Mastering data pre-processing for accurate quantitative molecular profiling with liquid chromatography coupled to mass spectrometry

Vikram Mitra

Mastering data pre-processing for accurate quantitative molecular profiling with liquid chromatography coupled to mass spectrometry

Vertaalde titel van de bijdrage: Mastering van data voorverwerking voor nauwkeurige kwantitatieve moleculaire profilering met vloeistofchromatografie gekoppeld aan massaspectrometrie

Vikram Mitra

Analytische Biochemie

Onderzoeksoutput: Thesis fully internal (DIV)

985 Downloads (Pure)

Samenvatting

Identificatie van verstoringen in biologische systemen is de hoeksteen van biomedisch onderzoek. Het meten van veranderingen op het moleculaire niveau heeft het mogelijk gemaakt biomedisch onderzoek te begrijpen van belangrijke fysiologische en moleculaire mechanismen in levende systemen. Proteomics laat de analyse van deze systeemniveau moleculaire veranderingen en interacties toe in complexe biologische monsters. Gegevensafhankelijke acquisitie (DDA) is de meest gebruikte aanpak voor het uitgebreide profileren van eiwitten en metaboliet in bottom-up proteomics experimenten die zijn verkregen met behulp van LC-MS / MS. Dit proefschrift beschrijft nieuwe kwaliteitsbeoordelingsmethoden die gebruikt worden om orthogonaliteit te bestuderen in het retentietijddomein (scheidingsdimensie) en vitale pre-analytische factoren die de ionenintensiteit (uitleesdimensie) van LC-MS / MS datasets beïnvloeden. Dus volgende opmerkingen vormen de hoofddoelen van het proefschrift -
- Samenvatting van diverse gegevensvoorbereidende stappen die betrokken zijn bij de behandeling van labelvrije LC-MS (/ MS) datasets, verkregen voor een typisch proteomics experiment.
- Beschrijf het MS1-stadium van een LC-MS (/ MS) dataset als tweede-orde tensor (driedimensionale data). Bespreek verschillende fysio-chemische oorsprong en effecten van
Orthogonaliteit in de twee scheidingsafmetingen (m / z en retentietijd) en uitlezing
Dimensie (ionintensiteit).
- Presentatie van een kwaliteitsbeoordelingsbenadering, die orthogonaliteit in de retentietijddimensie voor een paar LC-MS (/ MS) chromatogrammen evalueert na correctie
Van monotone verschuivingen.
- Propositie van een methode om ongeëvenaarde spectra te annoteren op basis van het begrip "identificatieoverdracht" na correctie van monotone verschuivingen tussen datasets en
Beoordeel de FDR geassocieerd met bijpassende functies op basis van retentietijd en m / z-coördinaten tussen datasets.
- Toepassing van Anova-Simultaneous Component Analysis (ASCA) om te bepalen welke pre-analytische factoren het ionintensiteitsdomein van LC-MS-eigenschappen beïnvloeden.

Vertaalde titel van de bijdrage	Mastering van data voorverwerking voor nauwkeurige kwantitatieve moleculaire profilering met vloeistofchromatografie gekoppeld aan massaspectrometrie
Originele taal-2	English
Kwalificatie	Doctor of Philosophy
Toekennende instantie	Rijksuniversiteit Groningen
Begeleider(s)/adviseur	Horvatovich, Peter, Supervisor Bischoff, Rainer, Supervisor Smilde, Age, Supervisor
Datum van toekenning	3-jul.-2017
Plaats van publicatie	[Groningen]
Uitgever	University of Groningen
Gedrukte ISBN's	978-90-367-9834-1
Elektronische ISBN's	978-90-367-9833-4
Status	Published - 2017

Toegang tot document

Title and contentsFinal publisher's version, 245 KB
Chapter 1Final publisher's version, 307 KB
Chapter 2Final publisher's version, 1,75 MB
Chapter 3Final publisher's version, 1,17 MB
Chapter 4Final publisher's version, 1,23 MB
Chapter 5Final publisher's version, 1,31 MB
Chapter 6Final publisher's version, 243 KB
SamenvattingFinal publisher's version, 184 KB
Supporting informationFinal publisher's version, 8,19 MB
AcknowledgementsFinal publisher's version, 134 KB
List of publicationsFinal publisher's version, 161 KB
Complete thesisFinal publisher's version, 12 MB
PropositionsFinal publisher's version, 197 KB

Handle.net

Permanente koppeling

Citeer dit

@phdthesis{69400d7ea8ba4629aa40f079eea09798,

title = "Mastering data pre-processing for accurate quantitative molecular profiling with liquid chromatography coupled to mass spectrometry",

abstract = "Identification of perturbations in biological systems is the corner stone of biomedical research. Measuring changes at molecular level has allowed biomedical research to understand key physiological and molecular mechanisms in living systems. Proteomics allows analysis of these system level molecular changes and interactions in complex biological samples. Data dependent acquisition (DDA) is the most widely used approach for comprehensive profiling of proteins and metabolites in bottom-up proteomics experiments acquired using LC-MS/MS. This thesis describes novel quality assessment methods used to study orthogonality in the retention time domain (separation dimension) and vital pre-analytical factors affecting the ion intensity (readout dimension) of LC-MS/MS datasets. Thus following statements form the main goals of the thesis - - Summary of various data pre-processing steps involved in the treatment of label-free LC-MS(/MS) datasets, obtained for a typical proteomics experiment. - Describe the MS1 stage of a LC-MS(/MS) dataset as a second order tensor (three dimensional data). Discuss various physio-chemical origins and effects oforthogonality in the two separation dimensions (m/z and retention time) and readoutdimension (ion intensity).- Presentation of a quality assessment approach, which evaluates orthogonality in the retention time dimension for a pair LC-MS(/MS) chromatograms following correctionof monotonic shifts.- Proposition of a method to annotate unmatched spectra based on the concept of “identification transfer” after correction of monotonic shifts between datasets andassess the FDR associated in matching features based on retention time and m/z coordinates between datasets.- Application of Anova-Simultaneous Component Analysis (ASCA) to determine, which pre-analytical factors influences the ion intensity domain of LC-MS features.",

author = "Vikram Mitra",

year = "2017",

language = "English",

isbn = "978-90-367-9834-1",

publisher = "University of Groningen",

school = "University of Groningen",

}

TY - THES

T1 - Mastering data pre-processing for accurate quantitative molecular profiling with liquid chromatography coupled to mass spectrometry

AU - Mitra, Vikram

PY - 2017

Y1 - 2017

N2 - Identification of perturbations in biological systems is the corner stone of biomedical research. Measuring changes at molecular level has allowed biomedical research to understand key physiological and molecular mechanisms in living systems. Proteomics allows analysis of these system level molecular changes and interactions in complex biological samples. Data dependent acquisition (DDA) is the most widely used approach for comprehensive profiling of proteins and metabolites in bottom-up proteomics experiments acquired using LC-MS/MS. This thesis describes novel quality assessment methods used to study orthogonality in the retention time domain (separation dimension) and vital pre-analytical factors affecting the ion intensity (readout dimension) of LC-MS/MS datasets. Thus following statements form the main goals of the thesis - - Summary of various data pre-processing steps involved in the treatment of label-free LC-MS(/MS) datasets, obtained for a typical proteomics experiment. - Describe the MS1 stage of a LC-MS(/MS) dataset as a second order tensor (three dimensional data). Discuss various physio-chemical origins and effects oforthogonality in the two separation dimensions (m/z and retention time) and readoutdimension (ion intensity).- Presentation of a quality assessment approach, which evaluates orthogonality in the retention time dimension for a pair LC-MS(/MS) chromatograms following correctionof monotonic shifts.- Proposition of a method to annotate unmatched spectra based on the concept of “identification transfer” after correction of monotonic shifts between datasets andassess the FDR associated in matching features based on retention time and m/z coordinates between datasets.- Application of Anova-Simultaneous Component Analysis (ASCA) to determine, which pre-analytical factors influences the ion intensity domain of LC-MS features.

AB - Identification of perturbations in biological systems is the corner stone of biomedical research. Measuring changes at molecular level has allowed biomedical research to understand key physiological and molecular mechanisms in living systems. Proteomics allows analysis of these system level molecular changes and interactions in complex biological samples. Data dependent acquisition (DDA) is the most widely used approach for comprehensive profiling of proteins and metabolites in bottom-up proteomics experiments acquired using LC-MS/MS. This thesis describes novel quality assessment methods used to study orthogonality in the retention time domain (separation dimension) and vital pre-analytical factors affecting the ion intensity (readout dimension) of LC-MS/MS datasets. Thus following statements form the main goals of the thesis - - Summary of various data pre-processing steps involved in the treatment of label-free LC-MS(/MS) datasets, obtained for a typical proteomics experiment. - Describe the MS1 stage of a LC-MS(/MS) dataset as a second order tensor (three dimensional data). Discuss various physio-chemical origins and effects oforthogonality in the two separation dimensions (m/z and retention time) and readoutdimension (ion intensity).- Presentation of a quality assessment approach, which evaluates orthogonality in the retention time dimension for a pair LC-MS(/MS) chromatograms following correctionof monotonic shifts.- Proposition of a method to annotate unmatched spectra based on the concept of “identification transfer” after correction of monotonic shifts between datasets andassess the FDR associated in matching features based on retention time and m/z coordinates between datasets.- Application of Anova-Simultaneous Component Analysis (ASCA) to determine, which pre-analytical factors influences the ion intensity domain of LC-MS features.

M3 - Thesis fully internal (DIV)

SN - 978-90-367-9834-1

PB - University of Groningen

CY - [Groningen]

ER -