Dataset Information

Unified Behavioral Scoring for Preclinical Models.

ABSTRACT: Preclinical mental health research relies upon animal models, and whilst many encouraging advances are being made, reproducibility and translational relevance may be limited by sub-optimal testing or model choices. Animal behaviors are complex and test batteries should be designed to include their multifaceted nature. However, multiple behavioral testing is often avoided due to cost, availability or statistical rigor. Additionally, despite the disparity in the incidence of mental health problems between the sexes, a move toward reducing animal numbers could be a deterrent to including both male and female animals. The current study introduces a unified scoring system for specific behavioral traits with the aim of maximizing the use of all data generated whilst reducing the incidence of statistical errors. Female and male mice from two common background strains were tested on behavior batteries designed to probe multiple aspects of anxiety-related and social behavioral traits. Results for every outcome measure were normalized to generate scores for each test and combined to give each mouse a single unified score for each behavioral trait. The unified behavioral scores revealed clear differences in the anxiety and stress-related, and sociability traits of mice. Principle component analysis of data demonstrated significant clustering of animals into their experimental groups. In contrast, individual tests returned an ambiguous mixture of non-significant trends and significant effects for various outcome measures. Utilizing a range of behavioral measures and combining all outcome measure data to produce unified scores provides a useful tool for detecting subtle behavioral traits in preclinical models.

SUBMITTER: Harrison DJ

PROVIDER: S-EPMC7154181 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Unified Behavioral Scoring for Preclinical Models.

Harrison David J DJ Creeth Hugo D J HDJ Tyson Hannah R HR Boque-Sastre Raquel R Isles Anthony R AR Palme Rupert R Touma Chadi C John Rosalind M RM

Frontiers in neuroscience 20200407

Preclinical mental health research relies upon animal models, and whilst many encouraging advances are being made, reproducibility and translational relevance may be limited by sub-optimal testing or model choices. Animal behaviors are complex and test batteries should be designed to include their multifaceted nature. However, multiple behavioral testing is often avoided due to cost, availability or statistical rigor. Additionally, despite the disparity in the incidence of mental health problems ...[more]

PMID: 32317926

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Project description:The statistical validation of peptide and protein identifications in mass spectrometry proteomics is a critical step in the analytical workflow. This is particularly important in discovery experiments to ensure only confident identifications are accumulated for downstream analysis and biomarker consideration. However, the inherent nature of discovery proteomics experiments leads to scenarios where the search space will inflate substantially due to the increased number of potential proteins that are being queried in each sample. In these cases, issues will begin to arise when the machine learning algorithms that are trained on an experiment specific basis cannot accurately distinguish between correct and incorrect identifications and will struggle to accurately control the false discovery rate. Here, we propose an alternative validation algorithm trained on a curated external data set of 2.8 million extracted peakgroups that leverages advanced machine learning techniques to create a generalizable peakgroup scoring (GPS) method for data independent acquisition (DIA) mass spectrometry. By breaking the reliance on the experimental data at hand and instead training on a curated external dataset, GPS can confidently control the false discovery rate while increasing the number of identifications and providing more accurate quantification in different search space scenarios. To first test the performance of GPS in a standard experimental environment and to provide a benchmark against other methods, a novel spike-in data set with known varying concentrations was analyzed. When compared to existing methods GPS increased the nunmber of identifications by 5-18\% and was able to provide more accurate quantification by increasing the number of ratio validated identifications by 24-74\%. To evaluate GPS in a larger search space, a novel data set of 141 blood plasma samples from patients developing acute kidney injury after sepsis was searched with a human tissue spectral library (10000+ proteins). Using GPS, we were able to provide a 207-377\% increase in the number of candidate differentially abundant proteins compared to the existing methods while maintaining competitive numbers of global identifications. Finally, using an optimized human tissue library and workflow we were able to identify 1205 proteins from the 141 plasma samples and increase the number of candidate differentially abundant proteins by 70.87\%. With the addition of machine learning aided differential expression, we were able to identify potential new biomarkers for stratifying subphenotypes of acute kidney injury in sepsis. These findings suggest that by using a generalized model such as GPS in tandem with a massive scale spectral library it is possible to expand the boundaries of discovery experiments in DIA proteomics. GPS is open source and freely available on github at (\url{https://github.com/InfectionMedicineProteomics/gps})

Dataset Information

Unified Behavioral Scoring for Preclinical Models.

Publications

Unified Behavioral Scoring for Preclinical Models.

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