Back

Spatially resolved single-cell atlas of the lung in fatal Covid19 in an African population reveals a distinct cellular signature and an interferon gamma dominated response

Nyirenda, J.; Hardy, O.; Silva Filho, J. L.; Herder, V.; Attipa, C.; Ndovi, C.; Siwombo, M.; Namalima, T.; Suwedi, L.; Nyasulu, W.; Ngulube, T.; Nyirenda, D.; Mvaya, L.; Phiri, J.; Chasweka, D.; Eneya, C.; Makwinja, C.; Phiri, C.; Ziwoya, F.; Tembo, A.; Makwangwala, K.; Khoswe, S.; Banda, P.; Morton, B.; Hilton, O.; Lawrence, S.; Frere dos Reis, M.; Cardoso Melo, G.; Vinicious Guimaros de Lecerda, M.; Trindande Maranhao Costa, F.; Marcelo Monteiro, W.; Carlos de Lima Fereirra, L.; Johnson, C.; Mcguinness, D.; Jambo, K.; Haley, M. J.; Kumwenda, B.; Palmarini, M.; Barnes, K. G.; Denno, D. M.; Vo

2023-12-12 immunology
10.1101/2023.11.16.566964 bioRxiv
Show abstract

Postmortem single-cell studies have transformed understanding of lower respiratory tract diseases (LRTD) including Covid19 but there is almost no data from African settings where HIV, malaria and other environmental exposures may affect disease pathobiology and treatment targets. We used histology and high-dimensional imaging to characterise fatal lung disease in Malawian adults with (n=9) and without (n=7) Covid19, and generated single-cell transcriptomics data from lung, blood and nasal cells. Data integration with other cohorts showed a conserved Covid19 histopathological signature, driven by contrasting immune and inflammatory mechanisms: in the Malawi cohort, by response to interferon-gamma (IFN-{gamma}) in lung-resident alveolar macrophages, in USA, European and Asian cohorts by type I/III interferon responses, particularly in blood-derived monocytes. HIV status had minimal impact on histology or immunopathology. Our study provides data resources and highlights the importance of studying the cellular mechanisms of disease in underrepresented populations, indicating shared and distinct targets for treatment.

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.