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. A number of uniquely human diseases, including Parkinson’s disease, would greatly benefit from a comprehensive human cellular in vitro model that recapitulates key characteristics of midbrain tissues in a high-throughput-compatible format. Three-dimensional (3D) cell culture in general and the ability to generate organ-like aggregates (‘organoids’) in particular have found a rapid following over the past few years  due to t3D culture in the form of spheroids has long been established, especially in the field of cancer biology , and useIn contrast, complex organoids have emerged as a promising research tool due to their unique resemblance to human tissues, defined by an organ-like architecture composed of different tissue-specific cell types and the capability to mimic organ functions . One strHere, we present a fully automated workflow in a standard 96-well format that combines generation, maintenance, whole mount immunostaining, tissue clearing, and high-content imaging of automated midbrain organoids (AMOs) see . The resScreening applications require biological systems that operate within predictable physiological parameters. In order to limit cellular heterogeneity during differentiation, we produced human AMOs starting from small molecule neural precursor cells (smNPCs) , which iIn order to characterize protein localization in our AMOs (>600 μm diameter) and assess the efficiency of their neural/midbrain differentiation at a cellular resolution and in a HTS-compatible manner, we adapted an extended 3D-staining protocol  and combThe immunostaining results are depicted as either single confocal optical slices see  or maximOver time, AMOs matured further. Expression of synapsin  see Fig and 2 asIn the cortex, neurons form cortical layers with distinct markers. Cortical 3D models recapitulate this layer organization to a degree . In contUltrastructural analysis of AMOs see  supporteFurther quantitative real-time PCR (qPCR) analysis demonstrated increasing expression levels of various neural , midbrain , and glia-specific  markers at different developmental stages with concomitant decreases in neural precursor markers , confirming neural maturation toward a midbrain identity over time . FinallyTo assess functional coupling of individual cells within the AMOs we first performed Fluo-4 acetoxymethyl ester (AM)-based calcium imaging, which can be used as a readout for spiking activity of neurons . In addiGiven the high reproducibility of synchronized calcium activity across all tested samples, we decided to evaluate the feasibility of using it as a functional readout in screening settings. Thus, we performed Fluo-4 AM-based calcium imaging on younger (day 35) AMOs and measured the resulting fluorescence signals on a standard plate reader. While they showed a shorter periodicity than the older samples, all tested AMOs displayed distinct peaks in fluorescence intensity resembling the synchronous activity patterns seen during spinning disk microscopy analysis see , also inMultielectrode array (MEA) measurements revealed spontaneous electrical activity in 35-day-old AMOs see . The fieFinally, we characterized the electrophysiological properties of single cells from the AMOs using voltage patch-clamping. A stepwise increase of the membrane holding potential from −70 to +60 mV with 10 mV increments elicited transmembrane currents that consisted of a fast-activating, fast-inactivating inward current followed by a slower activating, slowly deactivating outward current ranging from a few hundred pA to several nA see . The I–VTo characterize AMOs on the level of global gene expression, we performed RNA sequencing of single organoids from three independent batches of AMOs  and compared the results with published RNA sequencing data sets of primary human tissues  and estaSince homogeneity and reproducibility are crucial for screening applications, we next examined the variance of AMOs on the gene expression level and compared it to that of published midbrain organoids . This readj. < 0.05) in AMOs compared to the previously used published midbrain organoids   analysisrganoids . This anrganoids . Consistrganoids , this fu2 >0.99), illustrating the quantitative nature of our optical HTS 3D whole mount analysis workflow.While immunofluorescence-based screening-compatible techniques of whole 3D aggregates have been reported, they can only detect cells in the outer layers of large organoids , or theySox2 = 5%, CVMap2 = 9%).Next, we demonstrated the homogeneity of AMOs at the protein level. A fully automated 96-well based whole mount optical analysis see  left illPositional analysis detected effects of plate position (edge effects) for Map2 levels but not Sox2 levels with about 10% reduced Map2 brightness of samples in the center of the plate  comparedSince differentiation outcomes and kinetics are known to vary considerably between cell lines, we decided to benchmark our AMOs against a protocol that can be implemented using the same starting cell line as our AMOs and that is adaptable to the same automation and analysis workflow that we established for our midbrain model. In this comparison, smNPC-derived AMOs (line 2) and the hiPSC organoids share the same cell line of origin. We compared our smNPC-derived cultures to hiPSC-derived 3D neural organoids based on a core protocol by Compared to smNPC-derived AMOs, morphology and zonal arrangement of neural subpopulations in hiPSC organoids varied to a larger degree see . Three iWe performed high-content analyses at the protein level analogous to the data in In conclusion, AMOs are more homogeneous with regard to their morphology, cellular structure, size, viability, and protein expression than the automated hiPSC organoids generated from the same cell line and under strictly standardized conditions.A clinically relevant midbrain model requires dopaminergic activity. To further confirm midbrain-specific function of AMOs, we generated electrophysiological data from multielectrode arrays together with specific molecular agonists and antagonists for dopaminergic, GABAergic, and glutamatergic pathways. Functional responses were consistent with a midbrain identity of AMOs see . ElectriTo further evaluate cellular maturity as one key factor in physiologically relevant screens, we determined the percentage of Sox2-negative mature cell types within a large number of AMOs via high-content imaging. Testing AMOs from two separate lines with three independently cultured batches each, we detected between 70% and 80% mature cell types after only 30 days of differentiation with very little variation within and between batches and cell lines see .The maturity and dopaminergic midbrain identity of AMOs was further confirmed by the spontaneous and unstimulated secretion of dopamine into the culture medium at a similar level as found in adult human cerebrospinal fluid CSF, see . SequestTo further assess the physiological relevance of our workflow as well as its ability to quantify drug effects in 3D cell cultures, we treated AMOs from two different cell lines with increasing concentrations of toxins specific for dopaminergic neurons, namely 6-Hydroxy Dopamine (6OHD) and 1-Methyl-4-phenylpyridinium (MPP+) . High-coWe next evaluated the use of cleaved caspase 3 (cCasp3) as a general marker for cell death in AMOs for HTS toxicity studies see . If usedHowever, cCasp3 only labels cells that are currently undergoing apoptosis, and this signal disappears when dead cells are cleared from the tissue. Consequently, we observed that the cCasp3 signal dropped at high concentrations of toxicants see , necessiOverall, this data highlights the suitability of AMOs as an ideal tool for midbrain-specific drug and toxicity screening with various high-throughput-compatible and functional readouts that can be directly applied to large-scale screening campaigns as well as more detailed secondary analyses to gain mechanistic insight into the behaviors of distinct cellular subpopulations in the context of their niche.In this report, we present a fully scalable, automated workflow for the generation, maintenance, fixation, immunostaining, clearing, and optical high-content analysis of human midbrain organoids. In designing our workflow, we used a standard 96-well format along with optimized protocols providing full compatibility with standard liquid handlers in most screening facilities. By omitting cumbersome matrix embedding steps, we eradicate variation originating from positional differences of cells within extracellular matrix (ECM) droplets during embedding. Importantly, our workflow is also compatible with manual pipetting, and thus can benefit labs without access to liquid handlers. By starting with neural precursor cells rather than PSCs, we can accelerate and streamline the neural differentiation process resulting in highly reproducible, homogeneous, functionally coupled, and electrophysiologically active human 3D microtissues. In contrast to the highly organized layers of the cortex, which can be partially recapitulated in cerebral/cortical organoids , differeWhile other groups have previously reported the use of human neural precursor cell (hNPC)-derived neurospheres for toxicity testing , as wellOur automated whole mount optical analysis can also be directly applied to other established 3D protocols and may help uncover phenotypes that manifest themselves primarily in 3D or in distinct subpopulations of cells. The neural precursor-based proof-of-principle demonstrated here may be suitable, for example, in screens for diseases affecting primarily neural precursors including Zika virus infection. While this condition has been intensively studied using organoids, platform technologies for 3D high-throughput screens are still lacking and in high demand .Furthermore, other non-midbrain organoid protocols may benefit from our workflow; for example, we have adapted cortical organoids by Despite our efforts to standardize iPSC-based organoids in a modified automated workflow, AMOs displayed a distinctly lower variance in a broad set of parameters, including size, cellular subpopulations, and survival in toxicity studies. This may indicate that mechanical standardization alone cannot compensate for the innate variability of PSCs during their self-organization.Although the use of precursors cells as a starting population provides benefits, the comparison to hiPSC-based organoids also highlights certain limitations of this approach. Our choice of generating AMOs by seeding more committed neural precursor cells instead of hiPSCs improves the predictability of the differentiation outcome while maintaining the ability to form self-organized tissues. However, it also sacrifices some of the hiPSCs’ broader cell fate potential and complexity resulting in simpler structures compared to hiPSC-based organoids. In addition, smNPCs can only give rise to cells of the ectoderm, which excludes, for example, the formation of microglia. Although smNPCs are capable of efficiently generating oligodendrocytes , they geThe emerging science of 3D cell culture promises to probe the effects of drugs on single cell types (here: dopaminergic neurons) in an intact niche as part of a complex 3D in vitro culture. Understanding the effects in context will be an essential factor to better understand biology at the tissue level. Currently, 3D cell culture science is in a phase of transition, where traditional manual low throughput protocols need to be adapted to unbiased higher throughput workflows. This is a prerequisite to mine the information necessary to merge organoid science with the promises of big data and machine learning to better tackle the complexities of understanding 3D biology. Therefore, we see our workflow as a contribution to help others translate successful strategies such as cell painting  and othe2. The human small molecule precursor cells (smNPCs) were generated and characterized during a previous study  were performed by a Beckman Coulter Biomek FXP liquid handling station equipped with a 96-channel-pipetting head and an attached Cytomat incubator (Thermo Fisher). After digestion by accutase, we seeded 9000 smNPCs in each well of a conical 96-well plate (Thermo Fisher) in smNPC medium and allowed them to aggregate for 2 days. To increase inter-cell adhesion, we added 0.4% (w/v) polyvinyl alcohol . Starting at day 2, cells undergo ventral patterning over 4 days in two feedings by removal of CHIR99021 in the continued presence of 1 μM SAG and the addition of 1 ng/mL brain derived neurotrophic factor  and 1 ng/mL glial cell line-derived neurotrophic factor . After ventralization, we removed SAG on day 6, further supported midbrain differentiation and maturation by the addition of 1 ng/mL transforming growth factor beta 3 , and 100 μM dibutyryl cyclic adenosine monophosphate . To boost maturation and cell survival during the rest of the neural maturation, we increased the concentration of BDNF and GDNF to 2 ng/mL each starting at day 6. A single dose of 5 ng/mL Activin A (eBioscience) was added on day 6 only. Depending on the desired degree of maturity, the duration of the maturation phase can be prolonged to 100 days and longer. Organoids were fed every other day for the entire duration of culture.For size measurements, we took brightfield images of randomly selected AMOs or hiPSC organoids using a stereo microscope . Images were processed with ImageJ/Fiji  using a In order to analyze protein expression in 3D in a HTS-compatible manner, we adapted a whole mount staining protocol based on We performed RNA isolation for quantitative real-time PCR (qPCR) analysis using the NucleoSpin RNA XS kit (Macherey-Nagel) according to the manufacturer’s instructions. Depending on the age and protocol used, we pooled organoids from one batch in order to yield enough RNA for downstream analysis; AMOs: 32 (d6), 24 (d16), or 18 (d22 and d30), hiPSC organoids: 32 (d6), 14 (d22), or 10 (d30). We determined RNA concentration and purity using a NanoDrop 8000 spectrophotometer (Thermo Fisher) and performed reverse transcription according to standard protocols using 1000 ng RNA per reaction. qPCR was done in triplicates on a Quantstudio 5 Real-Time PCR System (Applied Biosystems) with iTaq Universal SYBR Green Supermix (Bio-Rad) and 3.2 ng RNA equivalents per 10 μL reaction. Cycling conditions were 2 min at 95°C followed by 40 cycles of 15 s at 95°C and 60 s at 60°C. We calculated the relative expression using the ΔΔct method and normalized to undifferentiated smNPCs (AMO line 1) collected before aggregation as well as using GAPDH as a housekeeping gene. Alternatively, gene expression was quantified with the Biomark 48.48 integrated fluidic circuit (IFC) Delta Gene assay (Fluidigm) according to the manufacturer’s instructions. Briefly, following 14 cycles of preamplification, the samples were subjected to an exonuclease I (New England Biolabs) treatment (37°C for 30 min and 80°C for 15 min) and diluted twentyfold with DNA Suspension buffer (TEKnova). The samples (in duplicates) and assay mixtures were loaded onto a 48.48 microfluidic IFC chip and run on the BioMark real-time PCR reader (Fluidigm) where they were amplified and measured according to manufacturer’s instructions. Here, data analysis was performed using the BioMark real-time PCR analysis software 4.3.1 (Fluidigm) with standard settings. Again, undifferentiated smNPCs (AMO line 1) were used as a reference and GAPDH served as housekeeping gene. All data was transferred to Microsoft Excel for further processing and GraphPad Prism v8.4.2 for plotting. A list of all used primers can be found in For calcium imaging, we added 10 μM cell-permeant Fluo-4 AM (Thermo Fisher) diluted in AMO medium to the aggregates and incubated for 60 min at 37°C. For inhibitor studies cobalt(II) chloride (Sigma-Aldrich) was added to the medium at a concentration of 2 mM together with the Fluo-4 AM. Imaging was performed using a Dragonfly spinning disc confocal microscope  at a frequency of 10 Hz for 4 min. Data analysis was performed using ImageJ/Fiji . First, Electrophysiological measurements on microelectrode arrays  were performed on electrode areas of 9-well MEAs as previously described . The MEAhttp://scidavis.sourceforge.net/) and Origin Pro 2019 (Origin Lab). To reveal the shape of I-V curves, single traces were normalized to the peak amplitude and then averaged.Due to the morphology of AMOs , it was technically impossible to perform the patch-clamp measurements on intact aggregates. Therefore, the organoids were treated with 1 mg/ml trypsin and then mechanically dispersed to obtain single cells. These were seeded on PDL-coated coverslips and cultured for 1–3 days in AMO medium (we stated the age of AMOs at the time of dissociation). The transmembrane currents were recorded from isolated cells using the whole-cell configuration of the patch-clamp technique . The patTo isolate RNA of single AMOs and organoids we used the Direct-zol-96 RNA kit (Zymo Research) according to the manufacturer’s instructions. We assessed RNA concentration and purity using a NanoDrop 8000 spectrophotometer and RNA integrity with a Bioanalyzer (Agilent Technologies) per standard protocols. Next, mRNA was enriched using the NEBNext Poly(A) Magnetic Isolation Module (NEB) followed by strand-specific cDNA NGS library preparation . The size of the resulting library was controlled by use of a D1000 ScreenTape (Agilent 2200 TapeStation) und quantified using the NEBNext Library Quant Kit for Illumina (NEB). Equimolar pooled libraries were sequenced in a single read mode (75 cycles) on the NextSeq 500 System (Illumina) using v2 chemistry yielding in an average QScore distribution of 95% >= Q30 score and subsequent demultiplexed and converted to FASTQ files by means of bcl2fastq v2.20 Conversion software (Illumina).www.nature.com/neuro/journal/v18/n1/extref/nn.3898-S9.zip. Reads were mapped to the human genome as described above. RPKM values (Reads Per Kilobase of transcript per Million mapped reads) for each gene were computed by Cufflinks (v2.2.1). We selected genes with high expression (log RPKM > 1) for further analysis. Based on the expression of the selected genes, Pearson correlations were calculated. We averaged the correlation coefficients for biological replicates. PCA and differential expression analysis were performed with raw counts using the R package DESeq2 (v1.18.1). Genes were considered as deregulated if |log2FC| > 2 and FDR < 0.05 using Benjamini-Hochberge multiple test adjustment  , using djustment . Gene Onjustment  and visujustment .For the comparison with cerebral organoids , the dishttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE119060.All RNA sequencing data generated by us was deposited to the NCBI GEO database (GSE119060) and can be accessed at To assess how quantitative our imaging workflow is, we performed a dilution experiment. We mixed unlabeled smNPCs with different percentages  of CellTracker deep red dye (Life Technologies)-labeled cells  and aggregated them in smNPC maintenance medium with 0.4% PVA. To explore the effects of overall aggregate size on quantitation, we generated aggregates with 100,000 as well as 200,000 cells in total. After 1 day of aggregation, the aggregates were fixed with 4% PFA, subjected to BABB-based tissue clearing, imaged, and analyzed as described below.After staining and clearing, we achieved uniform aggregate positioning within the wells by tilting the plates off the horizontal plane at 60 degrees for 1 min. Image acquisition was carried out in an Operetta high content imager (Perkin Elmer) and images were analyzed in Harmony 4.1 software and/or Columbus version 2.6.0.Each high-content imaging experiment requires a customized set of parameters adjusted for the size of the aggregates, marker-wavelengths, their morphology and distribution within the sample, marker combinations, and signal and background intensities. As the details of each workflow are particular to our hardware and software setup, we first provide a general description of our workflow that focuses on explaining the principal steps in a manner that allows platform-independent reproduction. We then provide the detailed analysis pipeline for the data shown in As our version of high content imager did not include automatic sample-finding capability, we started by acquiring well overview images at 2x magnification. Although uniform, locations of aggregates were not identical from well to well, necessitating acquisition of tiled 3D stacks of adjacent fields of view (FOV) with 10x magnification covering all possible locations of aggregates within a small area of given wells. The 2x overview images guided the choice of location for the 10x FOVs. This resulted in tiled non-overlapping stacks of images, some of which contained aggregates, and some of which did not. These 10x images entered further image analysis.In general, after hardware-based background correction, image analysis was performed separately for each FOV and confocal plane, and results were either reported as mean or sum for all FOVs and planes per well as indicated in the figures and figure legends. Further analysis consisted of three principal stages: (1) sample identification, (2) background correction, (3) segmentation, and (4) data reporting.To identify aggregates and distinguish them from occasional contaminations, we summed all acquired channels  and smoothed the resulting image with either a gaussian or a median kernel to remove local outlier pixels. Simple thresholding identified organoids as regions of interest (ROI), which were further refined via morphology (e.g. area and roundness) and intensity to exclude incorrectly identified objects such as dust or fibers.As large spherical aggregates possessed a non-uniform background signal, we performed local background correction before image segmentation. Subtracting a blurred version of the image from the raw image removed most of the non-uniform haze in each plane. To boost fine structures, sliding-parabola algorithms provided additional local contrast when needed.Structures of interest within each bona fide aggregate identified in (1), e.g. nuclei or cells, were segmented by either simple thresholds or by dedicated proprietary algorithms  and further refined by morphology and intensity if needed.Finally, the parameters of interest  were measured from the raw image channel based on the previously defined and selected ROIs. If object segmentation was not possible , raw/total integrated signal intensity within the defined sample region was reported. In order to generate results for the entire 3D aggregate, which may span several FOVs, datasets for all planes and fields were combined. Often, it is also necessary to normalize the data to the aggregate area to account for sample variations across planes and wells.2 size. In order to better isolate Sox2+ nuclei from the general background, we ran a sliding parabola algorithm with a curvature setting of 2 across each image plane in the 488 nm channel. Nuclei were then identified within each aggregate region via the ‘find nuclei’ function, algorithm 'M' and further selected to be Sox2+ if they were larger than 10 µm2 and brighter than 1200 abu. We excluded image artifacts, small dust particles, and overlapping nuclei by omitting nuclei brighter than 6000 abu and larger than 70 μm2 from further quantification. For final output, the number and total brightness of nuclei in 488 nm and of aggregate regions in 647 nm were summed for all planes and all fields of view in each well and transferred to Microsoft Excel and TIBCO Spotfire for further annotation, analysis, and plotting. We omitted data from wells that contained dust particles within the same FOV as organoids, incompletely imaged aggregates due to improper positioning, or AMOs that have been damaged or lost during culture or downstream processing. Plate 1, 2, and 3 represent independent batches of separately thawed and cultured cells of the same frozen batch.After staining and clearing, we achieved uniform aggregate positioning within the wells by tilting the plates off the horizontal plane at 60° for 1 min. Image acquisition was carried out in an Operetta high content imager (Perkin Elmer) and images were analyzed in Harmony 4.1 software. We acquired a total of 16 confocal planes in three channels  with an inter-plane spacing of 36.6 μm for a total stack of 549 μm, covering the entire aggregate height (clearing and dehydration steps tend to shrink and flatten aggregates slightly). To define the aggregate region on each image plane, all three channels were summed, filtered with a median filter to remove small localized features, and bright areas were identified via the 'find image region' function. After cleaning the edge of the aggregate region by dilation and erosion steps of 10 and 3 pixels, respectively, we identified bona fide AMOs by selecting for regions with a minimum of 300 arbitrary brightness units (abu) and 4000 μmFor cortical organoid generation (see method section below), human induced Pluripotent Stem Cells (hiPSCs)  were generated and characterized during a previous study . Cells wFor the generation of iPSC-based cerebral organoids  according to the manufacturer’s instructions. The entire procedure was performed using an automated liquid handling system (Beckman Coulter) and is thus fully scalable and HTS-compatible. In short, the reagent and the AMOs were brought to room temperature in their 96-well culture plates for 30 min and the media volume of each 96-well was adjusted to 55 µl. We added an equal volume (55 µl) of the CellTiter Glo 3D reagent and let it shake on a Thermomixer (Eppendorf) at 900 rpm for 5 min before incubating the samples protected from light at room temperature for 25 min. To prevent cross-talk between wells when measuring the luminescence, we next transferred the contents from the clear 96-well culture plates to opaque white 384-well Lumitrac plates (Greiner) with two technical replicates per sample. Luminescence was recorded immediately after transfer with a Synergy Mx plate reader (BioTek). The results were outputted to Microsoft Excel, reformatted and then transferred to GraphPad Prism v8.4.2 for plotting. Coefficients of variation (CVs) were calculated via CV = standard deviation/mean.2. For Map2, an additional sliding parabola algorithm with a curvature of 10 was run across the subtracted image to further reduce background noise. Map2+ cells were then also identified using the ‘find cells’ function algorithm ‘C’. As final output, the total intensity of the identified TH+ cells in the raw 647 nm channel and the Map2+ cells in the raw 488 nm channel were summed for all fields of view and confocal planes per well. The data was then transferred to Microsoft Excel for further analysis, including normalization to the organoid area, and plotted using GraphPad Prism v8.4.2.On day 47 of differentiation, AMOs received Tox medium (TM) overnight to remove antioxidants present in the B27 media supplement. TM consisted of DMEM-F12 supplemented with 1% N2 and 1% penicillin/streptomycin/glutamine. The next day, AMOs were treated with different concentrations  of either 6-Hydroxydopamine hydrochloride (Sigma-Aldrich) or 1-Methyl-4-phenylpyridinium iodide  in TM. To refresh compounds, AMOs received TM including toxins at the same concentrations as before 24 hr after the first application. After a total incubation of 48 hr, the medium was changed back to standard AMO medium, and the samples were cultured for 6 more days to allow cell death to occur and the dead cells to be cleared from the organoids. After 6 days, the samples were fixed with 4% PFA, whole mount stained for Map2 and TH, BABB-cleared, and subjected to high-content confocal imaging as described above. We performed image analysis following the steps as outlined in the high-content analysis section with slight modifications to accommodate the individual brightness, morphology, and background characteristics of the staining. Briefly, after identifying AMOs, we ran a Gaussian smoothing algorithm across the TH 647 nm (10 px width) and Map2 488 nm (five px width) channels and subtracted the smoothed images from the raw images to better isolate TH+ and Map2+ cells from the background. In the TH 647 nm channel, cells were then identified with the ‘find cells’ function, algorithm ‘C’ and further selected to be TH+ if they were brighter than 200 abu and larger than 25 μmWe collected the cell culture supernatant from 35 days old organoids 40 hr after feeding and measured its dopamine content using a Dopamine ELISA Kit (Abnova) according to the manufacturer’s instructions. Measurements were performed in duplicates and the sample concentrations were calculated based on a standard curve.Since there is no single marker that identifies all different mature cell populations within the AMOs, but all immature smNPCs express the precursor marker Sox2, which is downregulated upon maturation, we defined Sox2-negative cells as mature for the purpose of this analysis. Using confocal high-content imaging analysis as described above, we quantified the number of Sox2-positive cells as well as the total cell number (based on DAPI-stained nuclei) within the entire organoids. We then calculated the ratio of Sox2-negative cells as 1 - (number of Sox2-positive cells) / . Calculations were performed in Microsoft Excel and data was transferred to GraphPad Prism v8.4.2 for plotting.ScalesSQ tissue clearing was performed as previously described by We performed X-Clarity tissue clearing using an X-CLARITY Polymerization System (Logos biosystems) per manufacturer’s instructions. First, organoids were fixed with 4% PFA overnight at 4°C. The next day, the organoids were embedded in a hydrogel monomer solution and incubated 12–24 hr, followed by a 3-hr polymerization step at −90 bar and 37°C in the X-CLARITY Polymerization System. The embedded organoids were transferred to a tissue container and cleared in the electrophoretic tissue chamber for 2 hr at 1.2 A and 37°C.For ClearT tissue clearing we followed a protocol by CUBIC tissue clearing was modified after We stained the organoids with 0.5 μg/mL DAPI (Sigma) in PBS for 24 hr and subsequently subjected them to the different tissue clearing protocols. Afterwards, a LSM 700 scanning confocal microscope (Zeiss) was used to acquire z-stacks of the stained and cleared organoids. Three XZ and three YZ cross-sections per aggregate (n = 10 aggregates per clearing method) were used to quantify the maximum depth at which the DAPI signal could still be detected at a given brightness threshold. The depth of each cross section was measured manually at n = 5 different positions for each slice and n = 10 organoids per clearing protocol using ImageJ/Fiji. The data was exported to Microsoft Excel for further processing and GraphPad Prism v7.0 for plotting and statistical analysis.Following clearing, we took brightfield images of the organoids  using an upright stereomicroscope  under standardized brightfield transmission conditions. The mean brightness of the organoid area, measured by ImageJ/Fiji, served as measure for the amount of light transmitted through the aggregate. The data was exported to Microsoft Excel for further processing and GraphPad Prism v7.0 for plotting and statistical analysis.For statistical analysis of the clearing protocol comparisons, we used GraphPad Prism v7.0 and performed unpaired, two-tailed t-tests with α = 0.05 as normal distribution and equal variances can be assumed for the analyzed data.For electron microscopy analysis, we used n = 3 AMOs from one batch at day 32. Samples were initially fixed for 3 hr with 2% glutaraldehyde, 2% paraformaldehyde in 0.2 M cacodylate buffer, pH 7.2. Afterwards, the specimen was dissected into smaller pieces and post-fixed with 1% osmium tetroxide containing 1.5% potassium hexacyanoferrate. Samples were dehydrated stepwise, including an over-night uranyl-block staining step in 70% ethanol. The specimen was orientated and flat embedded in epon. In total, three samples sectioned under different angles were analyzed at the electron microscope to visualize different aspects of the AMOs.2 suspension tissue culture dishes (Sarstedt) in cerebral organoid differentiation medium  1:4000, and 2-mercaptoethanol 1:285714). We placed the culture dishes on a shaker at 37°C and 5% CO2 and fed the organoids every other day. On day 20 the B27 supplement was replaced by B27 with Vitamin A (Thermo Fisher) and organoids were cultured until day 30 or 45.For iPSC-derived cerebral organoid generation, we followed the protocol by 2, smaller than 100 μm2, and brighter than 2700 abu. We considered cells to be Sox2/cCasp3 double-positive if they fulfilled the criteria for both filters at the same time. The results were outputted to Microsoft Excel, reformatted and then transferred to GraphPad Prism v8.0.2 for plotting, data analysis, and curve fitting.At day 50, we treated AMOs with increasing concentrations  of G418 (Sigma-Aldrich) added directly to the culture medium. After 2 days, we renewed the medium  and fixed the aggregates after a total of 4 days of treatment. Fixation, whole mount immunostaining for cCasp3 and Sox2 as well as BABB-based clearing was performed as outlined above. Image analysis followed the steps as outlined in the high-content analysis section with slight modifications to accommodate the individual brightness, morphology, and background characteristics of the cCasp3 staining. Briefly, after identifying AMOs and Sox2+ cells as described previously, the cCasp3 channel was background corrected by running a sliding parabola algorithm with a curvature setting of 10 across each confocal slice of the AMO. We identified apoptotic cells via the ‘find nuclei’ function in the 647 nm channel, algorithm 'M' and further selected them to be cCasp3+ if they were larger than 11 μmOn day 30 of differentiation, AMOs received Tox medium (TM) overnight to remove antioxidants present in the B27 media supplement. TM consisted of DMEM-F12 supplemented with 1% N2 and 1% penicillin/streptomycin/glutamine. On day 31, we added different concentrations of G418  dissolved in TM and renewed the medium (including the different compound concentrations) once two days later. After a total treatment time of 96 hr, the samples were analyzed by their size, the CellTiter-Glo 3D Cell Viability Assay, and cCasp3 staining as described before. In the case of cCasp3 staining, the image analysis parameters were used with minor modifications to account for the individual characteristics of the staining, for example signal intensity, aggregate size, background. The calculation of the organoid size/area plotted in  In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.Acceptance summary:Adapting organoid cultures, which are highly complex, for high throughput screening applications is an important goal for the field. This Tools and Resources article establishes a protocol for generating and processing midbrain organoids in 96 well plates in an automated fashion, with a high degree of homogeneity between the structures. The resultant organoids express critical structural and transcriptomic features of midbrain, and respond functionally to chemical treatments, opening the door to myriad possibilities for chemical screening in this important tissue lineage.Decision letter after peer review:eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by Didier Stainier as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Jessica Young (Reviewer #2).Thank you for submitting your article "A fully automated high throughput workflow for 3D-based drug screening in human midbrain organoids" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.Summary:The manuscript by Renner et al. describes the development of an HTS compatible workflow for the generation of human midbrain organoids starting from partially differentiated iPSC. By using neural precursor cells (smNPC) generated using protocols previously described by some of the authors as starting point, the authors reduce the cellular and organ-like complexity compared to iPSC-derived organoids, but gain in reproducibility in well-to-well sphere production, as measured by size and expression of neuronal markers. The claim is made that the new protocol is superior to other protocols in this field because it produces more homogenous organoids. In general, this is a valuable effort to establish automation friendly protocols to generate reproducible neural organoids for HTS, and this article could be appropriate for a Tools and Resources article. However, all of the reviewers agree that new experiments are required to increase the rigor of the work, support the conclusions of the authors, and clearly demonstrate the advance that the technique produces over current methodologies, in addition to a more detailed evaluation of the new method's limitations.Essential revisions:1) In accordance with the general requirements for a Tools and Resources article, as this is not a totally novel technology but rather an improvement of neural organoid HTS, the new method should be properly compared and benchmarked against existing methods used in the field. The reviewers felt that the current manuscript lacks direct comparisons (meaning different protocols compared side by side in the same lab/batch). This is a weakness, especially given the strong emphasis in the paper on how this method is superior to others. Thus, a direct comparison of this protocol to a published neural spheroid/organoid protocol is required. This may include an iPS-to-neuron protocol, or alternatively plain neuronal protocol . The latter may be most appropriate as the question is how these new organoids are better than existing neuronal spheroids, e.g. for toxicity studies. The comparisons between protocols should ideally include both functional as well as gene expression data measurements taken side by side , and quantified in an HTS-appropriate manner.2) Because drug screening is the stated purpose of developing such automated organoid-based assay platform, it would have been more informative if the authors would have included data to demonstrate % of mature cell populations and more mechanism-based functional validation with specific pharmacological tools (e.g. dopamine receptor antagonists), in different HTS friendly readouts, in addition to calcium fluorescence signal and MEA, such as dopamine secretion (since these are midbrain organoids with dopaminergic neurons) and cell viability (e.g. CellTiterGlo3D). There is very good characterization of neural progenitor/precursor cells, neurons, and astrocytes in the AMOs, what about oligodendrocytes? Can steps be taken to ensure a representative mix of CNS cell types in the AMOs?3) Deeper assessment of line to line and batch to batch variability, with more detailed QC analysis and assessment of cells at higher magnification and better quantification, would be valuable in demonstrating the homogeneity and quality of the structures. The authors should comment on % of wells produce organoids that pass QC and then % of automated transfer of AMOs from production plate to clearing/imaging plate, and what % of the wells in an imaging plate do not pass QC because of high fluorescence signal. The authors should better characterize the reproducibility between AMOs from independent cell lines. In Figure 2—figure supplement 2 they describe the protocol on an independent line but simply characterize via morphology. As it is well known that different pluripotent stem cell lines, especially hiPSCs, have inherently different differentiation abilities, quantification of neural and neuronal cell markers should also be provided to show that organoids from independent lines can be directly compared in terms of cellular responses. A qPCR analysis, similar to what is presented in Figure 3 should be done on an AMOs from an independent line.4) There needs to be a detailed explanation and acknowledgement of the limitations of the new protocol in the text. The use of neural progenitor cells has been reported by others to produce neural spheroids with cortical neurons and astrocytes, spontaneous synchronous calcium waves and action potentials, and with good reproducibility to enable HTS. Therefore, the strategy described here to use NPCs to obtain more reproducible neural aggregates is not entirely novel, although by using the smNPCs and the differentiation protocol outlined, the authors obtain aggregates that include mature neurons , neural progenitors, and astrocytes (as measured by IHC and qPCR), with organized cell distribution in different concentric zones and with cellular orientations, including radial arrangements, which therefore classify them as organoids, although not as complex as those previously described starting with iPSC. The authors call this aggregates Automated Midbrain Organoids (AMOs). The authors should acknowledge the sacrifice of complexity that would normally be achieved in iPSC-derived neuronal organoids, resulting from dissociation, and absence of microglia. Starting from smNPCs suggests that the AMOs are nearly entirely derived from neural ectoderm. This precludes some important neural cell types like microglia, which come from mesoderm/hematopoietic lineages. Can the authors comment on whether this is a drawback to their protocol if not all CNS cell types are represented? Or comment on whether a modified automated protocol beginning with pluripotent stem cells and including a step to drive hematopoietic progenitor cells could be included?5) Spontaneous synchronized calcium waves were detected in the AMOs showing neuronal functional coupling. Functional network activity is also shown in the AMOs using MEA measurements. At least a couple of publications have shown spontaneous synchronized calcium wave and MEA signal from neural spheroids and organoids. , so we don't believe this is the first-time oscillatory waves have been observed in neural aggregates, as the authors state. In general, the authors should retract priority claims ('first time this has been done').eLife. Your revised article has been evaluated by Didier Stainier (Senior Editor) and a Reviewing Editor.Thank you for resubmitting your work entitled "A fully automated high throughput workflow for 3D-based drug screening in human midbrain organoids" for further consideration by The manuscript has been improved but there are some remaining issues that need to be addressed before acceptance, as outlined below:The consensus of the reviewers is that the authors have addressed most of the issues raised by the reviewers by the including relevant new data, as was requested. The authors have greatly strengthened this paper. They provide adequate discussion of the limitations  and provide a more detailed analysis of midbrain-relevant cell types and measurements of toxicity. We also appreciate the more detailed description of the MEA recordings as this will be useful for other groups attempting similar experiments. For a Tools and Resources article, this is a protocol that will be useful in screening applications.The reviewers have also identified a number of areas that require revision. In particular it is critical that the authors tone down some of the language described in their estimation of the protocol, relative to other protocols and approaches in the field. The reviewers are not wholly convinced that the Pasca lab organoid differentiation protocol was successfully adopted and scaled up, and the comparisons may be affected by this. There are also numerous minor errors within the manuscript that require attention.A short list of important revisions is described below. Addressing these will greatly improve the manuscript and make it more suitable for consideration as a published manuscript.1) Figure 4A-D, F-G need to include units in the y-axis.2) Figure 8E-J, as displayed, it is not possible to distinguish any effects by the treatments in any of the panels; perhaps the Fourier transform should be displayed to better illustrate the effects of the treatments and the currently shown traces should be moved to a supplemental figure.3) Figure 8K, it should indicate what MEA measurement was used as "summed absolute signal", and be good to include errors bars and indication of statistical significance between the conditions.4) Figure 2—figure supplement 2 needs to include concentration units in the dose response plots: log(concentration) in ug/ml?5) Nice to have but not required. In Figure 2—figure supplement 2: For HTS for toxic effects, it is important to establish readouts that are robust, fast and amenable to screening; CellTiterGlo is one of them and it would also be interesting to see whether G418 induced a reduction in spheroid size, as measure by brightfield microscopy?6) The analysis and comparison between other, commonly used brain organoid protocols is clearer than before. However, the Sloan et al. protocol part raises some major questions. How efficient was that protocol? It is said to have been performed with modifications. Did the authors attempt the original, unmodified protocol as a positive control? To my limited understanding some of the differences include;a) Growing cells feeder free to begin with instead of on MEFs (is this the case?).b) Harvesting with accutase instead of dispase which ends up dissociating the iPS cells instead of forming embryoid body like structures (this is a big difference!).c) Not using the neural induction media described in that paper.As the Sloan et al. protocol's performance is presented as evidence of the authors' claim that their protocol is an improvement over other protocols, this really needs to be carefully stated. The best course of action would be to clearly delineate the modifications made, compared to the original protocol, for instance in a supplemental figure. The authors should also tone down their conclusions and make the point that the modifications may have affected the other protocol. One difference is at the detachment step for the cultures – in the Sloan et al. protocol, this is at the iPS cell stage, whereas in the current manuscript the detachment/dissociation step is at the neural progenitor stage. This is likely to benefit the uniformity of the structures/cultures, but may also result in simpler structures than doing the whole protocol from start to finish with the same plate of iPS cells. Making this point in the paper and acknowledging the limitation of starting with a homogenized neural progenitor culture would be worthwhile. In general, this will not detract from the authors' data but will make their conclusions more accurate.7) The clarification of batches and comparison with an independent cell line is helpful in the conclusion that this is a highly reproducible protocol. But we have concerns about reporting the efficiencies as % without any errors. The authors do not explain where these numbers come from and what they mean, and how they were calculated. If there is a set of reproducible data to back these up, it should be provided, otherwise the numbers should be clearly described as one representative sample experiment rather than a general assessment.8) The figures and panels should be referenced in order within the text . Please ensure that this is adhered to throughout the paper, as there is a lot of data here and it can be confusing otherwise.9) "The addition of ectopic matrices, including Matrigel, is known to introduce a large amount of variability." The authors suggest that Matrigel introduces variability, but don't show any data to support this. Nor has Matrigel been completely eliminated, as the iPS cells must start out on matrix. As Matrigel is a commonly used ingredient, claims regarding its effects need to be carefully controlled for, rather than relying on citations from the literature. The authors should tone down claims of Matrigel-induced variability for this paper, and simply say that they drew a comparison to another protocol that does not use Matrigel, similar to the one they have developed.10) In the legend for Figure 4K:"k) Fast Fourier Transformation (FFT) based on the data shown in k." I believe it is based on the data shown in j, not k. Essential revisions:1) In accordance with the general requirements for a Tools and Resources article, as this is not a totally novel technology but rather an improvement of neural organoid HTS, the new method should be properly compared and benchmarked against existing methods used in the field. The reviewers felt that the current manuscript lacks direct comparisons (meaning different protocols compared side by side in the same lab/batch). This is a weakness, especially given the strong emphasis in the paper on how this method is superior to others. Thus, a direct comparison of this protocol to a published neural spheroid/organoid protocol is required. This may include an iPS-to-neuron protocol, or alternatively plain neuronal protocol . The latter may be most appropriate as the question is how these new organoids are better than existing neuronal spheroids, e.g. for toxicity studies. The comparisons between protocols should ideally include both functional as well as gene expression data measurements taken side by side , and quantified in an HTS-appropriate manner.We appreciate the reviewers’ demand for additional rigorous comparison and have conducted extensive additional experiments with over 10,000 organoids total. In order to have a stringent and fair comparison, we stipulated a number of requirements:1) We wanted to compare protocols using the same starting cell lines, since differentiation outcomes may vary widely between different cell lines, as the reviewers have mentioned themselves.2) To accommodate for the concerns raised by the reviewers regarding lab to lab variability, we wanted to automate the comparison protocol on our platform in house, so that differences in variability are inherent to the protocol and do not stem from other factors including manual handling, thawing, passaging, etc.3) Ideally, the comparison protocol does not use matrigel, as it is known to induce high variability . We felt that it would be more straightforward to compare other ECM-free protocols to ours for the most conservative comparison possible.These requirements exclude the use of cortical spheroids as used by Sirenko et al., as they used commercially available spheroids which can only be bought in an already aggregated state/as a ready-to-use product. The company selling these spheroids does not make their protocols publicly available, preventing us from replicating their work in house and using the same starting cell lines for a fair comparison. On a technical level, the spheroids from Sirenko et al. do not possess any discernable self-organization, but are merely 2D-differentiated, re-aggregated neural cultures. Our AMOs are considerably more complex than this, showing distinct self-organized architectures which bring them up to par with organoid rather than spheroid protocols.Thus, we chose to compare our AMOs to another widely used and accepted organoid protocol from the Pasca lab , which has several advantages:1) We can use the same starting cell line that gave rise to our AMOs.2) Pasca organoids do not require the addition of matrigel to properly mature.3) We managed to fully automate the entire generation and analysis of the Pasca organoids in house, using the exact same platform as our organoids.According to the reviewers’ requests, we then compared this established protocol to our protocol side-by-side in terms of:- gene expression - functionality/variability:> overall morphology and size > viability of single organoids > cellular architecture/structure and quantitative marker expression by high content imaging analysis > electrical activity - suitability for toxicity testing .We can quantitatively demonstrate that the widely accepted hPSC-based organoid protocol by Sloan et al. has a significantly larger degree of variability than our AMO protocol even when fully standardized and automated.We would like to point out that the adaptation of the protocol by Sloan et al. into a freely scalable workflow is a significant achievement in itself, and has not been reported in the literature to our knowledge.2) Because drug screening is the stated purpose of developing such automated organoid-based assay platform, it would have been more informative if the authors would have included data to demonstrate % of mature cell populations and more mechanism-based functional validation with specific pharmacological tools (e.g. dopamine receptor antagonists), in different HTS friendly readouts, in addition to calcium fluorescence signal and MEA, such as dopamine secretion (since these are midbrain organoids with dopaminergic neurons) and cell viability (e.g. CellTiterGlo3D). There is very good characterization of neural progenitor/precursor cells, neurons, and astrocytes in the AMOs, what about oligodendrocytes? Can steps be taken to ensure a representative mix of CNS cell types in the AMOs?We agree with the reviewers that our manuscript benefits from additional functional characterization of our AMOs. We have carried out a number of additional analyses to bolster this aspect of our manuscript.- We have quantified the number of mature cells in our organoids for two separate cell lines and 3 independently thawed and cultured batches each .- We have demonstrated functional characteristics of midbrain identity using pharmacological agonists and antagonists for different neuronal subpopulations .- We demonstrate dopamine release into the supernatant via ELISA with levels that match or exceed those found in cerebrospinal fluid in vivo .- We have quantified cell viability via CellTiterGlo3D, demonstrating superior homogeneity when compared to hIPSC-derived organoids .- We have separately quantified the survival of dopaminergic neurons and other neural populations after specific pharmacological ablation of dopaminergic cells. This clearly underlines the relevance and suitability of our model system for setting up screens for Parkinson’s disease, for example .Oligodendrocytes arise very late in neural development, and other protocols demonstrate presence of oligodendrocytes after 100 days of differentiation or more . These long time scales are not very attractive for the design of screening strategies. While our smNPCs are capable of efficiently generating oligodendrocytes, as has been published by colleagues of ours , this will not occur within the 30-50 day maturation period suggested for screening from our side. Instead, we favor strategies that ectopically add a known number of oligodendrocytes to our AMOs and thus reach a screenable state much earlier (see our Discussion section) in a system with syngeneic but ectopically added oligodendrocytes.3) Deeper assessment of line to line and batch to batch variability, with more detailed QC analysis and assessment of cells at higher magnification and better quantification, would be valuable in demonstrating the homogeneity and quality of the structures. The authors should comment on % of wells produce organoids that pass QC and then % of automated transfer of AMOs from production plate to clearing/imaging plate, and what % of the wells in an imaging plate do not pass QC because of high fluorescence signal. The authors should better characterize the reproducibility between AMOs from independent cell lines. In Figure 2—figure supplement 2 they describe the protocol on an independent line but simply characterize via morphology. As it is well known that different pluripotent stem cell lines, especially hiPSCs, have inherently different differentiation abilities, quantification of neural and neuronal cell markers should also be provided to show that organoids from independent lines can be directly compared in terms of cellular responses. A qPCR analysis, similar to what is presented in Figure 3 should be done on an AMOs from an independent line.We thank the reviewers and have added the requested information:- QC data with details about the efficiency of the different steps of the workflow can now be found in Figure 1D. Our workflow has undergone many iterations of optimization, and overall, we achieve a sample retention rate of 90% from beginning (organoid seeding) to end (data output from high content imaging). We were quite rigorous in reporting inefficiencies, as the 4.2% of samples lost in the transfer of organoids from culture to imaging plates can be virtually eliminated by simply repeating the automated transfer protocol to catch the small number of remaining “stragglers”.- We have added quantitative high content analyses for a second, independent cell line and a different hiPSC-derived protocol and compared them side by side in Figure 7. The data confirm our claim of higher homogeneity and reproducibility, even across cell lines.- We have also quantitatively compared different cell lines and protocols via qPCR and present the data side by side .We are not sure what the request “assessment of cells at higher magnification” means. We demonstrate single cell resolution in high content settings , and generally provide fluorescence data that provide both overview images of whole organoids (rarely done in the organoid literature due to high variance of structures), and we provide enlargements that facilitate understanding cellular identities and detailed single-cell level morphologies where possible . If we can provide further high-resolution images, please specify, and we will provide them.4) There needs to be a detailed explanation and acknowledgement of the limitations of the new protocol in the text. The use of neural progenitor cells has been reported by others to produce neural spheroids with cortical neurons and astrocytes, spontaneous synchronous calcium waves and action potentials, and with good reproducibility to enable HTS. Therefore, the strategy described here to use NPCs to obtain more reproducible neural aggregates is not entirely novel, although by using the smNPCs and the differentiation protocol outlined, the authors obtain aggregates that include mature neurons , neural progenitors, and astrocytes (as measured by IHC and qPCR), with organized cell distribution in different concentric zones and with cellular orientations, including radial arrangements, which therefore classify them as organoids, although not as complex as those previously described starting with iPSC. The authors call this aggregates Automated Midbrain Organoids (AMOs). The authors should acknowledge the sacrifice of complexity that would normally be achieved in iPSC-derived neuronal organoids, resulting from dissociation, and absence of microglia. Starting from smNPCs suggests that the AMOs are nearly entirely derived from neural ectoderm. This precludes some important neural cell types like microglia, which come from mesoderm/hematopoietic lineages. Can the authors comment on whether this is a drawback to their protocol if not all CNS cell types are represented? Or comment on whether a modified automated protocol beginning with pluripotent stem cells and including a step to drive hematopoietic progenitor cells could be included?According to the reviewers’ request, we have amended the text in several places including the discussion to acknowledge previously published NPC-based protocols as well as the relative sacrifice of complexity compared to published iPSC organoids. We added a whole section describing the impact of reproducibility for phenotypic screening in the field of 3D culture, and we point out the lack of oligodendrocytes and microglia in our protocol.While other organoid protocols do, in theory, allow for the formation of microglia “in situ” , they are, in practice, not common . Because spontaneous generation of microglia in organoids is so rare, variable, and inefficient, other labs have resorted to ectopically adding them to their organoids . This approach is absolutely feasible for us but an exploration of ectopic addition of other cell types may be outside the scope of this manuscript. We have added a short discussion on the use of our reproducible platform as a cellular scaffold to systematically add other cell types of interest, including microglia and oligodendrocytes.We are not sure what is meant by “… resulting from dissociation”. Our protocol does not include any dissociation steps other than generating single cell suspensions for initial organoid aggregation, which are common to all organoid protocols.5) Spontaneous synchronized calcium waves were detected in the AMOs showing neuronal functional coupling. Functional network activity is also shown in the AMOs using MEA measurements. At least a couple of publications have shown spontaneous synchronized calcium wave and MEA signal from neural spheroids and organoids. , so we don't believe this is the first-time oscillatory waves have been observed in neural aggregates, as the authors state. In general, the authors should retract priority claims ('first time this has been done').We thank the reviewers for pointing is out. The claim has been removed.The reviewers have also identified a number of areas that require revision. In particular it is critical that the authors tone down some of the language described in their estimation of the protocol, relative to other protocols and approaches in the field.We have toned down the language of a number of passages in the text, removing claims of higher estimation of our protocol compared to other protocols.The reviewers are not wholly convinced that the Pasca lab organoid differentiation protocol was successfully adopted and scaled up, and the comparisons may be affected by this.Please see detailed discussion below.There are also numerous minor errors within the manuscript that require attention.A short list of important revisions is described below. Addressing these will greatly improve the manuscript and make it more suitable for consideration as a published manuscript.1) Figure 4A-D, F-G need to include units in the y-axis.Thank you – we have added the missing labels.2) Figure 8E-J, as displayed, it is not possible to distinguish any effects by the treatments in any of the panels; perhaps the Fourier transform should be displayed to better illustrate the effects of the treatments and the currently shown traces should be moved to a supplemental figure.We agree with the reviewers that the changes in MEA traces were hard to discern. We have followed the reviewers’ recommendation and changed the plots to FFTs, which unfortunately also resulted in figures that suffered from a similar lack of clarity. We have thus decided to present the MEA data in a way that visually better separates the different agonist/antagonist treatments and at the same time better represents the temporal relationship between different parts of the experimental procedure. After recording electric field baseline activity for a given organoid, we first added an agonist, and, after recording the signal under the influence of the agonist, an inhibitor to the same sample in the same dish. During this time, we continued recording electric field strength and compared the resulting changes after brief equilibration. By presenting these plots in the same coordinate axis in a temporal sequence from left to right, we hope to make it clear that these measurements stem from one and the same sample and are temporally related, while decluttering the prior overlapping graphs. Our purpose with the traces in Figure 8E-J is to convey a direct impression of the raw electric field activity of the organoids as a representative plot; for quantitative comparison of the MEA traces, we have included all available replicate MEA measurements and plotted absolute changes electric field strength in Figure 8K.Of note, electric field strength varies dependent on the distance to the recording electrode. The organoids are essentially loosely and randomly attached spheres on the underlying array of fixed 2D electrodes, and their random positioning introduces strong sample-to sample variation of the electric field signal just by the nature of their placement. Hence, to achieve quantitative and comparable measurements, we had to normalize any potential changes of activity to an initial basal recording of the very same sample in the very same location of the MEA dish, taking utmost care not to dislodge the organoid while introducing liquids.We also measured these organoids after only a brief period of attachment, so that they retain their spherical geometry. This course of experiments is our best approximation to reach this goal. We hope that our new Figure 8E-J now better reflect this.3) Figure 8K should indicate what MEA measurement was used as "summed absolute signal", and be good to include errors bars and indication of statistical significance between the conditions.Thank you for pointing this out. The exact nature of quantification for Figure 8K was indeed not clear. The figure shows the sum of the absolute electric field potential oscillations over 15 seconds of time. The rationale behind this is that this would detect both changes in frequency and amplitude of the oscillating signal. This way, even small changes should appear clearly when the signal is summed. The original Figure 8K only graphed the one representative dataset depicted in 8E-J; however, the new version now includes all n’s for all conditions including error bars. We edited the text and figure legend to this end.We have also amended the corresponding Materials and methods section in order help clear up the details of the MEA experiments and their analysis.4) Figure 2—figure supplement 2 needs to include concentration units in the dose response plots: log(concentration) in ug/ml?Thank you for pointing this out. We have added the corresponding labels.5) Nice to have but not required. In Figure 2—figure supplement 2: For HTS for toxic effects, it is important to establish readouts that are robust, fast and amenable to screening; CellTiterGlo is one of them and it would also be interesting to see whether G418 induced a reduction in spheroid size, as measure by brightfield microscopy?Size measurements are certainly an interesting, simple, and solid option for readout in HTS settings. We have added organoid size measurements for the dose response experiments to Figure 8—figure supplement 1 as suggested. The clearing procedure renders the organoids almost fully transparent, so that they can no longer be detected in brightfield imaging. However, we routinely measure organoid size via fluorescence microscopy as part of our standard optical analysis pipeline for later signal normalization. Here, we retrieved this data.In this particular experimental setup, with our biological system and in the time frames and concentrations tested, we did not see any appreciable changes in organoid size.6) The analysis and comparison between other, commonly used brain organoid protocols is clearer than before. However, the Sloan et al. protocol part raises some major questions. How efficient was that protocol?The automated generation of iPSC-based organoids was highly efficient; we lost one organoid out of a grand total of 12x 96 well plates over 30 days of culture .Out of 126 iPSC-derived cortical organoids subjected to high content immunostaining, 120 correctly differentiated towards a neural fate confirmed by MAP2 expression . One organoid was lost during processing and 5 did not pass imaging quality control, and MAP2 staining could not be confirmed.At the RNA level, we confirmed expression of a number of relevant early and late neural and glial markers over time, encompassing a total of 168 organoids arranged in different time points and batches . Of note, these organoids did not undergo any selection before analysis and represent an unbiased sampling.More detailed analyses of protein expression confirmed expected cortical markers includingFoxG1, Ctip2, TBR1 and TBR2, Satb2 at day 30, 40, and 50 post aggregation . This figure shows one representative sample chosen from n=3 immunostained samples, all of which were randomly drawn from 96 well plates. All of the samples showed expression of the markers as shown in Figure 7—figure supplement 2.We show very early time points for iPSC-based cortical organoids. When we compare our structures (here day 40) with some of the earliest images available from the original publication by Pasca et al., 2015, our morphologies resemble the ones shown there at day 52 .It is said to have been performed with modifications. Did the authors attempt the original, unmodified protocol as a positive control? To my limited understanding some of the differences include;a) Growing cells feeder free to begin with instead of on MEFs (is this the case?)b) Harvesting with accutase instead of dispase which ends up dissociating the iPS cells instead of forming embryoid body like structures (this is a big difference!).c) Not using the neural induction media described in that paperAs the Sloan et al., protocol's performance is presented as evidence of the authors' claim that their protocol is an improvement over other protocols, this really needs to be carefully stated. The best course of action would be to clearly delineate the modifications made, compared to the original protocol, for instance in a supplemental figure. The authors should also tone down their conclusions and make the point that the modifications may have affected the other protocol.All of these are very important points to make, and we thank the reviewers for suggesting a more careful and differentiated approach to our comparisons.We have followed the reviewers’ recommendations and include a new supplementary figure  that details the alterations to the original publications by the Pasca lab. We have amended the text to detail these alterations and their potential impact on the resulting organoids. We have toned down the language pertaining to direct comparisons and clearly state that the modifications may affect the biological outcome (see changes in Results section and Discussion section).We would have preferred to compare our automated protocol with the original; however, it was not possible to automate the original protocol without amendments. For example, as the reviewers correctly pointed out, we used single cell solutions from feeder-free culture instead of lifted colonies from feeder cell cultures. We acknowledge that starting with a single cell suspension as opposed to colonies alters the biology of the starting conditions and may affect the outcome. However, for automated systems, digested colonies are challenging to handle, as they tend to settle quickly via gravity and it is not possible to deposit a single colony per well with a high degree of confidence. With single cell solutions, we could easily homogenize the starting conditions for each well. We thought it crucial to produce both kinds of organoids in an unbiased, automated, and mechanically standardized fashion to clearly attribute all differences to the biological differences instead of manual handling. We made these changes not to favor our protocol, but to test it in the most rigorous manner available to us and in a format that allows high throughput applications, the central tenet of this manuscript.One difference is at the detachment step for the cultures – in the Sloan et al. protocol, this is at the iPS cell stage, whereas in the current manuscript the detachment/dissociation step is at the neural progenitor stage. This is likely to benefit the uniformity of the structures/cultures, but may also result in simpler structures than doing the whole protocol from start to finish with the same plate of iPS cells. Making this point in the paper and acknowledging the limitation of starting with a homogenized neural progenitor culture would be worthwhile. In general, this will not detract from the authors' data but will make their conclusions more accurate.Thank you for the feedback, we have implemented that point in the Discussion section.7) The clarification of batches and comparison with an independent cell line is helpful in the conclusion that this is a highly reproducible protocol. But we have concerns about reporting the efficiencies as % without any errors. The authors do not explain where these numbers come from and what they mean, and how they were calculated. If there is a set of reproducible data to back these up, it should be provided, otherwise the numbers should be clearly described as one representative sample experiment rather than a general assessment.We apologize for not including this information earlier. It is now part of both the main text and Figure 1. In the interest of data transparency, we also list the full raw data numbers leading to these conclusions as new Supplementary file 1.A few comments on the on the number of n’s in this data table:a) We check culture efficiency  regularly, so we have a lot of data/n’s on this step.b) The efficiency of the transfer step from v-bottom culture plate to flat-bottom imaging plate is usually not critical for us, as we just run the transfer protocol twice to get 100% transfer efficiency and catch any stragglers. Here, we ran a few plates through the transfer step once for the explicit purpose of generating the “single shot” transfer numbers, hence we have fewer n’s here. We have since run additional transfers and have updated the relevant numbers in the manuscript.We have the lowest n’s for imaging, as the bulk of our samples are used for a variety of other analyses, e.g. cell titer glo, RNA, calcium, or MEA analyses.8) The figures and panels should be referenced in order within the text . Please ensure that this is adhered to throughout the paper, as there is a lot of data here and it can be confusing otherwise.We have changed the text as requested.9) "The addition of ectopic matrices, including Matrigel, is known to introduce a large amount of variability." The authors suggest that Matrigel introduces variability, but don't show any data to support this. Nor has Matrigel been completely eliminated, as the iPS cells must start out on matrix. As Matrigel is a commonly used ingredient, claims regarding its effects need to be carefully controlled for, rather than relying on citations from the literature. The authors should tone down claims of Matrigel-induced variability for this paper, and simply say that they drew a comparison to another protocol that does not use Matrigel, similar to the one they have developed.We have amended the text as requested.10) In the legend for Figure 4K:"k) Fast Fourier Transformation (FFT) based on the data shown in k." I believe it is based on the data shown in j, not k.Thank you for your careful review – we have changed the text accordingly.


However, most patients were treated with adjuvant chemotherapy so the prognostic value of SOX2 could not be assessed independently from its value as a predictive marker for non-response to chemotherapy. This study aimed to assess whether SOX2 was a true prognostic marker or a marker for chemotherapy response in a historical cohort of patients, a high proportion of whom were chemotherapy-naïve. SOX2 immunostaining was performed on tissue micro-arrays containing tumor cores from 797 patients with stage II and III colorectal cancer. SOX2+ cell densities were then quantified with StrataQuest digital image analysis software. Overall survival was assessed using Kaplan–Meier estimates and Cox regression. It was found that high SOX2+ cell densities were not associated with poor overall survival. Furthermore, all patients had a significant improvement in survival after 5-fluorouracil (5-FU) treatment, irrespective of their SOX2+ cell density. Therefore, SOX2+ cell densities were not associated with prognosis or chemotherapy benefit in this study. This is in contrast to our previous study, in which most patients received oxaliplatin as part of their treatment, in addition to 5-FU. This suggests SOX2 may predict response to oxaliplatin treatment, but not 5-FU.SOX2 (sex-determining region-Y homeobox-2) is a transcription factor essential for the maintenance of pluripotency and is also associated with stem-cell-like properties in preclinical cancer models. Our previous study on a cohort of stage III colon cancer patients demonstrated high SOX2 Colorectal cancer (CRC) is one of the most common cancers worldwide with nearly 1.8 million new cases diagnosed globally in 2018 [+ cancer cells exhibit characteristic CSC properties [+ cancer cells have been shown to be quiescent, potentially conferring treatment resistance, are able to avoid immune recognition and can also form metastatic niches [+ cancer cells may contribute to disease progression, potentially having negative impacts on patient prognoses. These studies also collectively indicate that SOX2 may be an accurate marker for the identification of CSC in tumors.Cancer stem-like cells (CSC) are a sub-population of cancer cells with enhanced tumorigenicity, unlimited self-renewal capacity and chemoresistance due to their quiescent nature . SOX2  for this analysis to allow adequate power for detection of chemotherapy benefit in the SOX2low and SOX2high groups separately  patients who received chemotherapy and 193 (54.8%) who did not receive any adjuvant chemotherapy treatment. Patients with no record of adjuvant chemotherapy treatment  were excluded from this analysis. High SOX2tely see . Both gr+ cells would have a significantly poorer prognosis and/or would not benefit from chemotherapy. The results did not indicate that SOX2 was a marker for poor prognosis nor that a higher density was associated with poorer response to chemotherapy in this patient cohort. Patients had improved OS following adjuvant chemotherapy, irrespective of the SOX2+ cell density in their tumors. With regard to CSS, we found that patients with SOX2low tumors had no benefit from chemotherapy, but those with SOX2high tumors did. The differing results for OS and CSS may be due to misclassification of CRC-related deaths as deaths from other causes or vice versa. This is a common limitation in retrospective cohort studies as the cause of death information was retrieved from the state registry database which relies on accurate reporting of this data.The aim of this study was to determine if high tumor SOX2 expression is a prognostic marker and/or predicts survival benefit from chemotherapy in stage II–III CRC. It was hypothesized that patients with a high density of SOX2Our findings are in contrast to our previous study demonstrating that high SOX2 density was significantly associated with poor prognosis in patients with stage III colon cancer . We alsoA historic cohort (treated from 1990 to 1999) was specifically selected for this study as the lower rate of adjuvant chemotherapy prescription at the time provided an opportunity to assess prognostic value independently from chemotherapy response. However, for those patients who did receive chemotherapy, the regimen also differed from the previous study due to changes to the treatment guidelines over time. In the current study, patients received a combination of 5-fluorouracil (5-FU) and leucovorin post-operatively, whereas patients in the previous study (treated from 2006 to 2010) mostly received 5-FU, leucovorin and oxaliplatin, following demonstration of a survival benefit from the addition of oxaliplatin . This raPatients in this study were treated from 1990 to 1999, compared to 2006–2010 in our previous study . Surgica+ cells, compared to 10% of patients in the current study when using StrataQuest for quantification. The use of scattergram-based thresholding in StrataQuest allowed for the removal of false-positive signals , which likely contributed to the reduction in the proportion of tumors considered to be SOX2high  for the assessment of prognostic value. Calculations using the observed proportion of SOX2high patients and the estimated median survival time for those with stage II disease suggest that the prognostic analysis with these patients was underpowered . However, while the effect size was not used in these calculations, there are limitations when using observed variables in post-hoc power calculations [TMA design and the method of SOX2 quantification were also key differences between the two cohorts, affecting the proportion of patients considered to have high expression. SOX2 is a heterogeneously expressed marker so it is possible that the use of TMAs, particularly where there are not multiple cores available for analysis, may increase the number of false-negative cases thereby decreasing the number of patients considered to have high expression. This is a limitation common to studies utilizing TMAs for analysis. With regards to digital image analysis, Aperio Imagescope Software suite v. 11 was used to quantify SOX2 expression in the previous study and 18% of patients had tumors with a high density of SOX2SOX2high . This wa2 in CRC . RevisedThe result that SOX2 density was not prognostic in patients with stage II nor stage III disease, while acknowledging that the stage II analysis was likely underpowered, is in contrast to the findings of Lundberg et al.  and a meThe cohort contained 445 patients with stage II and 352 patients with stage III CRC treated via surgical resection with curative intent with or without adjuvant chemotherapy at Sir Charles Gairdner Hospital (SCGH), Perth, Western Australia, between the years 1990 and 1999. 5-FU/leucovorin-based regimens were prescribed for adjuvant chemotherapy, as per the recommendations at the time. This study and the use of pathological samples and deidentified data were approved by SCGH Human Research Ethics Committee (HREC) (RGS00788) and St John of God Healthcare HREC (SJ-1360) under a waiver of consent.Tissue micro-arrays (TMAs) were constructed from formalin-fixed paraffin-embedded tissue as previously described . The TMASections 4 µm thick were cut from each TMA block and mounted on positively charged slides. Manual immunohistochemistry protocols were then performed for SOX2, as previously described . Deparaf+ nuclei to count the number of positive cells . SOX2 expression was analyzed using StrataQuest v. 6 software . Tissue areas were automatically detected by StrataQuest using thresholds on a grayscale image created from the RGB values and reviewed to ensure analyzed tissue areas matched the core histology description . SOX2 exve cells . Threshove cells . Data we+ cell density were determined by visual review of tissue cores while blinded to all clinical, pathological and outcome information or by use of the median value, as appropriate. The cut-off determined by visual review was 40 cells/mm2 and the median value was 1.50 cells/mm2. A case was determined as “high” if the mean SOX2 density across all available cores was above the cut-off. Overall survival (OS), defined as time from surgery until death from any cause, was used as the primary endpoint for survival analyses. Cancer-specific survival (CSS), defined as time from surgery until death from colorectal cancer, was used as a secondary endpoint for survival analysis. Kaplan–Meier curves were used to assess survival endpoints and the differences between curves were compared using log-rank estimates. Univariate regression analyses were performed using Cox regression. All results with p < 0.05 were deemed significant. The censor date for survival analyses was taken as five years after the surgery date for the last patient in the cohort.Cut-offs for high SOX2SAS v. 9.4  and GraphPad Prism v. 7  were used for statistical analyses.Based on our previous study , we anti+ cell density in this cohort. Further investigation of the prognostic and predictive effects of SOX2 in other cohorts is warranted to evaluate the negative findings of this study. The results of this study contrast to those of our previous study and this may be partially explained by the different chemotherapy regimens that were prescribed for each cohort. Patients in this cohort received 5-FU/leucovorin-based chemotherapy whereas patients in our previous cohort [+ cell densities and oxaliplatin-based chemotherapy response may be an avenue for future study.This study found no evidence that SOX2 is a significant prognostic marker for poor survival or that benefit from 5-FU-based chemotherapy is affected by SOX2s cohort  had oxal