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MouseBytes, an open-access high-throughput pipeline and database for rodent touchscreen-based cognitive assessment

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Cite this article as: eLife 2019;8:e49630 doi: 10.7554/eLife.49630

Abstract

Open Science has changed research by making data accessible and shareable, contributing to replicability to accelerate and disseminate knowledge. However, for rodent cognitive studies the availability of tools to share and disseminate data is scarce. Automated touchscreen-based tests enable systematic cognitive assessment with easily standardised outputs that can facilitate data dissemination. Here we present an integration of touchscreen cognitive testing with an open-access database public repository (mousebytes.ca), as well as a Web platform for knowledge dissemination (https://touchscreencognition.org). We complement these resources with the largest dataset of age-dependent high-level cognitive assessment of mouse models of Alzheimer’s disease, expanding knowledge of affected cognitive domains from male and female mice of three strains. We envision that these new platforms will enhance sharing of protocols, data availability and transparency, allowing meta-analysis and reuse of mouse cognitive data to increase the replicability/reproducibility of datasets.

Introduction

The public nature of research and increased rigor applied to research outputs have encouraged new approaches to enhance transparency, data sharing, and reproducibility (Button et al., 2013). Over the past 10 years, Open Science initiatives featuring increased data sharing and high-throughput automated data collection have increased the efficiency, quality, integrity and reproducibility of data gathering (Johnson and O'Donnell, 2009; Rahman and Watabe, 2018). In genomics, for example, researchers have made major progress in understanding the genetic basis of diseases by establishing multi-research site consortia and by providing access to these data through different open repositories (Boucas, 2018; Diehl and Boyle, 2016; Gerstein, 2012). In neuroimaging, data sharing and large open-access databases have enabled the development of new analytic tools allowing researchers to address questions that could not be answered using single data sets (Biswal et al., 2010; Poldrack and Gorgolewski, 2014).

Recently, there have been several attempts to build databases of rodent behaviour data. The Jackson Laboratory has developed the Mouse Phenome Database, a repository of mouse data taken from several studies (Grubb et al., 2009). Additionally, resources such as the International Mouse Phenotyping Resource of Standardised Screens provide different pipelines for the characterisation of mouse lines (Koscielny et al., 2014). Although these databases represent a necessary and fundamental shift in the availability of data, these repositories provide only limited information on high-level cognitive testing in mouse models.

Conventional cognitive assessments in mouse models are subject to large variation (Kafkafi et al., 2018; Wahlsten et al., 2003), which may be in part the result of lack of automation. Additionally, the methodology used for cognitive assessments can significantly vary among research groups. For example, a recent analysis focusing on transgenic mouse models of Alzheimer’s disease found a significant amount of variation in the parameters used in the Morris Water Maze, including pool size, pool temperature, number of trials per day, and number of acquisition days (Egan et al., 2016). Further evidence shows that even when protocol parameters are controlled for, different experimenters can still obtain different behavioural results in conventional behavioural tasks (Chesler et al., 2002; Crabbe et al., 1999; Kafkafi et al., 2018). Overall, there are converging domains of evidence to suggest that non-automated and non-standardised conventional behavioural assessments may be prone to several sources of bias.

Efforts to address these important gaps in data collection, automation, and translational research led to the development of the rodent touchscreen testing method (Horner et al., 2013; Mar et al., 2013; Oomen et al., 2013). This technology allows the use of tests in rodents that are highly similar, and in some cases identical, to human cognitive tests (Heath et al., 2019; Nilsson et al., 2016; Nithianantharajah et al., 2015; Romberg et al., 2013a). Touchscreen testing systems have standardised behavioural protocols that are under the control of a computer system, allowing for increased standardisation of outcomes. Furthermore, the automation of high-level cognitive testing can provide significant reductions in experimenter and environmental influence, by providing a standard operant environment and standardised output file formats (Horner et al., 2013; Mar et al., 2013). This feature makes results generated amenable to storage in a central repository, allowing for data categorisation, searching and comparison between multiple laboratories.

Here we used data obtained with male and female mice from three distinct mouse lines commonly used in Alzheimer's disease (AD) research to highlight the use of a new repository and Web-based software, MouseBytes (mousebytes.ca). We reveal longitudinal heterogeneity as well as commonalities in cognitive function between the various strains modelling AD. For example, 3xTG-AD mice, males and females, present early attention deficits (at 3–6 months of age) when compared to their age matched controls, demonstrating reproducibility of earlier results. Overall, our cognitive assessment suggests which mouse models can be used to model cognitive phenotypes consistent with Alzheimer’s disease.

MouseBytes is available to researchers worldwide (mousebytes.ca), so they can pre-process, run automated quality control scripts, store, visualise, and analyse their data alone or alongside other researchers’ stored data. Moreover, researchers can use a knowledge sharing tool https://touchscreencognition.org to disseminate community-driven information, including standard operating procedures (SOPs) and protocols. We foresee this repository for touchscreen data as a major step towards increasing the availability of datasets, including negative results, that can serve to evaluate reproducibility, decrease publication bias and to bring high-level cognitive assessment into the Open Science era.

Results and discussion

Open-access database and repository

To highlight the potential strengths of MouseBytes, we acquired data from male and female mice from three commonly used transgenic mouse models that have pathological similarities to AD at three ages in two different laboratories on three clinically-relevant touchscreen-based cognitive tests: attention [5-choice serial reaction time task (5-CSRTT)] (Beraldo et al., 2015; Mar et al., 2013; Romberg et al., 2011), behavioural flexibility [pairwise visual discrimination reversal (PD)] (Bussey et al., 2008; Kolisnyk et al., 2013; Mar et al., 2013) and long-term learning and memory [paired-associates learning (PAL)] (Al-Onaizi et al., 2016; Bartko et al., 2011; Horner et al., 2013). The mouse lines (3xTG-AD, 5xFAD and APP/PS1) were chosen due to their extensive use in AD research, as well as their differences in pathology development and AD familial genetic mutations (Egan et al., 2016; Jankowsky et al., 2004; Lee and Han, 2013; Oakley et al., 2006; Oddo et al., 2003). Moreover, the 3xTG-AD mouse line had been tested before using touchscreen attention tests providing a framework for reproducibility testing (Romberg et al., 2011).

Following completion of individual experiments, Extensive Markup Language (XML) files were generated using the Animal Behaviour Environment Test II (ABET II by Campden Instruments Ltd, Loughborough, England) software. XML files were uploaded into MouseBytes and screened using an automated quality control (QC) procedure which is a tool available at MouseBytes.ca (mousebytesQC). The rules and codes for the QC are available for download and modification in GitHub (GitHub_Touchscreen_Pipeline; copy archived at https://github.com/elifesciences-publications/Mousebytes-An-open-access-high-throughput-pipeline-and-database-for-rodent-touchscreen-based-data) (Memar et al., 2019) . Files that did not meet the QC criteria were automatically identified. Following QC, only XML files (one mouse unique ID/session) that passed QC were automatically uploaded to the database (mousebytes.ca) and integrated to the analytics TIBCO Spotfire to generate an interactive visualisation platform for 5-CSRTT, PAL, and PD (Figure 1, see also the online data visualisation - https://mousebytes.ca/data-visualization). Briefly, to navigate through the data in mousebytes visualisation (Spotfire) the users should select the cognitive task in the dropdown menu. After the selection of the cognitive task, corresponding features are selected (e.g. 5-SCRTT Probe trial for the 5-CSRTT data, etc.). A glossary with the description of the training and probe phases is found in MouseBytes (mousebytes.ca_description). Moreover, the user can check or uncheck the filter boxes on the right side of the page to define the data to be visualised and export or analyse specific graphs using the side tabs. This allows features selected, such as site, mouse strain, genotype, sex and age for example to be quickly compared (for more information on how to use the data visualisation please check the methods - Data Quality Control and availability).

Figure 1 with 1 supplement see all
Schematic overview of the automated touchscreen cognition platform.

Males and females of three different AD mouse lines were each evaluated in three different touchscreen tasks. The mice were food restricted and tested longitudinally and at two different sites (The University of Western Ontario (UWO) and University of Guelph (UoG) - Canada). Data were submitted to an automated QC process. Following automated QC, data were uploaded to an open-access database (mousebytes.ca) for post-processing analysis and visualisation using the analytics tool TIBCO Spotfire.

Our pipeline enabled collection of extensive amounts of data from different ages, mouse strains, and sex. In total, we tested 652 different mice and generated 62,411 xml files (27,440 for 5-CSRTT, 17,230 for PAL, and 17,892 for PD). Importantly, by scanning the files through our automated QC procedure, we identified 487 files (0.8%) that did not meet the QC system criteria. 62,393 xml files (99.2%) passed the automated QC criteria and were transferred to the database. After QC, files that did not meet the criteria, or could not be fixed, were automatically discarded and were not used for analysis (see Materials and methods).

Due to the amount of data we generated in this work, classical graphical format to visualise all information contained in these datasets would require close to 30–40 figures with 6–10 panels each, depending on the kind of comparisons being performed. This situation underscores the need for online and on-the-fly data assessment, multidimensional visualisation, and comparison using online visualisation tools (Dunn et al., 2016), such as TIBCO Spotfire (Dunn et al., 2016; Pechter et al., 2016), which we used here.

Currently, our system has been optimised for the intake of data from the Bussey-Saksida Operant Chamber systems (Campden Instruments, Lafayette Instruments). There are alternative commercial touchscreen systems available (i.e. Med Associate K-Limbic touchscreen operant chambers), as well as several open source alternatives (O'Leary et al., 2018; Pineño, 2014; Wolf et al., 2014). In order to open the MouseBytes platform to all researchers using touchscreens, we have incorporated codes for download and modification in GitHub (GitHub codes; Memar et al., 2019) to easily convert the formats of output XML files from other systems to the format used in MouseBytes.

High-level cognitive testing in AD mouse models

The sample sizes for all experiments/tasks can be found in Supplementary file 1. Key parameters that were analysed for each experiment can be seen for 5-CSRTT: 5-CSRTT MouseBytes data link, PAL: PAL MouseBytes data link, and PD: PD mousebytes data link. One of the features of this open-access database is the possibility of downloading a standardised dataset (using a hyperlink generated by MouseBytes) related to particular experiments (i.e. linked to a particular figure of a paper) to perform customised analyses (A series of videos is available on the website that demonstrates how to use MouseBytes: MouseBytes-Guidelines).

Statistical analysis of the performance of distinct AD mouse models was conducted using R, taking advantage of the fact that CSV files can be generated for specific datasets using MouseBytes, which facilitates the use of open-source statistical packages. A summary of the split-plot ANOVA of all behavioural measures for each genotype can be found in Supplementary file 2 (5-CSRTT), Supplementary file 3 (PAL), and Supplementary file 4 (PD). In addition, a second set of planned ANOVAs was conducted separately isolating cohorts by age and sex to identify potential genotype effects within select subpopulations of mice. Summary information with the complete secondary ANOVA statistics for all three tasks can be found in Supplementary file 5, Supplementary file 6 and Supplementary file 7. A summary of the results of these statistical analyses can be found in Table 1 (5-CSRTT) and Table 2 (PD and PAL). Specific analyses and links to each dataset for figures are presented below.

Table 1
5-CSRTT analyses.

Summary of conventional genotype analyses on the 5-CSRTT task. Summary results were based on simple 2 (genotype) x 4 (stimulus duration) split-plot ANOVA. Impairment or Facilitation was determined by looking for a significant genotype effect or interaction. (3x – 3xTG-AD, 5x – 5xFAD and APP – APP/PS1) mouse lines. Impairment (↓), Improvement (↑) No Effect (-). See also Supplementary file 2 and 5.

AccuracyOmissionsPremature ResponsesPerseverative ResponsesTouch LatencyReward Latency
SexAge (months)3x5xAPP3x5xAPP3x5xAPP3x5xAPP3x5xAPP3x5xAPP
Female3-6--------
7-10-----------
11-13------------
Male3-6-------------
7-10-----------
11-13-------
Table 2
PD and PAL analyses.

Summary of conventional genotype analyses on the PD and PAL tasks. Summary results were based on simple 2 (genotype) x 4 (stimulus duration) split-plot ANOVA. Impairment or Facilitation was determined by looking for a significant genotype effect or interaction. (3x – 3xTG-AD, 5x – 5xFAD and APP – APP/PS1) mouse lines. Impairment (↓), Improvement (↑) No Effect (-). See also Supplementary file 3, 4, 6, 7.

AccuracyCorrection TrialsTouch LatencyReward Latency
TaskSexAge (months)3x5xAPP3x5xAPP3x5xAPP3x5xAPP
PDFemale3-6----
7-10----
11-13---------
Male3-6---------
7-10-----------
11-13---------
PALFemale3-6-----
11-13-------
Male3-6--
11-13--------

Reliability in touchscreen testing

Variability of mouse performance in behavioural tests across different laboratories is an important issue for replicability (Crabbe et al., 1999; Kafkafi et al., 2018; Wahlsten et al., 2003). The use of automated and standardized testing can help decrease variability, although a wide range of factors including colony genetic drift (Zeldovich, 2017), light-dark cycle, types of cages and housing (single housed or group-housed), source of food, type and amount of reward, different types of environmental enrichment and colony room temperature/humidity conditions can still potentially contribute to variability (Kim et al., 2017). Furthermore, even though touchscreen tasks are automated and standardised, there is some level of flexibility in these tasks. We are aware that researchers, depending on the scientific question, may modify the experimental design (set of images, length of inter-trial intervals, number of trials and sessions per day, type and or amount of reward, etc.), which can increase the number of variables for analysis. To control these variables, we included in MouseBytes features that allow the users to describe these variables as Metadata. For example, when uploading XML files, the user must check boxes indicating the light-cycle and whether mice were single or group housed. In addition, in experimental description users can describe how mice were tested (e.g. number of trials and sessions per day). Furthermore, users can also link the digital object identifier (DOI) of their published article to datasets. With these additional sources of metadata information, one can begin to determine which variables can influence behavioural performance within the touchscreen environment.

To directly assess potential site variability in the current dataset stored in MouseBytes, we included site as a factor in our 5-CSRTT analyses. The 5-CSRTT task was chosen due to the larger cohorts of mice used in these experiments at the two sites. Throughout the analyses of 5-CSRTT measurements (see Materials and methods), no consistent pattern of main effects or interactions emerged for site between age, sex, genotype, strain, or measure (stimulus length) (Supplementary file 2, tabs 1, 2 and 3). For example, for interactions between test site, genotype, sex, age and stimulus length, only APP/PS1 mice presented a statistical difference in accuracy, whereas all other parameters (% correct, number of premature responses, number of perseverative responses, reward collection latency and correct touch latency) for the three strains were not significantly different (Supplementary file 2, tabs 1, 2 and 3, lines 28 #a). Interactions with test site that were significant typically had a small effect size and lacked consistency across behavioural domains and mouse strains (Supplementary file 2, tabs 1, 2 and 3, #a). Overall, the evidence suggests low site-to-site variability and high replicability for touchscreen test performance. For example, low variability was observed between sites when we compared longitudinally the performance of wild-type female mice (B6129SF2/J) and their AD-mouse model counterpart, 3xTG-AD, in the 5-CSRTT task (attention). We observed a difference in accuracy (0.6 s stimulus duration) in wild-type females at 3–6 months of age between the two sites (Figure 2A dataset 1). Other than that, no statistically significant differences were found for either accuracy or omission for both B6129SF2/J females (Figure 2B dataset 2, Figure 2C dataset 3, Figure 2D dataset 4) or 3xTG females (Figure 2E and F dataset 5 and dataset 6, Figure 2G and H dataset 7 and dataset 8) between the two sites. We observed similar reproducible results for mouse touchscreen performance across the other AD models and their wild-type counterparts (see mousebytes.ca for more comparisons). As an important feature, MouseBytes allows the generation of dataset hyperlinks to easily identify and download the raw data used to generate each figure panel (dataset 1, 2, etc.).

Figure 2 with 3 supplements see all
Performance and response measures of Male and Female mice during 5-CSRTT probe trials.

Mice were subjected to a series of probe trials and the averages of accuracy (% correct), omissions (%) and premature responses (number) were plotted at different ages. The plots were generated with data downloaded from MouseBytes and the links (datasets) for the individual analysis can be found in the results section. (A-D), longitudinal site comparison of the performance (accuracy and omissions) of female Wild-type controls (B6129SF2/J) at 3–6 and 11 to 13 months of age; (E-H) longitudinal site comparison of the performance (accuracy and omissions) of female 3xTG-AD at 3–6 and 11 to 13 months of age respectively; (I-L) comparison of the performance (accuracy and omissions) of 3xTG-AD male and their Wild-type controls (B6129SF2/J) at 3–6 and 11 to 13 months of age; (M-P) comparison of the performance (accuracy and omissions) of 3xTG-AD female mice and Wild-type controls (B6129SF2/J) at 3–6 and 11 to 13 months of age. Results are presented as means ± s.e.m.; data were analysed and compared using Repeated measure Two-Way ANOVA and Bonferroni multiple comparisons post-hoc test; *p<0.05, compared with control.

Previous experiments have detected robust attentional deficits in 11- month-old male 3xTG-AD mice (Romberg et al., 2011), with lower accuracy in the 5-CSRTT and no differences in omissions compared to wild-type controls (Romberg et al., 2011). We tested male 3xTG-AD mice at the same age and reproduced the cognitive signature pattern of attentional deficit as previously published for male mice (Figure 2J, dataset nine for accuracy, Figure 2L, dataset 10 for omissions). In addition, we also tested female mice and similar to the males, 3xTG-AD female mice also presented lower accuracy (Figure 2N dataset 11) and no difference in omissions (Figure 2P, dataset 12) when compared to the wild-type controls. Moreover, both male and female 3xTG-AD mice that were tested starting at 4 months of age also presented lower accuracy (Figure 2I, dataset 13 and M, dataset 14) and no difference in omissions (Figure 2K, dataset 15 and 2 O dataset 16) when compared to controls (Table 1 and Supplementary file 2 and Supplementary file 5 - Tab 1, #b). We also examined vigilance (the ability to maintain concentrated attention over a prolonged period of time), which was also previously reported to be affected in this mouse line (Romberg et al., 2011), by characterising performance across blocks of 10 trials. A complete breakdown of all the vigilance analyses can be found in Supplementary file 8 (Tab 1). Reduced vigilance across trials was reflected in a deficit in accuracy in 3xTG-AD males (10–11 month-old mice used as example, Figure 3A–D and Supplementary file 8- Tab 1, #c) or 3xTG- AD female mice (3–6 -month-old mice used as example, Figure 3I–L and Supplementary file 8 - Tab 1, #c). No differences were observed for omissions (Figure 3E–H,M–P and Supplementary file 8 – Tab 1). These experiments support the replicability we observed between sites and suggest that 3xTG-AD mice present robust attentional deficits that can be observed across several laboratories even when a different genetic background is used. Because genetic drifting can potentially affect reproducibility in mouse behaviour testing (Zeldovich, 2017), identification of robust deficits of high-level cognition resulting from AD-related pathology is important to develop drug treatments. It seems that attention deficit in the 3xTG-AD is one such outcome.

Sustained attention (vigilance) of 3xTG-AD male and female mice during the 5-CSRTT probe trials.

Response accuracy and omissions in Wild-type and 3xTG-AD male (A-D for accuracy and E-H for omission) and female (I-L for accuracy and M-P for omission) mice were analysed across 10-trials blocks within the daily sessions of 50 trials with different stimulus durations. Results are presented as means ± s.e.m.; data were analysed and compared using Repeated measure Two-Way ANOVA and Bonferroni multiple comparisons post-hot test; **p<0.01 compared with control.

The ability to compare mouse performance between sites can provide important insights on sources of variability for experiments. Replication experiments are the gold standard to validate scientific discoveries, but particularly in conventional rodent cognitive testing, variability of results is an issue. For example, different mouse models of AD present behavioural changes that are quite variable between laboratories when using conventional behaviour testing (Arendash et al., 2001; Clinton et al., 2007; Ding et al., 2008; Holcomb et al., 1999; Ostapchenko et al., 2015; Stevens and Brown, 2015). The combination of touchscreen cognitive testing and MouseBytes may help to identify sources of variability to overcome issues of replication in rodent high-level cognitive analysis.

In order to expand and further refine our understanding of the cognitive deficits in 3xTG-AD mice, we conducted two additional touchscreen-based cognitive assessments. Of particular interest was the PAL task, a relevant test for AD progression as the CANTAB version of PAL has been found to be predictive of conversion from mild cognitive impairment to AD (Junkkila et al., 2012). Moreover, forebrain cholinergic dysfunction, which is found in AD, impairs performance of mice in the PAL test (Al-Onaizi et al., 2016). We observed a small but significant deficit in the PAL task for 3xTG-AD male mice at four months of age (Table 2, Supplementary file 3 and Supplementary file 6 - Tab 1, #d). 3xTG-AD mice did not show any sign of deficits in visual discrimination learning or behavioural flexibility in PD (Supplementary file 4 and Supplementary file 7 - Tab 1). Overall, the cognitive phenotype of these mice resembled patients with early AD, presenting early deficits in sustained attention (Perry et al., 2000) and visual-spatial learning (Blackwell et al., 2004), but not in behavioural flexibility (Sahakian et al., 1989).

In addition to testing 3xTG-AD mice, we also tested APP/PS1 and the widely used 5xFAD mouse line. We chose to use the 5xFAD mice in a mixed genetic background (C57Bl6 and Swiss Jim Lambert -SJL), as this was the original background in which this mouse line was generated (Oakley et al., 2006), and it is the most commonly used background across several laboratories (Qosa and Kaddoumi, 2016). However, the SJL genetic background presents the Pdebrd1 mutation that can lead to retinal degeneration (Clapcote et al., 2005) (see Materials and methods), which causes severe visual impairment in homozygosis. Given that some of the 5xFAD mice could be heterozygous for the Pdebrd1 mutation, we evaluated whether carrying one Pdebrd allele affected the performance of mice in touchscreens using the PD task, which directly measures visual discrimination. The performance of mice carrying one Pdebrd allele did not differ from those who did not (Figure 2—figure supplement 1A, B and C). Moreover, as touchscreen testing requires food restriction for motivation, we also assessed whether the food restriction protocols used for touchscreen cognitive testing affected amyloid production in 3xTG-AD and 5xFAD mice. Ultimately, we failed to find any differences in amyloid production and deposition between food restricted and non-food restricted animals (Figure 2—figure supplement 2 and Figure 2—figure supplement 3).

The 5xFAD transgenic mouse line displayed a complex cognitive phenotype. Female 5xFAD mice displayed deficits in sustained attention that begin at 7 months in the 5-CSRTT task, while males show deficits by 11 months (Table 1, Supplementary file 2 and Supplementary file 5 - Tab 2, #e). Initial training on the PAL task did not generate robust results as both 5xFAD and their controls (both male and female) were poor performers (Supplementary file 3 and Supplementary file 6 – Tab 2). This highlights the utility of MouseBytes in assessing cognitive testing of a given mouse line by comparing with other lines in the database. However, a simplified version of the test revealed significant visual-spatial deficits at 10 months of age for both male and female 5xFAD mice (Supplementary file 3 and Supplementary file 6 – Tab 2, #f). No deficits in behavioural flexibility or visual discrimination learning were observed for 5xFAD mice when compared to their respective controls (Supplementary file 4 and Supplementary file 7 – Tab 2). The 5xFAD mouse line displays a subtler behavioural phenotype than the 3xTG-AD, but is still consistent with the impairments observed in AD. Interestingly, amyloidosis has been reported to start earlier and to be more aggressive in the 5XFAD (Oakley et al., 2006) compared to the 3xTG-AD line (Oddo et al., 2003). However, our results showed earlier deficits development in 3xTG-AD mice compared to the 5xFAD line, suggesting that this could be related to abnormal Tau function in the 3xTG-AD mouse line (Oddo et al., 2003).

APP/PS1 mice (male or female) did not show any sign of attentional deficits in the 5-CSRTT task at any age (Supplementary file 2 and Supplementary file 5 – Tab 3) similar to what was observed independently in a different background for this strain (Shepherd et al., 2019). However, APP/PS1 male mice presented with an early deficit in visual-spatial integration learning in the PAL task, which is consistent with the 3xTG-AD mouse phenotype (Supplementary file 3 and Supplementary file 6 – Tab 3, #g). Furthermore, an early deficit in behavioural flexibility was observed for female APP/PS1 mice at four months of age in the PD reversal task, which is interesting from the point of the translational utility of this mouse model, as behavioural flexibility deficits are not typically associated with AD at early stages of the disease progression (Sahakian et al., 1989) (Supplementary file 4 and Supplementary file 7 – Tab 3, #h).

Although each touchscreen task is generally run across labs using the same set of task-specific stimuli, task stimuli are being optimised continuously (Horner et al., 2013; Mar et al., 2013 ). Furthermore, some researchers have run tasks multiple times within a cohort of mice using different stimuli (Bartko et al., 2011) and have found that the performance of animals may vary with the stimulus set used (Bussey et al., 2008). We extracted cross-site data from mice using different stimulus sets to show that indeed, depending on the image set used in PD or PAL, mice can reach higher or lower levels of discrimination accuracy (Figure 1—figure supplement 1). Our data indicate that, for PAL and PD or other touchscreen tasks using complex visual stimuli, longitudinal testing should be preceded by appropriate control experiments to avoid potential bias with image sets. We anticipate that when more data are available in MouseBytes, the touchscreen community will be able to compare a larger number of images sets and identify optimal stimulus combinations.

Genetic background and touchscreen performance

The choice of background strain for mouse models of disease can have major implications for cognitive assessment (Sittig et al., 2016). However, due to the absence of framework within which to aggregate behavioural data, comparison of the performance by different mouse strains has been limited. For example, in previous work data acquisition needed to be standardised across laboratories to gather information on how genetic background influences performance (Graybeal et al., 2014). We compared the performance of mice from three different wild-type strains in the initial dataset deposited in MouseBytes (B6129SF2/J, B6SJLF1/J and C57BL6/j background). To directly assess strain variability in touchscreen test performance, data from 5-CSRTT experiments were extracted from MouseBytes and analysed (similar analyses can be performed for other tests by extracting the datasets from MouseBytes). Interestingly, both female (Figure 4A, dataset 17) and males (Figure 4C, dataset 18) B6129SF2/J presented higher levels of accuracy on 5-CSRTT at 3–6 months of age, but not at 11–13 months of age (Figure 4B, dataset 19 and 4D, dataset 20), when compared to the other two wild-type strains tested (B6SJLF1/J and C57BL6/j). Moreover, both male and female B6SJLF1/J mice (3–6 and 11–13 months of age) were found to engage in more premature responses than the B6129SF2/J and C57BL6/j lines (4E-H, datasets 21, 22, 23 and 24). This suggests a general phenotype of impulsiveness inherent to these B6SJLF1/J mice. We envision that with multiple users depositing their data in MouseBytes, it will be relatively easy to make comparisons of performance for thousands of mice from different strains. Ultimately, these overarching analyses could help to inform the background strains to be used for new mouse lines to investigate specific high-level cognitive domains, for example, models that can now be generated using new genome-editing techniques such as CRISPR/Cas.

Performance and response measures of male and female mice during the 5-CSRTT probe trials.

(A-D) Strain/mouse background comparison (accuracy) of female and male Wild-type controls (B6129SF2/J, B6SJLF1/J, C57Bl6/J) at 3–6 and 11–13 months of age; (E-H) Strain/mouse background comparison (premature responses) of female and male Wild-type controls (B6129SF2/J, B6SJLF1/J, C57Bl6/J) at 3–6 and 11–13 months of age; (I-L) Sex comparison (accuracy) of B6129SF2/J and 3xTG-AD females and males at 3–6 and 11–13 months of age. Results are presented as means ± s.e.m.; data were analysed and compared using Repeated measure Two-Way ANOVA and Bonferroni multiple comparisons post-hot test; *p<0.05, **p<0.01 and ***p<0.001 compared with control.

Sex variability

Recognition that behavioural rodent research is biased towards using male mice has led funding institutions to establish specific guidelines in the choice of animals for research (McCullough et al., 2014). Several neurobiological differences are present between male and female brains (Grissom and Reyes, 2019; Ruigrok et al., 2014). In the scope of AD, there are sex differences in the pathological development of plaques and tangles (Corder et al., 2004) and sex steroid hormones’ levels can contribute to some of these effects (Carroll et al., 2010; Carroll et al., 2007). To highlight the potential for sex comparisons in high-level cognitive assessment, we initially compared 3xTG-AD mice, a mouse line that presents sex variability in pathology (Carroll et al., 2010; Carroll et al., 2007). We found no major differences in attentional performance (accuracy) when we compared male and female mice in 5-CSRTT, as shown for the B6129SF2/J at 3–6 months (Figure 4I, dataset 25) or 11–13 months of age (Figure 4J, dataset 26). Similarly, no differences were found when 3xTG-AD males and females were compared at 3–6 (Figure 4K, dataset 27) or 11–13 months of age (Figure 4L, dataset 28). These results suggest little or no difference for high-level cognitive performance between male and female mice. To the best of our knowledge, the dataset presented here and deposited in MouseBytes provides the most extensive evaluation of performance of female mice in touchscreen tests.

Unbiased analysis of behavioural performance

While conventional ANOVA-based statistics can be employed to measure changes in behaviour, large datasets can make this approach difficult. In order to address these challenges, alternative types of analyses may be necessary. One potential solution is to employ machine learning algorithms or artificial intelligence systems. To provide an example of these high-level analyses to describe large behavioural datasets that can be extracted easily from MouseBytes, we generated a summary of the touchscreen behavioural data utilising a k-mean classification approach. This approach represents a class of unsupervised learning algorithms that can identify group clustering without any bias towards the behavioural measures or the sample identification such as of the genotype, age, sex, or test site. Recently, researchers have used longitudinal k-mean algorithms to subcategorise different cohorts of AD patient populations that had been previously classified in one large group based on disease progression (Genolini et al., 2016). We used the R package kml3d (Genolini et al., 2013) to conduct longitudinal k-mean unsupervised grouping of all the data for the three mouse lines. For all tasks, data were grouped into three categories loaded onto a similar progression of behavioural metrics of high performance, moderate performance, and low performance (Figure 5—figure supplement 1). We decided to choose three groups across all of our tasks in order to be consistent across domains, as well as to try and capture more subtlety in the clustering of behavioural characteristics. Because we tested animals at two or three temporally separated intervals during our cognitive testing, we decided to treat each animal’s observations for each testing interval as independent. This was done to allow for mice to change membership across our K-mean groups. We expected that changes in k-mean group membership would likely indicate cognitive decline in our animal populations. Following k-mean grouping, we then used Fisher’s exact test to determine if significant differences existed in the k-mean group membership between transgenic mice and their respective control strains. In order to separate out potential sex effects from genotype variation, we conducted comparisons separately between male and female mice for these analyses. Because animals were considered independent across age, we also conducted the analyses separately for each testing period. In order to account for multiple comparisons with Fisher’s Exact Test, the Benjamini-Hochberg correction for false discovery rate was applied (Table 3). Visualisation of the k-mean group memberships by strain, task, age, and sex can be found in Figure 5.

Figure 5 with 1 supplement see all
Heatmap visualisations of k-mean group membership across experiments.

The percentage of group representation per strain is shown by the cell color. Cell color closer to red indicates a higher representation of mice in the k-mean grouping. Mice are divided by sex (male and female), genotype (W for wild type and T for transgenic), and age (3–6, 7–10, and 11–13). Analysis of the PAL (A) task data uncovered early group membership variability in the APP/PS1 mouse line, as well as later group membership differences in the male 5xFAD mice. In the PD (B) experiment, only APP/PS1 transgenic mice showed an increase in membership of the low performing group compared to wildtype. See also S5. In the 5-CSRTT (C), transgenic 3xTG-AD and 5xFAD mice were found to shift to the lowest performing group of mice.

Table 3
p-values from Fisher Exact Test for K-Mean Group.

Group Differences between wildtype and Transgenic mice across behavioural experiments. Fisher’s exact test was conducted to compare the % membership of the high, mid, and low k-mean groups between wildtype and transgenic mice for each strain, sex, and age. All p-values have been adjusted with the Benjamini and Hochberg Correction.

3xTG-AD5xFADAPP/PS1
TaskAgeFemaleMaleFemaleMaleFemaleMale
5-CSRTT3-6.03.01.03.06.17.13
7-10.19.06.02.01.77.07
11-13.01.39.02<0.0011.00.19
PD3-6.34.25<0.001.92.02.69
7-10.81.16<0.001.06.42.61
11-131.00.36<0.001.03.18.81
PAL3-6.59.19.33.19.06.03
11-13.77.95.03.011.00.19

In the PAL task, the behaviour of high performing mice was characterised by high accuracy (% correct) and low numbers of correction trials (Figure 5—figure supplement 1A and B). Mid-performing mice had reduced accuracy and slow correct and reward collection response latencies (Figure 5—figure supplement 1A,C and D). The low-performing mice showed consistently low accuracy and high numbers of correction trials (errors) (Figure 5—figure supplement 1A and B). The number of mice belonging to each cluster can be found in Figure 5—figure supplement 1E. No significant differences in k-mean group membership were found for the 3xTG-AD mice at any age (Figure 5 and Table 3). Differences in membership were found to be significant for 5xFAD mice at 10–11 months of age, as more males 5xFAD transgenic were found to be in the low performing group, while more females 5xFAD transgenic were found to be in the mid performing group (Table 3). Interestingly, at four months of age, most 5xFAD wild-type and transgenic mice were low performers, suggesting the background strain may affect performance on this task, confirming our observation with traditional analysis. Significant group membership differences were observed for APP/PS1 mice at four months of age, and more female transgenic mice were found in the high performing group (Figure 5), while male APP/PS1 transgenic mice were in the low performing group (Figure 5 and Table 3). These data suggest that while the PAL task might be a good behavioural predictor in human AD, further studies should be conducted to ensure that this effect is consistently observed across multiple AD mouse models. This is consistent with the small effect sizes in PAL, except for 10-month-old 5xFAD mice (Supplementary file 3 and Supplementary file 6, Tab 2).

In the PD tasks, behaviour of high performing mice was characterized by high response accuracy (% correct) and low number of correction trials (errors) (Figure 5—figure supplement 1F and G). The typical behaviour of the mid performing mice included slower correct and reward collection response latencies to the test stimuli (Figure 5—figure supplement 1H and I). Low performing mice showed a pattern of low accuracy (% correct) and high number of correction trials (Figure 5—figure supplement 1F and G). The number of mice belonging to each cluster can be found in Figure 5—figure supplement 1J. No significant group composition differences were observed for 3xTG-AD mice (Table 3). Fisher’s exact test revealed significant differences in k-mean group composition for 5xFAD mice as more transgenic mice were found to occupy the mid performing group, while wild-type control mice occupied the low performing group (Table 3). Only a significant difference in group composition for APP/PS1 females was observed at four months, as there was a larger group of transgenic mice in the low performing group compared to wild type (Table 3). Overall, the different pattern of results in PD suggests it was not particularly sensitive to AD-related pathological changes. Separation of performance between strains was only observed for the female APP/PS1 mice.

In the 5-CSRTT, high performing mice were characterised by high accuracy (% correct), low omissions, and higher perseverative responses, Figure 5—figure supplement 1K,L and M). Mid performing mice had average response accuracy and rates of omissions (Figure 5—figure supplement 1K, L), but showed an increase in premature responses (Figure 5—figure supplement 1N). The low performing mice showed low accuracy, high omission, slow response times and slow reward collection latency (Figure 5—figure supplement 1K, L, O and P). The number of mice belonging to each cluster can be found in Figure 5—figure supplement 1Q. Significant k-mean membership differences were observed for 3xTG-AD mice, consistently at 4 months of age, and transgenic mice were usually clustered as low performers (Figure 5 and Table 3). Fisher’s exact test analysis revealed significant k-mean membership differences for 5xFAD transgenic mice and their respective control mice across all ages (Table 3), and the 5xFAD mice presented consistently low performance. These results suggest that both 5xFAD and 3xTG-AD transgenic mice consistently are lower performers (shifted to the lower performance group) than their WT counterparts (shifted to the higher and moderate performers) in the 5-CSRTT, suggesting that this test might be a good candidate for screening cognitive symptoms in these two mouse models of AD. Interestingly, these differences were not observed for the APP/PS1 mice. In fact, there was no difference in the performance of females APP/PS1 mice (3–6, 7–10 or 11–13). However, surprisingly, APP/PS1 male mice tended to shift their performance to the higher and moderate performers while the WT shifted to the lower performers.

Curiously, across all touchscreen paradigms, male and female 5xFAD mice had a consistent phenotype displaying increases in reward collection latency (Tables 1 and 2, Supplementary file 2, 3, 4, 5, 6 and 7 – Tab 2, #i), suggesting the potential that these mice present lack of motivation or abnormal motor function at the ages tested, which has been described previously (O'Leary et al., 2018).

Collectively, the data show different patterns of cognition abnormalities between the three AD models, which may be related to different human AD mutations and the pathophysiology associated with them, including the tau mutation in the 3xTG-AD mouse line. Overall, two of the three lines showed a consistent deficit in attention and all lines presented modest but significantly lower performance in PAL. In addition, we also observed sex dissimilarities in PAL for 3xTG-AD and APP/PS1 (Supplementary file 3, tabs 1 and 3 #d and #g respectively), which could be due to differences in cellular and molecular mechanisms in brain development (Grissom and Reyes, 2019; Ruigrok et al., 2014) and/or the differences of AD-type pathology, disease onset and progression rate in males and females.

Conclusions and next steps

Here we introduce an open-access high-throughput pipeline and a Web application database that facilitates data repository, searching, and analysis of touchscreen data. The MouseBytes data integration platform introduces quality control of high-throughput approaches using touchscreen analysis in an open source platform for dissemination of high-level cognitive data. Including standardised data from different laboratories around the world will bring the advantages of open-access data sharing and greatly enhance validation, comparison and post-publication analysis of large datasets by independent researchers. Furthermore, this approach also facilitates collaboration to increase replicability/reproducibility and re-use of cognitive data and ultimately increases the accuracy of predictions regarding cognitive phenotypes and outcomes in drug efficacy studies.

Currently, several different species can be tested using touchscreens for cognitive assessment, including rats, primates, monkeys, birds, and dogs (Bussey et al., 2008; Charles et al., 2004; Guigueno et al., 2015; Horner et al., 2013; Kangas and Bergman, 2012; Mar et al., 2013; Nagahara et al., 2010; Rodriguez et al., 2011; Schmitt, 2018; Steurer et al., 2012; Wallis, 2017). Our current scripts can facilitate the formatting of files from such studies and ultimately data from different species, including rats, will be easily incorporated into MouseBytes or similar platforms. Moreover, one can easily envision outputs from unidentified human touchscreen cognitive testing being stored and accessed using similar repository and Web applications. Given the potential for identical touchscreen tests in mice and humans, these data may prove valuable for understanding the consequences of specific mutations for high-level cognition (Nithianantharajah et al., 2015).

A major publication bias is the lack of published null datasets, which are important to avoid waste of resources. MouseBytes provides a platform for the dissemination of datasets for touchscreen cognitive assessment even when results show no change in high-level cognition. We anticipate that researchers using automated touchscreen tests will benefit by making their original data available for the community as an integral part of scientific record and publication. This database will become exponentially more valuable when data from more strains of mouse models of disease, drug treatments and genetic manipulations are deposited. Furthermore, as an open source, MouseBytes will be built as a platform where the research community can contribute to new features and share new codes for data analysis. Indeed, MouseBytes is part of a large open initiative for the touchscreen/cognitive behaviour community which includes the touchscreencognition.org platform as well, a knowledge sharing platform that allows storage of protocols and community-driven discussions.

We envision that with MouseBytes it will become easier to connect transcriptomic and different modalities of imaging data from mouse models to their cognitive performance. Ultimately, the integration of current and new touchscreen tests with the use of MouseBytes will change how cognitive function is evaluated in rodents facilitating the discovery of new therapeutic approaches for neurodegenerative and neuropsychiatric disorders.

Materials and methods

Key resources table
Reagent type
(species) or Resource
DesignationSource or referenceIdentifiersAdditional
Information
Antibody6E10 Primary Antibody (Human Monoclonal)CovanceRRID:AB_2564652
Lot#: D13EF01399
Cat#: SIG-39320
IF (1:200)
Antibody488 Goat Anti-Mouse Secondary Antibody (Mouse Polyclonal)InvitrogenRRID:AB_2534069
Cat#: A-11001
IF(1:1000)
Commercial Assay KitAmyloid Beta 42 Human ELISA Kit – UltrasensitiveInvitrogenCat#:KHB3544
StrainB6.Cg-Tg[APPswe,PSEN1dE9]85Dbo/Mmja (Mouse, Male, Female)Jackson LaboratoriesRRID:MGI:034832-JAX
Stock#: 034832-JAX
Strain(B6;129-Psen1tm1MpmTg[APPSwe,tauP301L-1Lfa 0 (Mouse, Male, Female)Jackson LaboratoriesRRID:MGI:101045-JAX
Stock#: 101045-JAX
StrainB6SJL-Tg(APPSwFlLon,PSEN1*M146L*L286V)6799Vas/Mmja (Mouse, Male, Female)Jackson LaboratoriesRRID:MGI:034840-JAX
Stock#: 034840-JAX
StrainC57BL/6 (Mouse, Male, Female)Jackson LaboratorieRRID:MGI:000664-JAX
Stock#: 000664
StrainB6129SF1/J(Mouse, Male, Female)Jackson LaboratorieRRID:MGI:101043
Stock#: 101043
StrainB6SJLF1/J(Mouse, Male, Female)Jackson LaboratorieRRID:MGI:100012
Stock#: 100012
SoftwareABET II TouchLafayette NeuroscienceModel#: 89505
SoftwareSpotfireTIBCOhttps://www.tibco.com/products/tibco-spotfir
SoftwareTouchscreen Quality Control SysteBrainsCANhttps://github.com/srmemar/Mousebytes-QualityControl

Contact for reagent and resource sharing

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All cognitive data are deposited in MouseBytes (www.mousebytes.ca). Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Marco Prado.

Experimental model and subject details

Animals

The choice of AD mouse lines considered mice with different rates of accumulating pathology, their use by a variety of researchers, and commercial availability from a single source. Three AD mouse lines were tested: 3xTG-AD (B6;129-Psen1tm1Mpm Tg[APPSwe,tauP301L-1Lfa 0] – RRID:MGI:101045-JAX) and its age-matched control mice (B6129SF2/J RRID:MGI:101045); 5xFAD (B6.Cg Tg [APPSwFlLon,PSEN1*M146L*L286V] 6799Vas/J RRID:MGI:034840-JAX) and control mice B6SJLF1/J (RRID:MGI:1000120; and APP/PS1 [(APPswe PS1dE9 B6.Cg-Tg[APPswe,PSEN1dE9]85Dbo/Mmjax RRID:MGI:034832-JAX) and control mice C57Bl/6J (RRID RRID:MGI:000664). All mice used in this study were bred at the Jackson Laboratory in Bar Harbor, Maine and shipped to Canada. Procedures were conducted in accordance with approved animal protocols at the University of Western Ontario (2016/104) and the University of Guelph (3481) following the Canadian Council of Animal Care and National Institutes of Health guidelines. The N values for each group of animals can be found in Supplementary file 1. We did not formally calculate power analysis a priori. Typical experiments using the Bussey-Saksida touchscreen technology use samples sizes between 7 and 13 mice per group (Beraldo et al., 2015; Kolisnyk et al., 2015; Kolisnyk et al., 2013; Lim et al., 2019; Romberg et al., 2013b; Romberg et al., 2011). Based on these previous studies, and depending on cohort availability, we assigned a minimum of 10 animals per genotype, sex and site, anticipating some mortality. Due to death of mice, which was a particular problem for APP/PS1, final numbers for some groups were lower than the initial starting numbers (see Supplementary file 1).

Animal housing and food restriction protocols

Mice were housed at two different sites. Half of the mice used in this study were shipped by Jackson Laboratory and housed at the University of Guelph, (Guelph, ON, Canada), while the other half was housed at The University of Western Ontario (London, ON, Canada). For all the 5-CSRTT experiments, an equal distribution of male and female mice was kept at both sites, allowing for comparison and reproducibility studies. For PD and PAL experiments, all male mice were tested at The University of Western Ontario and all the females were tested at the University of Guelph. At each site, mice were housed in a single colony room maintained on a standard 12 hr light cycle (8 am lights on, 8 pm lights off). All the experiments were conducted during the light phase of the cycle. The colony rooms were typically maintained at a temperature of 22–26°C. Mice were initially housed in groups of two mice per cage (16 cm x12cm x 26 cm) and they had their tail tattooed with a unique animal identification number. During each experiment, mice were maintained on a restricted food diet to ensure adequate levels of motivation and to maintain their body weight at 85% of their original weight. Male mice were fed on average 2.5 g of chow, while female mice were given 2.0 g per day (Tekland Chow – Harlan). We set a maximum initial weight of 25 g and if a mouse was over that weight we slowly decreased its weight to 25 g and then set this as a 100% (initial weight). Mice were weighed every other day to ensure maintenance of body weight at 85% of original weight and water was available ad libitum throughout the course of the experiment. After each session, mice received food according to their body weight. Mice are social animals, but it has been described previously that group housed mice can exhibit aggressive behaviour towards their cage mates and establish social hierarchy. This can also affect, in the non-dominant mouse, gene expression and induces depression and anxiety-like behaviours (Horii et al., 2017). After few weeks on food restriction, all the mice in the study (for all three strains) had to be separated and singly housed due to fighting.

3xTG-AD mice

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3xTG-AD mice present three mutations associated with familial AD forms: human familial AD amyloid-beta precursor protein (APPSWE), microtubule-associated protein tau (P301L) and presenilin1 (M146V) (PSEN1, APPSWE, and tauP301L as previously described) (Oddo et al., 2003). Briefly, single-cell embryos, harvest from homozygous presenilinM146V knocking (129/C57BL6 background) mice, were co-microinjected with human mutant tauP301L and the double mutant APPSwe (MK670/671 NL). Both are under the control of Thy1.2 expression cassette (Berardi et al., 2005). Age, sex and genetic background-matched controls (B6129SF2/J) were used. The mice were between twelve to sixteen weeks of age at the start of behavioural testing. At this initial age 3xTG-AD mice exhibit few or no extracellular Aβ deposits and relatively low levels of hyperphosphorylated tau (Berardi et al., 2005).

5xFAD mice

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The generation of the 5XFAD mice by Oakley and colleagues in 2006 has been described previously (Oakley et al., 2006). Briefly, 5xFAD mice overexpress three familial AD mutations in human APP(695); the K670N/M671L (Swedish - APPSWE), I716V (Florida - APPFL), and V717I (London - APPLON) mutations. In addition, these mice express the M146L and L286V mutations in human PSEN1. Transgene expression is driven by the mouse neuron specific Thy1 promoter (Oakley et al., 2006). These five familial, AD mutations are additive in driving Aβ overproduction (Oakley et al., 2006). 5xFAD mice present intraneuronal Aβ starting at 1.5 months of age (Oakley et al., 2006). 5xFAD mice and wild-type control males and females were twelve weeks of age at the start of behavioural testing. 5xFAD mice have a mixed background: C57Bl6 and Swiss Jim Lambert (SJL). SJL mice are homozygous for the recessive Pdebrd1 allele, which codes for the β-subunit of cGMP phosphodiesterase on mouse chromosome 5 (Clapcote et al., 2005; Giménez and Montoliu, 2001). Thus, F1 5xFAD mice should be heterozygous for the mutation. The mutated allele is a nonsense mutation that decreases the transcription of the phosphodiesterase, leading to retinal degeneration and blindness by wean age at approximately 3 weeks and rendering mice homozygous for the Pdebrd1 allele unsuitable for use in some experiments (Giménez and Montoliu, 2001). This same mutation is seen in FVB/NJ (Friend Virus B/National Health Institute Jackson) mice as well (Giménez and Montoliu, 2001). To confirm the absence of homozygous mice for the Pdebrd1 allele we genotyped samples of 5xFAD mice and controls. Briefly, DNA was extracted from mouse ear tissue and amplified using the REDExtract-N-Amp Tissue PCR Kit Protocol (Sigma-Aldrich, Oakville, Ontario). Polymerase chain reaction (PCR) was done using the Bio-Rad T100 Thermal Cycler (Bio-Rad Laboratories, Hercules, California) with a 500 bp x 40 cycle schedule (94°C x 3 min followed by 40 x [94°C x 30 s] + 60°C x 30 s + 72°C x 30 s then 72°C x 2 min). The tubes were held at 10°C until use. The following reagents were used for each sample: 5 μl of 2x premix, 0.5 μl of retinal degeneration (RD) three oligonucleotide primer (concentration: 0.5 μM; 28-mer, 5’-TGACAATTACTCCTTTTCCCTCAGTCTG-3’, accession number L02109, nucleotides 84 to 111), 0.1 μl of RD4 oligonucleotide primer (concentration: 0.02 μM; 28-mer, 5’-GTAAACAGCAAGAGGCTTTATTGGGAAC-3’, accession number L02109, nucleotides 644 to 617) and 2.9 μl of RD6 oligonucleotide primer (concentration: 14.5 μM; 28-mer, 5’-TACCCACCCTTCCTAATTTTTCTCAGC-3’, accession number L02110, nucleotides 2539 to 2512). RD3 and RD4 amplify a 0.55 kb PCR product from the Pdebrd1 mutant allele, while RD3 and RD6 amplifiy a 0.40 kb PCR product from the WT allele (Giménez and Montoliu, 2001). The PCR products are then run on an agarose gel along with a 100 bp ladder (Gene DireX, Frogga Bio, Toronto, Ontario) and imaged with FluorChem Q (Alpha Innotec Corp., San Leandro, California). The positive control for Pdebrd1 was ear tissue from a Friend Virus B NIH Jackson mouse (FVB/NJ; Jax stock #001800), an inbred strain of mouse known to be homozygous for the Pdebrd1 mutation. This mouse was purchased from the Jackson Laboratory (Bar Harbor, Maine). The control for the WT allele of Pdeb for this gel was ear tissue obtained from a B6SJLF1/J mouse.

APP/PS1 mice

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APP/PS1 are double transgenic mice expressing a chimera of mouse/human APP (Mo/HuAPP695swe) and a mutant human presenilin 1 (PS1-dE9). Generation of this mouse line has been previously described (Jankowsky et al., 2004). Transgene expression is driven by the mouse prion protein (PRP) promoter, which results in expression relatively restricted to the central nervous system.

Methods details

Touchscreen operant platform

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All the behavioural tests were conducted in the automated Bussey-Saksida Mouse Touchscreen Systems model 81426 (Campden Instruments Limited, Loughborough, EN) (Horner et al., 2013; Mar et al., 2013; Oomen et al., 2013). Mice were trained to operate the touchscreens by a series of shaping procedures for PD, 5-CSRTT and PAL. The screens in the touchscreen chamber were blocked with barriers during the experiment. For the 5-CSRTT, the screen was divided into five partitions (132 pixels x 132 pixesl) that were 50 pixels above the screen. For the PAL task, the screen was divided into three partitions (228 pixels x 228 pixels) that were 50 pixels above the screen. For the PD task, the screen was divided into two partitions (240 pixels x 240 pixels) that were 50 pixels above the screen. All the schedules were designed and pre-installed and the data were collected using the ABET II Touch software v.2.20.3 as previously described (Lafayette Instruments, Lafayette) (Horner et al., 2013; Mar et al., 2013; Oomen et al., 2013).

Behavioural Pre-Training

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Mice (10–12 weeks of age) experienced several pre-training stages (shaping) prior to probes in each task. For the first four days of pre-training, the mice were habituated to the testing chambers (Habituation schedules). On Day 1 (Habituation 1), the mice were placed in the testing chambers for 10 min with the house lights off, with no stimuli displayed and no reward presented. On Days 2–4 (Habituation 2), the mice were placed into the testing chambers for 20 min (Days 2 and 3) and for 40 min (Day 4). On Days 2–4, the reward tray light was turned on and the reward (strawberry milkshake; Saputo Dairy Products, Canada) was presented and paired with a sound (tone) for 280 ms every 10 s. During this phase, the mice should strengthen the association between the reward tray light and tone with the reward; however, no performance criteria were in place at the habituation phase.

Following habituation, the mice were subjected to the ‘Initial Touch’ schedule (Phase I), which involves pairing the reward with the presentation of stimuli (random images for PAL and PD and a white square for 5-CSRTT) on the touchscreen. In this phase, a single stimulus appears randomly in one of the windows. After 30 s the stimulus is turned off and the illumination of the reward tray light is paired with a tone and delivery of the reward (7 μl of strawberry milkshake). If the mouse touches the screen during the time that the image is displayed, a reward is immediately presented with a tone. A new trial starts when the mouse collects the reward, and Initial Touch sessions are repeated daily until the subject completes 30 trials within 60 min.

The next stage of pre-training, ‘Must Touch’ (Phase II) involves displaying a stimulus randomly in one of the windows, as before. However, in this phase, differently from Phase I, the mouse is required to touch the stimulus on the screen in order to receive the reward paired with a tone. If the mouse touches any window other than the one in which the stimulus is present, it receives no reward. Daily sessions are repeated until the mouse completes 30 trials in 60 min. The next phase of shaping introduces the animals to the initiation procedure, a schedule called ‘Must Initiate’ (Phase III). At the beginning of each trial, the reward tray is illuminated, and the mouse is required to initiate the stimulus delivery by a nose poke into the reward tray. Successful initiation extinguishes the tray light, and a stimulus is presented in one of the windows on the screen. After touching the stimulus and collecting the reward, the mouse is subjected to a 5 s inter-trial interval (ITI – houselights off, reward tray inactive and no stimulus presented) before the illumination of the reward tray light signals the beginning of the next trial. A criterion of 30 correct trials within 60 min must be met in order for the mouse to proceed to the next phase. The last pre-training phase (Phase IV) is called ‘Punish Incorrect’ and requires the mouse to both initiate and touch the stimulus but if an incorrect choice is made, it receives a 5 s timeout, during which the lights are turned on and no reward is delivered. The mice continue performing this phase of shaping until they are able to obtain at least 80% of trials correct within 60 min for two consecutive days. Intertrial intervals of 5 s for 5-CSRTT and 20 s for PD and PAL were used in all the phases. All the Standard Operant Procedures (SOPs) for 5-CSRTT (Horner et al., 2013; Mar et al., 2013; Oomen et al., 2013), PD (SOP2) and PAL (SOP3) and other touchscreen tasks can be found in the Touchscreen Cognition (www.touchscreencognition.org).

Pairwise visual discrimination (PD) training

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After reaching the criteria on Punish Incorrect schedules, mice were trained on PD Acquisition sessions. In this task, mice must initiate the trials by poking their head into the reward tray after a light signal is displayed. Immediately after exiting the reward tray, two different images appear on the screen. Mice are required to learn that a rewarded response is determined by the correct visual image (S+ correct image and S- incorrect image) (S4A). If a correct response is made, reward is delivered, but if an incorrect response is made, a 5 s time-out is initiated with activation of the house light. Following the time-out period, mice are required to complete the same trial repeatedly until a correct response is made. These correction trials do not count towards the overall trial count. To move to the next phase a criterion of 24 correct out of 30 trials (2 days in a row) is required.

Pairwise visual discrimination (PD) baseline

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Following the achievement of acquisition criteria, each mouse was subjected to two baseline sessions according to the same schedule used for the acquisition sessions. However, there is no criterion required to pass this stage.

Pairwise visual discrimination (PD) reversal

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After completion of the baseline sessions, each mouse was subjected to 10 consecutive daily sessions to assess reversal learning and cognitive flexibility. In these sessions, the contingencies between stimuli and reward were reversed such that the original S+ was now an S- and vice versa. There were no performance criteria for reversal sessions.

Pairwise visual discrimination (PD) maintenance

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Following the completion of the reversal sessions, maintenance sessions were run once weekly until each mouse was old enough to begin the subsequent acquisition sessions with new stimuli. This maintenance procedure is identical to the Punish Incorrect schedule previously described. There were no performance criteria, and each session ended after 30 trials were completed or after 60 min had elapsed.

Five choice serial reaction time task (5-CSRTT) training

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After reaching criterion on the Punish Incorrect stage of pre-training (Phase IV of pre-training), mice were trained on the 5-CSRTT, which requires responses to brief flashes of light pseudo-randomly displayed in one of the five response windows on the touchscreen chamber, as described previously (Beraldo et al., 2015; Kolisnyk et al., 2015; Romberg et al., 2013b; Romberg et al., 2011). All mice, at both sites (The University of Western Ontario and University of Guelph), were tested 5–6 days per week for 50 trials or 60 min per day, whichever occurred first. Each trial started with the illumination of the reward tray where the mouse was required to poke its head. After a 5–10 s variable delay, one of the windows was illuminated and the mouse had up to five extra seconds (limited hold) following stimulus presentation to respond on the screen in order to make a correct response. If a mouse touched the screen during the variable delay prior to stimulus presentation, the response was recorded as a ‘premature response’ and the mouse was punished with a 5 s time-out followed by a 5 s ITI. The touchscreen stimulus duration (i.e., the duration for which the window is lit) was initially set to 4 s. The first response to a window during the stimulus presentation or the limited holding period was recorded and initiated the next phase of the trial. If a correct choice was made the reward was presented and any incorrect response was punished with a 5 s time-out followed by a 5 s ITI (Mar et al., 2013). Failure to respond to any window by the end of the limited hold period was recorded as an ‘omission’ and punished with a 5 s time out, followed by the 5 s ITI before the start of the next trial. The mice continued on the 4 s stimulus duration until performance was stabilised at greater than 80% accuracy, less than 20% omissions, and 30–50 trials completed for three consecutive days. Once mice reached criteria, training continued on the same task, but with a 2 s stimulus duration; criteria for this phase were the same as for the 4 s version. If mice failed to reach criteria within 30 sessions, they were eliminated from the study.

Five choice serial reaction time task (5-CSRTT) probe sessions

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Once mice reached criterion on the 2 s variation of the 5-CSRTT, they were exposed to a series of probe sessions. During each probe session, one of four test stimulus durations was used: 1.5 s, 1.0 s, 0.8 s, and 0.6 s. Each mouse completed two consecutive days of probe trials with each of the stimulus durations. Following each probe session, mice were returned to the 2 s stimulus duration version for two consecutive baseline days before beginning the next probe session (two days with a different stimulus duration). The order of presentation for probe sessions was counterbalanced across all mice. In order to assess attentional performance longitudinally (which to the best of our knowledge has not been performed in AD mice), the same mice were tested on probe sessions at 4, 7, and 10–11 months of age. To ensure maintenance of sufficient baseline performance on the task between probe sessions, mice were given a single day of training with the 2 s stimulus duration each week until the next set of probe sessions. Prior to the start of the 7 month and 10–11 month probe sessions, mice were given 5 days of 2 s 5-CSRTT sessions in order to re-baseline them on the task. The same reduced stimulus durations (1.5 s, 1.0 s, 0.8 s, and 0.6 s) were used for probe sessions at each testing age, and the order of stimulus duration sessions was counterbalanced between mice at each age as well as for each mouse across each of the testing ages.

Paired associate learning (PAL) training

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After reaching the criteria on Punish Incorrect schedules (Phase IV of pre-training), mice were trained on PAL Acquisition sessions. In this task, mice must initiate the trials by poking their head into the reward tray when the light on the feeder turns on. Immediately after exiting the reward tray, two different images appear in two of three positions on the screen. Mice must learn that each specific visual image is associated with only one correct spatial location on the touchscreen, and only one image per trial is presented in the correct location. If a correct response is made a reward is delivered (7 μL of strawberry milkshake), but if an incorrect response is made, a 5 s time-out is implemented with activation of the house light. Following the time-out period, mice are required to complete the same trial repeatedly until a correct response is made. These correction trials were not counted towards the overall trial count. The criterion for this training phase is the completion of 36 trials in 60 min (per day). All the mice from both groups and all the time points were able to reach this criterion in the first session.

Paired associate learning (PAL) probe sessions

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After successfully completing the training phase, mice were tested either on different PAL (dPAL) or on same PAL (sPAL) tasks. 3xTG-AD and APP-PS1 were tested in dPAL at 4 and 11 months of age. Due to a poor performance in dPAL at 4 months of age, 5xFAD mice (10–11 months of age) were tested in sPAL, which is relatively easier test when compared to dPAL. In both tasks, a mouse initiates the task by touching the reward tray, which triggers the display of both S+ and S- on the screen. As described above, S+ refers to the stimulus presented in the correct location and S- refers to a stimulus presented in the incorrect location. In this task, the mice are required to learn to associate a stimulus with its correct location. Similar to the PAL Acquisition phase, if a correct response is made (S+), the reward is delivered. However, touching the S- stimulus results in a 10 s time-out and illumination of the light in the chamber (10 s). Following the punishment period, mice are required to initiate the same trial repeatedly until a correct response is made; these correction trials did not count towards the overall trial count. The only difference between dPAL and sPAL is the fact that on dPAL S+ and S- stimuli are different images and on sPAL S+ and S- are the same image (Horner et al., 2013). Mice were tested on 45 sessions for dPAL or sPAL tasks regardless of performance.

Longitudinal behavioural testing protocol

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Mice were tested longitudinally as they aged at different time points. Different cohorts of 3xTG-AD, 5xFAD and APP/PS1 mice were tested longitudinally starting at approximately 4, 7 and 10–11 months of age on 5-CSRTT and PD and at 4 and 10–11 months of age on PAL. The first time points of testing, for each experiment, were completed when the mice were 5–6 months of age. After the completion of the first set of experiments, all mice were kept on a maintenance schedule (2 s stimulus duration for 5-CSRTT and random images for PD and PAL) once a week until the commencement of the second set of probe trials that started at 7 months of age. The images used for the training and maintenance were removed from the probe trial database and were not displayed to the mice during the probes for PAL or PD tasks. This was done to ensure that the mice did not forget the basic aspects of touchscreen task performance and hence would not require retraining prior to the subsequent 7- and 11 month trials. Upon completion of the second set of trials, mice were put back on a weekly maintenance schedule until the commencement of the third set of trials at 11 months of age.

Image set control experiments

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For PD and PAL, different sets of images were used at each testing age. All the images were randomly selected from the ABET II imaging databank and, according to the manufacturer, the images present the same number of pixels. Image sets I, II and III were used for testing mice on PD at 4, 7 and 10–11 months of age, respectively (S4). Image sets IV, V and VI were used to test mice on dPAL or sPAL at 4 and 10–11 months of age (S4). It is well known that rodents prefer some stimuli over others (Bussey et al., 2008). To evaluate the potential stimulus biases, we tested different cohorts of wild-type male mice (B6129SF2/J MMRCC stock number 101045), at 4 months of age, with three different images sets used for testing the WT and transgenic mice on PD (Image sets I, II, and III) and PAL (Image sets IV, V, and VI). (S4). PD testing for image biases was done at The University of Western Ontario, and the PAL testing was done at the University of Guelph.

Tissue preparation

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Mice were anaesthetized using ketamine (100 mg/kg) and xylazine (25 mg/kg) in 0.9% sodium chloride solution, and then transcardially perfused with 1x phosphate buffered saline (PBS, pH = 7.4) for 5 min. For each mouse, one harvested hemibrain was post-fixed in 4% paraformeldahyde overnight and subsequently used for immunostaining, while the other harvested hemibrain was stored at −80°C for biochemical analyses.

The hemibrains for immunohistochemistry were cryopreserved using increasing concentrations of sucrose (15%, 20%, 30%), embedded in optimal cutting temperature (OCT) compound and frozen at −80°C. Sagittal sections (10 μm) were cut using the Cryostat (Leica Biosystems), directly mounted and frozen. All slides were immersed in 70% ethanol for 1 min followed by distilled water for another minute before they are stained as described below.

Thioflavin-S

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Slides were stained with filtered 1.25% Thioflavin-S solution in 50% ethanol for 8 min at room temperature. Slides were then washed twice with 80% ethanol, once with 95% ethanol, and then three times with distilled water before mounting.

Aβ immunoflourescence

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Slides were washed twice with Tris Buffered Saline (TBS) 1x and then permeabilised with 1% Triton X-100 (tx) in 1x TBS for 15 min. Non-specific binding was prevented by incubating the slides for one hour in 2% horse serum (HS) and 2% bovine serum albumin (BSA) in TBS 1x with 0.3% Triton-x. Slides were then stained overnight at 4°C with 6E10 primary antibody (RRID:AB_2564652) diluted 1:200 in TBS 1x. Following two TBS 1x washes, slides were incubated at 4°C in 488 goat-anti-mouse secondary antibody (RRID:AB_2564652) diluted 1:1000 in TBS 1x, 1% HS and 1% BSA. Nuclei were stained with To-Pro-3-Iodide (Life Technologies. Gibco, Carlsbad, CA, USA) diluted in PBS1x (1:1000) for 15 min. Slides were then rinsed three times with TBS 1x and mounted.

Aβ(1-42) ELISA

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For Aβ (1-42) quantification we used food-restricted and non-food restricted 3xTG-AD and 5xFAD (males and females) by six months of age. Hippocampus fractionation was performed as described previously (Ostapchenko et al., 2015). ELISA was performed using the ultrasensitive kit for human Aβ(1-42) (cat#KHB3544, ThermoFisher Scientific, Mississauga, ON, Canada).

Imaging

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Mounted slices were visualised by confocal microscopy using Leica-TSC SP8 or SP5 (Leica Microsystems, Wentzler, Germany) (20x/0.75 objective, 488 nm laser and 647 nm laser). Images were analysed using ImageJ (National Institute of Health-NIH, Bethesda, Maryland, USA). For each mouse, the cortex and hippocampus (dentate gyrus, CA3, CA1b and CA1a) of 3–4 slices were imaged and quantified in terms of percentage area. The experimenter was blind to genotype during image acquisition and quantification.

Quantification and statistical analysis

Touchscreen data analysis, quality control and storage

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To ensure the quality of acquired the data when using touchscreens in a high-throughput mode, several actions have been taken before data processing and analysis. All the procedures (schedules, images, food restriction, database and animal identification nomenclatures, etc.) were standardised between the two sites before the start of experimentation and the SOPs can be found in touchscreencognition.org. Monthly conference calls were made, and reports were exchanged between the researchers to assure the maximum standardisation of the procedures across the sites. After automated collection, the data were automatically saved and backed-up onto two different servers at The University of Western Ontario and the University of Guelph. For data protection, the database and back-up were strictly controlled and logged. ABETT II files were converted to XML files (each XML file corresponds to the data from a unique mouse ID session/day of training or testing) and uploaded into mousebytes.ca. The XML files were then automatically checked by automated quality control (QC) algorithm and the codes are available for free download and modification in GitHub (https://github.com/srmemar/Mousebytes-QualityControl) (Memar et al., 2019) Files with potential errors, due to human input and/or machine/software failure, were automatically flagged by the QC procedure. The discrepancies, flagged by the QC, were fixed manually when possible and corrupted files were discarded and not used for data analysis. For example, in most of cases the flagged files were due to a software failure at the beginning of the session or wrong animal ID number input. If there was a software failure at the beginning, a new session was started in the same day. This generates two XML files for the same mouse ID in the same day and the incomplete running session file is flagged and not transferred to the database based on the QC rules. If there is a wrong mouse ID input, MouseBytes will flag the XML file related to the wrong ID and the user is able to enter the right ID so the file can be transferred to MouseBytes. The Interquartile ranges (IQRs) method was used to filter out outliers from the temporal features for each cognitive task. So, any feature value beyond the sum of third quartile (Q3) and 3*IQRs (i.e. Q3 + 3*IQRs) was considered as extreme outlier and automatically removed from the dataset (Parrinello et al., 2016).

The processed data were transferred to the open-access application (Parrinello et al., 2016). The complete data set is also available for visualisation and customised analyses on the analytics platform TIBCO Spotfire (Dunn et al., 2016; Pechter et al., 2016), integrated in MouseMytes. Guidelines to access and visualise the data on Spotfire and in MouseBytes can be found in mousebytes.ca/spotfire and mousebytes.ca/tutorial.

5-CSRTT analysis

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In all pre-training stages for the 5-CSRTT task (phases I-IV), the number of sessions to reach criterion were analysed to determine any differences in learning of the task. A similar analysis was performed for both 4 s and 2 s stages of 5-CSRTT training. For the probe sessions, several parameters were analysed as an average between each set of two probe sessions with each stimulus duration: Accuracy – percentage of correct trials; Omissions – percentage of trials on which no response is made; Correct Response Latency – reaction time for correct response; Reward Collection Latency – reaction time to collect the reward on correct trials; Premature responses – number of responses made prior to the stimulus presentation; Perseverations – number repeated responses at a previously rewarded window before onset of the next trial. Omissions and Premature responses did not count towards Accuracy. Analysis of the probe trial data was conducted using a 3 (age) x 4 (stimulus duration) x 2 (research site) x 2 (genotype) x 2 (sex) split-plot ANOVA. In addition, vigilance in the 5-CSRTT was analysed with a 4 (stimulus duration) x 5 (block) x 2 (genotype) split-plot ANOVA. To characterise genotype-specific effects, we a priori decided to conduct 4 (stimulus duration) x 2 (genotype) split-plot ANOVA analysis between wildtype and transgenic mice for each sex, strain, and age.

PD analysis

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For the pre-training stages for the PD task (Phases I-IV) and acquisition sessions, the number of sessions to reach criterion were analysed to determine any differences in task learning. In addition, several behavioural parameters were analysed for the PD and reversal phases: Accuracy – percentage of correct trials; Correction trials – number of trials until a correct choice is made. Correct Response Latency – reaction times for correct response; Reward Collection Latency – reaction time to collect the reward on correct trials. To analyse PD reversal data, a 10 (session) x 2 (genotype) x 2 (sex) split-plot ANOVA was conducted. We decided a priori to investigate genotype-specific effects within each mouse line, so we additionally conducted 10 (session) x 2 (genotype) split-plot ANOVA within each sex, strain, and age.

PAL analysis

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In the pre-training stages for the PAL task, the number of sessions to reach criterion were analyzed to determine any differences in basic touchscreen acquisition. In addition, several behavioural parameters were analysed for the dPAL/sPAL and retention phases: Accuracy – percentage of correct trials; Correction trials – number of trials until a correct choice is made. Correct Response Latency – reaction times for correct response; Reward Collection Latency – reaction response to collect the reward on correct trials. Data from the 45 sessions (days) of dPAL/sPAL were binned in intervals of 5 sessions or one week of testing (e.g. sessions 1–5 is binned as week 1, session 6–10 is binned as week 2, etc.). Data for the PAL training was analysed with a 9 (bin) x 2 (sex) x 2 (genotype) split-plot ANOVA. All data were analysed separately at 4 months and 11 months of age and for males and females. To characterise genotype effects specifically, we a priori decided to conduct an additional 9 (bin) x 2 (genotype) ANOVA models separating by sex, strain, and age.

Longitudinal k-means clustering

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In order to categorise the overall performance of the mice over the duration of the tasks we employed a k-means clustering approach. Given the longitudinal nature of the behavioural tasks, repeated performances across multiple trials, a three-dimensional version of the analysis is required. To do so, we used the kml3d R package (Genolini et al., 2016; Genolini et al., 2013). This allows for clustering analysis to be performed across trials. The clusters were renamed post-hoc based on the overall performance of the mice on key metrics in the task, which resulted in a high, mid, and low performing cluster. In order to determine if differences in k-mean group composition existed between wildtype and transgenic mice, Fisher’s exact test was conducted.

Aβ immunofluorescence

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For all immunofluoresecence studies, a total of 8 B6SJLF/1 (4 female, four male) and 8 5xFAD (four female, four male) mice were used. Immunofluorescent analysis was conducted on four slices from each brain sample. Comparison of 6E10 and thioflavin-S expression between genotypes was conducted using two-tailed independent samples t-tests.

Aβ ELISA

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A total of 6 B6129SF2/J (three male, three female), 8 B6SJLF/1 (4 female, four male), 6 3xTG-AD (three male, three female), and 8 5xFAD (four male, four female) mice were used for the ELISA analysis of Aβ. In order to quantify differences between wildtype and transgenic mouse Aβ expression, two-tailed independent samples t-tests were used.

Data and software availability

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The data that support the findings of this study are available from the https://mousebytes.ca data repository. The code used for analysis of these data can be found in https://github.com/srmemar/Mousebytes-QualityControl (Memar et al., 2019).

Additional resources

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For access to the raw data from these behavioural experiments, upload new experiments, or visualise current experiments, please visit the (Memar et al., 2019) data repository.

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Decision letter

  1. Andrew Holmes
    Reviewing Editor; NIH, United States
  2. Huda Y Zoghbi
    Senior Editor; Texas Children's Hospital, United States
  3. John G Howland
    Reviewer; University of Saskatchewan, Canada
  4. Yogita Chudasama
    Reviewer; NIMH IRP, United States

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Acceptance summary:

Neuroscience is currently benefiting from rapid technological advances that enable the monitoring and manipulation of neural systems and circuits with unprecedented power. However, progress in designing novel approaches to measure, collate and analyze behavioral correlates of neural function has lagged these developments. Here, the authors introduce an open-access online platform and data repository to facilitate the analysis and sharing of large-scale datasets obtained from an increasingly widely-used computerized, touchscreen-based, apparatus for assaying a range of cognitive processes in rodents. They go on illustrate the utility of this resource for revealing novel sex- and genetic-background related cognitive domains in a multi-site cohort of Alzheimer's disease model mice. This publication serves as a timely example of the potential for open-science in an era in which establishing effective networks connecting investigators and their growing repositories of ('big') data is becoming increasingly important.

Decision letter after peer review:

Thank you for submitting your article "An open-access high-throughput pipeline and database for rodent touchscreen-based cognitive assessment" for consideration by eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Huda Zoghbi as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: John G Howland (Reviewer #2); Yogita Chudasama (Reviewer #3).

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

Summary and Essential revisions:

All three reviewers expressed enthusiasm for efforts to standardize and share data generated by this increasingly popular behavioral testing paradigm. The major concerns raised at this stage for the authors to address relate to: 1) the ease of 'adoptability' of the analysis across laboratories that are not working in collaboration with the authors, and 2) the strength of the narrative justifying the collation of the analysis methods with the example empirical data in this one paper. In my opinion, these issues should be addressable in a compelling rebuttal to the reviewers and a carefully revised version of the manuscript.

Reviewer #1:

With more and more researchers adopting touchscreen systems for high-level cognitive assessment, there is certainly a need for a well maintained repository to allow for sharing of published datasets, large meta-analysis, and importantly, but not mentioned, the dissemination of null datasets. Co-authors and institutions involved in the current effort are leaders in the field of using this technology and these tools have the potential to lead to advancements in the field. However, the structure of the manuscript, and in particular the lack of mention of major difficulties in comparing datasets across labs, make the current results unclear, and the tools, such as the K-mean classification approach, difficult to assess.

- The authors repeatedly stress the strength of the touchscreen systems is the standardization of outcomes. However, even within the 3 tasks the authors use here, there are an incredibly amount of variables, including stimulus choices, inter-trial intervals, trials and sessions per day, reward type and amount, stimulus durations for 5-CSRTT etc. that can vary across experiments and labs. Much more discussion and description needs to be given to how these factors can be controlled for, or at least noted in order for Mousebytes users to make well-designed comparisons across datasets.

- The current study stresses the high-degree of collaboration and communication across sites including pre-standardization, use of the same systems and software, monthly conference calls and collaboration on QC. This can't possibly be expected to happen with an open access database. How will the system account for data collected on behavioral systems from other providers, or open source built systems? Gurley, J. Exp Analysis of Beh 2019; Butler and Kennerly, Behav. Res. Methods 2018 A discussion of how the system can or cannot deal with files using different category labels, different orders of variables, etc. needs to be added.

- The authors state both in the Introduction and Materials and methods that during QC "files" with potential errors are flagged by the procedure, fixed manually, or discarded and not used for analysis if not fixable. What is a "file"? Is this a daily data file for an individual animal? If so, how does the software account for the "hole" in the data? Are those data points simply ignored, wouldn't this decrease the total errors, alter the percent correct for that animal?

- While the authors correctly note the huge number of graphs and figures that can be generated from this dataset, the data included in Figure 2 seem arbitrary. Site comparison is included for 3-6 M/Males but not 11-13. Strain is shown for males and not females. Given only 4 figures are included in the main document, there is room to add these panels and aid the reader in the logical assessment of the variables.

- Application of unbiased assessment via machine learning is exciting and potentially important, but the approach is not clear. If the authors wish to unbias the approach, why were sex and age specifically conducted separately? Does this analysis require the assumption of independent samples? Inclusion of each animal as a separate data point at each age would violate this.

- The data presented in Figure 3 (or 4?) do not seem to support the authors' conclusions in the text. The statement "5xFAD and 3xTG-AD transgenic mice consistently are lower performers than their WT counterparts in the 5-CSRTT" is confusing, Based on the figure, it seems that WT 3xFAD males and females are consistently worse at most ages. APP/PS1 results are not discussed, although females show almost no differences (except at 7 months) and in males, WT seem to be consistently worse than TG.

- Follow up motor testing on the 5xFAD males is potentially interesting but detracts from the main point of the manuscript. It is unconvincing that increased reward collection latency "uncover(ed) unexpected phenotypes in mouse models" as 1) general phenotyping of mouse models for characteristics (motor, sight, motivation) that would alter touchscreen results should be done prior to testing and 2) this phenotype was previously reported as the authors note.

- Similarly, the inclusion of the lack of effect of AB burden based on ad lib vs. food restriction is potentially interesting, but here just adds more density to an already overpacked manuscript

- The longitudinal analysis is potentially powerful, but also raises questions. Some discussion of how previous training might alter later performance needs to be included. There is a strong case to be made that touchscreen testing could be considered enrichment. How does previous experience alter later cognitive performance in these models versus standard housed non-tested mice?

Reviewer #2:

Beraldo and colleagues present a novel open source analysis pipeline for data coming from a suite of tasks used in touchscreen-equipped operant conditioning chambers. They use this pipeline to analyze the behaviour of 3 mouse lines used to study Alzheimer's disease. The pipeline and on-line interface provide an exciting opportunity for combining research from different laboratories to increase understanding of cognition using touchscreen-equipped operant conditioning chambers. Results from the mice tested in the present experiments show results related to the reliability of touchscreen-based assessments, the effects of genetic background, sex, and age on cognition, and also provide an example of using K-mean clustering to analyze behaviour of hundreds of mice in the 3 tasks. Overall, the study is important, exciting, and will provide a useful resource for behavioural neuroscientists studying cognition in rodents. Below are a series of queries for the attention of the authors.

The title of the paper indicates that the pipeline will be used to study "rodent touchscreen-based cognitive assessment". However, the manuscript deals exclusively with mice. The absence of discussion of rats (or other species) appears to be a missed opportunity in this regard. Will the MouseBytes site support datasets from researchers studying rats, or other species?

The Introduction of the manuscript provides a compelling case for the importance of open science and the researchers should be lauded for pushing forward with this pipeline related to cognitive assessment. However, a more detailed discussion of the hardware used to collect the data should be provided. As the hardware used at both sites was the Bussey-Saksida Mouse Systems, it would of considerable interest for other open source hardware options were discussed (e.g., https://labrigger.com/blog/2018/03/09/custom-touchscreen-behavior-systems-for-rodents/). Will the data coming from these systems be compatible with the MouseBytes pipeline?

There are two important components to this study: 1) the introduction of MouseBytes; and 2) the results of assessing the AD-related mice. I found that the specifics of the findings related to the AD-mice were lost in places, particularly the summary and end of the Introduction. How do the authors' experimental results advance our understanding of the 3 mouse models and what specific future research questions can/should be answered with them?

Reviewer #3:

This paper is essentially a methods paper. The authors have taken a bold step in putting together a much warranted open-access database that enables transparency, collaboration and potential likelihood of reproducibility. The paper is well written with clear detail of the database repository, mousebytes.ca.

I have only two comments which are essentially suggestions that I think might help make the paper stronger.

1) The question is how far can mice carry Alzheimer Disease research or any other disease related research, and is data sharing and transparency the answer to translation? The growing disillusionment with murine models of human disease especially in drug discovery, have questioned mouse models constructed with highly penetrant alleles of human disease because of the large number of compounds which apparently 'cure' the mouse but not the human. This discrepancy is repeatedly argued to be due to failure in data/experimental reproducibility causing journals to emphasize more statistical details as if statistics will provide greater translation. Seems to me that the mousebyte.ca database is one approach that could feasibly enable a better mouse to human translation or at least work in this direction. The truth of the matter is that it is virtually impossible to replicate data even in the same lab, let alone between labs for a multitude of reasons. Can mousebytes.ca facilitate translation?

2) At the moment, the paper focuses on three mouse lines thought to model Alzheimers disease (AD) to highlight the strength of the database. The database is also specific to data acquired using the operant touchscreen platform because non-automated methods, the authors argue, are subject to large variations. Are their plans to expand the database to a much larger cohort of mice that are not necessarily tested for cognitive behaviors tested on a touchscreen platform? The large array of autistic mouse models come to mind for which social communication and interaction, motor, cognitive and emotional behaviors are all variable leaving a very confused state in the field.

https://doi.org/10.7554/eLife.49630.sa1

Author response

Summary and Essential revisions:

All three reviewers expressed enthusiasm for efforts to standardize and share data generated by this increasingly popular behavioral testing paradigm. The major concerns raised at this stage for the authors to address relate to: 1) the ease of 'adoptability' of the analysis across laboratories that are not working in collaboration with the authors, and 2) the strength of the narrative justifying the collation of the analysis methods with the example empirical data in this one paper. In my opinion, these issues should be addressable in a compelling rebuttal to the reviewers and a carefully revised version of the manuscript.

We thank the editors for summarizing the main concerns and the reviewers for the positive evaluation. We have addressed these two concerns below in the response to reviewers.

Regarding point 1, the intrinsic features of MouseBytes, and particularly those now added in response to the reviewers’ comments, are specifically designed to promote ease of use for all touchscreen users, especially those with whom we do not collaborate directly. MouseBytes allows users to enter Metadata that facilitates comparison and combination of data from different sites and also different touchscreen apparatuses (see reply to reviewers’ comments below). It is also important to emphasize that MouseBytes is by no means immutable; it will continue to evolve based on user’s feedback.

Thanks to the feedback provided by the reviewers, we made substantial changes to the manuscript, and also to the software. We now can collect even more Metadata on, e.g., experimental conditions. We have also developed codes to easily convert output files from any touchscreen apparatus to the MouseBytes format.

Regarding point 2, we improved the narrative concerning the empirical data, and in particular made it clearer how the data illustrate the advantages of MouseBytes. This is now explained more clearly in the Abstract and Introduction.

Reviewer #1:

With more and more researchers adopting touchscreen systems for high-level cognitive assessment, there is certainly a need for a well maintained repository to allow for sharing of published datasets, large meta-analysis, and importantly, but not mentioned, the dissemination of null datasets. Co-authors and institutions involved in the current effort are leaders in the field of using this technology and these tools have the potential to lead to advancements in the field. However, the structure of the manuscript, and in particular the lack of mention of major difficulties in comparing datasets across labs, make the current results unclear, and the tools, such as the K-mean classification approach, difficult to assess.

We thank the reviewer for raising these important points. We have now mentioned throughout the manuscript the potential difficulties in comparing datasets. In particular, we now emphasize collection of Metadata by MouseBytes as a way to greatly facilitate comparison of datasets. Users depositing data can now specify in detail all conditions of the experiments, and further are able to link datasets to published manuscripts which yield further details. Examples include added fields in the repository to account for potential variations in conditions between different laboratories such as light-dark cycle, and group v single housing. The greater availability of these details will increase the power of meta-analysis using MouseBytes.

We have also improved the discussion and explanation about the k-mean analysis. We clearly state now in the manuscript both in the Introduction and Discussion the utility for depositing null-datasets.

- The authors repeatedly stress the strength of the touchscreen systems is the standardization of outcomes. However, even within the 3 tasks the authors use here, there are an incredibly amount of variables, including stimulus choices, inter-trial intervals, trials and sessions per day, reward type and amount, stimulus durations for 5-CSRTT etc. that can vary across experiments and labs. Much more discussion and description needs to be given to how these factors can be controlled for, or at least noted in order for Mousebytes users to make well-designed comparisons across datasets.

The reviewer is right regarding the potential for many variables in any task to be changed using touchscreens. At the moment, major variations in these parameters are identified by the QC codes, and therefore experiments that deviate from standard operating parameters are flagged and can only be uploaded after communication between the user and administrator (subsections “Open-Access Database and repository” and “Touchscreen operant platform” describe these procedures). When experiments deviate from standard parameters, users are requested to provide information in the experiment field and also link the datasets to the published manuscript or preprint providing further details regarding the experimental conditions. Datasets that differ from standard protocols will still be uploadable in MouseBytes and can then be identified and curated for meta-analysis by using the information provided by the experimenter and the QC codes can be also modified by users. This is mentioned in the Results and Discussion and Materials and methods (all new information labelled).

- The current study stresses the high-degree of collaboration and communication across sites including pre-standardization, use of the same systems and software, monthly conference calls and collaboration on QC. This can't possibly be expected to happen with an open access database. How will the system account for data collected on behavioral systems from other providers, or open source built systems? Gurley, J. Exp Analysis of Beh 2019; Butler and Kennerly, Behav. Res. Methods 2018 A discussion of how the system can or can not deal with files using different category labels, different orders of variables, etc. needs to be added.

We would like to thank the reviewer for this suggestion. It has always been our intention to allow Mousebytes to incorporate data not only from Bussey-Saksida touchscreens systems, but from any variant of touchscreen operant chambers as long the output data can be made to match the Mousebytes specifications. We have now developed codes to do just that, and have added this MouseBytes feature to the manuscript. These codes can be downloaded and modified by different users to convert XML files from any touchscreen system output to files that can be readable by MouseBytes (https://github.com/srmemar/Mousebytes-An-open-access-high-throughput-pipeline-and-database-for-rodent-touchscreen-based-data/blob/master/XML_Output.ipynb). This is now described in the Results and Discussion.

- The authors state both in the Introduction and Materials and methods that during QC "files" with potential errors are flagged by the procedure, fixed manually, or discarded and not used for analysis if not fixable. What is a "file"? Is this a daily data file for an individual animal? If so, how does the software account for the "hole" in the data? Are those data points simply ignored, wouldn't this decrease the total errors, alter the percent correct for that animal?

Files are XML outputs generated from a unique session (day) from a unique mouse ID. We have now addressed this point in the subsections “Open-Access Database and repository” and “Touchscreen operant platform”. If a file is flagged and cannot be fixed for some reason, these data are not transferred to MouseBytes and will not be part of the downloadable dataset or used for visualization. In the specific datasets already in MouseBytes, it is unlikely that the discarded files would affect the final results reported in the manuscript. For example, for the 5-choice data less than 0.6% of files were terminally flagged and not used to compose the datasets, and only 0.09% were from probe trials (26 probe trial files flagged files out of 27,440 total files from 5 choice). Hence, the system will not use these data, but it should not have affected the final analysis in the manuscript.

- While the authors correctly note the huge number of graphs and figures that can be generated from this dataset, the data included in Figure 2 seem arbitrary. Site comparison is included for 3-6 M/Males but not 11-13. Strain is shown for males and not females. Given only 4 figures are included in the main document, there is room to add these panels and aid the reader in the logical assessment of the variables.

We thank the reviewer for this suggestion. We now provide several new panels comparing the performance of mice longitudinally, between sites, sex and strains. (Figures 2 and 4) to the current version of the manuscript which is now described in the Results and Discussion

- Application of unbiased assessment via machine learning is exciting and potentially important, but the approach is not clear. If the authors wish to unbias the approach, why were sex and age specifically conducted separately? Does this analysis require the assumption of independent samples? Inclusion of each animal as a separate data point at each age would violate this.

In the k-mean clustering approach that we present in the manuscript, we did not separate sex in the primary clustering algorithm to define Low, Mid and High performers. However, following the k-mean analysis, we determined if group memberships were significantly different by utilizing Fisher’s test. In this secondary analysis, we did separate the data by age and sex. We did this to prioritize looking at group membership and genotype in the Fisher’s Exact Test. In order to address the multiple tests created by separating age and sex, we used a False Discovery Rate correction analyses.

In the k-mean analysis, we assumed that performance could change with age, so age was treated as an independent between factor, where each animal had two or three cluster points for each of the ages of assessment. The k-mean clustering algorithm does not require independence for the initial clustering. The Fisher’s exact test we conducted had independent data points as part of our statistical analysis.

The decision to treat animals at different ages as separate data points was done in order to allow for mice to switch k-mean categories, based on the assumption that performance would change with age. In a given experiment, wildtype mice may naturally have reductions in performance on cognitive tasks related to age. We were interested in detecting if transgenic mice would shift from a category of “high performers” into a category of “low performers” or vice-versa as they were assessed at later time points. More specifically, we were interested in if this shift from high to low performers was larger for transgenic mice than wildtype mice.

Future efforts with the MouseBytes data system will certainly take advantage of other unbiased classification systems (e.g. Mean Shift, DBSCAN, etc.) with larger datasets to begin to better determine how to classify behavioural data from mouse models.

- The data presented in Figure 3 (or 4?) do not seem to support the authors' conclusions in the text. The statement "5xFAD and 3xTG-AD transgenic mice consistently are lower performers than their WT counterparts in the 5-CSRTT" is confusing, Based on the figure, it seems that WT 3xFAD males and females are consistently worse at most ages. APP/PS1 results are not discussed, although females show almost no differences (except at 7 months) and in males, WT seem to be consistently worse than TG.

These data are presented now in Figure 5. Analysis of the dataset shows that indeed, the 3xTG AD and 5xFAD AD mice are shifted to the lower performance group in the 5-Choice Task. Otherwise, their WT counterpart mice are shifted to the higher and moderate performers. For the APP/PS1 mice we did not observed any difference in the performance of females (comparing WT with the TG) in 5 choice. However, interestingly, APP/PS1 male mice shifted their performance to the high and moderate performers while WT shifted to lower performers in the 5-Choice Task. The presentation and discussion of this information are in the Results and Discussion.

- Follow up motor testing on the 5xFAD males is potentially interesting but detracts from the main point of the manuscript. It is unconvincing that increased reward collection latency "uncover(ed) unexpected phenotypes in mouse models" as 1) general phenotyping of mouse models for characteristics (motor, sight, motivation) that would alter touchscreen results should be done prior to testing and 2) this phenotype was previously reported as the authors note.

We agree with the reviewer and accordingly removed the dataset from the manuscript.

- Similarly, the inclusion of the lack of effect of AB burden based on ad lib vs. food restriction is potentially interesting, but here just adds more density to an already overpacked manuscript

We thank the reviewer for the suggestion. However, given that environmental enrichment and decreased food consumption could affect pathology in mouse models, we find these results important for readers to understand that lack of change in cognitive domains is not related to changes in pathology. We have decreased as much as possible the discussion on these issues and only provide information for readers to get the main message.

- The longitudinal analysis is potentially powerful, but also raises questions. Some discussion of how previous training might alter later performance needs to be included. There is a strong case to be made that touchscreen testing could be considered enrichment. How does previous experience alter later cognitive performance in these models versus standard housed non-tested mice?

This is an important point raised by the reviewer. We elaborated on this point in the manuscript (Results and Discussion), but the present work did not directly address this question.

However, one possible source of insight into this question might be in the data from mice performing the 5-Choice serial reaction time task. In our manuscript, we present data from the same mice tested at 3 ages, with final timepoint at 11-13 months, when mice would have been probed twice in this task. Romberg and colleagues, 2011, also tested a 3xTG-AD mice previously with the 5-Choice task at 10 months of age, however, these mice were naïve when tested at this age. The data and results between these two experiments are very similar, suggesting that prior cognitive training may not have a dramatic effect on subsequent touchscreen behavior in the 5-Choice test. Moreover, comparison of performance of mice longitudinally in the 5-Choice task suggests that accuracy does not change with age, although omission is improved after repeated training (See datasets from Figure 2 and Figure 4 and compare 2-6- and 11-13-month-old mice).

We have also analyzed the performance in pairwise visual discrimination and reversal with distinct images in different ages and found that depending on the image mouse performance varied, but previous training did not seem to affect the performance. This is now discussed in the Results and Discussion. We anticipate that with increased datasets being deposited, this important question will be addressed in the future for multiple tasks.

Reviewer #2:

[…] Below are a series of queries for the attention of the authors.

The title of the paper indicates that the pipeline will be used to study "rodent touchscreen-based cognitive assessment". However, the manuscript deals exclusively with mice. The absence of discussion of rats (or other species) appears to be a missed opportunity in this regard. Will the MouseBytes site support datasets from researchers studying rats, or other species?

The Introduction of the manuscript provides a compelling case for the importance of open science and the researchers should be lauded for pushing forward with this pipeline related to cognitive assessment. However, a more detailed discussion of the hardware used to collect the data should be provided. As the hardware used at both sites was the Bussey-Saksida Mouse Systems, it would of considerable interest for other open source hardware options were discussed (e.g., https://labrigger.com/blog/2018/03/09/custom-touchscreen-behavior-systems-for-rodents/). Will the data coming from these systems be compatible with the MouseBytes pipeline?

As described in response to reviewer 1, we have created a way for Mousebytes to incorporate data not only from Bussey-Saksida touchscreens systems, but from any variant of touchscreen operant chambers. To do that, we have developed codes that can be downloaded and modified by different users to convert XML files from any touchscreen system output to files that can be readable by MouseBytes (https://github.com/srmemar/Mousebytes-An-open-access-high-throughput-pipeline-and-database-for-rodent-touchscreen-based-data/blob/master/XML_Output.ipynb). These codes can be easily adapted by users with data obtained in other touchscreen systems. This is now mentioned in the Results and Discussion.

There are two important components to this study: 1) the Introduction of MouseBytes; and 2) the results of assessing the AD-related mice. I found that the specifics of the findings related to the AD-mice were lost in places, particularly the summary and end of the Introduction. How do the authors' experimental results advance our understanding of the 3 mouse models and what specific future research questions can/should be answered with them?

These issues have now been addressed in the Introduction and the Discussion. We identify common age-dependent attentional deficits in 3xTG-AD and 5xFAD mice. We also found that behavioral flexibility did not seem to be consistently affected in most mouse lines tested. This dataset provides the foundation to test new drugs to correct attentional deficits, which were consistently reproduced in 2 out of 3 mouse lines.

Reviewer #3:

[…] I have only two comments, which are essentially suggestions that I think might help make the paper stronger.

1) The question is how far can mice carry Alzheimer Disease research or any other disease related research, and is data sharing and transparency the answer to translation? The growing disillusionment with murine models of human disease especially in drug discovery, have questioned mouse models constructed with highly penetrant alleles of human disease because of the large number of compounds which apparently 'cure' the mouse but not the human. This discrepancy is repeatedly argued to be due to failure in data/experimental reproducibility causing journals to emphasize more statistical details as if statistics will provide greater translation. Seems to me that the mousebyte.ca database is one approach that could feasibly enable a better mouse to human translation or at least work in this direction. The truth of the matter is that it is virtually impossible to replicate data even in the same lab, let alone between labs for a multitude of reasons. Can mousebytes.ca facilitate translation?

The reviewer summarizes one of our motivations to create a repository for touchscreen datasets. Databases such as MouseBytes can provide more transparency, to allow others to analyze datasets and confirm experiments. We hope that our database can serve as an interface for researchers interested in studying replication and reproducibility.

Additionally, we are hoping to provide a platform for researchers to identify critical features in the lab environment that may contribute to behavioural variation. We have recently begun to address this by incorporation of new meta-data into the MouseBytes experiment records for factors such as colony light cycle, visual stimuli selection, and type of animal housing. We plan on expanding our meta-data system to include several more factors, but these can also be informed in manuscripts that are linked to datasets via their DOI. Eventually, with enough labs and experiments in the system, we can begin to systematically identify which factors may be contributing to experimental variation, and address the issues of translation and reproducibility.

2) At the moment, the paper focuses on three mouse lines thought to model Alzheimers disease (AD) to highlight the strength of the database. The database is also specific to data acquired using the operant touchscreen platform because non-automated methods, the authors argue, are subject to large variations. Are their plans to expand the database to a much larger cohort of mice that are not necessarily tested for cognitive behaviors tested on a touchscreen platform? The large array of autistic mouse models come to mind for which social communication and interaction, motor, cognitive and emotional behaviors are all variable leaving a very confused state in the field.

: We appreciate the desire and need to expand open platforms of behavioural sharing to non-touchscreen behaviour. At this time, we are not considering expanding our system to non-touchscreen behavior, because most of these conventional methods are not automated and have no standardized outputs. We have chosen to focus on touchscreen behaviour with the MouseBytes data, due to the inherent benefits of these systems for replication, experimental control, and translational validity. We are however interested in expanding the touchscreen repertoire to include tasks that are more emotional/social in nature and could probe different types of deficits in other mouse models. Indeed, we are aware of other researchers who are using videos in touchscreens to assess murine social behaviour. We also anticipate the contribution of datasets by the community of researchers using touchscreens using mouse models of developmental disorders.

Our view would be that although touchscreens are ideally suited for databasing applications, the hope is that other behaviours will eventually join the open science/datasharing movement. We think that MouseBytes could help to lead the way.

https://doi.org/10.7554/eLife.49630.sa2

Article and author information

Author details

  1. Flavio H Beraldo

    1. Robarts Research Institute, The University of Western Ontario, Ontario, Canada
    2. Graduate Program in Neuroscience, The University of Western Ontario, Ontario, Canada
    3. Department of Physiology and Pharmacology, The University of Western Ontario, Ontario, Canada
    Contribution
    Conceptualization, Data curation, Formal analysis, Supervision, Investigation, Visualization, Methodology, Project administration
    Contributed equally with
    Daniel Palmer, Sara Memar and David I Wasserman
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7638-2007
  2. Daniel Palmer

    1. Robarts Research Institute, The University of Western Ontario, Ontario, Canada
    2. Department of Psychology and Neuroscience Program, University of Guelph, Guelph, Canada
    Contribution
    Conceptualization, Data curation, Software, Formal analysis, Investigation, Visualization, Methodology
    Contributed equally with
    Flavio H Beraldo, Sara Memar and David I Wasserman
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3419-8647
  3. Sara Memar

    Robarts Research Institute, The University of Western Ontario, Ontario, Canada
    Contribution
    Data curation, Software, Visualization, Methodology
    Contributed equally with
    Flavio H Beraldo, Daniel Palmer and David I Wasserman
    Competing interests
    No competing interests declared
  4. David I Wasserman

    1. Robarts Research Institute, The University of Western Ontario, Ontario, Canada
    2. Department of Psychology and Neuroscience Program, University of Guelph, Guelph, Canada
    Contribution
    Conceptualization, Data curation, Investigation
    Contributed equally with
    Flavio H Beraldo, Daniel Palmer and Sara Memar
    Competing interests
    No competing interests declared
  5. Wai-Jane V Lee

    1. Robarts Research Institute, The University of Western Ontario, Ontario, Canada
    2. Graduate Program in Neuroscience, The University of Western Ontario, Ontario, Canada
    Contribution
    Formal analysis, Investigation
    Competing interests
    No competing interests declared
  6. Shuai Liang

    Rotman Research Institute, Baycrest Hospital, Toronto, Canada
    Contribution
    Data curation, Software
    Competing interests
    No competing interests declared
  7. Samantha D Creighton

    Department of Psychology and Neuroscience Program, University of Guelph, Guelph, Canada
    Contribution
    Formal analysis, Investigation
    Competing interests
    No competing interests declared
  8. Benjamin Kolisnyk

    1. Robarts Research Institute, The University of Western Ontario, Ontario, Canada
    2. Graduate Program in Neuroscience, The University of Western Ontario, Ontario, Canada
    Present address
    Deep Genomics, Toronto, Canada
    Contribution
    Data curation, Software, Formal analysis
    Competing interests
    No competing interests declared
  9. Matthew F Cowan

    Robarts Research Institute, The University of Western Ontario, Ontario, Canada
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  10. Justin Mels

    1. Robarts Research Institute, The University of Western Ontario, Ontario, Canada
    2. Graduate Program in Neuroscience, The University of Western Ontario, Ontario, Canada
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  11. Talal S Masood

    1. Robarts Research Institute, The University of Western Ontario, Ontario, Canada
    2. Graduate Program in Neuroscience, The University of Western Ontario, Ontario, Canada
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  12. Chris Fodor

    Robarts Research Institute, The University of Western Ontario, Ontario, Canada
    Contribution
    Formal analysis, Investigation
    Competing interests
    No competing interests declared
  13. Mohammed A Al-Onaizi

    1. Robarts Research Institute, The University of Western Ontario, Ontario, Canada
    2. Department of Anatomy and Cell Biology, The University of Western Ontario, Ontario, Canada
    Present address
    Department of Anatomy, Health Sciences Center, Kuwait University, Kuwait City, Kuwait
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  14. Robert Bartha

    1. Robarts Research Institute, The University of Western Ontario, Ontario, Canada
    2. Department of Medical Biophysics, The University of Western Ontario, London, Canada
    Contribution
    Conceptualization, Resources, Funding acquisition
    Competing interests
    No competing interests declared
  15. Tom Gee

    Rotman Research Institute, Baycrest Hospital, Toronto, Canada
    Contribution
    Data curation, Software
    Competing interests
    No competing interests declared
  16. Lisa M Saksida

    1. Robarts Research Institute, The University of Western Ontario, Ontario, Canada
    2. Department of Physiology and Pharmacology, The University of Western Ontario, Ontario, Canada
    3. Brain and Mind Institute, The University of Western Ontario, Ontario, Canada
    Contribution
    Conceptualization, Resources, Supervision, Funding acquisition
    Competing interests
    Lisa Saksida has established a series of targeted cognitive tests for animals, administered via touchscreen within a custom environment known as the “Bussey-Saksida touchscreen chamber”. Cambridge Enterprise, the technology transfer office of the University of Cambridge, supported commercialization of the Bussey-Saksida chamber, culminating in a license to Campden Instruments. Any financial compensation received from commercialization of the technology is fully invested in further touchscreen development and/or maintenance
  17. Timothy J Bussey

    1. Robarts Research Institute, The University of Western Ontario, Ontario, Canada
    2. Department of Physiology and Pharmacology, The University of Western Ontario, Ontario, Canada
    3. Brain and Mind Institute, The University of Western Ontario, Ontario, Canada
    Contribution
    Conceptualization, Resources, Supervision, Funding acquisition
    Competing interests
    Tim Bussey has established a series of targeted cognitive tests for animals, administered via touchscreen within a custom environment known as the “Bussey-Saksida touchscreen chamber”. Cambridge Enterprise, the technology transfer office of the University of Cambridge, supported commercialization of the Bussey-Saksida chamber, culminating in a license to Campden Instruments. Any financial compensation received from commercialization of the technology is fully invested in further touchscreen development and/or maintenance
  18. Stephen S Strother

    1. Rotman Research Institute, Baycrest Hospital, Toronto, Canada
    2. Department of Medical Biophysics, University of Toronto, Toronto, Canada
    Contribution
    Conceptualization, Resources, Supervision, Funding acquisition
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3198-217X
  19. Vania F Prado

    1. Robarts Research Institute, The University of Western Ontario, Ontario, Canada
    2. Graduate Program in Neuroscience, The University of Western Ontario, Ontario, Canada
    3. Department of Physiology and Pharmacology, The University of Western Ontario, Ontario, Canada
    4. Department of Anatomy and Cell Biology, The University of Western Ontario, Ontario, Canada
    Contribution
    Conceptualization, Resources, Supervision, Funding acquisition
    Competing interests
    No competing interests declared
  20. Boyer D Winters

    Department of Psychology and Neuroscience Program, University of Guelph, Guelph, Canada
    Contribution
    Conceptualization, Resources, Supervision, Funding acquisition
    For correspondence
    bwinters@uoguelph.ca
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3613-4441
  21. Marco AM Prado

    1. Robarts Research Institute, The University of Western Ontario, Ontario, Canada
    2. Graduate Program in Neuroscience, The University of Western Ontario, Ontario, Canada
    3. Department of Physiology and Pharmacology, The University of Western Ontario, Ontario, Canada
    4. Department of Anatomy and Cell Biology, The University of Western Ontario, Ontario, Canada
    Contribution
    Conceptualization, Resources, Supervision, Funding acquisition
    For correspondence
    mprado@robarts.ca
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3028-5778

Funding

Weston Brain Institute

  • Robert Bartha
  • Stephen S Strother
  • Boyer D Winters
  • Marco AM Prado

Canadian Institutes of Health Research (MOP136930)

  • Marco AM Prado

Alzheimer Society

  • Vania F Prado
  • Marco AM Prado

Canada First Research Excellence Fund (BrainsCAN)

  • Robert Bartha
  • Lisa M Saksida
  • Timothy J Bussey
  • Vania F Prado
  • Marco AM Prado

Brain Canada (Multi-Investigator Research Grant)

  • Vania F Prado
  • Marco AM Prado

Canadian Institutes of Health Research (MOP126000)

  • Vania F Prado
  • Marco AM Prado

Canadian Institutes of Health Research (MOP89919)

  • Vania F Prado
  • Marco AM Prado

Natural Sciences and Engineering Research Council of Canada

  • Lisa M Saksida
  • Timothy J Bussey
  • Vania F Prado

Canada Research Chairs

  • Lisa M Saksida
  • Marco AM Prado

Brain Canada (Canada Open Neuroscience Platform)

  • Sara Memar
  • Timothy J Bussey
  • Marco AM Prado

Mitacs

  • Daniel Palmer
  • Lisa M Saksida
  • Timothy J Bussey

CIFAR

  • Lisa M Saksida

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We thank Chris Gorgolewsky for helpful discussion, Jue Fan and Sanda Raulic for mouse genotyping support, Suro Lee, Hillary Kim, Meghan Thorne, Jocelyn Shubert, Martine Grenon, and Theresa Martin, for the help with mouse weighing and feeding.

This work was supported by the Weston Brain Institute (Canada), Canadian Institute of Health Research (MOP136930, MOP126000 and MOP89919), NSERC, Alzheimer’s Society of Canada, Canadian First Research Excellence Fund (BrainsCAN) and Brain Canada. LMS is supported by CIFAR, SM is a Canadian Open Neuroscience Platform (CONP) Scholar, DP received a Post-Doctoral fellowship from MITACS, LMS and MAMP are Tier 1 Canada Research Chairs.

Ethics

Animal experimentation: Procedures were conducted in accordance with approved animal protocols at the University of Western Ontario (2016/104) and the University of Guelph (3481) following the Canadian Council of Animal Care and National Institutes of Health guidelines.

Senior Editor

  1. Huda Y Zoghbi, Texas Children's Hospital, United States

Reviewing Editor

  1. Andrew Holmes, NIH, United States

Reviewers

  1. John G Howland, University of Saskatchewan, Canada
  2. Yogita Chudasama, NIMH IRP, United States

Publication history

  1. Received: June 24, 2019
  2. Accepted: December 11, 2019
  3. Accepted Manuscript published: December 11, 2019 (version 1)
  4. Version of Record published: December 27, 2019 (version 2)

Copyright

© 2019, Beraldo et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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