High-content Data Research Articles

A scalable, data analytics workflow for image-based morphological profiles

Cell Painting is an established community-based microscopy-assay platform that provides high-throughput, high-content data for biological readouts. In November 2022, the JUMP-Cell Painting Consortium released the largest publicly available Cell Painting dataset with CellProfiler features, comprising more than 2 billion cell images. This dataset is designed for predicting the activity and toxicity of 115k drug compounds, with the aim to make cell images as computable as genomes and transcriptomes. In this context, our paper introduces a scalable and computationally efficient data analytics workflow created to meet the needs of researchers. This data-driven workflow facilitates the comparison of drug treatment effects through significant and biologically relevant insights. The workflow consists of two parts: first, the Equivalence score (Eq. score), a straightforward yet sophisticated metric highlighting relevant deviations from negative controls based on cell image morphology; second, the scalability of the workflow, by utilizing the Eq. scores on a large scale to predict and classify the subtle morphological changes in cell image profiles. By doing so, we show classification improvements compared to using the raw CellProfiler features on the CPJUMP1-pilot dataset on three types of perturbations.We hope that our workflow’s contributions will enhance drug screening efficiency and streamline the drug development process. As this process is resource-intensive, every incremental improvement is valuable. Through our collective efforts in advancing the understanding of high-throughput image-based data, we aim to reduce both the time and cost of developing new, life-saving treatments.

Guiding organs-on-chips towards applications: a balancing act between integration of advanced technologies and standardization

Organs-on-chips (OoC) are in vitro models that emulate key functionalities of tissues or organs in a miniaturized and highly controlled manner. Due to their high versatility, OoC have evolved as promising alternatives to animal testing for a more effective drug development pipeline. Additionally, OoC are revealing increased predictive power for toxicity screening applications as well as (patho-) physiology research models. It is anticipated that enabling technologies such as biofabrication, multimodality imaging, and artificial intelligence will play a critical role in the development of the next generation of OoC. These domains are expected to increase the mimicry of the human micro-physiology and functionality, enhance screening of cellular events, and generate high-content data for improved prediction. Although exponentially growing, the OoC field will strongly benefit from standardized tools to upgrade its implementational power. The complexity derived from the integration of multiple technologies and the current absence of concrete guidelines for establishing standards may be the reason for the slower adoption of OoC by industry, despite the fast progress of the field. Therefore, we argue that it is essential to consider standardization early on when using new enabling technologies, and we provide examples to illustrate how to maintain a focus on technology standards as these new technologies are used to build innovative OoC applications. Moreover, we stress the importance of informed design, use, and analysis decisions. Finally, we argue that this early focus on standards in innovation for OoC will facilitate their implementation.

Improving the power of drug toxicity measurements by quantitative nuclei imaging

Imaging-based anticancer drug screens are becoming more prevalent due to development of automated fluorescent microscopes and imaging stations, as well as rapid advancements in image processing software. Automated cell imaging provides many benefits such as their ability to provide high-content data, modularity, dynamics recording and the fact that imaging is the most direct way to access cell viability and cell proliferation. However, currently most publicly available large-scale anticancer drugs screens, such as GDSC, CTRP and NCI-60, provide cell viability data measured by assays based on colorimetric or luminometric measurements of NADH or ATP levels. Although such datasets provide valuable data, it is unclear how well drug toxicity measurements can be integrated with imaging data. Here we explored the relations between drug toxicity data obtained by XTT assay, two quantitative nuclei imaging methods and trypan blue dye exclusion assay using a set of four cancer cell lines with different morphologies and 30 drugs with different mechanisms of action. We show that imaging-based approaches provide high accuracy and the differences between results obtained by different methods highly depend on drug mechanism of action. Selecting AUC metrics over IC50 or comparing data where significantly drugs reduced cell numbers noticeably improves consistency between methods. Using automated cell segmentation protocols we analyzed mitochondria activity in more than 11 thousand drug-treated cells and showed that XTT assay produces unreliable data for CDK4/6, Aurora A, VEGFR and PARP inhibitors due induced cell size growth and increase in individual mitochondria activity. We also explored several benefits of image-based analysis such as ability to monitor cell number dynamics, dissect changes in total and individual mitochondria activity from cell proliferation, and ability to identify chromatin remodeling drugs. Finally, we provide a web tool that allows comparing results obtained by different methods.

Abstract 5375: Learned phenotypic embeddings enable scalable imputation of high-content molecular data elucidating prognostic chromatin signatures

Abstract Emerging high-content data modalities like functional genomics and spatial proteomics have enormous potential to reveal determinants of phenotypic plasticity that underlie variability in clinical outcomes, but to date these modalities are only collected in modestly sized research cohorts (< 200-400 patients), where we lack power to detect subtype-specific or prognostic signatures. To study intertumor heterogeneity on a much larger scale (>10,000 patients), we developed a machine learning framework based on self-supervised embeddings that allows scalable imputation of high-content data on large standard of care datasets. Our framework starts by learning a phenotypic embedding of tumor state based solely on H&E histology images, allowing the embedding to be trained on large patient cohorts regardless of availability of molecular covariates. It then learns to predict genomic or proteomic labels from the lower-dimensional phenotypic embeddings. This model can be used for imputation in much larger cohorts, where only clinical outcome and histology are available. To demonstrate our method, we use the TCGA ATAC-seq data, which is available for 400 patients across 23 cancer types. By learning self-supervised embeddings of histology, our framework was able to impute ATAC-seq for 5,000 peaks in 11,000 patients across 31 cancer types, with high accuracy in held out samples (R2 = 0.61). To our knowledge, this represents the broadest available pan-cancer chromatin landscape. The imputed ATAC-seq reveals a subset of peaks that are significantly associated with overall survival (OS) in multiple cancer types (e.g., Breast (BC) ATAC-only HR 1.75, p: 8.6E-3). Genes proximal to these peaks are strongly enriched for well characterized oncogenes, and also several novel genes with functions in cellular metabolism and chromatin remodeling whose expression is not known to be prognostic in our disease settings. Finally, we developed models to predict OS from H&E slide embeddings and from imputed ATAC-seq, both pan-cancer and in specific tumor types. Both models significantly outperform baseline stage and molecular subtype clinical risk predictors (e.g., BC baseline HR: 2.44, p: 3E-6 vs. embedding/imputed ATAC HR 3.78, 2E-9, p for improvement 2E-9) and, interestingly, we find that adding an ATAC-seq based risk score to an embedding-based risk score significantly improves disease-specific survival prediction (HCC embedding-only HR: 2.13, p: 8E-5 vs. HCC embedding/imputed ATAC HR: 2.65, p: 1E-6). This suggests that histopathology images are a rich source of prognostic information beyond that which is captured by traditional pathologist grading. Overall, our work highlights the ability to use self-supervised embeddings of histopathology to impute biological covariates on large, standard-of-care cohorts, empowering novel insights into disease mechanisms and patient outcome. Citation Format: Christopher S. Probert, Zachary R. McCaw, Navami Jain, Daphne Koller. Learned phenotypic embeddings enable scalable imputation of high-content molecular data elucidating prognostic chromatin signatures. [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 5375.

Celastrus orbiculatus Thunb. extract inhibits EMT and metastasis of gastric cancer by regulating actin cytoskeleton remodeling

Ethnopharmacological relevanceThe traditional Chinese medicine herb Celastrus orbiculatus Thunb. is an important folk medicinal plant in China that has been used as an anti-inflammatory, antitumor, and analgesic in various diseases. Recent years, many studies have reported the significant effects of Celastrus orbiculatus Thunb. extract (COE) on gastric cancer. However, the specific mechanism by which COE regulates gastric cancer cytoskeleton remodeling and thus inhibits EMT has not yet been reported. Aim of studyTo study the effect and mechanism of COE in inhibiting the epithelial-mesenchymal transition (EMT) and metastasis of gastric cancer cells, laying an experimental foundation for the clinical application and further development of COE. MethodsThe high-content cell dynamic tracking system was used to continuously track the trajectory of cell movement in real time. Through the high-content data, the average movement distance and movement speed of the cells are calculated. Additionally, the dynamic images of the cell movement in the high-content imaging system are derived to analyze the impact of COE on the movement of gastric cancer cells. Cytoskeleton staining experiment was performed to detect the effect of COE on the assembly of gastric cancer cell cytoskeleton proteins. Western blot was employed to detect the changes of EMT and metastasis-related proteins in the gastric cancer cells treated by COE. The effect of COE on the key regulatory protein Cofilin-1 (CFL1) of cell movement was examined by Western blot and protein degradation experiment. The effect of COE on EMT and metastasis of the gastric cancer cells lacking CFL1 was assessed by a transwell assay. The in vivo inhibitory effect of COE on EMT and metastasis of gastric cancer was determined by the animal living image system. IHC assays were used to detect the levels of EMT-related proteins in COE reversal in vivo. ResultThe results showed that the movement distance and average movement speed of gastric cancer cells after COE treatment were significantly lower than those of the control group. Cytoskeleton staining experiments revealed that COE can significantly change the distribution of skeletal proteins in gastric cancer cells. Additionally, COE treatment significantly reduced the expression of Matrix metalloproteinases (MMP-2, MMP-9) and other proteins. Furthermore, COE can significantly accelerate the degradation of CFL1 protein, and both COE treatment and CFL1 deletion can significantly inhibit EMT and metastasis of gastric cancer cells. Lastly, the number of peritoneal metastases of gastric cancer cells was significantly reduced in animals after COE treatment. COE can reverse the levels of EMT-related proteins while reducing the expression levels of CFL1 protein in vivo. ConclusionCOE can significantly inhibit EMT and metastasis of gastric cancer cells in vivo and in vitro. This effect may be achieved by reducing the stability of CFL1 and inhibiting the assembly of actin in gastric cancer cells.

LiveCellMiner: A new tool to analyze mitotic progression.

Live-cell imaging has become state of the art to accurately identify the nature of mitotic and cell cycle defects. Low- and high-throughput microscopy setups have yield huge data amounts of cells recorded in different experimental and pathological conditions. Tailored semi-automated and automated image analysis approaches allow the analysis of high-content screening data sets, saving time and avoiding bias. However, they were mostly designed for very specific experimental setups, which restricts their flexibility and usability. The general need for dedicated experiment-specific user-annotated training sets and experiment-specific user-defined segmentation parameters remains a major bottleneck for fully automating the analysis process. In this work we present LiveCellMiner, a highly flexible open-source software tool to automatically extract, analyze and visualize both aggregated and time-resolved image features with potential biological relevance. The software tool allows analysis across high-content data sets obtained in different platforms, in a quantitative and unbiased manner. As proof of principle application, we analyze here the dynamic chromatin and tubulin cytoskeleton features in human cells passing through mitosis highlighting the versatile and flexible potential of this tool set.

Abstract 1888: Identification and optimization of novel small molecule modulators of immune checkpoint resistance with a unified representation space for genomic and chemical perturbations

Abstract Immune checkpoint inhibitors have revolutionized cancer treatment, producing a durable response consistent with immunologic memory in a subset of patients. However, the majority of patients demonstrate innate or acquired resistance that must be characterized and overcome to induce successful treatment. Advancements in human reverse translation and scaled in vivo CRISPR screening have uncovered novel molecular and genomic correlates of resistance, and promising druggable mechanisms - driven by highly complex interactions between tumor cells and the immune system. It is this core biology that must be disentangled to build the treatment paradigms of the future. Here we demonstrate a technique for massively parallel prioritization of new immuno-oncology hypotheses using industrial-scale experimentation and machine learning. Leveraging high-content imaging data from whole-genome CRISPR knockout and a library of >250,000 compounds, a deep learning model was trained to construct a batch-invariant low dimensional representation of each perturbation. Millions of perturbations in multiple cell types were embedded in a unified representation space that was leveraged to increase the rate of discovery, accelerate reverse translation, yield novel biological insights, and guide the advancement of lead molecular series through SAR. Here we highlight multiple discovery programs driven by inferred relationships between small molecules and gene knockout with translation from inference to in vivo efficacy. Specifically, we prioritize molecules with activity in STK11-deficient tumors and additional immune checkpoint sensitizers. Citation Format: Ashish Bhandari, Michael F. Cuccarese, Kevin Fales, Kiran Nadella, Rebecca Sarto Basso, Daria Beshnova, Hayley Donnella, Bahar Shamloo, Jacob Cooper, Imran Haque, Ron Alfa, Jacob Rinaldi. Identification and optimization of novel small molecule modulators of immune checkpoint resistance with a unified representation space for genomic and chemical perturbations [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1888.

A Data-Driven Lens to Understand Human Biology: An Interview with Daphne Koller

A Data-Driven Lens to Understand Human Biology: An Interview with Daphne Koller

“Tonga”: A Novel Toolbox for Straightforward Bioimage Analysis

Techniques to acquire and analyze biological images are central to life science. However, the workflow downstream of imaging can be complex and involve several tools, leading to creation of very specialized scripts and pipelines that are difficult to reproduce by other users. Although many commercial and open-source software are available, non-expert users are often challenged by a knowledge gap in setting up analysis pipelines and selecting correct tools for extracting data from images. Moreover, a significant share of everyday image analysis requires simple tools, such as precise segmentation, cell counting, and recording of fluorescent intensities. Hence, there is a need for user-friendly platforms for everyday image analysis that do not require extensive prior knowledge on bioimage analysis or coding. We set out to create a bioimage analysis software that has a straightforward interface and covers common analysis tasks such as object segmentation and analysis, in a practical, reproducible, and modular fashion. We envision our software being useful for analysis of cultured cells, histological sections, and high-content data.

A Model of Human Small Airway on a Chip for Studies of Subacute Effects of Inhalation Toxicants.

Testing for acute inhalation hazards is conducted in animals; however, a number of robust in vitro human cell-based alternatives have been developed and tested. These models range in complexity from cultures of cell lines or primary cells in air-liquid interface on Transwells, to more complex and physiologically relevant flow- and mechanical stimulation-enabled tissue chips. Although the former models are relatively straightforward to establish and can be tested in medium/high throughput, the latter require specialized equipment and lack in throughput. In this study, we developed a device that can be easily manufactured while allowing for the production of a differentiated lung tissue. This multilayered microfluidic device enables coculture of primary human small airway epithelial cells and lung microvascular endothelial cells under physiological conditions for up to 18 days and recreates the parenchymal-vascular interface in the distal lung. To explore the potential of this airway on a chip for applications in inhalation toxicology, we also devised a system that allows for direct gas/aerosol exposures of the engineered airway epithelium to noxious stimuli known to cause adverse respiratory effects, including dry flowing air, lipopolysaccharide, particulate matter, and iodomethane. This study generated quantitative, high-content data that were indicative of aberrant changes in biochemical (lactate dehydrogenase), barrier (dextran permeability), functional (ciliary beating), and molecular (imaging for various markers) phenotypes of the small airway epithelium due to inhalational exposures. This study is significant because it established an in vitro model of human small airway on a chip that can be used in medium/high-throughput studies of subacute effects of inhalation toxicants.

Bayesian inference of molecular kinetic parameters from astrocyte calcium imaging data

Model-based Bayesian inference from high-content data obtained on live specimens is a burgeoning field with demonstrated applications to neuroscience. In parallel, computer vision methods for extracting the calcium signaling information from imaging data have advanced in application to neuronal physiology. Here, we are describing in detail a method we have recently developed to study calcium dynamics in astrocytes, which combines computer vision with model-based Bayesian learning to deduce the most likely molecular kinetic parameters underlying the observed calcium activity. As reported in the companion experimental study, this method allowed us to identify the key molecular changes downstream of a multi-gene deletion modeling the human 22q11.2 deletion syndrome, the most common human microdeletion and the genetic factor with the highest penetrance for schizophrenia.•Methodological details are laid out, from our imaging approach to our adaptation of the VBA-CaBBI algorithm previously developed primarily for brain functional imaging data.•The analytical pipeline is suited for further applications to glial cells and adaptable to other cell types exhibiting complexcalcium dynamics.

Electrical monitoring approaches in 3-dimensional cell culture systems: Toward label-free, high spatiotemporal resolution, and high-content data collection in vitro

3-dimensional (3D) cell cultures better mimic natural environment of cells than 2-dimensional (2D) cell cultures to obtain in vivo like inter and intracellular responses. However, third dimension brings complexity to cell culture. Therefore, high-resolution/high-content screening in 3D is one of the most important challenges with this type of cell cultures. Although optical monitoring techniques, well-established in 2D area, are enhanced to monitor 3D cell cultures, they are generally endpoint, static, time inefficient, and labor intensive. Alternatively, electrical sensing can become a solution to achieve dynamic, real-time, and label-free monitoring of cells in both 2D and 3D cell cultures. Developments in electrical monitoring of cell culture have led to novel approaches, proposed by adapting fundamentals of 2D electrical techniques to 3D to obtain high spatiotemporal systems. In this review, we classified these approaches into five main groups: (i) 3D impedance measurement approach (ii) electrical impedance tomography, (iii) 3D microelectrode array approach, (iv) 3D nanoelectronics scaffold approach, and (v) microphysiometry. We also defined the challenges in the adaptation of electrical monitoring techniques to 3D cultures and explained possible solutions in terms of specific applications and technical point of views, including methods particular to our group. In conclusion, 3D electrical monitoring in cell cultures is considerably challenging but highly accelerated recently by significant advances of microfabrication technology, bioengineering, and material science. Novel approaches reviewed here have a lot of potential and offer opportunities for further developments to find solutions, fit to serve the (bio)medical needs.

High-throughput label-free detection of DNA-to-RNA transcription inhibition using brightfield microscopy and deep neural networks

Drug discovery is in constant evolution and major advances have led to the development of in vitro high-throughput technologies, facilitating the rapid assessment of cellular phenotypes. One such phenotype is immunogenic cell death, which occurs partly as a consequence of inhibited RNA synthesis. Automated cell-imaging offers the possibility of combining high-throughput with high-content data acquisition through the simultaneous computation of a multitude of cellular features. Usually, such features are extracted from fluorescence images, hence requiring labeling of the cells using dyes with possible cytotoxic and phototoxic side effects. Recently, deep learning approaches have allowed the analysis of images obtained by brightfield microscopy, a technique that was for long underexploited, with the great advantage of avoiding any major interference with cellular physiology or stimulatory compounds. Here, we describe a label-free image-based high-throughput workflow that accurately detects the inhibition of DNA-to-RNA transcription. This is achieved by combining two successive deep convolutional neural networks, allowing (1) to automatically detect cellular nuclei (thus enabling monitoring of cell death) and (2) to classify the extracted nuclear images in a binary fashion. This analytical pipeline is R-based and can be easily applied to any microscopic platform.

Measuring Endocytosis During Proliferative Cell Quiescence.

Quiescence (also called "G0") is the state in which cells have exited the cell cycle but are capable to reenter as required. Though poorly understood, it represents one of the most prevalent cell states across all life. Many biologically important cell types reside in quiescence including mature hepatocytes, endothelial cells, and dormant adult stem cells. Furthermore, the quiescence program occurs in both short- and long-term varieties, depending on the physiological environments. A barrier slowing our understanding of quiescence has been a scarcity of available in vitro model systems to allow for the exploration of key regulatory pathways, such as endocytosis. Endocytosis, the internalization of extracellular material into the cell, is a fundamental and highly regulated process that impacts many cell biological functions. Accordingly, we have developed an in vitro model of deep quiescence in hTERT-immortalized RPE1 cells, combining both long-term contact inhibition and mitogen removal, to measure endocytosis. In addition, we present an analytical approach employing automated high-throughput microscopy and image analysis that yields high-content data allowing for meaningful and statistically robust interpretation. Importantly, the methods presented herein provide a suitable platform that can be easily adapted to investigate other regulatory processes across the cell cycle.

Approaches to Investigating the Protein Interactome of PTEN

The tumor suppressor phosphatase and tensin homologue (PTEN) is a redox-sensitive dual specificity phosphatase with an essential role in the negative regulation of the PI3K-AKT signaling pathway, affecting metabolic and cell survival processes. PTEN is commonly mutated in cancer, and dysregulation in the metabolism of PIP3 is implicated in other diseases such as diabetes. PTEN interactors are responsible for some functional roles of PTEN beyond the negative regulation of the PI3K pathway and are thus of great importance in cell biology. Both high-data content proteomics-based approaches and low-data content PPI approaches have been used to investigate the interactome of PTEN and elucidate further functions of PTEN. While low-data content approaches rely on co-immunoprecipitation and Western blotting, and as such require previously generated hypotheses, high-data content approaches such as affinity pull-down proteomic assays or the yeast 2-hybrid system are hypothesis generating. This review provides an overview of the PTEN interactome, including redox effects, and critically appraises the methods and results of high-data content investigations into the global interactome of PTEN. The biological significance of findings from recent studies is discussed and illustrates the breadth of cellular functions of PTEN that can be discovered by these approaches.

Abstract 5963: NGS-based murine pan-cancer gene expression panel for immuno-oncology and tumor microenvironment studies

Abstract INTRODUCTION: Cancer is a heterogeneous disease and highly related to the immunological system. The significance of fully understanding the interactions between cancer and immunity in cancer therapeutics has been broadly noted, particularly for immunotherapies. Experimental murine models derived from a variety of biological technologies are essential preclinical model systems utilized before clinical studies. Molecular pathology techniques, such as gene expression biomarkers, are widely used to systematically profile and characterize these models. So far, however, there has been no robust and cost-effective assay designed for murine clinical immuno-oncology (I/O) research to characterize tumor-immune interactions at a gene expression level. METHODS: Marker genes were identified by combining public and in-house reference genomic data for non-tumor bearing mice as well as tumor mouse models. Oligonucleotide-based hybridization/capture techniques analogous to exome sequencing, targeting specific regions are well established on Illumina Novaseq and BGI MGIseq platforms. These methods were applied to the murine I/O panel assay, with accuracy and reproducibility verified across these two sequencing platforms. The results were further qualified using a PCR array platform. RESULTS: A mouse gene panel involving 1080 genes that encompass surface markers and transcriptomic biomarkers for the immune system, key pathways at the interface of the tumor, tumor microenvironment (TME), and immune response, as well as internal reference genes for data normalization has been established. By combining advanced NGS technologies and specific bioinformatic pipelines, we were able to detect and distinguish various immune and stromal cells involved in the complex interaction between the tumor and its microenvironment, allowing for a multifaceted characterization of disease biology and the interrogation of immune response and evasion. We validated the sensitivity, specificity, and repeatability of the panel by qPCR experiments. CONCLUSIONS: We have established a murine-I/O NGS panel involving 1080 genes to characterize tumor-immune interactions more efficiently and in a cost-effective manner for preclinical studies. This provides an enhanced insight into the TME, with high-content data that has the potential to translate more effectively to the clinic as well as for early identification of immunotherapy companion diagnostics. Citation Format: Xiaobo Chen, Jia Xue, Qixiang Li, Sheng Guo. NGS-based murine pan-cancer gene expression panel for immuno-oncology and tumor microenvironment studies [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5963.

All-Optical Electrophysiology Refines Populations of In Silico Human iPSC-CMs for Drug Evaluation

High-throughput in vitro drug assays have been impacted by recent advances in human induced pluripotent stem cell-derived cardiomyocyte (hiPSC-CM) technology and by contact-free all-optical systems simultaneously measuring action potentials (APs) and Ca2+ transients (CaTrs). Parallel computational advances have shown that in silico simulations can predict drug effects with high accuracy. We combine these in vitro and in silico technologies and demonstrate the utility of high-throughput experimental data to refine in silico hiPSC-CM populations and to predict and explain drug action mechanisms. Optically obtained hiPSC-CM APs and CaTrs were used from spontaneous activity and under optical pacing in control and drug conditions at multiple doses. An updated version of the Paci2018 model was developed to refine the description of hiPSC-CM spontaneous electrical activity; a population of in silico hiPSC-CMs was constructed and calibrated using simultaneously recorded APs and CaTrs. We tested in silico five drugs (astemizole, dofetilide, ibutilide, bepridil, and diltiazem) and compared the outcomes to in vitro optical recordings. Our simulations showed that physiologically accurate population of models can be obtained by integrating AP and CaTr control records. Thus, constructed population of models correctly predicted the drug effects and occurrence of adverse episodes, even though the population was optimized only based on control data and in vitro drug testing data were not deployed during its calibration. Furthermore, the in silico investigation yielded mechanistic insights; e.g., through simulations, bepridil’s more proarrhythmic action in adult cardiomyocytes compared to hiPSC-CMs could be traced to the different expression of ion currents in the two. Therefore, our work 1) supports the utility of all-optical electrophysiology in providing high-content data to refine experimentally calibrated populations of in silico hiPSC-CMs, 2) offers insights into certain limitations when translating results obtained in hiPSC-CMs to humans, and 3) shows the strength of combining high-throughput in vitro and population in silico approaches.

Biomarkers for Traumatic Brain Injury: Data Standards and Statistical Considerations.

Recent biomarker innovations hold potential for transforming diagnosis, prognostic modeling, and precision therapeutic targeting of traumatic brain injury (TBI). However, many biomarkers, including brain imaging, genomics, and proteomics, involve vast quantities of high-throughput and high-content data. Management, curation, analysis, and evidence synthesis of these data are not trivial tasks. In this review, we discuss data management concepts and statistical and data sharing strategies when dealing with biomarker data in the context of TBI research. We propose that application of biomarkers involves three distinct steps-discovery, evaluation, and evidence synthesis. First, complex/big data has to be reduced to useful data elements at the stage of biomarker discovery. Second, inferential statistical approaches must be applied to these biomarker data elements for assessment of biomarker clinical utility and validity. Last, synthesis of relevant research is required to support practice guidelines and enable health decisions informed by the highest quality, up-to-date evidence available. We focus our discussion around recent experiences from the International Traumatic Brain Injury Research (InTBIR) initiative, with a specific focus on four major clinical projects (Transforming Research and Clinical Knowledge in TBI, Collaborative European NeuroTrauma Effectiveness Research in TBI, Collaborative Research on Acute Traumatic Brain Injury in Intensive Care Medicine in Europe, and Approaches and Decisions in Acute Pediatric TBI Trial), which are currently enrolling subjects in North America and Europe. We discuss common data elements, data collection efforts, data-sharing opportunities, and challenges, as well as examine the statistical techniques required to realize successful adoption and use of biomarkers in the clinic as a foundation for precision medicine in TBI.

Systems analysis of benign bladder disorders: insights from omics analysis.

The signaling pathways and effectors that drive the response of the bladder to nonmalignant insults or injury are incompletely defined. Interrogation of biological systems has been revolutionized by the ability to generate high-content data sets that capture information on a variety of biomolecules in cells and tissues, from DNA to RNA to proteins. In oncology, such an approach has led to the identification of cancer subtypes, improved prognostic capability, and has provided a basis for precision treatment of patients. In contrast, systematic molecular characterization of benign bladder disorders has lagged behind, such that our ability to uncover novel therapeutic interventions or increase our mechanistic understanding of such conditions is limited. Here, we discuss existing literature on the application of omics approaches, including transcriptomics and proteomics, to urinary tract conditions characterized by pathological tissue remodeling. We discuss molecular pathways implicated in remodeling, challenges in the field, and aspirations for omics-based research in the future.

Abstract B60: Perturbation biology of colorectal cancer organoids reveals patient-specific signaling rewiring and interference with immunity

Abstract Colorectal cancer (CRC), a cancer with 1.4 million new cases diagnosed annually worldwide, is refractory to immunotherapy (with the exception of a minority of tumors with microsatellite instability). This is somehow paradoxical as CRC is a cancer which we have shown is under immunologic control and that tumor-infiltrating lymphocytes represent a strong independent predictor of survival (1). Based on our previous work showing that the immunophenotypes are determined by the genotypes (2), we hypothesized that mutations are rewiring signaling pathways and thereby modulate the recognition of tumor cells by T cells. In order to investigate rewiring of signaling networks and their interference with immunity for individual patients, we developed an experimental-computational concept using perturbation experiments with patient-derived tumor organoids. A biobank of CRC organoids was generated from histologically verified tumor samples, normal tissue, and liver metastases obtained from CRC patients (n=22). Comprehensive characterization of the organoids (exome sequencing, RNA sequencing, and proteomics) and of the tumors (multiplexed immunofluorescence for 6 immune cell types) was carried out and the resulting data were used to prioritize perturbation experiments. Patient-derived organoids were then perturbed with kinase inhibitors (MEKi, PI3Ki, mTORi, TBK1i, IKKi, BRAFi, and TAKi) and large-scale phosphoproteomic profiling using data-independent acquisition (SWATH-MS) was carried out. Deep phosphoproteomic profiling of perturbed organoids enabled reconstruction of patient-specific signaling networks and revealed profound rewiring by targeted drugs and interference with immune-related pathways, suggesting possible pharmacologic modulation by approved targeted agents to induce immunogenic effects. We show for the first time that systematic and comprehensive analysis of the signaling rewiring can provide a mechanistic rationale for immunotherapy-based combination regimens in CRC. This work is an important step towards the development of a precision immuno-oncology platform that integrates tumor organoids with high-throughput and high-content data for making therapeutic recommendations for individual patients.