Dataset Information

Automated querying and identification of novel peptides using MALDI mass spectrometric imaging.

ABSTRACT: MSI is a molecular imaging technique that allows for the generation of topographic 2D maps for various endogenous and some exogenous molecules without prior specification of the molecule. In this paper, we start with the premise that a region of interest (ROI) is given to us based on preselected morphological criteria. Given an ROI, we develop a pipeline, first to determine mass values with distinct expression signatures, localized to the ROI, and second to identify the peptides corresponding to these mass values. To identify spatially differentiated masses, we implement a statistic that allows us to estimate, for each spectral peak, the probability that it is over- or under-expressed within the ROI versus outside. To identify peptides corresponding to these masses, we apply LC-MS/MS to fragment endogenous (nonprotease digested) peptides. A novel pipeline based on constructing sequence tags de novo from both original and decharged spectra and a subsequent database search is used to identify peptides. As the MSI signal and the identified peptide are only related by a single mass value, we isolate the corresponding transcript and perform a second validation via in situ hybridization of the transcript. We tested our approach, MSI-Query, on a number of ROIs in the medicinal leech, Hirudo medicinalis, including the central nervous system (CNS). The Hirudo CNS is capable of regenerating itself after injury, thus forming an important model system for neuropeptide identification. The pipeline helps identify a number of novel peptides. Specifically, we identify a gene that we name HmIF4, which is a member of the intermediate filament family involved in neural development and a second novel, uncharacterized peptide. A third peptide, derived from the histone H2B, is also identified, in agreement with the previously suggested role of histone H2B in axon targeting.

SUBMITTER: Bruand J

PROVIDER: S-EPMC4845900 | biostudies-literature | 2011 Apr

REPOSITORIES: biostudies-literature

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Automated querying and identification of novel peptides using MALDI mass spectrometric imaging.

Bruand Jocelyne J Sistla Srinivas S Mériaux Céline C Dorrestein Pieter C PC Gaasterland Terry T Ghassemian Majid M Wisztorski Maxence M Fournier Isabelle I Salzet Michel M Macagno Eduardo E Bafna Vineet V

Journal of proteome research 20110315 4

MSI is a molecular imaging technique that allows for the generation of topographic 2D maps for various endogenous and some exogenous molecules without prior specification of the molecule. In this paper, we start with the premise that a region of interest (ROI) is given to us based on preselected morphological criteria. Given an ROI, we develop a pipeline, first to determine mass values with distinct expression signatures, localized to the ROI, and second to identify the peptides corresponding to ...[more]

PMID: 21332220

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Project description:Human proteomic databases required for MS peptide identification are frequently updated and carefully curated, yet are still incomplete because it has been challenging to acquire every protein sequence from the diverse assemblage of proteoforms expressed in every tissue and cell type. In particular, alternative splicing has been shown to be a major source of this cell-specific proteomic variation. Many new alternative splice forms have been detected at the transcript level using next generation sequencing methods, especially RNA-Seq, but it is not known how many of these transcripts are being translated. Leveraging the unprecedented capabilities of next generation sequencing methods, we collected RNA-Seq and proteomics data from the same cell population (Jurkat cells) and created a bioinformatics pipeline that builds customized databases for the discovery of novel splice-junction peptides. Eighty million paired-end Illumina reads and ?500,000 tandem mass spectra were used to identify 12,873 transcripts (19,320 including isoforms) and 6810 proteins. We developed a bioinformatics workflow to retrieve high-confidence, novel splice junction sequences from the RNA data, translate these sequences into the analogous polypeptide sequence, and create a customized splice junction database for MS searching. Based on the RefSeq gene models, we detected 136,123 annotated and 144,818 unannotated transcript junctions. Of those, 24,834 unannotated junctions passed various quality filters (e.g. minimum read depth) and these entries were translated into 33,589 polypeptide sequences and used for database searching. We discovered 57 splice junction peptides not present in the Uniprot-Trembl proteomic database comprising an array of different splicing events, including skipped exons, alternative donors and acceptors, and noncanonical transcriptional start sites. To our knowledge this is the first example of using sample-specific RNA-Seq data to create a splice-junction database and discover new peptides resulting from alternative splicing.

Project description:Hypoxic areas are a common feature of rapidly growing malignant tumors and their metastases and are typically spatially heterogeneous. Hypoxia has a strong impact on tumor cell biology and contributes to tumor progression in multiple ways. To date, only a few molecular key players in tumor hypoxia, such as hypoxia-inducible factor-1 (HIF-1), have been discovered. The distribution of biomolecules is frequently heterogeneous in the tumor volume and may be driven by hypoxia and HIF-1?. Understanding the spatially heterogeneous hypoxic response of tumors is critical. Mass spectrometric imaging (MSI) provides a unique way of imaging biomolecular distributions in tissue sections with high spectral and spatial resolution. In this paper, breast tumor xenografts grown from MDA-MB-231-HRE-tdTomato cells, with a red fluorescent tdTomato protein construct under the control of a hypoxia response element (HRE)-containing promoter driven by HIF-1?, were used to detect the spatial distribution of hypoxic regions. We elucidated the 3D spatial relationship between hypoxic regions and the localization of lipids and proteins by using principal component analysis-linear discriminant analysis (PCA-LDA) on 3D rendered MSI volume data from MDA-MB-231-HRE-tdTomato breast tumor xenografts. In this study, we identified hypoxia-regulated proteins active in several distinct pathways such as glucose metabolism, regulation of actin cytoskeleton, protein folding, translation/ribosome, splicesome, the PI3K-Akt signaling pathway, hemoglobin chaperone, protein processing in endoplasmic reticulum, detoxification of reactive oxygen species, aurora B signaling/apoptotic execution phase, the RAS signaling pathway, the FAS signaling pathway/caspase cascade in apoptosis, and telomere stress induced senescence. In parallel, we also identified colocalization of hypoxic regions and various lipid species such as PC(16:0/18:0), PC(16:0/18:1), PC(16:0/18:2), PC(16:1/18:4), PC(18:0/18:1), and PC(18:1/18:1), among others. Our findings shed light on the biomolecular composition of hypoxic tumor regions, which may be responsible for a given tumor's resistance to radiation or chemotherapy.

Dataset Information

Automated querying and identification of novel peptides using MALDI mass spectrometric imaging.

Publications

Automated querying and identification of novel peptides using MALDI mass spectrometric imaging.

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