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. 2022 Apr 21;3(2):101379.
doi: 10.1016/j.xpro.2022.101379. eCollection 2022 Jun 17.

Analyzing single cell transcriptome data from severe COVID-19 patients

Nasna Nassir  1   2 , Richa Tambi  1   2 , Asma Bankapur  1 , Noushad Karuvantevida  1   2 , Hamdah Hassan Khansaheb  3 , Binte Zehra  1 , Ghausia Begum  1 , Reem Abdel Hameid  1 , Awab Ahmed  1 , Zulfa Deesi  3 , Abdulmajeed Alkhajeh  4 , K M Furkan Uddin  5 , Hosneara Akter  5 , Seyed Ali Safizadeh Shabestari  1 , Mellissa Gaudet  6 , Mahmood Yaseen Hachim  1 , Alawi Alsheikh-Ali  1   7 , Bakhrom K Berdiev  1 , Saba Al Heialy  1   6 , Mohammed Uddin  1   8
Affiliations

Analyzing single cell transcriptome data from severe COVID-19 patients

Nasna Nassir et al. STAR Protoc. .

Abstract

We describe the protocol for identifying COVID-19 severity specific cell types and their regulatory marker genes using single-cell transcriptomics data. We construct COVID-19 comorbid disease-associated gene list using multiple databases and literature resources. Next, we identify specific cell type where comorbid genes are upregulated. We further characterize the identified cell type using gene enrichment analysis. We detect upregulation of marker gene restricted to severe COVID-19 cell type and validate our findings using in silico, in vivo, and in vitro cellular models. For complete details on the use and execution of this protocol, please refer to Nassir et al. (2021b).

Keywords: Bioinformatics; Gene Expression; Genomics; Health Sciences; Immunology; Molecular Biology; RNAseq.

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Conflict of interest statement

The authors declare no competing interests.

Figures

None
Graphical abstract
Figure 1
Figure 1
Steps involved in the compilation of unique marker gene database using various sources Marker gene information is present in literature in the form of heat map (Gene expression plotted against cell types using color gradients for expression levels), dot plot (circles denoting the gene expression across various cell types), tables or databases. In case of dot plot, the size and color intensity are proportional to the level of expression in the percent of cells and degree of expression, respectively (in ascending order). In order to constitute the final marker database, the cell types and gene list were combined from all the sources retaining only unique genes into the list for each cell type (Removing any duplicate genes within inter and intra cell types).
Figure 2
Figure 2
Assigning cell type identity for severe COVID-19 cluster 11 (A) Mean expression of CCL3L1 across all clusters in severe COVID-19 dataset. The highest expression was observed for cluster 11, which was annotated as monocyte derived alveolar macrophages (MoAM), marked by CCL3L1. Red dotted line indicates the global median expression. A cluster was assigned a particular cell type if it had the highest median expression (across the clusters and expression value was more than 99th percentile overall expression. (B) Dot plot showing expression of macrophage and its subtype (TRAM and MoAM) marker genes for the severe COVID-19 dataset. The y-axis represents the cell types based on the marker database and x-axis represents the marker genes. Cluster 11 is marked by MoAM_CCL3L1.
Figure 3
Figure 3
Steps in identifying cluster associated with comorbid disease gene expression and finding gene restricted to that cell type Enrichment analysis of severe COVID-19 clusters with comorbid gene set. Higher expression (above global median) is indicated by a star. The cluster having maximum number of upregulated gene set is selected for further downstream analysis and identifying candidate genes with restrictive expression in severe COVID-19 cluster. The representative feature plots are reused from Figure 3 of (Nassir et al., 2021b).
Figure 4
Figure 4
Steps to perform Cytoscape analysis Flowchart representing pathway network creation using Enrichment Map and Autoannotate tool. The tab selections are highlighted in red.
See this image and copyright information in PMC

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