Identifying FUS amyotrophic lateral sclerosis disease signatures in patient dermal fibroblasts.

Amyotrophic lateral sclerosis (ALS) is a rapidly progressing, highly heterogeneous neurodegenerative disease, underscoring the importance of obtaining information to personalize clinical decisions quickly after diagnosis. Here, we investigated whether ALS-relevant signatures can be detected directly from biopsied patient fibroblasts. We profiled familial ALS (fALS) fibroblasts, representing a range of mutations in the fused in sarcoma (FUS) gene and ages of onset. To differentiate FUS fALS and healthy control fibroblasts, machine-learning classifiers were trained separately on high-content imaging and transcriptional profiles. "Molecular ALS phenotype" scores, derived from these classifiers, captured a spectrum from disease to health. Interestingly, these scores negatively correlated with age of onset, identified several pre-symptomatic individuals and sporadic ALS (sALS) patients with FUS-like fibroblasts, and quantified "movement" of FUS fALS and "FUS-like" sALS toward health upon FUS ASO treatment. Taken together, these findings provide evidence that non-neuronal patient fibroblasts can be used for rapid, personalized assessment in ALS.

Small molecule in situ resin capture provides a compound first approach to natural product discovery.

Culture-based microbial natural product discovery strategies fail to realize the extraordinary biosynthetic potential detected across earth's microbiomes. Here we introduce Small Molecule In situ Resin Capture (SMIRC), a culture-independent method to obtain natural products directly from the environments in which they are produced. We use SMIRC to capture numerous compounds including two new carbon skeletons that were characterized using NMR and contain structural features that are, to the best of our knowledge, unprecedented among natural products. Applications across diverse marine habitats reveal biome-specific metabolomic signatures and levels of chemical diversity in concordance with sequence-based predictions. Expanded deployments, in situ cultivation, and metagenomics facilitate compound discovery, enhance yields, and link compounds to candidate producing organisms, although microbial community complexity creates challenges for the later. This compound-first approach to natural product discovery provides access to poorly explored chemical space and has implications for drug discovery and the detection of chemically mediated biotic interactions.

A PINK1 input threshold arises from positive feedback in the PINK1/Parkin mitophagy decision circuit.

Mechanisms that prevent accidental activation of the PINK1/Parkin mitophagy circuit on healthy mitochondria are poorly understood. On the surface of damaged mitochondria, PINK1 accumulates and acts as the input signal to a positive feedback loop of Parkin recruitment, which in turn promotes mitochondrial degradation via mitophagy. However, PINK1 is also present on healthy mitochondria, where it could errantly recruit Parkin and thereby activate this positive feedback loop. Here, we explore emergent properties of the PINK1/Parkin circuit by quantifying the relationship between mitochondrial PINK1 concentrations and Parkin recruitment dynamics. We find that Parkin is recruited to mitochondria only if PINK1 levels exceed a threshold and then only after a delay that is inversely proportional to PINK1 levels. Furthermore, these two regulatory properties arise from the input-coupled positive feedback topology of the PINK1/Parkin circuit. These results outline an intrinsic mechanism by which the PINK1/Parkin circuit can avoid errant activation on healthy mitochondria.

Small Molecule in situ Resin Capture - A Compound First Approach to Natural Product Discovery.

Microbial natural products remain an important resource for drug discovery. Yet, commonly employed discovery techniques are plagued by the rediscovery of known compounds, the relatively few microbes that can be cultured, and laboratory growth conditions that do not elicit biosynthetic gene expression among myriad other challenges. Here we introduce a culture independent approach to natural product discovery that we call the Small Molecule In situ Resin Capture (SMIRC) technique. SMIRC exploits in situ environmental conditions to elicit compound production and represents a new approach to access poorly explored chemical space by capturing natural products directly from the environments in which they are produced. In contrast to traditional methods, this compound-first approach can capture structurally complex small molecules across all domains of life in a single deployment while relying on Nature to provide the complex and poorly understood environmental cues needed to elicit biosynthetic gene expression. We illustrate the effectiveness of SMIRC in marine habitats with the discovery of numerous new compounds and demonstrate that sufficient compound yields can be obtained for NMR-based structure assignment. Two new compound classes are reported including one novel carbon skeleton that possesses a functional group not previously observed among natural products and a second that possesses potent biological activity. We introduce expanded deployments, in situ cultivation, and metagenomics as methods to facilitate compound discovery, enhance yields, and link compounds to producing organisms. This compound first approach can provide unprecedented access to new natural product chemotypes with broad implications for drug discovery.

Searching for molecular hypoxia sensors among oxygen-dependent enzymes.

The ability to sense and respond to changes in cellular oxygen levels is critical for aerobic organisms and requires a molecular oxygen sensor. The prototypical sensor is the oxygen-dependent enzyme PHD: hypoxia inhibits its ability to hydroxylate the transcription factor HIF, causing HIF to accumulate and trigger the classic HIF-dependent hypoxia response. A small handful of other oxygen sensors are known, all of which are oxygen-dependent enzymes. However, hundreds of oxygen-dependent enzymes exist among aerobic organisms, raising the possibility that additional sensors remain to be discovered. This review summarizes known and potential hypoxia sensors among human O2-dependent enzymes and highlights their possible roles in hypoxia-related adaptation and diseases.

Dissecting the effects of GTPase and kinase domain mutations on LRRK2 endosomal localization and activity.

Parkinson's disease-causing leucine-rich repeat kinase 2 (LRRK2) mutations lead to varying degrees of Rab GTPase hyperphosphorylation. Puzzlingly, LRRK2 GTPase-inactivating mutations-which do not affect intrinsic kinase activity-lead to higher levels of cellular Rab phosphorylation than kinase-activating mutations. Here, we investigate whether mutation-dependent differences in LRRK2 cellular localization could explain this discrepancy. We discover that blocking endosomal maturation leads to the rapid formation of mutant LRRK2+ endosomes on which LRRK2 phosphorylates substrate Rabs. LRRK2+ endosomes are maintained through positive feedback, which mutually reinforces membrane localization of LRRK2 and phosphorylated Rab substrates. Furthermore, across a panel of mutants, cells expressing GTPase-inactivating mutants form strikingly more LRRK2+ endosomes than cells expressing kinase-activating mutants, resulting in higher total cellular levels of phosphorylated Rabs. Our study suggests that the increased probability that LRRK2 GTPase-inactivating mutants are retained on intracellular membranes compared to kinase-activating mutants leads to higher substrate phosphorylation.

Selection of Optimal Cell Lines for High-Content Phenotypic Screening.

High-content microscopy offers a scalable approach to screen against multiple targets in a single pass. Prior work has focused on methods to select "optimal" cellular readouts in microscopy screens. However, methods to select optimal cell line models have garnered much less attention. Here, we provide a roadmap for how to select the cell line or lines that are best suited to identify bioactive compounds and their mechanism of action (MOA). We test our approach on compounds targeting cancer-relevant pathways, ranking cell lines in two tasks: detecting compound activity ("phenoactivity") and grouping compounds with similar MOA by similar phenotype ("phenosimilarity"). Evaluating six cell lines across 3214 well-annotated compounds, we show that optimal cell line selection depends on both the task of interest (e.g., detecting phenoactivity vs inferring phenosimilarity) and distribution of MOAs within the compound library. Given a task of interest and a set of compounds, we provide a systematic framework for choosing optimal cell line(s). Our framework can be used to reduce the number of cell lines required to identify hits within a compound library and help accelerate the pace of early drug discovery.

Biallelic TET2 mutations confer sensitivity to 5'-azacitidine in acute myeloid leukemia.

Precision medicine can significantly improve outcomes for patients with cancer, but implementation requires comprehensive characterization of tumor cells to identify therapeutically exploitable vulnerabilities. Here, we describe somatic biallelic TET2 mutations in an elderly patient with acute myeloid leukemia (AML) that was chemoresistant to anthracycline and cytarabine but acutely sensitive to 5'-azacitidine (5'-Aza) hypomethylating monotherapy, resulting in long-term morphological remission. Given the role of TET2 as a regulator of genomic methylation, we hypothesized that mutant TET2 allele dosage affects response to 5'-Aza. Using an isogenic cell model system and an orthotopic mouse xenograft, we demonstrate that biallelic TET2 mutations confer sensitivity to 5'-Aza compared with cells with monoallelic mutations. Our data argue in favor of using hypomethylating agents for chemoresistant disease or as first-line therapy in patients with biallelic TET2-mutated AML and demonstrate the importance of considering mutant allele dosage in the implementation of precision medicine for patients with cancer.

Integrative spatial analysis of cell morphologies and transcriptional states with MUSE.

Spatial transcriptomics enables the simultaneous measurement of morphological features and transcriptional profiles of the same cells or regions in tissues. Here we present multi-modal structured embedding (MUSE), an approach to characterize cells and tissue regions by integrating morphological and spatially resolved transcriptional data. We demonstrate that MUSE can discover tissue subpopulations missed by either modality as well as compensate for modality-specific noise. We apply MUSE to diverse datasets containing spatial transcriptomics (seqFISH+, STARmap or Visium) and imaging (hematoxylin and eosin or fluorescence microscopy) modalities. MUSE identified biologically meaningful tissue subpopulations and stereotyped spatial patterning in healthy brain cortex and intestinal tissues. In diseased tissues, MUSE revealed gene biomarkers for proximity to tumor region and heterogeneity of amyloid precursor protein processing across Alzheimer brain regions. MUSE enables the integration of multi-modal data to provide insights into the states, functions and organization of cells in complex biological tissues.

Modulating environmental signals to reveal mechanisms and vulnerabilities of cancer persisters.

Cancer persister cells are able to survive otherwise lethal doses of drugs through nongenetic mechanisms, which can lead to cancer regrowth and drug resistance. The broad spectrum of molecular differences observed between persisters and their treatment-naïve counterparts makes it challenging to identify causal mechanisms underlying persistence. Here, we modulate environmental signals to identify cellular mechanisms that promote the emergence of persisters and to pinpoint actionable vulnerabilities that eliminate them. We found that interferon-γ (IFNγ) can induce a pro-persistence signal that can be specifically eliminated by inhibition of type I protein arginine methyltransferase (PRMT) (PRMTi). Mechanistic investigation revealed that signal transducer and activator of transcription 1 (STAT1) is a key component connecting IFNγ's pro-persistence and PRMTi's antipersistence effects, suggesting a previously unknown application of PRMTi to target persisters in settings with high STAT1 expression. Modulating environmental signals can accelerate the identification of mechanisms that promote and eliminate cancer persistence.

Differential toxicity to murine small and large intestinal epithelium induced by oncology drugs.

Gastrointestinal toxicity is a major concern in the development of drugs. Here, we establish the ability to use murine small and large intestine-derived monolayers to screen drugs for toxicity. As a proof-of-concept, we applied this system to assess gastrointestinal toxicity of ~50 clinically used oncology drugs, encompassing diverse mechanisms of action. Nearly all tested drugs had a deleterious effect on the gut, with increased sensitivity in the small intestine. The identification of differential toxicity between the small and large intestine enabled us to pinpoint differences in drug uptake (antifolates), drug metabolism (cyclophosphamide) and cell signaling (EGFR inhibitors) across the gut. These results highlight an under-appreciated distinction between small and large intestine toxicity and suggest distinct tissue properties important for modulating drug-induced gastrointestinal toxicity. The ability to accurately predict where and how drugs affect the murine gut will accelerate preclinical drug development.

Analysis of growth cone extension in standardized coordinates highlights self-organization rules during wiring of the Drosophila visual system.

A fascinating question in neuroscience is how ensembles of neurons, originating from different locations, extend to the proper place and by the right time to create precise circuits. Here, we investigate this question in the Drosophila visual system, where photoreceptors re-sort in the lamina to form the crystalline-like neural superposition circuit. The repeated nature of this circuit allowed us to establish a data-driven, standardized coordinate system for quantitative comparison of sparsely perturbed growth cones within and across specimens. Using this common frame of reference, we investigated the extension of the R3 and R4 photoreceptors, which is the only pair of symmetrically arranged photoreceptors with asymmetric target choices. Specifically, we found that extension speeds of the R3 and R4 growth cones are inherent to their cell identities. The ability to parameterize local regularity in tissue organization facilitated the characterization of ensemble cellular behaviors and dissection of mechanisms governing neural circuit formation.

Transit-Amplifying Cells Coordinate Changes in Intestinal Epithelial Cell-Type Composition.

Renewing tissues have the remarkable ability to continually produce both proliferative progenitor and specialized differentiated cell types. How are complex milieus of microenvironmental signals interpreted to coordinate tissue-cell-type composition? Here, we investigate the responses of intestinal epithelium to individual and paired perturbations across eight epithelial signaling pathways. Using a high-throughput approach that combines enteroid monolayers and quantitative imaging, we identified conditions that enrich for specific cell types as well as interactions between pathways. Importantly, we found that modulation of transit-amplifying cell proliferation changes the ratio of differentiated secretory to absorptive cell types. These observations highlight an underappreciated role for transit-amplifying cells in the tuning of differentiated cell-type composition.

Systematic alteration of in vitro metabolic environments reveals empirical growth relationships in cancer cell phenotypes.

Cancer cells, like microbes, live in complex metabolic environments. Recent evidence suggests that microbial behavior across metabolic environments is well described by simple empirical growth relationships, or growth laws. Do such empirical growth relationships also exist in cancer cells? To test this question, we develop a high-throughput approach to extract quantitative measurements of cancer cell behaviors in systematically altered metabolic environments. Using this approach, we examine relationships between growth and three frequently studied cancer phenotypes: drug-treatment survival, cell migration, and lactate overflow. Drug-treatment survival follows simple linear growth relationships, which differ quantitatively between chemotherapeutics and EGFR inhibition. Cell migration follows a weak grow-and-go growth relationship, with substantial deviation in some environments. Finally, lactate overflow is mostly decoupled from growth rate and is instead determined by the cells' ability to maintain high sugar uptake rates. Altogether, this work provides a quantitative approach for formulating empirical growth laws of cancer.

Generation and Quantitative Imaging of Enteroid Monolayers.

The intestinal epithelium is a single layer of cells that plays a critical role in digestion, absorbs nutrients from food, and coordinates the delicate interplay between microbes in the gut lumen and the immune system. Epithelial homeostasis is crucial for maintaining health; disruption of homeostasis results in disorders including inflammatory bowel disease and cancer. The advent of 3D intestinal epithelial organoids has greatly advanced our understanding of the molecular underpinnings of epithelial homeostasis and disease. Recently, we developed an enteroid monolayer (2D) culture system that recapitulates important features of 3D organoids and the in vivo intestinal epithelium such as tissue renewal, representation of diverse epithelial cell types, self-organization, and apical-basolateral polarization. Enteroid monolayers are cultured in microtiter plates, enabling high-throughput experiments. Furthermore, their 2D nature makes it easier to distinguish individual cells by fluorescent microscopy, enabling quantitative analysis of single cell behaviors within the epithelial tissue.Here we describe experimental methods for generating enteroid monolayers and computational methods for analyzing immunofluorescence images of enteroid monolayers. We outline experimental methods for generating enteroid monolayers from freshly isolated intestinal crypts, frozen intestinal crypts, and 3D organoids. Fresh crypts are easily obtained from murine or human intestinal samples, and the ability to derive enteroid monolayers from both frozen crypts and 3D organoids enables genetic modification and/or biobanking of patient samples for future studies. We outline computational methods for identifying distinct epithelial cell types (goblet, stem, EdU+) in immunofluorescence images of enteroid monolayers and, importantly, individual nuclei, enabling truly single cell measurements of epithelial cell behaviors to be made. Taken together, these methods will enable detailed studies of epithelial homeostasis and intestinal disease.

Loss of TET2 Affects Proliferation and Drug Sensitivity through Altered Dynamics of Cell-State Transitions.

A persistent puzzle in cancer biology is how mutations, which neither alter growth signaling pathways nor directly interfere with drug mechanism, can still recur and persist in tumors. One example is the mutation of the DNA demethylase tet methylcytosine dioxygenase 2 (TET2) in acute myeloid leukemias (AMLs) that frequently persists from diagnosis through remission and relapse, but whose fitness advantage in chemotherapy is unclear. Here, we use isogenic human AML cell lines to show that TET2 loss of function alters the dynamics of transitions between differentiated and stem-like states. A conceptual mathematical model and experimental validation suggest that these altered cell-state dynamics can benefit the cell population by slowing population decay during drug treatment and lowering the number of survivor cells needed to re-establish the initial population. These studies shed light on the functional and phenotypic effects of a TET2 mutation in AML and illustrate how a single gene mutation can alter a cells' phenotypic plasticity. A record of this paper's transparent peer review process is included in the Supplemental Information.

A multi-modal data resource for investigating topographic heterogeneity in patient-derived xenograft tumors.

Patient-derived xenografts (PDXs) are an essential pre-clinical resource for investigating tumor biology. However, cellular heterogeneity within and across PDX tumors can strongly impact the interpretation of PDX studies. Here, we generated a multi-modal, large-scale dataset to investigate PDX heterogeneity in metastatic colorectal cancer (CRC) across tumor models, spatial scales and genomic, transcriptomic, proteomic and imaging assay modalities. To showcase this dataset, we present analysis to assess sources of PDX variation, including anatomical orientation within the implanted tumor, mouse contribution, and differences between replicate PDX tumors. A unique aspect of our dataset is deep characterization of intra-tumor heterogeneity via immunofluorescence imaging, which enables investigation of variation across multiple spatial scales, from subcellular to whole tumor levels. Our study provides a benchmark data resource to investigate PDX models of metastatic CRC and serves as a template for future, quantitative investigations of spatial heterogeneity within and across PDX tumor models.

Patterns of Early p21 Dynamics Determine Proliferation-Senescence Cell Fate after Chemotherapy.

Chemotherapy is designed to induce cell death. However, at non-lethal doses, cancer cells can choose to remain proliferative or become senescent. The slow development of senescence makes studying this decision challenging. Here, by analyzing single-cell p21 dynamics before, during, and days after drug treatment, we link three distinct patterns of early p21 dynamics to final cell fate. Surprisingly, while high p21 expression is classically associated with senescence, we find the opposite at early times during drug treatment: most senescence-fated cells express much lower p21 levels than proliferation-fated cells. We demonstrate that these dynamics lead to a p21 "Goldilocks zone" for proliferation, in which modest increases of p21 expression can lead to an undesirable increase of cancer cell proliferation. Our study identifies a counter-intuitive role for early p21 dynamics in the cell-fate decision and pinpoints a source of proliferative cancer cells that can emerge after exposure to non-lethal doses of chemotherapy.

Scalable analysis of cell-type composition from single-cell transcriptomics using deep recurrent learning.

Recent advances in large-scale single-cell RNA-seq enable fine-grained characterization of phenotypically distinct cellular states in heterogeneous tissues. We present scScope, a scalable deep-learning-based approach that can accurately and rapidly identify cell-type composition from millions of noisy single-cell gene-expression profiles.