Aviv Regev’s research while affiliated with Genentech and other places

Human airways contain specialized rare epithelial cells including CFTR-rich ionocytes that regulate airway surface physiology and chemosensory tuft cells that produce asthma-associated inflammatory mediators. Here, using a lung cell atlas of 311,748 single cell RNA-Seq profiles, we identify 687 ionocytes (0.45%). In contrast to prior reports claiming a lack of ionocytes in the small airways, we demonstrate that ionocytes are present in small and large airways in similar proportions. Surprisingly, we find only 3 mature tuft cells (0.002%), and demonstrate that previously annotated tuft-like cells are instead highly replicative progenitor cells. These tuft-ionocyte progenitor (TIP) cells produce ionocytes as a default lineage. However, Type 2 and Type 17 cytokines divert TIP cell lineage in vitro, resulting in the production of mature tuft cells at the expense of ionocyte differentiation. Our dataset thus provides an updated understanding of airway rare cell composition, and further suggests that clinically relevant cytokines may skew the composition of disease-relevant rare cells.

High-content phenotypic screens provide a powerful strategy for studying biological systems, but the scale of possible perturbations and cell states makes exhaustive experiments unfeasible. Computational models that are trained on existing data and extrapolate to correctly predict outcomes in unseen contexts have the potential to accelerate biological discovery. Here, we present CellFlow, a flexible framework based on flow matching that can model single cell phenotypes induced by complex perturbations. We apply CellFlow to various phenotypic screens, accurately predicting expression responses to a wide range of perturbations, including cytokine stimulation, drug treatments and gene knockouts. CellFlow successfully modeled developmental perturbations at the whole-embryo scale and guided cell fate and organoid engineering by predicting heterogeneous cell populations arising from combinatorial morphogen treatments and by performing a virtual organoid protocol screen. Taken together, CellFlow has the potential to accelerate discovery from phenotypic screens by learning from existing data and generating phenotypes induced by unseen conditions.

Microglia are the resident immune cells in the brain and have pivotal roles in neurodevelopment and neuroinflammation1,2. This study investigates the function of the immune-checkpoint molecule TIM-3 (encoded by HAVCR2) in microglia. TIM-3 was recently identified as a genetic risk factor for late-onset Alzheimer’s disease³, and it can induce T cell exhaustion⁴. However, its specific function in brain microglia remains unclear. We demonstrate in mouse models that TGFβ signalling induces TIM-3 expression in microglia. In turn, TIM-3 interacts with SMAD2 and TGFBR2 through its carboxy-terminal tail, which enhances TGFβ signalling by promoting TGFBR-mediated SMAD2 phosphorylation, and this process maintains microglial homeostasis. Genetic deletion of Havcr2 in microglia leads to increased phagocytic activity and a gene-expression profile consistent with the neurodegenerative microglial phenotype (MGnD), also referred to as disease-associated microglia (DAM). Furthermore, microglia-targeted deletion of Havcr2 ameliorates cognitive impairment and reduces amyloid-β pathology in 5×FAD mice (a transgenic model of Alzheimer’s disease). Single-nucleus RNA sequencing revealed a subpopulation of MGnD microglia in Havcr2-deficient 5×FAD mice characterized by increased pro-phagocytic and anti-inflammatory gene expression alongside reduced pro-inflammatory gene expression. These transcriptomic changes were corroborated by single-cell RNA sequencing data across most microglial clusters in Havcr2-deficient 5×FAD mice. Our findings reveal that TIM-3 mediates microglia homeostasis through TGFβ signalling and highlight the therapeutic potential of targeting microglial TIM-3 in Alzheimer’s disease.

Background The molecular underpinnings of organ dysfunction in severe COVID-19 and its potential long-term sequelae are under intense investigation. To shed light on these in the context of liver function, we perform single-nucleus RNA-seq and spatial transcriptomic profiling of livers from 17 COVID-19 decedents. Results We identify hepatocytes positive for SARS-CoV-2 RNA with an expression phenotype resembling infected lung epithelial cells, and a central role in a pro-fibrotic TGFβ signaling cell–cell communications network. Integrated analysis and comparisons with healthy controls reveal extensive changes in the cellular composition and expression states in COVID-19 liver, providing the underpinning of hepatocellular injury, ductular reaction, pathologic vascular expansion, and fibrogenesis characteristic of COVID-19 cholangiopathy. We also observe Kupffer cell proliferation and erythrocyte progenitors for the first time in a human liver single-cell atlas. Despite the absence of a clinical acute liver injury phenotype, endothelial cell composition is dramatically impacted in COVID-19, concomitantly with extensive alterations and profibrogenic activation of reactive cholangiocytes and mesenchymal cells. Conclusions Our atlas provides novel insights into liver physiology and pathology in COVID-19 and forms a foundational resource for its investigation and understanding.

Single-cell RNA-seq data have provided new insights into intracellular and intercellular processes. Because multiple processes are active in each cell simultaneously, such as its cell type program, differentiation, the cell cycle, and environmental responses, their respective signals can confound one another, requiring methods that can separate and filter different complex biological signals. Each such signal is based on different gene activities and can define different relationships between cells. However, existing methods often focus on a single process or rely on overly restrictive assumptions, thus removing, rather than disentangling biological signals. Here, we develop CellUntangler, a deep generative model that embeds cells into a flexible latent space composed of multiple subspaces, each designed with an appropriate geometry to capture a distinct signal. We apply CellUntangler to datasets containing only cycling cells and both cycling and non-cycling cells, generating embeddings in which the cell cycle signal is disentangled from non-cell cycle specific signals, such as cell type or differentiation trajectory. We demonstrate CellUntangler's extensibility by using it to capture and separate spatial from non-spatial signals. With CellUntangler, we can obtain latent embeddings that capture various biological signals and perform enhancement or filtering at the gene expression level for downstream analyses.

Single-cell RNA sequencing has profiled hundreds of millions of human cells across organs, diseases, development and perturbations to date. Mining these growing atlases could reveal cell–disease associations, identify cell states in unexpected tissue contexts and relate in vivo biology to in vitro models. These require a common measure of cell similarity across the body and an efficient way to search. Here we develop SCimilarity, a metric-learning framework to learn a unified and interpretable representation that enables rapid queries of tens of millions of cell profiles from diverse studies for cells that are transcriptionally similar to an input cell profile or state. We use SCimilarity to query a 23.4-million-cell atlas of 412 single-cell RNA-sequencing studies for macrophage and fibroblast profiles from interstitial lung disease¹ and reveal similar cell profiles across other fibrotic diseases and tissues. The top scoring in vitro hit for the macrophage query was a 3D hydrogel system², which we experimentally demonstrated reproduces this cell state. SCimilarity serves as a foundation model for single-cell profiles that enables researchers to query for similar cellular states across the human body, providing a powerful tool for generating biological insights from the Human Cell Atlas.

The Human Cell Atlas (HCA) is a global partnership "to create comprehensive reference maps of all human cells-the fundamental units of life - as a basis for both understanding human health and diagnosing, monitoring, and treating disease." ( https://www.humancellatlas.org/ ) The atlas shall characterize cells from diverse individuals across the globe to better understand human biology. HCA proactively considers the priorities of, and benefits accrued to, contributing communities. Here, we lay out principles and action items that have been adopted to affirm HCA's commitment to equity so that the atlas is beneficial to all of humanity.