Transdifferentiation of Human Circulating Monocytes Into Neuronal-Like Cells in 20 Days and Without Reprograming.
Ontology highlight
ABSTRACT: Despite progress, our understanding of psychiatric and neurological illnesses remains poor, at least in part due to the inability to access neurons directly from patients. Currently, there are in vitro models available but significant work remains, including the search for a less invasive, inexpensive and rapid method to obtain neuronal-like cells with the capacity to deliver reproducible results. Here, we present a new protocol to transdifferentiate human circulating monocytes into neuronal-like cells in 20 days and without the need for viral insertion or reprograming. We have thoroughly characterized these monocyte-derived-neuronal-like cells (MDNCs) through various approaches including immunofluorescence (IF), flow cytometry, qRT-PCR, single cell mRNA sequencing, electrophysiology and pharmacological techniques. These MDNCs resembled human neurons early in development, expressed a variety of neuroprogenitor and neuronal genes as well as several neuroprogenitor and neuronal proteins and also presented electrical activity. In addition, when these neuronal-like cells were exposed to either dopamine or colchicine, they responded similarly to neurons by retracting their neuronal arborizations. More importantly, MDNCs exhibited reproducible differentiation rates, arborizations and expression of dopamine 1 receptors (DR1) on separate sequential samples from the same individual. Differentiation efficiency measured by cell morphology was on average 11.9 ± 1.4% (mean, SEM, n = 38,819 cells from 15 donors). To provide context and help researchers decide which in vitro model of neuronal development is best suited to address their scientific question,we compared our results with those of other in vitro models currently available and exposed advantages and disadvantages of each paradigm.
SUBMITTER: Bellon A
PROVIDER: S-EPMC6156467 | biostudies-literature | 2018
REPOSITORIES: biostudies-literature
ACCESS DATA