Webinar: Single-Cell Multiomics Resolves a Sharp Disease-State Shift between Mild and Moderate COVID-19


Host immune responses play a central role in controlling SARS-CoV-2 infection, but they remain incompletely characterized and understood.


In this webinar, Dr. Yapeng Su of the Institute for Systems Biology presents an integrated analysis of the clinical measurements, immune cells, and plasma multiomics of 139 COVID-19 patients representing all levels of disease severity, from serial blood draws collected during the first week of infection following diagnosis.


As published in Cell, Dr. Su’s team identified a major shift between mild and moderate disease, at which point elevated inflammatory signaling is accompanied by the loss of specific classes of metabolites and metabolic processes. Within this stressed plasma environment at moderate disease, multiple unusual immune cell phenotypes emerge and amplify with increasing disease severity.


Dr. Su and colleagues condensed more than 120,000 immune features into a single axis to capture how different immune cell classes coordinate in response to SARS-CoV-2. This immune-response axis independently aligns with the major plasma composition changes, with clinical metrics of blood clotting, and with the sharp transition between mild and moderate disease.


This study suggests that moderate disease may provide the most effective setting for therapeutic intervention.

Additional Q&A With Dr. Yapeng Su


Due to great interest in this talk, many submitted questions weren’t answered during the webinar. Dr. Su graciously answered these questions, below.


1. First, congratulations for your work. Where can we have access to the data?


Thanks! In our paper, we have a data availability section explicitly provided, where the 10x data and other clinical data for those patients can be found. The link to our paper is here: https://www.cell.com/cell/fulltext/S0092-8674(20)31444-6


2. What technology platform did you use to process the multiomic data?


There are various types of multiomic datasets that are being collected and utilized in this study, and they were processed and further analyzed using different platforms. All of the details are provided in the Methods section of our paper.


3. Did you have a chance to look at tissue residing immune cells?


We actually did not have the chance to check that, but all of our data is released to the public for experts in different fields to utilize, to answer those further biological questions related to COVID.


4. Is this being used currently to monitor patients, and does it also apply to the unique pediatric overwhelming immune response associated with a secondary infection or response?


The comprehensive multiomic analysis of these different patient samples will take time to perform and the turnaround time could be long, so it may not be directly used to monitor patients in real-time. However, many markers coming out from the study may serve as simplified markers for monitoring patient status. We have not yet analyzed it on pediatric patients, but we believe such deep multiomic analysis of patients will surely be useful to investigate pediatric patients.


5. Great talk. Do you detect any Innate lymphoid cells (ILCs) in COVID progression?


Thanks. Yes. We have monitored NK cells and their association with COVID severity. Detailed content can be found in our paper.


6. Are there empirical data elucidating the cytotoxic activity of CD4 helper cells? How does it compare to CD8%2B cytotoxic T cells?


There are rare cases in which people do report cytotoxic CD4 helper cells in mice, and recently in humans, too. Those cells are CD4 T cells, so they recognize antigens presented by MHC-class-II rather than MHC-class-I. Therefore, we suspect that these cells can potentially kill antigen-presenting cells, which generally present antigens via MHC-class-II to educate T cells.


7. What are the additional differences between cluster 5 and cluster 8 besides one is CD4%2B and the other CD8%2B?


Sorry for the confusion. Both clusters are CD4 T cells. the cluster8 CD4 T cell is the proliferative exhausted CD4. And the Cluster5 CD4 T cells are the cytotoxic CD4 T cells. There are so many differences between them. For example, cluster8 is proliferating and cluster5 is not. Cluster5 can secrete cytotoxic molecules to kill, but cluster8 does not, etc.


8. What is the y-axis scale?


The y-axis for the box plots in most of my slides are % of certain cell types.


9. You speculated that antigen-specific CD4%2B T cells are likely within the cytotoxic CD4%2B population. Can you comment on the frequency of all CD4%2B T cells that are part of this population?


The cytotoxic CD4 accounts for ~10% of all CD4 T cells in the PBMC, but the variation is very large from patient to patient.


10. How are the cytotoxic and Th1 signature scores calculated? Are there any genes shared between these two scores?


All of these detailed computational methods are provided in the Methods section of our paper. There are no overlapping genes between the two scores.


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