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Parkinson's disease (PD)‐specific neurons, grown in standard 2D cultures, typically only display weak endophenotypes. The cultivation of PD patient‐specific neurons, derived from induced pluripotent stem cells carrying the LRRK2‐G2019S mutation, is optimized in 3D microfluidics. The automated image analysis algorithms are implemented to enable pharmacophenomics in disease‐relevant conditions. In contrast to 2D cultures, this 3D approach reveals robust endophenotypes. High‐content imaging data show decreased dopaminergic differentiation and branching complexity, altered mitochondrial morphology, and increased cell death in LRRK2‐G2019S neurons compared to isogenic lines without using stressor agents. Treatment with the LRRK2 inhibitor 2 (Inh2) rescues LRRK2‐G2019S‐dependent dopaminergic phenotypes. Strikingly, a holistic analysis of all studied features shows that the genetic background of the PD patients, and not the LRRK2‐G2019S mutation, constitutes the strongest contribution to the phenotypes. These data support the use of advanced in vitro models for future patient stratification and personalized drug development. The identification of robust phenotypes recapitulating key cellular defects of Parkinson's disease (PD) is hampered by the lack of sufficiently representative in vitro models. Here, the cultivation of PD patients' derived dopaminergic neurons carrying the LRRK2‐G2019S mutation in 3D microfluidics is optimized. This system shows time‐dependent differentiation, viability, and mitochondrial phenotypes in LRRK2‐G2019S compared to LRRK2‐WT.

High‐content image‐based cell phenotyping provides fundamental insights into a broad variety of life science disciplines. Striving for accurate conclusions and meaningful impact demands high reproducibility standards, with particular relevance for high‐quality open‐access data sharing and meta‐analysis. However, the sources and degree of biological and technical variability, and thus the reproducibility and usefulness of meta‐analysis of results from live‐cell microscopy, have not been systematically investigated. Here, using high‐content data describing features of cell migration and morphology, we determine the sources of variability across different scales, including between laboratories, persons, experiments, technical repeats, cells, and time points. Significant technical variability occurred between laboratories and, to lesser extent, between persons, providing low value to direct meta‐analysis on the data from different laboratories. However, batch effect removal markedly improved the possibility to combine image‐based datasets of perturbation experiments. Thus, reproducible quantitative high‐content cell image analysis of perturbation effects and meta‐analysis depend on standardized procedures combined with batch correction. Synopsis Analyses of the sources of variability of cell migration data obtained by live cell imaging produced by independent labs with identical protocol and key reagents show that the highest variability occurs between labs and the variance can be substantially reduced by batch effect removal. To quantify the sources of variability, a live cell imaging design of cell migration in 2D and 3D culture was replicated by expert labs, different team members and in independent replicates in a hierarchical structure. Lab to lab variance and, to lesser extent, variation between team members were key sources of technical variance, based on Linear Mixed Effect model analysis. Batch effect removal dramatically reduced the variance and was verified using a 3D cell migration dataset. Graphical Abstract Analyses of the sources of variability of cell migration data obtained by live cell imaging produced by independent labs with identical protocol and key reagents show that the highest variability occurs between labs and the variance can be substantially reduced by batch effect removal.

Batten diseases (BDs) are a group of lysosomal storage disorders characterized by seizure, visual loss, and cognitive and motor deterioration. We discovered increased levels of globotriaosylceramide (Gb3) in cellular and murine models of CLN3 and CLN7 diseases and used fluorescent‐conjugated bacterial toxins to label Gb3 to develop a cell‐based high content imaging (HCI) screening assay for the repurposing of FDA‐approved compounds able to reduce this accumulation within BD cells. We found that tamoxifen reduced the lysosomal accumulation of Gb3 in CLN3 and CLN7 cell models, including neuronal progenitor cells (NPCs) from CLN7 patient‐derived induced pluripotent stem cells (iPSC). Here, tamoxifen exerts its action through a mechanism that involves activation of the transcription factor EB (TFEB), a master gene of lysosomal function and autophagy. In vivo administration of tamoxifen to the CLN7 Δex2 mouse model reduced the accumulation of Gb3 and SCMAS, decreased neuroinflammation, and improved motor coordination. These data strongly suggest that tamoxifen may be a suitable drug to treat some types of Batten disease. SYNOPSIS The neuronal ceroid lipofuscinoses (NCL), commonly known as Batten disease (BD), are a group of recessively inherited fatal diseases of the nervous system that typically arise in childhood. There is neither cure nor drugs to revert the course of these diseases. Neural accumulation of lysosomal Gb3 is a novel hallmark of CLN3 and CLN7 batten diseases. The FDA‐approved drug tamoxifen reverts pathological phenotype of CLN3 and CLN7 diseases in vitro and in vivo . Tamoxifen effects are independent of the modulation of estrogen receptors but require the activation of TFEB. Graphical Abstract The neuronal ceroid lipofuscinoses (NCL), commonly known as Batten disease (BD), are a group of recessively inherited fatal diseases of the nervous system that typically arise in childhood. There is neither cure nor drugs to revert the course of these diseases.

Small molecules often affect multiple targets, elicit off‐target effects, and induce genotype‐specific responses. Chemical genetics, the mapping of the genotype dependence of a small molecule's effects across a broad spectrum of phenotypes can identify novel mechanisms of action. It can also reveal unanticipated effects and could thereby reduce high attrition rates of small molecule development pipelines. Here, we used high‐content screening and image analysis to measure effects of 1,280 pharmacologically active compounds on complex phenotypes in isogenic cancer cell lines which harbor activating or inactivating mutations in key oncogenic signaling pathways. Using multiparametric chemical–genetic interaction analysis, we observed phenotypic gene–drug interactions for more than 193 compounds, with many affecting phenotypes other than cell growth. We created a resource termed the Pharmacogenetic Phenome Compendium (PGPC), which enables exploration of drug mode of action, detection of potential off‐target effects, and the generation of hypotheses on drug combinations and synergism. For example, we demonstrate that MEK inhibitors amplify the viability effect of the clinically used anti‐alcoholism drug disulfiram and show that the EGFR inhibitor tyrphostin AG555 has off‐target activity on the proteasome. Taken together, this study demonstrates how combining multiparametric phenotyping in different genetic backgrounds can be used to predict additional mechanisms of action and to reposition clinically used drugs. Synopsis This study defines a quantitative map of phenotypic pharmacogenetic interactions in human cancer cells using high‐content imaging screens in a panel of isogenic cell lines. The resource is used to predict effective drug combinations, compound mode‐of‐action and off‐target effects. We developed a robust and scalable approach to integrate multiparametric phenotypic profiling and quantitative pharmacogenetic interaction mapping in human cancer cells. We used high‐content screening and automated image analysis to measure genotype‐specific effects of 1,280 drugs on complex phenotypes in a panel of 12 isogenic cancer cell lines, resulting in more than 14,000,000 measurements. We observed a total of 2,359 significant chemical–genetic interactions, only 16 of which affected cell number. Our approach provided increased coverage for gene–drug interaction mapping as compared to strategies that solely rely on cell growth as a phenotypic readout. We created a resource termed the Pharmacogenetic Phenome Compendium (PGPC), comprising information about over 300,000 drug–gene–phenotype interactions. The PGPC can be explored to predict compound mode of action and off‐target effects, pathway crosstalk and effective drug combinations. Graphical Abstract This study defines a quantitative map of phenotypic pharmacogenetic interactions in human cancer cells using high‐content imaging screens in a panel of isogenic cell lines. The resource is used to predict effective drug combinations, compound mode of action and off‐target effects.

Cultured human bone marrow stromal (mesenchymal) stem cells (hBM‐MSCs) are heterogenous cell populations exhibiting variable biological properties. Quantitative high‐content imaging technology allows identification of morphological markers at a single cell resolution that are determinant for cellular functions. We determined the morphological characteristics of cultured primary hBM‐MSCs and examined their predictive value for hBM‐MSC functionality. BM‐MSCs were isolated from 56 donors and characterized for their proliferative and differentiation potential. We correlated these data with cellular and nuclear morphological features determined by Operetta; a high‐content imaging system. Cell area, cell geometry, and nucleus geometry of cultured hBM‐MSCs exhibited significant correlation with expression of hBM‐MSC membrane markers: ALP, CD146, and CD271. Proliferation capacity correlated negatively with cell and nucleus area and positively with cytoskeleton texture features. In addition, in vitro differentiation to osteoblasts as well as in vivo heterotopic bone formation was associated with decreased ratio of nucleus width to length. Multivariable analysis applying a stability selection procedure identified nuclear geometry and texture as predictors for hBM‐MSCs differentiation potential to osteoblasts or adipocytes. Our data demonstrate that by employing a limited number of cell morphological characteristics, it is possible to predict the functional phenotype of cultured hBM‐MSCs and thus can be used as a screening test for “quality” of hBM‐MSCs prior their use in clinical protocols.

INTRODUCTION The risk of developing Alzheimer's disease is associated with genes involved in microglial function. Inositol polyphosphate‐5‐phosphatase (INPP5D), which encodes Src homology 2 (SH2) domain–containing inositol polyphosphate 5‐phosphatase 1 (SHIP1), is a risk gene expressed in microglia. Because SHIP1 binds receptor immunoreceptor tyrosine‐based inhibitory motifs (ITIMs), competes with kinases, and converts PI(3,4,5)P3 to PI(3,4)P2, it is a negative regulator of microglia function. Validated inhibitors are needed to evaluate SHIP1 as a potential therapeutic target. METHODS We identified inhibitors and screened the enzymatic domain of SHIP1. A protein construct containing two domains was used to evaluate enzyme inhibitor potency and selectivity versus SHIP2. Inhibitors were tested against a construct containing all ordered domains of the human and mouse proteins. A cellular thermal shift assay (CETSA) provided evidence of target engagement in cells. Phospho‐AKT levels provided further evidence of on‐target pharmacology. A high‐content imaging assay was used to study the pharmacology of SHIP1 inhibition while monitoring cell health. Physicochemical and absorption, distribution, metabolism, and excretion (ADME) properties were evaluated to select a compound suitable for in vivo studies. RESULTS SHIP1 inhibitors displayed a remarkable array of activities and cellular pharmacology. Inhibitory potency was dependent on the protein construct used to assess enzymatic activity. Some inhibitors failed to engage the target in cells. Inhibitors that were active in the CETSA consistently destabilized the protein and reduced pAKT levels. Many SHIP1 inhibitors were cytotoxic either at high concentration due to cell stress or they potently induced cell death depending on the compound and cell type. One compound activated microglia, inducing phagocytosis at concentrations that did not result in significant cell death. A pharmacokinetic study demonstrated brain exposures in mice upon oral administration. DISCUSSION 3‐((2,4‐Dichlorobenzyl)oxy)‐5‐(1‐(piperidin‐4‐yl)‐1H‐pyrazol‐4‐yl) pyridine activated primary mouse microglia and demonstrated exposures in mouse brain upon oral dosing. Although this compound is our recommended chemical probe for investigating the pharmacology of SHIP1 inhibition at this time, further optimization is required for clinical studies. Highlights Cellular thermal shift assay (CETSA) and signaling (pAKT) assays were developed to provide evidence of src homology 2 (SH2) domain‐contaning inositol phosphatase 1 (SHIP1) target engagement and on‐target activity in cellular assays. A phenotypic high‐content imaging assay with simultaneous measures of phagocytosis, cell number, and nuclear intensity was developed to explore cellular pharmacology and monitor cell health. SHIP1 inhibitors demonstrate a wide range of activity and cellular pharmacology, and many reported inhibitors are cytotoxic. The chemical probe 3‐((2,4‐dichlorobenzyl)oxy)−5‐(1‐(piperidin‐4‐yl)−1H‐pyrazol‐4‐yl) pyridine is recommended to explore SHIP1 pharmacology.

Combining high‐throughput generation and high‐content imaging of embryo models will enable large‐scale screening assays in the fields of (embryo) toxicity, drug development, embryogenesis, and reproductive medicine. This study shows the continuous culture and in situ (i.e., in microwell) imaging‐based readout of a 3D stem cell‐based model of peri‐implantation epiblast (Epi)/extraembryonic endoderm (XEn) development with an expanded pro‐amniotic cavity (PAC) (E3.5 E5.5), namely XEn/EPiCs. Automated image analysis and supervised machine learning permit the identification of embryonic morphogenesis, tissue compartmentalization, cell differentiation, and consecutive classification. Screens with signaling pathway modulators at different time windows provide spatiotemporal information on their phenotypic effect on developmental processes leading to the formation of XEn/EPiCs. Exposure of the biological model in the microwell platform to pathway modulators at two time windows, namely 0–72 h and 48–120 h, show that Wnt and Fgf/MAPK pathway modulators affect Epi differentiation and its polarization, while modulation of BMP and Tgfβ/Nodal pathway affects XEn specification and epithelialization. Further, their collective role is identified in the timing of the formation and expansion of PAC. The newly developed, scalable culture and analysis platform, thereby, provides a unique opportunity to quantitatively and systematically study effects of pathway modulators on early embryonic development. This article describes a novel and scalable microwell platform for performing high‐throughput screens of stem cell‐derived embryo‐like structures. The modular analysis pipeline uses discrete classifiers to phenotypically quantify the structures. Screening of signaling pathway modulators demonstrate that the formation and patterning of embryonic morphogenesis, including pro‐amniotic cavity (PAC) formation, is spatiotemporally controlled by Wnt, Fgf/MAPK, BMP, and Nodal pathways.

Interference in cell cycle progression has been noted as one of the important properties of anticancer drugs. In this study, we developed the cell cycle prediction model using high‐content imaging data of recipient cells after drug exposure and DNA‐staining with a low‐toxic DNA dye, SiR‐DNA. For this purpose, we exploited HeLa and MCF7 cells introduced with a fluorescent ubiquitination‐based cell cycle indicator (Fucci). Fucci‐expressing cancer cells were subjected to high‐content imaging analysis using OperettaCLS after 36‐h exposure to anticancer drugs; the nuclei were segmented, and the morphological and intensity properties of each nucleus characterized by SiR‐DNA staining were calculated using imaging analysis software, Harmony. For the use of training, we classified cells into each phase of the cell cycle using the Fucci system. Training data (n = 7500) and validation data (n = 2500) were randomly sampled and the binary classification prediction models for G1, early S, and S/G2/M phases of the cell cycle were developed using four supervised machine learning algorithms. We selected random forest as the model with the best performance through 10‐fold cross‐validation; the accuracy rate was approximately 75%–87%. Regarding feature importance, variables expected to be biologically related to the cell cycle, for example, signal intensity and nuclear size, were highly ranked, suggesting the validity of the model. These results showed that the cell cycle can be predicted in cancer cells by simply exploiting the current prediction model using fluorescent images of DNA‐staining dye, and the model could be applied for the use of future ex vivo drug sensitivity diagnosis. The top 10 variables in feature importance of the G1 prediction model and S/G2/M prediction model in HeLa cells (A, B), and those in MCF7 cells (C, D).

Ovarian cancer is one of the most lethal gynecological cancers worldwide and has one of the highest recurrence rates. Recently developed Chimeric Antigen Receptor (CAR) ‐T cell therapy has shown potent clinical efficacy against hematological malignancies. However, solid tumors, including ovarian cancer, possess several mechanisms that hinder T cell activity, and metabolic alteration of cancer cells has been shown to contribute to resistance to immune cell attack against solid tumors. Here, we explore the metabolic response of ovarian cancer cells to CAR‐T cell attack using label‐free high‐content hyperspectral stimulated Raman scattering (h2SRS) imaging. Utilizing visible h2SRS imaging with much improved spatial resolution, we find an altered cholesterol metabolism, featured by increased storage of cholesteryl ester in lipid droplets and free cholesterol, in ovarian cancer cells that survive the CAR‐T treatment. Administration of Avasimibe, an inhibitor of cholesteryl esterification, further enhances CAR‐T cytotoxicity. Our study shows the promise of implementing metabolic modulation to facilitate CAR‐T cell treatment of solid tumors. High‐content Stimulated Raman Scattering (SRS) Imaging reveals that ovarian cancer cells surviving Chimeric Antigen Receptor (CAR) ‐T cell challenge exhibit increased cholesterol esterification. Pharmacological inhibition of this pathway with Avasimibe significantly enhances CAR‐T induced killing of ovarian cancer cells by reducing cancer cell aggressiveness. This work presents a promising metabolic modulation strategy to overcome therapy resistance in solid tumors.

Three-dimensional (3D) culture systems have fueled hopes to bring about the next generation of more physiologically relevant high-throughput screens (HTS). However, current protocols yield either complex but highly heterogeneous aggregates (‘organoids’) or 3D structures with less physiological relevance (‘spheroids’). Here, we present a scalable, HTS-compatible workflow for the automated generation, maintenance, and optical analysis of human midbrain organoids in standard 96-well-plates. The resulting organoids possess a highly homogeneous morphology, size, global gene expression, cellular composition, and structure. They present significant features of the human midbrain and display spontaneous aggregate-wide synchronized neural activity. By automating the entire workflow from generation to analysis, we enhance the intra- and inter-batch reproducibility as demonstrated via RNA sequencing and quantitative whole mount high-content imaging. This allows assessing drug effects at the single-cell level within a complex 3D cell environment in a fully automated HTS workflow. In 1907, the American zoologist Ross Granville Harrison developed the first technique to artificially grow animal cells outside the body in a liquid medium. Cells are still grown in much the same way in modern laboratories: a single layer of cells is placed in a warm incubator with nutrient-rich broth. These cell layers are often used to test new drugs, but they cannot recapitulate the complexity of a real organ made from multiple cell types within a living, breathing human body. Growing three-dimensional miniature organs or 'organoids' that behave in a similar way to real organs is the next step towards creating better platforms for drug screening, but there are several difficulties inherent to this process. For one thing, it is hard to recreate the multitude of cell types that make up an organ. For another, the cells that do grow often fail to connect and communicate with each other in biologically realistic ways. It is also tough to grow a large number of organoids that all behave in the same way, making it hard to know whether a particular drug works or whether it is just being tested on a 'good' organoid. Renner et al. have been able to overcome these issues by using robotic technology to create thousands of identical, mid-brain organoids from human cells in the lab. The robots perform a series of precisely controlled tasks – including dispensing the initial cells into wells, feeding organoids as they grow and testing them at different stages of development. These mini-brains, which are the size of the head of a pin, mimic the part of the brain where Parkinson's disease first manifests. They can be used to test new drugs for Parkinson's, and to better understand the biology of the brain. Perhaps more importantly, other types of organoids can be created using the same technique to model diseases that affect other areas of the brain, or other organs altogether. For example, Renner et al. also generated forebrain organoids using an automated approach for both generation and analysis. This research, which shows that organoids can be grown and tested in a fully automated, reproducible and scalable way, creates a platform to quickly, cheaply and easily test thousands of drugs for Parkinson's and other difficult-to-treat diseases in a human setting. This approach has the potential to reduce research waste by increasing the chances that a drug that works in the lab will also ultimately work in a patient; and reduce animal experiments, as drugs that do not work in human tissues will not proceed to animal testing.