Abstract Understanding how cell states change in response to genetic perturbations is critical for gene-function and therapeutics discovery. However, state-of-the-art deep-learning models trained on large single-cell omics datasets still struggle to accurately predict cellular responses to perturbations, highlighting the need for a better understanding of the cell-state space and how cells move through this space. Here, we present a contrastive learning model that integrates diverse scRNA-seq datasets into a global, interpretable cell-state manifold. We further develop a framework to integrate this global cell-state manifold with genome-scale perturbation data to identify gene-expression programs that define principal axes of cell-state transitions and functional embeddings of genes that define major perturbation classes. Applying this framework across Perturb-seq datasets on different cell types reveals conserved cellular responses to perturbations, as well as cell-type-specific rewiring of stress responses. Moreover, we perform a genome-scale Perturb-seq screen in human embryonic stem cells, validating and extending these findings and uncovering a class of mesenchymal transitions induced by diverse perturbations to cellular stress-response pathways.

Abstract Introduction The tumor microenvironment is a complex ecosystem of cancer, immune, and stromal cells. Within this environment, cancer progression is coordinated by both cell-intrinsic and -extrinsic factors. Targeting signaling interactions between cancer and immune cells has proven to be an effective therapeutic strategy, as evidenced by the remarkable success of cancer immunotherapies. However, clinical responses vary across patients and tumor types. Developing effective cancer therapies and defining critical windows for clinical intervention requires a detailed understanding of the phenotypic and spatiotemporal dynamics of the cell types that make up the tumor microenvironment. Methods To enable high-resolution reconstruction of cell fate decisions in the tumor microenvironment, we developed PEtracer, a prime editing-based lineage tracing system compatible with single-cell sequencing and multimodal imaging, allowing for joint analysis of cell state and lineage in dissociated cells or in intact tissues with high spatial resolution. Results Using PEtracer coupled with single-cell and spatial transcriptomic profiling in syngeneic mouse models of cancer, we reconstructed the growth of individually seeded tumors in vivo, uncovered distinct modules of cell-intrinsic and -extrinsic factors that coordinate tumor growth, and mapped the recruitment and differentiation dynamics of tumor-infiltrating immune cells. Conclusion PEtracer enables high-resolution reconstruction of cell state and lineage dynamics within the tumor microenvironment in vivo. By leveraging prime editing-based lineage recording and multimodal imaging, PEtracer allows systematic dissection of how tumor, immune, and stromal cells evolve during cancer progression and therapeutic response. The resulting spatially resolved multimodal datasets provide a foundation for predictive models of tumor microenvironment dynamics. Funding Source National Institutes of Health Topic Categories Tumor Immunology: Cellular Responses and Tumor Microevironment (TIME)

The clinical success of cancer drug candidates depends on efficacy across many different individuals. Because xenografts are challenging to scale, we currently rely on a limited set of in vivo preclinical models. Here, to address this limitation, we introduce GENEVA, a scalable single-cell-resolution platform for measuring responses to drug perturbations. GENEVA models cancer genetic diversity by combining multiple patient-derived cell lines and cancer cell lines into pooled three-dimensional cultures and xenograft models, allowing us to study drug responses across a wide range of genetic backgrounds within a single experiment. We apply GENEVA to investigate KRAS-G12C inhibitors and demonstrate that mitochondrial activation is a key driver of cell death following KRAS inhibition, while epithelial-to-mesenchymal transition is a prominent resistance mechanism. These findings highlight the utility of GENEVA to identify therapeutic targets and optimize combination therapies with the potential to bridge the gap between preclinical cancer models and patient outcomes.

Localized translation broadly enables spatiotemporal control of gene expression. Here, we present LOV-domain-controlled ligase for translation localization (LOCL-TL), an optogenetic approach for monitoring translation with codon resolution at any defined subcellular location under physiological conditions. Application of LOCL-TL to mitochondrially localized translation revealed that ∼20% of human nuclear-encoded mitochondrial genes are translated on the outer mitochondrial membrane (OMM). Mitochondrially translated messages form two classes distinguished by encoded protein length, recruitment mechanism, and cellular function. An evolutionarily ancient mechanism allows nascent chains to drive cotranslational recruitment of long proteins via an unanticipated bipartite targeting signal. Conversely, mRNAs of short proteins, especially eukaryotic-origin electron transport chain (ETC) components, are specifically recruited by the OMM protein A-kinase anchoring protein 1 (AKAP1) in a translation-independent manner that depends on mRNA splicing. AKAP1 loss lowers ETC levels. LOCL-TL thus reveals a hierarchical strategy that enables preferential translation of a subset of proteins on the OMM.

Understanding how genes influence drug responses is critical for advancing personalized cancer treatments. However, identifying these gene-drug interactions in a physiologically relevant human system remains a challenge, as it requires a model that reflects the complexity and heterogeneity among individuals. Here we show that large-scale CRISPR-based genetic screens, including knockout, interference (CRISPRi), activation (CRISPRa), and single-cell approaches, can be applied in primary human 3D gastric organoids to systematically identify genes that affect sensitivity to cisplatin. Our screens uncover genes that modulate cisplatin response. By combining CRISPR perturbations with single-cell transcriptomics, we resolve how genetic alterations interact with cisplatin at the level of individual cells and uncover an unexpected link between fucosylation and cisplatin sensitivity. We identify TAF6L as a regulator of cell recovery from cisplatin-induced cytotoxicity. These results highlight the utility of human organoid models for dissecting gene-drug interactions and offer insights into therapeutic vulnerabilities in gastric cancer.

A major scientific drive is to characterize the protein-coding genome as it provides the primary basis for the study of human health. But the fundamental question remains: what has been missed in prior genomic analyses? Over the past decade, the translation of non-canonical open reading frames (ncORFs) has been observed across human cell types and disease states, with major implications for proteomics, genomics, and clinical science. However, the impact of ncORFs has been limited by the absence of a large-scale understanding of their contribution to the human proteome. Here, we report the collaborative efforts of stakeholders in proteomics, immunopeptidomics, Ribo-seq ORF discovery, and gene annotation, to produce a consensus landscape of protein-level evidence for ncORFs. We show that at least 25% of a set of 7,264 ncORFs give rise to translated gene products, yielding over 3,000 peptides in a pan-proteome analysis encompassing 3.8 billion mass spectra from 95,520 experiments. With these data, we developed an annotation framework for ncORFs and created public tools for researchers through GENCODE and PeptideAtlas. This work will provide a platform to advance ncORF-derived proteins in biomedical discovery and, beyond humans, diverse animals and plants where ncORFs are similarly observed.

While oncogenes promote cancer cell growth, unrestrained proliferation represents a significant stressor to cellular homeostasis networks such as the DNA damage response (DDR). To enable oncogene tolerance, many cancers disable tumor suppressive DDR signaling through genetic loss of DDR pathways and downstream effectors (e.g., ATM or p53 tumor suppressor mutations). Whether and how oncogenes can help “self-tolerize” by creating analogous functional deficiencies in physiologic DDR networks is not known. Here we focus on Ewing sarcoma, a FET fusion oncoprotein (EWS-FLI1) driven pediatric bone tumor, as a model for the class of FET rearranged cancers. Native FET protein family members are among the earliest factors recruited to DNA double-strand breaks (DSBs) during the DDR, though the function of both native FET proteins and FET fusion oncoproteins in DNA repair remains to be defined. Using preclinical mechanistic studies of the DDR and clinical genomic datasets from patient tumors, we discover that the EWS-FLI1 fusion oncoprotein is recruited to DNA DSBs and interferes with native FET (EWS) protein function in activating the DNA damage sensor ATM. As a consequence of FET fusion-mediated interference with the DDR, we establish functional ATM deficiency as the principal DNA repair defect in Ewing sarcoma and the compensatory ATR signaling axis as a collateral dependency and therapeutic target in multiple FET rearranged cancers. More generally, we find that aberrant recruitment of a fusion oncoprotein to sites of DNA damage can disrupt physiologic DSB repair, revealing a mechanism for how growth-promoting oncogenes can also create a functional deficiency within tumor suppressive DDR networks.

Metazoan life requires the coordinated activities of thousands of genes in spatially organized cell types. Understanding the basis of tissue function requires approaches to dissect the genetic control of diverse cellular and tissue phenotypes in vivo. Here, we present Perturb-Multimodal (Perturb-Multi), a paired imaging and sequencing method to construct large-scale, multimodal genotype-phenotype maps in tissues with pooled genetic perturbations. Using imaging, we identify perturbations in individual cells while simultaneously measuring their gene expression profiles and subcellular morphology. Using single-cell sequencing, we measure full transcriptomic responses to the same perturbations. We apply Perturb-Multi to study hundreds of genetic perturbations in the mouse liver. Our data suggest the genetic regulators and mechanisms underlying the dynamic control of hepatocyte zonation, the unfolded protein response, and steatosis. Perturb-Multi accelerates discoveries of the genetic basis of complex cell and tissue physiology and provides critical training data for emerging machine learning models of cellular function.

Lysosomal dysfunction is a well-recognized feature of aging, yet its systematic molecular investigation remains limited. Here, we employ a suite of tools for rapid lysosomal isolation to construct a multi-tissue atlas of the metabolite changes that murine lysosomes undergo during aging. Aged lysosomes in brain, heart, muscle and adipose accumulate glycerophosphodiesters and cystine, metabolites that are causally linked to juvenile lysosomal storage disorders like Batten disease. Levels of these metabolites increase linearly with age, preceding organismal decline. Caloric restriction, a lifespan-extending intervention, mitigates these changes in the heart but not the brain. Our findings link lysosomal storage disorders to aging-related dysfunction, uncover a metabolic lysosomal "aging clock," and open avenues for the mechanistic investigation of how lysosomal functions deteriorate during aging and in age-associated diseases. One-Sentence Summary:Aging in mice is tracked by a lysosomal "clock", where glycerophosphodiesters and cystine - metabolites causally linked to juvenile lysosomal storage disorders - gradually accumulate in lysosomes of the brain, heart, skeletal muscle and adipose tissue.

Oct4, Sox2, and Klf4 (OSK) Yamanaka factors induce pluripotency and reverse age-related epigenetic changes, yet the mechanisms by which they promote rejuvenation remain poorly explored. Oxidative stress contributes to CNS aging and retinal pigmented epithelium (RPE) degeneration in age-related macular degeneration. We find that OSK expression in RPE restores retinal structure and visual function in aged mice and promotes oxidative resilience through a non-canonical, Tet2-independent pathway. Integrative functional genomics identifies GSTA4, a detoxifying enzyme that clears the lipid peroxidation byproduct 4-HNE, as a necessary and sufficient OSK effector. Dynamic GSTA4 regulation by OSK recapitulates a stem cell derived stress resilience program. GSTA4 overexpression alone enhances mitochondrial resilience, rejuvenates the aged RPE transcriptome, and reverses visual decline. GSTA4 is consistently upregulated across diverse lifespan-extending interventions suggesting a broader pro-longevity role. These findings uncover a previously unrecognized protective axis driven by Yamanaka factors that circumvents reprogramming, providing therapeutic insights for age-related diseases. HIGHLIGHTS:OSK-GSTA4 provides a dynamic, Tet2-independent stress-resilience axis.Functional genomics pinpoints GSTA4 as a direct downstream effector activated by OSK.RPE aging involves progressive accumulation of 4-HNE that can be detoxified by GSTA4.Enhancing GSTA4 rejuvenates RPE cells, restores vision and is associated with lifespan-extending interventions.

Organ function requires coordinated activities of thousands of genes in distinct, spatially organized cell types. Understanding the basis of emergent tissue function requires approaches to dissect the genetic control of diverse cellular and tissue phenotypes in vivo. Here, we develop paired imaging and sequencing methods to construct large-scale, multi-modal genotype-phenotypes maps in tissue with pooled genetic perturbations. Using imaging, we identify genetic perturbations in individual cells while simultaneously measuring their gene expression and subcellular morphology. Using single-cell sequencing, we measure transcriptomic responses to the same genetic perturbations. We apply this approach to study hundreds of genetic perturbations in the mouse liver. Our study reveals regulators of hepatocyte zonation and liver unfolded protein response, as well as distinct pathways that cause hepatocyte steatosis. Our approach enables new ways of interrogating the genetic basis of complex cellular and organismal physiology and provides crucial training data for emerging machine-learning models of cellular function.

Charting the spatiotemporal dynamics of cell fate determination in development and disease is a long-standing objective in biology. Here we present the design, development, and extensive validation of PEtracer, a prime editing-based, evolving lineage tracing technology compatible with both single-cell sequencing and multimodal imaging methodologies to jointly profile cell state and lineage in dissociated cells or while preserving cellular context in tissues with high spatial resolution. Using PEtracer coupled with MERFISH spatial transcriptomic profiling in a syngeneic mouse model of tumor metastasis, we reconstruct the growth of individually-seeded tumors in vivo and uncover distinct modules of cell-intrinsic and cell-extrinsic factors that coordinate tumor growth. More generally, PEtracer enables systematic characterization of cell state and lineage relationships in intact tissues over biologically-relevant temporal and spatial scales.

Single-cell CRISPR screens such as Perturb-seq enable transcriptomic profiling of genetic perturbations at scale. However, the data produced by these screens are noisy, and many effects may go undetected. Here we introduce transcriptome-wide analysis of differential expression (TRADE)-a statistical model for the distribution of true differential expression effects that accounts for estimation error appropriately. TRADE estimates the 'transcriptome-wide impact', which quantifies the total effect of a perturbation across the transcriptome. Analyzing several large Perturb-seq datasets, we show that many transcriptional effects remain undetected in standard analyses but emerge in aggregate using TRADE. A typical gene perturbation affects an estimated 45 genes, whereas a typical essential gene affects over 500. We find moderate consistency of perturbation effects across cell types, identify perturbations where transcriptional responses vary qualitatively across dosage levels and clarify the relationship between genetic and transcriptomic correlations across neuropsychiatric disorders.

Prion disease is caused by misfolding of the prion protein (PrP) into pathogenic self-propagating conformations, leading to rapid-onset dementia and death. However, elimination of endogenous PrP halts prion disease progression. In this study, we describe Coupled Histone tail for Autoinhibition Release of Methyltransferase (CHARM), a compact, enzyme-free epigenetic editor capable of silencing transcription through programmable DNA methylation. Using a histone H3 tail-Dnmt3l fusion, CHARM recruits and activates endogenous DNA methyltransferases, thereby reducing transgene size and cytotoxicity. When delivered to the mouse brain by systemic injection of adeno-associated virus (AAV), Prnp -targeted CHARM ablates PrP expression across the brain. Furthermore, we have temporally limited editor expression by implementing a kinetically tuned self-silencing approach. CHARM potentially represents a broadly applicable strategy to suppress pathogenic proteins, including those implicated in other neurodegenerative diseases.

Learning and memory require activity-induced changes in dendritic translation, but which mRNAs are involved and how they are regulated are unclear. In this study, to monitor how depolarization impacts local dendritic biology, we employed a dendritically targeted proximity labeling approach followed by crosslinking immunoprecipitation, ribosome profiling and mass spectrometry. Depolarization of primary cortical neurons with KCl or the glutamate agonist DHPG caused rapid reprogramming of dendritic protein expression, where changes in dendritic mRNAs and proteins are weakly correlated. For a subset of pre-localized messages, depolarization increased the translation of upstream open reading frames (uORFs) and their downstream coding sequences, enabling localized production of proteins involved in long-term potentiation, cell signaling and energy metabolism. This activity-dependent translation was accompanied by the phosphorylation and recruitment of the non-canonical translation initiation factor eIF4G2, and the translated uORFs were sufficient to confer depolarization-induced, eIF4G2-dependent translational control. These studies uncovered an unanticipated mechanism by which activity-dependent uORF translational control by eIF4G2 couples activity to local dendritic remodeling. Precise profiling of dendritic RNA regulation reveals how neuronal depolarization leads to ribosome switching onto short upstream open reading frames and new coding sequences to acutely modulate local protein synthesis.

Cells must adapt to environmental changes to maintain homeostasis. One of the most striking environmental adaptations is entry into hibernation during which core body temperature can decrease from 37°C to as low at 4°C. How mammalian cells, which evolved to optimally function within a narrow range of temperatures, adapt to this profound decrease in temperature remains poorly understood. In this study, we conducted the first genome-scale CRISPR-Cas9 screen in cells derived from Syrian hamster, a facultative hibernator, as well as human cells to investigate the genetic basis of cold tolerance in a hibernator and a non-hibernator in an unbiased manner. Both screens independently revealed glutathione peroxidase 4 (GPX4), a selenium-containing enzyme, and associated proteins as critical for cold tolerance. We utilized genetic and pharmacological approaches to demonstrate that GPX4 is active in the cold and its catalytic activity is required for cold tolerance. Furthermore, we show that the role of GPX4 as a suppressor of cold-induced cell death extends across hibernating species, including 13-lined ground squirrels and greater horseshoe bats, highlighting the evolutionary conservation of this mechanism of cold tolerance. This study identifies GPX4 as a central modulator of mammalian cold tolerance and advances our understanding of the evolved mechanisms by which cells mitigate cold-associated damage—one of the most common challenges faced by cells and organisms in nature.

Single cell CRISPR screens such as Perturb-seq enable transcriptomic profiling of genetic perturbations at scale. However, the data produced by these screens are often noisy due to cost and technical constraints, limiting power to detect true effects with conventional differential expression analyses. Here, we introduce TRanscriptome-wide Analysis of Differential Expression (TRADE), a statistical framework which estimates the transcriptome-wide distribution of true differential expression effects from noisy gene-level measurements. Within TRADE, we derive multiple novel, interpretable statistical metrics, including the 'transcriptome-wide impact', an estimator of the overall transcriptional effect of a perturbation which is stable across sampling depths. We analyze new and published large-scale Perturb-seq datasets to show that many true transcriptional effects are not statistically significant, but detectable in aggregate with TRADE. In a genome-scale Perturb-seq screen, we find that a typical gene perturbation affects an estimated 45 genes, whereas a typical essential gene perturbation affects over 500 genes. An advantage of our approach is its ability to compare the transcriptomic effects of genetic perturbations across contexts and dosages despite differences in power. We use this ability to identify perturbations with cell-type dependent effects and to find examples of perturbations where transcriptional responses are not only larger in magnitude, but also qualitatively different, as a function of dosage. Lastly, we expand our analysis to case/control comparison of gene expression for neuropsychiatric conditions, finding that transcriptomic effect correlations are greater than genetic correlations for these diagnoses. TRADE lays an analytic foundation for the systematic comparison of genetic perturbation atlases, as well as differential expression experiments more broadly.