Here, we review single-cell sequencing techniques for individual and multiomics profiling in single cells. We mainly describe single-cell genomic, epigenomic, and transcriptomic methods, and examples of their applications. For the integration of multilayered data sets, such as the transcriptome data derived from single-cell RNA sequencing and chromatin accessibility data derived from single-cell ATAC-seq, there are several computational integration methods. We also describe single-cell experimental methods for the simultaneous measurement of two or more omics layers. We can achieve a detailed understanding of the basic molecular profiles and those associated with disease in each cell by utilizing a large number of single-cell sequencing techniques and the accumulated data sets.

Immune responses are different between individuals and personal health histories and unique environmental conditions should collectively determine the present state of immune cells. However, the molecular systems underlying such heterogeneity remain elusive. Here, 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 profiles between different individuals. These patterns showed daily fluctuations even within the same individual. Similar diversities were also observed for the T-cell and B-cell receptor repertoires. Detailed immune-cell profiles at healthy statuses should give 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. In fact, we detected distinct responses to vaccines between individuals, although 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.

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.

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