Predicting the functional states of human iPSC-derived neurons with single-cell RNA-seq and electrophysiology

Cedric Bardy, M. van den Hurk, B. Kakaradov, B. N. Jaegger, J. A. Erwin, R. V. Hernandez, T. Eames, A. A. Paucar, M. Gorris, C Marchand, R. Japelli, J. Barron, A. K. Bryant, M. Kellog, R. S. Lasken, B. P. F. Rutten, H. W. M. Steinbusch, G. W. Yeo, F. H. Gage

    Research output: Contribution to journalArticlepeer-review

    100 Citations (Scopus)


    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.

    Original languageEnglish
    Pages (from-to)1573-1588
    Number of pages16
    Issue number11
    Publication statusPublished - 1 Nov 2016


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