Phenotypic Readout Research Articles

Phenomics approaches to understand genetic networks and gene function in yeast.

Over the past decade, major efforts have been made to systematically survey the characteristics or phenotypes associated with genetic variation in a variety of model systems. These so-called phenomics projects involve the measurement of ‘phenomes’, or the set of phenotypic information that describes an organism or cell, in various genetic contexts or states, and in response to external factors, such as environmental signals. Our understanding of the phenome of an organism depends on the availability of reagents that enable systematic evaluation of the spectrum of possible phenotypic variation and the types of measurements that can be taken. Here, we highlight phenomics studies that use the budding yeast, a pioneer model organism for functional genomics research. We focus on genetic perturbation screens designed to explore genetic interactions, using a variety of phenotypic read-outs, from cell growth to subcellular morphology.

Drosophila functional screening of de novo variants in autism uncovers damaging variants and facilitates discovery of rare neurodevelopmental diseases.

SUMMARYIndividuals with autism spectrum disorder (ASD) exhibit an increased burden of de novo mutations (DNMs) in a broadening range of genes. While these studies have implicated hundreds of genes in ASD pathogenesis, which DNMs cause functional consequences in vivo remains unclear. We functionally test the effects of ASD missense DNMs using Drosophila through “humanization” rescue and overexpression-based strategies. We examine 79 ASD variants in 74 genes identified in the Simons Simplex Collection and find 38% of them to cause functional alterations. Moreover, we identify GLRA2 as the cause of a spectrum of neurodevelopmental phenotypes beyond ASD in 13 previously undiagnosed subjects. Functional characterization of variants in ASD candidate genes points to conserved neurobiological mechanisms and facilitates gene discovery for rare neurodevelopmental diseases.

Image-Based Annotation of Chemogenomic Libraries for Phenotypic Screening.

Phenotypical screening is a widely used approach in drug discovery for the identification of small molecules with cellular activities. However, functional annotation of identified hits often poses a challenge. The development of small molecules with narrow or exclusive target selectivity such as chemical probes and chemogenomic (CG) libraries, greatly diminishes this challenge, but non-specific effects caused by compound toxicity or interference with basic cellular functions still pose a problem to associate phenotypic readouts with molecular targets. Hence, each compound should ideally be comprehensively characterized regarding its effects on general cell functions. Here, we report an optimized live-cell multiplexed assay that classifies cells based on nuclear morphology, presenting an excellent indicator for cellular responses such as early apoptosis and necrosis. This basic readout in combination with the detection of other general cell damaging activities of small molecules such as changes in cytoskeletal morphology, cell cycle and mitochondrial health provides a comprehensive time-dependent characterization of the effect of small molecules on cellular health in a single experiment. The developed high-content assay offers multi-dimensional comprehensive characterization that can be used to delineate generic effects regarding cell functions and cell viability, allowing an assessment of compound suitability for subsequent detailed phenotypic and mechanistic studies.

Multiplexed Genome Editing for Efficient Phenotypic Screening in Zebrafish.

Zebrafish are widely used to investigate candidate genes for human diseases. While the emergence of CRISPR-Cas9 technology has revolutionized gene editing, the use of individual guide RNAs limits the efficiency and application of this technology in functional genetics research. Multiplexed genome editing significantly enhances the efficiency and scope of gene editing. Herein, we describe an efficient multiplexed genome editing strategy to generate zebrafish mutants. Following behavioural tests and histological examination, we identified one new candidate gene (tmem183a) for hearing loss. This study provides a robust genetic platform to quickly obtain zebrafish mutants and to identify candidate genes by phenotypic readouts.

Extracting functional insights from loss-of-function screens using deep link prediction

SummaryWe present deep link prediction (DLP), a method for the interpretation of loss-of-function screens. Our approach uses representation-based link prediction to reprioritize phenotypic readouts by integrating screening experiments with gene-gene interaction networks. We validate on 2 different loss-of-function technologies, RNAi and CRISPR, using datasets obtained from DepMap. Extensive benchmarking shows that DLP-DeepWalk outperforms other methods in recovering cell-specific dependencies, achieving an average precision well above 90% across 7 different cancer types and on both RNAi and CRISPR data. We show that the genes ranked highest by DLP-DeepWalk are appreciably more enriched in drug targets compared to the ranking based on original screening scores. Interestingly, this enrichment is more pronounced on RNAi data compared to CRISPR data, consistent with the greater inherent noise of RNAi screens. Finally, we demonstrate how DLP-DeepWalk can infer the molecular mechanism through which putative targets trigger cell line mortality.

Pooled genetic perturbation screens with image-based phenotypes.

Discovery of the genetic components underpinning fundamental and disease-related processes is being rapidly accelerated by combining efficient, programmable genetic engineering with phenotypic readouts of high spatial, temporal and/or molecular resolution. Microscopy is a fundamental tool for studying cell biology, but its lack of high-throughput sequence readouts hinders integration in large-scale genetic screens. Optical pooled screens using in situ sequencing provide massively scalable integration of barcoded lentiviral libraries (e.g., CRISPR perturbation libraries) with high-content imaging assays, including dynamic processes in live cells. The protocol uses standard lentiviral vectors and molecular biology, providing single-cell resolution of phenotype and engineered genotype, scalability to millions of cells and accurate sequence reads sufficient to distinguish >106 perturbations. In situ amplification takes ~2 d, while sequencing can be performed in ~1.5 h per cycle. The image analysis pipeline provided enables fully parallel automated sequencing analysis using a cloud or cluster computing environment.

Unsupervised discovery of dynamic cell phenotypic states from transmitted light movies.

Identification of cell phenotypic states within heterogeneous populations, along with elucidation of their switching dynamics, is a central challenge in modern biology. Conventional single-cell analysis methods typically provide only indirect, static phenotypic readouts. Transmitted light images, on the other hand, provide direct morphological readouts and can be acquired over time to provide a rich data source for dynamic cell phenotypic state identification. Here, we describe an end-to-end deep learning platform, UPSIDE (Unsupervised Phenotypic State IDEntification), for discovering cell states and their dynamics from transmitted light movies. UPSIDE uses the variational auto-encoder architecture to learn latent cell representations, which are then clustered for state identification, decoded for feature interpretation, and linked across movie frames for transition rate inference. Using UPSIDE, we identified distinct blood cell types in a heterogeneous dataset. We then analyzed movies of patient-derived acute myeloid leukemia cells, from which we identified stem-cell associated morphological states as well as the transition rates to and from these states. UPSIDE opens up the use of transmitted light movies for systematic exploration of cell state heterogeneity and dynamics in biology and medicine.

Spatial genomics enables multi-modal study of clonal heterogeneity in tissues.

The state and behaviour of a cell can be influenced by both genetic and environmental factors. In particular, tumour progression is determined by underlying genetic aberrations1-4 as well as the makeup of the tumour microenvironment5,6. Quantifying the contributions of these factors requires new technologies that can accurately measure the spatial location of genomic sequence together with phenotypic readouts. Here we developed slide-DNA-seq, a method for capturing spatially resolved DNA sequences from intact tissue sections. We demonstrate that this method accurately preserves local tumour architecture and enables thede novo discovery of distinct tumour clones and their copy number alterations. We then apply slide-DNA-seq to a mouse model ofmetastasis and a primary human cancer, revealing that clonal populations are confined to distinct spatial regions. Moreover, through integration with spatial transcriptomics, we uncover distinct sets of genes that are associated with clone-specific genetic aberrations, the local tumour microenvironment, or both. Together, this multi-modal spatial genomics approach provides a versatile platform for quantifying how cell-intrinsic and cell-extrinsic factors contribute to gene expression, protein abundance and other cellular phenotypes.

Harnessing CD8+CD28- Regulatory T Cells as a Tool to Treat Autoimmune Disease.

T regulatory cell therapy presents a novel therapeutic strategy for patients with autoimmune diseases or who are undergoing transplantation. At present, the CD4+ Treg population has been extensively characterized, as a result of defined phenotypic and functional readouts. In this review article, we discuss the development and biology of CD8+ Tregs and their role in murine and human disease indications. A subset of CD8+ Tregs that lack the surface expression of CD28 (CD8+CD28− Treg) has proved efficacious in preclinical models. CD8+CD28− Tregs are present in healthy individuals, but their impaired functionality in disease renders them less effective in mediating immunosuppression. We primarily focus on harnessing CD8+ Treg cell therapy in the clinic to support current treatment for patients with autoimmune or inflammatory conditions.

Quantitative high-throughput phenotypic screening for environmental estrogens using the E-Morph Screening Assay in combination with in silico predictions

BackgroundExposure to environmental chemicals that interfere with normal estrogen function can lead to adverse health effects, including cancer. High-throughput screening (HTS) approaches facilitate the efficient identification and characterization of such substances. ObjectivesWe recently described the development of the E-Morph Assay, which measures changes at adherens junctions as a clinically-relevant phenotypic readout for estrogen receptor (ER) alpha signaling activity. Here, we describe its further development and application for automated robotic HTS. MethodsUsing the advanced E-Morph Screening Assay, we screened a substance library comprising 430 toxicologically-relevant industrial chemicals, biocides, and plant protection products to identify novel substances with estrogenic activities. Based on the primary screening data and the publicly available ToxCast dataset, we performed an insilico similarity search to identify further substances with potential estrogenic activity for follow-up hit expansion screening, and built seven insilico ER models using the conformal prediction (CP) framework to evaluate the HTS results. ResultsThe primary and hit confirmation screens identified 27 ‘known’ estrogenic substances with potencies correlating very well with the published ToxCast ER Agonist Score (r=+0.95). We additionally detected potential ‘novel’ estrogenic activities for 10 primary hit substances and for another nine out of 20 structurally similar substances from insilico predictions and follow-up hit expansion screening. The concordance of the E-Morph Screening Assay with the ToxCast ER reference data and the generated CP ER models was 71% and 73%, respectively, with a high predictivity for ER active substances of up to 87%, which is particularly important for regulatory purposes. DiscussionThese data provide a proof-of-concept for the combination of in vitro HTS approaches with insilico methods (similarity search, CP models) for efficient analysis of large substance libraries in order to prioritize substances with potential estrogenic activity for subsequent testing against higher tier human endpoints.

Comparison of electrophysiological and motility assays to study anthelmintic effects in Caenorhabditis elegans

Currently, only a few chemical drug classes are available to control the global burden of nematode infections in humans and animals. Most of these drugs exert their anthelmintic activity by interacting with proteins such as ion channels, and the nematode neuromuscular system remains a promising target for novel intervention strategies. Many commonly-used phenotypic readouts such as motility provide only indirect insight into neuromuscular function and the site(s) of action of chemical compounds. Electrophysiological recordings provide more specific information but are typically technically challenging and lack high throughput for drug discovery. Because drug discovery relies strongly on the evaluation and ranking of drug candidates, including closely related chemical derivatives, precise assays and assay combinations are needed for capturing and distinguishing subtle drug effects.Past studies show that nematode motility and pharyngeal pumping (feeding) are inhibited by most anthelmintic drugs. Here we compare two microfluidic devices (“chips”) that record electrophysiological signals from the nematode pharynx (electropharyngeograms; EPGs) ─ the ScreenChip™ and the 8-channel EPG platform ─ to evaluate their respective utility for anthelmintic research. We additionally compared EPG data with whole-worm motility measurements obtained with the wMicroTracker instrument. As references, we used three macrocyclic lactones (ivermectin, moxidectin, and milbemycin oxime), and levamisole, which act on different ion channels. Drug potencies (IC50 and IC95 values) from concentration-response curves, and the time-course of drug effects, were compared across platforms and across drugs. Drug effects on pump timing and EPG waveforms were also investigated. These experiments confirmed drug-class specific effects of the tested anthelmintics and illustrated the relative strengths and limitations of the different assays for anthelmintic research.

Multimodal FACED imaging for large-scale single-cell morphological profiling

Free-space angular-chirp-enhanced delay (FACED) is an ultrafast laser-scanning technique that allows for high imaging speed at the scale orders of magnitude greater than the current technologies. However, this speed advantage has only been restricted to bright-field and fluorescence imaging—limiting the variety of image contents and hindering its applicability in image-based bioassay, which increasingly demands rich phenotypic readout at a large scale. Here, we present a new high-speed quantitative phase imaging (QPI) based on time-interleaved phase-gradient FACED image detection. We further integrate this system with a microfluidic flow cytometer platform that enables synchronized and co-registered single-cell QPI and fluorescence imaging at an imaging throughput of 77 000 cells/s with sub-cellular resolution. Combined with deep learning, this platform empowers comprehensive image-based profiling of single-cell biophysical phenotypes that can offer not only sufficient label-free power for cell-type classification but also cell-cycle phase tracking with high accuracy comparable to the gold-standard fluorescence method. This platform further enables correlative, compartment-specific single-cell analysis of the spatially resolved biophysical profiles at the throughput inaccessible with existing QPI methods. The high imaging throughput and content given by this multimodal FACED imaging system could open new opportunities in image-based single-cell analysis, especially systematic analysis that correlates the biophysical and biochemical information of cells, and provide new mechanistic insights into biophysical heterogeneities in many biological processes.

LMK235, a small molecule inhibitor of HDAC4/5, protects dopaminergic neurons against neurotoxin- and α-synuclein-induced degeneration in cellular models of Parkinson's disease

Epigenetic modifications in neurodegenerative disease are under investigation for their roles in disease progression. Alterations in acetylation rates of certain Parkinson's disease (PD)-linked genes have been associated with the pathological progression of this disorder. In light of this, and given the lack of disease-modifying therapies for PD, HDAC inhibitors (HDIs) are under consideration as potential pharmacological agents. The neuroprotective effects of pan-HDACs and some class-specific inhibitors have been tested in in vivo and in vitro models of PD, with varying outcomes. Here we used gene co-expression analysis to identify HDACs that are associated with human dopaminergic (DA) neuron development. We identified HDAC3, HDAC5, HDAC6 and HDAC9 as being highly correlated with the DA markers, SLC6A3 and NR4A2. RT-qPCR revealed that mRNA expression of these HDACs exhibited similar temporal profiles during embryonic mouse midbrain DA (mDA) neuron development. We tested the neuroprotective potential of a number of class-specific small molecule HDIs on human SH-SY5Y cells, using neurite growth as a phenotypic readout of neurotrophic action. Neither the class I-specific HDIs, RGFP109 and RGFP966, nor the HDAC6 inhibitor ACY1215, had significant effects on neurite outgrowth. However, the class IIa HDI, LMK235 (a HDAC4/5 inhibitor), significantly increased histone acetylation and neurite outgrowth. We found that LMK235 increased BMP-Smad-dependent transcription in SH-SY5Y cells and that this was required for its neurite growth-promoting effects on SH-SY5Y cells and on DA neurons in primary cultures of embryonic day (E) 14 rat ventral mesencephalon (VM). These effects were also seen in SH-SY5Y cells transfected with HDAC5 siRNA. Furthermore, LMK235 treatment exerted neuroprotective effects against degeneration induced by the DA neurotoxin 1-methyl-4-phenylpyridinium (MPP+), in both SH-SY5Y cells and cultured DA neurons. Treatment with LMK235 was also neuroprotective against axonal degeneration induced by overexpression of wild-type (WT) or A53T mutant α-synuclein in both SH-SY5Y cells and primary cultures of DA neurons. In summary, these data show the neuroprotective potential of the class IIa HDI, LMK235, in cell models of relevance to PD.

Differential levels of IFNα subtypes in autoimmunity and viral infection

Type I interferons are essential for host response to viral infections, while dysregulation of their response can result in autoinflammation or autoimmunity. Among IFNα (alpha) responses, 13 subtypes exist that signal through the same receptor, but have been reported to have different effector functions. However, the lack of available tools for discriminating these closely related subtypes, in particular at the protein level, has restricted the study of their differential roles in disease. We developed a digital ELISA with specificity and high sensitivity for the IFNα2 subtype. Application of this assay, in parallel with our previously described pan-IFNα assay, allowed us to study different IFNα protein responses following cellular stimulation and in diverse patient cohorts. We observed different ratios of IFNα protein responses between viral infection and autoimmune patients. This analysis also revealed a small percentage of autoimmune patients with high IFNα2 protein measurements but low pan-IFNα measurements. Correlation with an ISG score and functional activity showed that in this small sub group of patients, IFNα2 protein measurements did not reflect its biological activity. This unusual phenotype was partly explained by the presence of anti-IFNα auto-antibodies in a subset of autoimmune patients. This study reports ultrasensitive assays for the study of IFNα proteins in patient samples and highlights the insights that can be obtained from the use of multiple phenotypic readouts in translational and clinical studies.

Cell-based siRNA screens highlight triple-negative breast cancer cell epigenetic vulnerability

<p><strong>Background:</strong> Triple-negative breast cancer (TNBC) is a heterogeneous disease defined by ER-, PR- and HER2-negative phenotype and in most cases, a relatively aggressive clinical behaviour. The lack of specific targeted therapies and low efficiency of currently available chemotherapies spurred several clinical trials in the last few years. Despite encouraging results, TNBC still remains a major unmet medical need that prompted us to explore the role of 863 epigenetic modulators in TNBC cell survival.</p><p><strong>Methods:</strong> A comprehensive siRNA library was screened to explore the role of known epigenetic modulators in TNBC cell viability and growth. The knock-down effect was evaluated for 863 epigenetic genes using 4 siRNAs/gene in two TNBC and a non-TNBC cell lines using ATP-based luminescence and nuclei count image-based assays. Considering siRNA off-target effects, four analysis methods including a classical threshold-based analysis and three ranking methods were applied to determine on-target hits for each screen readout. Hit genes common to both phenotypic readouts highlighted strong epigenetic players involved in TNBC cell survival.</p><p><strong>Results:</strong> Overall, knock-down of many epigenetic modulator genes mitigates cell survival in TNBC and a non-TNBC cell lines depicted from both phenotypic readouts. Interestingly, ranking-based analysis confirmed hit genes identified in threshold-based analysis and also revealed additional hits enabling us to confirm CDK1 and KMT5A as important regulators in TNBC cell viability and growth. Surprisingly, CHAF1A appeared as a new candidate gene involved in TNBC cell survival.</p><p><strong>Conclusions:</strong> Taken together, siRNA epigenetic screening results identified CHAF1A as a novel regulator of TNBC cell survival.</p>

Efficient CRISPR/Cas9 mutagenesis for neurobehavioral screening in adult zebrafish

Abstract Adult zebrafish are widely used to interrogate mechanisms of disease development and tissue regeneration. Yet, the prospect of large-scale genetics in adult zebrafish has traditionally faced a host of biological and technical challenges, including inaccessibility of adult tissues to high-throughput phenotyping and the spatial and technical demands of adult husbandry. Here, we describe an experimental pipeline that combines high-efficiency CRISPR/Cas9 mutagenesis with functional phenotypic screening to identify genes required for spinal cord repair in adult zebrafish. Using CRISPR/Cas9 dual-guide ribonucleic proteins, we show selective and combinatorial mutagenesis of 17 genes at 28 target sites with efficiencies exceeding 85% in adult F0 “crispants”. We find that capillary electrophoresis is a reliable method to measure indel frequencies. Using a quantifiable behavioral assay, we identify seven single- or duplicate-gene crispants with reduced functional recovery after spinal cord injury. To rule out off-target effects, we generate germline mutations that recapitulate the crispant regeneration phenotypes. This study provides a platform that combines high-efficiency somatic mutagenesis with a functional phenotypic readout to perform medium- to large-scale genetic studies in adult zebrafish.

12621 Targeted Chemical-Genetic Screen Platform for Identifying Drug Modes-of-Action

ABSTRACT IMPACT: The key to advancing precision medicine is to deepen our understanding of drug modes-of-action (MOA). This project aims to develop a novel method for predicting MOA of potential drug compounds, providing an experimental and computational platform for more efficient drug discovery. OBJECTIVES/GOALS: To develop (1) a targeted CRISPR-Cas9 chemical-genetic screen approach, and (2) a computational method to predict drug mode-of-action from chemical-genetic interaction profiles. METHODS/STUDY POPULATION: Screening drugs against a gene deletion library can identify knockouts that modulate drug sensitivity. These chemical-genetic interaction (CGI) screens can be performed in human cell lines using a pooled lentiviral CRISPR-Cas9 approach to assess drug sensitivity/resistance of single-gene knockouts across the human genome. A targeted, rather than genome-wide, library can enable scaling these screens across many drugs.CGI profiles can be derived from phenotypic screen readouts. These profiles are analogous to genetic interaction (GI) profiles, which represent sensitivity/resistance of gene knockouts to a second gene knockout rather than a drug. To computationally predict a drug’s genetic target, we leverage the property that a drug’s CGI profile will be similar to its target’s GI profile. RESULTS/ANTICIPATED RESULTS: Five proof-of-principle screens will be conducted with compounds that have existing genome-wide profiles and well-characterized MOA. I will generate CGI profiles for these five compounds and identify genes that are drug-sensitizers or drug-suppressors. I will then evaluate whether targeted library screens can recapitulate the CGIs found in genome-wide screens. Finally, I will develop a computational tool to integrate these CGI profiles with GI profiles (derived from another project) to predict gene-level and bioprocess-level drug targets. These predictions (from both targeted and genome-wide profiles) will be benchmarked against a drug-target and drug-bioprocess standard. DISCUSSION/SIGNIFICANCE OF FINDINGS: This work will develop a scalable, targeted chemical-genetic screen approach to discovering how putative therapeutics work. The targeted screen workflow provides a method for higher-throughput drug screening. The computational pipeline provides a powerful tool for exploring the MOA of uncharacterized drugs or repurposing FDA-approved drugs.

Abstract PO-048: Visual nucleotyping identifies chromatin phenotypes triggered by genome editing

Abstract Optical microscopy has the potential to provide rapid phenotypic readout of cellular states. Here we show that automated optical phenotyping of nuclei is capable of rapidly and reliably discriminating the effects of targeted mutations to chromatin remodelers, which are major players in oncogenesis and targets for therapeutic intervention. Smarca4 is a chromatin remodeler frequently mutated in cancer. We investigated whether nuclear morphometry and concomitant supervised machine learning approaches on standard optical microscopy images of nuclear-DNA stained cells could reliably discern Smarca4-/- lung cancer cells from isogenic Smarca4+/+ clones that were derived by precision genome editing. We used TALEN-based editing on A549 lung cancer cells to generate isogenic Smarca4+/+ clones, performed conventional 2D 40x optical imaging of DAPI-stained cell nuclei from parental (Smarca4-/-) and edited (Smarca4+/+) cells, quantified a set of numerical parameters (features) that reflected various intuitive attributes of nuclear morphology and chromatin texture of the cell nuclei, and finally tested the efficacy of supervised machine learning algorithms containing these features to distinguish between the two cell categories. We found that the parental and edited A549 cells exhibited similar nuclear size and shape attributes but showed measurable differences in subtle chromatin texture. Additionally, classifiers based on these chromatin texture features were able to accurately distinguish the smarca4-rescued cells from the smarca4-mutant cells. Our results thus demonstrate the promise of quantitative chromatin phenotyping for genome editing applications in oncology. Citation Format: Vivek Nandakumar, Sandra Stehling-Sun, William Kerwin, Alister Funnell, John Stamatoyannopoulos. Visual nucleotyping identifies chromatin phenotypes triggered by genome editing [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-048.

High-Content Analysis-Based Sensitivity Prediction and Novel Therapeutics Screening for c-Met-Addicted Glioblastoma.

Simple SummaryReal-time ex vivo drug testing tailors individual therapeutics based on predicted drug responses. Most technologies to date rely on conventional drug screening that provides low confidence data. Here, we present high-content analysis-based drug testing of glioblastoma patients to identify the right glioblastoma patients for a given drug. This generates multi-parameter biomarker and phenotype readouts providing a better reliability of the assay. Additionally, we showed a high-content drug repurposing screen and defined a new c-Met-inhibiting function of the CDK4/6 inhibitor Abemaciclib. Large-scale high throughput screening results demonstrate that Abemaciclib sensitivity in glioblastoma patients is highly correlated with the c-Met inhibitors sensitivity, further supporting the accuracy of the platform and important new clinical implications regarding multiple functions of Abemaciclib.(1) Background: Recent advances in precision oncology research rely on indicating specific genetic alterations associated with treatment sensitivity. Developing ex vivo systems to identify cancer patients who will respond to a specific drug remains important. (2) Methods: cells from 12 patients with glioblastoma were isolated, cultured, and subjected to high-content screening. Multi-parameter analyses assessed the c-Met level, cell viability, apoptosis, cell motility, and migration. A drug repurposing screen and large-scale drug sensitivity screening data across 59 cancer cell lines and patient-derived cells were obtained from 125 glioblastoma samples. (3) Results: High-content analysis of patient-derived cells provided robust and accurate drug responses to c-Met-targeted agents. Only the cells of one glioblastoma patient (PDC6) showed elevated c-Met level and high susceptibility to the c-Met inhibitors. Multi-parameter image analysis also reflected a decreased c-Met expression and reduced cell growth and motility by a c-Met-targeting antibody. In addition, a drug repurposing screen identified Abemaciclib as a distinct CDK4/6 inhibitor with a potent c-Met-inhibitory function. Consistent with this, we present large-scale drug sensitivity screening data showing that the Abemaciclib response correlates with the response to c-Met inhibitors. (4) Conclusions: Our study provides a new insight into high-content screening platforms supporting drug sensitivity prediction and novel therapeutics screening.

High Content Analysis Across Signaling Modulation Treatments for Subcellular Target Identification Reveals Heterogeneity in Cellular Response.

Cellular phenotypes on bioactive compound treatment are a result of the downstream targets of the respective treatment. Here, a computational approach is taken for downstream subcellular target identification to understand the basis of the cellular response. This response is a readout of cellular phenotypes captured from cell-painting-based light microscopy images. The readouts are morphological profiles measured simultaneously from multiple cellular organelles. Cellular profiles generated from roughly 270 diverse treatments on bone cancer cell line form the high content screen used in this study. Phenotypic diversity across these treatments is demonstrated, depending on the image-based phenotypic profiles. Furthermore, the impact of the treatments on specific organelles and associated organelle sensitivities are determined. This revealed that endoplasmic reticulum has a higher likelihood of being targeted. Employing multivariate regression overall cellular response is predicted based on fewer organelle responses. This prediction model is validated against 1,000 new candidate compounds. Different compounds despite driving specific modulation outcomes elicit a varying effect on cellular integrity. Strikingly, this confirms that phenotypic responses are not conserved that enables quantification of signaling heterogeneity. Agonist-antagonist signaling pairs demonstrate switch of the targets in the cascades hinting toward evidence of signaling plasticity. Quantitative analysis of the screen has enabled the identification of these underlying signatures. Together, these image-based profiling approaches can be employed for target identification in drug and diseased states and understand the hallmark of cellular response.