no

no. comparison. This table shows the correlation of scRNA-seq and CyTOF in the indicated cell types. Table S7 Sequence statistics scVDJ-seq used in the current study. This table provides the sequence statistics of the scRNA-seq TCR and B cell receptor V(D)J sequencing of daily variance, individual variance, influenza vaccination, and SARS-CoV-2 vaccination study. Table S8 Frequency of top used clonotypes in H1 and H2 TCR. This table provides the statistics of the CDR3 sequence and the frequency of the daily variance of H1 and H2 study. Table S9 Statistics of the clonotypes and T cellCrecognizing CMV and EBV. (A, B) This table provides the information about the clonotypes and T cellCrecognizing CMV (A) and EBV (B). Table S10 Detected antibody level during influenza computer virus vaccination. This table shows the antibody level of the anti-influenza computer virus, type A H1, type A H3, 3,5-Diiodothyropropionic acid type B Yamagata, and type B Victoria, during the influenza vaccination in H1 and H2. Table S11 Detected antibody level during SARS-CoV-2 vaccination. This table shows the level of IgG and IgM antibody anti-SARS-CoV-2 computer virus in the S region during the SARS-CoV-2 vaccination in H1, H6, and H7. Table S12 Markers used in the CyTOF analysis. This table provides the markers, channels, and clones used in the CyTOF study. We used a commercial antibody set. Details are shown in the Materials and Methods section. Table S13 Summary table of scRNA-seq metadata. This table provides the metadata of scRNA-seq datasets used in the current study. The metadata table format is following MINSEQE standards. Table S14 Summary table of scVDJ-seq metadata. This table provides the metadata of scVDJ-seq datasets used in the current study. The metadata table format is following AIRR standards. Table S15 Summary table of CyTOF metadata. This table provides the metadata of CyTOF datasets used in the current study. The metadata table format is following MiFlowcyte requirements. Reviewer feedback LSA-2022-01398_review_history.pdf (179K) GUID:?259A9085-8072-4E1F-A1A8-37D45562A7EE Data Availability StatementThe natural data have been deposited to the National Bioscience Database Center as study number: https://ddbj.nig.ac.jp/resource/jga-study/JGAS000321 (https://ddbj.nig.ac.jp/resource/jga-study/JGAS000321). Metadata of deposit datasets are included in Table S13 (scRNA-seq), S14 (scVDJ) and S15 (mass cytometry) following FAIR principles. Details of deposited data are shown below. JGA accession quantity of the study: https://ddbj.nig.ac.jp/resource/jga-study/JGAS000321, JGA accession number for dataset: https://ddbj.nig.ac.jp/resource/jga-dataset/JGAD000432, study title: Comprehensive analysis of conversation between human gene expression and PIK3C3 environmental metagenomes (https://ddbj.nig.ac.jp/resource/jga-study/JGAS000321), dataset title: WGS, 10 scRNA, 10 TCR, 10 BCR and CyTOF data of blood samples (https://ddbj.nig.ac.jp/resource/jga-dataset/JGAD000432), quantity of samples: 153, quantity of data files: 459 (fastq:459), quantity of analysis files: 30 (tab:30), file size: 2.2 TB (2,157,143,498,962 bytes), and usage restriction policy: NBDC Policy (https://ddbj.nig.ac.jp/resource/jga-policy/JGAP000001). In addition to the NBDC database, we also put processed dataset on our private database (https://kero.hgc.jp/longread_viewer/single_cell/open/data/Expression/). The present study did not develop any new software. All codes used in the present study can be available upon request to the lead contact, Yutaka Suzuki (pj.ca.oykot-u.k@ikuzusy). Table S13 Summary table of scRNA-seq metadata. This table provides the metadata of scRNA-seq datasets used in the current study. The metadata table format is following MINSEQE standards. Table S14 Summary table of scVDJ-seq metadata. This 3,5-Diiodothyropropionic acid table provides the metadata of scVDJ-seq datasets used in the current study. The metadata table format is following AIRR standards. Table S15 Summary table of CyTOF metadata. This table provides the metadata of CyTOF datasets used in the current study. The metadata table format is following MiFlowcyte requirements. Abstract 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. Comparable diversities 3,5-Diiodothyropropionic acid 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 numerous environmental conditions. To demonstrate this idea, we conducted the comparable analysis for the same individuals around the vaccination of influenza and SARS-CoV-2. In fact, we detected unique 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. Introduction The human immune system consists of ingenious.