Novel ultrasensitive immunoassay for the selective quantification of tau oligomers and related soluble aggregates.

INTRODUCTION: Tau aggregation into paired helical filaments and neurofibrillary tangles is characteristic of Alzheimer's disease (AD) and related disorders. However, biochemical assays for the quantification of soluble, earlier-stage tau aggregates are lacking. We describe an immunoassay that is selective for tau oligomers and related soluble aggregates over monomers. METHODS: A homogeneous (single-antibody) immunoassay was developed using a novel anti-tau monoclonal antibody and validated with recombinant and brain tissue-derived tau. RESULTS: The assay signals were concentration dependent for recombinant tau aggregates in solution but not monomers, and recognized peptides within, but not outside, the aggregation-prone microtubule binding region. The signals in inferior and middle frontal cortical tissue homogenates increased with neuropathologically determined Braak staging, and were higher in insoluble than soluble homogenized brain fractions. Autopsy-verified AD gave stronger signals than other neurodegenerative diseases. DISCUSSION: The quantitative oligomer/soluble aggregate-specific assay can identify soluble tau aggregates, including oligomers, from monomers in human and in vitro biospecimens. HIGHLIGHTS: The aggregation of tau to form fibrils and neurofibrillary tangles is a key feature of Alzheimer's disease. However, biochemical assays for the quantification of oligomers/soluble aggregated forms of tau are lacking. We developed a new assay that preferentially binds to soluble tau aggregates, including oligomers and fibrils, versus monomers. The assay signal increased corresponding to the total protein content, Braak staging, and insolubility of the sequentially homogenized brain tissue fractions in an autopsy-verified cohort. The assay recognized tau peptides containing the microtubule binding region but not those covering the N- or C-terminal regions only.

Application of robust regression in translational neuroscience studies with non-Gaussian outcome data.

Linear regression is one of the most used statistical techniques in neuroscience, including the study of the neuropathology of Alzheimer's disease (AD) dementia. However, the practical utility of this approach is often limited because dependent variables are often highly skewed and fail to meet the assumption of normality. Applying linear regression analyses to highly skewed datasets can generate imprecise results, which lead to erroneous estimates derived from statistical models. Furthermore, the presence of outliers can introduce unwanted bias, which affect estimates derived from linear regression models. Although a variety of data transformations can be utilized to mitigate these problems, these approaches are also associated with various caveats. By contrast, a robust regression approach does not impose distributional assumptions on data allowing for results to be interpreted in a similar manner to that derived using a linear regression analysis. Here, we demonstrate the utility of applying robust regression to the analysis of data derived from studies of human brain neurodegeneration where the error distribution of a dependent variable does not meet the assumption of normality. We show that the application of a robust regression approach to two independent published human clinical neuropathologic data sets provides reliable estimates of associations. We also demonstrate that results from a linear regression analysis can be biased if the dependent variable is significantly skewed, further indicating robust regression as a suitable alternate approach.