Image-based Profiling Research Articles

Probing platelet activity before and after COVID-19 vaccinations by image-based profiling and temporal monitoring

Probing platelet activity before and after COVID-19 vaccinations by image-based profiling and temporal monitoring

Probing platelet activity before and after COVID-19 vaccinations by image-based profiling and temporal monitoring

Probing platelet activity before and after COVID-19 vaccinations by image-based profiling and temporal monitoring

Probing platelet activity before and after COVID-19 vaccinations by image-based profiling and temporal monitoring

Probing platelet activity before and after COVID-19 vaccinations by image-based profiling and temporal monitoring

Massive image-based single-cell profiling reveals high levels of circulating platelet aggregates in patients with COVID-19

A characteristic clinical feature of COVID-19 is the frequent incidence of microvascular thrombosis. In fact, COVID-19 autopsy reports have shown widespread thrombotic microangiopathy characterized by extensive diffuse microthrombi within peripheral capillaries and arterioles in lungs, hearts, and other organs, resulting in multiorgan failure. However, the underlying process of COVID-19-associated microvascular thrombosis remains elusive due to the lack of tools to statistically examine platelet aggregation (i.e., the initiation of microthrombus formation) in detail. Here we report the landscape of circulating platelet aggregates in COVID-19 obtained by massive single-cell image-based profiling and temporal monitoring of the blood of COVID-19 patients (n = 110). Surprisingly, our analysis of the big image data shows the anomalous presence of excessive platelet aggregates in nearly 90% of all COVID-19 patients. Furthermore, results indicate strong links between the concentration of platelet aggregates and the severity, mortality, respiratory condition, and vascular endothelial dysfunction level of COVID-19 patients.

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

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

TMOD-04. IMAGE-BASED DRUG RESPONSE PROFILING FROM PEDIATRIC TUMOR CELL SPHEROIDS USING PATIENT-BY-PATIENT DEEP TRANSFER LEARNING

Abstract Introduction Image-based phenotypic drug profiling is receiving increasing attention in drug discovery and precision medicine. Compared to classical end-point measurements quantifying drug response, image-based profiling enables both the quantification of drug response and characterization of disease entities and drug mechanisms of actions. In pediatric precision oncology, we aim to study drug response in patient-derived 3D spheroid tumor cell cultures and tackle the challenges of a lack of image-segmentation methods and limited patient-derived material. Methods We investigate deep transfer learning with patient-by-patient fine-tuning for cell-viability quantification. We fine-tune a convolutional neural network (pre-trained on ImageNet) with many cell-line-specific and few patient-specific assay controls. The method is validated using 3D cell cultures in 384-well microplates derived from cell lines with known drug sensitivities and tested with primary patient-derived samples. Network outputs at different drug concentrations are used for drug-sensitivity scoring; dense-layer activations are used in t-distributed stochastic neighbor embedding and clustering of drugs. Results Cell-line experiments confirm expected hits, such as effective treatment with BRAF inhibitors in a BRAF V600E mutated brain tumor model and NTRK inhibitors in a cell line harboring an NTRK-fusion, indicating the predictive power of deep learning to identify drug-hit candidates for individual patients. In patient-derived samples, clustering of drugs further confirms phenotypic similarity according to their mechanisms of actions. Combining drug scoring with phenotypic clustering may provide opportunities for complementary combination treatments. Conclusion Deep transfer learning with patient-by-patient fine-tuning is a promising, segmentation-free image-analysis approach for precision medicine and drug discovery based on 3D spheroid cell cultures.

Which image-based phenotypes are most promising for using AI to understand cellular functions and why?

Which image-based phenotypes are most promising for using AI to understand cellular functions and why?

FP12.04 Intelligent Label-Free Image-Based Profiling for Lung Cancer Cell Detection and Classification

FP12.04 Intelligent Label-Free Image-Based Profiling for Lung Cancer Cell Detection and Classification

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

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

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

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.

Development of a Kidney Calcification Inhibitor Employing Image-Based Profiling: A Proof-of-Concept Study.

Kidney calcification increases the risk of chronic kidney disease. However, to date, renal calcium phosphate crystallization, a main initiating and driving factor of kidney calcification, has not been explored as a drug target. Pre-clinical drug development is hampered by limited knowledge on the broad range of kidney calcification disorders, characterized by a multifactorial process of disease progression. In this work, we first established an in vitro calcification profiling platform to accelerate pre-clinical drug discovery. The image-based profiling assay allowed the rapid testing of several ionic stimuli and/or inhibitory molecules. We then leveraged a previously established library of inositol hexakisphosphate analogues to identify a renal calcium phosphate inhibitor. A lead compound showed in vitro and in vivo efficacy to prevent calcium phosphate-induced kidney damage. In conclusion, this work reports a renal calcium phosphate inhibitor that could efficiently reduce kidney damage and emphasizes the utility and translational value of the in vitro calcification platform.

Applications of image-based high-throughput phenotypic profiling (HTPP) for hazard evaluation of environmental chemicals

Poster presented to the Intelligent Systems for Molecular Biology virtual meeting July 2020

A novel therapeutic antibody screening method using bacterial high-content imaging reveals functional antibody binding phenotypes of Escherichia coli ST131

The increase of antimicrobial resistance (AMR), and lack of new classes of licensed antimicrobials, have made alternative treatment options for AMR pathogens increasingly attractive. Recent studies have demonstrated anti-bacterial efficacy of a humanised monoclonal antibody (mAb) targeting the O25b O-antigen of Escherichia coli ST131. To evaluate the phenotypic effects of antibody binding to diverse clinical E. coli ST131 O25b bacterial isolates in high-throughput, we designed a novel mAb screening method using high-content imaging (HCI) and image-based morphological profiling to screen a mAb targeting the O25b O-antigen. Screening the antibody against a panel of 86 clinical E. coli ST131 O25:H4 isolates revealed 4 binding phenotypes: no binding (18.60%), weak binding (4.65%), strong binding (69.77%) and strong agglutinating binding (6.98%). Impaired antibody binding could be explained by the presence of insertion sequences or mutations in O-antigen or lipopolysaccharide core biosynthesis genes, affecting the amount, structure or chain length of the O-antigen. The agglutinating binding phenotype was linked with lower O-antigen density, enhanced antibody-mediated phagocytosis and increased serum susceptibly. This study highlights the need to screen candidate mAbs against large panels of clinically relevant isolates, and that HCI can be used to evaluate mAb binding affinity and potential functional efficacy against AMR bacteria.

MS17.03 Quantitative Image Analysis, Image-Based Profiling Including 3D Digital Printing and AI

MS17.03 Quantitative Image Analysis, Image-Based Profiling Including 3D Digital Printing and AI

Capturing single-cell heterogeneity via data fusion improves image-based profiling

Single-cell resolution technologies warrant computational methods that capture cell heterogeneity while allowing efficient comparisons of populations. Here, we summarize cell populations by adding features’ dispersion and covariances to population averages, in the context of image-based profiling. We find that data fusion is critical for these metrics to improve results over the prior alternatives, providing at least ~20% better performance in predicting a compound’s mechanism of action (MoA) and a gene’s pathway.

Image-Based Profiling of Synaptic Connectivity in Primary Neuronal Cell Culture.

Neurological disorders display a broad spectrum of clinical manifestations. Yet, at the cellular level, virtually all these diseases converge into a common phenotype of dysregulated synaptic connectivity. In dementia, synapse dysfunction precedes neurodegeneration and cognitive impairment by several years, making the synapse a crucial entry point for the development of diagnostic and therapeutic strategies. Whereas high-resolution imaging and biochemical fractionations yield detailed insight into the molecular composition of the synapse, standardized assays are required to quickly gauge synaptic connectivity across large populations of cells under a variety of experimental conditions. Such screening capabilities have now become widely accessible with the advent of high-throughput, high-content microscopy. In this review, we discuss how microscopy-based approaches can be used to extract quantitative information about synaptic connectivity in primary neurons with deep coverage. We elaborate on microscopic readouts that may serve as a proxy for morphofunctional connectivity and we critically analyze their merits and limitations. Finally, we allude to the potential of alternative culture paradigms and integrative approaches to enable comprehensive profiling of synaptic connectivity.

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

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

Image-Based Profiling of Patient-Derived Pancreatic Tumor-Stromal Cell Interactions Within a Micropatterned Tumor Model.

Pancreatic cancer is one of the most aggressive cancers with a 5-year patient survival rate of 8.2% and limited availability of therapeutic agents to target metastatic disease. Pancreatic cancer is characterized by a dense stromal cell population with unknown contribution to the progression or suppression of tumor growth. In this study, we describe a microengineered tumor stromal assay of patient-derived pancreatic cancer cells to study the heterotypic interactions of patient pancreatic cancer cells with different types of stromal fibroblasts under basal and drug-treated conditions. The population dynamics of tumor cells in terms of migration and viability were visualized as a functional end point. Coculture with cancer-associated fibroblasts increased the migration of cancer cells when compared to dermal fibroblasts. Finally, we imaged the response of a bromodomain and extraterminal inhibitor on the viability of pancreatic cancer clusters surrounding by stroma in microengineered tumor stromal assay. We visualized a codynamic reduction in both cancer and stromal cells with bromodomain and extraterminal treatment compared to the dimethyl sulfoxide-treated group. This study demonstrates the ability to engineer tumor–stromal assays with patient-derived cells, study the role of diverse types of stromal cells on cancer progression, and precisely visualize a coculture during the screening of therapeutic compounds.

High-Content Analysis of Breast Cancer Using Single-Cell Deep Transfer Learning

High-content analysis has revolutionized cancer drug discovery by identifying substances that alter the phenotype of a cell, which prevents tumor growth and metastasis. The high-resolution biofluorescence images from assays allow precise quantitative measures enabling the distinction of small molecules of a host cell from a tumor. In this work, we are particularly interested in the application of deep neural networks (DNNs), a cutting-edge machine learning method, to the classification of compounds in chemical mechanisms of action (MOAs). Compound classification has been performed using image-based profiling methods sometimes combined with feature reduction methods such as principal component analysis or factor analysis. In this article, we map the input features of each cell to a particular MOA class without using any treatment-level profiles or feature reduction methods. To the best of our knowledge, this is the first application of DNN in this domain, leveraging single-cell information. Furthermore, we use deep transfer learning (DTL) to alleviate the intensive and computational demanding effort of searching the huge parameter’s space of a DNN. Results show that using this approach, we obtain a 30% speedup and a 2% accuracy improvement.

Morphological Profiles of RNAi-Induced Gene Knockdown Are Highly Reproducible but Dominated by Seed Effects.

RNA interference and morphological profiling—the measurement of thousands of phenotypes from individual cells by microscopy and image analysis—are a potentially powerful combination. We show that morphological profiles of RNAi-induced knockdown using the Cell Painting assay are in fact highly sensitive and reproducible. However, we find that the magnitude and prevalence of off-target effects via the RNAi seed-based mechanism make morphological profiles of RNAi reagents targeting the same gene look no more similar than reagents targeting different genes. Pairs of RNAi reagents that share the same seed sequence produce image-based profiles that are much more similar to each other than profiles from pairs designed to target the same gene, a phenomenon previously observed in small-scale gene-expression profiling experiments. Various strategies have been used to enrich on-target versus off-target effects in the context of RNAi screening where a narrow set of phenotypes are measured, mostly based on comparing multiple sequences targeting the same gene; however, new approaches will be needed to make RNAi morphological profiling (that is, comparing multi-dimensional phenotypes) viable. We have shared our raw data and computational pipelines to facilitate research.