Morphological profiling of small molecules

LIN28-Targeting Chromenopyrazoles and Tetrahydroquinolines Induced Cellular Morphological Changes and Showed High Biosimilarity with BRD PROTACs

The probing of small molecules with heterocyclic scaffolds covering unexplored chemical space and the evaluation of their biological relevance are essential parts of forward chemical genetics approaches and for the development of potential small-molecule therapeutics. In this study, we profiled sets of chromenopyrazoles (CMPs) and tetrahydroquinolines (THQs), originally developed to target the protein-RNA interaction of LIN28-let-7, in a cell painting assay (CPA) measuring cellular morphological changes. Selected LIN28-inactive CMPs and THQs induced cellular morphological changes to different extents. The most CPA-active CMPs 2 and 3 exhibited high bio-similarity with the LCH and BET clusters, while the most CPA-active THQs 13 and 20 indicated a mechanism of action beyond the currently established biosimilarity clusters. Overall, this work demonstrated that CPA is useful in revealing "hidden" biological targets and mechanisms of action for biologically inactive small molecules, which are CMPs and THQs targeting the RNA-binding protein LIN28 in this case, evaluated in target-based strategies. When compared with annotated reference compounds, CMP 3 exhibited a high biosimilarity with the dual BRD7/9 degrading PROTAC VZ185, suggesting that CPA could potentially function as a new phenotypic approach to identify degrader molecules.

Cytotoxic and proliferation-inhibitory activity of natural and synthetic fungal tropolone sesquiterpenoids in various cell lines

Fungal specialized metabolites are known for their potent biological activities, among which tropolone sesquiterpenoids (TS) stand out for their diverse bioactivities. Here, we report cytotoxic and proliferation inhibitory effects of the recently discovered TS compounds 4-hydroxyxenovulene B and 4-dihydroxy norpycnidione, and the structurally related 4-hydroxy norxenovulene B and xenovulene B. Inhibition of metabolic activity after TS treatment was observed in Jurkat, PC-3 and FAIK3-5 cells, whereas MDA-MB-231 cells were unresponsive to treatment. Structurally similar epolones were shown to induce erythropoietin (EPO). Therefore, FAIK3-5 cells, which can naturally produce EPO, were applied to test the compounds in this regard. While no effect on EPO production in FAIK3-5 cells could be demonstrated, effects on their proliferation, viability, and morphology were observed depending on the presence of tropolone moieties in the molecules. Our study underlines the importance of relevant cell models for bioactivity testing of compounds with unknown mechanisms of action.

Insights into the Identification of iPSC- and Monocyte-Derived Macrophage-Polarizing Compounds by AI-Fueled Cell Painting Analysis Tools

Macrophage polarization critically contributes to a multitude of human pathologies. Hence, modulating macrophage polarization is a promising approach with enormous therapeutic potential. Macrophages are characterized by a remarkable functional and phenotypic plasticity, with pro-inflammatory (M1) and anti-inflammatory (M2) states at the extremes of a multidimensional polarization spectrum. Cell morphology is a major indicator for macrophage activation, describing M1(-like) (rounded) and M2(-like) (elongated) states by different cell shapes. Here, we introduced cell painting of macrophages to better reflect their multifaceted plasticity and associated phenotypes beyond the rigid dichotomous M1/M2 classification. Using high-content imaging, we established deep learning- and feature-based cell painting image analysis tools to elucidate cellular fingerprints that inform about subtle phenotypes of human blood monocyte-derived and iPSC-derived macrophages that are characterized as screening surrogate. Moreover, we show that cell painting feature profiling is suitable for identifying inter-donor variance to describe the relevance of the morphology feature 'cell roundness' and dissect distinct macrophage polarization signatures after stimulation with known biological or small-molecule modulators of macrophage (re-)polarization. Our novel established AI-fueled cell painting analysis tools provide a resource for high-content-based drug screening and candidate profiling, which set the stage for identifying novel modulators for macrophage (re-)polarization in health and disease.

Imaging-based profiling for elucidation of antibacterial mechanisms of action

In this review, we aim to summarize experimental data and approaches to identifying cellular targets or mechanisms of action of antibacterials based on imaging techniques. Imaging-based profiling methods, such as bacterial cytological profiling, dynamic bacterial morphology imaging, and others, have become a useful research tool for mechanistic studies of new antibiotics as well as combinations with conventional ones and other therapeutic options. The main methodological and experimental details and obtained results are summarized and discussed. The review covers the literature up to February 2024.

Scalable, compressed phenotypic screening using pooled perturbations

High-throughput phenotypic screens using biochemical perturbations and high-content readouts are constrained by limitations of scale. To address this, we establish a method of pooling exogenous perturbations followed by computational deconvolution to reduce required sample size, labor and cost. We demonstrate the increased efficiency of compressed experimental designs compared to conventional approaches through benchmarking with a bioactive small-molecule library and a high-content imaging readout. We then apply compressed screening in two biological discovery campaigns. In the first, we use early-passage pancreatic cancer organoids to map transcriptional responses to a library of recombinant tumor microenvironment protein ligands, uncovering reproducible phenotypic shifts induced by specific ligands distinct from canonical reference signatures and correlated with clinical outcome. In the second, we identify the pleotropic modulatory effects of a chemical compound library with known mechanisms of action on primary human peripheral blood mononuclear cell immune responses. In sum, our approach empowers phenotypic screens with information-rich readouts to advance drug discovery efforts and basic biological inquiry.

Profiling Cellular Morphological Changes Induced by Dual-Targeting PROTACs of Aurora Kinase and RNA-Binding Protein YTHDF2

Proteolysis targeting chimeras (PROTACs) are new chemical modalities that degrade proteins of interest, including established kinase targets and emerging RNA-binding proteins (RBPs). Whereas diverse sets of biochemical, biophysical and cellular assays are available for the evaluation and optimizations of PROTACs in understanding the involved ubiquitin-proteasome-mediated degradation mechanism and the structure-degradation relationship, a phenotypic method profiling the cellular morphological changes is rarely used. In this study, first, we reported the only examples of PROTACs degrading the mRNA-binding protein YTHDF2 via screening of multikinase PROTACs. Second, we reported the profiling of cellular morphological changes of the dual kinase- and RBP-targeting PROTACs using the unbiased cell painting assay (CPA). The CPA analysis revealed the high biosimilarity with the established aurora kinase cluster and annotated aurora kinase inhibitors, which reflected the association between YTHDF2 and the aurora kinase signaling network. Broadly, the results demonstrated that the cell painting assay can be a straightforward and powerful approach to evaluate PROTACs. Complementary to the existing biochemical, biophysical and cellular assays, CPA provided a new perspective in characterizing PROTACs at the cellular morphology.

Dispersion-free inertial focusing (DIF) for high-yield polydisperse micro-particle filtration and analysis

Inertial focusing excels at the precise spatial ordering and separation of microparticles by size within fluid flows. However, this advantage, resulting from its inherent size-dependent dispersion, could turn into a drawback that challenges applications requiring consistent and uniform positioning of polydisperse particles, such as microfiltration and flow cytometry. To overcome this fundamental challenge, we introduce Dispersion-Free Inertial Focusing (DIF). This new method minimizes particle size-dependent dispersion while maintaining the high throughput and precision of standard inertial focusing, even in a highly polydisperse scenario. We demonstrate a rule-of-thumb principle to reinvent an inertial focusing system and achieve an efficient focusing of particles ranging from 6 to 30 μm in diameter onto a single plane with less than 3 μm variance and over 95% focusing efficiency at highly scalable throughput (2.4-30 mL h-1) - a stark contrast to existing technologies that struggle with polydispersity. We demonstrated that DIF could be applied in a broad range of applications, particularly enabling high-yield continuous microparticle filtration and large-scale high-resolution single-cell morphological analysis of heterogeneous cell populations. This new technique is also readily compatible with the existing inertial microfluidic design and thus could unleash more diverse systems and applications.

Detection of a Mitochondrial Fragmentation and Integrated Stress Response Using the Cell Painting Assay

Mitochondria are cellular powerhouses and are crucial for cell function. However, they are vulnerable to internal and external perturbagens that may impair mitochondrial function and eventually lead to cell death. In particular, small molecules may impact mitochondrial function, and therefore, their influence on mitochondrial homeostasis is at best assessed early on in the characterization of biologically active small molecules and drug discovery. We demonstrate that unbiased morphological profiling by means of the cell painting assay (CPA) can detect mitochondrial stress coupled with the induction of an integrated stress response. This activity is common for compounds addressing different targets, is not shared by direct inhibitors of the electron transport chain, and enables prediction of mitochondrial stress induction for small molecules that are profiled using CPA.

Artificial intelligence for high content imaging in drug discovery

Artificial intelligence (AI) and high-content imaging (HCI) are contributing to advancements in drug discovery, propelled by the recent progress in deep neural networks. This review highlights AI's role in analysis of HCI data from fixed and live-cell imaging, enabling novel label-free and multi-channel fluorescent screening methods, and improving compound profiling. HCI experiments are rapid and cost-effective, facilitating large data set accumulation for AI model training. However, the success of AI in drug discovery also depends on high-quality data, reproducible experiments, and robust validation to ensure model performance. Despite challenges like the need for annotated compounds and managing vast image data, AI's potential in phenotypic screening and drug profiling is significant. Future improvements in AI, including increased interpretability and integration of multiple modalities, are expected to solidify AI and HCI's role in drug discovery.

A divergent intermediate strategy yields biologically diverse pseudo-natural products

The efficient exploration of biologically relevant chemical space is essential for the discovery of bioactive compounds. A molecular design principle that possesses both biological relevance and structural diversity may more efficiently lead to compound collections that are enriched in diverse bioactivities. Here the diverse pseudo-natural product (PNP) strategy, which combines the biological relevance of the PNP concept with synthetic diversification strategies from diversity-oriented synthesis, is reported. A diverse PNP collection was synthesized from a common divergent intermediate through developed indole dearomatization methodologies to afford three-dimensional molecular frameworks that could be further diversified via intramolecular coupling and/or carbon monoxide insertion. In total, 154 PNPs were synthesized representing eight different classes. Cheminformatic analyses showed that the PNPs are structurally diverse between classes. Biological investigations revealed the extent of diverse bioactivity enrichment of the collection in which four inhibitors of Hedgehog signalling, DNA synthesis, de novo pyrimidine biosynthesis and tubulin polymerization were identified from four different PNP classes.

Discovery of a Novel Pseudo-Natural Product Aurora Kinase Inhibitor Chemotype through Morphological Profiling

The pseudo-natural product (pseudo-NP) concept aims to combine NP fragments in arrangements that are not accessible through known biosynthetic pathways. The resulting compounds retain the biological relevance of NPs but are not yet linked to bioactivities and may therefore be best evaluated by unbiased screening methods resulting in the identification of unexpected or unprecedented bioactivities. Herein, various NP fragments are combined with a tricyclic core connectivity via interrupted Fischer indole and indole dearomatization reactions to provide a collection of highly three-dimensional pseudo-NPs. Target hypothesis generation by morphological profiling via the cell painting assay guides the identification of an unprecedented chemotype for Aurora kinase inhibition with both its relatively highly 3D structure and its physicochemical properties being very different from known inhibitors. Biochemical and cell biological characterization indicate that the phenotype identified by the cell painting assay corresponds to the inhibition of Aurora kinase B.

Morphological profiling for drug discovery in the era of deep learning

Morphological profiling is a valuable tool in phenotypic drug discovery. The advent of high-throughput automated imaging has enabled the capturing of a wide range of morphological features of cells or organisms in response to perturbations at the single-cell resolution. Concurrently, significant advances in machine learning and deep learning, especially in computer vision, have led to substantial improvements in analyzing large-scale high-content images at high throughput. These efforts have facilitated understanding of compound mechanism of action, drug repurposing, characterization of cell morphodynamics under perturbation, and ultimately contributing to the development of novel therapeutics. In this review, we provide a comprehensive overview of the recent advances in the field of morphological profiling. We summarize the image profiling analysis workflow, survey a broad spectrum of analysis strategies encompassing feature engineering- and deep learning-based approaches, and introduce publicly available benchmark datasets. We place a particular emphasis on the application of deep learning in this pipeline, covering cell segmentation, image representation learning, and multimodal learning. Additionally, we illuminate the application of morphological profiling in phenotypic drug discovery and highlight potential challenges and opportunities in this field.

Meta-learning-based Inductive logistic matrix completion for prediction of kinase inhibitors

Protein kinases become an important source of potential drug targets. Developing new, efficient, and safe small-molecule kinase inhibitors has become an important topic in the field of drug research and development. In contrast with traditional wet experiments which are time-consuming and expensive, machine learning-based approaches for predicting small molecule inhibitors for protein kinases are time-saving and cost-effective, which are highly desired for us. However, the issue of sample scarcity (known active and inactive compounds are usually limited for most kinases) poses a challenge to the research and development of machine learning-based kinase inhibitors' active prediction methods. To alleviate the data scarcity problem in the prediction of kinase inhibitors, in this study, we present a novel Meta-learning-based inductive logistic matrix completion method for the Prediction of Kinase Inhibitors (MetaILMC). MetaILMC adopts a meta-learning framework to learn a well-generalized model from tasks with sufficient samples, which can fast adapt to new tasks with limited samples. As MetaILMC allows the effective transfer of the prior knowledge learned from kinases with sufficient samples to kinases with a small number of samples, the proposed model can produce accurate predictions for kinases with limited data. Experimental results show that MetaILMC has excellent performance for prediction tasks of kinases with few-shot samples and is significantly superior to the state-of-the-art multi-task learning in terms of AUC, AUPR, etc., various performance metrics. Case studies also provided for two drugs to predict Kinase Inhibitory scores, further validating the proposed method's effectiveness and feasibility. SCIENTIFIC CONTRIBUTION: Considering the potential correlation between activity prediction tasks for different kinases, we propose a novel meta learning algorithm MetaILMC, which learns a prior of strong generalization capacity during meta-training from the tasks with sufficient training samples, such that it can be easily and quickly adapted to the new tasks of the kinase with scarce data during meta-testing. Thus, MetaILMC can effectively alleviate the data scarcity problem in the prediction of kinase inhibitors.

Chemical Proteomics and Morphological Profiling Revealing MYDGF as a Target for Synthetic Anticancer Macromolecules

Biodegradable guanidinium-functionalized polycarbonates kill cancer cells via membrane translocation without causing resistance after repeated use, but the exact molecular targets of the polycarbonates are unknown. Here, we investigate the protein targets of the polycarbonates through affinity-based protein profiling and report myeloid-derived growth factor (MYDGF) as the main protein target. Direct binding of the polycarbonates to MYDGF protein is validated through biolayer interferometry. MYDGF is overexpressed in a range of cancer cells, and knockdown of MYDGF is shown to reduce cell proliferation in cancer cells. Through morphological profiling, we also identify similarities in phenotypic effects of the functionalized polycarbonates with topoisomerase I inhibitors, MDM2 inhibitors, and phosphatidylinositol 3kinase inhibitors against cancer cells, suggesting a common mechanism through the PIK3/AKT pathway leading to apoptosis. These findings present the first macromolecular compound targeting MYDGF and may serve as an example for MYDGF modulation as a potential new target for macromolecular chemotherapeutic development.

Collective Synthesis of Sarpagine and Macroline Alkaloid-Inspired Compounds

Design strategies that can access natural-product-like chemical space in an efficient manner may facilitate the discovery of biologically relevant compounds. We have employed a divergent intermediate strategy to construct an indole alkaloid-inspired compound collection derived from two different molecular design principles, i.e. biology-oriented synthesis and pseudo-natural products. The divergent intermediate was subjected to acid-catalyzed or newly discovered Sn-mediated conditions to selectively promote intramolecular C- or N-acylation, respectively. After further derivatization, a collection totalling 84 compounds representing four classes was obtained. Morphological profiling via the cell painting assay coupled with a subprofile analysis showed that compounds derived from different design principles have different bioactivity profiles. The subprofile analysis suggested that a pseudo-natural product class is enriched in modulators of tubulin, and subsequent assays led to the identification of compounds that suppress in vitro tubulin polymerization and mitotic progression.

Synthetic Matching of Complex Monoterpene Indole Alkaloid Chemical Space

Monoterpene indole alkaloids (MIAs) are endowed with high structural and spatial complexity and characterized by diverse biological activities. Given this complexity-activity combination in MIAs, rapid and efficient access to chemical matter related to and with complexity similar to these alkaloids would be highly desirable, since such compound classes might display novel bioactivity. We describe the design and synthesis of a pseudo-natural product (pseudo-NP) collection obtained by the unprecedented combination of MIA fragments through complexity-generating transformations, resulting in arrangements not currently accessible by biosynthetic pathways. Cheminformatic analyses revealed that both the pseudo-NPs and the MIAs reside in a unique and common area of chemical space with high spatial complexity-density that is only sparsely populated by other natural products and drugs. Investigation of bioactivity guided by morphological profiling identified pseudo-NPs that inhibit DNA synthesis and modulate tubulin. These results demonstrate that the pseudo-NP collection occupies similar biologically relevant chemical space that Nature has endowed MIAs with.

Sensing of Antibiotic-Bacteria Interactions

Sensing of antibiotic-bacteria interactions is an important area of research that has gained significant attention in recent years. Antibiotic resistance is a major public health concern, and it is essential to develop new strategies for detecting and monitoring bacterial responses to antibiotics in order to maintain effective antibiotic development and antibacterial treatment. This review summarizes recent advances in sensing strategies for antibiotic-bacteria interactions, which are divided into two main parts: studies on the mechanism of action for sensitive bacteria and interrogation of the defense mechanisms for resistant ones. In conclusion, this review provides an overview of the present research landscape concerning antibiotic-bacteria interactions, emphasizing the potential for method adaptation and the integration of machine learning techniques in data analysis, which could potentially lead to a transformative impact on mechanistic studies within the field.

Evaluating the utility of brightfield image data for mechanism of action prediction

Fluorescence staining techniques, such as Cell Painting, together with fluorescence microscopy have proven invaluable for visualizing and quantifying the effects that drugs and other perturbations have on cultured cells. However, fluorescence microscopy is expensive, time-consuming, labor-intensive, and the stains applied can be cytotoxic, interfering with the activity under study. The simplest form of microscopy, brightfield microscopy, lacks these downsides, but the images produced have low contrast and the cellular compartments are difficult to discern. Nevertheless, by harnessing deep learning, these brightfield images may still be sufficient for various predictive purposes. In this study, we compared the predictive performance of models trained on fluorescence images to those trained on brightfield images for predicting the mechanism of action (MoA) of different drugs. We also extracted CellProfiler features from the fluorescence images and used them to benchmark the performance. Overall, we found comparable and largely correlated predictive performance for the two imaging modalities. This is promising for future studies of MoAs in time-lapse experiments for which using fluorescence images is problematic. Explorations based on explainable AI techniques also provided valuable insights regarding compounds that were better predicted by one modality over the other.

Morphological subprofile analysis for bioactivity annotation of small molecules

Fast prediction of the mode of action (MoA) for bioactive compounds would immensely foster bioactivity annotation in compound collections and may early on reveal off-targets in chemical biology research and drug discovery. Morphological profiling, e.g., using the Cell Painting assay, offers a fast, unbiased assessment of compound activity on various targets in one experiment. However, due to incomplete bioactivity annotation and unknown activities of reference compounds, prediction of bioactivity is not straightforward. Here we introduce the concept of subprofile analysis to map the MoA for both, reference and unexplored compounds. We defined MoA clusters and extracted cluster subprofiles that contain only a subset of morphological features. Subprofile analysis allows for the assignment of compounds to, currently, twelve targets or MoA. This approach enables rapid bioactivity annotation of compounds and will be extended to further clusters in the future.

Morphological Profiling Identifies the Motor Protein Eg5 as Cellular Target of Spirooxindoles

Oxindoles and iso-oxindoles are natural product-derived scaffolds that provide inspiration for the design and synthesis of novel biologically relevant compound classes. Notably, the spirocyclic connection of oxindoles with iso-oxindoles has not been explored by nature but promises to provide structurally related compounds endowed with novel bioactivity. Therefore, methods for their efficient synthesis and the conclusive discovery of their cellular targets are highly desirable. We describe a selective RhIII -catalyzed scaffold-divergent synthesis of spirooxindole-isooxindoles and spirooxindole-oxindoles from differently protected diazooxindoles and N-pivaloyloxy aryl amides which includes a functional group-controlled Lossen rearrangement as key step. Unbiased morphological profiling of a corresponding compound collection in the Cell Painting assay efficiently identified the mitotic kinesin Eg5 as the cellular target of the spirooxindoles, defining a unique Eg5 inhibitor chemotype.

Scaffold Remodelling of Diazaspirotricycles Enables Synthesis of Diverse sp3 -Rich Compounds With Distinct Phenotypic Effects

A 'top down' scaffold remodelling approach to library synthesis was applied to spirotricyclic ureas prepared by a complexity-generating oxidative dearomatisation. Eighteen structurally-distinct, sp3 -rich scaffolds were accessed from the parent tricycle through ring addition, cleavage and expansion strategies. Biological screening of a small compound library based on these scaffolds using the cell-painting assay demonstrated distinctive phenotypic responses engendered by different library members, illustrating the functional as well as structural diversity of the compounds.

Morphological profiling by high-throughput single-cell biophysical fractometry

Complex and irregular cell architecture is known to statistically exhibit fractal geometry, i.e., a pattern resembles a smaller part of itself. Although fractal variations in cells are proven to be closely associated with the disease-related phenotypes that are otherwise obscured in the standard cell-based assays, fractal analysis with single-cell precision remains largely unexplored. To close this gap, here we develop an image-based approach that quantifies a multitude of single-cell biophysical fractal-related properties at subcellular resolution. Taking together with its high-throughput single-cell imaging performance (~10,000 cells/sec), this technique, termed single-cell biophysical fractometry, offers sufficient statistical power for delineating the cellular heterogeneity, in the context of lung-cancer cell subtype classification, drug response assays and cell-cycle progression tracking. Further correlative fractal analysis shows that single-cell biophysical fractometry can enrich the standard morphological profiling depth and spearhead systematic fractal analysis of how cell morphology encodes cellular health and pathological conditions.

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.

Optofluidic imaging meets deep learning: from merging to emerging

Propelled by the striking advances in optical microscopy and deep learning (DL), the role of imaging in lab-on-a-chip has dramatically been transformed from a silo inspection tool to a quantitative "smart" engine. A suite of advanced optical microscopes now enables imaging over a range of spatial scales (from molecules to organisms) and temporal window (from microseconds to hours). On the other hand, the staggering diversity of DL algorithms has revolutionized image processing and analysis at the scale and complexity that were once inconceivable. Recognizing these exciting but overwhelming developments, we provide a timely review of their latest trends in the context of lab-on-a-chip imaging, or coined optofluidic imaging. More importantly, here we discuss the strengths and caveats of how to adopt, reinvent, and integrate these imaging techniques and DL algorithms in order to tailor different lab-on-a-chip applications. In particular, we highlight three areas where the latest advances in lab-on-a-chip imaging and DL can form unique synergisms: image formation, image analytics and intelligent image-guided autonomous lab-on-a-chip. Despite the on-going challenges, we anticipate that they will represent the next frontiers in lab-on-a-chip imaging that will spearhead new capabilities in advancing analytical chemistry research, accelerating biological discovery, and empowering new intelligent clinical applications.

Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence

Only around 20% of the human proteome is considered to be druggable with small-molecule antagonists. This leaves some of the most compelling therapeutic targets outside the reach of ligand discovery. The concept of targeted protein degradation (TPD) promises to overcome some of these limitations. In brief, TPD is dependent on small molecules that induce the proximity between a protein of interest (POI) and an E3 ubiquitin ligase, causing ubiquitination and degradation of the POI. In this perspective, we want to reflect on current challenges in the field, and discuss how advances in multiomics profiling, artificial intelligence, and machine learning (AI/ML) will be vital in overcoming them. The presented roadmap is discussed in the context of small-molecule degraders but is equally applicable for other emerging proximity-inducing modalities.

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 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.

Annotation of the Effect of Chemogenomic Compounds on Cell Health Using High-Content Microscopy in Live-Cell Mode

The characterization of chemogenomic libraries with respect to their general effect on cellular health represents essential data for the annotation of phenotypic responses. Here, we describe a multidimensional high-content live cell assay that allows to examine cell viability in different cell lines, based on their nuclear morphology as well as modulation of small molecules of tubulin structure, mitochondrial health, and membrane integrity. The protocol monitors cells during a time course of 48 h using osteosarcoma cells, human embryonic kidney cells, and untransformed human fibroblasts as an example. The described protocol can be easily established and it can be adapted to other cell lines or other parameters important for cellular health.

Identification of Dihydroorotate Dehydrogenase Inhibitors Using the Cell Painting Assay

Profiling approaches have been increasingly employed for the characterization of disease-relevant phenotypes or compound perturbation as they provide a broad, unbiased view on impaired cellular states. We report that morphological profiling using the cell painting assay (CPA) can detect modulators of de novo pyrimidine biosynthesis and of dihydroorotate dehydrogenase (DHODH) in particular. The CPA can differentiate between impairment of pyrimidine and folate metabolism, which both affect cellular nucleotide pools. The identified morphological signature is shared by inhibitors of DHODH and the functionally tightly coupled complex III of the mitochondrial respiratory chain as well as by UMP synthase, which is downstream of DHODH. The CPA appears to be particularly suited for the detection of DHODH inhibitors at the site of their action in cells. As DHODH is a validated therapeutic target, the CPA will enable unbiased identification of DHODH inhibitors and inhibitors of de novo pyrimidine biosynthesis for biological research and drug discovery.

Validating Small Molecule Chemical Probes for Biological Discovery

Small molecule chemical probes are valuable tools for interrogating protein biological functions and relevance as a therapeutic target. Rigorous validation of chemical probe parameters such as cellular potency and selectivity is critical to unequivocally linking biological and phenotypic data resulting from treatment with a chemical probe to the function of a specific target protein. A variety of modern technologies are available to evaluate cellular potency and selectivity, target engagement, and functional response biomarkers of chemical probe compounds. Here, we review these technologies and the rationales behind using them for the characterization and validation of chemical probes. In addition, large-scale phenotypic characterization of chemical probes through chemical genetic screening is increasingly leading to a wealth of information on the cellular pharmacology and disease involvement of potential therapeutic targets. Extensive compound validation approaches and integration of phenotypic information will lay foundations for further use of chemical probes in biological discovery. Expected final online publication date for the Annual Review of Biochemistry, Volume 91 is June 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

BioProfiling.jl: profiling biological perturbations with high-content imaging in single cells and heterogeneous populations

MOTIVATION

High-content imaging screens provide a cost-effective and scalable way to assess cell states across diverse experimental conditions. The analysis of the acquired microscopy images involves assembling and curating raw cellular measurements into morphological profiles suitable for testing biological hypotheses. Despite being a critical step, general-purpose and adaptable tools for morphological profiling are lacking and no solution is available for the high-performance Julia programming language.

RESULTS

Here, we introduce BioProfiling.jl, an efficient end-to-end solution for compiling and filtering informative morphological profiles in Julia. The package contains all the necessary data structures to curate morphological measurements and helper functions to transform, normalize and visualize profiles. Robust statistical distances and permutation tests enable quantification of the significance of the observed changes despite the high fraction of outliers inherent to high-content screens. This package also simplifies visual artifact diagnostics, thus streamlining a bottleneck of morphological analyses. We showcase the features of the package by analyzing a chemical imaging screen, in which the morphological profiles prove to be informative about the compounds' mechanisms of action and can be conveniently integrated with the network localization of molecular targets.

AVAILABILITY AND IMPLEMENTATION

The Julia package is available on GitHub: https://github.com/menchelab/BioProfiling.jl. We also provide Jupyter notebooks reproducing our analyses: https://github.com/menchelab/BioProfilingNotebooks. The data underlying this article are available from FigShare, at https://doi.org/10.6084/m9.figshare.14784678.v2.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Chemical Evolution of Natural Product Structure

Natural products are the result of Nature’s exploration of biologically relevant chemical space through evolution and an invaluable source of bioactive small molecules for chemical biology and medicinal chemistry. Novel concepts for the discovery of new bioactive compound classes based on natural product structure may enable exploration of wider biologically relevant chemical space. The pseudo-natural product concept merges the relevance of natural product structure with efficient exploration of chemical space by means of fragment-based compound development to inspire the discovery of new bioactive chemical matter through de novo combination of natural product fragments in unprecedented arrangements. The novel scaffolds retain the biological relevance of natural products but are not obtainable through known biosynthetic pathways which can lead to new chemotypes that may have unexpected or unprecedented bioactivities. Herein, we cover the workflow of pseudo-natural product design and development, highlight recent examples, and discuss a cheminformatic analysis in which a significant portion of biologically active synthetic compounds were found to be pseudo-natural products. We compare the concept to natural evolution and discuss pseudo-natural products as the human-made equivalent, i.e. the chemical evolution of natural product structure.

Pseudo Natural Products-Chemical Evolution of Natural Product Structure

Pseudo-natural products (PNPs) combine natural product (NP) fragments in novel arrangements not accessible by current biosynthesis pathways. As such they can be regarded as non-biogenic fusions of NP-derived fragments. They inherit key biological characteristics of the guiding natural product, such as chemical and physiological properties, yet define small molecule chemotypes with unprecedented or unexpected bioactivity. We iterate the design principles underpinning PNP scaffolds and highlight their syntheses and biological investigations. We provide a cheminformatic analysis of PNP collections assessing their molecular properties and shape diversity. We propose and discuss how the iterative analysis of NP structure, design, synthesis, and biological evaluation of PNPs can be regarded as a human-driven branch of the evolution of natural products, that is, a chemical evolution of natural product structure.