Abstract
Purpose :
Lens cell fiber proteomes change throughout a subject’s life, but it is unknown if cells of equal age present with heterogenous compositions. Additionally, precise systems-level changes in lens fibers of different age is sparsely understood. Single-cell proteomics with Mass Spectrometry (MS) can be used to quantitatively identify proteome variations at greater depth than alternative single-cell methods. Therefore, we have developed a method for high-throughput quantitative characterization of single lens fiber cell proteomes.
Methods :
Freshly enucleated bovine eyes were dissected to isolate the lens. Fiber cells were then gently dissociated from the lens and classified based on the ratio of reduced circumference to circumference of un-dissociated lens bulk. Intact fibers were individually collected and digested using a microscale single-pot approach. High-throughput analysis was enabled by pooling up to 12 single-cells tagged with isobaric labels. To control signal-to-noise ratios and isotopic crossover, a multi-cell carrier channel was used alongside a blank channel respectively. Data were collected on a high-resolution orbitrap LC-MS/MS platform and processed with MaxQuant. Custom R scripts were used for post-processing and analysis.
Results :
Bovine lens fiber cells have been analyzed to identify changes in cells of proximity to primary fiber cells. We have developed a method for rapid and reproducible capture of single lens fibers. We have also developed digestion and acquisition methods for characterization of proteomes in the limit of cell quantity. This method has been successfully applied to identify over 200 protein groups in 6 pooled cortical fibers – these protein groups were largely cytoskeletal or from the crystallin family. Initial findings suggest that further improvements in acquisition are possible for optimized proteome characterization. With optimized collection, ontological comparisons will be made.
Conclusions :
New methodology has allowed rapid and reproducible capture of single lens fiber cells. Additionally, data directed towards high-throughput single-cell proteomics can detect heterogenous populations and/or ontological features of fiber cell aging.
This is a 2020 ARVO Annual Meeting abstract.