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Related Concept Videos

Proteomics01:33

Proteomics

A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term proteomics...
Peptide Identification Using Tandem Mass Spectrometry01:33

Peptide Identification Using Tandem Mass Spectrometry

Tandem mass spectrometry, also known as MS/MS or MS2, is an analytical technique that employs two mass analyzers. Essentially it is a series of mass spectrometers that helps isolate a particular biomolecule and then helps study its chemical properties.
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Multiple Sclerosis l: Introduction01:19

Multiple Sclerosis l: Introduction

Multiple sclerosis is a chronic autoimmune disease of the central nervous system (CNS) that affects the brain, spinal cord, and optic nerves. It is an inflammatory demyelinating disorder and a leading cause of neurological disability in young adults.EpidemiologyMS commonly begins between 20 and 40 years of age and is twice as common in women. Its exact cause remains unclear, but genetic susceptibility contributes, with higher risk in first-degree relatives and identical twins. A greater...

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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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Published on: June 26, 2013

Proteomic pattern analysis discriminates among multiple sclerosis-related disorders.

Mika Komori1, Yumiko Matsuyama, Takashi Nirasawa

  • 1Department of Neurology, Graduate School of Medicine, Kyoto University, Japan.

Annals of Neurology
|April 24, 2012
PubMed
Summary
This summary is machine-generated.

Cerebrospinal fluid proteomic patterns can distinguish multiple sclerosis (MS) subtypes, aiding diagnosis. This biomarker discovery strategy improves accuracy for MS-related disorders and guides treatment decisions.

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A Streamlined Approach for Mass Spectrometry-Based Proteomics Using Selected Tissue Regions
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A Streamlined Approach for Mass Spectrometry-Based Proteomics Using Selected Tissue Regions

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Area of Science:

  • Neuroimmunology
  • Proteomics
  • Biomarker Discovery

Background:

  • Multiple Sclerosis (MS)-related disorders encompass a spectrum of inflammatory neurological diseases.
  • Accurate differential diagnosis among MS subtypes and related conditions is crucial for effective management.
  • Current diagnostic methods may require enhancement for precise differentiation.

Purpose of the Study:

  • To employ an unbiased proteomic strategy for cerebrospinal fluid (CSF) analysis in MS-related disorders.
  • To identify potential CSF proteomic biomarkers for distinguishing between different MS subtypes and related neurological conditions.
  • To assess the diagnostic utility of CSF proteomic patterns in clinical decision-making.

Main Methods:

  • Cerebrospinal fluid (CSF) protein profiles were analyzed from 107 patients with MS-related disorders (including relapsing remitting MS [RRMS], primary progressive MS [PPMS], anti-aquaporin4 antibody seropositive-neuromyelitis optica spectrum disorder [SP-NMOSD], and seronegative-NMOSD [SN-NMOSD]), amyotrophic lateral sclerosis (ALS), or other inflammatory neurological diseases.
  • Proteins were purified using magnetic beads and analyzed via matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS).
  • Multivariate statistics and pattern matching algorithms were applied, with validation in an independent cohort of 84 patients.

Main Results:

  • CSF proteomic profiles significantly differed among MS-related disorders.
  • SP-NMOSD and SN-NMOSD were distinguishable from RRMS with high accuracy, particularly during relapse phases.
  • Proteomic pattern analysis revealed substantial spectral differences between RRMS and PPMS, exceeding those between PPMS and ALS.

Conclusions:

  • CSF proteomic pattern analysis demonstrates potential for increasing diagnostic accuracy in MS-related disorders.
  • This approach can assist clinicians in making more informed therapeutic decisions.
  • The findings highlight the utility of unbiased proteomic strategies in neuroinflammatory disease diagnostics.