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A novel pipeline for drug discovery in neuropsychiatric disorders using high-content single-cell screening of signalling network responses ex vivo

The current work entails the development of a novel high content platform for the measurement of kinetic ligand responses across cell signalling networks at the single-cell level in distinct PBMC subtypes ex vivo. Using automated sample preparation, fluorescent cellular barcoding and flow cytometry the platform is capable of detecting 21, 840 parallel cell signalling responses in each PBMC sample. We apply this platform to characterize the effects of neuropsychiatric treatments and CNS ligands on the T cell signalling repertoire. We apply it to define cell signalling network abnormalities in PBMCs from drug-naïve first-onset schizophrenia patients (n=12) relative to healthy controls (n=12) which are subsequently normalized in PBMCs from the same patients (n=10) after a six week course of clinical treatment with the atypical antipsychotic olanzapine. We then validate the abnormal cell signalling responses in PBMCs from an independent cohort of drug-naïve first-onset schizophrenia patients (n=25) relative to controls (n=25) and investigate the specificity of the abnormal PBMC responses in schizophrenia as compared to major depression (n=25), bipolar disorder (n=25) and autism spectrum disorder (n=25). Subsequently we conduct a phenotypic drug screen using the US Food and Drug Administration (FDA) approved compound library, in addition to experimental neuropsychiatric drug candidates and nutraceuticals, to identify compounds which selectively normalize the schizophrenia-associated cell signalling response. Finally these candidate compounds are characterized using structure-activity relationships to reveal specific chemical moieties implicated in the putative therapeutic effect.

Identiferoai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:744241
Date January 2016
CreatorsLago Cooke, Santiago Guillermo
ContributorsBahn, Sabine
PublisherUniversity of Cambridge
Source SetsEthos UK
Detected LanguageEnglish
TypeElectronic Thesis or Dissertation
Sourcehttps://www.repository.cam.ac.uk/handle/1810/270297

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