Life Science AllianceGenomicsPeer-reviewed journal article
Intensive Single Cell Analysis Reveals Immune Cell Diversity among Healthy Individuals
Yukie KashimaiD, Keiya Kaneko, Patrick RetengiD, Nina Yoshitake, Lucky Ronald RuntuweneiD, Satoi NagasawaiD, Masaya Onishi, Masahide SekiiD, Ayako SuzukiiD, Sumio SuganoiD, Mamiko Sakata‐YanagimotoiD, Yumiko ImaiiD, Kaori Nakayama‐Hosoya, Ai Kawana‐TachikawaiD, Taketoshi MizutaniiD, Yutaka SuzukiiD
- Citations
- 10
- FWCI
- 0.73
- Field percentile
- top 37%
Abstract
It is believed that immune responses are different between individuals and at different times. In addition, personal health histories and unique environmental conditions should collectively determine the present state of immune cells. However, the cellular and molecular system mechanisms underlying such heterogeneity remain largely elusive. In this study, we conducted a systematic time-lapse single-cell analysis, using 171 single-cell libraries and 30 mass cytometry datasets intensively for seven healthy individuals. We found substantial diversity in immune cell populations and their gene expression patterns between different individuals. These patterns showed daily fluctuations even within the same individual spending a usual life. Similar diversities were also observed for the T cell receptor and B cell receptor repertoires. Detailed immune cell profiles at healthy statuses should give an essential background information to understand their immune responses, when the individual is exposed to various environmental conditions. To demonstrate this idea, we conducted the similar analysis for the same individuals on the vaccination of Influenza and SARS-CoV-2, since the date and the dose of the antigens are well-defined in these cases. In fact, we found that the distinct responses to vaccines between individuals, althougth key responses are common. Single cell immune cell profile data should make fundamental data resource to understand variable immune responses, which are unique to each individual.
- organism: human
- assay: scRNA-seq
- research focus: cell type annotation
- research focus: differential expression
- approach: statistical model
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