Image-based Phenotypic Profiling Research Articles

Anomaly detection for high-content image-based phenotypic cell profiling.

High-content image-based phenotypic profiling combines automated microscopy and analysis to identify phenotypic alterations in cell morphology and provide insight into the cell's physiological state. Classical representations of the phenotypic profile can not capture the full underlying complexity in cell organization, while recent weakly machine-learning based representation-learning methods are hard to biologically interpret. We used the abundance of control wells to learn the in-distribution of control experiments and use it to formulate a self-supervised reconstruction anomaly-based representation that encodes the intricate morphological inter-feature dependencies while preserving the representation interpretability. The performance of our anomaly-based representations was evaluated for downstream tasks with respect to two classical representations across four public Cell Painting datasets. Anomaly-based representations improved reproducibility, Mechanism of Action classification, and complemented classical representations. Unsupervised explainability of autoencoder-based anomalies identified specific inter-feature dependencies causing anomalies. The general concept of anomaly-based representations can be adapted to other applications in cell biology.

Abstract LB238: Image-based phenotypic profiling of a chemogenomic screening library identifies nobel targets of known inhibitors

Abstract The gene encoding epidermal growth factor receptor (EGFR) is a major driver gene in cancer. Many drugs targeting EGFR-associated molecules have been developed, yet many have failed in clinical trials due to a lack of efficacy and/or unexpected side effects. In this study, I used image-based phenotypic profiling to screen a pharmacologically active compound library with the aim of identifying new druggable targets in the EGFR pathway. As anticipated, the phenotypic screen identified compounds that produce phenotypes resulting from targeting a known specific molecule or pathway. The assay also showed that compounds with diverse known mechanisms of action produced similar, EGFR-related cellular phenotypes. Biochemical assays revealed that those compounds share a previously unappreciated common target/pathway, showing that the image-based assay can identify new target molecules that are independent of the compound’s known target. Citation Format: Kenji Tanabe. Image-based phenotypic profiling of a chemogenomic screening library identifies nobel targets of known inhibitors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB238.

Senescence-associated morphological profiles (SAMPs): an image-based phenotypic profiling method for evaluating the inter and intra model heterogeneity of senescence.

Senescence occurs in response to a number of damaging stimuli to limit oncogenic transformation and cancer development. As no single, universal senescence marker has been discovered, the confident classification of senescence induction requires the parallel assessment of a series of hallmarks. Therefore, there is a growing need for “first-pass” tools of senescence identification to streamline experimental workflows and complement conventional markers.Here, we utilise a high content, multidimensional phenotypic profiling-based approach, to assess the morphological profiles of senescent cells induced via a range of stimuli. In the context of senescence, we refer to these as senescence-associated morphological profiles (SAMPs), as they facilitate distinction between senescent and proliferating cells. The complexity of the profiles generated also allows exploration of the heterogeneity both between models of senescence and within an individual senescence model, providing a level of insight at the single cell level. Furthermore, we also demonstrate that these models are applicable to the assessment of senescence in vivo, which remains a key challenge for the field. Therefore, we believe SAMPs has the potential to serve as a useful addition in the repertoire of senescence researchers, either as a first-pass tool or as part of the established senescence hallmarks.

Live-Imaging Readouts and Cell Models for Phenotypic Profiling of Mitochondrial Function.

Mitochondria are best known as the powerhouses of the cells but their cellular role goes far beyond energy production; among others, they have a pivotal function in cellular calcium and redox homeostasis. Mitochondrial dysfunction is often associated with severe and relatively rare disorders with an unmet therapeutic need. Given their central integrating role in multiple cellular pathways, mitochondrial dysfunction is also relevant in the pathogenesis of various other, more common, human pathologies. Here we discuss how live-cell high content microscopy can be used for image-based phenotypic profiling to assess mitochondrial (dys) function. From this perspective, we discuss a selection of live-cell fluorescent reporters and imaging strategies and discuss the pros/cons of human cell models in mitochondrial research. We also present an overview of live-cell high content microscopy applications used to detect disease-associated cellular phenotypes and perform cell-based drug screening.

Image-Based Profiling Can Discriminate the Effects of Inhibitors on Signaling Pathways under Differential Ligand Stimulation

A major advantage of image-based phenotypic profiling of compounds is that numerous image features can be sampled and quantitatively evaluated in an unbiased way. However, since this assay is a discovery-oriented screening, it is difficult to determine the optimal experimental setup in advance. In this study, we examined whether variable cellular stimulation affects the efficacy of the image-based profiling of compounds. Seven different epidermal growth factor receptor (EGFR) ligands were used, and the expression of EGFR signaling molecules was monitored at various time points. Significant quantitative differences in image features were detected among the differentially treated samples. Next, 14 different compounds that affect EGFR signaling were profiled. Nearly half of the compounds were classified into distinct clusters, irrespective of differential ligand stimulation. The results suggest that image-based phenotypic profiling is quite robust in its ability to predict compound interaction with its target. Although this method will have to be validated in other experimental systems, the robustness of image-based compound profiling demonstrated in this work provides a valid basis for further study and its extended application.

Time series modeling of live-cell shape dynamics for image-based phenotypic profiling.

Live-cell imaging can be used to capture spatio-temporal aspects of cellular responses that are not accessible to fixed-cell imaging. As the use of live-cell imaging continues to increase, new computational procedures are needed to characterize and classify the temporal dynamics of individual cells. For this purpose, here we present the general experimental-computational framework SAPHIRE (Stochastic Annotation of Phenotypic Individual-cell Responses) to characterize phenotypic cellular responses from time series imaging datasets. Hidden Markov modeling is used to infer and annotate morphological state and state-switching properties from image-derived cell shape measurements. Time series modeling is performed on each cell individually, making the approach broadly useful for analyzing asynchronous cell populations. Two-color fluorescent cells simultaneously expressing actin and nuclear reporters enabled us to profile temporal changes in cell shape following pharmacological inhibition of cytoskeleton-regulatory signaling pathways. Results are compared with existing approaches conventionally applied to fixed-cell imaging datasets, and indicate that time series modeling captures heterogeneous dynamic cellular responses that can improve drug classification and offer additional important insight into mechanisms of drug action. The software is available at http://saphire-hcs.org.

Comparison of Methods for Image-Based Profiling of Cellular Morphological Responses to Small-Molecule Treatment

Quantitative microscopy has proven a versatile and powerful phenotypic screening technique. Recently, image-based profiling has shown promise as a means for broadly characterizing molecules’ effects on cells in several drug-discovery applications, including target-agnostic screening and predicting a compound’s mechanism of action (MOA). Several profiling methods have been proposed, but little is known about their comparative performance, impeding the wider adoption and further development of image-based profiling. We compared these methods by applying them to a widely applicable assay of cultured cells and measuring the ability of each method to predict the MOA of a compendium of drugs. A very simple method that is based on population means performed as well as methods designed to take advantage of the measurements of individual cells. This is surprising because many treatments induced a heterogeneous phenotypic response across the cell population in each sample. Another simple method, which performs factor analysis on the cellular measurements before averaging them, provided substantial improvement and was able to predict MOA correctly for 94% of the treatments in our ground-truth set. To facilitate the ready application and future development of image-based phenotypic profiling methods, we provide our complete ground-truth and test data sets, as well as open-source implementations of the various methods in a common software framework.