Metabolic labeling of plant cell cultures with K(15)NO(3 )as a tool for quantitative analysis of proteins and metabolites

Strategies for robust quantitative comparison between different biological samples are of high importance in experiments that address biological questions beyond the establishment of protein lists. Here, we propose the use of (15)N-KNO(3 )as the only nitrogen source in Arabidopsis cell cultures in order to achieve a metabolically fully labeled cell population. Proteins from such metabolically labeled culture are distinguishable from unlabeled protein populations by a characteristic mass shift that depends on the amino acid composition of the tryptic peptide analyzed. In addition, the metabolically labeled cell extracts are also suitable for comparative quantitative analysis of nitrogen-containing cellular metabolic complement. Protein extracts from unlabeled and from standardized (15)N-labeled cells were combined into one sample for joined analytical processing. This has the advantage of (i) reduced experimental variability and (ii) immediate relative quantitation at the level of single extracted peptide and metabolite spectra. Together ease and accuracy of relative quantitation for profiling experiments is substantially improved. The metabolic labeling strategy has been validated by mixtures of protein extracts and metabolite extracts from the same cell cultures in known ratios of labeled to unlabeled extracts (1:1, 1:4, and 4:1). We conclude that saturating metabolic (15)N-labeling provides a robust and affordable integrative strategy to answer questions in quantitative proteomics and nitrogen focused metabolomics

Quantitative proteomics in the characterization of T helper lymphocyte differentiation

The term proteome is used to define the complete set of proteins expressed in cells or tissues of an organism at a certain timepoint. Respectively, proteomics is used to describe the methods, which are used to study such proteomes. These methods include chromatographic and electrophoretic techniques for protein or peptide fractionation, mass spectrometry for their identification, and use of computational methods to assist the complicated data analysis. A primary aim in this Ph.D. thesis was to set-up, optimize, and develop proteomics methods for analysing proteins extracted from T-helper (Th) lymphocytes. First, high-throughput LC-MS/MS and ICAT labeling methods were set-up and optimized for analysing the microsomal fraction proteins extracted from Th lymphocytes. Later, iTRAQ method was optimized to study cytokine regulated protein expression in the nuclei of Th lymphocytes. High-throughput LC-MS/MS analyses, like ICAT and iTRAQ, produce large quantities of data and robust software and data analysis pipelines are needed. Therefore, different software programs used for analysing such data were evaluated. Moreover, a pre-filtering algorithm was developed to classify good-quality and bad-quality spectra prior to the database searches. Th-lymphocytes can differentiate into Th1 or Th2 cells based on surrounding antigens, co-stimulatory molecules, and cytokines. Both subsets have individual cytokine secretion profiles and specific functions. Th1 cells participate in the cellular immunity against intracellular pathogens, while Th2 cells have important role in the humoral immunity against extracellular parasites. An abnormal response of Th1 and Th2 cells and imbalance between the subsets are charasteristic of several diseases. Th1 specific reactions and cytokines have been detected in autoimmune diseases, while Th2 specific response and cytokine profile is common in allergy and asthma. In this Ph. D. thesis mass spectrometry-based proteomics was used to study the effects of Th1 and Th2 promoting cytokines IL-12 and IL-4 on the proteome of Th lymphocytes. Characterization of microsomal fraction proteome extracted from IL-12 treated lymphobasts and IL-4 stimulated cord blood CD4+ cells resulted in finding of cytokine regulated proteins. Galectin-1 and CD7 were down-regulated in IL-12 treated cells, while IL-4 stimulation decreased the expression of STAT1, MXA, GIMAP1, and GIMAP4. Interestingly, the transcription of both GIMAP genes was up-regulated in Th1 polarized cells and down-regulated in Th2 promoting conditions.Proteomilla tarkoitetaan organismin solujen tai kudosten tietyllä ajanhetkellä ilmentämiä proteiineja. Proteomiikka puolestaan käsittää menetelmät, joiden avulla tutkitaan proteomeja. Näihin menetelmiin kuuluvat kromatografiset ja elektroforeettiset tekniikat proteiinien ja peptidien fraktiointiin, massaspektrometria niiden tunnistamiseen sekä tietojenkäsittely menetelmät avustamaan tietojen analysointia. Väitöskirjan osatöiden tarkoituksena oli pystyttää, optimoida ja kehittää proteomiikan tutkimusmenetelmiä T-auttajasoluista (Th) eristettyjen proteiinien tutkimiseen. Aluksi pystytettiin ja optimoitiin LC-MS/MS- ja ICAT-menetelmät. Th-solujen mikrosomaalisten fraktioiden analysoimiseksi. Myöhemmin iTRAQ-menetelmä optimoitiin Th-solujen tuman proteomin tutkimiseksi. ICAT- ja iTRAQ-menetelmien kaltaiset LC-MS/MS-menetelmät tuottavat paljon tietoa, minkä analysoimiseksi tarvitaan tehokkaita tietokoneohjelmia ja tietojen analysointijärjestelmiä. Tämän vuoksi väitöskirjatyössä arvioitiin massaspektrometrillä mitattujen spektrien analysointiin soveltuvia tietokoneohjelmia. Lisäksi kehitettiin algoritmi, jonka avulla voidaan erotella laadultaan hyvät ja huonot spektrit toisistaan ennen tietokantahakuja. Th-solut voivat erilaistua Th1- tai Th2-soluiksi ympäröivien antigeenien, ko-stimuloivien molekyylien ja sytokiinien vaikutuksesta. Molemmilla alatyypeillä on yksilölliset sytokiinien tuottoprofiilit ja spesifiset tehtävät. Th1-solut osallistuvat soluvälitteiseen immuniteettiin solunsisäisiä taudinaiheuttajia vastaan, kun taas Th2-soluilla on tärkeä rooli vasta-ainevälitteisessä immuniteetissä solunulkoisia loisia vastaan. Th1- ja Th2-solujen epänormaali vaste ja solupopulaatioiden välinen epätasapaino voivat johtaa sairauksiin. Th1-soluille ominaisia reaktioita ja sytokiineja on havaittu autoimmuunisairauksissa, kun taas Th2-soluille tyypillinen vaste ja sytokiiniprofiili ovat ominaisia allergiassa ja astmassa. Tässä väitöskirjatyössä tutkittiin massaspektrometriaan perustuvan proteomiikan avulla Th1 ja Th2 erilaistavien sytokiinien IL-12 ja IL-4 vaikutusta Th-solujen proteomiin. IL-12:lla stimuloiduista perifeerisen veren Th-soluista ja IL-4:llä stimuloiduista napaveren Th-soluista eristettiin mikrosomaaliset fraktiot, joiden proteomeissa havaittiin sytokiinien säätelemiä proteiineja. Galektiini-1:n ja CD7:n ekspressio väheni IL-12:n vaikutuksesta ja IL-4 puolestaan vähensi STAT1:n, MXA:n, GIMAP1:n ja GIMAP4:n ilmenemistä. Molempien GIMAP geenien transkription havaittiin vähenevän IL-4:n vaikutuksesta ja lisääntyvän IL-12:n vaikutuksesta.Siirretty Doriast

Development and optimization of a workflow to enable mass spectrometry-based quantitative membrane proteomics of mature and tolerogenic dendritic cells

PhD ThesisTolerogenic dendritic cells are monocyte-derived dendritic cells (DC) cultured such that they adopt an immunoregulatory phenotype. In vitro, these cells are able to induce and maintain T cell tolerance through deviation of naive T cells to an anti-inflammatory phenotype and induction of anergy in memory T cells. Equivalent cells suppress established arthritis in murine models and tolerogenic DC are presently the subject of a phase I safety and efficacy trial at Newcastle University as part of the AutoDeCRA study. However, in spite of these promising data, we are yet to rigorously explore the basis of the phenotype of tolerogenic DC and lack markers to unequivocally distinguish them from other types of DC. This body of work is concerned with the development of a workflow to enable these questions to be addressed using mass spectrometry-based quantitative proteomics. Specifically, methods have been optimized and validated to enable a) enrichment and proteolytic digestion of membrane proteins, favouring their detection over more abundant cytoplasmic and nuclear proteins in LC/MS; b) differential stable isotope labelling of peptide N- and C-termini, enabling ‘isobaric peptide termini labelling’-based relative quantitation at the MS2 level; c) pipette-tip based anion exchange fractionation of IPTL-labelled peptides prior to LC/MS analysis, broadening depth of proteome coverage. Efforts to apply aspects of the workflow to perform quantitative comparisons of the whole cell proteomes and qualitative profiling of the membrane proteomes of mature and tolerogenic DC are also documented. It is envisaged that future application of this optimized workflow as a whole will enable the identification and relative quantitation of significant numbers of mature and tolerogenic DC plasma membrane proteins. Differentially expressed proteins of interest identified through this approach may then be further investigated for putative roles in tolerance induction

Development and application of quantitative proteomics strategies to analyze molecular mechanisms of neurodegeneration

Neurodegenerative disorders such as Alzheimer’s disease, Huntington’s disease, Parkinson’s disease, Amyotrophic Lateral Sclerosis or Prion diseases are chronic, incurable and often fatal. A cardinal feature of all neurodegenerative disorders is the accumulation of misfolded and aggregated proteins. Depending on the disease, these aggregated proteins are cell type specific and have distinct cellular localizations, compositions and structures. Despite intensive research, the contribution of protein misfolding and aggregation to the cell type specific toxicity is not completely understood. In recent years, quantitative proteomics has matured into an exceptionally powerful technology providing accurate quantitative information on almost all cellular proteins as well as protein interactions, modifications, and subcellular localizations, which cannot be addressed by other omics technologies. The aim of this thesis is to investigate key features of neurodegeneration such as misfolded proteins and toxic protein aggregates with cutting edge proteomics, presenting a technological “proof of concept” and novel insights into the (patho)physiology of neurodegeneration

Développement de nouveaux outils bioinformatiques pour l'exploitation des données de spectrométrie de masse en protéomique haut-débit

En biologie, la spectrométrie de masse est devenue l'outil incontournable pour l'identification des protéines. Associée à des techniques de séparation, elle est aussi utilisée pour mesurer la variation d'abondance des protéines entre plusieurs échantillons. Cependant, la très grande quantité et complexité des données liées à ce type d'analyse requièrent des programmes informatiques sophistiqués et adaptés. Mon travail de doctorat a consisté à répondre aux différentes problématiques liées à l'exploitation des données nanoLC-MS/MS, à savoir la validation des résultats d'identification ainsi que la quantification relative des protéines pour des approches mettant en œuvre ou non un marquage isotopique. Le logiciel MFPaQ, dont deux versions sont présentées dans ce document, en est le principal résultat. La version 3 intègre des fonctionnalités telle que la validation des données Mascot, la génération de listes non-redondantes de protéines et la quantification d'analyses ICAT. La version 4, évolution majeure du logiciel, incorpore des algorithmes adaptés à l'analyse quantitative de données MS sans marquage, ainsi que la gestion des stratégies de marquage SILAC et 14N/15N. Son utilisation a facilité la réalisation d'études protéomiques, dont certaines, auxquelles j'ai plus particulièrement participé, sont présentées. Afin de répondre aux futurs enjeux informatiques de la protéomique, j'ai entrepris dans un second temps le développement du logiciel Prosper, qui dispose d'une architecture d'organisation des données permettant de réaliser des requêtes croisées sur l'ensemble des échantillons analysés. Il constitue aussi un outil prototype pour l'élaboration de nouveaux algorithmes.In biology, mass spectrometry has become an indispensable tool for protein identification. Associated with separation techniques, it can also be used to measure the variation of protein abundance between different samples. However, due to the huge quantity and complexity of the data produced by this kind of analysis, sophisticated and suitable computer programs are needed. My PhD work was to address the different issues related to the processing of nanoLC-MS/MS data, namely the validation of the identification results, and the relative quantification of proteins using approaches based or not on isotopic labeling. The MFPaQ program, two versions of which are presented here, is the main result of this work. Version 3 includes features such as Mascot data validation, generation of non-redundant protein lists and quantification of ICAT analyses. Version 4, which represents a major upgrade of the software, incorporates additional algorithms for quantitative analysis of label-free MS data, as well as for the handling of the 14N/15N and SILAC labeling strategies. This bioinformatic tool has been used for various proteomic studies, some of which are discussed here. In order to meet future IT challenges in proteomics, I undertook later the development of the Prosper software, which is based on an optimized architecture for organizing data, and allows performing cross-queries on all analysed samples. It also constitutes a prototype tool for the development and evaluation of new algorithms