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Characterization of Cerebrospinal Fluid via Data-Independent Acquisition Mass Spectrometry
journal contribution
posted on 2018-09-11, 21:29 authored by Katalin Barkovits, Andreas Linden, Sara Galozzi, Lukas Schilde, Sandra Pacharra, Brit Mollenhauer, Nadine Stoepel, Simone Steinbach, Caroline May, Julian Uszkoreit, Martin Eisenacher, Katrin MarcusCerebrospinal
fluid (CSF) is in direct contact with the brain and
serves as a valuable specimen to examine diseases of the central nervous
system through analyzing its components. These include the analysis
of metabolites, cells as well as proteins. For identifying new suitable
diagnostic protein biomarkers bottom-up data-dependent acquisition
(DDA) mass spectrometry-based approaches are most popular. Drawbacks
of this method are stochastic and irreproducible precursor ion selection.
Recently, data-independent acquisition (DIA) emerged as an alternative
method. It overcomes several limitations of DDA, since it combines
the benefits of DDA and targeted methods like selected reaction monitoring
(SRM). We established a DIA method for in-depth proteome analysis
of CSF. For this, four spectral libraries were generated with samples
from native CSF (n = 5), CSF fractionation (15 in
total) and substantia nigra fractionation (54 in total) and applied
to three CSF DIA replicates. The DDA and DIA methods for CSF were
conducted with the same nanoLC parameters using a 180 min gradient.
Compared to a conventional DDA method, our DIA approach increased
the number of identified protein groups from 648 identifications in
DDA to 1574 in DIA using a comprehensive spectral library generated
with DDA measurements from five native CSF and 54 substantia nigra
fractions. We also could show that a sample specific spectral library
generated from native CSF only increased the identification reproducibility
from three DIA replicates to 90% (77% with a DDA method). Moreover,
by utilizing a substantia nigra specific spectral
library for CSF DIA, over 60 brain-originated proteins could be identified
compared to only 11 with DDA. In conclusion, the here presented optimized
DIA method substantially outperforms DDA and could develop into a
powerful tool for biomarker discovery in CSF. Data are available via
ProteomeXchange with the identifiers PXD010698, PXD010708, PXD010690,
PXD010705, and PXD009624.
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mass spectrometry-based approaches54 substantia nigra fractionsCSF DIA replicatesPXDoptimized DIA methodprotein biomarkers bottom-up data-dependent acquisitionDDA methodirreproducible precursor ion selection60 brain-originated proteinsSRM180 min gradientData-Independent Acquisition Mass Spectrometry Cerebrospinal fluid
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