LAT encodes T cell activation pathway balance.

Immune cells transduce environmental stimuli into responses essential for host health via complex signaling cascades. T cells, in particular, leverage their unique T cell receptors (TCRs) to detect specific Human Leukocyte Antigen (HLA)-presented peptides. TCR activation is then relayed via linker for activation of T cells (LAT), a TCR-proximal disordered adapter protein, which organizes protein partners and mediates the propagation of signals down diverse pathways including NFAT and AP-1. Here, we studied how balanced downstream pathway activation is encoded in the amino acid sequence of LAT. To comprehensively profile the sequence-function relationship of LAT, we developed a pooled, single-cell, high-content screening approach in which a large series of mutants in the LAT protein were analyzed to characterize their effects on T cell activation. Measuring epigenetic, transcriptomic, and cell surface protein dynamics of single cells harboring distinct LAT mutants, we found functional regions spanning over 40% of the LAT amino acid sequence. Conserved sequence motifs for protein interactions along with charge distribution are critical sequence features, and contribute to interpretation of human genetic variation in LAT. While mutant defect severity spans from moderate to complete loss of function, nearly all defective mutants, irrespective of their position in LAT, confer balanced defects across all downstream pathways. To understand the molecular basis for this observation, we performed proximal protein labeling which demonstrated that disruption of LAT interaction with a single partner protein indirectly disrupts other partner interactions, likely through the dual roles of these proteins as effectors of downstream pathways and bridging factors between LAT molecules. Overall, we report widely distributed functional regions throughout a disordered adapter and a precise physical organization of LAT and interacting molecules which constrains signaling outputs. More broadly, we describe an approach for interrogating sequence-function relationships for proteins with complex activities across regulatory layers of the cell.

Toward a foundation model of causal cell and tissue biology with a Perturbation Cell and Tissue Atlas.

Comprehensively charting the biologically causal circuits that govern the phenotypic space of human cells has often been viewed as an insurmountable challenge. However, in the last decade, a suite of interleaved experimental and computational technologies has arisen that is making this fundamental goal increasingly tractable. Pooled CRISPR-based perturbation screens with high-content molecular and/or image-based readouts are now enabling researchers to probe, map, and decipher genetically causal circuits at increasing scale. This scale is now eminently suitable for the deployment of artificial intelligence and machine learning (AI/ML) to both direct further experiments and to predict or generate information that was not-and sometimes cannot-be gathered experimentally. By combining and iterating those through experiments that are designed for inference, we now envision a Perturbation Cell Atlas as a generative causal foundation model to unify human cell biology.

Cellular communities reveal trajectories of brain ageing and Alzheimer's disease.

Alzheimer's disease (AD) has recently been associated with diverse cell states1-11, yet when and how these states affect the onset of AD remains unclear. Here we used a data-driven approach to reconstruct the dynamics of the brain's cellular environment and identified a trajectory leading to AD that is distinct from other ageing-related effects. First, we built a comprehensive cell atlas of the aged prefrontal cortex from 1.65 million single-nucleus RNA-sequencing profiles sampled from 437 older individuals, and identified specific glial and neuronal subpopulations associated with AD-related traits. Causal modelling then prioritized two distinct lipid-associated microglial subpopulations-one drives amyloid-ß proteinopathy while the other mediates the effect of amyloid-ß on tau proteinopathy-as well as an astrocyte subpopulation that mediates the effect of tau on cognitive decline. To model the dynamics of cellular environments, we devised the BEYOND methodology, which identified two distinct trajectories of brain ageing, each defined by coordinated progressive changes in certain cellular communities that lead to (1) AD dementia or (2) alternative brain ageing. Thus, we provide a cellular foundation for a new perspective on AD pathophysiology that informs personalized therapeutic development, targeting different cellular communities for individuals on the path to AD or to alternative brain ageing.

Experimental and Computational Methods for Allelic Imbalance Analysis from Single-Nucleus RNA-seq Data.

Single-cell RNA-seq (scRNA-seq) is emerging as a powerful tool for understanding gene function across diverse cells. Recently, this has included the use of allele-specific expression (ASE) analysis to better understand how variation in the human genome affects RNA expression at the single-cell level. We reasoned that because intronic reads are more prevalent in single-nucleus RNA-Seq (snRNA-Seq), and introns are under lower purifying selection and thus enriched for genetic variants, that snRNA-seq should facilitate single-cell analysis of ASE. Here we demonstrate how experimental and computational choices can improve the results of allelic imbalance analysis. We explore how experimental choices, such as RNA source, read length, sequencing depth, genotyping, etc., impact the power of ASE-based methods. We developed a new suite of computational tools to process and analyze scRNA-seq and snRNA-seq for ASE. As hypothesized, we extracted more ASE information from reads in intronic regions than those in exonic regions and show how read length can be set to increase power. Additionally, hybrid selection improved our power to detect allelic imbalance in genes of interest. We also explored methods to recover allele-specific isoform expression levels from both long- and short-read snRNA-seq. To further investigate ASE in the context of human disease, we applied our methods to a Parkinson's disease cohort of 94 individuals and show that ASE analysis had more power than eQTL analysis to identify significant SNP/gene pairs in our direct comparison of the two methods. Overall, we provide an end-to-end experimental and computational approach for future studies.

Pan-cancer mapping of single CD8+ T cell profiles reveals a TCF1:CXCR6 axis regulating CD28 co-stimulation and anti-tumor immunity.

CD8+ T cells must persist and function in diverse tumor microenvironments to exert their effects. Thus, understanding common underlying expression programs could better inform the next generation of immunotherapies. We apply a generalizable matrix factorization algorithm that recovers both shared and context-specific expression programs from diverse datasets to a single-cell RNA sequencing (scRNA-seq) compendium of 33,161 CD8+ T cells from 132 patients with seven human cancers. Our meta-single-cell analyses uncover a pan-cancer T cell dysfunction program that predicts clinical non-response to checkpoint blockade in melanoma and highlights CXCR6 as a pan-cancer marker of chronically activated T cells. Cxcr6 is trans-activated by AP-1 and repressed by TCF1. Using mouse models, we show that Cxcr6 deletion in CD8+ T cells increases apoptosis of PD1+TIM3+ cells, dampens CD28 signaling, and compromises tumor growth control. Our study uncovers a TCF1:CXCR6 axis that counterbalances PD1-mediated suppression of CD8+ cell responses and is essential for effective anti-tumor immunity.

BACH2 regulates diversification of regulatory and proinflammatory chromatin states in TH17 cells.

Interleukin-17 (IL-17)-producing helper T (TH17) cells are heterogenous and consist of nonpathogenic TH17 (npTH17) cells that contribute to tissue homeostasis and pathogenic TH17 (pTH17) cells that mediate tissue inflammation. Here, we characterize regulatory pathways underlying TH17 heterogeneity and discover substantial differences in the chromatin landscape of npTH17 and pTH17 cells both in vitro and in vivo. Compared to other CD4+ T cell subsets, npTH17 cells share accessible chromatin configurations with regulatory T cells, whereas pTH17 cells exhibit features of both npTH17 cells and type 1 helper T (TH1) cells. Integrating single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq) and single-cell RNA sequencing (scRNA-seq), we infer self-reinforcing and mutually exclusive regulatory networks controlling different cell states and predicted transcription factors regulating TH17 cell pathogenicity. We validate that BACH2 promotes immunomodulatory npTH17 programs and restrains proinflammatory TH1-like programs in TH17 cells in vitro and in vivo. Furthermore, human genetics implicate BACH2 in multiple sclerosis. Overall, our work identifies regulators of TH17 heterogeneity as potential targets to mitigate autoimmunity.

Brain Chimeroids reveal individual susceptibility to neurotoxic triggers.

Interindividual genetic variation affects the susceptibility to and progression of many diseases1,2. However, efforts to study how individual human brains differ in normal development and disease phenotypes are limited by the paucity of faithful cellular human models, and the difficulty of scaling current systems to represent multiple people. Here we present human brain Chimeroids, a highly reproducible, multidonor human brain cortical organoid model generated by the co-development of cells from a panel of individual donors in a single organoid. By reaggregating cells from multiple single-donor organoids at the neural stem cell or neural progenitor cell stage, we generate Chimeroids in which each donor produces all cell lineages of the cerebral cortex, even when using pluripotent stem cell lines with notable growth biases. We used Chimeroids to investigate interindividual variation in the susceptibility to neurotoxic triggers that exhibit high clinical phenotypic variability: ethanol and the antiepileptic drug valproic acid. Individual donors varied in both the penetrance of the effect on target cell types, and the molecular phenotype within each affected cell type. Our results suggest that human genetic background may be an important mediator of neurotoxin susceptibility and introduce Chimeroids as a scalable system for high-throughput investigation of interindividual variation in processes of brain development and disease.

Tissue and cellular spatiotemporal dynamics in colon aging.

Tissue structure and molecular circuitry in the colon can be profoundly impacted by systemic age-related effects, but many of the underlying molecular cues remain unclear. Here, we built a cellular and spatial atlas of the colon across three anatomical regions and 11 age groups, encompassing ~1,500 mouse gut tissues profiled by spatial transcriptomics and ~400,000 single nucleus RNA-seq profiles. We developed a new computational framework, cSplotch, which learns a hierarchical Bayesian model of spatially resolved cellular expression associated with age, tissue region, and sex, by leveraging histological features to share information across tissue samples and data modalities. Using this model, we identified cellular and molecular gradients along the adult colonic tract and across the main crypt axis, and multicellular programs associated with aging in the large intestine. Our multi-modal framework for the investigation of cell and tissue organization can aid in the understanding of cellular roles in tissue-level pathology.

Protective role for kidney TREM2high macrophages in obesity- and diabetes-induced kidney injury.

Diabetic kidney disease (DKD), the most common cause of kidney failure, is a frequent complication of diabetes and obesity, and yet to date, treatments to halt its progression are lacking. We analyze kidney single-cell transcriptomic profiles from DKD patients and two DKD mouse models at multiple time points along disease progression-high-fat diet (HFD)-fed mice aged to 90-100 weeks and BTBR ob/ob mice (a genetic model)-and report an expanding population of macrophages with high expression of triggering receptor expressed on myeloid cells 2 (TREM2) in HFD-fed mice. TREM2high macrophages are enriched in obese and diabetic patients, in contrast to hypertensive patients or healthy controls in an independent validation cohort. Trem2 knockout mice on an HFD have worsening kidney filter damage and increased tubular epithelial cell injury, all signs of worsening DKD. Together, our studies suggest that strategies to enhance kidney TREM2high macrophages may provide therapeutic benefits for DKD.

Cell-type-specific mRNA transcription and degradation kinetics in zebrafish embryogenesis from metabolically labeled single-cell RNA-seq.

During embryonic development, pluripotent cells assume specialized identities by adopting particular gene expression profiles. However, systematically dissecting the relative contributions of mRNA transcription and degradation to shaping those profiles remains challenging, especially within embryos with diverse cellular identities. Here, we combine single-cell RNA-Seq and metabolic labeling to capture temporal cellular transcriptomes of zebrafish embryos where newly-transcribed (zygotic) and pre-existing (maternal) mRNA can be distinguished. We introduce kinetic models to quantify mRNA transcription and degradation rates within individual cell types during their specification. These models reveal highly varied regulatory rates across thousands of genes, coordinated transcription and destruction rates for many transcripts, and link differences in degradation to specific sequence elements. They also identify cell-type-specific differences in degradation, namely selective retention of maternal transcripts within primordial germ cells and enveloping layer cells, two of the earliest specified cell types. Our study provides a quantitative approach to study mRNA regulation during a dynamic spatio-temporal response.

An esophagus cell atlas reveals dynamic rewiring during active eosinophilic esophagitis and remission.

Coordinated cell interactions within the esophagus maintain homeostasis, and disruption can lead to eosinophilic esophagitis (EoE), a chronic inflammatory disease with poorly understood pathogenesis. We profile 421,312 individual cells from the esophageal mucosa of 7 healthy and 15 EoE participants, revealing 60 cell subsets and functional alterations in cell states, compositions, and interactions that highlight previously unclear features of EoE. Active disease displays enrichment of ALOX15+ macrophages, PRDM16+ dendritic cells expressing the EoE risk gene ATP10A, and cycling mast cells, with concomitant reduction of TH17 cells. Ligand-receptor expression uncovers eosinophil recruitment programs, increased fibroblast interactions in disease, and IL-9+IL-4+IL-13+ TH2 and endothelial cells as potential mast cell interactors. Resolution of inflammation-associated signatures includes mast and CD4+ TRM cell contraction and cell type-specific downregulation of eosinophil chemoattractant, growth, and survival factors. These cellular alterations in EoE and remission advance our understanding of eosinophilic inflammation and opportunities for therapeutic intervention.

Cell subtype-specific effects of genetic variation in the Alzheimer's disease brain.

The relationship between genetic variation and gene expression in brain cell types and subtypes remains understudied. Here, we generated single-nucleus RNA sequencing data from the neocortex of 424 individuals of advanced age; we assessed the effect of genetic variants on RNA expression in cis (cis-expression quantitative trait loci) for seven cell types and 64 cell subtypes using 1.5 million transcriptomes. This effort identified 10,004 eGenes at the cell type level and 8,099 eGenes at the cell subtype level. Many eGenes are only detected within cell subtypes. A new variant influences APOE expression only in microglia and is associated with greater cerebral amyloid angiopathy but not Alzheimer's disease pathology, after adjusting for APOEε4, providing mechanistic insights into both pathologies. Furthermore, only a TMEM106B variant affects the proportion of cell subtypes. Integration of these results with genome-wide association studies highlighted the targeted cell type and probable causal gene within Alzheimer's disease, schizophrenia, educational attainment and Parkinson's disease loci.

Evaluation and optimization of sequence-based gene regulatory deep learning models.

Neural networks have emerged as immensely powerful tools in predicting functional genomic regions, notably evidenced by recent successes in deciphering gene regulatory logic. However, a systematic evaluation of how model architectures and training strategies impact genomics model performance is lacking. To address this gap, we held a DREAM Challenge where competitors trained models on a dataset of millions of random promoter DNA sequences and corresponding expression levels, experimentally determined in yeast, to best capture the relationship between regulatory DNA and gene expression. For a robust evaluation of the models, we designed a comprehensive suite of benchmarks encompassing various sequence types. While some benchmarks produced similar results across the top-performing models, others differed substantially. All top-performing models used neural networks, but diverged in architectures and novel training strategies, tailored to genomics sequence data. To dissect how architectural and training choices impact performance, we developed the Prix Fixe framework to divide any given model into logically equivalent building blocks. We tested all possible combinations for the top three models and observed performance improvements for each. The DREAM Challenge models not only achieved state-of-the-art results on our comprehensive yeast dataset but also consistently surpassed existing benchmarks on Drosophila and human genomic datasets. Overall, we demonstrate that high-quality gold-standard genomics datasets can drive significant progress in model development.

Toward the Identifiability of Comparative Deep Generative Models.

Deep Generative Models (DGMs) are versatile tools for learning data representations while adequately incorporating domain knowledge such as the specification of conditional probability distributions. Recently proposed DGMs tackle the important task of comparing data sets from different sources. One such example is the setting of contrastive analysis that focuses on describing patterns that are enriched in a target data set compared to a background data set. The practical deployment of those models often assumes that DGMs naturally infer interpretable and modular latent representations, which is known to be an issue in practice. Consequently, existing methods often rely on ad-hoc regularization schemes, although without any theoretical grounding. Here, we propose a theory of identifiability for comparative DGMs by extending recent advances in the field of non-linear independent component analysis. We show that, while these models lack identifiability across a general class of mixing functions, they surprisingly become identifiable when the mixing function is piece-wise affine (e.g., parameterized by a ReLU neural network). We also investigate the impact of model misspecification, and empirically show that previously proposed regularization techniques for fitting comparative DGMs help with identifiability when the number of latent variables is not known in advance. Finally, we introduce a novel methodology for fitting comparative DGMs that improves the treatment of multiple data sources via multi-objective optimization and that helps adjust the hyperparameter for the regularization in an interpretable manner, using constrained optimization. We empirically validate our theory and new methodology using simulated data as well as a recent data set of genetic perturbations in cells profiled via single-cell RNA sequencing.

Prediction of single-cell RNA expression profiles in live cells by Raman microscopy with Raman2RNA.

Single-cell RNA sequencing and other profiling assays have helped interrogate cells at unprecedented resolution and scale, but are inherently destructive. Raman microscopy reports on the vibrational energy levels of proteins and metabolites in a label-free and nondestructive manner at subcellular spatial resolution, but it lacks genetic and molecular interpretability. Here we present Raman2RNA (R2R), a method to infer single-cell expression profiles in live cells through label-free hyperspectral Raman microscopy images and domain translation. We predict single-cell RNA sequencing profiles nondestructively from Raman images using either anchor-based integration with single molecule fluorescence in situ hybridization, or anchor-free generation with adversarial autoencoders. R2R outperformed inference from brightfield images (cosine similarities: R2R >0.85 and brightfield <0.15). In reprogramming of mouse fibroblasts into induced pluripotent stem cells, R2R inferred the expression profiles of various cell states. With live-cell tracking of mouse embryonic stem cell differentiation, R2R traced the early emergence of lineage divergence and differentiation trajectories, overcoming discontinuities in expression space. R2R lays a foundation for future exploration of live genomic dynamics.

A cellular and spatial atlas of TP53 -associated tissue remodeling in lung adenocarcinoma.

TP53 is the most frequently mutated gene across many cancers and is associated with shorter survival in lung adenocarcinoma (LUAD). To define how TP53 mutations affect the LUAD tumor microenvironment (TME), we constructed a multi-omic cellular and spatial tumor atlas of 23 treatment-naïve human lung tumors. We found that TP53 -mutant ( TP53 mut ) malignant cells lose alveolar identity and upregulate highly proliferative and entropic gene expression programs consistently across resectable LUAD patient tumors, genetically engineered mouse models, and cell lines harboring a wide spectrum of TP53 mutations. We further identified a multicellular tumor niche composed of SPP1 + macrophages and collagen-expressing fibroblasts that coincides with hypoxic, pro-metastatic expression programs in TP53 mut tumors. Spatially correlated angiostatic and immune checkpoint interactions, including CD274 - PDCD1 and PVR - TIGIT , are also enriched in TP53 mut LUAD tumors, which may influence response to checkpoint blockade therapy. Our methodology can be further applied to investigate mutation-specific TME changes in other cancers.

Leukemia-intrinsic determinants of CAR-T response revealed by iterative in vivo genome-wide CRISPR screening.

CAR-T therapy is a promising, novel treatment modality for B-cell malignancies and yet many patients relapse through a variety of means, including loss of CAR-T cells and antigen escape. To investigate leukemia-intrinsic CAR-T resistance mechanisms, we performed genome-wide CRISPR-Cas9 loss-of-function screens in an immunocompetent murine model of B-cell acute lymphoblastic leukemia (B-ALL) utilizing a modular guide RNA library. We identified IFNγR/JAK/STAT signaling and components of antigen processing and presentation pathway as key mediators of resistance to CAR-T therapy in vivo; intriguingly, loss of this pathway yielded the opposite effect in vitro (sensitized leukemia to CAR-T cells). Transcriptional characterization of this model demonstrated upregulation of these pathways in tumors relapsed after CAR-T treatment, and functional studies showed a surprising role for natural killer (NK) cells in engaging this resistance program. Finally, examination of data from B-ALL patients treated with CAR-T revealed an association between poor outcomes and increased expression of JAK/STAT and MHC-I in leukemia cells. Overall, our data identify an unexpected mechanism of resistance to CAR-T therapy in which tumor cell interaction with the in vivo tumor microenvironment, including NK cells, induces expression of an adaptive, therapy-induced, T-cell resistance program in tumor cells.

A deep lung cell atlas reveals cytokine-mediated lineage switching of a rare cell progenitor of the human airway epithelium.

The human airway contains specialized rare epithelial cells whose roles in respiratory disease are not well understood. Ionocytes express the Cystic Fibrosis Transmembrane Conductance Regulator (CFTR), while chemosensory tuft cells express asthma-associated alarmins. However, surprisingly, exceedingly few mature tuft cells have been identified in human lung cell atlases despite the ready identification of rare ionocytes and neuroendocrine cells. To identify human rare cell progenitors and define their lineage relationship to mature tuft cells, we generated a deep lung cell atlas containing 311,748 single cell RNA-Seq (scRNA-seq) profiles from discrete anatomic sites along the large and small airways and lung lobes of explanted donor lungs that could not be used for organ transplantation. Of 154,222 airway epithelial cells, we identified 687 ionocytes (0.45%) that are present in similar proportions in both large and small airways, suggesting that they may contribute to both large and small airways pathologies in CF. In stark contrast, we recovered only 3 mature tuft cells (0.002%). Instead, we identified rare bipotent progenitor cells that can give rise to both ionocytes and tuft cells, which we termed tuft-ionocyte progenitor cells (TIP cells). Remarkably, the cycling fraction of these TIP cells was comparable to that of basal stem cells. We used scRNA-seq and scATAC-seq to predict transcription factors that mark this novel rare cell progenitor population and define intermediate states during TIP cell lineage transitions en route to the differentiation of mature ionocytes and tuft cells. The default lineage of TIP cell descendants is skewed towards ionocytes, explaining the paucity of mature tuft cells in the human airway. However, Type 2 and Type 17 cytokines, associated with asthma and CF, diverted the lineage of TIP cell descendants in vitro , resulting in the differentiation of mature tuft cells at the expense of ionocytes. Consistent with this model of mature tuft cell differentiation, we identify mature tuft cells in a patient who died from an asthma flare. Overall, our findings suggest that the immune signaling pathways active in asthma and CF may skew the composition of disease-relevant rare cells and illustrate how deep atlases are required for identifying physiologically-relevant scarce cell populations.

Multi-modal skin atlas identifies a multicellular immune-stromal community associated with altered cornification and specific T cell expansion in atopic dermatitis.

In healthy skin, a cutaneous immune system maintains the balance between tolerance towards innocuous environmental antigens and immune responses against pathological agents. In atopic dermatitis (AD), barrier and immune dysfunction result in chronic tissue inflammation. Our understanding of the skin tissue ecosystem in AD remains incomplete with regard to the hallmarks of pathological barrier formation, and cellular state and clonal composition of disease-promoting cells. Here, we generated a multi-modal cell census of 310,691 cells spanning 86 cell subsets from whole skin tissue of 19 adult individuals, including non-lesional and lesional skin from 11 AD patients, and integrated it with 396,321 cells from four studies into a comprehensive human skin cell atlas in health and disease. Reconstruction of human keratinocyte differentiation from basal to cornified layers revealed a disrupted cornification trajectory in AD. This disrupted epithelial differentiation was associated with signals from a unique immune and stromal multicellular community comprised of MMP12 + dendritic cells (DCs), mature migratory DCs, cycling ILCs, NK cells, inflammatory CCL19 + IL4I1 + fibroblasts, and clonally expanded IL13 + IL22 + IL26 + T cells with overlapping type 2 and type 17 characteristics. Cell subsets within this immune and stromal multicellular community were connected by multiple inter-cellular positive feedback loops predicted to impact community assembly and maintenance. AD GWAS gene expression was enriched both in disrupted cornified keratinocytes and in cell subsets from the lesional immune and stromal multicellular community including IL13 + IL22 + IL26 + T cells and ILCs, suggesting that epithelial or immune dysfunction in the context of the observed cellular communication network can initiate and then converge towards AD. Our work highlights specific, disease-associated cell subsets and interactions as potential targets in progression and resolution of chronic inflammation.