Image-based profiling for drug discovery: due for a machine-learning upgrade?

Abstract

Image-based profiling is a maturing strategy by which the rich information present in biological images is reduced to a multidimensional profile, a collection of extracted image-based features. These profiles can be mined for relevant patterns, revealing unexpected biological activity that is useful for many steps in the drug discovery process. Such applications include identifying disease-associated screenable phenotypes, understanding disease mechanisms and predicting a drug’s activity, toxicity or mechanism of action. Several of these applications have been recently validated and have moved into production mode within academia and the pharmaceutical industry. Some of these have yielded disappointing results in practice but are now of renewed interest due to improved machine-learning strategies that better leverage image-based information. Although challenges remain, novel computational technologies such as deep learning and single-cell methods that better capture the biological information in images hold promise for accelerating drug discovery.

Introduction

In a drug development programme, evaluation of the efficacy and safety of all candidate compounds in humans, or even rodents, is ethically and practically unfeasible. Therefore, simpler model systems (cells, tissues and small model organisms) are used to map clinical efficacy and safety to a screening-amenable molecular target, pathway or phenotype in the process of screening1 (Box 1). The design of a screening assay balances on the one hand practicality and affordability, allowing broad exploration of chemical space, and on the other hand biological relevance to the disorder being studied or to possible safety concerns. Thus, the output, or readout, of a screening assay is typically chosen to be one or a few readily interpretable features that reflect biology already understood to be relevant for efficacy or safety2. Multiple such assays are used to test thousands to millions of small molecules to identify and triage hits (that is, attractive molecular starting points). The assays are also then used to advance hits to more drug-like leads and finally to optimize leads before preclinical development.

Profiling is an alternative strategy to screening. The word ‘profiling’ has two meanings: representing a sample by a profile (that is, a collection of features), and making predictions about a sample based on such a representation. Profiling aims to capture a wide variety of features, few or none of which may have previously validated relevance to a disease or potential treatment. It may, therefore, reveal unanticipated biology at play. Profiling often relies on the same or similar model systems as screening assays (for example, fluorescently stained cells), but profiling represents these model systems with a more comprehensive set of features3,4,5. More features can be powerful — for example, to uncover a previously unexpected mechanism of the disease or to provide sensitive quality control for the stability of an assay system — but they can also add unhelpful noise or be more difficult to interpret than a carefully selected screening feature.

Feature profiles can be constructed in many ways using many assay types6. Readouts can be generated from panels of separate assays; examples widely used in industry7 include cell viability across cell line panels8, enzymatic activity across a kinase panel9 and binding to a panel of safety-relevant targets10. More cost-efficient profiles can be acquired in a single multiplexed assay11 by combining one of several kinds of high-dimensional readout technologies with cell-based model systems. Proteomic profiling and metabolomic profiling would be very powerful but are too low throughput due to their cost12,13,14,15. Increasingly affordable sequencing approaches enable transcriptional profiling16 to rival bead-based alternatives17,18; they also underlie highly multiplexed cell viability assays19.

To date, image-based profiling using automated microscopy is the least expensive of these high-dimensional profiling techniques, and it inherently offers single-cell resolution, which can capture important heterogeneous cell behaviours. Computer vision techniques have advanced dramatically in the past few years, enabling the extraction of a huge quantity of unbiased morphological information from images. Cell-based microscopy assays have also advanced (Box 2), with assays such as Cell Painting inexpensively combining multiple stains in a robust assay yielding single-cell profiles composed of thousands of features20. The few reported comparative analyses indicate that image-based profiling may capture more biological information than high-throughput transcriptional profiling21,22.

Profiling is not a widely deployed strategy at the primary screening phase in the pharmaceutical industry, where well-validated, bespoke screening assays remain the preferred approach. However, profiling does play a role at other stages of the drug discovery process. In the discovery stages before screening — including target identification, target validation, phenotype discovery and assay development — up to thousands of compounds are profiled in exploratory assays that combine imaging, primary or induced pluripotent stem cell-derived cells and/or genetic editing1,23,24. As a consequence, profiling in the early stages of drug discovery can reveal key biological readouts that can be used for the subsequent phase of screening at high throughput.

Image-based profiling is also regularly applied downstream of screening to the hits that emerge from target-based, pathway-based or phenotypic screening and validation. It then functions as an unbiased secondary assay, applicable to hits from any screen and in any disorder. At minimum, profiling can organize hits into groups with biologically similar effects. At best, it can hint at a compound’s mechanism of action (MOA) and previously unsuspected off-target activities. However, it requires a considerable interpretation effort to derive these actionable insights from phenotypic features. With conventional analytical approaches, this interpretation step has been too onerous to scale up to screening full libraries.

Recently, machine-learning models were trained to predict the outcomes of hundreds of assays from a set of existing high-content images that were originally collected at high throughput for an image-based screen with a focused readout25. The study illustrated how machine learning can leverage side information — in this case, a large volume of assay activity labels. In another study, machine learning showed potential for image-based lead optimization26. Both of these studies were led by researchers at large pharmaceutical companies and reveal a renewed interest in profiling-inspired drug discovery. Not coincidentally, both studies were enabled by machine learning.

A new wave of biotechnology companies, including insitro and Recursion, are developing image-based profiling strategies with the explicit intent to feed a machine-learning effort27,28. They rely on moderately sized libraries and unbiased image-based profiling for phenotype discovery and primary screening, rather than on extensive decks of compounds in more time-consuming, customized assays. This enables more rapid exploration of hundreds of model systems that emulate disease states with genetic perturbations, even though some disease states may be less well represented in a generic cell system without customized assay readouts (Box 2). Machine learning is deployed to learn to map chemical, genetic or pathological perturbations to their in vitro, in vivo or clinical effects and to transform raw profiles into a screening assay, reducing the need for expert-crafted screening assays.

In this Review, we focus on applications of image-based profiling that are immediately applicable to the drug discovery process. We begin with a brief introduction to image-based profiling assays and analysis approaches and then discuss the status, successes and limitations of various pharmaceutical applications of this technology.

Image-based profiling in a nutshell

Image-based profiling does not require specialized equipment or reagents. All that is needed are images of biological samples that represent different cases (for example, categories of human patients) or treatment conditions (for example, chemical, genetic, time-point or other perturbations of the biological system) (Fig. 1).

Fig. 1: Image-based profiling.
figure1

a | Overview of the typical steps in the workflow for generating image-based profiles from biological samples. b | Example images from the Cell Painting assay often used for image-based profiling. It includes six stains labelling eight cellular components, which are imaged in five channels20. ER, endoplasmic reticulum.

First, the biological samples are prepared. This is typically done in arrayed multiwell plate format, although living cell microarrays and pooled imaging strategies are higher-throughput options29. Next, the samples are subjected to treatment conditions of interest and incubated. The samples are imaged, typically after fixation and staining, although one can instead conduct live, time-lapse imaging30,31 and/or use label-free techniques such as those that predict a staining pattern from brightfield images using machine learning32,33.

The images are processed to extract features, which are aggregated into profiles. Methods for this step are developing rapidly, in particular moving from expert-defined feature extraction to data-driven deep learning34,35. Finally, the extracted profiles are analysed for biologically meaningful similarities and differences in a portion of the computational workflow that differs depending on which application has been chosen.

In essence, any set of images can be used for image-based profiling (Box 2). The workflow described above requires adaptation depending on the samples, perturbations, stains and imaging modality used, but the overall strategy is the same. That said, profiling will be most powerful if each image contains a large number of instances (for example, cells or organisms) and a large amount of visible information about each instance (for example, high-resolution images of multiple corresponding stains).

Analysis techniques evolve

Challenges in profile analysis

Thousands of features can be extracted from images, but not all are equally useful for a given task. Different disease states or different compound mechanisms of action (MOAs) are best captured by reading out changes in distinct combinations of features, such as the shape and size of cell structures or the intensity and texture of various stains. An extensive image-based profile encompasses many feature combinations, overlapping or not, that together contain the information to document a broad spectrum of diseases and MOAs at once.

Notably, for a given goal, the presence of the relevant features is often not enough; we also need differential weighting: the amplification of features that are relevant to the goal and the suppression of features that are not. Importantly, the more extensive the profile, the more biological information it can potentially encode, but also the harder it is to extract the information for any given individual task from among irrelevant information. This phenomenon is referred to as the curse of dimensionality: the masking of the task-specific signal derived from comparatively few relevant features by the cumulative noise (related to technical variation) and confounding signal (related to unrelated biology) across the many other features. The problem can be addressed by combining features by weighted aggregation and/or more powerful representations produced by machine learning, which can have various levels of supervision (Box 3). In a way, an assay developer applies feature weighting when exploring and optimizing biological models, reagents and readouts for a screening assay. Computationally extracting relevant information from feature weighting of a profile can be thought of as the virtual development of a profile-based screening assay.

Early analysis techniques

Since the first proposals to leverage image-based profiling for drug discovery36,37,38, the analysis of image-based profiles has largely relied on unsophisticated unsupervised clustering of the profiles, including some profiles for well-known controls. Profiles are used to place each sample into a single, shared high-dimensional representation space (panels a–c of the figure in Box 3) — also referred to as a (weighted) feature or phenotypic space — where the closeness between pairs of compounds corresponds to their profile similarity. ‘Shared’ here refers to adjusting all features in the same way, regardless of which samples or classes are being studied.

The nearest-neighbour strategy is an approach to add an element of supervision. It predicts the biological activity or MOA of a query compound on the basis of that of a reference compound with a suitably similar profile. The biggest advantage of the nearest-neighbour approach is its information efficiency: in contrast to more advanced methods, it can be applied even if only a handful of reference compounds with existing annotation are available. Nevertheless, like those advanced methods, the approach improves with more annotated references. Importantly, reference compounds must be sufficiently active, fairly selective and, of course, accurately annotated — which is not assured even for the current marketed pharmacopoeia. As a result, the nearest-neighbour strategy has limited power.

Feature adjustment, transformation and normalization techniques are recommended to increase signal-to-noise ratios and thereby mitigate the curse of dimensionality39 (panel b of the figure in Box 3). These techniques, however, affect only the shared representation space in which samples are placed. Hence, they do not remedy the vulnerability of unsupervised approaches to confounding signals from sample biases or polypharmacology (Box 4) in the shared representation space. Classical feature selection methods reduce thousands of raw features to hundreds according to feature correlation, but typically without a dramatic increase of discriminative power. The first step in the analysis of an image-based profiling study is often a further reduction of the high-dimensional phenotypic space to a 2D or 3D representation for visualization purposes using computational methods such as principal component analysis (PCA), t-distributed stochastic neighbour embedding (t-SNE), and uniform manifold approximation and projection (UMAP)40,41,42. Although such visualizations are a non-optimal representation of the rich information of high-dimensional profiles, they can provide a first sense of the similarity patterns in the experiment.

A substantial proportion of known bioactive compounds yield a significant change in image-based profiles: 68% in one study using the Cell Painting assay22. Yet, due to the curse of dimensionality, a limited number of MOAs can be readily distinguished by the nearest-neighbour strategy: typically only one or two dozen form well-delineated clusters, and rarely are new clusters identified. The commonly observed clusters tend to represent compounds with disruptive mechanisms, such as histone deacetylase inhibition and microtubule interference. The ability of image-based profiling to yield distinctive clusters of compounds for other, often subtler mechanisms is limited and has not increased much over the years43. In a recent study where 15 reporter cell lines were evaluated, even after feature selection only 20 of 83 MOA classes were readily distinguishable by image-based profiles in the best single cell line, and 41 were readily distinguishable, cumulatively across all 15 cell lines44.

Machine learning: powering a new wave of image-based profiling

Driven by the recent availability of ever-larger volumes of images, the field has begun to turn to machine learning, and specifically deep learning, to improve extraction of relevant signal from profiles45. In essence, a deep neural network is a concatenation of multiple layers, each trained to reweight and transform data to achieve a goal, such as detecting objects in images or predicting the compound to which a cell has been exposed. An input layer introduces data such as image-based profiles or raw images into the network. Each layer then reshapes the data as it flows through the network. Finally, the output layer exports the completely transformed data.

These techniques thus learn a more sophisticated representation than simple reweighting. Self-supervised methods focus on information that can be learnt from different documentations of the same object (perturbation, sample or cell type) (panel c of the figure in Box 3). For example, if the same microscopy field is documented with two views with different stains, a method can be trained to predict one view from the other; in the process, it learns to filter out the noise in either view to leave only the robust signal46. Related methods train to differentiate multiple cells from a single view or multiple views (that is, replicates) that document a specific perturbation (for example, chemical or genetic) from those documenting any other perturbations47,48. These machine-learning methods require more substantial volumes of images for training but position all samples in a cleaner shared representation space than unsupervised methods. The more samples that are included, the more mechanisms and biological processes this shared representation space will try to simultaneously capture, challenging the representation’s discriminatory power.

Like unsupervised or self-supervised approaches, supervised methods require a large volume of images for training, but in addition they need training data, including training data points with activity labels for predefined tasks, and they benefit from side information. If large label sets are available that annotate compounds with their activities in validated assays, supervised methods can be trained, for example, to deconvolute the relevant signal from raw images or image-based profiles, including the output of self-supervised methods25. In contrast to unsupervised or self-supervised approaches, which learn a single representation space to position samples irrespectively of tasks of interest, the output layers of advanced supervised approaches produce a representation for each predefined task (panel d of the figure in Box 3).

Drug development companies are increasingly treating their existing data as a resource to be leveraged for machine learning. The promise of improved profile resolution through rich data and machine learning is currently rekindling industrial interest in phenotypic profiling. Although the pay-off may be great, serious machine-learning effort comes at a high cost, largely in terms of recruiting and retaining highly sought-after experts and giving them the time and computational resources to build and maintain suitable software. Machine-learning strategies also risk problems such as overfitting, technical artefacts or confounders in the data, and bias49,50 (Box 4). The ideal experimental set-up is often cost-prohibitive, and avoiding all possible confounding factors is impossible. Thus, practically speaking, practitioners must carefully design a combined experimental/data strategy that diminishes the risks; this requires deep understanding of the problem. One mitigant of machine-learning confounders in drug discovery is that for many of the applications we will discuss, the goal is simply to triage compounds that will subsequently be tested thoroughly, such that false positives do not proceed. Nevertheless, in biomedicine, machine-learning methods that are interpretable — that is, where one can learn what features of samples are being used in decision-making — can sometimes ensure that technical artefacts are not the source of the signal and can provide insight into disease and drug mechanisms.

Profile-based phenotype discovery and screening

It takes many months to years to develop a conventional image-based assay for screening, even after years of basic research into the mechanisms of a disease. One time-consuming step is hypothesizing and engineering one or a few relevant assay readouts, most often the staining of a molecule or other cell component. The time spent on this step can be dramatically reduced by using a generic staining approach such as Cell Painting (Box 2); applying image-based profiling to such images typically yields more extensive feature sets than customized staining. In conventional image-based assay development, researchers also spend considerable effort selecting suitable conditions for testing a drug’s impact on putative disease-associated cell phenotypes, which might include the cell type and culture conditions, appropriate stimuli and the duration of drug exposure. Here too, timelines can be cut by taking a more generic approach; for instance, one that relies on a simple cultured cell system and standardized assay conditions that are not customized to the diseases at hand. However, developing customized assay readouts with a careful selection of sample material, stimuli and time points may yield a higher probability of finding a disease-relevant phenotype, perhaps justifying much longer assay development timelines. Furthermore, many disease programmes will involve highly customized assays in the long run as secondary assays, so one could argue these may as well be developed up front for primary screening unless their throughput is limiting.

Regardless of where an image-based assay is on the spectrum from unbiased to customized, the typical steps to profile-based phenotype discovery and screening (also known as signature discovery and signature-based screening) are as follows.

  • Prepare sets of biological samples that represent the disease state and the healthy state via strategies described in detail later in this section (Table 1).

    Table 1 Strategies for identifying a ‘disease state in a dish’
  • Capture image-based profiles and attempt to identify any reproducible phenotypic difference between the diseased and healthy samples. This phenotypic difference will become the screening objective — that is, the phenotypic assay readout. This readout might be a single feature extracted from a single image channel (in essence, a conventional high-content assay), or it might be a multifeature profile that discriminates between the diseased and healthy states. Machine learning and side information may be required to filter out confounding signals and noise. The discovery of novel phenotypes associated with a disease may itself yield new mechanistic insights into the disorder.

  • Optionally, simplify the assay (for example, remove unnecessary fluorescent markers) to reduce its cost, or add markers that serve a useful triaging function for hits.

  • Use the identified processed phenotype or profile to (a) test thousands to millions of chemicals for their ability to reverse the disease morphology to resemble the healthy state or (b) virtually query an existing dataset of image-based profiles from chemical perturbations of healthy cells to identify those whose perturbation yields the ‘opposite’ (anticorrelated) phenotype, indicating a favourable impact on the same pathways as are impacted by the disease. In addition, compounds that produce the same (correlated) profile as the disease can potentially provide useful mechanistic information.

  • Optionally, identify or validate novel targets for the disorder by (a) testing a genome-scale set of genetic perturbations for their ability to modify the disease-related phenotype or (b) virtually querying an existing genome-scale dataset of image-based profiles from genetic perturbations of healthy cells to identify or validate genes whose perturbation yields the same (correlated) or opposite (anticorrelated) phenotype. Novel, validated targets could then be fed into conventional target-based drug discovery pipelines.

Identifying a disease-associated phenotype

The first step, identifying a disease-associated phenotype in images, is crucial51. Several strategies exist for identifying a cellular disease state with a profile that differs from that of the healthy state (Table 1). First, patient-derived cells are a physiologically relevant choice, assuming a sufficient number of independent patients are available to yield confidence that phenotypic differences are associated with the disease rather than due to the inherent morphological variability of cell lines across patients. Caution must be exercised, as high-dimensional profiles are prone to confounding factors (Box 4), whereby features that seemingly distinguish between healthy and diseased states may in fact reflect age, genetic, exposure or sample biases that are not relevant to the disease. Nevertheless, many reproducible image-based phenotypes have been discovered, often inadvertently, as scientists stained and visually examined cells, typically using common markers such as organelle dyes. For example, unusual mitochondrial structure was identified in fibroblasts and lymphocytes from patients with bipolar disorder52 and in fibroblasts from patients with Leigh syndrome53, and normal human fibroblasts can be differentiated from Huntington disease fibroblasts using only tubulin staining54. Image-based profiling offers a way to scale-up and systematize this kind of serendipitous discovery.

A second approach to identifying a disease-associated phenotype is especially suited to disorders caused by loss-of-function mutations in single genes. A gene’s expression is decreased using RNAi or CRISPR and then the morphological impact on cells is examined, taking care to identify off-target effects55. This approach was used to detect an impact on cell structure (as detected by stains for DNA, actin and VE-cadherin), which was obvious by eye, of RNAi knockdown of CCM2, the gene associated with the loss-of-function disorder cerebral cavernous malformation56. Through screening, the researchers identified small molecules that reverse the phenotype. Remarkably, compounds chosen by a computational analysis outperformed those chosen by eye in secondary physiological experiments. This study, conducted at the University of Utah, led to the launch of the biotechnology company Recursion. The company has since identified hundreds of disease-associated image-based phenotypes available for parallel screening and has placed four drugs into clinical trials. In a similar strategy, an academic research team took a comprehensive approach to investigate alleles from genome sequencing studies: in adipocytes differentiated in vitro, they ablated 125 genes associated with type 2 diabetes and clustered the resulting image-based profiles, identifying novel lipodystrophy genes57. Another team mutated zebrafish orthologues near 132 schizophrenia-associated alleles and created behavioural and brain structural image-based profiles, prioritizing candidates for further study58.

A third approach to phenotype identification for screening, gene overexpression, is especially suited for testing alleles in protein-coding regions of genes that are known or hypothesized to cause disorders. The mutant form of a given protein is exogenously expressed in cells, and its image-based profile is compared with that of cells overexpressing the wild-type form59. Although overexpression may itself impact the cell’s structure and function, observing a differential signature between wild-type and mutant overexpression yields a strong hypothesis for disease-related impact. The Taipale laboratory at the University of Toronto is using this strategy for a set of monogenic disorders, with the twist that each disease-associated protein is flag-tagged so that disease-associated changes in protein localization can be detected in addition to changes in cell morphology (M. Taipale and J. Lacoste, personal communication). One could envision this strategy becoming the routine next step after every sequencing study that yields long lists of hypothesized disease-associated variants: generate appropriate genetic reagents for all alleles and test their image-based impact in a suitable cell line. This approach might allow clustering of alleles into different functional groups, such that a subset of each group could be studied in more disease-specific assays. Already, image-based profiling of gene overexpression is showing promise in determining the impact of so-called variants of unknown significance in patient tumours (J. Caicedo and J. Boehm, personal communication), analogous to a prior successful mRNA profiling approach60.

A fourth strategy is to genetically modify cells, which, unlike overexpression, allows interrogation of both coding and non-coding variants. For now, the single-cell cloning procedures needed for gene editing techniques are too slow and inconsistent for large-scale use, and lineage artefacts may confound accurate phenotype detection61. Nevertheless, the approach can work well for testing individual alleles. Recently a change in image-based profile was detected for cancer cells genetically modified to express mutant focal adhesion kinase (FAK) versus its wild-type form62. This genetic perturbation was intended to mimic pharmacological treatment targeted to FAK and simplify the pathway engagement while avoiding off-target concerns with FAK small-molecule inhibitors. The study authors then screened small molecules to identify putative synergistic combinations with FAK inhibitors, identifying histone deacetylase inhibitors as potential novel kinase inhibitor drug combinations for cancer. It may soon be feasible to rapidly and systematically genetically engineer cell lines containing disease-associated alleles in their endogenous locations, which opens the door for this approach to be conducted more systematically. One can even engineer model organisms to carry human disease-associated alleles to create ‘avatars’ for identifying image-based phenotypes and subsequent testing of compounds63,64,65,66,67.

A fifth approach to identify a screenable phenotype is to identify changes in image-based profiles associated with existing drugs for a particular disease of interest, then use those phenotypes to identify new candidate drugs and often their MOA as well. This approach has identified new behavioural phenotypes and potential therapeutics in zebrafish time-lapse imaging using existing psychotropics68, antipsychotics69, appetite modulators70 and anaesthetics71 as the query compounds. Image-based profiling also revealed morphological changes associated with 61 structurally diverse free fatty acids, identifying those that are associated with lipotoxicity in insulin-secreting pancreatic β-cells (N. Wieder and A. Greka, personal communication).

These five strategies for identifying screenable phenotypes differ in terms of the ease of assay development, the degree of customization required and how closely they reflect the human disease state. At one end of the spectrum, one can choose a single cell type and set of staining conditions to search for many disease phenotypes in parallel. In this case, the assay development time for each disorder is zero, but many phenotypes will be missed due to a lack of a suitable stain or appropriate biological conditions. Still, the simplicity, cost-efficiency and scalability of the strategy make it an attractive approach: although customized follow-up validation assays must still be created, they can be more expensive and of lower throughput, and they need to be made only for the subset of disorders showing promising hits. By contrast, there will be more chance of identifying a phenotype for any particular disease if the assay is more specifically tailored to the disorder, although this increases the assay development time. For example, one could explore several healthy and diseased systems to be imaged, such as several cell lines, primary cell types, mixtures of co-cultured cells or even differentiated cell cultures. In addition to more physiologically relevant cell/organism systems, stains can be customized to reflect several biological processes associated with the disorder. At the limit, these customizations end up as equivalent to conventional phenotypic assay development.

It should be noted that reversing disease-associated phenotypes identified via the strategies described above will not always yield effective drugs. First, cultured cells do not reflect the full intricacies seen in a human organism; many disease mechanisms are not cell autonomous, and many drugs will have different effects in patients’ whole bodies, especially considering their different genetic backgrounds and environmental exposure. These limitations are shared with all biochemical or cell-based drug screening methods and are fairly well appreciated. However, profile-based phenotype discovery introduces additional concerns. For example, phenotypes detected as described above might be completely incidental to the phenotype that causes symptoms for patients, such that drugs reversing the phenotype are ineffective. Even worse, the detected phenotype might reflect the cells’ attempt to mitigate the impact of the disease perturbation, such that drugs reversing the phenotype would aggravate the condition in patients. Nevertheless, the speed that profiling offers allows extra time to rule out these kinds of problems.

The drug industry as a whole has begun to adopt image-based profiling to inform target identification and validation, phenotype discovery and assay development before screening. However, for screening itself, wherever possible, the industry tends to prefer a customized assay focused on a molecularly defined target or pathway that adequately reproduces the profile-based findings. Focused assays facilitate determining structure–activity relationships (SARs), selecting mechanistically inspired biomarkers of efficacy and identifying on-target liabilities that correlate with desired on-target activity. Moreover, contrary to expectations, compounds identified in early mRNA and image profile-based phenotypic screening efforts do not seem to have been validated in animal models more often than compounds identified in more straightforward target or pathway-based screening approaches — although, admittedly, no systematic analysis has been reported to date. Large pharmaceutical companies also tend to focus on selected disease areas, and in those areas they prefer to maximize the chances of success by screening large compound libraries, which can be more efficiently accommodated in streamlined customized assays, especially if profiling will nevertheless eventually be used in follow-up assays. However, thousands of diseases fall out of industry scope. As a result, biotechnology start-ups (Recursion and insitro, most notably27) are exploring this space. As mentioned in the introduction, they rely on profile-based phenotypic screening of smaller libraries in generic assays to more efficiently evaluate the potential of compound intervention across that less frequently studied disease spectrum.

Lead generation

In the drug discovery process, screening is followed by lead generation, in which hundreds of screening hits are narrowed down to just a few lead candidates. The various applications of image-based profiling for lead generation can use either unbiased assays (which could be systematically applied across disease areas) or customized assays with relevant biomarkers (which can be tailored to particular disease areas) as described in the previous section and Box 2.

Hit expansion, lead optimization and SAR studies

During lead generation, two complementary activities — hit expansion and lead optimization — are pursued to triage and modify compounds for the most favourable attributes for further drug development. Conventionally, lead generation is guided by several individual assays, including the primary screening assay. These typically have simple and rapid readouts, reflecting single effects of molecules, such as enzyme activity, reporter expression, aggregation of a target protein or cell viability. Image-based profiling can be a tractable path for lead optimization, because it is quick, is sensitive and covers a broad, though not comprehensive, range of biology. Rather than relying on multiple assay readouts to select compounds with desired qualities, image-based profiling presents the possibility of selecting compounds with several desired qualities using a single assay72. Moreover, image-based profiling can quickly and comprehensively establish relationships among hits into biological clusters, independently of compound structure, providing direction for prioritization37,73,74. In addition, hits can be triaged quickly across disease-specific cell types with different genetic backgrounds using the same image-based profiling assay to assess the efficacy of compounds75,76.

Recently, several SAR studies applied image-based profiling using Cell Painting to assess the biological activity of newly synthesized compounds and build diversity sets for focused libraries77,78,79,80. The quick deployment and richness of the readouts enabled researchers to efficiently examine the effects of compound modifications across a wide range of biological activity. The unbiased assay proved to be sensitive enough to distinguish the effects of stereochemistry on activity. Furthermore, because an unbiased image-based profiling assay covers such a broad spectrum of activity, modifications that act on different mechanisms can be identified78,79 — an advantage over conventional SAR studies.

Because these readouts reflect the consequences of molecular modifications for biological activity, this approach can be used to examine biological activity of hits from targeted screening, provided the cellular model system contains the molecular target in a biologically relevant context. Importantly, one must confirm that observed effects are indeed the result of target modification by the test compound and not the result of cellular adaptation via an alternative mechanism or polypharmacology. Furthermore, and more challenging, is the possibility of no observed response. The absence of a response may not be due to the absence of a target or target binding, but could be due to complex cellular pharmacology — a combination of compound concentration, duration of treatment and cellular metabolism — which yields an observable morphological profile similar to no-treatment controls.

In this context, it is helpful to understand the relative association of image-based features with the desired phenotype or with off-target activities. However, this interpretation step is still a challenge beyond simplistic cases in which a small number of image-based features are strongly affected and easily interpretable, such as a change in protein localization. Although it has yet to be demonstrated for lead optimization, machine learning is emerging as a powerful tool to deconvolute multiplexed profiles of studied hits into virtual assays for distinct biological activities (as described in the next section), which can be favourable, neutral or unfavourable. Whether translated into interpretable activities by nearest-reference or by more advanced machine-learning approaches, the rich information in image-based profiles for newly synthesized compounds can be used to track the incremental effects of subsequent compound modifications during lead optimization.

Predicting assay activity

Compared with traditional hit expansion approaches, machine learning offers some attractive alternatives that make use of unbiased image data and provide an advantage over conventional SAR studies. By nature, image-based prediction can generate hits that are structurally diverse because predictions are based on activity in a biological system rather than chemical structure. Also, being inherently multiplexed, image-based methods are potentially capable of predicting the activity of hit compounds in assays unrelated to the assay that generated the image-based profiles.

This hypothesis was verified by a multi-institution team using image-based profiles from Janssen to successfully predict the activity of structurally diverse hit compounds in screening assays. In two validation studies, the strategy yielded a 60-fold to 250-fold increase in hit rates compared with the original screening assays25. Using the program CellProfiler to perform classical image segmentation and feature extraction, they leveraged supervised machine learning to predict the activity of a large set of compounds in given assays, based on archived imaging data on those compounds in an unrelated assay. Building on this research, a later team constructed a novel network architecture, GapNet, to predict ChEMBL-derived81 compound annotations for a 30,000-compound Cell Painting dataset82: 32% of the assays could be well predicted83. In addition to the practical implication — that many expensive screens might be replaced effectively with a computational prediction step — this result also indicates that a substantial proportion of biological pathways of interest are captured in a single imaging assay. Furthermore, by combining image profiles with compound structural information, machine learning was able to predict active chemical structures de novo26.

If these machine-learning strategies work well in practice, one could envision the sizes of primary screens being so substantially reduced in the future as to fundamentally change the typical drug discovery process, with heavier reliance on computational predictions rather than experimentation in early stages.

Predicting toxicity

Identifying a compound’s liabilities during lead generation is as important as assessing its efficacy; roughly 17% of phase III trial failures are due to safety concerns84. A simple cell-based assay such as Cell Painting, which could readily be scaled to an entire million-compound collection, is appealing for toxicity prediction given widespread enthusiasm for reducing the use of intact animals for testing both pharmaceuticals and environmental chemicals85,86.

Recently, a team at the US Environmental Protection Agency used image-based profiles from the Cell Painting assay to characterize selected chemicals’ bioactivity and toxicity87. One nuance of toxicity testing is that the dose at which effects are seen is paramount to discerning whether the compound will be toxic in humans. By comparing the toxic concentration thresholds for each chemical generated with other methods with thresholds generated with image-based profiling, the team concluded that image-based profiling is a viable, cost-effective alternative for chemical safety assessments. Image-based profiling of full dose–response curves for large-scale chemical libraries (for example, six dose points for millions of compounds) would require significant investment, but subsets of doses or subsets of compounds are readily feasible. Cell Painting’s ability to predict various specific cell health assay readouts that provide mechanistic information, such as stalling in various cell cycle states88, provides further motivation for the collection of large-scale Cell Painting data on compounds for pharmaceutical, agricultural and environmental use.

Other methods combine image-based profiling with machine learning to identify the MOAs of toxicants in different types of cells. Image-based features can predict nephrotoxicity of drugs, chemicals and toxicants targeting human renal proximal tubular cells89. A high-throughput in vitro phenotypic profiling for toxicity prediction (HIPPTox) system predicted pulmonotoxicity more accurately than cell viability assays in human lung cell lines90. Image-based profiling identified rosmarinic acid as a candidate with cardioprotective effects against the toxicity of doxorubicin91. This approach also revealed the toxic effects of bisphenol A and its analogues on a testicular cell co-culture model by simultaneously measuring multiple adverse end points such as changes to nuclear morphology and cytoskeletal structure92.

Identifying the MOA

Elucidating the MOA of a drug provides a deeper understanding of its biological activity68,93,94,95,96, increases its chances of clinical approval and enables the design of novel drugs97, and provides insights into the potential to separate unfavourable effects from favourable effects if they are driven by different targets98.

Identifying the MOA and/or targets of a hit or lead presents a major challenge, particularly for candidates arising from phenotypic rather than target-based screens. No single experimental technology can definitively identify a compound’s MOA. Instead, the most likely MOA is often inferred from a combination of several complementary methods99. Proteomics approaches can be used to identify unknown protein targets after pull-down with a tagged compound or if an untagged compound shifts the target’s stability100. Assay panels can readily identify the engagement of a compound with a set of predefined targets such as kinases, but only a fraction of the druggable proteome can be queried in this way, and every assay has its limitations. Finally, phenotypic profiling, including image-based profiling, can also contribute to MOA identification, as we describe in this section.

There are three broad categories of image-based profiling approaches to determining the MOA. The first approach, known as guilt-by-association with annotated compounds, involves comparing image-based profiles of compounds with unknown MOAs with those of well-annotated compounds to identify neighbours101. In this approach, image-based profiles are clustered according to the assumption that drugs with similar MOAs generate similar phenotypic signatures102. This strategy has been successfully implemented across studies spanning more than a decade73,75,102,103,104,105,106. However, as described in the section Analysis techniques evolve, image-based profiles and classical methods have effectively grouped compounds for many classes of MOAs but are ineffective for other classes, and polypharmacology is confounding. Nevertheless, there are many examples of novel MOA discoveries being made by this route, including identifying novel inhibitory activity of silmitasertib107, the MOA of autoquin (an autophagy inhibitor with a non-protein target)108, the translation inhibition of phenomycin109, the activity of synthesized pseudo-natural products, pyranofuropryridones, which remained inactive in other common assays110, and useful components of natural products111,112,113,114.

We expect that in coming years this guilt-by-association strategy will become increasingly sophisticated by leveraging advancements in machine learning, availability of more extensive reference annotation, integration with other data sources and optimization of cell lines. As the choice of cell line informs MOA prediction accuracy21, effort has been made towards engineering reporter cell lines43 and identifying cell line-invariant features115. Others have interrogated genetic heterogeneity, finding distinct morphological responses to serotonin modulators across breast cancer cell lines116. The integration of image and transcriptome data into ensemble approaches has shown promise to improve MOA determination for synthetic small molecules, natural products and identified bioactive metabolites21,117. Image-based profiles of cells treated with natural products have also been combined with mass spectral features of the same natural products to discover a novel family that causes endoplasmic reticulum stress118. If image-based and other profile types carry complementary information, then creating and integrating both kinds of profiles will be useful; if they are instead largely redundant, then researchers could acquire only one modality and build translators to convert one type of profile to the other119.

In the second approach, known as guilt-by-association with perturbed genes, the image-based profile of the drug is matched to that of cells perturbed with a specific genetic reagent, thereby yielding a hypothesis for the drug’s target and MOA120. As pioneered with yeast mutants121,122,123, this approach has shown success in cell-based, image-based profiling experiments, although it is not yet widely used. Genetic perturbation can be achieved by a variety of techniques. Historically, perturbation by small interfering RNA (siRNA) has been most common124,125,126, although seed effects can be confounding127. Another strategy is to use CRISPR–Cas9 (ref.128) to knock out genes of interest as it is less susceptible to undesirable and confounding off-target effects of siRNA55,129. Overexpressing genes is another option that can yield distinctive image-based profiles59, which in theory could be matched to a chemical’s image-based profile. Most chemical compounds inhibit a protein’s function, but it is currently unclear whether matching a suppressed gene more effectively determines the MOA as opposed to looking for a profile that is anticorrelated to that of an overexpressed gene.

The third approach, which is based on rescue experiments, might be more accurate than the similarity-matching strategies described above, but would require new, large-scale experiments for each query molecule rather than a simple computational matching exercise. In a rescue experiment to determine the MOA, cells treated with a given drug that induces a particular image-based profile would be treated with genetic perturbations to identify any that can reverse the profile, such that the cells resemble untreated cells. There are currently no published examples of this approach, although pooled optical profiling puts this type of experiment closer to reality by offering the capability to test hundreds to thousands of genetic reagents in parallel29.

As mentioned in the section Analysis techniques evolve, most image-based MOA prediction studies use a nearest-neighbour approach. This procedure assumes that each drug has a single MOA, but it is now well appreciated that few chemicals impact only a single protein within a cell. Instead, polypharmacology, including off-target effects, is commonly seen (Box 4). One or two of these targets may be relevant in the context of the disease of interest, but others may also be reflected in the unweighted profile and obscure the profiles resulting from the targets of interest. For certain MOAs, particularly those with a strong and broad impact on cellular morphology, clusters of compounds are easily identified with straightforward methods130 but such methods fail for many MOA classes. Hence there is a need for MOA determination methods that account for polypharmacology. Deep learning-based approaches have been developed25,83 that are inherently well suited for tackling this problem, as they can produce MOA-specific, reweighted image-based profiles, thereby deconvoluting the complex phenotypes arising from polypharmacology44. We are only beginning to scratch the surface of image-based profiling of single perturbations, but testing pairs of reagents has begun for limited sets of small molecules131 and genetic perturbations132. Larger such datasets should lead to a better understanding of how to identify and deconvolute polypharmacology in profiles. Regardless of how MOA hypotheses arise, ideally more than one structurally different and accurately annotated compound targeting the same target/pathway will be available to lend evidence to a hypothesis.

Beyond drug discovery

Outside the scope of this Review are many other successes of image-based profiling applied to a wide variety of important biological phenomena and cellular structures, including identifying relevant genes via functional genomics and studying cell responses to growth topologies and differentiation factors133,134,135,136,137,138. Although identifying biomarkers of disease or drug response can be a first step towards drug discovery and a helpful aid in clinical trials, we do not cover in this Review the tremendous strides that have been made in image-based diagnostics, in some cases by using deep learning on unlabelled samples to create label-free diagnostics139. Examples range from suggesting a diagnosis140 to predicting patient outcomes141,142,143 or even molecular phenotypes144,145. Lastly, we do not cover interventional, personalized medicine applications here, where patient samples are treated with various potential therapies and imaging is used to measure responses. Examples include the image-based profiling of the response of a bacterial strain isolated from a patient to various antibiotics146, of organoids derived from a patient with cystic fibrosis to drugs147 and of tumour cells or organoids to various chemotherapies148,149,150.

Future directions

We fully expect advancements in the field of image-based profiling — both computational and biological — will progress rapidly in the next 5 years as the approach gains attention.

On the computational side, deep learning is already beginning to accelerate drug discovery by tackling diverse problems in the process151, and image-based profiling will be among the major beneficiaries of advancements in computer vision and predictive algorithms152,153,154. Deep learning can process raw microscopy images to produce representations that are better suited for downstream analysis and interpretation; cells or cellular substructures can be identified more accurately155,156, and improved image-based descriptors can be derived during feature extraction157. Deep learning may therefore eventually replace classical image processing and feature extraction algorithms, such as those in the currently most commonly used software program, CellProfiler158, or one of its commercial counterparts. Deep networks have been trained for the interpretation of image-based profiles: they can recognize the biological states of imaged cells159 or predict the biological activities in validated assays for imaged compounds25. Convolutional neural networks can also integrate bespoke feature extraction and interpretive tasks in a single process160. Emerging anecdotal evidence suggests that provided with enough activity labels, these single-step, end-to-end networks have a predictive performance superior to that of conventional feature extraction and profiling. This may stem from deep learning’s ability to learn specialized composite image features beyond the ones predefined by feature extraction software. Alternatively, it may reflect an implicit encoding of the heterogeneity of single cells within a microscopy image134. Capturing single-cell heterogeneity after feature extraction has indeed been found to improve image-based phenotypic clustering161. Finally, the flexible architecture of neural networks enables information to flow in from alternative data sources and formats, as input, or as side information.

On the biology side, drug discovery scientists are adopting increasingly complex model systems for their image-based profiling, such as differentiated cell types, tissues, organoids and whole model organisms. These assay systems may more routinely become higher-resolution and multidimensional systems, including 3D and time-lapse image capture. Sequencing-based barcoding methods are enabling larger-scale genetic perturbation libraries to be profiled by imaging29. We also expect that data generation and machine-learning approaches will become increasingly intertwined. For example, networks can learn to predict fluorescence patterns from transmitted light images33 when provided with enough training pairs. This may in the future enable cost-efficient label-free image capture.

The ability to make broad activity predictions on the basis of microscopy screen images that were originally collected for a single mechanism of interest25 has created an appetite to combine similar approaches with the richer profiles from multiplexed generic staining protocols such as Cell Painting83. Biotechnology and pharmaceutical companies are already investing in generating purpose-built image sets that document genetic and compound-induced perturbations. This investment occurs in proprietary settings, but also in the context of public–private partnerships involving multiple pharmaceutical companies, which will ultimately boost the availability of such datasets in the public domain27. It remains to be seen whether image-based profiles have reproducibility problems as recently suggested for mRNA profiles in the largest single-site public dataset162, and whether machine-learning approaches can reconcile image-based profiles across experimental batches and data generation sites. Nevertheless, we hope that increased availability of high-quality queryable image datasets163 paired with side information on imaged compounds, genetic perturbations or disease models will in turn inspire the design of yet more powerful machine-learning methods, driving a virtuous circle of discovery.

References

  1. 1.

    Haasen, D. et al. How phenotypic screening influenced drug discovery: lessons from five years of practice. Assay Drug Dev. Technol. 15, 239–246 (2017). This primer describes the hype and eventual more nuanced positioning of phenotypic screening, providing a pharmaceutical industry perspective as machine learning is ready to spark a new wave of opportunities in this space.

    CAS  PubMed  Google Scholar 

  2. 2.

    Singh, S., Carpenter, A. E. & Genovesio, A. Increasing the content of high-content screening: an overview. J. Biomol. Screen. 19, 640–650 (2014).

    PubMed  PubMed Central  Google Scholar 

  3. 3.

    Dorval, T., Chanrion, B., Cattin, M.-E. & Stephan, J. P. Filling the drug discovery gap: is high-content screening the missing link? Curr. Opin. Pharmacol. 42, 40–45 (2018).

    CAS  PubMed  Google Scholar 

  4. 4.

    Boutros, M., Heigwer, F. & Laufer, C. Microscopy-based high-content screening. Cell 163, 1314–1325 (2015).

    CAS  PubMed  Google Scholar 

  5. 5.

    Caicedo, J. C., Singh, S. & Carpenter, A. E. Applications in image-based profiling of perturbations. Curr. Opin. Biotechnol. 39, 134–142 (2016).

    CAS  PubMed  Google Scholar 

  6. 6.

    Herholt, A., Galinski, S., Geyer, P. E., Rossner, M. J. & Wehr, M. C. Multiparametric assays for accelerating early drug discovery. Trends Pharmacol. Sci. https://doi.org/10.1016/j.tips.2020.02.005 (2020).

    Article  PubMed  Google Scholar 

  7. 7.

    David, L. et al. Applications of deep-learning in exploiting large-scale and heterogeneous compound data in industrial pharmaceutical research. Front. Pharmacol. 10, 1303 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Stinson, S. F. et al. Morphological and immunocytochemical characteristics of human tumor cell lines for use in a disease-oriented anticancer drug screen. Anticancer. Res. 12, 1035–1053 (1992).

    CAS  PubMed  Google Scholar 

  9. 9.

    Merget, B., Turk, S., Eid, S., Rippmann, F. & Fulle, S. Profiling prediction of kinase inhibitors: toward the virtual assay. J. Med. Chem. 60, 474–485 (2017).

    CAS  PubMed  Google Scholar 

  10. 10.

    Bowes, J. et al. Reducing safety-related drug attrition: the use of in vitro pharmacological profiling. Nat. Rev. Drug Discov. 11, 909–922 (2012).

    CAS  PubMed  Google Scholar 

  11. 11.

    Kurita, K. L. & Linington, R. G. Connecting phenotype and chemotype: high-content discovery strategies for natural products research. J. Nat. Prod. 78, 587–596 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Litichevskiy, L. et al. A library of phosphoproteomic and chromatin signatures for characterizing cellular responses to drug perturbations. Cell Syst. 6, 424–443.e7 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Bouzekri, A., Esch, A. & Ornatsky, O. Multidimensional profiling of drug‐treated cells by imaging mass cytometry. FEBS Open Bio 9, 1652–1669 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Zampieri, M., Sekar, K., Zamboni, N. & Sauer, U. Frontiers of high-throughput metabolomics. Curr. Opin. Chem. Biol. 36, 15–23 (2017).

    CAS  PubMed  Google Scholar 

  15. 15.

    Dubuis, S., Ortmayr, K. & Zampieri, M. A framework for large-scale metabolome drug profiling links coenzyme A metabolism to the toxicity of anti-cancer drug dichloroacetate. Commun. Biol. 1, 101 (2018).

    PubMed  PubMed Central  Google Scholar 

  16. 16.

    Ye, C. et al. DRUG-seq for miniaturized high-throughput transcriptome profiling in drug discovery. Nat. Commun. 9, 4307 (2018).

    PubMed  PubMed Central  Google Scholar 

  17. 17.

    Keenan, A. B. et al. Connectivity mapping: methods and applications. Annu. Rev. Biomed. Data Sci. 2, 69–92 (2019).

    Google Scholar 

  18. 18.

    Subramanian, A. et al. A next generation connectivity map: L1000 platform and the first 1,000,000 profiles. Cell 171, 1437–1452.e17 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Corsello, S. M. et al. Discovering the anticancer potential of non-oncology drugs by systematic viability profiling. Nat. Cancer 1, 235–248 (2020).

    PubMed  PubMed Central  Google Scholar 

  20. 20.

    Bray, M.-A. et al. Cell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes. Nat. Protoc. 11, 1757–1774 (2016). This step-by-step protocol article details Cell Painting, the most popular image-based profiling assay; it combines six stains to label eight cell components in a straightforward microscopy assay.

    CAS  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Lapins, M. & Spjuth, O. Evaluation of gene expression and phenotypic profiling data as quantitative descriptors for predicting drug targets and mechanisms of action. Preprint at bioRxiv https://doi.org/10.1101/580654 (2019).

    Article  Google Scholar 

  22. 22.

    Wawer, M. J. et al. Toward performance-diverse small-molecule libraries for cell-based phenotypic screening using multiplexed high-dimensional profiling. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.1410933111 (2014).

    Article  PubMed  Google Scholar 

  23. 23.

    Weigle, S., Martin, E., Voegtle, A., Wahl, B. & Schuler, M. Primary cell-based phenotypic assays to pharmacologically and genetically study fibrotic diseases in vitro. J. Biol. Methods 6, e115 (2019).

    PubMed  PubMed Central  Google Scholar 

  24. 24.

    Johnson, K. et al. A stem cell-based approach to cartilage repair. Science 336, 717–721 (2012).

    CAS  PubMed  Google Scholar 

  25. 25.

    Simm, J. et al. Repurposing high-throughput image assays enables biological activity prediction for drug discovery. Cell Chem. Biol. 25, 611–618.e3 (2018). This study applies machine learning to microscopy images designed in the context of a specific screening campaign to inform on the activity of compounds in a broad spectrum of biological processes; this has applications in early drug discovery.

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Méndez-Lucio, O., Zapata, P. A. M., Wichard, J., Rouquié, D. & Clevert, D.-A. Cell morphology-guided de novo hit design by conditioning generative adversarial networks on phenotypic image features. Preprint at ChemRxiv https://doi.org/10.26434/chemrxiv.11594067 (2020). This study proposes an approach to image-based lead optimization: a generative adversarial network is trained on small-molecule structures with and without matching image-based profiles to generate new small-molecule structures that mimic the morphological profiles induced by overexpression of 12 individual genes.

    Article  Google Scholar 

  27. 27.

    Mullard, A. Machine learning brings cell imaging promises into focus. Nat. Rev. Drug Discov. 18, 653–655 (2019).

    CAS  PubMed  Google Scholar 

  28. 28.

    Mullard, A. Daphne Koller. Nat. Rev. Drug Discov. 18, 576–577 (2019).

    CAS  PubMed  Google Scholar 

  29. 29.

    Feldman, D. et al. Optical pooled screens in human. Cells. Cell 179, 787–799.e17 (2019).

    CAS  PubMed  Google Scholar 

  30. 30.

    Cooper, S., Sadok, A., Bousgouni, V. & Bakal, C. Apolar and polar transitions drive the conversion between amoeboid and mesenchymal shapes in melanoma cells. Mol. Biol. Cell 26, 4163–4170 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Eismann, B. et al. Automated 3D light-sheet screening with high spatiotemporal resolution reveals mitotic phenotypes. J. Cell Sci. 133, jcs245043 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Christiansen, E. M. et al. In silico labeling: predicting fluorescent labels in unlabeled images. Cell 173, 792–803.e19 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nat. Methods 15, 917–920 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Grys, B. T. et al. Machine learning and computer vision approaches for phenotypic profiling. J. Cell Biol. 216, 65–71 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Caicedo, J. C. et al. Data-analysis strategies for image-based cell profiling. Nat. Methods 14, 849–863 (2017). Members of the CytoData Society across most laboratories in the field of image-based profiling come together to define best practices for informatics; remarkably, these are still relatively up to date, with the exception of deep learning advancements.

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Price, J. H. et al. Advances in molecular labeling, high throughput imaging and machine intelligence portend powerful functional cellular biochemistry tools. J. Cell. Biochem. Suppl. 39, 194–210 (2002).

    PubMed  Google Scholar 

  37. 37.

    Perlman, Z. E. et al. Multidimensional drug profiling by automated microscopy. Science 306, 1194–1198 (2004). This article arguably launched the field of image-based profiling, as it presents the high-dimensional analysis of images of cells in the presence of chemical perturbations.

    CAS  PubMed  Google Scholar 

  38. 38.

    Abraham, V. C., Taylor, D. L. & Haskins, J. R. High content screening applied to large-scale cell biology. Trends Biotechnol. 22, 15–22 (2004).

    CAS  PubMed  Google Scholar 

  39. 39.

    Michael Ando, D., McLean, C. Y. & Berndl, M. Improving phenotypic measurements in high-content imaging screens. Preprint at bioRxiv https://doi.org/10.1101/161422 (2017).

    Article  Google Scholar 

  40. 40.

    van der Maaten, L. Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008).

    Google Scholar 

  41. 41.

    McInnes, L., Healy, J. & Melville, J. UMAP: uniform manifold approximation and projection for dimension reduction. Preprint at arXiv https://arxiv.org/abs/1802.03426 (2018).

  42. 42.

    Pearson, K. LIII. On lines and planes of closest fit to systems of points in space. Lond. Edinb. Dublin Philos. Mag. J. Sci. 2, 559–572 (1901).

    Google Scholar 

  43. 43.

    Kang, J. et al. Improving drug discovery with high-content phenotypic screens by systematic selection of reporter cell lines. Nat. Biotechnol. 34, 70–77 (2016).

    CAS  PubMed  Google Scholar 

  44. 44.

    Cox, M. J. et al. Tales of 1,008 small molecules: phenomic profiling through live-cell imaging in a panel of reporter cell lines. Sci. Rep. 10, 13262 (2020). This study quantifies the improvement in the ability of image-based profiling using Cell Painting to distinguish compound MOAs in 15 cell lines versus the best single cell line (approximately twofold); use of data from all 15 cell lines distinguished only about half of the MOAs.

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Scheeder, C., Heigwer, F. & Boutros, M. Machine learning and image-based profiling in drug discovery. Curr. Opin. Syst. Biol. 10, 43–52 (2018).

    PubMed  PubMed Central  Google Scholar 

  46. 46.

    Lu, A. X., Kraus, O. Z., Cooper, S. & Moses, A. M. Learning unsupervised feature representations for single cell microscopy images with paired cell inpainting. PLoS Comput. Biol. 15, e1007348 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Zhou, Z.-H. A brief introduction to weakly supervised learning. Natl Sci. Rev. 5, 44–53 (2018).

    Google Scholar 

  48. 48.

    Caicedo, J. C., McQuin, C., Goodman, A., Singh, S. & Carpenter, A. E. Weakly supervised learning of single-cell feature embeddings. Proc IEEE Comput. Soc. Conf. Comput Vis. Pattern Recognit. https://doi.org/10.1109/CVPR.2018.00970 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  49. 49.

    Teschendorff, A. E. Avoiding common pitfalls in machine learning omic data science. Nat. Mater. 18, 422–427 (2019).

    CAS  PubMed  Google Scholar 

  50. 50.

    Riley, P. Three pitfalls to avoid in machine learning. Nature 572, 27–29 (2019).

    CAS  PubMed  Google Scholar 

  51. 51.

    Pegoraro, G. & Misteli, T. High-throughput imaging for the discovery of cellular mechanisms of disease. Trends Genet. 33, 604–615 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Cataldo, A. M. et al. Abnormalities in mitochondrial structure in cells from patients with bipolar disorder. Am. J. Pathol. 177, 575–585 (2010).

    PubMed  PubMed Central  Google Scholar 

  53. 53.

    Blanchet, L. et al. Quantifying small molecule phenotypic effects using mitochondrial morpho-functional fingerprinting and machine learning. Sci. Rep. 5, 8035 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. 54.

    Hung, C. L.-K. et al. A patient-derived cellular model for Huntington’s disease reveals phenotypes at clinically relevant CAG lengths. Mol. Biol. Cell 29, 2809–2820 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. 55.

    Smith, I. et al. Evaluation of RNAi and CRISPR technologies by large-scale gene expression profiling in the Connectivity Map. PLoS Biol. 15, e2003213 (2017).

    PubMed  PubMed Central  Google Scholar 

  56. 56.

    Gibson, C. C. et al. Strategy for identifying repurposed drugs for the treatment of cerebral cavernous malformation. Circulation 131, 289–299 (2015).

    CAS  PubMed  Google Scholar 

  57. 57.

    Jiao, Y. et al. Discovering metabolic disease gene interactions by correlated effects on cellular morphology. Mol. Metab. 24, 108–119 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. 58.

    Thyme, S. B. et al. Phenotypic landscape of schizophrenia-associated genes defines candidates and their shared functions. Cell 177, 478–491.e20 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. 59.

    Rohban, M. H. et al. Systematic morphological profiling of human gene and allele function via cell painting. eLife 6, e24060 (2017).

    PubMed  PubMed Central  Google Scholar 

  60. 60.

    Berger, A. H. et al. High-throughput phenotyping of lung cancer somatic mutations. Cancer Cell 32, 884 (2017).

    CAS  PubMed  Google Scholar 

  61. 61.

    Ben-David, U. et al. Genetic and transcriptional evolution alters cancer cell line drug response. Nature 560, 325–330 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. 62.

    Dawson, J. C. et al. A synergistic anti-cancer FAK and HDAC inhibitor combination discovered by a novel chemical-genetic high-content phenotypic screen. Mol. Cancer Ther. https://doi.org/10.1101/590802 (2020).

    Article  PubMed  Google Scholar 

  63. 63.

    Papanikolopoulou, K., Mudher, A. & Skoulakis, E. An assessment of the translational relevance of Drosophila in drug discovery. Expert Opin. Drug Discov. 14, 303–313 (2019).

    CAS  PubMed  Google Scholar 

  64. 64.

    Costa, B., Estrada, M. F., Mendes, R. V. & Fior, R. Zebrafish avatars towards personalized medicine-a comparative review between avatar models. Cells 9, 293 (2020).

    CAS  PubMed Central  Google Scholar 

  65. 65.

    Astone, M., Dankert, E. N., Alam, S. K. & Hoeppner, L. H. Fishing for cures: the alLURE of using zebrafish to develop precision oncology therapies. NPJ Precis. Oncol. 1, 39 (2017).

    PubMed  PubMed Central  Google Scholar 

  66. 66.

    Vashi, N. & Justice, M. J. Treating Rett syndrome: from mouse models to human therapies. Mamm. Genome 30, 90–110 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  67. 67.

    Dar, A. C., Das, T. K., Shokat, K. M. & Cagan, R. L. Chemical genetic discovery of targets and anti-targets for cancer polypharmacology. Nature 486, 80–84 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  68. 68.

    Rihel, J. et al. Zebrafish behavioral profiling links drugs to biological targets and rest/wake regulation. Science 327, 348–351 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  69. 69.

    Bruni, G. et al. Zebrafish behavioral profiling identifies multitarget antipsychotic-like compounds. Nat. Chem. Biol. 12, 559–566 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  70. 70.

    Jordi, J. et al. High-throughput screening for selective appetite modulators: a multibehavioral and translational drug discovery strategy. Sci. Adv. 4, eaav1966 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  71. 71.

    McCarroll, M. N. et al. Zebrafish behavioural profiling identifies GABA and serotonin receptor ligands related to sedation and paradoxical excitation. Nat. Commun. 10, 4078 (2019).

    PubMed  PubMed Central  Google Scholar 

  72. 72.

    Kümmel, A. et al. Differentiation and visualization of diverse cellular phenotypic responses in primary high-content screening. J. Biomol. Screen. 17, 843–849 (2012).

    PubMed  Google Scholar 

  73. 73.

    Loo, L.-H., Wu, L. F. & Altschuler, S. J. Image-based multivariate profiling of drug responses from single cells. Nat. Methods 4, 445–453 (2007).

    CAS  PubMed  Google Scholar 

  74. 74.

    Vial, M.-L. et al. A grand challenge. 2. Phenotypic profiling of a natural product library on Parkinson’s patient-derived cells. J. Nat. Prod. 79, 1982–1989 (2016).

    CAS  PubMed  Google Scholar 

  75. 75.

    Caie, P. D. et al. High-content phenotypic profiling of drug response signatures across distinct cancer cells. Mol. Cancer Ther. 9, 1913–1926 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  76. 76.

    Hughes, R. E. et al. High-content phenotypic profiling in esophageal adenocarcinoma identifies selectively active pharmacological classes of drugs for repurposing and chemical starting points for novel drug discovery. SLAS Discov. https://doi.org/10.1177/2472555220917115 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  77. 77.

    Gerry, C. J. et al. Real-time biological annotation of synthetic compounds. J. Am. Chem. Soc. 138, 8920–8927 (2016). This study launched many further examples using Cell Painting interactively with synthetic chemistry for medicinal chemistry and SAR determination.

    CAS  PubMed  PubMed Central  Google Scholar 

  78. 78.

    Nelson, S. D. Jr. Wawer, M. J. & Schreiber, S. L. Divergent synthesis and real-time biological annotation of optically active tetrahydrocyclopenta[c]pyranone derivatives. Org. Lett. 18, 6280–6283 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  79. 79.

    Melillo, B. et al. Synergistic effects of stereochemistry and appendages on the performance diversity of a collection of synthetic compounds. J. Am. Chem. Soc. 140, 11784–11790 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  80. 80.

    Zimmermann, S. et al. A scaffold-diversity synthesis of biologically intriguing cyclic sulfonamides. Chemistry 25, 15498–15503 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  81. 81.

    Gaulton, A. et al. The ChEMBL database in 2017. Nucleic Acids Res. 45, D945–D954 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  82. 82.

    Bray, M.-A. et al. A dataset of images and morphological profiles of 30,000 small-molecule treatments using the Cell Painting assay. Gigascience https://doi.org/10.1093/gigascience/giw014 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  83. 83.

    Hofmarcher, M., Rumetshofer, E., Clevert, D.-A., Hochreiter, S. & Klambauer, G. Accurate prediction of biological assays with high-throughput microscopy images and convolutional networks. J. Chem. Inf. Model. 59, 1163–1171 (2019). This article describes how combining convolutional neural networks with images of broad, generic stains boosts biological information extraction from microscopy images.

    CAS  PubMed  Google Scholar 

  84. 84.

    Hwang, T. J. et al. Failure of investigational drugs in late-stage clinical development and publication of trial results. JAMA Intern. Med. 176, 1826–1833 (2016).

    PubMed  Google Scholar 

  85. 85.

    Thomas, R. S. et al. The next generation blueprint of computational toxicology at the US Environmental Protection Agency. Toxicol. Sci. 169, 317–332 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  86. 86.

    Paul Friedman, K. et al. Utility of in vitro bioactivity as a lower bound estimate of in vivo adverse effect levels and in risk-based prioritization. Toxicol. Sci. 173, 202–225 (2020).

    PubMed  Google Scholar 

  87. 87.

    Nyffeler, J. et al. Bioactivity screening of environmental chemicals using imaging-based high-throughput phenotypic profiling. Toxicol. Appl. Pharmacol. 389, 114876 (2020). This careful analysis describes the use of Cell Painting to report on various aspects of toxicity.

    CAS  PubMed  Google Scholar 

  88. 88.

    Way, G. P. et al. Predicting cell health phenotypes using image-based morphology profiling. Preprint at bioRxiv https://doi.org/10.1101/2020.07.08.193938 (2020).

    Article  Google Scholar 

  89. 89.

    Su, R., Xiong, S., Zink, D. & Loo, L.-H. High-throughput imaging-based nephrotoxicity prediction for xenobiotics with diverse chemical structures. Arch. Toxicol. 90, 2793–2808 (2016).

    CAS  PubMed  Google Scholar 

  90. 90.

    Lee, J.-Y. J., Miller, J. A., Basu, S., Kee, T.-Z. V. & Loo, L.-H. Building predictive in vitro pulmonary toxicity assays using high-throughput imaging and artificial intelligence. Arch. Toxicol. 92, 2055–2075 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  91. 91.

    Zhang, Q., Li, J., Peng, S., Zhang, Y. & Qiao, Y. Rosmarinic acid as a candidate in a phenotypic profiling cardio-/cytotoxicity cell model induced by doxorubicin. Molecules 25, 836 (2020).

    CAS  PubMed Central  Google Scholar 

  92. 92.

    Yin, L. et al. High-content image-based single-cell phenotypic analysis for the testicular toxicity prediction induced by bisphenol A and its analogs bisphenol S, bisphenol AF, and tetrabromobisphenol A in a three-dimensional testicular cell co-culture model. Toxicol. Sci. 173, 313–335 (2020).

    CAS  PubMed  Google Scholar 

  93. 93.

    Godinez, W. J. et al. Morphological deconvolution of beta-lactam polyspecificity in E. coli. ACS Chem. Biol. 14, 1217–1226 (2019).

    CAS  PubMed  Google Scholar 

  94. 94.

    Tanaka, M. et al. An unbiased cell morphology–based screen for new, biologically active small molecules. PLoS Biol. 3, e128 (2005).

    PubMed  PubMed Central  Google Scholar 

  95. 95.

    Lin, S. et al. Diversity focused semisyntheses of tetronate polyether ionophores. Preprint at ChemRxiv https://doi.org/10.26434/chemrxiv.8299715 (2019).

    Article  Google Scholar 

  96. 96.

    Mayer, T. U. et al. Small molecule inhibitor of mitotic spindle bipolarity identified in a phenotype-based screen. Science 286, 971–974 (1999).

    CAS  PubMed  Google Scholar 

  97. 97.

    Mechanism matters. Nat. Med. 16, 347 (2010).

  98. 98.

    MacDonald, M. L. et al. Identifying off-target effects and hidden phenotypes of drugs in human cells. Nat. Chem. Biol. 2, 329–337 (2006).

    CAS  PubMed  Google Scholar 

  99. 99.

    Schenone, M., Dančík, V., Wagner, B. K. & Clemons, P. A. Target identification and mechanism of action in chemical biology and drug discovery. Nat. Chem. Biol. 9, 232–240 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  100. 100.

    Kubota, K., Funabashi, M. & Ogura, Y. Target deconvolution from phenotype-based drug discovery by using chemical proteomics approaches. Biochim. Biophys. Acta Proteins Proteom. 1867, 22–27 (2019).

    CAS  PubMed  Google Scholar 

  101. 101.

    Woehrmann, M. H. et al. Large-scale cytological profiling for functional analysis of bioactive compounds. Mol. Biosyst. 9, 2604–2617 (2013).

    CAS  PubMed  Google Scholar 

  102. 102.

    Slack, M. D., Martinez, E. D., Wu, L. F. & Altschuler, S. J. Characterizing heterogeneous cellular responses to perturbations. Proc. Natl Acad. Sci. USA 105, 19306–19311 (2008).

    CAS  PubMed  Google Scholar 

  103. 103.

    Ljosa, V. et al. Comparison of methods for image-based profiling of cellular morphological responses to small-molecule treatment. J. Biomol. Screen. 18, 1321–1329 (2013).

    CAS  PubMed  Google Scholar 

  104. 104.

    Gustafsdottir, S. M. et al. Multiplex cytological profiling assay to measure diverse cellular States. PLoS ONE 8, e80999 (2013).

    PubMed  PubMed Central  Google Scholar 

  105. 105.

    Gebre, A. A. et al. Profiling of the effects of antifungal agents on yeast cells based on morphometric analysis. FEMS Yeast Res. 15, fov040 (2015).

    PubMed  Google Scholar 

  106. 106.

    Futamura, Y. et al. Morphobase, an encyclopedic cell morphology database, and its use for drug target identification. Chem. Biol. 19, 1620–1630 (2012).

    CAS  PubMed  Google Scholar 

  107. 107.

    Reisen, F. et al. Linking phenotypes and modes of action through high-content screen fingerprints. ASSAY. Drug Dev. Technol. 13, 415–427 (2015).

    CAS  PubMed  Google Scholar 

  108. 108.

    Laraia, L. et al. Image-based morphological profiling identifies a lysosomotropic, iron-sequestering autophagy inhibitor. Angew. Chem. Int. Ed. https://doi.org/10.1002/ange.201913712 (2020).

    Article  Google Scholar 

  109. 109.

    Hansen, B. K. et al. Structure and function of the bacterial protein toxin phenomycin. Structure 28, 528–539.e9 (2020).

    CAS  PubMed  Google Scholar 

  110. 110.

    Christoforow, A. et al. Design, synthesis, and phenotypic profiling of pyrano-furo-pyridone pseudo natural products. Angew. Chem. Int. Ed. 58, 14715–14723 (2019).

    CAS  Google Scholar 

  111. 111.

    Peters, C. E. et al. Rapid inhibition profiling identifies a keystone target in the nucleotide biosynthesis pathway. ACS Chem. Biol. 13, 3251–3258 (2018).

    CAS  PubMed  Google Scholar 

  112. 112.

    Schulze, C. J. et al. ‘Function-first’ lead discovery: mode of action profiling of natural product libraries using image-based screening. Chem. Biol. 20, 285–295 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  113. 113.

    Ochoa, J. L., Bray, W. M., Lokey, R. S. & Linington, R. G. Phenotype-guided natural products discovery using cytological profiling. J. Nat. Prod. 78, 2242–2248 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  114. 114.

    Kremb, S., Müller, C., Schmitt-Kopplin, P. & Voolstra, C. R. Bioactive potential of marine macroalgae from the central red sea (Saudi Arabia) assessed by high-throughput imaging-based phenotypic profiling. Mar. Drugs 15, 80 (2017).

    PubMed Central  Google Scholar 

  115. 115.

    Boyd, J. C., Pinheiro, A., Nery, E. D., Reyal, F. & Walter, T. Domain-invariant features for mechanism of action prediction in a multi-cell-line drug screen. Bioinformatics https://doi.org/10.1093/bioinformatics/btz774 (2019).

    Article  PubMed Central  Google Scholar 

  116. 116.

    Warchal, S. J. et al. High content phenotypic screening identifies serotonin receptor modulators with selective activity upon breast cancer cell cycle and cytokine signaling pathways. Bioorganic Medicinal Chem. 28, 115209 (2020).

    CAS  Google Scholar 

  117. 117.

    Hight, S. K. et al. High-throughput functional annotation of natural products by integrated activity profiling. Pharmacol. Toxicol. https://doi.org/10.1101/748129 (2019).

    Article  Google Scholar 

  118. 118.

    Kurita, K. L., Glassey, E. & Linington, R. G. Integration of high-content screening and untargeted metabolomics for comprehensive functional annotation of natural product libraries. Proc. Natl Acad. Sci. USA 112, 11999–12004 (2015).

    CAS  PubMed  Google Scholar 

  119. 119.

    Nassiri, I. & McCall, M. N. Systematic exploration of cell morphological phenotypes associated with a transcriptomic query. Nucleic Acids Res. 46, e116 (2018).

    PubMed  PubMed Central  Google Scholar 

  120. 120.

    Breinig, M., Klein, F. A., Huber, W. & Boutros, M. A chemical–genetic interaction map of small molecules using high-throughput imaging in cancer cells. Mol. Syst. Biol. 11, 846 (2015). This is a landmark study on the interaction of genes and compounds by use of isogenic cell lines, all mapped using image-based phenotypes.

    PubMed  PubMed Central  Google Scholar 

  121. 121.

    Ohnuki, S., Oka, S., Nogami, S. & Ohya, Y. High-content, image-based screening for drug targets in yeast. PLoS ONE 5, e10177 (2010). This is a pioneering study in yeast demonstrating successful identification of small-molecule mimics of gene signatures (and vice versa for target identification) using image-based data.

    PubMed  PubMed Central  Google Scholar 

  122. 122.

    Piotrowski, J. S. et al. Plant-derived antifungal agent poacic acid targets β-1,3-glucan. Proc. Natl Acad. Sci. USA 112, E1490–E1497 (2015).

    CAS  PubMed  Google Scholar 

  123. 123.

    Iwaki, A., Ohnuki, S., Suga, Y., Izawa, S. & Ohya, Y. Vanillin inhibits translation and induces messenger ribonucleoprotein (mRNP) granule formation in saccharomyces cerevisiae: application and validation of high-content, image-based profiling. PLoS ONE 8, e61748 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  124. 124.

    Sundaramurthy, V. et al. Integration of chemical and RNAi multiparametric profiles identifies triggers of intracellular mycobacterial killing. Cell Host Microbe 13, 129–142 (2013).

    CAS  PubMed  Google Scholar 

  125. 125.

    Sundaramurthy, V. et al. Deducing the mechanism of action of compounds identified in phenotypic screens by integrating their multiparametric profiles with a reference genetic screen. Nat. Protoc. 9, 474–490 (2014).

    CAS  PubMed  Google Scholar 

  126. 126.

    Eggert, U. S. et al. Parallel chemical genetic and genome-wide RNAi screens identify cytokinesis inhibitors and targets. PLoS Biol. 2, e379 (2004).

    PubMed  PubMed Central  Google Scholar 

  127. 127.

    Singh, S. et al. Morphological profiles of RNAi-induced gene knockdown are highly reproducible but dominated by seed effects. PLoS ONE 10, e0131370 (2015).

    PubMed  PubMed Central  Google Scholar 

  128. 128.

    Jinek, M. et al. A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity. Science 337, 816–821 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  129. 129.

    Jackson, A. L. et al. Expression profiling reveals off-target gene regulation by RNAi. Nat. Biotechnol. 21, 635–637 (2003).

    CAS  PubMed  Google Scholar 

  130. 130.

    Young, D. W. et al. Integrating high-content screening and ligand-target prediction to identify mechanism of action. Nat. Chem. Biol. 4, 59–68 (2008).

    CAS  PubMed  Google Scholar 

  131. 131.

    Caldera, M. et al. Mapping the perturbome network of cellular perturbations. Nat. Commun. 10, 5140 (2019).

    PubMed  PubMed Central  Google Scholar 

  132. 132.

    Fischer, B. et al. A map of directional genetic interactions in a metazoan cell. eLife 4, e05464 (2015).

    PubMed Central  Google Scholar 

  133. 133.

    Yin, Z. et al. A screen for morphological complexity identifies regulators of switch-like transitions between discrete cell shapes. Nat. Cell Biol. 15, 860–871 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  134. 134.

    Usaj, M. M. et al. Systematic genetics and single-cell imaging reveal widespread morphological pleiotropy and cell-to-cell variability. Mol. Syst. Biol. 16, e9243 (2020).

    PubMed  PubMed Central  Google Scholar 

  135. 135.

    Chong, Y. T. et al. Yeast proteome dynamics from single cell imaging and automated analysis. Cell 161, 1413–1424 (2015).

    CAS  PubMed  Google Scholar 

  136. 136.

    Neumann, B. et al. Phenotypic profiling of the human genome by time-lapse microscopy reveals cell division genes. Nature 464, 721–727 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  137. 137.

    Unadkat, H. V. et al. An algorithm-based topographical biomaterials library to instruct cell fate. Proc. Natl Acad. Sci. USA 108, 16565–16570 (2011).

    CAS  PubMed  Google Scholar 

  138. 138.

    Ruan, X. et al. Image-derived models of cell organization changes during differentiation of PC12 cells. Preprint at bioRxiv https://doi.org/10.1101/522763 (2019).

    Article  Google Scholar 

  139. 139.

    Doan, M. & Carpenter, A. E. Leveraging machine vision in cell-based diagnostics to do more with less. Nat. Mater. 18, 414–418 (2019).

    CAS  PubMed  Google Scholar 

  140. 140.

    De Fauw, J. et al. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat. Med. 24, 1342–1350 (2018).

    Google Scholar 

  141. 141.

    Zhu, Y. et al. An image informatics pipeline for imaging mass cytometry to characterize the immune landscape in pre- and on-treatment immune therapy and its application in recurrent platinium-resistant epithelial ovarian cancer. in 2019 IEEE EMBS International Conference on Biomedical Health Informatics (BHI) 1–4 (IEEE, 2019).

  142. 142.

    Mobadersany, P. et al. Predicting cancer outcomes from histology and genomics using convolutional networks. Proc. Natl Acad. Sci. USA 115, E2970–E2979 (2018).

    CAS  PubMed  Google Scholar 

  143. 143.

    Jackson, H. W. et al. The single-cell pathology landscape of breast cancer. Nature 578, 615–620 (2020).

    CAS  PubMed  Google Scholar 

  144. 144.

    Coudray, N. et al. Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nat. Med. 24, 1559–1567 (2018).

    CAS  PubMed  Google Scholar 

  145. 145.

    Ash, J. T., Darnell, G., Munro, D. & Engelhardt, B. E. Joint analysis of gene expression levels and histological images identifies genes associated with tissue morphology. Preprint at bioRxiv https://doi.org/10.1101/458711 (2018).

    Article  Google Scholar 

  146. 146.

    Quach, D. T., Sakoulas, G., Nizet, V., Pogliano, J. & Pogliano, K. Bacterial cytological profiling (BCP) as a rapid and accurate antimicrobial susceptibility testing method for staphylococcus aureus. EBioMedicine 4, 95–103 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  147. 147.

    Dekkers, J. F. et al. A functional CFTR assay using primary cystic fibrosis intestinal organoids. Nat. Med. 19, 939–945 (2013).

    CAS  PubMed  Google Scholar 

  148. 148.

    Snijder, B. et al. Image-based ex-vivo drug screening for patients with aggressive haematological malignancies: interim results from a single-arm, open-label, pilot study. Lancet Haematol. 4, e595–e606 (2017).

    PubMed  PubMed Central  Google Scholar 

  149. 149.

    Irmisch, A. et al. The Tumor Profiler Study: integrated, multi-omic, functional tumor profiling for clinical decision support. Oncology https://doi.org/10.1101/2020.02.13.20017921 (2020).

    Article  Google Scholar 

  150. 150.

    Betge, J. et al. Multiparametric phenotyping of compound effects on patient derived organoids. Preprint at bioRxiv https://doi.org/10.1101/660993 (2019).

    Article  Google Scholar 

  151. 151.

    Chen, H., Engkvist, O., Wang, Y., Olivecrona, M. & Blaschke, T. The rise of deep learning in drug discovery. Drug Discov. Today 23, 1241–1250 (2018).

    PubMed  Google Scholar 

  152. 152.

    Kraus, O. Z. & Frey, B. J. Computer vision for high content screening. Crit. Rev. Biochem. Mol. Biol. 51, 102–109 (2016).

    PubMed  Google Scholar 

  153. 153.

    Moen, E. et al. Deep learning for cellular image analysis. Nat. Methods 16, 1233–1246 (2019).

    CAS  PubMed  Google Scholar 

  154. 154.

    Chessel, A. & Carazo Salas, R. E. From observing to predicting single-cell structure and function with high-throughput/high-content microscopy. Essays Biochem. 63, 197–208 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  155. 155.

    Wollmann, T. et al. GRUU-Net: integrated convolutional and gated recurrent neural network for cell segmentation. Med. Image Anal. 56, 68–79 (2019).

    CAS  PubMed  Google Scholar 

  156. 156.

    Caicedo, J. C. et al. Nucleus segmentation across imaging experiments: the 2018 Data Science Bowl. Nat. Methods 16, 1247–1253 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  157. 157.

    Jackson, P. T. et al. Phenotypic profiling of high throughput imaging screens with generic deep convolutional features. in 2019 16th International Conference on Machine Vision Applications (MVA) 1–4 (IEEE, 2019).

  158. 158.

    McQuin, C. et al. CellProfiler 3.0: next-generation image processing for biology. PLoS Biol. 16, e2005970 (2018).

    PubMed  PubMed Central  Google Scholar 

  159. 159.

    Eulenberg, P. et al. Reconstructing cell cycle and disease progression using deep learning. Nat. Commun. 8, 463 (2017).

    PubMed  PubMed Central  Google Scholar 

  160. 160.

    Kensert, A., Harrison, P. J. & Spjuth, O. Transfer learning with deep convolutional neural networks for classifying cellular morphological changes. SLAS Discov. 24, 466–475 (2019).

    PubMed  PubMed Central  Google Scholar 

  161. 161.

    Rohban, M. H., Abbasi, H. S., Singh, S. & Carpenter, A. E. Capturing single-cell heterogeneity via data fusion improves image-based profiling. Nat. Commun. 10, 2082 (2019).

    PubMed  PubMed Central  Google Scholar 

  162. 162.

    Lim, N. & Pavlidis, P. Evaluation of connectivity map shows limited reproducibility in drug repositioning. Preprint at bioRxiv https://doi.org/10.1101/845693 (2019).

    Article  Google Scholar 

  163. 163.

    Ellenberg, J. et al. A call for public archives for biological image data. Nat. Methods 15, 849–854 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  164. 164.

    Swinney, D. C. The contribution of mechanistic understanding to phenotypic screening for first-in-class medicines. J. Biomol. Screen. 18, 1186–1192 (2013).

    PubMed  Google Scholar 

  165. 165.

    Eder, J., Sedrani, R. & Wiesmann, C. The discovery of first-in-class drugs: origins and evolution. Nat. Rev. Drug Discov. 13, 577–587 (2014).

    CAS  PubMed  Google Scholar 

  166. 166.

    Warchal, S. J., Unciti-Broceta, A. & Carragher, N. O. Next-generation phenotypic screening. Future Med. Chem. 8, 1331–1347 (2016).

    CAS  PubMed  Google Scholar 

  167. 167.

    Swinney, D. C. & Anthony, J. How were new medicines discovered? Nat. Rev. Drug Discov. 10, 507–519 (2011).

    CAS  PubMed  Google Scholar 

  168. 168.

    Vincent, F., Loria, P., Pregel, M. & Stanton, R. Developing predictive assays: the phenotypic screening ‘rule of 3’. Sci. Transl Med. https://doi.org/10.1126/scitranslmed.aab1201 (2015).

    Article  PubMed  Google Scholar 

  169. 169.

    Lau, T. A., Bray, W. M. & Lokey, R. S. macrophage cytological profiling and anti-inflammatory drug discovery. Assay. Drug Dev. Technol. 17, 14–16 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  170. 170.

    RxRx. https://www.rxrx.ai/.

  171. 171.

    Lin, J.-R. et al. Highly multiplexed immunofluorescence imaging of human tissues and tumors using t-CyCIF and conventional optical microscopes. eLife 7, e31657 (2018).

    PubMed  PubMed Central  Google Scholar 

  172. 172.

    Gerdes, M. J. et al. Highly multiplexed single-cell analysis of formalin-fixed, paraffin-embedded cancer tissue. Proc. Natl Acad. Sci. USA 110, 11982–11987 (2013).

    CAS  PubMed  Google Scholar 

  173. 173.

    Bolognesi, M. M. et al. Multiplex staining by sequential immunostaining and antibody removal on routine tissue sections. J. Histochem. Cytochem. 65, 431–444 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  174. 174.

    Glass, G., Papin, J. A. & Mandell, J. W. SIMPLE: a sequential immunoperoxidase labeling and erasing method. J. Histochem. Cytochem. 57, 899–905 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  175. 175.

    Baharlou, H., Canete, N. P., Cunningham, A. L., Harman, A. N. & Patrick, E. Mass cytometry imaging for the study of human diseases-applications and data analysis strategies. Front. Immunol. 10, 2657 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  176. 176.

    Rappez, L. et al. Spatial single-cell profiling of intracellular metabolomes in situ. Preprint at bioRxiv https://doi.org/10.1101/510222 (2019).

    Article  Google Scholar 

  177. 177.

    Kang, Z. B. et al. Fluopack screening platform for unbiased cellular phenotype profiling. Sci. Rep. 10, 2097 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  178. 178.

    Perez-Gomez, A. et al. A phenotypic caenorhabditis elegans screen identifies a selective suppressor of antipsychotic-induced hyperphagia. Nat. Commun. 9, 5272 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  179. 179.

    Cassar, S. et al. Measuring drug absorption improves interpretation of behavioral responses in a larval zebrafish locomotor assay for predicting seizure liability. J. Pharmacol. Toxicol. Methods 88, 56–63 (2017).

    CAS  PubMed  Google Scholar 

  180. 180.

    Becker, T., Caicedo, J. C., Singh, S., Weckmann, M. & Carpenter, A. E. Combining morphological and migration profiles of in vitro time-lapse data. in 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) 965–968 (IEEE, 2018).

  181. 181.

    Qian, W. W. et al. Batch equalization with a generative adversarial network. Preprint at bioRxiv https://doi.org/10.1101/2020.02.07.939215 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  182. 182.

    Rastelli, G. & Pinzi, L. Computational polypharmacology comes of age. Front. Pharmacol. 6, 157 (2015).

    PubMed  PubMed Central  Google Scholar 

  183. 183.

    Proschak, E., Stark, H. & Merk, D. Polypharmacology by design: a medicinal chemist’s perspective on multitargeting compounds. J. Med. Chem. 62, 420–444 (2019).

    CAS  PubMed  Google Scholar 

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Acknowledgements

The authors appreciate helpful comments from S. Jaensch, S. Singh, J. Caicedo, N. Rindtorff and all members of the Carpenter laboratory. The authors acknowledge funding support for S.N.C. and A.E.C. from the US National Institutes of Health (R35 GM122547 to A.E.C.).

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The authors contributed equally to all aspects of the article.

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Correspondence to Anne E. Carpenter.

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H.C. and J.D.B. are employed by Janssen and Pfizer, respectively. A.E.C. is on the Scientific and Technical Advisory Board of, has optional ownership interest in and receives income from Recursion. S.N.C. declares no conflict of interest.

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Glossary

Proteomic profiling

Measuring the levels of a large number of proteins in a sample, sometimes including their post-translationally modified forms.

Metabolomic profiling

Measuring the levels of a large number of metabolites in a sample.

Mechanism of action

(MOA). The description of how a compound interacts with a target and affects a biological system.

Side information

Further available measurements or metadata about samples that indirectly improve predictive performance.

Labels

Values for particular parameters in a given set of samples. For example, each compound in a dataset might have a mechanism of action label or a toxicity label.

Lead optimization

The process of narrowing down compounds after hit expansion to those with desired activity.

Brightfield images

Images captured from a sample without using any fluorescent illumination light.

Supervision

In machine learning, supervised learning aims for the system to predict the correct answers for each input, on the basis of examples. By contrast, in unsupervised learning the goal is to learn useful representations of each sample such that the similarities and differences among them can be observed.

Polypharmacology

The property of a compound whereby it interacts with more than a single target.

Neural network

A machine-learning architecture whereby features of a sample (for example, image pixels or image-derived metrics) are fed into a network of nodes, which collectively learn to produce the correct answer for that sample by each node adjusting its contribution (weight) to the final answer.

Hit expansion

The selection of compounds that were not tested in the primary screen, to broaden the diversity of the chemical space for hit selection. Compounds are selected on the basis of similarities in structure or biological activity to candidate hits.

SAR studies

An iterative process for lead optimization in which assays are applied to determine the effect of successive structural modifications to a compound on activity.

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Chandrasekaran, S.N., Ceulemans, H., Boyd, J.D. et al. Image-based profiling for drug discovery: due for a machine-learning upgrade?. Nat Rev Drug Discov 20, 145–159 (2021). https://doi.org/10.1038/s41573-020-00117-w

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