Dinithi Sumanaweera

. Here in our SARS-CoV-2 human challenge study, we used single-cell multi-omics profiling of nasopharyngeal swabs and blood to temporally resolve abortive, transient and sustained infections in seronegative individuals challenged with pre-Alpha SARS-CoV-2. Our analyses revealed rapid changes in cell-type proportions and dozens of highly dynamic cellular response states in epithelial and immune cells associated with specific time points and infection status. We observed that the interferon response in blood preceded the nasopharyngeal response. Moreover, nasopharyngeal immune infiltration occurred early in samples from individuals with only transient infection and later in samples from individuals with sustained infection. High expression of HLA-DQA2 before inoculation was associated with preventing sustained infection. Ciliated cells showed multiple immune responses and were most permissive for viral replication, whereas nasopharyngeal T cells and macrophages were infected non-productively. We resolved 54 T cell states, including acutely activated T cells that clonally expanded while carrying convergent SARS-CoV-2 motifs. Our new computational pipeline Cell2TCR identifies activated antigen-responding T cells based on a gene expression signature and clusters these into clonotype groups and motifs. Overall, our detailed time series data can serve as a Rosetta stone for epithelial and immune cell responses and reveals early dynamic responses associated with protection against infection.

Human prenatal skin is populated by innate immune cells, including macrophages, but whether they act solely in immunity or have additional functions in morphogenesis is unclear. Here we assembled a comprehensive multi-omics reference atlas of prenatal human skin (7–17 post-conception weeks), combining single-cell and spatial transcriptomics data, to characterize the microanatomical tissue niches of the skin. This atlas revealed that crosstalk between non-immune and immune cells underpins the formation of hair follicles, is implicated in scarless wound healing and is crucial for skin angiogenesis. We systematically compared a hair-bearing skin organoid (SkO) model derived from human embryonic stem cells and induced pluripotent stem cells to prenatal and adult skin1. The SkO model closely recapitulated in vivo skin epidermal and dermal cell types during hair follicle development and expression of genes implicated in the pathogenesis of genetic hair and skin disorders. However, the SkO model lacked immune cells and had markedly reduced endothelial cell heterogeneity and quantity. Our in vivo prenatal skin cell atlas indicated that macrophages and macrophage-derived growth factors have a role in driving endothelial development. Indeed, vascular network remodelling was enhanced following transfer of autologous macrophages derived from induced pluripotent stem cells into SkO cultures. Innate immune cells are therefore key players in skin morphogenesis beyond their conventional role in immunity, a function they achieve through crosstalk with non-immune cells. A comprehensive multi-omics reference atlas of prenatal human skin shows that innate immune cells crosstalk with non-immune cells to perform pivotal roles in skin morphogenesis, including the formation of hair follicles.

Abstract The gastrointestinal tract is a multi-organ system crucial for efficient nutrient uptake and barrier immunity. Advances in genomics and a surge in gastrointestinal diseases 1,2 has fuelled efforts to catalogue cells constituting gastrointestinal tissues in health and disease 3 . Here we present systematic integration of 25 single-cell RNA sequencing datasets spanning the entire healthy gastrointestinal tract in development and in adulthood. We uniformly processed 385 samples from 189 healthy controls using a newly developed automated quality control approach (scAutoQC), leading to a healthy reference atlas with approximately 1.1 million cells and 136 fine-grained cell states. We anchor 12 gastrointestinal disease datasets spanning gastrointestinal cancers, coeliac disease, ulcerative colitis and Crohn’s disease to this reference. Utilizing this 1.6 million cell resource (gutcellatlas.org), we discover epithelial cell metaplasia originating from stem cells in intestinal inflammatory diseases with transcriptional similarity to cells found in pyloric and Brunner’s glands. Although previously linked to mucosal healing 4 , we now implicate pyloric gland metaplastic cells in inflammation through recruitment of immune cells including T cells and neutrophils. Overall, we describe inflammation-induced changes in stem cells that alter mucosal tissue architecture and promote further inflammation, a concept applicable to other tissues and diseases.

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease where substantial heterogeneity in clinical presentation urgently requires a better stratification of patients for the development of drug trials and clinical care. In this study we explored stratification through a crowdsourcing approach, the DREAM Prize4Life ALS Stratification Challenge. Using data from >10,000 patients from ALS clinical trials and 1479 patients from community-based patient registers, more than 30 teams developed new approaches for machine learning and clustering, outperforming the best current predictions of disease outcome. We propose a new method to integrate and analyze patient clusters across methods, showing a clear pattern of consistent and clinically relevant sub-groups of patients that also enabled the reliable classification of new patients. Our analyses reveal novel insights in ALS and describe for the first time the potential of a crowdsourcing to uncover hidden patient sub-populations, and to accelerate disease understanding and therapeutic development.

Single-cell data analysis can infer dynamic changes in cell populations, for example across time, space or in response to perturbation, thus deriving pseudotime trajectories. Current approaches comparing trajectories often use dynamic programming but are limited by assumptions such as the existence of a definitive match. Here we describe Genes2Genes, a Bayesian information-theoretic dynamic programming framework for aligning single-cell trajectories. It is able to capture sequential matches and mismatches of individual genes between a reference and query trajectory, highlighting distinct clusters of alignment patterns. Across both real world and simulated datasets, it accurately inferred alignments and demonstrated its utility in disease cell-state trajectory analysis. In a proof-of-concept application, Genes2Genes revealed that T cells differentiated in vitro match an immature in vivo state while lacking expression of genes associated with TNF signaling. This demonstrates that precise trajectory alignment can pinpoint divergence from the in vivo system, thus guiding the optimization of in vitro culture conditions.