A
Exploring the synaptic proteome
Synaptic transmission is a fundamental component of nervous system function, and its dysfunction is implicated in virtually every neurological or psychiatric disease.
Thus, identification and functional annotation of its molecular components provide a foundational resource for neuroscience that is as important as more ubiquitous cellular organelle-related proteomes or transcriptomes are for biology in general.
Proteomics is probably the method of choice for identification of specific synaptic components because unless more complex experimental designs that include network analysis25,37 are used, transcriptional profiling cannot usually provide organelle-specific data.
-> https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3624047/pdf/emss-27912.pdf -> the largest expansion in the number of synaptic proteins was observed during the transition between the invertebrate and vertebrate lineages.
-> https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1810326/pdf/zpq4658.pdf -> the multi-electrode recording has been combined with microarrays to correlate genome-wide mRNA expression with synaptogenesis
and synaptic activity in dissociated hippocampal neurons cultured on multi-electrode grids
http://casestudies.brain-map.org/celltax
Integrating genetic and phenotypic data
Another approach to adding systems-level structure to transcriptome data is the analysis of these data in concert with genetic and phenotypic data to integrate across all three levels of observation.
Thus, the advent of expression quantitative trait locus (eQTL) analysis is a major advance in integrating large-scale genomic or genetic data sets to understand a model system or cohort of patients at a systems level.
In eQTL mapping, gene expression data are used as a phenotype on which to base quantitative genetic association mapping (Fig. 1),
the rationale being that gene expression is a more proximal, intermediate quantitative phenotype to the underlying genetic risk than heterogeneous behavioural or anatomical phenotypes.
49. Ghazalpour A, et al. Integrating genetic and network analysis to characterize genes related to mouse weight. PLoS Genet. 2006; 2:e130. [PubMed: 16934000]
50. Chen Y, et al. Variations in DNA elucidate molecular networks that cause disease. Nature. 2008; 452:429–435. [PubMed: 18344982]
