High-throughput phenotyping reveals expansive genetic and structural underpinnings of immune variation

Abstract

By developing a high-density murine immunophenotyping platform compatible with high-throughput genetic screening, we have established profound contributions of genetics and structure to immune variation (http://www.immunophenotype.org). Specifically, high-throughput phenotyping of 530 unique mouse gene knockouts identified 140 monogenic ‘hits’, of which most had no previous immunologic association. Furthermore, hits were collectively enriched in genes for which humans show poor tolerance to loss of function. The immunophenotyping platform also exposed dense correlation networks linking immune parameters with each other and with specific physiologic traits. Such linkages limit freedom of movement for individual immune parameters, thereby imposing genetically regulated ‘immunologic structures’, the integrity of which was associated with immunocompetence. Hence, we provide an expanded genetic resource and structural perspective for understanding and monitoring immune variation in health and disease.

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Fig. 1: Variation in immune cell subset composition with sex as a contributory driver.
Fig. 2: Correlations exist between immune parameters.
Fig. 3: Correlations between immune and non-immune parameters form a sex-specific network of interactions.
Fig. 4: Of 530 genes, 140 perturb the immunophenotype.
Fig. 5: Examples of genes with specific impacts on the immune system.
Fig. 6: Examples of genes that impact on the immune system and physiology.
Fig. 7: Genes affecting baseline immunophenotypes and challenge responses.
Fig. 8: Phenodeviants preserve or break immunological structures.

Data availability

The flow cytometry files that support the findings of this study are available from http://www.flowrepository.org with the following identifiers: 3i PBMC panel 1, FR-FCM-ZYPJ; 3i PBMC panel 2, FR-FCM-ZYPK; 3i T-cell Spleen IMPC, FR-FCM-ZYX9; 3i T-cell MLN IMPC, FR-FCM-ZYXB; 3i B-cell Spleen IMPC, FR-FCM-ZYXC; 3i B-cell MLN IMPC, FR-FCM-ZYXE; 3i M-cell Spleen IMPC, FR-FCM-ZYXF; 3i M-cell MLN IMPC, FR-FCM-ZYXG; 3i P2 SPL IMPC, FR-FCM-ZYXN; 3i BM IMPC, FR-FCM-ZYXQ. Vignettes showing gating of affected cell populations in KO mice and controls are available for all phenotypes deemed important in the present study in flow cytometry assays through https://www.mousephenotype.org (access through Associated Images on a gene page). Microscopy image files from ear epidermis assay, ANA assay and DSS histology are available from the corresponding author upon request and have been submitted to https://www.mousephenotype.org (access through Associated Images on a gene page). All assay results on mouse and strain level that support the findings of the present study are available through http://www.immunophenotype.org (website entirely devoted to this project) and https://www.mousephenotype.org (access via gene name). Source data for Figs. 1–3 and 5–8 are presented with the paper. Supporting data are also available from https://github.com/AnnaLorenc/3i_heatmapping. All mouse lines analyzed in this work are available from repositories linked to the IMPC (https://www.mousephenotype.org) or from WSI (email mouseinterest@sanger.ac.uk). Cell lines are available upon request.

Code availability

Code used for initial hit calling and preprocessed per-mouse data for flow cytometry, ear epidermis and DSS assays are available from https://github.com/AnnaLorenc/3i_heatmapping. The PhenStat R package used for influenza analysis is available on Bioconductor (http://www.bioconductor.org)61. The ImageJ macro and the Python code used to score ANA positivity, the Definiens Developer code to assess ear epidermis images and the R code used to assess the FPRs are available upon request.

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Acknowledgements

We thank many colleagues for advice, particularly: S. Heck of the Biomedical Research Centre of Guy’s and St Thomas’ Hospital and King’s College London; D. Davies of the Francis Crick Institute; F. Geissmann, R. Noelle, J. Strid and O. Sobolev in the early stages of program planning; and N. Karamanis and K. Hawkins for UX testing of the website. The project was funded by a Wellcome Trust grant (no. 100156/Z/12/Z). M.D.-C. received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska–Curie grant agreement no. 656347. K.O.B., J.W. and T.M. were supported by the National Institutes of Health (NIH) Common Fund (grant no. UM1 HG006370). The development of automated flow analysis in R.B.’s group was supported by Genome BC (grant no. SOF152), the Natural Sciences and Engineering Research Council of Canada, the International Society for Advancement of Cytometry Genome Canada, Genome BC (grant no. 252FLO), the National Institute of General Medical Sciences (grant no. R01GM118417) and the Canadian Institutes of Health Research. G.D. was supported by a National Institute for Health Research grant to the Cambridge Biomedical Centre. W.J. was supported by an NIH grant (no. AI026170). C.M.L. is supported by a Wellcome Senior Fellowship in Basic Biomedical Science (no. 107059/Z/15/Z). R.J.C. is a principal investigator of the Medical Research Council’s Human Immunology Unit. K.J.M. is funded by a Wellcome Trust Investigator Award (no. 102972/Z/13/Z). R.K.G. holds a Wellcome Trust Investigator Award (no. Z10661/Z/18/Z) and is part of the Wellcome Trust Centre for Cell Matrix Research funded by an award (no. Z03128/Z/16/Z). In addition, the project was supported by grants and facilities provided to A.C.H. by the Francis Crick Institute, which receives its core funding from Cancer Research UK (grant no. FC001093), the UK Medical Research Council (grant no. FC001093) and the Wellcome Trust (grant no. FC001093).

Author information

J.K.W., F.P., G.D., W.J., C.M.L., R.J.C., K.J.M., R.K.G., G.M.G., D.J.A. and A.C.H. conceptualized the present study. L.A.-D., A.G.L., A.L., D.S.U., S. Clare, A.O.S., N. Saran, M.A.D.-C., K.R.B., B.M., J.I. P.R.B., K.I.H., E. Cambridge, S.F., T.L.C., B.W., A.R., S.D., J.M., A.Y., M.L., G.X.S.-Z., A.C., R.B., G.D., W.J., C.M.L., R.J.C., K.J.M., R.K.G., G.M.G., D.J.A. and A.C.H. provided the methodology. A.L., M.G., N.A.K., D.M., A.R., S.D., J.M., A.Y., M.L., J.A. and R.B. provided the software. L.A.-D., A.L., K.O.B., J.W., J.C.M., A.M. and T.F.M. dealt with the website. L.A.-D., A.G.L., A.L., D.S.U., S. Clare, A.O.S., N. Saran, M.A.D.-C., K.R.B., B.M., J.I., P.R.B. and E. Cambridge provided the validation. L.A.-D., A.G.L., A.L., D.S.U., S. Caetano, A.O.S., N. Saran, M.A.D.-C., K.R.B., B.M., J.I., P.R.B., E. Cambridge and A.C.H. did the formal analysis. L.A.-D., A.G.L., D.S.U., S. Clare, A.O.S., M.A.D.-C., N. Saran, K.R.B., B.M., J.I., P.R.B., S. Caetano, K.I.H., E. Cambridge, S.F., T.L.C., L.K., K.H., C.B., G.N., E. Cawthorne, B.W., G.X.S.-Z., A.C., C.B.R., H.W., A.P-K., M.E., N. Strevens and M.P. carried out the investigations. L.A.-D. and A.L. curated the data. A.C.H. wrote the original draft. L.A.-D., A.G.L., A.L., D.S.U., A.O.S., N. Saran, N.A.K., J.A., G.D., R.J.C., R.K.G., D.J.A. and A.C.H. reviewed and edited the manuscript. L.A.-D., A.G.L., A.L. and A.C.H. visualized it. T.F.M., R.B., G.D., W.J., C.M.L., R.J.C., K.J.M., R.K.G., G.M.G., D.J.A. and A.C.H. supervised. L.A.-D., J.K.W., S. Clare, A.O.S., R.R-S. and A.C.H. were the project administrators. R.B., F.P., G.D., W.J., C.M.L., R.J.C., K.J.M., R.K.G., G.M.G., D.J.A. and A.C.H. carried out the funding acquisition.

Correspondence to Adrian C. Hayday.

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Peer review information Z. Fehervari was the primary editor on this article, and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Abeler-Dörner, L., Laing, A.G., Lorenc, A. et al. High-throughput phenotyping reveals expansive genetic and structural underpinnings of immune variation. Nat Immunol 21, 86–100 (2020). https://doi.org/10.1038/s41590-019-0549-0

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