Biology subjects

Zou, J.

Publications and source records attributed to Zou, J..

At least 73 records · Page 4Linked to original sources

bioRxiv2025

AMPK- and cGAS-Mediated Dual Sensing of Glucose and dsDNA Synergizes to Enhance Antitumor Immunity

Intratumoral glucose availability is linked considerably to tumor growth, immune evasion, and metastasis. However, the precise molecular mechanisms by which glucose levels govern cellular and immune responses against tumors remain incompletely understood. Here, we explored the context of tumors of intersections between AMP-activated protein kinase (AMPK) and cGAS-STING signaling, a key pathway initiating antitumor immunity. We unexpectedly found that AMPK activation robustly enhances cGAS-STING signaling within cancer and immune cells, which potently impacts melanoma and colorectal cell senescence, organoid apoptosis, and the infiltration of CD4+ and CD8+ T lymphocytes. Using classic or newly developed AMPK agonist Aldometanib, TBK1S511E/S511E knock-in (KI) mice, glucose metabolic modulator, as well as AMPK1/2 dKO melanoma, we demonstrated that dual sensing of intratumoral glucose and dsDNA integrated by the AMPK-TBK1 cascade is essential for initiating antitumor immunity and suppressing cancer progressions. Notably, novel AMPK agonists or intervening in glucose glycolysis by Lonidamine synergized effectively with STING agonists, substantially inhibiting melanoma growth. Therefore, these findings unravel a concise mechanism integrating glucose metabolism and innate DNA sensing into cancer cell fate and propose an effective therapeutic strategy to enhance antitumor immunity. HIGHLIGHTSO_LIDual and synergistic sensing of glucose and dsDNA facilitates antitumor immunity in melanoma and colorectal cancer; C_LIO_LIAMPK substitutes intratumoral cGAS-STING-IRF3 signaling through TBK1 phosphorylation at S511; C_LIO_LIGlucose deficiency induces cancer cell senescence, apoptosis, and T lymphocyte infiltration via the AMPK-TBK1 cascade; C_LIO_LINewly developed AMPK agonists synergize with STING agonists to retrieve striking tumor suppression. C_LI

cancer biology↗

bioRxiv2025

Learning single-cell spatial context through integrated spatial multiomics with CORAL

Cellular organization is central to tissue function and homeostasis, influencing development, disease progression, and therapeutic outcomes. The emergence of spatial omics technologies, including spatial transcriptomics and proteomics, has enabled the integration of molecular and histological features within tissues. Analyzing these multimodal data presents unique challenges, including variable resolutions, imperfect tissue alignment, and limited or variable spatial coverage. To address these issues, we introduce CORAL, a probabilistic deep generative model that leverages graph attention mechanisms to learn expressive, integrated representations of multimodal spatial omics data. CORAL deconvolves low-resolution spatial data into high-resolution single-cell profiles and detects functional spatial domains. It also characterizes cell-cell interactions and elucidates disease-relevant spatial features. Validated on synthetic data and experimental datasets, including Stero-CITE-seq data from mouse thymus, and paired CODEX and Visium data from hepatocellular carcinoma, CORAL demonstrates robustness and versatility. In hepatocellular carcinoma, CORAL uncovered key immune cell subsets that drive the failure of response to immunotherapy, highlighting its potential to advance spatial single-cell analyses and accelerate translational research.

bioinformatics↗

bioRxiv2024

CellMemory: Hierarchical Interpretation of Out-of-Distribution Cells Using Bottlenecked Transformer

Identifying the genetic and molecular drivers of phenotypic heterogeneity among individuals is vital for understanding human health and for diagnosing, monitoring, and treating diseases. To this end, international consortia such as the Human Cell Atlas and the Tabula Sapiens are creating comprehensive cellular references. Due to the massive volume of data generated, machine learning methods, especially transformer architectures, have been widely employed in related studies. However, applying machine learning to cellular data presents several challenges. One such challenge is making the methods interpretable with respect to both the input cellular information and its context. Another less explored challenge is the accurate representation of cells outside existing references, referred to as out-of-distribution (OOD) cells. The out-of-distribution could be attributed to various physiological conditions, such as comparing diseased cells, particularly tumor cells, with healthy reference data, or significant technical variations, such as using transfer learning from single-cell reference to spatial query data. Inspired by the global workspace theory in cognitive neuroscience, we introduce CellMemory, a bottlenecked Transformer with improved generalization capabilities designed for the hierarchical interpretation of OOD cells unseen during reference building. Even without pre-training, it exceeds the performance of large language models pre-trained with tens of millions of cells. In particular, when deciphering spatially resolved single-cell transcriptomics data, CellMemory demonstrates the ability to interpret data at the granule level accurately. Finally, we harness CellMemorys robust representational capabilities to elucidate malignant cells and their founder cells in different patients, providing reliable characterizations of the cellular changes caused by the disease.

bioinformatics↗

bioRxiv2024

Mechanistic Insights into Dengue Virus Inhibition by a Clinical Trial Compound NITD-688

Dengue, caused by the dengue virus (DENV), presents a significant public health challenge with limited effective treatments. NITD-688 is a potent pan-serotype DENV inhibitor currently in Phase II clinical trials. However, its mechanism of action is not fully understood. Here, we present the molecular details of how NITD-688 inhibits DENV. NITD-688 binds directly to the nonstructural protein 4B (NS4B) with nanomolar affinities across all four DENV serotypes and specifically disrupts the interaction between NS4B and nonstructural protein 3 (NS3) without significantly changing the interactions between NS4B and other viral or host proteins. NS4B mutations that confer resistance to NITD-688 reduce both NITD-688 binding to NS4B and disruption of the NS4B/NS3 interaction. Specifically, NITD-688 blocks the interaction of NS3 with a cytosolic loop within NS4B. This inhibits the formation of new NS4B/NS3 complexes and disrupts pre-existing complexes in vitro and DENV-infected cells, ultimately inhibiting viral replication. Consistent with this mechanism, NITD-688 retains greater potency in cellular assays with delayed treatment compared to JNJ-1802, another NS4B inhibitor that has been studied in Phase II clinical trials. Together, these findings provide critical insights into the mechanism of action of NITD-688, facilitating the development of novel flavivirus NS4B inhibitors and informing future clinical interventions against DENV.

microbiology↗

bioRxiv2024

Microglia promote neurodegeneration and hyperkatifeia during withdrawal and prolonged abstinence from chronic binge alcohol

Proinflammatory microglial polarization, neuronal death, and hyperkatifeia/negative affect during withdrawal are key features of alcohol use disorder (AUD). However, the role microglia play in the development of AUD-related neuronal and behavioral pathology is unclear. Given the ability of microglia to regulate neuronal function, it was hypothesized that proinflammatory microglia promote neuronal death and hyperkatifeia during prolonged abstinence from binge alcohol. Proinflammatory signaling and affective state were assessed in mice either during acute withdrawal (24h) or abstinence (>4 weeks) to binge alcohol exposure. Ten days of binge alcohol increased proinflammatory gene signaling 24h after EtOH, which lasted weeks into withdrawal. Alcohol reduced brain-derived neurotrophic factor (BDNF) in hyperkatifeia-associated regions (i.e., the central amygdala and infralimbic cortex) during acute withdrawal and caused persistent microglial structural changes and loss of microglial BDNF in the BNST during abstinence. This was associated with increased anxiety-like behavior and hyperarousal, with persistent enhancement of conditioned fear memory during abstinence. Inhibition of proinflammatory microglia with Gi designer receptors exclusively activated by designer drugs (DREADDs) blocked neuronal death and prevented persistent proinflammatory gene induction and hyperkatifeia in female mice. Thus, this identifies a direct role for microglia in the development of AUD-related neuropathology and behavioral dysfunction, implicating microglia as cellular targets for the prevention of AUD phenotypes.

neuroscience↗

bioRxiv2024

Squidiff: Predicting cellular development and responses to perturbations using a diffusion model

Single-cell sequencing has revolutionized our understanding of cellular heterogeneity and responses to environmental stimuli. However, mapping transcriptomic changes across diverse cell types in response to various stimuli and elucidating underlying disease mechanisms remains challenging. Studies involving physical stimuli, such as radiotherapy, or chemical stimuli, like drug testing, demand labor-intensive experimentation, hindering mechanistic insight and drug discovery. Here we present Squidiff, a diffusion model-based generative framework that predicts transcriptomic changes across diverse cell types in response to environmental changes. We demonstrate Squidiffs robustness across cell differentiation, gene perturbation, and drug response prediction. Through continuous denoising and semantic feature integration, Squidiff learns transient cell states and predicts high-resolution transcriptomic landscapes over time and conditions. Furthermore, we applied Squidiff to model blood vessel organoid development and cellular responses to neutron irradiation and growth factors. Our results demonstrate that Squidiff enables in silico screening of molecular landscapes, facilitating rapid hypothesis generation and providing valuable insights for precision medicine.

bioinformatics↗

bioRxiv2024

InterPLM: Discovering Interpretable Features in Protein Language Models via Sparse Autoencoders

AO_SCPLOWBSTRACTC_SCPLOWProtein language models (PLMs) have demonstrated remarkable success in protein modeling and design, yet their internal mechanisms for predicting structure and function remain poorly understood. Here we present a systematic approach to extract and analyze interpretable features from PLMs using sparse autoencoders (SAEs). By training SAEs on embeddings from the PLM ESM-2, we identify thousands of human-interpretable latent features per layer that highlight hundreds of known biological concepts such as binding sites, structural motifs, and functional domains. In contrast, examining individual neurons in ESM-2 reveals significantly less conceptual alignment, suggesting that PLMs represent most concepts in superposition. We further demonstrate that a larger PLM (ESM-2 with 650M parameters) captures substantially more interpretable concepts than a smaller PLM (ESM-2 with 8M parameters). Beyond capturing known annotations, we show that ESM-2 learns coherent concepts that do not map onto existing annotations and propose a pipeline using language models to automatically interpret novel latent features learned by the SAEs. As practical applications, we demonstrate how these latent features can fill in missing annotations in protein databases and enable targeted steering of protein sequence generation. Our results demonstrate that PLMs encode rich, interpretable representations of protein biology and we propose a systematic framework to uncover and understand these latent features. In the process, we recover both known biology and potentially new protein motifs. As community resources, we introduce InterPLM (interPLM.ai), an interactive visualization platform for investigating learned PLM features, and release code for training and analysis at github.com/ElanaPearl/interPLM.

bioinformatics↗

bioRxiv2024

The Virtual Lab: AI Agents Design New SARS-CoV-2 Nanobodies with Experimental Validation

Science frequently benefits from teams of interdisciplinary researchers. However, most scientists dont have access to experts from multiple fields. Fortunately, large language models (LLMs) have recently shown an impressive ability to aid researchers across diverse domains by answering scientific questions. Here, we expand the capabilities of LLMs for science by introducing the Virtual Lab, an AI-human research collaboration to perform sophisticated, interdisciplinary science research. The Virtual Lab consists of an LLM principal investigator agent guiding a team of LLM agents with different scientific backgrounds (e.g., a chemist agent, a computer scientist agent, a critic agent), with a human researcher providing high-level feedback. We design the Virtual Lab to conduct scientific research through a series of team meetings, where all the agents discuss a scientific agenda, and individual meetings, where an agent accomplishes a specific task. We demonstrate the power of the Virtual Lab by applying it to design nanobody binders to recent variants of SARS-CoV-2, which is a challenging, open-ended research problem that requires reasoning across diverse fields from biology to computer science. The Virtual Lab creates a novel computational nanobody design pipeline that incorporates ESM, AlphaFold-Multimer, and Rosetta and designs 92 new nanobodies. Experimental validation of those designs reveals a range of functional nanobodies with promising binding profiles across SARS-CoV-2 variants. In particular, two new nanobodies exhibit improved binding to the recent JN.1 or KP.3 variants of SARS-CoV-2 while maintaining strong binding to the ancestral viral spike protein, suggesting exciting candidates for further investigation. This demonstrates the ability of the Virtual Lab to rapidly make impactful, real-world scientific discovery.

bioinformatics↗

bioRxiv2024

ROSIE: AI generation of multiplex immunofluorescence staining from histopathology images

Hematoxylin and eosin (H&E) is a common and inexpensive histopathology assay. Though widely used and information-rich, it cannot directly inform about specific molecular markers, which require additional experiments to assess. To address this gap, we present ROSIE, a deep-learning framework that computationally imputes the expression and localization of dozens of proteins from H&E images. Our model is trained on a dataset of over 1000 paired and aligned H&E and multiplex immunofluorescence (mIF) samples from 20 tissues and disease conditions, spanning over 16 million cells. Validation of our in silico mIF staining method on held-out H&E samples demonstrates that the predicted biomarkers are effective in identifying cell phenotypes, particularly distinguishing lymphocytes such as B cells and T cells, which are not readily discernible with H&E staining alone. Additionally, ROSIE facilitates the robust identification of stromal and epithelial microenvironments and immune cell subtypes like tumor-infiltrating lymphocytes (TILs), which are important for understanding tumor-immune interactions and can help inform treatment strategies in cancer research.

pathology↗

bioRxiv2024

Identification of molecular and functional subtypes using chronic pancreatitis patient-derived organoid models

Chronic pancreatitis (CP) affects [~]3 million people worldwide, yet altering the course of disease is challenging. We developed a patient-derived organoid (PDO) platform to investigate the molecular pathogenesis of this disease and identify therapeutic strategies. We generated 36 PDOs from patients with idiopathic, hereditary, and alcohol-related CP with high genetic concordance. PDOs retained inflammation-associated transcriptional and proteomic features. Transcriptomic profiling revealed three molecular subtypes of CP independent of etiology. We discovered widespread dysfunction of the cystic fibrosis transmembrane conductance regulator (CFTR) in half of the CP PDOs, including those with wildtype CFTR. Clinically available CFTR modulators stabilized mutant or wildtype CFTR, restored CFTR function, and decreased mitogenic and inflammatory signaling. This work provides the first comprehensive PDO platform for modeling CP. We demonstrate the utility of this platform for precision therapeutic investigations. Our findings reveal CFTR modulators as a broadly applicable and effective therapeutic strategy.

cell biology↗

bioRxiv2024

GenePert: Leveraging GenePT Embeddings for Gene Perturbation Prediction

Predicting how perturbation of a target gene affects the expression of other genes is a critical component of understanding cell biology. This is a challenging prediction problem as the model must capture complex gene-gene relationships and the output is high-dimensional and sparse. To address this challenge, we present GenePert, a simple approach that leverages GenePT embeddings, which are derived using ChatGPT from text descriptions of individual genes, to predict gene expression changes due to perturbations via regularized regression models. Benchmarked on eight CRISPR perturbation screen datasets across multiple cell types and five different pretrained gene embedding models, GenePert consistently outperforms all the state-of-the-art prediction models measured in both Pearson correlation and mean squared error metrics. Even with limited training data, our model generalizes effectively, offering a scalable solution for predicting perturbation outcomes. These findings underscore the power of informative gene embeddings in predicting the outcomes of unseen genetic perturbation experiments in silico. GenePert is available at https://github.com/zou-group/GenePert.

bioinformatics↗

bioRxiv2024

Systemic and local chronic inflammation and hormone disposition promote a tumor-permissive environment for breast cancer in older women

Estrogen receptor positive (ER+) breast cancer is the most common subtype of breast cancer and is an age-related disease. The peak incidence of diagnosis occurs around age 70, even though these post-menopausal patients have low circulating levels of estradiol (E2). Despite the hormone sensitivity of age-related tumors, we have a limited understanding of the interplay between systemic and local hormones, chronic inflammation, and immune changes that contribute to the growth and development of these tumors. Here, we show that aged F344 rats treated with the dimethylbenz(a)anthracene / medroxyprogestrone acetate (DMBA/MPA) carcinogen develop more tumors at faster rates than their younger counterparts, suggesting that the aged environment promotes tumor initiation and impacts growth. Single-nuclei RNA-seq (snRNA-seq) of the tumors showed broad local immune dysfunction that was associated with circulating chronic inflammation. Across a broad cohort of specimens from patients with ER+ breast cancer and age-matched donors of normal breast tissue, we observe that even with an estrone (E1)-predominant estrogen disposition in the systemic circulation, tumors in older patients increase HSD17B7 expression to convert E1 to E2 in the tumor microenvironment (TME) and have local E2 levels similar to pre-menopausal patients. Concurrently, trackable increases in several chemokines, defined most notably by CCL2, promote a chronically inflamed but immune dysfunctional TME. This unique milieu in the aged TME, characterized by high local E2 and chemokine-enriched chronic inflammation, promotes both accumulation of tumor-associated macrophages (TAMs), which serve as signaling hubs, as well as polarization of TAMs towards a CD206+/PD-L1+, immunosuppressive phenotype. Pharmacologic targeting of estrogen signaling (either by HSD17B7 inhibition or with fulvestrant) and chemokine inflammation both decrease local E2 and prevent macrophage polarization. Overall, these findings suggest that chronic inflammation and hormonal disposition are critical contributors to the age-related nature of ER+ breast cancer development and growth and offer potential therapeutic insight to treat these patients. Translational SummaryWe uncover the unique underpinnings establishing how the systemic host environment contributes to the aged breast tumor microenvironment, characterized by high local estradiol and chronic inflammation with immune dysregulation, and show that targeting avenues of estrogen conversion and chronic inflammation work to restore anti-tumor immunity. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=135 SRC="FIGDIR/small/616978v1_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@1ecd90borg.highwire.dtl.DTLVardef@9d7ac8org.highwire.dtl.DTLVardef@e7fb74org.highwire.dtl.DTLVardef@2da524_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2024

Maintenance of neuronal TDP-43 expression requires axonal lysosome transport

TDP-43 mislocalization and pathology occurs across a range of neurodegenerative diseases, but the pathways that modulate TDP-43 in neurons are not well understood. We generated a Halo-TDP-43 knock-in iPSC line and performed a genome-wide CRISPR interference FACS-based screen to identify modifiers of TDP-43 levels in neurons. A meta-analysis of our screen and publicly available screens identified both specific hits and pathways present across multiple screens, the latter likely responsible for generic protein level maintenance. We identified BORC, a complex required for anterograde lysosome transport, as a specific modifier of TDP-43 protein, but not mRNA, levels in neurons. BORC loss led to longer half-life of TDP-43 and other proteins, suggesting lysosome location is required for proper protein turnover. As such, lysosome location and function are crucial for maintaining TDP-43 protein levels in neurons.

neuroscience↗

bioRxiv2024

Mechano-osmotic signals control chromatin state and fate transitions in pluripotent stem cells

Acquisition of specific cell shapes and morphologies is a central component of cell fate transitions. Although signaling circuits and gene regulatory networks that regulate pluripotent stem cell differentiation have been intensely studied, how these networks are integrated in space and time with morphological transitions and mechanical deformations to control state transitions remains a fundamental open question. Here, we focus on two distinct models of pluripotency, primed pluripotent stem cells and pre-implantation inner cell mass cells of human embryos to discover that cell fate transitions associate with rapid changes in nuclear shape and volume which collectively alter the nuclear mechanophenotype. Mechanistic studies in human induced pluripotent stem cells further reveal that these phenotypical changes and the associated active fluctuations of the nuclear envelope arise from growth factor signaling-controlled changes in chromatin mechanics and cytoskeletal confinement. These collective mechano-osmotic changes trigger global transcriptional repression and a condensation-prone environment that primes chromatin for a cell fate transition by attenuating repression of differentiation genes. However, while this mechano-osmotic chromatin priming has the potential to accelerate fate transitions and differentiation, sustained biochemical signals are required for robust induction of specific lineages. Our findings uncover a critical mechanochemical feedback mechanism that integrates nuclear mechanics, shape and volume with biochemical signaling and chromatin state to control cell fate transition dynamics.

cell biology↗

bioRxiv2024

Integrative structural analysis of NF45-NF90 heterodimers reveals architectural rearrangements and oligomerisation on binding dsRNA.

Complexes of nuclear factors 45 and 90 (NF45-NF90) play a multitude of roles in co- and post-transcriptional RNA processing, including regulating adenosine-to-inosine editing, cassette exon and back splicing, and splicing fidelity. NF45-NF90 complexes recognise dsRNA and, in human cells, primarily interact with inverted Alu repeats (AluIRs) that are commonly inserted into introns and other non-coding RNA regions. Intronic AluIRs of [~]300 bp can regulate splicing outcomes, such as generation of circRNAs. We examined domain reorganisation of NF45-NF90 domains on dsRNAs exceeding 50 bp to gain insight into its RNA recognition properties on longer dsRNAs. Using a combination of phylogenetic analysis, solution methods (including small angle X-ray scattering and quantitative cross-linking mass spectrometry), machine learning and negative stain electron microscopy, we generated a model of NF45-NF90 complex formation on dsRNA. Our data reveal that different interactions of NF45-NF90 complexes allow these proteins to coat long stretches of dsRNA. This property of the NF45-NF90 complex has important implications for how long, nuclear dsRNAs are recognised in the nucleus and how this might promote (co)-regulation of specific RNA splicing and editing events that shape the mammalian transcriptome.

biochemistry↗

bioRxiv2024

Comprehensive evaluation and practical guideline of gating methods for high-dimensional cytometry data: manual gating, unsupervised clustering, and auto-gating

Cytometry is an advanced technique for simultaneously identifying and quantifying many cell surface and intracellular proteins at a single-cell resolution. Analyzing high-dimensional cytometry data involves identifying and quantifying cell populations based on their marker expressions. This study provided a quantitative review and comparison of various ways to phenotype cellular populations within the cytometry data, including manual gating, unsupervised clustering, and supervised auto-gating. Six datasets from diverse species and sample types were included in the study, and manual gating with two hierarchical layers was used as the truth for evaluation. For manual gating, results from five researchers were compared to illustrate the gating consistency among different raters. For unsupervised clustering, 22 tools were quantitatively compared in terms of accuracy with the truth and computing cost. While no method outperformed all others, several tools, including PAC-MAN, CCAST, FlowSOM, flowClust, and DEPECHE, generally demonstrated strong performance. For supervised auto-gating methods, four algorithms were evaluated, where DeepCyTOF and CyTOF Linear Classifier performed the best. We further provided practical recommendations on prioritizing gating methods based on different application scenarios. This study offers comprehensive insights for biologists to understand diverse gating methods and choose the best-suited ones for their applications.

bioinformatics↗

bioRxiv2024

A novel method (RIM-Deep) enhances imaging depth and resolution stability of deep-cleared brain tissue in inverted confocal microscopy

The increasing use of tissue clearing techniques underscores the urgent need for cost-effective and simplified deep imaging methods. While traditional inverted confocal microscopes excel in high-resolution imaging of tissue sections and cultured cells, they face limitations in deep imaging of cleared tissues due to refractive index mismatches between the immersion media of objectives and sample container. To overcome these challenges, the RIM-Deep was developed to significantly improve deep imaging capabilities without compromising the normal function of the confocal microscope. This system facilitates deep immunofluorescence imaging of the prefrontal cortex in cleared macaque tissue, extending imaging depth from 2 mm to 5 mm. Applied to an intact and cleared Thy1-EGFP mouse brain, the system allowed for clear axonal visualization at high imaging depth. Moreover, this advancement enables large-scale, deep 3D imaging of intact tissues. In principle, this concept can be extended to any imaging modality, including existing inverted wide-field, confocal, and two-photon microscopy. This would significantly upgrade traditional laboratory configurations and facilitate the study of connectomics in the brain and other tissues.

neuroscience↗

bioRxiv2024

Spatiotemporal transcriptomic profiling and modeling of mouse brain at single-cell resolution reveals cell proximity effects of aging and rejuvenation

Old age is associated with a decline in cognitive function and an increase in neurodegenerative disease risk1. Brain aging is complex and accompanied by many cellular changes2-20. However, the influence that aged cells have on neighboring cells and how this contributes to tissue decline is unknown. More generally, the tools to systematically address this question in aging tissues have not yet been developed. Here, we generate spatiotemporal data at single-cell resolution for the mouse brain across lifespan, and we develop the first machine learning models based on spatial transcriptomics ( spatial aging clocks) to reveal cell proximity effects during brain aging and rejuvenation. We collect a single-cell spatial transcriptomics brain atlas of 4.2 million cells from 20 distinct ages and across two rejuvenating interventions--exercise and partial reprogramming. We identify spatial and cell type-specific transcriptomic fingerprints of aging, rejuvenation, and disease, including for rare cell types. Using spatial aging clocks and deep learning models, we find that T cells, which infiltrate the brain with age, have a striking pro-aging proximity effect on neighboring cells. Surprisingly, neural stem cells have a strong pro-rejuvenating effect on neighboring cells. By developing computational tools to identify mediators of these proximity effects, we find that pro-aging T cells trigger a local inflammatory response likely via interferon-{gamma} whereas pro-rejuvenating neural stem cells impact the metabolism of neighboring cells possibly via growth factors (e.g. vascular endothelial growth factor) and extracellular vesicles, and we experimentally validate some of these predictions. These results suggest that rare cells can have a drastic influence on their neighbors and could be targeted to counter tissue aging. We anticipate that these spatial aging clocks will not only allow scalable assessment of the efficacy of interventions for aging and disease but also represent a new tool for studying cell-cell interactions in many spatial contexts.

neuroscience↗