TY - JOUR
T1 - Neuronal differentiation strategies
T2 - Insights from single-cell sequencing and machine learning
AU - Konstantinides, Nikolaos
AU - Desplan, Claude
N1 - Funding Information:
Work in the lab is supported by grants from the National Institutes of Health (R01 EY017916) and from the New York State Stem Cell Science (DOH01-C32604GG to C.D.). N.K. was supported by a postdoctoral Human Frontier Science Program fellowship (LT000122/2015-L) and is currently supported by the National Eye Institute (K99 EY029356-01). Deposited in PMC for release after 12 months.
Publisher Copyright:
© 2020. Published by The Company of Biologists Ltd
PY - 2020/12
Y1 - 2020/12
N2 - Neuronal replacement therapies rely on the in vitro differentiation of specific cell types from embryonic or induced pluripotent stem cells, or on the direct reprogramming of differentiated adult cells via the expression of transcription factors or signaling molecules. The factors used to induce differentiation or reprogramming are often identified by informed guesses based on differential gene expression or known roles for these factors during development. Moreover, differentiation protocols usually result in partly differentiated cells or the production of a mix of cell types. In this Hypothesis article, we suggest that, to overcome these inefficiencies and improve neuronal differentiation protocols, we need to take into account the developmental history of the desired cell types. Specifically, we present a strategy that uses single-cell sequencing techniques combined with machine learning as a principled method to select a sequence of programming factors that are important not only in adult neurons but also during differentiation.
AB - Neuronal replacement therapies rely on the in vitro differentiation of specific cell types from embryonic or induced pluripotent stem cells, or on the direct reprogramming of differentiated adult cells via the expression of transcription factors or signaling molecules. The factors used to induce differentiation or reprogramming are often identified by informed guesses based on differential gene expression or known roles for these factors during development. Moreover, differentiation protocols usually result in partly differentiated cells or the production of a mix of cell types. In this Hypothesis article, we suggest that, to overcome these inefficiencies and improve neuronal differentiation protocols, we need to take into account the developmental history of the desired cell types. Specifically, we present a strategy that uses single-cell sequencing techniques combined with machine learning as a principled method to select a sequence of programming factors that are important not only in adult neurons but also during differentiation.
KW - In vitro differentiation
KW - Machine learning
KW - Neuronal development
KW - Neuronal differentiation protocols
KW - Neuronal replacement therapy
KW - Single-cell sequencing
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U2 - 10.1242/dev.193631
DO - 10.1242/dev.193631
M3 - Article
C2 - 33293292
AN - SCOPUS:85097514897
SN - 0950-1991
VL - 147
JO - Journal of Embryology and Experimental Morphology
JF - Journal of Embryology and Experimental Morphology
IS - 23
M1 - dev193631
ER -