18
Nov 22, 2021
Research square preprints
DOI :
10.21203/rs.3.rs-1071141/v1
Article
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Background The myeloid cells play a vital role in health and disease of central nervous system (CNS). However, how to clearly distinguish them is still a knotty problem. At present, single cell RNA Sequencing (scRNA Seq) technology can sequence thousands of cells at the single cell level, and then divide the cells into different clusters according to the similarity of gene expression, but it is still difficult to further identity these cell clusters. Generally, there are some specific marker genes for cell type identities. However, it is difficult to distinguish a variety of myeloid cells in the CNS, because these cells often have the same or cross gene markers, and some markers will change significantly in different pathological states. Therefore, establishing a simple and practical method to distinguish these cell populations is of great significance for the analysis of scRNA Seq data.
Method Referring to CellMarker (http://biocc.hrbmu.edu.cn/CellMarker/), PanglaoDB (https://panglaodb.se/) and Mouse Cell Atlas (http://bis.zju.edu.cn/MCA/gallery.html), combining with the recent literatures, a simple Excel template was designed, in which a panel of gene makers corresponding to the myeloid cells were included. The 83 cell clusters from several recently reported single cell data were used to verify the accuracy of this template.
Result This template could easily distinguish myeloid cell subtypes and non myeloid cells. Comparing with literatures, the overall consistency rate was 93.98%. There was no statistically significant difference between the two groups (Bowkers test, P >0.05). Kappa symmetric measures showed that the Kappa value = 0.642 (P < 0.01).
Conclusions The cell identities of scRNA Seq cluster data could be performed using our simple Excel formulae, a panel of gene markers and ideal cell clustering data are the basis for accurate identification of CNS myeloid cell subtypes.

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