title: Automated sample preparation for streamlined  proteomic profiling of clinical specimens
creator: Müller, Torsten Michael
subject: ddc-500
subject: 500 Natural sciences and mathematics
subject: ddc-570
subject: 570 Life sciences
subject: ddc-600
subject: 600 Technology (Applied sciences)
description: The genetic information of all life is encoded within DNA molecules that are translated into functional entities, so-called proteins. They are responsible for operating and controlling a vast array of molecular mechanisms in any biological system and ubiquitous in  (patho)physiology as a result. Besides, proteins are the primary target of drugs and can  have a central role as biomarkers for diagnostic, prognostic, or predictive purposes. Here,  many regulatory mechanisms and spatiotemporal influences prevent an accurate  prediction of a proteins’ abundance and its associated functionality based on the genome  information alone. Nowadays, it has become possible to measure and quantify thousands  of proteins simultaneously, however, involving comprehensive sample preparation  procedures. Currently, no universally standardized method enables a routine application of  proteome profiling in a clinical environment.  In this thesis, an automated workflow for the efficient processing of the most common and  quantity-limited specimens is described. In order to demonstrate the usefulness of the end-to-  end pipeline, which was termed autoSP3, it was applied to the proteome profiling of  histologically defined and WHO recognized growth patterns of pulmonary adenocarcinoma  (ADC) that currently have a limited clinical implication. Secondly, we investigated the  proteome composition of a molecularly well-defined cohort of Ependymoma (EPN)  pediatric brain tumors. Despite the availability of substantial NGS data and their ability to  differentiate nine distinct subgroups, the majority of tumors remained without a functional  insight. Here, the proteome profiling could provide a missing link and emphasize several  subgroup-specific protein targets.  In summary, this thesis describes the optimization of SP3 and its automation into a robust  and cost-efficient pipeline for quantity-limited sample preparation and biological insight  into the proteome composition of ADC growth patterns and EPN tumor subgroups.
date: 2020
type: Dissertation
type: info:eu-repo/semantics/doctoralThesis
type: NonPeerReviewed
format: application/pdf
identifier: https://archiv.ub.uni-heidelberg.de/volltextserverhttps://archiv.ub.uni-heidelberg.de/volltextserver/29046/1/Mueller_Torsten_Dissertation.pdf
identifier: DOI:10.11588/heidok.00029046
identifier: urn:nbn:de:bsz:16-heidok-290468
identifier:   Müller, Torsten Michael  (2020) Automated sample preparation for streamlined proteomic profiling of clinical specimens.  [Dissertation]     
relation: https://archiv.ub.uni-heidelberg.de/volltextserver/29046/
rights: info:eu-repo/semantics/openAccess
rights: http://archiv.ub.uni-heidelberg.de/volltextserver/help/license_urhg.html
language: eng