Arianne C. Richard

Bio: Arianne C. Richard is an academic researcher from University of Cambridge. The author has contributed to research in topics: Cytokine & Tumor necrosis factor alpha. The author has an hindex of 14, co-authored 27 publications receiving 919 citations. Previous affiliations of Arianne C. Richard include Columbia University & University of Illinois at Urbana–Champaign.

Detection and removal of barcode swapping in single-cell RNA-seq data.

Abstract: Barcode swapping results in the mislabelling of sequencing reads between multiplexed samples on patterned flow-cell Illumina sequencing machines. This may compromise the validity of numerous genomic assays; however, the severity and consequences of barcode swapping remain poorly understood. We have used two statistical approaches to robustly quantify the fraction of swapped reads in two plate-based single-cell RNA-sequencing datasets. We found that approximately 2.5% of reads were mislabelled between samples on the HiSeq 4000, which is lower than previous reports. We observed no correlation between the swapped fraction of reads and the concentration of free barcode across plates. Furthermore, we have demonstrated that barcode swapping may generate complex but artefactual cell libraries in droplet-based single-cell RNA-sequencing studies. To eliminate these artefacts, we have developed an algorithm to exclude individual molecules that have swapped between samples in 10x Genomics experiments, allowing the continued use of cutting-edge sequencing machines for these assays.

Testing for differential abundance in mass cytometry data

Abstract: When comparing biological conditions using mass cytometry data, a key challenge is to identify cellular populations that change in abundance. Here, we present a computational strategy for detecting 'differentially abundant' populations by assigning cells to hyperspheres, testing for significant differences between conditions and controlling the spatial false discovery rate. Our method (http://bioconductor.org/packages/cydar) outperforms other approaches in simulations and finds novel patterns of differential abundance in real data.

TL1A and DR3, a TNF family ligand‐receptor pair that promotes lymphocyte costimulation, mucosal hyperplasia, and autoimmune inflammation

Abstract: DR3 (TNFRSF25) is a member of the tumor necrosis factor receptor (TNFR) superfamily expressed primarily on lymphocytes and is a receptor for the TNF family cytokine TL1A (TNFSF15). DR3 costimulates T-cell activation, but it is unique among these receptors in that it signals through an intracytoplasmic death domain and the adapter protein TRADD (TNFR-associated death domain). TL1A costimulates T cells to produce a wide variety of cytokines and can promote expansion of activated and regulatory T cells in vivo. Studies in mice deficient in DR3 or TL1A or in animals treated with antibodies that block the activity of TL1A have revealed a specific role for DR3 in enhancing effector T-cell proliferation at the site of tissue inflammation in autoimmune disease models. DR3 appears to be required in autoimmune disease models dependent on a variety of different T-cell subsets and also invariant natural killer T (iNKT) cells. Chronic expression of TL1A induces a distinct interleukin-13-dependent pathology in the small intestine marked by goblet cell hyperplasia and other features associated with allergic and anti-parasitic responses. These studies suggest that TL1A may be a viable target for therapies designed to inhibit the T-cell-dependent component of diverse autoimmune diseases.

ILC2-driven innate immune checkpoint mechanism antagonizes NK cell antimetastatic function in the lung

Abstract: Metastasis constitutes the primary cause of cancer-related deaths, with the lung being a commonly affected organ. We found that activation of lung-resident group 2 innate lymphoid cells (ILC2s) orchestrated suppression of natural killer (NK) cell-mediated innate antitumor immunity, leading to increased lung metastases and mortality. Using multiple models of lung metastasis, we show that interleukin (IL)-33-dependent ILC2 activation in the lung is involved centrally in promoting tumor burden. ILC2-driven innate type 2 inflammation is accompanied by profound local suppression of interferon-γ production and cytotoxic function of lung NK cells. ILC2-dependent suppression of NK cells is elaborated via an innate regulatory mechanism, which is reliant on IL-5-induced lung eosinophilia, ultimately limiting the metabolic fitness of NK cells. Therapeutic targeting of IL-33 or IL-5 reversed NK cell suppression and alleviated cancer burden. Thus, we reveal an important function of IL-33 and ILC2s in promoting tumor metastasis via their capacity to suppress innate type 1 immunity.

Molecular mechanisms of T cell co-stimulation and co-inhibition

Abstract: Co-stimulatory and co-inhibitory receptors have a pivotal role in T cell biology, as they determine the functional outcome of T cell receptor (TCR) signalling. The classic definition of T cell co-stimulation continues to evolve through the identification of new co-stimulatory and co-inhibitory receptors, the biochemical characterization of their downstream signalling events and the delineation of their immunological functions. Notably, it has been recently appreciated that co-stimulatory and co-inhibitory receptors display great diversity in expression, structure and function, and that their functions are largely context dependent. Here, we focus on some of these emerging concepts and review the mechanisms through which T cell activation, differentiation and function is controlled by co-stimulatory and co-inhibitory receptors.

Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression

Abstract: Single-cell RNA-seq (scRNA-seq) data exhibits significant cell-to-cell variation due to technical factors, including the number of molecules detected in each cell, which can confound biological heterogeneity with technical effects. To address this, we present a modeling framework for the normalization and variance stabilization of molecular count data from scRNA-seq experiments. We propose that the Pearson residuals from “regularized negative binomial regression,” where cellular sequencing depth is utilized as a covariate in a generalized linear model, successfully remove the influence of technical characteristics from downstream analyses while preserving biological heterogeneity. Importantly, we show that an unconstrained negative binomial model may overfit scRNA-seq data, and overcome this by pooling information across genes with similar abundances to obtain stable parameter estimates. Our procedure omits the need for heuristic steps including pseudocount addition or log-transformation and improves common downstream analytical tasks such as variable gene selection, dimensional reduction, and differential expression. Our approach can be applied to any UMI-based scRNA-seq dataset and is freely available as part of the R package sctransform, with a direct interface to our single-cell toolkit Seurat.

The biology of innate lymphoid cells

Abstract: The innate immune system is composed of a diverse array of evolutionarily ancient haematopoietic cell types, including dendritic cells, monocytes, macrophages and granulocytes. These cell populations collaborate with each other, with the adaptive immune system and with non-haematopoietic cells to promote immunity, inflammation and tissue repair. Innate lymphoid cells are the most recently identified constituents of the innate immune system and have been the focus of intense investigation over the past five years. We summarize the studies that formally identified innate lymphoid cells and highlight their emerging roles in controlling tissue homeostasis in the context of infection, chronic inflammation, metabolic disease and cancer.

The GTEx Consortium atlas of genetic regulatory effects across human tissues

Abstract: The Genotype-Tissue Expression (GTEx) project was established to characterize genetic effects on the transcriptome across human tissues, and to link these regulatory mechanisms to trait and disease associations. Here, we present analyses of the v8 data, based on 17,382 RNA-sequencing samples from 54 tissues of 948 post-mortem donors. We comprehensively characterize genetic associations for gene expression and splicing in cis and trans, showing that regulatory associations are found for almost all genes, and describe the underlying molecular mechanisms and their contribution to allelic heterogeneity and pleiotropy of complex traits. Leveraging the large diversity of tissues, we provide insights into the tissue-specificity of genetic effects, and show that cell type composition is a key factor in understanding gene regulatory mechanisms in human tissues.