Cilia-iAPEX proximity labeling proteomics
Ontology highlight
ABSTRACT:
INSTRUMENT(S):
ORGANISM(S): Mus Musculus (mouse)
TISSUE(S): Epithelial Cell, Fibroblast
SUBMITTER: 
Frank Stein
LAB HEAD: David Mick
PROVIDER: PXD060138 | Pride | 2025-09-19
REPOSITORIES: Pride
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Project description:Background: Whereas cilia damage and reduced cilia beat frequency have been implicated as causative of reduced mucociliary clearance in smokers, theoretically mucociliary clearance could also be affected by cilia length. Based on models of mucociliary clearance predicting cilia length must exceed the 6 -7 μm airway surface fluid depth to generate force in the mucus layer, we hypothesized cilia height may be decreased in airway epithelium of normal smokers compared to nonsmokers. Methodology/Principal Findings: Cilia length in normal nonsmokers and smokers was evaluated in aldehyde-fixed, paraffin-embedded endobronchial biopsies, and air-dried and hydrated samples brushed from human airway epithelium via fiberoptic bronchoscopy. In 28 endobronchial biopsies, healthy smoker cilia length was reduced 15% compared to nonsmokers (p<0.05). In 47 air-dried samples of airway epithelial cells, smoker cilia length was reduced 13% compared to nonsmokers (p<0.0001). Analysis of the length of individual, detached cilia in 17 samples, smoker cilia length was reduced 9% compared to nonsmokers (p<0.05). Finally, in 16 fully hydrated, unfixed samples, smoker cilia length was reduced 7% compared to nonsmokers (p<0.05). Conclusions/significance: Models predict that a reduction in cilia length would reduce mucociliary clearance, suggesting that smoking-associated shorter airway epithelial cilia plays a significant role in the pathogenesis of smoking-induced lung disease.
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Project description:APEX was targeted to the primary cilium of mouse IMCD cells using a 5HT6-APEX-GFP fusion construct. Proximity labling was performed using incubation with biotinphenol and H2O2. For details, please see the methods section of the accompanying paper.
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Project description:Proximity labeling coupled with mass spectrometry (proximity proteomics) have emerged as a powerful technique to map proximal protein interactions in living cells. However, the large-scale and unbiased sample analysis in proximity proteomics necessitates a universal and high-throughput workflow to reduce hands-on time and increase quantitative reproducibility. To address this issue, we developed a systematic and automated approach, including generation and characterization of monoclonal cell lines, automated enrichment of biotinylated proteins in a 96-well format, optimization of quantitative mass spectrometry acquisition methods, and advanced computational framework to deconvolute spatial and temporal protein interaction networks. The quantitative reproducibility and accuracy of this streamlined workflow clearly outperforms the conventional manual enrichment. We first applied this approach to investigate subcellular proteomics, including cytosol, endosome, Golgi, lysosome, and plasma membrane. Moreover, using serotonin receptor (5HT2A) as a model, we mapped dynamic protein-protein interactions of GPCR induced by agonist. Collectively, this systematic approach could be widely used in proximity proteomics with high throughput, high reproducibility, and minimal manual intervention.
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