To design a robust quantitative proteomics study, an understanding of both the inherent heterogeneity of the biological samples being studied as well as the technical variability of the proteomics methods and platform is needed. Additionally, accurately identifying the technical steps associated with the largest variability would provide valuable information for the improvement and design of future processing pipelines. We present an experimental strategy that allows for a detailed examination of the variability of the quantitative LC-MS proteomics measurements. By replicating analyses at different stages of processing, various technical components can be estimated and their individual contribution to technical variability can be dissected. This design can be easily adapted to other quantitative proteomics pipelines. Herein, we applied this methodology to our label-free workflow for the processing of human brain tissue. For this application, the pipeline was divided into four critical components: Tissue dissection and homogenization (extraction), protein denaturation followed by trypsin digestion and SPE clean-up (digestion), short-term run-to-run instrumental response fluctuation (instrumental variance), and long-term drift of the quantitative response of the LC-MS/MS platform over the 2 week period of continuous analysis (instrumental stability). From this analysis, we found the following contributions to variability: extraction (72%) >> instrumental variance (16%) > instrumental stability (8.4%) > digestion (3.1%). Furthermore, the stability of the platform and its' suitability for discovery proteomics studies is demonstrated.

Recent evidence suggests that rare genetic variants within the TREM2 gene are associated with increased risk for Alzheimer’s disease. TREM2 mutations are the genetic basis for a condition characterized by polycystic lipomembranous osteodysplasia with sclerosing leukoencephalopathy (PLOSL) and an early-onset dementia syndrome. TREM2 is important in the phagocytosis of apoptotic neuronal cells by microglia in the brain. Loss of function might lead to an impaired clearance and accumulation of necrotic debris and subsequent neurodegeneration. In this study, we investigated a consanguineous family segregating autosomal recessive behavioral variant FTLD from Antioquia, Colombia. Exome sequencing identified a nonsense mutation in TREM2 (p.Trp198X) segregating with disease. Next, using a cohort of clinically characterized and neuropathologically verified sporadic AD cases and controls we report replication of the AD risk association at rs75932628 within TREM2. These data suggest that mutational burden in TREM2 may serve as a risk factor for neurodegenerative disease in general and that potentially this class of TREM2 variant carriers with dementia should be considered a molecularly distinct form of neurodegenerative disease.

By integrating genome, transcriptome and proteome data, Petyuk et al. identify HSPA2 as a driver of pathology in Alzheimer’s disease, replicating the finding in an independent cohort and validating it in two in vitro systems. The results highlight the power of systems approaches for identifying genes involved in disease pathways.

SUMMARY Recent genome wide association studies have identified CLU, CR1, ABCA7 BIN1, PICALM and MS4A6A/MS4A6E in addition to the long established APOE, as loci for Alzheimer’s disease. We have systematically examined each of these loci to assess whether common coding variability contributes to the risk of disease. We have also assessed the regional expression of all the genes in the brain and whether there is evidence of an eQTL explaining the risk. In agreement with other studies we find that coding variability may explain the ABCA7 association, but common coding variability does not explain any of the other loci. We were not able to show that any of the loci had eQTLs within the power of this study. Furthermore the regional expression of each of the loci did not match the pattern of brain regional distribution in Alzheimer pathology. Although these results are mainly negative, they allow us to start defining more realistic alternative approaches to determine the role of all the genetic loci involved in Alzheimer’s disease.

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