Einleitung 

Up to one fourth of all SCZ patients will eventually convert to a treatment resistant state and typically receive clozapine as last resort treatment. At present, the biological mechanisms underlying the emergence of treatment resistant schizophrenia (TRS) remain unclear, but multiple lines of evidence suggest a strong genetic component. In order to dissect this molecular genetic basis of TRS, the Ziller group has generated induced pluripotent stem cells (iPSCs) from a large cohort of healthy individuals, first line responder and TR SCZ patients. These iPSCs were differentiated into different types of cortical neurons and subsequently investigated under baseline and treatment in vivo conditions, administering aripiprazole, loxapin and clozapine. Subsequently, treated and untreated neuronal cultures were subjected to deep molecular (transcriptomics, metabolomics) and electrophysiological (multi-electrode-arrays) characterization. These datasets are all readily available. It is the central goal of the overarching project to pinpoint genes, metabolites and electrophysiological parameters associated with treatment resistance and treatment response in vivo for subsequent follow up studies.

Fragestellung 

Within this complex matrix of questions, the focus of offered PhD thesis is on the analysis of the transcriptomic data sets. In particular, it is the central goal of this thesis to identify genes pathways differentially regulated between patient groups under baseline and treatment conditions.

Bearbeitung

The PhD student will initially receive training datasets and familiarize her/himself with computational analysis methods for transcriptomic data. Following successful analysis and quality control of these training data, the student will receive the relevant pre-processed transcriptomic data from this study for analysis. 

Methoden

• Computational analysis of high dimensional data in R
• Quality control of gene expression data based on RNA-Seq
• Differential gene expression analysis based on advanced generalized linear model design (DESeq2)
• Use and analysis of single cell RNA-Seq data
• Identification and control of confounding factors in complex datasets using surrogate variable analysis, combat and in silico deconvolution of complex cell mixtures

Arbeitsprogramm

1.    Training in R, gene expression analysis using online tutorials and example datasets. Introduction  to github.
2.    Quality control and filtering of RNA-Seq data from treated and untreated SCZ patients and controls. Identification of confounders, evaluation of correction methods
3.    In silico deconvolution of RNA-Seq data using existing single cell transcriptomics data. This step is directly supported by a bioinformatician.
4.    Differential gene expression analysis of RNA-Seq data and identification of genes associated with TRS and treatment response in in vitro iPSC derived neurons
5.    Validation of differentially expressed genes using an independent RNA-Seq dataset of in vivo data obtained from rhesus macaque monkeys treated with clozapine.
6.    Documentation of analyses and results through online code repositories and Jupiter notebooks.