Patient-Derived Neurons Reveal Biomarkers of Antidepressant Response

Avior Y, Ron S, Kroitorou D, Albeldas C, Lerner V, Corneo B, Nitzan E, Laifenfeld D, Cohen Solal T. Depression patient-derived cortical neurons reveal potential biomarkers for antidepressant response. Transl Psychiatry 2021;11(1):201. doi: 10.1038/s41398-021-01319-5.

Background: Major depressive disorder (MDD) affects hundreds of millions globally, yet only about one-third of patients achieve remission after first-line antidepressant treatment. The lack of predictive biomarkers forces clinicians to rely on trial-and-error prescribing. Advances in patient-derived induced pluripotent stem cell (iPSC) technology offer a means to model neuronal characteristics linked to drug response in vitro, potentially enabling individualized antidepressant selection.

Hypothesis: This study hypothesized that neurons derived from depression patients’ lymphoblastoid cell lines (LCLs) would display cellular and molecular biomarkers predictive of clinical response to the antidepressant bupropion.

Methods: The authors reprogrammed LCLs from ten MDD patients in the STAR*D cohort into iPSCs, differentiated them into cortical neurons, and treated them with bupropion or vehicle. Synaptic morphology and dendritic spine characteristics were quantified using confocal microscopy, and dendritic and spine structures were analyzed with Neurolucida. Gene expression changes were examined through RNA sequencing.

Results: Neurons from bupropion responders exhibited significantly more synapsin puncta and colocalized pre- and postsynaptic markers than nonresponders, both before and after treatment. Responders’ neurons showed longer dendritic spines and distinct shifts in spine-type distributions following drug exposure. RNA sequencing revealed nine genes – such as MT1E, NPPB, NGF and ITGA2 – upregulated exclusively in responder-derived neurons after treatment.

Conclusions: Patient-derived cortical neurons recapitulated clinical antidepressant responses, identifying synaptic and transcriptional biomarkers that may predict bupropion efficacy. These findings support the feasibility of using in vitro neuronal assays for personalized antidepressant selection.