Dataset Information

Human cerebrospinal fluid proteoform profiling

ABSTRACT: . In this study, we have investigated the capacities of last-generation Ultra-High Resolution Q-Tof systems to deal with high complexity intact protein samples, have developed the tools to process the intact protein data in an existing label-free quantification pipeline, and have evaluated the approach on a cohort of patients suffering from neurodegenerative disease in a proof-of concept study. Several proteoforms have been pointed out for their capacity to distinguish Alzheimer disease patients from patients suffering from other neurodegenerative disease and have been characterized.

INSTRUMENT(S): impact II

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Cerebrospinal Fluid

DISEASE(S): Alzheimer's Disease

SUBMITTER: Pierre-Olivier SCHMIT

LAB HEAD: Dr. Christophe Hirtz

PROVIDER: PXD005314 | Pride | 2018-04-05

REPOSITORIES: Pride

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Publications

Towards a routine application of Top-Down approaches for label-free discovery workflows.

Schmit Pierre-Olivier PO Vialaret Jerome J Wessels Hans J C T HJCT van Gool Alain J AJ Lehmann Sylvain S Gabelle Audrey A Wood Jason J Bern Marshall M Paape Rainer R Suckau Detlev D Kruppa Gary G Hirtz Christophe C

Journal of proteomics 20170830

Thanks to proteomics investigations, our vision of the role of different protein isoforms in the pathophysiology of diseases has largely evolved. The idea that protein biomarkers like tau, amyloid peptides, ApoE, cystatin, or neurogranin are represented in body fluids as single species is obviously over-simplified, as most proteins are present in different isoforms and subjected to numerous processing and post-translational modifications. Measuring the intact mass of proteins by MS has the advan ...[more]

PMID: 28855124

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Project description:Purpose: Delonix regia or Gulmohor commonly grows here and there in Indian villages as well as it is used for megacity beautification and environmental management due to its evergreen nature and vibrant flower colour. However, an increasing incidence of seasonal pollinosis was observed among the inhabitants living in close vicinity to this tree suggesting a possible link between the airborne pollen load and the concomitant respiratory hazards. This prompted us to investigate the allergens in the pollen of this dominant avenue tree. Methodology: Allergenicity of D. regia pollen grains was first checked by Skin Prick Test (SPT) and further confirmed by in vitro tests, such as, ELISA, Dot Blot and histamine release assay. The total proteome profiling was done by 2D PAGE and it was confronted with the pooled sera of 10 patients. The IgE reactive proteins were identified by MALDI TOF/TOF in Autoflex speed (Bruker,Germany). The raw spectra were searched against NCBInr database using MASCOT search engine. Result: Delonix regia, pollen grains have been found in considerable amount in the air during its flowering season (May to July). Approximately 31% of atopic individuals were found allergic to D. regia, pollen with elevated level of specific IgE and histamine in the serum. Total 8 IgE reactive proteins have been identified by homology driven proteomics. These proteins are ATP synthase beta subunit (spot no 5), Actin (spot no 6), Hypothetical actin like protein (spot no 7), Recombinant S-adenosylmethionine synthase 2 (spot no 9), UDP-arabinopyranose mutase (spot no 13), Luminal-binding 5 (spot no 2), ELF3-like protein (spot no 8) and hypothetical protein OsJ_04810 (spot no 11). Among these eight identified allergenic proteins, five were previously reported as allergen from different sources whereas the rest three have been reported for the first time as novel allergens. Conclusion: Novelty of this study was to identify 8 allergens from D.regia for the first time using immune-biochemical and proteomic techniques. Further studies will open up new avenues in component resolved diagnosis of pollen allergy.

Dataset Information

Human cerebrospinal fluid proteoform profiling

Publications

Towards a routine application of Top-Down approaches for label-free discovery workflows.

Similar Datasets

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