Dataset Information

Peptidome analysis of cerebrospinal fluid by LC-MALDI MS.

ABSTRACT: We report on the analysis of endogenous peptides in cerebrospinal fluid (CSF) by mass spectrometry. A method was developed for preparation of peptide extracts from CSF. Analysis of the extracts by offline LC-MALDI MS resulted in the detection of 3,000-4,000 peptide-like features. Out of these, 730 peptides were identified by MS/MS. The majority of these peptides have not been previously reported in CSF. The identified peptides were found to originate from 104 proteins, of which several have been reported to be involved in different disorders of the central nervous system. These results support the notion that CSF peptidomics may be viable complement to proteomics in the search of biomarkers of CNS disorders.

SUBMITTER: Holtta M

PROVIDER: S-EPMC3412831 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Peptidome analysis of cerebrospinal fluid by LC-MALDI MS.

Hölttä Mikko M Zetterberg Henrik H Mirgorodskaya Ekaterina E Mattsson Niklas N Blennow Kaj K Gobom Johan J

PloS one 20120806 8

We report on the analysis of endogenous peptides in cerebrospinal fluid (CSF) by mass spectrometry. A method was developed for preparation of peptide extracts from CSF. Analysis of the extracts by offline LC-MALDI MS resulted in the detection of 3,000-4,000 peptide-like features. Out of these, 730 peptides were identified by MS/MS. The majority of these peptides have not been previously reported in CSF. The identified peptides were found to originate from 104 proteins, of which several have been ...[more]

PMID: 22880031

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Project description:Highly specialized cells are fundamental for proper functioning of complex organs. Variations in cell-type specific gene expression and protein composition have been linked to a variety of diseases. Although single cell technologies have emerged as valuable tools to address this cellular heterogeneity, a majority of these workflows lack sufficient in situ resolution for functional classification of cells and are associated with extremely long analysis time, especially when it comes to in situ proteomics. In addition, lack of understanding of single cell dynamics within their native environment limits our ability to explore the altered physiology in disease development. This limitation is particularly relevant in the mammalian brain, where different cell types perform unique functions and exhibit varying sensitivities to insults. The hippocampus, a brain region crucial for learning and memory, is of particular interest due to its obvious involvement in various neurological disorders. Here, we present a combination of experimental and data integration approaches for investigation of cellular heterogeneity and functional disposition within the mouse brain hippocampus using MALDI Imaging mass spectrometry (MALDI-IMS) and shotgun proteomics (LC-MS/MS) coupled with laser-capture microdissection (LCM) along with spatial transcriptomics. Within the dentate gyrus granule cells we identified two proteomically distinct cellular subpopulations that are characterized by a substantial number of discriminative proteins. These cellular clusters contribute to the overall functionality of the dentate gyrus by regulating redox homeostasis, mitochondrial organization, RNA processing, and microtubule organization. Importantly, most of the identified proteins matched their transcripts, verifying the in situ protein identification and supporting their functional analyses. By combining high-throughput spatial proteomics with transcriptomics, our approach enables reliable near-single-cell scale identification of proteins and profiling of inter-cellular heterogeneity within similar cell-types in tissues. This methodology has the potential to be applied to different biological conditions and tissues, providing a deeper understanding of cellular subpopulations in situ.

Dataset Information

Peptidome analysis of cerebrospinal fluid by LC-MALDI MS.

Publications

Peptidome analysis of cerebrospinal fluid by LC-MALDI MS.

Similar Datasets

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