High-throughput Imaging System Research Articles

Abstract 3107: SMASH: Single molecule antibody screening with high-throughput imaging system

Abstract Larger, more diverse antibody libraries are being screened in recent years. Despite enabling the high-throughput screening of extensive libraries, the process for identification and characterization of antibodies remains laborious and time-intensive. Here we report a platform that not only compiles hit finding and validation processes, but also provides high-resolution binding affinity as well as rank order among the library being screened. We streamlined the library construction and affinity screening via microscale transient expression in HEK293T cells with designed antibody sequence and direct quantification of antibody-antigen binding affinity which reduced the timeline from multiple weeks to a single week. We constructed the single-chain fragment antigen binding (scFab) libraries by replacing the duplex DNA fragment in CDR of interest while preserving the human framework of Fab. We have demonstrated that the SPID technique allows to detect antibody-antigen interactions from just dozens of pg of scFabs in total. This allowed us to bypass the conventional DNA cloning or even PCR amplification to achieve the minimum amount of DNA plasmids for expressing scFabs to be analyzed such as BLI and SPR. Therefore, we assembled the DNA plasmids through in vitro ligation with high efficiency and directly introduced it to HEK293T in 96-well microplate with microscale (200 ul) for transient transfection, which only takes three days for library generation. Next, we utilized SPID to quantify the number of single molecules between free and antigen-bound fractions of scFab, resulting in occupancy at the antibody-antigen binding equilibrium. This technique facilitated the determination of binding affinity (KD) for 200 antibody-antigen pairs within a 6-hour TAT encompassing both measurement and analysis. In total, we utilized 4,000 times less antibody for characterization while achieving enhanced sensitivity compared to both BLI and SPR methods. As a proof-of-concept, we generated a library consisting of 720 variants of Trastuzumab in scFab format to probe the landscape of CDRs affecting affinities against HER2 showing nM to low uM dynamic range of KD. Out of 720 analyzed variants, 5% were deleterious, 38% provided substantial changes, 40% showed little/no change, 17% enhanced affinity to HER2. To validate the robustness of the platform, we extensively expanded the library screening, assessing over 2,000 antibody variants for their affinity against more than three different therapeutic targets. To conclude, we demonstrated an efficient approach for monitoring changes in affinity resulting from single mutations in scFab. This rapid screening of site-saturated libraries would lead to a combinational variant library that our platform can screen. Ultimately, our platform aims to identify potential candidates for therapeutic antibody development throughput the phases of antibody discovery, maturation, and engineering. Citation Format: Jihye Jo, Changju Chun, Byeong-Kwon Sohn, Booyoung Yu, Jiyu Lee. SMASH: Single molecule antibody screening with high-throughput imaging system [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3107.

Abstract 1044: Low-cost, high-sensitivity InSituPlex® mIF assays on the Parhelia Omni-Stainer™

Abstract Background: Multiplex immunofluorescence (mIF) is an important tool for extracting complex phenotypic information from normal or diseased tissues, and provides accurate and predictive insights on the tissue microenvironment faster. For patient samples in translational and clinical research, high sensitivity, specificity, and reproducibility are critical; however, throughput and cost considerations are also important in designing clinical studies. Enabling an easy-to-use high-performance assay such as InSituPlex® on robust and affordable auto-stainers like the Parhelia Omni-Stainer™ can allow researchers to gain a deeper understanding of the tissue micro-environment through high-quality multiplexed biomarker detection in a fraction of the time and cost. Methods: We performed Ultivue InSituPlex® assays with OmniVUE™ panels using the Parhelia Omni-Stainer™, a low-cost, high-throughput tissue auto-stainer with reduced requirements for reagents with a minimal dead volume. We compared the whole-slide staining performance across replicates using human tonsil and tumor tissue arrays across multiple independent runs over multiple days. The stained slides were scanned using the Zeiss AxioScan.Z1 high-throughput imaging system, and image batches were processed using Ultivue’s UltiStacker.AI and UltiAnalyzer.AI software. Results: Tighter temperature control of the slide in the Omni-Stainer supports optimal InSituPlex® signal amplification. Intra-run and inter-day analysis on serial sections of human tonsil and tumor tissue arrays demonstrated a high degree of consistency in InSituPlex® assay performance, specifically with marker-specific signal intensities and cell densities. The Omni-Stainer facilitates the use of low reagent volumes at each step of the assay due to its efficient reagent use, thus lowering the cost of reagents per slide. Conclusions: This study demonstrated a fast, simple and robust workflow to perform high-performance InSituPlex® mIF assays on the low-cost, high-throughput Parhelia Omni-Stainer™. Ultivue’s configurable OmniVUE™ mIF panels can be run on the Omni-Stainer with reduced associated reagent costs per slide. This workflow can support larger clinical and translational studies in a more cost-effective fashion, leading to in-depth analysis of tissue microenvironment through highly precise multiplexed biomarker detection. Citation Format: Kevin Hwang, Lauren Duro, Alex Veith, Nelson Wedin, Nikolay Samusik, Je H. Lee. Low-cost, high-sensitivity InSituPlex® mIF assays on the Parhelia Omni-Stainer™ [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1044.

A Staphylococcus epidermidis strain inhibits the uptake of Staphylococcus aureus derived from atopic dermatitis skin into the keratinocytes

BackgroundVarious bacterial species form a microbiome in the skin. In the past, dead Staphylococcus aureus derived from atopic dermatitis (AD) are taken up by keratinocytes; however, whether live S. aureus can be taken up by keratinocytes is unknown. ObjectiveThis study aimed to examine whether live AD strains of S. aureus internalize into the keratinocytes and how the internalization changes under conditions in which other bacterial species including S. epidermidis are present. MethodsHaCaT cells were cultured with live S. aureus and S. epidermidis (live or heat-treated) or their culture supernatants. After coculture, the change in the amount of S. aureus in the cytoplasm of HaCaT cells was analyzed using, a high-throughput imaging system, Opera Phenix™. ResultsLive S. aureus were taken up in the cytoplasm of HaCaT cells. Coculturing live S. aureus with live S. epidermidis or the culture supernatants decreased the abundance of S. aureus in the cytoplasm. The heat-treated culture supernatants of live S. epidermidis or culture supernatants of other S. strains did not decrease the abundance of S. aureus in the cytoplasm. ConclusionLive S. aureus was internalized into the cytoplasm of HaCaT cells as does heat-treated S. aureus. In addition, the heat-sensitive substances secreted by coculture with S. epidermidis and keratinocytes inhibited the uptake of S. aureus by keratinocytes.

The rhizodynamics robot: Automated imaging system for studying long-term dynamic root growth.

The study of plant root growth in real time has been difficult to achieve in an automated, high-throughput, and systematic fashion. Dynamic imaging of plant roots is important in order to discover novel root growth behaviors and to deepen our understanding of how roots interact with their environments. We designed and implemented the Generating Rhizodynamic Observations Over Time (GROOT) robot, an automated, high-throughput imaging system that enables time-lapse imaging of 90 containers of plants and their roots growing in a clear gel medium over the duration of weeks to months. The system uses low-cost, widely available materials. As a proof of concept, we employed GROOT to collect images of root growth of Oryza sativa, Hudsonia montana, and multiple species of orchids including Platanthera integrilabia over six months. Beyond imaging plant roots, our system is highly customizable and can be used to collect time- lapse image data of different container sizes and configurations regardless of what is being imaged, making it applicable to many fields that require longitudinal time-lapse recording.

Analysis of an optical imaging system prototype for autonomously monitoring zooplankton in an aquaculture facility

Traditional approaches to biomonitoring in aquatic systems, such as sample collection, sorting, and identification, require significant time and effort, thereby limiting the spatiotemporal resolution of sample collection. Additionally, collection and preservation of samples for subsequent taxonomic identification and enumeration leads to mortality of organisms. Recent advances in technologies that utilize optical imaging and machine learning have provided new opportunities to expedite biomonitoring and lead to significant cost savings. These technologies can be advantageous to scientists or managers that conduct routine biomonitoring to inform operations, as in the case of aquaculture facilities. The Small Aquatic Organism optical imaging system (SAO) is a high-throughput optical imaging and classification prototype system that relies on computer vision and machine learning (Support Vector Machines, or SVMs) to autonomously identify and enumerate aquatic organisms. The SAO provides a more sustainable method of collecting large volumes of data and has the benefit of being used in situ. In this study, we tested the performance of the SAO in providing comparable results to manual zooplankton community monitoring in ten ponds at an aquaculture facility. We performed a side-by-side study comparing the sampling methods of plankton tow nets, where major zooplankton taxonomic classes were manually identified and enumerated, to sampling with the SAO. Vouchered samples were used to develop a training library for the SAO, where classes consisted of water boatman and zooplankton groups: cladocerans, copepod adults, copepod nauplii, and rotifers. SAO imagery was manually classified and compared with predicted results for validation. Accuracy for the SVM classifier of the SAO was 37.4 %. Convolutional Neural Networks (CNN) and Random Forest classifiers were also applied to SAO imagery and image features for comparison. The best CNN model and our Random Forest model had accuracies of 80.4 % and 46.6 % respectively. Challenges faced included the small size of copepod nauplii and rotifers and the limited resolution of the imaging camera, although there are tradeoffs between imaging resolution and the sample processing rate. Our comparison shows that advancement in both optical imaging and ML are needed in order for the SAO prototype to yield comparable results to manual community monitoring in an aquaculture facility.

Hyperspectral Analysis for Discriminating Herbicide Site of Action: A Novel Approach for Accelerating Herbicide Research

In agricultural weed management, herbicides are indispensable, yet innovation in their modes of action (MOA)—the general mechanisms affecting plant processes—has slowed. A finer classification within MOA is the site of action (SOA), the specific biochemical pathway in plants targeted by herbicides. The primary objectives of this study were to evaluate the efficacy of hyperspectral imaging in the early detection of herbicide stress and to assess its potential in accelerating the herbicide development process by identifying unique herbicide sites of action (SOA). Employing a novel SOA classification method, eight herbicides with unique SOAs were examined via an automated, high-throughput imaging system equipped with a conveyor-based plant transportation at Purdue University. This is one of the earliest trials to test hyperspectral imaging on a large number of herbicides, and the study aimed to explore the earliest herbicide stress detection/classification date and accelerate the speed of herbicide development. The final models, trained on a dataset with nine treatments with 320 samples in two rounds, achieved an overall accuracy of 81.5% 1 day after treatment. With the high-precision models and rapid screening of numerous compounds in only 7 days, the study results suggest that hyperspectral technology combined with machine learning can contribute to the discovery of new herbicide MOA and help address the challenges associated with herbicide resistance. Although no public research to date has used hyperspectral technology to classify herbicide SOA, the successful evaluation of herbicide damage to crops provides hope to accelerate the progress of herbicide development.

A Stare-Down Video-Rate High-Throughput Hyperspectral Imaging System and Its Applications in Biological Sample Sensing

A Stare-Down Video-Rate High-Throughput Hyperspectral Imaging System and Its Applications in Biological Sample Sensing

Accelerated high-throughput imaging and phenotyping system for small organisms.

Studying the complex web of interactions in biological communities requires large multifactorial experiments with sufficient statistical power. Automation tools reduce the time and labor associated with setup, data collection, and analysis in experiments that untangle these webs. We developed tools for high-throughput experimentation (HTE) in duckweeds, small aquatic plants that are amenable to autonomous experimental preparation and image-based phenotyping. We showcase the abilities of our HTE system in a study with 6,000 experimental units grown across 2,000 treatments. These automated tools facilitated the collection and analysis of time-resolved growth data, which revealed finer dynamics of plant-microbe interactions across environmental gradients. Altogether, our HTE system can run experiments with up to 11,520 experimental units and can be adapted for other small organisms.

Self-supervised maize kernel classification and segmentation for embryo identification.

Computer vision and deep learning (DL) techniques have succeeded in a wide range of diverse fields. Recently, these techniques have been successfully deployed in plant science applications to address food security, productivity, and environmental sustainability problems for a growing global population. However, training these DL models often necessitates the large-scale manual annotation of data which frequently becomes a tedious and time-and-resource- intensive process. Recent advances in self-supervised learning (SSL) methods have proven instrumental in overcoming these obstacles, using purely unlabeled datasets to pre-train DL models. Here, we implement the popular self-supervised contrastive learning methods of NNCLR Nearest neighbor Contrastive Learning of visual Representations) and SimCLR (Simple framework for Contrastive Learning of visual Representations) for the classification of spatial orientation and segmentation of embryos of maize kernels. Maize kernels are imaged using a commercial high-throughput imaging system. This image data is often used in multiple downstream applications across both production and breeding applications, for instance, sorting for oil content based on segmenting and quantifying the scutellum's size and for classifying haploid and diploid kernels. We show that in both classification and segmentation problems, SSL techniques outperform their purely supervised transfer learning-based counterparts and are significantly more annotation efficient. Additionally, we show that a single SSL pre-trained model can be efficiently finetuned for both classification and segmentation, indicating good transferability across multiple downstream applications. Segmentation models with SSL-pretrained backbones produce DICE similarity coefficients of 0.81, higher than the 0.78 and 0.73 of those with ImageNet-pretrained and randomly initialized backbones, respectively. We observe that finetuning classification and segmentation models on as little as 1% annotation produces competitive results. These results show SSL provides a meaningful step forward in data efficiency with agricultural deep learning and computer vision.

Laser ablation tomography (LATscan) as a new tool for anatomical studies of woody plants.

Traditionally, botanists study plant anatomy by carefully sectioning samples, histological staining to highlight tissues of interests, then imaging slides under light microscopy. This approach generates significant details; however, this workflow is laborious, particularly in woody vines (lianas) with heterogeneous anatomies, and ultimately yields two-dimensional (2D) images. Laser ablation tomography (LATscan) is a high-throughput imaging system that yields hundreds of images per minute. This method has proven useful for studying the structure of delicate plant tissues; however, its utility in understanding the structure of woody tissues is underexplored. We report LATscan-derived anatomical data from several stems of lianas (c. 20 mm) of seven species and compare these results with those obtained through traditional anatomical techniques. LATscan successfully allows the description of tissue composition by differentiating cell type, size, and shape, but also permits the recognition of distinct cell wall composition (e.g. lignin, suberin, cellulose) based on differential fluorescent signals on unstained samples. LATscan generate high-quality 2D images and 3D reconstructions of woody plant samples; therefore, this new technology is useful for both qualitative and quantitative analyses. This high-throughput imaging technology has the potential to bolster phenotyping of vegetative and reproductive anatomy, wood anatomy, and other biological systems.

Generation of 3D Tumor Spheroids for Drug Evaluation Studies.

In recent decades, in addition to monolayer-cultured cells, three-dimensional tumor spheroids have been developed as a potentially powerful tool for the evaluation of anticancer drugs. However, the conventional culture methods lack the ability to manipulate the tumor spheroids in a homogeneous manner at the three-dimensional level. To address this limitation, in this paper, we present a convenient and effective method of constructing average-sized tumor spheroids. Additionally, we describe a method of image-based analysis using artificial intelligence-based analysis software that can scan the whole plate and obtain data on three-dimensional spheroids. Several parameters were studied. By using a standard method of tumor spheroid construction and a high-throughput imaging and analysis system, the effectiveness and accuracy of drug tests performed on three-dimensional spheroids can be dramatically increased.

A high-throughput multispectral imaging system for museum specimens

We present an economical imaging system with integrated hardware and software to capture multispectral images of Lepidoptera with high efficiency. This method facilitates the comparison of colors and shapes among species at fine and broad taxonomic scales and may be adapted for other insect orders with greater three-dimensionality. Our system can image both the dorsal and ventral sides of pinned specimens. Together with our processing pipeline, the descriptive data can be used to systematically investigate multispectral colors and shapes based on full-wing reconstruction and a universally applicable ground plan that objectively quantifies wing patterns for species with different wing shapes (including tails) and venation systems. Basic morphological measurements, such as body length, thorax width, and antenna size are automatically generated. This system can increase exponentially the amount and quality of trait data extracted from museum specimens.

High-Throughput Imaging of Blood Flow Reveals Developmental Changes in Distribution Patterns of Hemodynamic Quantities in Developing Zebrafish.

Mechanical forces from blood flow and pressure (hemodynamic forces) contribute to the formation and shaping of the blood vascular network during embryonic development. Previous studies have demonstrated that hemodynamic forces regulate signaling and gene expression in endothelial cells that line the inner surface of vascular tubes, thereby modifying their cellular state and behavior. Given its important role in vascular development, we still know very little about the quantitative aspects of hemodynamics that endothelial cells experience due to the difficulty in measuring forces in vivo. In this study, we sought to determine the magnitude of wall shear stress (WSS) exerted on ECs by blood flow in different vessel types and how it evolves during development. Utilizing the zebrafish as a vertebrate model system, we have established a semi-automated high-throughput fluorescent imaging system to capture the flow of red blood cells in an entire zebrafish between 2- and 6-day post-fertilization (dpf). This system is capable of imaging up to 50 zebrafish at a time. A semi-automated analysis method was developed to calculate WSS in zebrafish trunk vessels. This was achieved by measuring red blood cell flow using particle tracking velocimetry analysis, generating a custom-made script to measure lumen diameter, and measuring local tube hematocrit levels to calculate the effective blood viscosity at each developmental stage. With this methodology, we were able to determine WSS magnitude in different vessels at different stages of embryonic and larvae growth and identified developmental changes in WSS, with absolute levels of peak WSS in all vessel types falling to levels below 0.3 Pa at 6 dpf. Additionally, we discovered that zebrafish display an anterior-to-posterior trend in WSS at each developmental stage.

Applying high‐throughput analysis to understand cell proliferation under different adhesion conditions

Mammalian cell culture is a staple of biomedical research, and yet it has long been recognized that the conditions in which cells are cultured do not reflect the cellular environment in vivo. Adherent cells are cultured on a plastic surface with an elastic modulus over 10,000‐fold stiffer than what is experienced by neuronal, epithelial and muscle cells in living tissues. These altered conditions have a variety of effects on cellular behaviors, especially in regard to cell shape, motility and division. Previous work in the lab suggest that cell adhesion and motility play a role in the mechanics of cytokinesis in adherent cells. To test this hypothesis, we are conducting a study of the cell cycle and cell division dynamics of human Retinal Pigmented Epithelial (RPE1) cells cultured on substrates of differing mechanical properties. We have genetically engineered lines of RPE1 cells that express either a nuclear Red Fluorescent Protein (RFP) marker or a two‐color fluorescent cell cycle biosensor that respectively quantify cell number and cell cycle timing using a high‐throughput imaging system. The doubling time as well as the lengths of each phase of the cell cycle will be measured in cells grown in matrices with decreasing stiffness, and these experiments will also allow us to define a minimal matrix stiffness that will still afford successful cytokinesis. These data will then form the foundation for future studies where we perturb different components of the cytokinetic machinery and test the ability of cells to compensate through increased cell adhesion and motility.

Holographic 3D particle reconstruction using a one-stage network.

Volumetric reconstruction of a three-dimensional (3D) particle field with high resolution and low latency is an ambitious and valuable task. As a compact and high-throughput imaging system, digital holography (DH) encodes the 3D information of a particle volume into a two-dimensional (2D) interference pattern. In this work, we propose a one-stage network (OSNet) for 3D particle volumetric reconstruction. Specifically, by a single feed-forward process, OSNet can retrieve the 3D coordinates of the particles directly from the holograms without high-fidelity image reconstruction at each depth slice. Evaluation results from both synthetic and experimental data confirm the feasibility and robustness of our method under different particle concentrations and noise levels in terms of detection rate and position accuracy, with improved processing speed. The additional applications of 3D particle tracking are also investigated, facilitating the analysis of the dynamic displacements and motions for micro-objects or cells. It can be further extended to various types of computational imaging problems sharing similar traits.

An improved automated zebrafish larva high-throughput imaging system.

As a typical multicellular model organism, the zebrafish has been increasingly used in biological research. Despite the efforts to develop automated zebrafish larva imaging systems, existing ones are still defective in terms of reliability and automation. This paper presents an improved zebrafish larva high-throughput imaging system, which makes improvements to the existing designs in the following aspects. Firstly, a single larva extraction strategy is developed to make larva loading more reliable. The aggregated larvae are identified, classified by their numbers and patterns, and separated by the aspiration pipette or water stream. Secondly, the dynamic model of larva motion in the capillary is established and an adaptive robust controller is designed for decelerating the fast-moving larva to ensure the survival rate. Thirdly, rotating the larva to the desired orientation is automated by developing an algorithm to estimate the larva's initial rotation angle. For validating the improved larva imaging system, a real-time heart rate monitoring experiment is conducted as an application example. Experimental results demonstrate that the goals of the improvements have been achieved. With these improvements, the improved zebrafish larva imaging system remarkably reduces human intervention and increases the efficiency and success/survival rates of larva imaging.

Compressive spectral image fusion via a single aperture high throughput imaging system

Spectral image fusion techniques combine the detailed spatial information of a multispectral (MS) image and the rich spectral information of a hyperspectral (HS) image into a high-spatial and high-spectral resolution image. Due to the data deluge entailed by such images, new imaging modalities have exploited their intrinsic correlations in such a way that, a computational algorithm can fuse them from few multiplexed linear projections. The latter has been coined compressive spectral image fusion. State-of-the-art research work have focused mainly on the algorithmic part, simulating instrumentation characteristics and assuming independently registered sensors to conduct compressed MS and HS imaging. In this manuscript, we report on the construction of a unified computational imaging framework that includes a proof-of-concept optical testbed to simultaneously acquire MS and HS compressed projections, and an alternating direction method of multipliers algorithm to reconstruct high-spatial and high-spectral resolution images from the fused compressed measurements. The testbed employs a digital micro-mirror device (DMD) to encode and split the input light towards two compressive imaging arms, which collect MS and HS measurements, respectively. This strategy entails full light throughput sensing since no light is thrown away by the coding process. Further, different resolutions can be dynamically tested by binning the DMD and sensors pixels. Real spectral responses and optical characteristics of the employed equipment are obtained through a per-pixel point spread function calibration approach to enable accurate compressed image fusion performance. The proposed framework is demonstrated through real experiments within the visible spectral range using as few as 5% of the data.

High-Throughput Whole-Plate Imaging of Cells for Multiple Biological Applications.

Advanced multipurpose cell imaging systems along with integrated rapid quantitation software can enhance and expedite cancer cell culture studies in a variety of applications. Though accurate cell culture studies are an important and necessary component of nearly all cancer biomarker detection and therapy studies, the methods we currently use are of low-throughput, time consuming, and lack accuracy. Hence, it is important to improve several features of the assays to increase the accuracy of their quantitative outputs in most studies. In general, we perform cell culture analysis semimanually by counting a small aliquot of suspended cells using a hemocytometer or viewing a small area of cells on a plate using a bright-field microscope, and then extrapolate the counts or observations to estimate the values for the total numbers of cells. The fundamental problem with this process lies in using techniques, such as extrapolation, which inherently introduces intrasample variability while collecting the cells by enzymatic trypsinization for these assays that are affecting cell growth and other downstream assessments. Fluorescence (FL) microscopy-based assays are also used to image and count cells for various applications, including cell viability, proliferation, apoptosis, cell death, transfection efficiency, protein expression, stem cell properties, colony formation, cytotoxicity, drug dose-response, and treatment efficacy studies. These methods are not optimal for many researches, as they require real-time visualization under a microscope plus manual analysis to determine the final results. Owing to long exposure times for cells under fluorescent light of a microscope, the cells may be exposed to suboptimal conditions that affect cell growth, and with occasional photobleaching of the expressed FL probes. Alternatively, the use of cell imaging systems that integrate both advanced bright-field and FL imaging for cell counting and quantification can be useful. In this protocol, we discuss the advantages of a high-throughput cell imaging system using a whole-plate imaging format when used in various bioimaging studies by highlighting a few applications of the system. The system is designed to fundamentally improve the accuracy and time of cell culture analysis while also allowing us to perform the assay without trypsinization, thus avoiding the need to replicate multiple wells for monitoring cell growth over time.

Automated Production of Human Induced Pluripotent Stem Cell-Derived Cortical and Dopaminergic Neurons with Integrated Live-Cell Monitoring

Manual culture and differentiation protocols for human induced pluripotent stem cells (hiPSC) are difficult to standardize, show high variability and are prone to spontaneous differentiation into unwanted cell types. The methods are labor-intensive and are not easily amenable to large-scale experiments. To overcome these limitations, we developed an automated cell culture system coupled to a high-throughput imaging system and implemented protocols for maintaining multiple hiPSC lines in parallel and neuronal differentiation. We describe the automation of a short-term differentiation protocol using Neurogenin-2 (NGN2) over-expression to produce hiPSC-derived cortical neurons within 6‒8 days, and the implementation of a long-term differentiation protocol to generate hiPSC-derived midbrain dopaminergic (mDA) neurons within 65 days. Also, we applied the NGN2 approach to a small molecule-derived neural precursor cells (smNPC) transduced with GFP lentivirus and established a live-cell automated neurite outgrowth assay. We present an automated system with protocols suitable for routine hiPSC culture and differentiation into cortical and dopaminergic neurons. Our platform is suitable for long term hands-free culture and high-content/high-throughput hiPSC-based compound, RNAi and CRISPR/Cas9 screenings to identify novel disease mechanisms and drug targets.

Automated Production of Human Induced Pluripotent Stem Cell-Derived Cortical and Dopaminergic Neurons with Integrated Live-Cell Monitoring.

Manual culture and differentiation protocols for human induced pluripotent stem cells (hiPSC) are difficult to standardize, show high variability and are prone to spontaneous differentiation into unwanted cell types. The methods are labor-intensive and are not easily amenable to large-scale experiments. To overcome these limitations, we developed an automated cell culture system coupled to a high-throughput imaging system and implemented protocols for maintaining multiple hiPSC lines in parallel and neuronal differentiation. We describe the automation of a short-term differentiation protocol using Neurogenin-2 (NGN2) over-expression to produce hiPSC-derived cortical neurons within 6‒8 days, and the implementation of a long-term differentiation protocol to generate hiPSC-derived midbrain dopaminergic (mDA) neurons within 65 days. Also, we applied the NGN2 approach to a small molecule-derived neural precursor cells (smNPC)transduced with GFP lentivirus and established a live-cell automated neurite outgrowth assay. We present an automated system with protocols suitable for routine hiPSC culture and differentiation into cortical and dopaminergic neurons. Our platform is suitable for long term hands-free culture and high-content/high-throughput hiPSC-based compound, RNAi and CRISPR/Cas9 screenings to identify novel disease mechanisms and drug targets.