Buckley, A. R. | Search bioRxiv

bioRxiv2018

Whole brain imaging reveals distinct spatial patterns of amyloid beta deposition in three mouse models of Alzheimer’s disease

A variety of Alzheimers disease (AD) mouse models overexpress mutant forms of human amyloid precursor protein (APP), producing high levels of amyloid {beta} (A{beta}) and forming plaques However, the degree to which these models mimic spatiotemporal patterns of A{beta} deposition in brains of AD patients is unknown. Here, we mapped the spatial distribution of A{beta} plaques across ages in three APP-overexpression mouse lines (APP/PS1, Tg2576, hAPP-J20) using in vivo labeling with methoxy-X04, high throughput whole brain imaging, and an automated informatics pipeline. Images were acquired with high resolution serial 2-photon tomography and labeled plaques were detected using custom-built segmentation algorithms. Image series were registered to the Allen Mouse Brain Common Coordinate Framework, a 3D reference atlas, enabling automated brain-wide quantification of plaque density, number, and location. In both APP/PS1 and Tg2576 mice, plaques were identified first in isocortex, followed by olfactory, hippocampal, and cortical subplate areas. In hAPP-J20 mice, plaque density was highest in hippocampal areas, followed by isocortex, with little to no involvement of olfactory or cortical subplate areas. Within the major brain divisions, distinct regions were identified with high (or low) plaque accumulation; e.g., the lateral visual area within the isocortex of APP/PS1 mice had relatively higher plaque density compared with other cortical areas, while in hAPP-J20 mice, plaques were densest in the ventral retrosplenial cortex. In summary, we show how whole brain imaging of amyloid pathology in mice reveals the extent to which a given model recapitulates the regional A{beta} deposition patterns described in AD.

neuroscience↗

bioRxiv2016

Pan-Cancer Analysis Reveals Technical Artifacts in The Cancer Genome Atlas (TCGA) Germline Variant Calls

The degree to which germline variation drives cancer development and shapes tumor phenotypes remains largely unexplored, possibly due to a lack of large scale publicly available germline data for a cancer cohort. Here we called germline variants on 9,618 cases from The Cancer Genome Atlas (TCGA) database representing 31 cancer types. We identified batch effects affecting loss of function (LOF) variant calls that can be traced back to differences in the way the sequence data were generated both within and across cancer types. Overall, LOF indel calls were more sensitive to technical artifacts than LOF Single Nucleotide Variant (SNV) calls. In particular, whole genome amplification of DNA prior to sequencing led to an artificially increased burden of LOF indel calls, which confounded association analyses relating germline variants to tumor type despite stringent indel filtering strategies. Due to the inherent noise we chose to remove all 614 amplified DNA samples, including all acute myeloid leukemia and virtually all ovarian cancer samples, from the final dataset. This study demonstrates how insufficient quality control can lead to false positive germlinetumor type associations and draws attention to the need to be sensitive to problems associated with a lack of uniformity in data generation in TCGA data.\n\nAuthor SummaryCancer research to date has largely focused on genetic aberrations specific to tumor tissue. In contrast, the degree to which germline, or inherited, variation contributes to tumorigenesis remains unclear, possibly due to a lack of accessible germline variant data. In this study we identify germline variants in 9,618 samples using raw germline exome data from The Cancer Genome Atlas (TCGA). There are substantial differences in the way exome sequence data was generated both across and within cancer types in TCGA. We observe that differences in sequence data generation introduced batch effects, or variation that is due to technical factors not true biological variation, in our variant data. Most notably, we observe that amplification of DNA prior to sequencing resulted in an excess of predicted damaging indel variants. We show how these batch effects can confound germline association analyses if not properly addressed. Our study highlights the difficulties of working with large public genomic datasets like TCGA where samples are collected over time and across data centers, and particularly cautions the use of amplified DNA samples for genetic association analyses.

genomics↗