Durens Madel, Nestor Jonathan, Williams Madeline, Herold Kevin, Niescier Robert F, Lunden Jason W, Phillips Andre W, Lin Yu-Chih, Dykxhoorn Derek M, Nestor Michael W
Program in Neuroscience, Hussman Institute for Autism, 801 W. Baltimore St., Suite 301, Baltimore, MD, 21201, United States.
Program in Neuroscience, Hussman Institute for Autism, 801 W. Baltimore St., Suite 301, Baltimore, MD, 21201, United States; Program in Molecular Medicine, University of Maryland, School of Medicine, 655 W. Baltimore St., Baltimore, MD, 21201, United States.
J Neurosci Methods. 2020 Apr 1;335:108627. doi: 10.1016/j.jneumeth.2020.108627. Epub 2020 Feb 4.
The need for scalable high-throughput screening (HTS) approaches for 3D human stem cell platforms remains a central challenge for disease modeling and drug discovery. We have developed a workflow to screen cortical organoids across platforms.
We used serum-free embryoid bodies (SFEBs) derived from human induced pluripotent stem cells (hiPSCs) and employed high-content imaging (HCI) to assess neurite outgrowth and cellular composition within SFEBs. We multiplexed this screening assay with both multi-electrode arrays (MEAs) and single-cell calcium imaging.
HCI was used to assess the number of excitatory neurons (VGlut) in experimental replicates of hiPSC-derived SFEBs, demonstrating experiment-to-experiment consistency. Neurite detection using HCI was applied to assess neurite morphology. MEA analysis showed that firing and burst rates in SFEBs decreased with blockade of NMDARs and AMPARs and increased with GABAR blockade. We also demonstrate effective combination of both MEA and HCI to analyze VGlut populations surrounding electrodes within MEAs. HCI-based (Ca) transient analysis revealed firing in individual cells surrounding active MEA electrodes.
Current methods to generate neural organoids show high degrees of variability, and often require sectioning or special handling for analysis. The protocol outlined in this manuscript generates SFEBs with high degree of consistency making them amenable to complex assays combining HTS and electrophysiology allowing for an in-depth, unbiased analysis.
SFEBs can be used in combination with HTS to compensate for experimental variability common in 3D cultures, while significantly decreasing processing speed, making this an efficient starting point for phenotypic drug screening.
对于三维人类干细胞平台而言，可扩展的高通量筛选（HTS）方法的需求仍然是疾病建模和药物发现的核心挑战。我们开发了一种工作流程，用于跨平台筛选皮质类器官。
我们使用了源自人类诱导多能干细胞（hiPSC）的无血清胚状体（SFEB），并采用高内涵成像（HCI）来评估SFEB内的神经突生长和细胞组成。我们将这种筛选测定与多电极阵列（MEA）和单细胞钙成像进行了多重联用。
HCI用于评估hiPSC衍生的SFEB实验复制品中兴奋性神经元（VGlut）的数量，证明了实验之间的一致性。使用HCI进行的神经突检测用于评估神经突形态。MEA分析表明，SFEB中的放电率和爆发率随着NMDAR和AMPAR的阻断而降低，随着GABAR的阻断而增加。我们还展示了MEA和HCI的有效结合，以分析MEA内电极周围的VGlut群体。基于HCI的（Ca）瞬态分析揭示了活跃MEA电极周围单个细胞的放电情况。
目前生成神经类器官的方法显示出高度的变异性，并且通常需要切片或特殊处理才能进行分析。本手稿中概述的方案生成的SFEB具有高度的一致性，使其适用于结合HTS和电生理学的复杂测定，从而实现深入、无偏的分析。
SFEB可与HTS结合使用，以补偿三维培养中常见的实验变异性，同时显著提高处理速度，使其成为表型药物筛选的有效起点。
