Macromolecular transport between the nucleus and the cytoplasm is central to a myriad of eukaryotic cellular processes. This process depends on facilitated diffusion through nuclear pore complexes (NPCs) which selectively allow permeation of cargoes bound to nuclear transport receptors while blocking most other large cargoes. The nucleocytoplasmic transport machinery is subject to various physiological and pathological regulation. For example, several types of viruses, including SARS-CoV-2, manipulate the nucleocytoplasmic transport machinery to nullify the innate anti-viral defense of the host cells. However, there has been no quantitative assay that measures the nucleocytoplasmic transport kinetics in live cells in a high-throughput manner, limiting a deeper mechanistic understanding of such regulations and translational research. To overcome this obstacle, we developed high-throughput assays based on optogenetic probes to quantify the kinetics of nuclear import and export in living human cells. In our recent publication, we validated these assays and studied the role of O-linked N-acetylglucosamine (O-GlcNAc) modification of NPCs in modulating the kinetics of nuclear import and export. We further expanded the use of these assays for quantitative assessment of pathological proteins that compromise the nucleocytoplasmic transport by combining the assays with a novel technique for measuring intracellular level of exogenously expressed protein. Using this combined assay, we simultaneous measured the intracellular level of SARS-CoV-2 ORF6 protein in transiently transfected cells and the resulting reduction in the nuclear transport kinetics. This enabled a dose-response characterization of the nuclear transport inhibition by ORF6 protein. Using this approach, we quantitatively compared various ORF6 variants, including those of SARS-CoV and SARS-CoV-2, providing a mechanistic insight into how ORF6 inhibits the nuclear transport.

The JUMP-CP consortium (Joint Undertaking for Morphological Profiling-Cell Painting) has committed to create a public Cell Painting dataset of over 140,000 genetic and chemical perturbations and establish experimental and computational best practices for image-based profiling. We believe this unprecedented public resource will catalyze drug discovery by enabling the prediction of compounds’ mode of action and toxicity, characterizing disease phenotypes, uncovering new biology, and more. During its first year, the consortium conducted pilot experiments which set down experimental guidelines and explored various computational approaches for profile generation and downstream applications. In this talk, we will present the latest results, discussing the applicability of Cell Painting to link genetic and chemical perturbations among other findings.  

Identification of novel drug targets is a key part of the drug discovery process required to address clinical unmet need in many disease areas. The functional genomics group at AstraZeneca have developed a platform capable of screening the entire genome in an arrayed format. This high-throughput multi-parametric confocal imaging platform, enables complex phenotypic assays and Cas9 genome editing at whole genome scale, for the identification of novel drug targets. This imaging platform has enabled identification of novel targets across several disease areas. This talk will focus on the application of arrayed CRISPR screening for the identification of novel targets for Idiopathic Pulmonary Fibrosis. We will describe the complex assay development required for arrayed CRISPR screening in primary human lung fibroblasts and how this multiparametric imaging platform has enabled us to identify and validate hits with high confidence from primary screening to bespoke target validation.

Genetic screens are critical for the systematic identification of genes underlying cellular phenotypes. While pooling gene perturbations can greatly increase screening throughput, this approach was not previously compatible with the high-content imaging of complex and dynamic cellular phenotypes. We set out to develop optical pooled screening, a method to link pooled perturbations with their associated visual phenotypic outcomes in mammalian cells. In this approach, libraries of genetic perturbations are demultiplexed following image-based phenotyping using targeted in situ sequencing. By applying improved sample preparation, in situ sequencing by synthesis, and microscopy protocols, we have established this approach at a genome-wide scale and report here the results of our first genome-wide optical pooled screen.

Intracellular responses to viral infection are mediated by a key innate immune signaling pathway detecting cytosolic dsRNA that results in phosphorylation and nuclear translocation of IRF3 upon detection of cytosolic dsRNA. In a screen of 80,000 sgRNA across >10 million HeLa cells infected with Sendai virus, we identified direct regulators of IRF3 translocation which modulate responses to RNA stimuli. In this project and others entailing high-content imaging of cell lines and primary cells, we apply machine learning to identify biological phenotypes associated with genomic perturbations in large pooled experiments.

Moving forward, we continue to improve optical pooled screening throughput to ease adoption of this approach by the research community and support large screening projects in a range of biological models. We also aim to integrate additional types of phenotypic readouts to further enrich cellular characterization and provide useful flexibility in screen design.

Microscopy has been instrumental to our understanding of gene functions at different system-levels. Recent convergence of technological advances in automated microscopy, image analysis and genome editing, such as CRISPR, open new avenues for studying how gene perturbations at the molecular level contribute to cellular processes and tissue phenotypes. Tremendous progress has been made towards systematic mapping of gene functions; from genome-wide CRISPR screens, target-annotated small molecule and functional protein screens at the cell and organism level utilizing automated data analysis. This session invites speakers who developed innovative image-based screening approaches for profiling gene and protein function. They will discuss how these approaches can be used to discover new therapeutic targets and disease genes as well as challenges and opportunities ahead.

The COVID-19 pandemic, caused by SARS-CoV-2, has presented a global need for research efforts enabling the interventions, as well as to discover the disease biology and pathology. Here, we described our two high-content, cell-based assays to 1) identify antivirals and, 2) understand the development of antigen responses/immunity against the virus in the infected individuals.

The drug sensitivity and resistance testing (DSRT) technology, established earlier by us to repurpose targeted therapies for cancer patients, was used to develop a high throughput platform for drug testing of >1000 compounds against the SARS-CoV-2 infection using different cell lines with cell viability -based readout, with further validation with image-based assays. The top hits are currently being tested in rodent infection models.

We have also set up a rapid and accurate high-capacity serological method to measure the antibodies against SARS-CoV-2. The assay is a next-generation miniaturized version of the indirect immunofluorescence assay (IFA) commonly applied in viral serodiagnostics. Here we show how the assay enables machine-learning -guided detection of up to three immunoglobulin (Ig) classes from a single sample in a high throughput manner with high sensitivity and specificity, enabling quantitative data interpretation and visualization.

SARS-CoV-2 is a novel coronavirus responsible for the COVID-19 pandemic, in which acute respiratory infections are associated with high socio-economic burden. We applied target based and high-content screening approach to a well-defined collection of 5632 compounds including 3488 that have undergone previous clinical investigations across 600 indications. In phenotypic assays,  compounds were screened by microscopy for their ability to inhibit SARS-CoV-2 cytopathicity in the human epithelial colorectal adenocarcinoma cell line, Caco-2. The primary screen identified 258 hits that inhibited cytopathicity by more than 75%, most of which were not previously known to be active against SARS-CoV-2 in vitro.  The high-content screening data are suitable for reanalysis across numerous drug classes and indications and may yield additional insights into SARS-CoV-2 mechanisms and potential therapeutic strategies. Target based screens were run against key viral targets including the main (M) and papain like (PL) proteases and structural biology approches used to conform the MoA

We applied deep-learning-driven analysis of cellular morphology to develop a scalable “phenomics” platform and here we demonstrate its ability to rapidly identify potential therapeutic stop-gaps for the COVID-19 pandemic. High-throughput screening on this platform demonstrates rapid identification and triage of hits for COVID-19. We deploy the platform to develop phenotypic models of active SARS-CoV-2 infection and of COVID-19-associated cytokine storm, surfacing compounds with demonstrated clinical benefit, such as Remdesivir, and deprioritizing compounds that had no benefit such as hydroxychloroquine. The associated library of images, deep learning features, and compound screening data from COVID-19 screens are available at rxrx.ai and serve as a resource for immune biology and cellular-model drug discovery with potential impact on the COVID-19 pandemic

COVID-19 is an ongoing emerging infectious disease and the causative agent of COVID-19 was identified as SARS-CoV-2. Although this virus is very similar to SARS-CoV based on the viral genome sequence, the transmission, pathogenesis and case fatality are substantially different from those caused by other coronaviruses such as SARS-CoV and MERS-CoV. The unprecedented socioeconomic burden by COVID-19 has prompted very fast development of diagnostic tools, vaccines, and therapeutics. Remarkably, within ten months after the release of the viral genome sequence, a couple of vaccines were successfully developed and after emergency use authorization, several of them are now being used for vaccination of people in many countries. Compared to the vaccine development, drug development has been partially successful so far, and the most potent antiviral drugs were developed as therapeutic antibodies which would inhibit interaction between viral spike protein and host receptor ACE2, thereby blocking viral entry. In this presentation, the COVID-19 drug development efforts will be discussed focusing on drug repositioning and the recent SARS-CoV-2 variants of concern.

The COVID-19 pandemic catalyzed an unprecedented effort to establish vaccine, diagnostic and therapeutic tools to fight SARS-CoV-2. Remarkable successes underlie todays’ deployment of high-efficacy vaccines, and fast, affordable diagnostics. However, the area of therapeutic drug discovery lagged behind, and as yet there are just a handful of clinically proven drugs showing promise to treat COVID-19. It is clear that in the future new technologies and strategies for high-content screening for early drug discovery could hold the key to rethinking how we go about drug-discovery, and our preparedness for future emergent infectious disease outbreaks. This session invites keynote research scientists to share with us their views on how existing technologies for early drug discovery might be better deployed against infectious disease targets like SARS-CoV-2, and what new technologies and strategies look promising for the future.

A fundamental question in cancer biology is how cancers evolve heterogeneity and treatment resistance. The evolution trajectory of cancer is dictated by selective pressures from treatments and the tumour ecosystem. Artificial intelligence (AI) allows us to measure geographical patterns of the microenvironment in pathological samples, to study cancer habitats and niches. Current challenges are establishing multidisciplinary platforms and developing reproducible AI tools by leveraging pathology, genetic, molecular, cellular, and clinical data to improve personalized oncology.

Charles Darwin described how the intimate coexistence between flowering plants and insects leads to reciprocal evolutionary changes; this is now known as coevolution. Today, through the demolition of disciplinary barriers, AI and pathology can coevolve to create evolutionary changes and new paradigms. I will discuss our latest progress on combining AI and experimental technologies for spatial histology and omics data analysis. We aim to understand how cancer evolves within diverse environmental conditions. Our work has revealed a high level of geospatial variation in the tumour microenvironment, with profound implications for early diagnosis, biomarker development, and cancer therapeutics.   

The field of 3D tissues and organoids has seen massive growth, with applications ranging from disease modeling, drug discovery, cell therapy, personalized medicine, and cosmetics to food & cultured meat. While there is lots of excitement about the possibilities of 3D models, many academic labs and commercial entities experience similar bottlenecks for successful development, application & translation, scaling up, and finally, commercialization of these models. In this presentation, Patrick will review bottlenecks, solutions, and new opportunities in the field, covering science, data & automation, as well as commercialization challenges.

The staggering high failure rate of clinical trials for oncology drugs can be attributed to many factors, including efficacy and safety issues not flagged during preclinical R&D. Too often, neither in vitro nor in vivo models can adequately recapitulate the complexity of the human tumor microenvironment (TME) or predict patient response to clinical treatment protocols. Translational human 3D cell culture models, such as patient-derived tumor organoids, have begun to bridge the gap between tissue culture systems and patients in the clinic. However, even in these advanced models, the endogenous cells in the TME, such as tumor infiltrating lymphocytes (TILs), fibroblasts, macrophages and other immune cells are typically absent. These TME components have been shown to express important drug targets and play a critical role in both tumor progression and modulation of the response to drugs.

Dr. Price will present  a novel platform that combines short-term 3D ex vivo tumor culture with high content image-based analysis. He will describe how fresh patient tumor tissue from pleural fluid, ascites, surgical resections or biopsy can be tested ex vivo to preserve tumor heterogeneity and resident immune cells, obviating the need for artificial tumor-immune cell co-culture systems.

There is a great need to understand the synergy between the areas of translational and regulatory science research as they pertain to microphysiological systems and their application in evaluating safety and efficacy for therapeutic indications for different disease areas. There has been much progress towards the experimental development of robust, scalable, and reproducible 3D cellular models, including spheroids, organoids, biofabricated tissues and microphysiological on chip systems, as assay platforms for preclinical drug testing.  However, there is a need for systematic physiological and pharmacological validation and benchmarking of the different 3D cellular models to establish their true clinical predictability and use for decision making in the drug discovery and development pipeline. This talk will focus on identifying these areas of synergy and focus on the development of microphysiological systems that are a best fit for different applications.

Human exposure to Per- and polyfluoroalkyl substances (PFAS) is ubiquitous and these compounds are some of the most prominent contaminants in humans. The toxicity of  perfluorooctane sulfonate (PFOS) and perfluorooctanoate (PFOA) is somewhat known, however data on lesser-understood PFAS are limited. Studies in rodents showed that exposure to PFAS resulted in liver toxicity and liver carcinogenesis.  Whether this is also the case in humans it is less clear. Thus we used primary human liver cell spheroids to identify toxic liver effects. New approach methodologies (NAMs) that apply bioinformatic tools to high-throughput data are being increasingly considered to inform risk assessment for data-poor chemicals. In this study we identified biological response potencies (i.e., benchmark concentrations: BMCs) following PFAS exposures to inform read-across for risk assessment of data-poor PFAS. Gene expression changes were measured in the primary human liver cell microtissues (i.e., 3D spheroids) after 1-day and 10-day exposures to increasing concentrations of 23 PFAS. The cells were treated with subgroups of PFAS: carboxylates (PFCAs), sulfonates (PFSAs), fluorotelomers, and sulfonamides. We identified differentially expressed genes and transcriptomic BMCs. Further, we examined phenotypic changes by microscopic imaging. The combined high-throughput transcriptomic and bioinformatic analyses and microscopy support the capability of NAMs to efficiently assess the effects of PFAS in liver microtissues.

Over the past ten years, we’ve seen tremendous advances in 3D cell technologies, such as 3D in vitro models that more closely reflect morphological characteristics, cellular complexity, and physiology of tissues and organ systems in the human body. In this session, our speakers will discuss how the era of 3D biology is transforming preclinical research today. They will share how they are applying 3D liver models to investigate the toxic response to exposure to forever chemicals (per- and polyfluoroalkyl substances), introduce novel approaches for developing immune-competent 3D tumor models from fresh patient tumor tissue to better predict patient response to immunotherapies, identify synergies between translational and regulatory science in the context of microphysiological systems that mirror interactions between multiple tissue types, and provide expert advice on how to successfully implement these types of new 3D cell technologies in your workflow.

The endometrium, the mucosal lining of the uterus, undergoes dynamic changes throughout the menstrual cycle in response to ovarian hormones. We have generated single-cell and spatial reference maps of the human uterus and 3D endometrial organoid cultures. We dissect the signalling pathways that determine cell fate of the epithelial lineages in the lumenal and glandular microenvironments. Our benchmark of the endometrial organoids highlights common pathways regulating the differentiation of secretory and ciliated lineage in vivo and in vitro. We show in vitro that downregulation of WNT or NOTCH pathways increases the differentiation efficiency along the secretory and ciliated lineages, respectively. These mechanistic insights provide a platform for future development of treatments for a range of common endometrial disorders including endometriosis and carcinoma.

Biological systems are functionally defined by the nature, amount and spatial location of the totality of their proteins. Resolving the spatiotemporal distribution of the human proteome at a subcellular level increases our understanding of human biology and disease. In the Human Protein Atlas project, we are systematically mapping the human proteome in a multitude of human cells and organs using microscopy. We have generated a high-resolution map of the subcellular distribution of the human proteome and have shown that as much as half of all proteins localize to multiple compartments. Such proteins may have context specific functions and ‘moonlight’ in different parts of the cell, thus increasing the functionality of the proteome and the complexity of the cell from a systems perspective.

Recently, we performed a single cell spatiotemporal dissection of the transcriptome and proteome over the cell cycle. We could identify 20% of the human proteome to display cell-to-cell variability, and present the first evidence of cell cycle association for 301 proteins. Our results show that cell cycle progression explains less than half of all cell-to-cell variability, and that most cycling proteins are regulated post-translationally, rather than by transcriptomic cycling.

All this work is critically dependent on computational image analysis, and I will discuss machine learning approaches for classification and embedding of spatial subcellular patterns, including the results from two recent Kaggle competitions.

In summary, I will demonstrate the importance of spatial proteomics data for improved single cell biology and present how the freely available Human Protein Atlas database (www.proteinatlas.org) can be used as a resource for life science.

Recent emergence of low-cost, high-throughput single-cell RNA-Sequencing technology (scRNA-Seq) has enabled systematic identification of new cell types. Molecular definition of cell types provides a way to classify cells and survey cellular heterogeneity. Establishing the spatial organization of cell types identified by scRNA-Seq analysis is essential and requires mapping of these cell types in three-dimensional (3D) tissue volumes. Here, we developed Expansion-Assisted Iterative Fluorescence In Situ Hybridization (EASI-FISH) to survey gene expression and molecular cell types in thick tissue. We also developed a turnkey, open-source computational pipeline that allows for rapid and automated data processing, facilitating adoption of high-plex FISH in thick tissue as a routine laboratory method.

In this talk I will give an overview of the computational steps in the analysis of a single cell-based large-scale microscopy experiments. First, I will present a novel microscopic image correction method designed to eliminate illumination and uneven background effects which, left uncorrected, corrupt intensity-based measurements. New single-cell image segmentation methods will be presented using differential geometry, energy minimization and deep learning methods (www.nucleAIzer.org). I will discuss the Advanced Cell Classifier (ACC) (www.cellclassifier.org), a machine learning software tool capable of identifying cellular phenotypes based on features extracted from the image. It provides an interface for a user to efficiently train machine learning methods to predict various phenotypes. For cases where discrete cell-based decisions are not suitable, we propose a method to use multi-parametric regression to analyze continuous biological phenomena. To improve the learning speed and accuracy, we propose an active learning scheme that selects the most informative cell samples.

Our recently developed single-cell isolation methods, based on laser-microcapturing and patch clamping, utilize the selection and extraction of specific cell(s) using the above machine learning models. I will show that we successfully performed DNA and RNA sequencing, proteomics, lipidomics and targeted electrophysiology measurements on the selected cells.

Spatial omics technologies use imaging methods to acquire localized molecular readouts. While powerful, these technologies pose many challenges. The field requires sophisticated computational infrastructure, novel analytical methods, and innovative approaches in order to realize its full potential. Combined with high-content screens, these applications will reveal the impact of perturbations on biological scales, such as tissues and organ systems, as never before possible. This session will highlight emerging solutions to increase information content of spatial omics data that will power the ultra-high-content screens of the future.

Deep learning is a class of machine learning techniques that uses multi-layered artificial neural networks for automated analysis of signals or data. Beyond its main stream applications such as the recognition and labeling of specific features in images, deep learning holds numerous opportunities for revolutionizing image formation, microscopic reconstruction and sensing fields. In this presentation, I will provide an overview of some of our recent work on the use of deep neural networks in advancing computational microscopy and sensing systems, also covering their biomedical applications.