Development of a Sensitive, Scalable Method for Spatial, Cell-Type-Resolved Proteomics of the Human Brain

While nearly comprehensive proteome
coverage can be achieved from
bulk tissue or cultured cells, the data usually lacks spatial resolution.
As a result, tissue based proteomics averages protein abundance across
multiple cell types and/or localizations. With proteomics platforms
lacking sensitivity and throughput to undertake deep single-cell proteome
studies in order to resolve spatial or cell type dependent protein
expression gradients within tissue, proteome analysis has been combined
with sorting techniques to enrich for certain cell populations. However,
the spatial resolution and context is lost after cell sorting. Here,
we report an optimized method for the proteomic analysis of neurons
isolated from post-mortem human brain by laser capture microdissection
(LCM). We tested combinations of sample collection methods, lysis
buffers and digestion methods to maximize the number of identifications
and quantitative performance, identifying 1500 proteins from 60 000
μm2 of 10 μm thick cerebellar molecular layer
with excellent reproducibility. To demonstrate the ability of our
workflow to resolve cell type specific proteomes within human brain
tissue, we isolated sets of individual Betz and Purkinje cells. Both
neuronal cell types are involved in motor coordination and were found
to express highly specific proteomes to a depth of 2800 to 3600 proteins.