Predicting the functional states of human iPSC-derived neurons with single-cell RNA-seq and electrophysiology
| Autor: | Fred H. Gage, A K Bryant, Bart P. F. Rutten, Roger S. Lasken, Mark A.J. Gorris, Roberto Jappelli, Jennifer A. Erwin, A A Paucar, Gene W. Yeo, Ruben V. Hernandez, M van den Hurk, Jerika J. Barron, Baptiste N. Jaeger, Mariko Kellogg, Cynthia Marchand, Boyko Kakaradov, Harry W.M. Steinbusch, Tameji Eames, Cedric Bardy |
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| Přispěvatelé: | RS: MHeNs - R3 - Neuroscience, Promovendi MHN, Psychiatrie & Neuropsychologie, Faculteit FHML Centraal |
| Rok vydání: | 2016 |
| Předmět: |
0301 basic medicine
Pluripotent Stem Cells Patch-Clamp Techniques Cancer development and immune defence Radboud Institute for Molecular Life Sciences [Radboudumc 2] Cellular differentiation Induced Pluripotent Stem Cells Action Potentials RNA-Seq Article Transcriptome Machine Learning 03 medical and health sciences Cellular and Molecular Neuroscience Single-cell analysis Humans Patch clamp Induced pluripotent stem cell Molecular Biology Cells Cultured Neurons Sequence Analysis RNA Cell Differentiation Neurophysiology Electrophysiology Psychiatry and Mental health 030104 developmental biology RNA Single-Cell Analysis Psychology Neuroscience |
| Zdroj: | Molecular Psychiatry, 21, 1573-1588 Molecular Psychiatry, 21, 11, pp. 1573-1588 Molecular Psychiatry, 21(11), 1573-1588. Nature Publishing Group |
| ISSN: | 1476-5578 1573-1588 1359-4184 |
| Popis: | Item does not contain fulltext Human neural progenitors derived from pluripotent stem cells develop into electrophysiologically active neurons at heterogeneous rates, which can confound disease-relevant discoveries in neurology and psychiatry. By combining patch clamping, morphological and transcriptome analysis on single-human neurons in vitro, we defined a continuum of poor to highly functional electrophysiological states of differentiated neurons. The strong correlations between action potentials, synaptic activity, dendritic complexity and gene expression highlight the importance of methods for isolating functionally comparable neurons for in vitro investigations of brain disorders. Although whole-cell electrophysiology is the gold standard for functional evaluation, it often lacks the scalability required for disease modeling studies. Here, we demonstrate a multimodal machine-learning strategy to identify new molecular features that predict the physiological states of single neurons, independently of the time spent in vitro. As further proof of concept, we selected one of the potential neurophysiological biomarkers identified in this study-GDAP1L1-to isolate highly functional live human neurons in vitro. |
| Databáze: | OpenAIRE |
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