High-throughput Image Analysis Research Articles

High-throughput optimized prime editing mediated endogenous protein tagging for pooled imaging of protein localization.

The subcellular organization of proteins carries important information on cellular state and gene function, yet currently there are no technologies that enable accurate measurement of subcellular protein localizations at scale. Here we develop an approach for pooled endogenous protein tagging using prime editing, which coupled with an optical readout and sequencing, provides a snapshot of proteome organization in a manner akin to perturbation-based CRISPR screens. We constructed a pooled library of 17,280 pegRNAs designed to exhaustively tag 60 endogenous proteins spanning diverse localization patterns and explore a large space of genomic and pegRNA design parameters. Pooled measurements of tagging efficiency uncovered both genomic and pegRNA features associated with increased efficiency, including epigenetic states and interactions with transcription. We integrate pegRNA features into a computational model with predictive value for tagging efficiency to constrain the design space of pegRNAs for large-scale peptide knock-in. Lastly, we show that combining in-situ pegRNA sequencing with high-throughput deep learning image analysis, enables exploration of subcellular protein localization patterns for many proteins in parallel following a single pooled lentiviral transduction, setting the stage for scalable studies of proteome dynamics across cell types and environmental perturbations.

Image Analysis Artificial Intelligence Technologies for Plant Phenotyping: Current State of the Art

Modern agriculture is characterized by the use of smart technology and precision agriculture to monitor crops in real time. The technologies enhance total yields by identifying requirements based on environmental conditions. Plant phenotyping is used in solving problems of basic science and allows scientists to characterize crops and select the best genotypes for breeding, hence eliminating manual and laborious methods. Additionally, plant phenotyping is useful in solving problems such as identifying subtle differences or complex quantitative trait locus (QTL) mapping which are impossible to solve using conventional methods. This review article examines the latest developments in image analysis for plant phenotyping using AI, 2D, and 3D image reconstruction techniques by limiting literature from 2020. The article collects data from 84 current studies and showcases novel applications of plant phenotyping in image analysis using various technologies. AI algorithms are showcased in predicting issues expected during the growth cycles of lettuce plants, predicting yields of soybeans in different climates and growth conditions, and identifying high-yielding genotypes to improve yields. The use of high throughput analysis techniques also facilitates monitoring crop canopies for different genotypes, root phenotyping, and late-time harvesting of crops and weeds. The high throughput image analysis methods are also combined with AI to guide phenotyping applications, leading to higher accuracy than cases that consider either method. Finally, 3D reconstruction and a combination with AI are showcased to undertake different operations in applications involving automated robotic harvesting. Future research directions are showcased where the uptake of smartphone-based AI phenotyping and the use of time series and ML methods are recommended.

AnNoBrainer, An Automated Annotation of Mouse Brain Images using Deep Learning.

Annotation of multiple regions of interest across the whole mouse brain is an indispensable process for quantitative evaluation of a multitude of study endpointsin neuroscience digital pathology. Prior experience and domain expert knowledge are the key aspects for image annotation quality and consistency. At present, image annotation is often achieved manually by certified pathologists or trained technicians, limiting the total throughput of studies performed at neuroscience digital pathology labs. It may also mean that simpler and quicker methods of examining tissue samples are used by non-pathologists, especially in the early stages of research and preclinical studies. To address these limitations and to meet the growing demand for image analysis in a pharmaceutical setting, we developed AnNoBrainer, an open-source software tool that leverages deep learning, image registration, and standard cortical brain templates to automatically annotate individual brain regions on 2D pathology slides. Application of AnNoBrainer to a published set of pathology slides from transgenic mice models of synucleinopathy revealed comparable accuracy, increased reproducibility, and a significant reduction (~ 50%) in time spent on brain annotation, quality control and labelling compared to trained scientists in pathology. Taken together, AnNoBrainer offers a rapid, accurate, and reproducible automated annotation of mouse brain images that largely meets the experts' histopathological assessment standards (> 85% of cases) and enables high-throughput image analysis workflows in digital pathology labs.

755 Pathology in the Digital Era of Neuro-oncology: A Pathomics-Based Approach to Modeling Outcomes in Medulloblastoma

INTRODUCTION: Pathomics is an emerging data science technique that may be used to extract intricate, sub-visual histopathologic features using high-throughput digital image analysis. METHODS: Using the Children’s Brain Tumor Network database, pediatric medulloblastoma patients were evaluated for inclusion in the study. After tumor segmentation, 49 quantitative pathomic features were extracted using a digital pathology software package (Qupath). LASSO cox proportional hazard model with stratified 5-fold cross-validation was used to identify high-performing features and create a predictive survival model by identifying low-, medium- and high-risk OS cohorts. A classification model was developed using histogram gradient boosting classifier to perform binary classification of Group3/4 vs. SHH/WNT with leave-one-subject-out cross validation. RESULTS: 84 patients with median age at diagnosis of 8.61 years (range 0.31-21.72), and median OS of 45.6 months (range 0.76-195.87) were included in the study. A survival model built using only clinical features yielded a concordance index (c-index) of 0.76 compared to actual outcomes (stratification: p < 0.001). However, a survival model built by combining clinical and high-performing pathomic features yielded the best performance, showing disparate outcomes between low-, medium- and high-risk groups (p < 0.001) with resultant c-index of 0.85. Furthermore, pathomic features classified subjects into Group 3/4 or SHH/WNT with classification accuracy of 71% (SEM = 0.06), and t-test against stratified chance accuracy (57%) showed significantly above-chance performance (p = .019). CONCLUSIONS: This study represents the largest pathomic-based machine learning analysis for predicting survival and molecular subtypes in medulloblastoma. The results demonstrate success in pathomic analysis using machine learning-based modeling to predict survival and molecular subtypes for patients with medulloblastoma. Further work will aim to perform our analysis on an external validation dataset to enhance model performance and improve its clinical applicability.

The serine/threonine protein kinase MpSTE1 directly governs hyphal branching in Monascus spp.

Monascus spp. are commercially important fungi due to their ability to produce beneficial secondary metabolites such as the cholesterol-lowering agent lovastatin and natural food colorants azaphilone pigments. Although hyphal branching intensively influenced the production of these secondary metabolites, the pivotal regulators of hyphal development in Monascus spp. remain unclear. To identify these important regulators, we developed an artificial intelligence (AI)–assisted image analysis tool for quantification of hyphae-branching and constructed a random T-DNA insertion library. High-throughput screening revealed that a STE kinase, MpSTE1, was considered as a key regulator of hyphal branching based on the hyphal phenotype. To further validate the role of MpSTE1, we generated an mpSTE1 gene knockout mutant, a complemented mutant, and an overexpression mutant (OE::mpSTE1). Microscopic observations revealed that overexpression of mpSTE1 led to a 63% increase in branch number while deletion of mpSTE1 reduced the hyphal branching by 68% compared to the wild-type strain. In flask cultures, the strain OE::mpSTE1 showed accelerated growth and glucose consumption. More importantly, the strain OE::mpSTE1 produced 9.2 mg/L lovastatin and 17.0 mg/L azaphilone pigments, respectively, 47.0% and 30.1% higher than those of the wild-type strain. Phosphoproteomic analysis revealed that MpSTE1 directly phosphorylated 7 downstream signal proteins involved in cell division, cytoskeletal organization, and signal transduction. To our best knowledge, MpSTE1 is reported as the first characterized regulator for tightly regulating the hyphal branching in Monascus spp. These findings significantly expanded current understanding of the signaling pathway governing the hyphal branching and development in Monascus spp. Furthermore, MpSTE1 and its analogs were demonstrated as promising targets for improving production of valuable secondary metabolites.Key points• MpSTE1 is the first characterized regulator for tightly regulating hyphal branching• Overexpression of mpSTE1 significantly improves secondary metabolite production• A high-throughput image analysis tool was developed for counting hyphal branching

Ferritinophagy: Assessing the Selective Degradation of Iron by Autophagy in Human Fibroblasts.

Mutations in the autophagy gene WDR45/WIPI4 are the cause of beta-propeller-associated neurodegeneration (BPAN), a subtype of human diseases known as neurodegeneration with brain iron accumulation (NBIA) due to the presence of iron deposits in the brains of patients. Intracellular iron levels are tightly regulated by a number of cellular mechanisms, including the critical mechanism of ferritinophagy. This paper describes how ferritinophagy can be assessed in primary, skin-derived human fibroblasts. In this protocol, we use iron-modulating conditions for inducing or inhibiting ferritinophagy at the cellular level, such as the administration of bafilomycin A1 to inhibit lysosome function and ferric ammonium citrate (FAC) or deferasiox (DFX) treatments to overload or deplete iron, respectively. Such treated fibroblasts are then subjected to high-throughput imaging and CellProfiler-based quantitative localization analysis of endogenous ferritin and autophagosomal/lysosomal markers, here LAMP2. Based on the level of autophagosomal/lysosomal ferritin, conclusions can be drawn regarding the level of ferritinophagy. This protocol can be used to assess ferritinophagy in BPAN patient-derived primary fibroblasts or other types of mammalian cells.

Abstract A032: Cellos: High-throughput deconvolution of 3D organoid dynamics at cellular resolution for cancer pharmacology

Abstract Three-dimensional (3D) culture models, such as organoids, are flexible systems to interrogate cellular evolution, cellular growth and morphology, multicellular spatial architecture, and cell interactions in response to drug treatment. However, new computational methods to segment and analyze 3D models at cellular resolution with sufficiently high throughput are needed to realize these possibilities. Here we report Cellos (Cell and Organoid Segmentation), an accurate, high throughput image analysis pipeline for 3D organoid and nuclear segmentation analysis. Cellos segments organoids in 3D using classical algorithms and segments nuclei using a Stardist-3D convolutional neural network which we trained on manually annotated dataset of 3,862 cells. To evaluate the capabilities of Cellos we then analyzed 74,450 organoids with 1.65 million cells, from multiple experiments on triple negative breast cancer organoids containing clonal mixtures with complex cisplatin sensitivies. Cellos was able to accurately distinguish ratios of distinct fluorescently labeled cell populations in organoids, with < 3% deviation from seeding ratios in each well and was effective for both fluorescently labelled nuclei and independent Hoechst stained datasets. Cellos was able to recapitulate traditional luminescence-based drug response quantification by analyzing 3D images, including parallel analysis of multiple cancer clones in the same well. Moreover, Cellos was able to identify organoid and nuclei morphology features associated with treatment and unique to each of the clones. Finally, Cellos enables 3D analysis of cell spatial relationships, which we used to detect ecological affinity between cancer clones beyond what arises from local cell division and organoid composition. Cellos provides powerful tools to perform high throughput analysis for pharmacological testing and biological investigation of organoids based on 3D imaging. Citation Format: Patience Mukashyaka, Pooja Kumar, David J. Mellert, Shadae Nicholas, Javad Noorbakhsh, Mattia Brugiolo, Olga Anczukow, Edison T. Liu, Jeffrey H. Chuang. Cellos: High-throughput deconvolution of 3D organoid dynamics at cellular resolution for cancer pharmacology [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr A032.

Comparison between a deep-learning and a pixel-based approach for the automated quantification of HIV target cells in foreskin tissue

The availability of target cells expressing the HIV receptors CD4 and CCR5 in genital tissue is a critical determinant of HIV susceptibility during sexual transmission. Quantification of immune cells in genital tissue is therefore an important outcome for studies on HIV susceptibility and prevention. Immunofluorescence microscopy allows for precise visualization of immune cells in mucosal tissues; however, this technique is limited in clinical studies by the lack of an accurate, unbiased, high-throughput image analysis method. Current pixel-based thresholding methods for cell counting struggle in tissue regions with high cell density and autofluorescence, both of which are common features in genital tissue. We describe a deep-learning approach using the publicly available StarDist method to count cells in immunofluorescence microscopy images of foreskin stained for nuclei, CD3, CD4, and CCR5. The accuracy of the model was comparable to manual counting (gold standard) and surpassed the capability of a previously described pixel-based cell counting method. We show that the performance of our deep-learning model is robust in tissue regions with high cell density and high autofluorescence. Moreover, we show that this deep-learning analysis method is both easy to implement and to adapt for the identification of other cell types in genital mucosal tissue.

Analysis of organoid and immune cell co-cultures by machine learning-empowered image cytometry.

Organoids are three-dimensional (3D) structures that can be derived from stem cells or adult tissue progenitor cells and exhibit an extraordinary ability to autonomously organize and resemble the cellular composition and architectural integrity of specific tissue segments. This feature makes them a useful tool for analyzing therapeutical relevant aspects, including organ development, wound healing, immune disorders and drug discovery. Most organoid models do not contain cells that mimic the neighboring tissue’s microenvironment, which could potentially hinder deeper mechanistic studies. However, to use organoid models in mechanistic studies, which would enable us to better understand pathophysiological processes, it is necessary to emulate the in situ microenvironment. This can be accomplished by incorporating selected cells of interest from neighboring tissues into the organoid culture. Nevertheless, the detection and quantification of organoids in such co-cultures remains a major technical challenge. These imaging analysis approaches would require an accurate separation of organoids from the other cell types in the co-culture. To efficiently detect and analyze 3D organoids in co-cultures, we developed a high-throughput imaging analysis platform. This method integrates automated imaging techniques and advanced image processing tools such as grayscale conversion, contrast enhancement, membrane detection and structure separation. Based on machine learning algorithms, we were able to identify and classify 3D organoids within dense co-cultures of immune cells. This procedure allows a high-throughput analysis of organoid-associated parameters such as quantity, size, and shape. Therefore, the technology has significant potential to advance contextualized research using organoid co-cultures and their potential applications in translational medicine.

Genetic Signature Controlling Root System Architecture in Diverse Spring Wheat Germplasm.

Roots are the main sensing organ, initiating multiple signaling pathways in response to abiotic factors, including nutrients, drought, and salt stress. A focus on improving the root system architecture is a key strategy to mitigate these stresses in wheat crop. In the present study, a diversity panel comprising indigenous landraces and historical cultivars from Pakistan was characterized for the root system architecture (RSA) and important loci were identified using a genome-wide association study (GWAS). RSA of the diversity panel was characterized 30 days after sowing in brunch tubes, and root images were taken. A high-throughput root imaging analysis using Rhizovision software was performed by setting the scale to extract the eight RSA traits and four plant biomass-related traits. GWAS identified 323 association signals for 12 root and biomass traits present on all wheat chromosomes, while the most important and reliable genetic loci (based on pleotropic loci and candidate genes) were identified on chromosomes 2A, 2B, 5A, 5D, 6A, 7B, and 7D for RSA. SNP annotation and transcriptome profiling identified nine candidate genes regulating the RSA and plant biomass traits, including ROOTLESS WITH UNDETECTABLE MERISTEM1, MYB TRANSCRIPTION FACTOR4, BRASSINOSTEROID INSENSITIVE1, SLENDER RICE1, AUXIN-RESPONSIVE FACTOR25, SCARECROW, NARROW LEAF2, PIN-FORMED1 AND PHOSPHATE TRANSCRIPTION FACTOR1. This study provided pre-breeding information for deep-rooting genotypes and associated markers that will accelerate the incorporation of such traits in breeding.

High-throughput digital imaging analysis for grain morphology of historical wheat cultivars of Pakistan

High-throughput digital imaging analysis for grain morphology of historical wheat cultivars of Pakistan

PTOLEMI: Personalized Cancer Treatment through Machine Learning-Enabled Image Analysis of Microfluidic Assays.

Contemporary personalized cancer diagnostic approaches encounter multiple challenges. The presence of cellular and molecular heterogeneity in patient samples introduces complexities to analysis protocols. Conventional analyses are manual, reliant on expert personnel, time-intensive, and financially burdensome. The copious data amassed for subsequent analysis strains the system, obstructing real-time diagnostics at the "point of care" and impeding prompt intervention. This study introduces PTOLEMI: Python-based Tensor Oncological Locator Examining Microfluidic Instruments. PTOLEMI stands out as a specialized system designed for high-throughput image analysis, particularly in the realm of microfluidic assays. Utilizing a blend of machine learning algorithms, PTOLEMI can process large datasets rapidly and with high accuracy, making it feasible for point-of-care diagnostics. Furthermore, its advanced analytics capabilities facilitate a more granular understanding of cellular dynamics, thereby allowing for more targeted and effective treatment options. Leveraging cutting-edge AI algorithms, PTOLEMI rapidly and accurately discriminates between cell viability and distinct cell types within biopsy samples. The diagnostic process becomes automated, swift, precise, and resource-efficient, rendering it well-suited for point-of-care requisites. By employing PTOLEMI alongside a microfluidic cell culture chip, physicians can attain personalized diagnostic and therapeutic insights. This paper elucidates the evolution of PTOLEMI and showcases its prowess in analyzing cancer patient samples within a microfluidic apparatus. While the integration of machine learning tools into biomedical domains is undoubtedly in progress, this study's innovation lies in the fusion of PTOLEMI with a microfluidic platform-an integrated, rapid, and independent framework for personalized drug screening-based clinical decision-making.

Imaging-Based Screening Identifies Modulators of the eIF3 Translation Initiation Factor Complex in Candida albicans.

Fungal pathogens like Candida albicans can cause devastating human disease. Treatment of candidemia is complicated by the high rate of resistance to common antifungal therapies. Additionally, there is host toxicity associated with many antifungal compounds due to the conservation between essential mammalian and fungal proteins. An attractive new approach for antimicrobial development is to target virulence factors: non-essential processes that are required for the organism to cause disease in human hosts. This approach expands the potential target space while reducing the selective pressure toward resistance, as these targets are not essential for viability. In C. albicans, a key virulence factor is the ability to transition to hyphal morphology. We developed a high-throughput image analysis pipeline to distinguish between yeast and filamentous growth in C. albicans at the single cell level. Based on this phenotypic assay, we screened the FDA drug repurposing library of 2,017 compounds for their ability to inhibit filamentation and identified 33 compounds that block the hyphal transition in C. albicans with IC50 values ranging from 0.2 to 150 μM. Multiple compounds showed a phenyl sulfone chemotype, prompting further analysis. Of these phenyl sulfones, NSC 697923 displayed the most efficacy, and by selecting for resistant mutants, we identified eIF3 as the target of NSC 697923 in C. albicans.

A Shared Pathogenic Mechanism for Valproic Acid and SHROOM3 Knockout in a Brain Organoid Model of Neural Tube Defects.

Neural tube defects (NTDs), including anencephaly and spina bifida, are common major malformations of fetal development resulting from incomplete closure of the neural tube. These conditions lead to either universal death (anencephaly) or severe lifelong complications (spina bifida). Despite hundreds of genetic mouse models of neural tube defect phenotypes, the genetics of human NTDs are poorly understood. Furthermore, pharmaceuticals, such as antiseizure medications, have been found clinically to increase the risk of NTDs when administered during pregnancy. Therefore, a model that recapitulates human neurodevelopment would be of immense benefit to understand the genetics underlying NTDs and identify teratogenic mechanisms. Using our self-organizing single rosette cortical organoid (SOSR-COs) system, we have developed a high-throughput image analysis pipeline for evaluating the SOSR-CO structure for NTD-like phenotypes. Similar to small molecule inhibition of apical constriction, the antiseizure medication valproic acid (VPA), a known cause of NTDs, increases the apical lumen size and apical cell surface area in a dose-responsive manner. GSK3β and HDAC inhibitors caused similar lumen expansion; however, RNA sequencing suggests VPA does not inhibit GSK3β at these concentrations. The knockout of SHROOM3, a well-known NTD-related gene, also caused expansion of the lumen, as well as reduced f-actin polarization. The increased lumen sizes were caused by reduced cell apical constriction, suggesting that impingement of this process is a shared mechanism for VPA treatment and SHROOM3-KO, two well-known causes of NTDs. Our system allows the rapid identification of NTD-like phenotypes for both compounds and genetic variants and should prove useful for understanding specific NTD mechanisms and predicting drug teratogenicity.

Superresolution Fluorescence Microscopy of Platelet Subcellular Structures as a Potential Tumor Liquid Biopsy.

Blood-based tumor liquid biopsies are promising as an alternative or complement to tissue biopsies due to their noninvasiveness, convenience, and safety, and there is still a great demand for the discovery of new biomarkers for these biopsies. Here, nanoscale distribution patterns of subcellular structures in platelets, as imaged by structured illumination superresolution fluorescence microscopy, as a new type of potential biomarker for tumor liquid biopsies are presented. A standardized protocol for platelet sample preparation and developed an automated high-throughput image analysis workflow is established. The diagnostic capability based on the statistical analysis of 280000 superresolution images of individual platelets from a variety of tumor patients, benign mass patients, and healthy volunteers (n = 206) is explored. These results suggest that the nanoscale distribution patterns of α-granules in platelets have the potential to be biomarkers for several cancers, including glioma and cervical, endometrial, and ovarian cancers, facilitating not only diagnosis but also therapeutic monitoring. This study provides a promising novel type of platelet parameter for tumor liquid biopsies at the subcellular level rather than the existing cellular or molecular level and opens up a new avenue for clinical applications of superresolution imaging techniques.

Quantification of Giant Unilamellar Vesicle Fusion Products by High-Throughput Image Analysis.

Artificial cells are based on dynamic compartmentalized systems. Thus, remodeling of membrane-bound systems, such as giant unilamellar vesicles, is finding applications beyond biological studies, to engineer cell-mimicking structures. Giant unilamellar vesicle fusion is rapidly becoming an essential experimental step as artificial cells gain prominence in synthetic biology. Several techniques have been developed to accomplish this step, with varying efficiency and selectivity. To date, characterization of vesicle fusion has relied on small samples of giant vesicles, examined either manually or by fluorometric assays on suspensions of small and large unilamellar vesicles. Automation of the detection and characterization of fusion products is now necessary for the screening and optimization of these fusion protocols. To this end, we implemented a fusion assay based on fluorophore colocalization on the membranes and in the lumen of vesicles. Fluorescence colocalization was evaluated within single compartments by image segmentation with minimal user input, allowing the application of the technique to high-throughput screenings. After detection, statistical information on vesicle fluorescence and morphological properties can be summarized and visualized, assessing lipid and content transfer for each object by the correlation coefficient of different fluorescence channels. Using this tool, we report and characterize the unexpected fusogenic activity of sodium chloride on phosphatidylcholine giant vesicles. Lipid transfer in most of the vesicles could be detected after 20 h of incubation, while content exchange only occurred with additional stimuli in around 8% of vesicles.

High-throughput image analysis with deep learning captures heterogeneity and spatial relationships after kidney injury

Recovery from acute kidney injury can vary widely in patients and in animal models. Immunofluorescence staining can provide spatial information about heterogeneous injury responses, but often only a fraction of stained tissue is analyzed. Deep learning can expand analysis to larger areas and sample numbers by substituting for time-intensive manual or semi-automated quantification techniques. Here we report one approach to leverage deep learning tools to quantify heterogenous responses to kidney injury that can be deployed without specialized equipment or programming expertise. We first demonstrated that deep learning models generated from small training sets accurately identified a range of stains and structures with performance similar to that of trained human observers. We then showed this approach accurately tracks the evolution of folic acid induced kidney injury in mice and highlights spatially clustered tubules that fail to repair. We then demonstrated that this approach captures the variation in recovery across a robust sample of kidneys after ischemic injury. Finally, we showed markers of failed repair after ischemic injury were correlated both spatially within and between animals and that failed repair was inversely correlated with peritubular capillary density. Combined, we demonstrate the utility and versatility of our approach to capture spatially heterogenous responses to kidney injury.

Abstract 186: Cellos: High-throughput deconvolution of 3D organoid dynamics at cellular resolution for cancer pharmacology

Abstract Three-dimensional (3D) culture models, such as organoids, are flexible systems to interrogate cellular growth, multicellular spatial architecture, and morphology in response to drug treatment, including the potential to resolve the individual behaviors and interactions of mixed cancer cell populations. However, new computational methods to segment and analyze 3D models at cellular resolution are needed to realize these possibilities. Here we report Cellos (Cell Organoid Segmentation), an accurate, high throughput image analysis pipeline for 3D organoid and nuclear segmentation analysis. Cellos segments 3D organoids using classical algorithms and segments nuclei using a Stardist-3D convolutional neural network trained on a manually annotated dataset of 3,862 cells from 36 organoids confocally imaged at 5 μm z-resolution. To evaluate the capabilities of Cellos, we then analyzed 74,450 organoids with 1.65 million cells from multiple experiments on triple negative breast cancer organoids containing clonal mixtures with complex cisplatin sensitivity. Cellos was able to accurately distinguish cell ratios in organoids in 96-well plates to within 2.99% and was effective for fluorescently labelled nuclei and independent DAPI stained datasets. Cellos was able to recapitulate traditional luminescence based drug responses by analyzing 3D images, including parallel analysis of multiple cancer clones in the same well. Moreover, Cellos was able to identify organoid and nuclear morphology feature changes associated with treatment. Finally, Cellos enables 3D analysis of cell spatial relationships, which we used to detect ecological affinity between cancer cells, beyond expectations from local cell division or organoid composition. Cellos provides powerful tools to perform high throughput analysis for pharmacological testing and biological investigation of organoids based on 3D imaging. Citation Format: Patience Mukashyaka, Pooja Kumar, Dave Mellert, Shadae Nicholas, Javad Noorbakhsh, Mattia Brugiolo, Olga Anczukow, Edison T. Liu, Jeffrey H. Chuang. Cellos: High-throughput deconvolution of 3D organoid dynamics at cellular resolution for cancer pharmacology [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 186.

Generation, High-Throughput Screening, and Biobanking of Human-Induced Pluripotent Stem Cell-Derived Cardiac Spheroids

Human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) are of paramount importance for human cardiac disease modeling and therapeutics. We recently published a cost-effective strategy for the massive expansion of hiPSC-CMs in two dimensions (2D). Two major limitations are cell immaturity and a lack of three-dimensional (3D) arrangement and scalability in high-throughput screening (HTS) platforms. To overcome these limitations, the expanded cardiomyocytes form an ideal cell source for the generation of 3D cardiac cell culture and tissue engineering techniques. The latter holds great potential in the cardiovascular field, providing more advanced and physiologically relevant HTS. Here, we describe an HTS-compatible workflow with easy scalability for the generation, maintenance, and optical analysis of cardiac spheroids (CSs) in a 96-well-format. These small CSs are essential to fill the gap present in current in vitro disease models and/or generation for 3D tissue engineering platforms. The CSs present a highly structured morphology, size, and cellular composition. Furthermore, hiPSC-CMs cultured as CSs display increased maturation and several functional features of the human heart, such as spontaneous calcium handling and contractile activity. By automatization of the complete workflow, from the generation of CSs to functional analysis, we increase intra- and inter-batch reproducibility as demonstrated by high-throughput (HT) imaging and calcium handling analysis. The described protocol allows modeling of cardiac diseases and assessing drug/therapeutic effects at the single-cell level within a complex 3D cell environment in a fully automated HTS workflow. In addition, the study describes a straightforward procedure for long-term preservation and biobanking of whole-spheroids, thereby providing researchers the opportunity to create next-generation functional tissue storage. HTS combined with long-term storage will substantially contribute to translational research in a wide range of areas, including drug discovery and testing, regenerative medicine, and the development of personalized therapies.

Generation, High-Throughput Screening, and Biobanking of Human-Induced Pluripotent Stem Cell-Derived Cardiac Spheroids.

Human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) are of paramount importance for human cardiac disease modeling and therapeutics. We recently published a cost-effective strategy for the massive expansion of hiPSC-CMs in two dimensions (2D). Two major limitations are cell immaturity and a lack of three-dimensional (3D) arrangement and scalability in high-throughput screening (HTS) platforms. To overcome these limitations, the expanded cardiomyocytes form an ideal cell source for the generation of 3D cardiac cell culture and tissue engineering techniques. The latter holds great potential in the cardiovascular field, providing more advanced and physiologically relevant HTS. Here, we describe an HTS-compatible workflow with easy scalability for the generation, maintenance, and optical analysis of cardiac spheroids (CSs) in a 96-well-format. These small CSs are essential to fill the gap present in current in vitro disease models and/or generation for 3D tissue engineering platforms. The CSs present a highly structured morphology, size, and cellular composition. Furthermore, hiPSC-CMs cultured as CSs display increased maturation and several functional features of the human heart, such as spontaneous calcium handling and contractile activity. By automatization of the complete workflow, from the generation of CSs to functional analysis, we increase intra- and inter-batch reproducibility as demonstrated by high-throughput (HT) imaging and calcium handling analysis. The described protocol allows modeling of cardiac diseases and assessing drug/therapeutic effects at the single-cell level within a complex 3D cell environment in a fully automated HTS workflow. In addition, the study describes a straightforward procedure for long-term preservation and biobanking of whole-spheroids, thereby providing researchers the opportunity to create next-generation functional tissue storage. HTS combined with long-term storage will substantially contribute to translational research in a wide range of areas, including drug discovery and testing, regenerative medicine, and the development of personalized therapies.