Data repurposing from digital home cage monitoring enlightens new perspectives on mouse motor behaviour and reduction principle

Abstract

In this longitudinal study we compare between and within-strain variation in the home-cage spatial preference of three widely used and commercially available mice strains—C57BL/6NCrl, BALB/cAnNCrl and CRL:CD1(ICR)—starting from the first hour post cage-change until the next cage-change, for three consecutive intervals, to further profile the circadian home-cage behavioural phenotypes. Cage-change can be a stressful moment in the life of laboratory mice, since animals are disturbed during the sleeping hours and must then rapidly re-adapt to a pristine environment, leading to disruptions in normal motor patterns. The novelty of this study resides in characterizing new strain-specific biological phenomena, such as activity along the cage walls and frontality, using the vast data reserves generated by previous experimental data, thus introducing the potential and exploring the applicability of data repurposing to enhance Reduction principle when running in vivo studies. Our results, entirely obtained without the use of new animals, demonstrate that also when referring to space preference within the cage, C57BL/6NCrl has a high variability in the behavioural phenotypes from pre-puberty until early adulthood compared to BALB/cAnNCrl, which is confirmed to be socially disaggregated, and CRL:CD1(ICR) which is conversely highly active and socially aggregated. Our data also suggest that a strain-oriented approach is needed when defining frequency of cage-change as well as maximum allowed animal density, which should be revised, ideally under the EU regulatory framework as well, according to the physiological peculiarities of the strains, and always avoiding the “one size fits all” approach.

A minimal metadata set (MNMS) to repurpose nonclinical in vivo data for biomedical research

“…The efficient description and retrieval of raw data enables their application for scopes beyond those intended during the original collection, therefore reducing the number of animals or, in some cases, replacing animals needed for novel experiments. An example of this approach was recently demonstrated by Fuochi et al, who reinterrogated data obtained by home-cage monitoring to gain new insights into mouse behavior 33 .…”

What Can Laboratory Animal Facility Managers Do to Improve the Welfare of Laboratory Animals and Laboratory Animal Facility Staff? A German Perspective

“…In other words, larger cages would be beneficial for mice because larger cages would allow more and better environmental refinements for their inhabitants. The need to provide animals with more floor space is also supported in terms of hygiene and welfare [52]. Fuochi et al 2023 clearly mention the need to review the minimum cage space requirements described in EU legislation.…”

“…The need to provide animals with more floor space is also supported in terms of hygiene and welfare [52]. Fuochi et al 2023 clearly mention the need to review the minimum cage space requirements described in EU legislation. However, regulatory changes in this direction would come at a substantial cost: existing cage systems would have to be replaced on a large scale, and maintenance capacity per facility would decrease.…”

Big data and its impact on the 3Rs: a home cage monitoring oriented review

“…In addition to these examples, Big Data generated by automatic home cage recording systems has the potential to be reused for a completely different decision/task than for what it was originally intended (Woodall, 2017 ) and to describe new biological phenomena without the use of new animals (Fuochi et al, 2023 ). The availability of open-source data repositories, collecting data originating from in vivo research, can be accessed and used by researchers before planning new in vivo experiments.…”

“…Key metrics, such as locomotor activity, distance traveled, and index of bedding status, are calculated from the raw data in the cloud and then provided to a web-based software application (DVC ® Analytics, Tecniplast S.p.A., Buguggiate VA, Italy). Other metrics can then be derived from these (e.g., Golini et al, 2020 ; Fuochi et al, 2023 ) to generate novel digital biomarkers that can lead to new valuable insight into phenotypes and significantly help the application of the principles of the 3Rs (Baran et al, 2022 ).…”