribution. Additionally, a signature derived from scRNA-seq of hepatic tumour-infiltrating lymphocytes after ex vivo peptide stimulation using a pool of SARS-CoV-2 peptides showed a strongly associated distribution, in line with a SARS-CoV2-specific immune response5 whereas that from using a pool of HBV peptides resulted in an anti-correlated distribution (figure 1F). These illustrate the ability of spatial transcriptomics to quantify with microenvironment-level resolution the SARS-CoV-2-specific immune response.Recapitulating the mIHC protein data, deconvolution of immune populations6 revealed marked spatial associations between SARS-CoV-2 viral presence and the distributions of lymphocytes but not of macrophages (figure 1G).

Conclusions We believe this is the first deep profiling report of non-post-mortem samples which adopts a multi-modal approach combining mIHC, spatial transcriptomics, and transcriptomic signatures derived from scRNA-seq to interrogate the in situ immune response to viral infection. Applying this to SARS-CoV-2 infection, we detected tissue spatial heterogeneity in viral presence and an associated lymphocyte-dominant immune response in the COVID-19-recovered patient, in contrast to post-mortem observations of scarce lymphocytes in cases of severe COVID-19.7 Ongoing work including further validation of the findings in local and overseas cohorts and their correlation with patient clinical outcomes.


  1. Cheung CCL, et al. Residual SARS-CoV-2 viral antigens detected in GI and hepatic tissues from five recovered patients with COVID-19. Gut, p. gutjnl-2021-324280, 2021. doi: 10.1136/gutjnl-2021-324280.

  2. Lim JCT, et al. An automated staining protocol for seven-colour immunofluorescence of human tissue sections for diagnostic and prognostic use. Pathology (Phila.) 2018;50(3):333–341. doi: 10.1016/j.pathol.2017.11.087.

  3. Shao X, Liao J, Lu X, Xue R, Ai N, Fan X. scCATCH: automatic annotation on cell types of Clusters from Single-Cell RNA Sequencing Data. iScience 2020;23(3):100882, doi: 10.1016/j.isci.2020.100882.

  4. Lee JS, et al. Immunophenotyping of COVID-19 and influenza highlights the role of type I interferons in development of severe COVID-19. Sci Immunol 2020;5(49):p.eabd1554. doi: 10.1126/sciimmunol.abd1554.

  5. Schub D, et al. High levels of SARS-CoV-2–specific T cells with restricted functionality in severe courses of COVID-19. JCI Insight 2020;5(20):p.e142167. doi: 10.1172/jci.insight.142167.

  6. Newman AM, et al. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods 2015;12(5):453–457. doi: 10.1038/nmeth.3337.

  7. Wang Y, et al. SARS-CoV-2 infection of the liver directly contributes to hepatic impairment in patients with COVID-19. J Hepatol 2020;73(4):807–816. doi: 10.1016/j.jhep.2020.05.002.

Ethics Approval This study was approved by the SingHealth Centralised Institutional Review Board (reference number: 2019/2653)

Abstract 626 Figure 1

Spatial heterogeneity of SARS-CoV-2 infection uncovers an association with a dominant lymphocytic response

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