Human Cellular Genetics of Innate Immunity

The type I interferon response is a key part of the innate immune system, responding to infection and inducing an antiviral intracellular state. While there is known to be variability in this signalling pathway between individuals, alongside cell-to-cell heterogeneity in a genetically identical cell population, the basis of this variation is not fully understood. In this PhD, I established large-scale single-cell RNA sequencing experiments to study cellular variation in the innate immune response in fibroblasts of 70 healthy human individuals from the HipSci initiative. Chapter 2 describes optimisation of stimulation conditions to induce an antiviral response, and the experimental work carried out on the panel of donors. In Chapter 3, I analyse heterogeneity in resting (unstimulated) fibroblasts. By comparing to ex vivo skin data containing multiple cell types, I confirm the relative homogeneity of the in vitro cultured fibroblasts used, mapping to one sub-population of ex vivo skin fibroblasts. Using matched whole exome sequencing data, somatic mutations in sub-populations of cells within each donor were detected, and clonal populations identified. A novel computational method, cardelino, was developed for inference of the clonal tree configuration and the clone of origin of individual cells that have been assayed using scRNA-seq. Applying cardelino to 32 fibroblast lines identifies hundreds of differentially expressed genes between cells from different somatic clones, with cell cycle and proliferation pathways frequently enriched. Returning to innate immunity, Chapters 4 and 5 centre on variability in the type I interferon response. I first describe work linking variability in the innate immune response and evolutionary divergence across mammalian species. Focusing on human variability, the large dataset described above is used to characterise the innate immune response at single cell resolution, elucidating the dynamics of the response across donors in Chapter 4. Chapter 5 describes the application of quantitative trait loci approaches to innate immune phenotypes. This work characterises both inter- and intra-individual heterogeneity in innate immunity.

Mapping small molecule binding data to structural domains.

RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are.BACKGROUND: Large-scale bioactivity/SAR Open Data has recently become available, and this has allowed new analyses and approaches to be developed to help address the productivity and translational gaps of current drug discovery. One of the current limitations of these data is the relative sparsity of reported interactions per protein target, and complexities in establishing clear relationships between bioactivity and targets using bioinformatics tools. We detail in this paper the indexing of targets by the structural domains that bind (or are likely to bind) the ligand within a full-length protein. Specifically, we present a simple heuristic to map small molecule binding to Pfam domains. This profiling can be applied to all proteins within a genome to give some indications of the potential pharmacological modulation and regulation of all proteins. RESULTS: In this implementation of our heuristic, ligand binding to protein targets from the ChEMBL database was mapped to structural domains as defined by profiles contained within the Pfam-A database. Our mapping suggests that the majority of assay targets within the current version of the ChEMBL database bind ligands through a small number of highly prevalent domains, and conversely the majority of Pfam domains sampled by our data play no currently established role in ligand binding. Validation studies, carried out firstly against Uniprot entries with expert binding-site annotation and secondly against entries in the wwPDB repository of crystallographic protein structures, demonstrate that our simple heuristic maps ligand binding to the correct domain in about 90 percent of all assessed cases. Using the mappings obtained with our heuristic, we have assembled ligand sets associated with each Pfam domain. CONCLUSIONS: Small molecule binding has been mapped to Pfam-A domains of protein targets in the ChEMBL bioactivity database. The result of this mapping is an enriched annotation of small molecule bioactivity data and a grouping of activity classes following the Pfam-A specifications of protein domains. This is valuable for data-focused approaches in drug discovery, for example when extrapolating potential targets of a small molecule with known activity against one or few targets, or in the assessment of a potential target for drug discovery or screening studies

Mapping interindividual dynamics of innate immune response at single-cell resolution

Common genetic variants across individuals modulate the cellular response to pathogens and are implicated in diverse immune pathologies, yet how they dynamically alter the response upon infection is not well understood. Here, we triggered antiviral responses in human fibroblasts from 68 healthy donors, and profiled tens of thousands of cells using single-cell RNA-sequencing. We developed GASPACHO (GAuSsian Processes for Association mapping leveraging Cell HeterOgeneity), a statistical approach designed to identify nonlinear dynamic genetic effects across transcriptional trajectories of cells. This approach identified 1,275 expression quantitative trait loci (local false discovery rate 10%) that manifested during the responses, many of which were colocalized with susceptibility loci identified by genome-wide association studies of infectious and autoimmune diseases, including the OAS1 splicing quantitative trait locus in a COVID-19 susceptibility locus. In summary, our analytical approach provides a unique framework for delineation of the genetic variants that shape a wide spectrum of transcriptional responses at single-cell resolution

Gene expression variability across cells and species shapes innate immunity.

As the first line of defence against pathogens, cells mount an innate immune response, which varies widely from cell to cell. The response must be potent but carefully controlled to avoid self-damage. How these constraints have shaped the evolution of innate immunity remains poorly understood. Here we characterize the innate immune response's transcriptional divergence between species and variability in expression among cells. Using bulk and single-cell transcriptomics in fibroblasts and mononuclear phagocytes from different species, challenged with immune stimuli, we map the architecture of the innate immune response. Transcriptionally diverging genes, including those that encode cytokines and chemokines, vary across cells and have distinct promoter structures. Conversely, genes that are involved in the regulation of this response, such as those that encode transcription factors and kinases, are conserved between species and display low cell-to-cell variability in expression. We suggest that this expression pattern, which is observed across species and conditions, has evolved as a mechanism for fine-tuned regulation to achieve an effective but balanced response

Cardelino:computational integration of somatic clonal substructure and single-cell transcriptomes

Bulk and single-cell DNA sequencing has enabled reconstructing clonal substructures of somatic tissues from frequency and cooccurrence patterns of somatic variants. However, approaches to characterize phenotypic variations between clones are not established. Here we present cardelino (https://github.com/single-cell-genetics/cardelino), a computational method for inferring the clonal tree configuration and the clone of origin of individual cells assayed using single-cell RNA-seq (scRNA-seq). Cardelino flexibly integrates information from imperfect clonal trees inferred based on bulk exome-seq data, and sparse variant alleles expressed in scRNA-seq data. We apply cardelino to a published cancer dataset and to newly generated matched scRNA-seq and exome-seq data from 32 human dermal fibroblast lines, identifying hundreds of differentially expressed genes between cells from different somatic clones. These genes are frequently enriched for cell cycle and proliferation pathways, indicating a role for cell division genes in somatic evolution in healthy skin

Mapping interindividual dynamics of innate immune response at single-cell resolution.

Common genetic variants across individuals modulate the cellular response to pathogens and are implicated in diverse immune pathologies, yet how they dynamically alter the response upon infection is not well understood. Here, we triggered antiviral responses in human fibroblasts from 68 healthy donors, and profiled tens of thousands of cells using single-cell RNA-sequencing. We developed GASPACHO (GAuSsian Processes for Association mapping leveraging Cell HeterOgeneity), a statistical approach designed to identify nonlinear dynamic genetic effects across transcriptional trajectories of cells. This approach identified 1,275 expression quantitative trait loci (local false discovery rate 10%) that manifested during the responses, many of which were colocalized with susceptibility loci identified by genome-wide association studies of infectious and autoimmune diseases, including the OAS1 splicing quantitative trait locus in a COVID-19 susceptibility locus. In summary, our analytical approach provides a unique framework for delineation of the genetic variants that shape a wide spectrum of transcriptional responses at single-cell resolution

Mapping interindividual dynamics of innate immune response at single-cell resolution

Acknowledgements: N.K., R.R., K.B.M., T.H. and S.A.T. were supported by Wellcome Sanger core funding (grant no. WT206194) and the Human Induced Pluripotent Stem Cell Initiative. T.H. was supported by the Israel Science Foundation (grant no. 435/20) and by the Chan Zuckerberg Initiative (Single-Cell Analysis of Inflammation, grant no. DAF2020-217532). R.R. was supported by the BBSRC Doctoral Training Programme. F.J.C.-N. and B.G. were supported by Wellcome (grant no. 206328/Z/17/Z) and MRC (grant no. MR/S036113/1). P.A.L. was supported by the Evelyn Trust (grant no. 20/75) and the UKRI/NIHR through the UK Coronavirus Immunology Consortium. K.B.W. acknowledges funding from University College London, Birkbeck MRC Doctoral Training Programme. M.Z.N. acknowledges funding from an MRC Clinician Scientist Fellowship (grant no. MR/W00111X/1) and the Rutherford Fund Fellowship allocated by the MRC and from the UK Regenerative Medicine Platform 2 (grant no. MR/5005579/1). M.Z.N. and K.B.M. have been funded by the Rosetrees Trust (grant no. M944) and from Action Medical Research (grant no. GN2911). We thank M. D. Morgan for careful reading of the manuscript and data sharing.Common genetic variants across individuals modulate the cellular response to pathogens and are implicated in diverse immune pathologies, yet how they dynamically alter the response upon infection is not well understood. Here, we triggered antiviral responses in human fibroblasts from 68 healthy donors, and profiled tens of thousands of cells using single-cell RNA-sequencing. We developed GASPACHO (GAuSsian Processes for Association mapping leveraging Cell HeterOgeneity), a statistical approach designed to identify nonlinear dynamic genetic effects across transcriptional trajectories of cells. This approach identified 1,275 expression quantitative trait loci (local false discovery rate 10%) that manifested during the responses, many of which were colocalized with susceptibility loci identified by genome-wide association studies of infectious and autoimmune diseases, including the OAS1 splicing quantitative trait locus in a COVID-19 susceptibility locus. In summary, our analytical approach provides a unique framework for delineation of the genetic variants that shape a wide spectrum of transcriptional responses at single-cell resolution

Mapping interindividual dynamics of innate immune response at single-cell resolution.

Common genetic variants across individuals modulate the cellular response to pathogens and are implicated in diverse immune pathologies, yet how they dynamically alter the response upon infection is not well understood. Here, we triggered antiviral responses in human fibroblasts from 68 healthy donors, and profiled tens of thousands of cells using single-cell RNA-sequencing. We developed GASPACHO (GAuSsian Processes for Association mapping leveraging Cell HeterOgeneity), a statistical approach designed to identify nonlinear dynamic genetic effects across transcriptional trajectories of cells. This approach identified 1,275 expression quantitative trait loci (local false discovery rate 10%) that manifested during the responses, many of which were colocalized with susceptibility loci identified by genome-wide association studies of infectious and autoimmune diseases, including the OAS1 splicing quantitative trait locus in a COVID-19 susceptibility locus. In summary, our analytical approach provides a unique framework for delineation of the genetic variants that shape a wide spectrum of transcriptional responses at single-cell resolution