Mass spectrometry identification of proteins of isolated auditory and vestibular hair cells using data-independent and data-dependent acquisition
Hair cells undergo postnatal development that leads to formation of their sensory organelles, synaptic machinery, and in the case of cochlear outer hair cells, their electromotile mechanism. To examine the proteome changes over development, we isolated pools of 5000 Pou4f3-Gfp positive or negative cells from the cochlea or utricles; these cell pools were analyzed by data-dependent and data-independent acquisition (DDA and DIA) mass spectrometry. DDA data were used to generate spectral libraries, which enabled identification and accurate quantitation of specific proteins using the DIA datasets. We also isolated and pooled individual inner and outer hair cells from adult cochlea and compared their proteomes to those of developing hair cells. The DDA and DIA datasets will be valuable for accurately quantifying proteins in hair cells and non-hair cells over this developmental window.
Sample Processing Protocol
Cells were isolated from postnatal day 0 (P0), P4, or P7 cochleas and utricles of Pou4f3-Gfp mice using fluorescence-activated cell sorting (FACS). The cells with the brightest signal in the GFP channel were sorted into the GFP+ pool and the cells with the lowest signal in the GFP channel were sorted into the GFP- pools. Details of cell isolation and sorting are described elsewhere (Scheffer et al., J. Neurosci 35, 6366-6380; 2015). Individual sorting runs generated from 100-15,000 cells, which were frozen as individual pools. In addition, samples of whole cochlea or whole utricle were isolated. After pooling samples appropriately, they were processed for mass spectrometry using the enhanced FASP method (Erde et al., J. Proteome Res. 13, 1885-1895). Samples were then subjected to liquid-chromatography mass spectrometry using data-dependent or data-independent acquisition settings.
Data Processing Protocol
For DDA data, we used MaxQuant, with its built-in Andromeda search engine, to match peptides to mouse protein database entries, to assemble peptides into proteins, and the quantify those proteins. We also searched the DDA with the SEQUEST search engine, using the Proteome Discoverer package. DIA data were all analyzed using Skyline.
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