Pharmacological Transdifferentiation of Human Nasal Olfactory Stem Cells into Dopaminergic Neurons.

The discovery of novel drugs for neurodegenerative diseases has been a real challenge over the last decades. The development of patient- and/or disease-specific in vitro models represents a powerful strategy for the development and validation of lead candidates in preclinical settings. The implementation of a reliable platform modeling dopaminergic neurons will be an asset in the study of dopamine-associated pathologies such as Parkinson's disease. Disease models based on cell reprogramming strategies, using either human-induced pluripotent stem cells or transcription factor-mediated transdifferentiation, are among the most investigated strategies. However, multipotent adult stem cells remain of high interest to devise direct conversion protocols and establish in vitro models that could bypass certain limitations associated with reprogramming strategies. Here, we report the development of a six-step chemically defined protocol that drives the transdifferentiation of human nasal olfactory stem cells into dopaminergic neurons. Morphological changes were progressively accompanied by modifications matching transcript and protein dopaminergic signatures such as LIM homeobox transcription factor 1 alpha (LMX1A), LMX1B, and tyrosine hydroxylase (TH) expression, within 42 days of differentiation. Phenotypic changes were confirmed by the production of dopamine from differentiated neurons. This new strategy paves the way to develop more disease-relevant models by establishing reprogramming-free patient-specific dopaminergic cell models for drug screening and/or target validation for neurodegenerative diseases.

The confirmation rate of primary hits: a predictive model.

HTS data from primary screening are usually analyzed by setting a cutoff for activity, in order to minimize both false-negative and false-positive rates. An alternative approach, based on a calculated probability of being active, is presented here. Given the predicted confirmation rate derived from this probability, the number of primary positives selected for follow-up can be optimized to maximize the number of true positives without picking too many false positives. Typical cutoff-determining methods are more serendipitous in their nature and not easily optimized in an effort to optimize screening efforts. An additional advantage of calculating a probability of being active for each compound screened is that orthogonal mixtures can be deconvoluted without presetting a deconvolution threshold. An important consequence of using the probability of being active with orthogonal mixtures is that individual compound screening results can be recorded irrespective of whether the assays were performed on single compounds or on cocktails.