Tissue clearing may alter emission and absorption properties of common fluorophores.

In recent years, 3D cell culture has been gaining a more widespread following across many fields of biology. Tissue clearing enables optical analysis of intact 3D samples and investigation of molecular and structural mechanisms by homogenizing the refractive indices of tissues to make them nearly transparent. Here, we describe and quantify that common clearing solutions including benzyl alcohol/benzyl benzoate (BABB), PEG-associated solvent system (PEGASOS), immunolabeling-enabled imaging of solvent-cleared organs (iDISCO), clear, unobstructed brain/body imaging cocktails and computational analysis (CUBIC), and ScaleS4 alter the emission spectra of Alexa Fluor fluorophores and fluorescent dyes. Clearing modifies not only the emitted light intensity but also alters the absorption and emission peaks, at times to several tens of nanometers. The resulting shifts depend on the interplay of solvent, fluorophore, and the presence of cells. For biological applications, this increases the risk for unexpected channel crosstalk, as filter sets are usually not optimized for altered fluorophore emission spectra in clearing solutions. This becomes especially problematic in high throughput/high content campaigns, which often rely on multiband excitation to increase acquisition speed. Consequently, researchers relying on clearing in quantitative multiband excitation experiments should crosscheck their fluorescent signal after clearing in order to inform the proper selection of filter sets and fluorophores for analysis.

Combining Automated Organoid Workflows with Artificial Intelligence-Based Analyses: Opportunities to Build a New Generation of Interdisciplinary High-Throughput Screens for Parkinson's Disease and Beyond.

Parkinson's disease (PD) is the second most common neurodegenerative disease and primarily characterized by the loss of dopaminergic neurons in the substantia nigra pars compacta of the midbrain. Despite decades of research and the development of various disease model systems, there is no curative treatment. This could be due to current model systems, including cell culture and animal models, not adequately recapitulating human PD etiology. More complex human disease models, including human midbrain organoids, are maturing technologies that increasingly enable the strategic incorporation of the missing components needed to model PD in vitro. The resulting organoid-based biological complexity provides new opportunities and challenges in data analysis of rich multimodal data sets. Emerging artificial intelligence (AI) capabilities can take advantage of large, broad data sets and even correlate results across disciplines. Current organoid technologies no longer lack the prerequisites for large-scale high-throughput screening (HTS) and can generate complex yet reproducible data suitable for AI-based data mining. We have recently developed a fully scalable and HTS-compatible workflow for the generation, maintenance, and analysis of three-dimensional (3D) microtissues mimicking key characteristics of the human midbrain (called "automated midbrain organoids," AMOs). AMOs build a reproducible, scalable foundation for creating next-generation 3D models of human neural disease that can fuel mechanism-agnostic phenotypic drug discovery in human in vitro PD models and beyond. Here, we explore the opportunities and challenges resulting from the convergence of organoid HTS and AI-driven data analytics and outline potential future avenues toward the discovery of novel mechanisms and drugs in PD research. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Cell-Type-Specific High Throughput Toxicity Testing in Human Midbrain Organoids.

Toxicity testing is a crucial step in the development and approval of chemical compounds for human contact and consumption. However, existing model systems often fall short in their prediction of human toxicity in vivo because they may not sufficiently recapitulate human physiology. The complexity of three-dimensional (3D) human organ-like cell culture systems ("organoids") can generate potentially more relevant models of human physiology and disease, including toxicity predictions. However, so far, the inherent biological heterogeneity and cumbersome generation and analysis of organoids has rendered efficient, unbiased, high throughput evaluation of toxic effects in these systems challenging. Recent advances in both standardization and quantitative fluorescent imaging enabled us to dissect the toxicities of compound exposure to separate cellular subpopulations within human organoids at the single-cell level in a framework that is compatible with high throughput approaches. Screening a library of 84 compounds in standardized human automated midbrain organoids (AMOs) generated from two independent cell lines correctly recognized known nigrostriatal toxicants. This approach further identified the flame retardant 3,3',5,5'-tetrabromobisphenol A (TBBPA) as a selective toxicant for dopaminergic neurons in the context of human midbrain-like tissues for the first time. Results were verified with high reproducibility in more detailed dose-response experiments. Further, we demonstrate higher sensitivity in 3D AMOs than in 2D cultures to the known neurotoxic effects of the pesticide lindane. Overall, the automated nature of our workflow is freely scalable and demonstrates the feasibility of quantitatively assessing cell-type-specific toxicity in human organoids in vitro.

Fluorescence-based Single-cell Analysis of Whole-mount-stained and Cleared Microtissues and Organoids for High Throughput Screening.

Three-dimensional (3D) cell culture, especially in the form of organ-like microtissues ("organoids"), has emerged as a novel tool potentially mimicking human tissue biology more closely than standard two-dimensional culture. Typically, tissue sectioning is the standard method for immunohistochemical analysis. However, it removes cells from their native niche and can result in the loss of 3D context during analyses. Automated workflows require parallel processing and analysis of hundreds to thousands of samples, and sectioning is mechanically complex, time-intensive, and thus less suited for automated workflows. Here, we present a simple protocol for combined whole-mount immunostaining, tissue-clearing, and optical analysis of large-scale (approx. 1 mm) 3D tissues with single-cell level resolution. While the protocol can be performed manually, it was specifically designed to be compatible with high-throughput applications and automated liquid handling systems. This approach is freely scalable and allows parallel automated processing of large sample numbers in standard labware. We have successfully applied the protocol to human mid- and forebrain organoids, but, in principle, the workflow is suitable for a variety of 3D tissue samples to facilitate the phenotypic discovery of cellular behaviors in 3D cell culture-based high-throughput screens. Graphic abstract: Automatable organoid clearing and high-content analysis workflow and timeline.

Generation and Maintenance of Homogeneous Human Midbrain Organoids.

Three-dimensional cell cultures ("organoids") promise to better recapitulate native tissue physiology than traditional 2D cultures and are becoming increasingly interesting for disease modeling and compound screening efforts. While a number of protocols for the generation of neural organoids have been published, most protocols require extensive manual handling and result in heterogeneous aggregates with high sample-to-sample variation, which can hinder screening-based strategies. We have now developed a fast and efficient protocol for the generation and maintenance of highly homogeneous and reproducible midbrain organoids. The protocol is streamlined for use in fully automated workflows but can also be performed manually without the need for highly specialized equipment. It relies on the aggregation of small molecule neural precursor cells (smNPCs) in standard 96-well V-bottomed plates under static culture conditions without cumbersome matrix embedding. The result is ready-to-assay uniform 3D human midbrain organoids available in freely scalable quantities for downstream analyses in 3D cell culture. Graphic abstract: Automated midbrain organoid generation workflow and timeline.

A fully automated high-throughput workflow for 3D-based chemical screening in human midbrain organoids.

Three-dimensional (3D) culture systems have fueled hopes to bring about the next generation of more physiologically relevant high-throughput screens (HTS). However, current protocols yield either complex but highly heterogeneous aggregates ('organoids') or 3D structures with less physiological relevance ('spheroids'). Here, we present a scalable, HTS-compatible workflow for the automated generation, maintenance, and optical analysis of human midbrain organoids in standard 96-well-plates. The resulting organoids possess a highly homogeneous morphology, size, global gene expression, cellular composition, and structure. They present significant features of the human midbrain and display spontaneous aggregate-wide synchronized neural activity. By automating the entire workflow from generation to analysis, we enhance the intra- and inter-batch reproducibility as demonstrated via RNA sequencing and quantitative whole mount high-content imaging. This allows assessing drug effects at the single-cell level within a complex 3D cell environment in a fully automated HTS workflow.

In 1907, the American zoologist Ross Granville Harrison developed the first technique to artificially grow animal cells outside the body in a liquid medium. Cells are still grown in much the same way in modern laboratories: a single layer of cells is placed in a warm incubator with nutrient-rich broth. These cell layers are often used to test new drugs, but they cannot recapitulate the complexity of a real organ made from multiple cell types within a living, breathing human body. Growing three-dimensional miniature organs or 'organoids' that behave in a similar way to real organs is the next step towards creating better platforms for drug screening, but there are several difficulties inherent to this process. For one thing, it is hard to recreate the multitude of cell types that make up an organ. For another, the cells that do grow often fail to connect and communicate with each other in biologically realistic ways. It is also tough to grow a large number of organoids that all behave in the same way, making it hard to know whether a particular drug works or whether it is just being tested on a 'good' organoid. Renner et al. have been able to overcome these issues by using robotic technology to create thousands of identical, mid-brain organoids from human cells in the lab. The robots perform a series of precisely controlled tasks ­ including dispensing the initial cells into wells, feeding organoids as they grow and testing them at different stages of development. These mini-brains, which are the size of the head of a pin, mimic the part of the brain where Parkinson's disease first manifests. They can be used to test new drugs for Parkinson's, and to better understand the biology of the brain. Perhaps more importantly, other types of organoids can be created using the same technique to model diseases that affect other areas of the brain, or other organs altogether. For example, Renner et al. also generated forebrain organoids using an automated approach for both generation and analysis. This research, which shows that organoids can be grown and tested in a fully automated, reproducible and scalable way, creates a platform to quickly, cheaply and easily test thousands of drugs for Parkinson's and other difficult-to-treat diseases in a human setting. This approach has the potential to reduce research waste by increasing the chances that a drug that works in the lab will also ultimately work in a patient; and reduce animal experiments, as drugs that do not work in human tissues will not proceed to animal testing.