Neuronal differentiation strategies: insights from single-cell sequencing and machine learning

Neuronal replacement therapies rely on the 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 differentiat...

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Vydané v:Development (Cambridge) Ročník 147; číslo 23
Hlavní autori: Konstantinides, Nikolaos, Desplan, Claude
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: England 08.12.2020
Predmet:
Animals
Cell Differentiation - genetics
Cellular Reprogramming - genetics
Embryonic Stem Cells - cytology
Embryonic Stem Cells - metabolism
Humans
Induced Pluripotent Stem Cells - cytology
Induced Pluripotent Stem Cells - metabolism
Machine Learning
Neural Stem Cells - cytology
Neural Stem Cells - metabolism
Neurons - cytology
Neurons - metabolism
Signal Transduction - genetics
Single-Cell Analysis - methods
Single-cell sequencing
Neuronal replacement therapy
Neuronal differentiation protocols
In vitro differentiation
Machine learning
Neuronal development
ISSN:1477-9129, 1477-9129
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Popis
Shrnutí:Neuronal replacement therapies rely on the 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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 23
ISSN:1477-9129
1477-9129
DOI:10.1242/dev.193631