From “Artificial” to “Real”: What 24/7 Home Cage Monitoring Teaches Us In Pre-Clinical Neurodegenerative Disease Models

From “Artificial” to “Real”: What 24/7 Home Cage
Monitoring Teaches Us In Pre-Clinical
Neurodegenerative Disease Models
Science of Aging Series
Stefano Gaburro, PhD
Scientific Director
Tecniplast S.p.A.
Brun Ulfhake, MD, PhD
Senior Professor
Karolinska Institutet

Throughout this presentation, Stefano and Brun will discuss continuous
home cage monitoring and how moving behavioral testing to a setting
that is familiar to the animals can decrease concerns of validity and
animal welfare.
Science of Aging Series
From “Artificial” to “Real”: What 24/7 Home Cage
Monitoring Teaches Us In Pre-Clinical
Neurodegenerative Disease Models

Digital Ventilated Cages (DVC®): See
what you have been missing with 24/7,
contactless “real” home cage monitoring
Copyright 2021 S. Gaburro, APS, and InsideScientific. All Rights Reserved.
Scientific Director
Tecniplast S.p.A.
E-mail: stefano.gaburro@tecniplast.it
LinkedIn: https://www.linkedin.com/in/stefanogaburro/

innovation through passion
1962 1992 1994 1997 2005
1949
Tecniplast
foundation
the first moulded
plastic cage
IVC first generation
Laminar Flow
solutions
the Aquatic Division
DVC®
the Digital (R)Evolution
TODAY
Tecniplast historical milestones in supporting science

Traditional
Standard laboratory animals testing

Experiment
• 24/7  Animals more active at night
• Unlimited observation time vs. 5 min
• Environmental factors controlled (age*environment)
Pillars of home cage monitoring

Animals (mice) spend 99% of their time
IVC conditions met!
Baran et al, Manuscript in preparation
Real Home ≠ Temporary Home (artificial)

DVC® system

DVC® system
DVC® system

Electrodes capacity
perturbations are
translated into
activations
RAW DATA METRIC
The corresponding data metric can be then grouped and displayed in different way to be analyzed
DVC® already validated metrics:
Animal Locomotion Index

https://www.frontiersin.org/articles/10.3389/fnbeh.2020.575434/
DVC® in Science

Effects of DVC microelectromagnetic fields (EMF) on
behavior and pathology
BEHAVIOUR PATHOLOGY

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6451168/
DVC® Individual Mouse Tracking

Example: Home Cage phenotyping of common inbred
and outbred strains

Study #1: Home Cage phenotyping of common inbred
and outbred strains

Average activity day/night Normalised activity day/night
Study #1: Home Cage phenotyping of common inbred
and outbred strains

• CD1 outbred mice have > activity vs. inbred mice
• Response to cage change is more prominent in CD1
mice (average activity)
• Sex differences mostly notable in CD1 mice
Study #1: Conclusions

Study #2: ALS progression in transgenic SOD mice

Goal of the experiment:
• Assess DVC® capabilities to detect ALS progression over time (ALS-related
locomotion impairments symptoms in SOD mice appear at ~16 weeks of age)
vs. standard behavioral test (e.g. grid test)
Experimental settings:
• 10 cages WT and KO male, 16 cages WT and KO female
• 2x mouse/cage
• > 4 months mice observation via DVC®
• 1x week cage change
• Behavioral Test Grip Test, Grid Test Sign of muscular Atrophy

Qualitative assessment of activity: Heatmap
Study #2: ALS progression in transgenic SOD
mice

Sample Entropy: Assess variation
with interrelated data series
Physiological time series (e.g. ECG)
To assess cardiac issues
Quantitative assessment of activity: Regularity Disruption Index (RDI)
Activity
Study #2: ALS progression in transgenic SOD
mice

Quantitative assessment: Regularity Disruption Index (RDI) vs. Grid Test

RDI is irrespective
of cage change

Activity in ‘least active hour’
RDI
Age 16-20 weeks
Day time Night time

Quantitative assessment: Regularity Disruption Index (RDI) vs. Body Weight

• Proven that RDI can be used as biomarkers for early
ALS identification
• Gender differences in the time development of the
disease in all parameters

Pilot Study Parkinson Model
(12 Months, male mice)

DVC® helps scientists addressing more scientific questions
regarding data reproducibility and at identifying clear
pathological symptoms in advance
Take-home Messages

Rhythms and bouts of rest and
activity of mice recorded 24/7
with a DVC® system
Copyright 2021 B.Ulfhake, APS, and InsideScientific. All Rights Reserved.
Senior Professor

Automated non-intrusive home-cage monitoring is ideal for obtaining cumulative records
of spontaneous in-cage behaviours of small rodents.
Collected data records can be used to build libraries of behaviours covering different
strains, sex, age and holding conditions. Such libraries can be used for unbiased data-
driven (multivariate, machine-learning, functional and spectral) analysis of small
laboratory rodent’s spontaneous behaviours.
…………
We have used HCM to analyze rhythmicities of mouse in-cage activity and rest.
Why home-cage monitoring (HCM) ?
Rhythms and bouts of rest and activity of mice recorded
24/7 with a DVC® system

Rhythmicities of small rodents in-cage behaviour
Circadian: internal oscillator (SCN; 21-27h) that entrains to light-off/-on (12h/24h); type 1*
Slow rhythmicity (seasonal-like): internal oscillator (period ~100 days). For laboratory mice
there is no known external timer (asynchronous across cages and seasons of the year) but the
group of mice in the cage entrain to the same rhythm. Type 1 or 2?
Internal oscillator (type 2): patterns of sleep (SWS, REM), seasons of life: prenatal and
postnatal development, menarche/maturity, post-reproductive era/aging. Internal clock;
growth of body height and menarche are highly heritable.
External oscillator (type 3): recurring husbandry/experimental routines. Doesn’t show if no
external trigger.
* Zucker, I. in Circannual rhythms Mammals in Handbook of Behavioral Neurobiology] Handbook of Behavioral Neurobiology Vol. Vol 12 509–529 (2001).

Data analyses
(non-exhaustive listing)
Spectral analysis can be used to analyze clustering of activities with different period
and if these changes as we age, or in response to interventions/ challenges or show
specific spatial patterns.
Functional analysis can be used to describe rhythmicities in behaviours.
Bouts of activity and rest can be extracted by decomposition analysis of the data set
and may provide details of mice behavioural repertoire.

Circadian rhythm of activity and rest
Male B6 (5/cage)
Female B6 (5/cage)

Activity and rest of the day
(by DVC in-cage recording)
Circadian rhythm of activity and metabolic rate
Respiratory exchange rate (RER) and energy expenditure EE
of the day (by indirect calorimetry)
Lights-off

Colour key to bouts:
Red: High activity
Grey: Low-to-medium activity
Black: Rest (short bouts)
White: Rest (long bouts)
M B6x5
Bouts of activity and rest by decomposition analysis
Fraction
of
total
time
h
-1
Hours of the day
F B6x5

Bouts of activity and rest by decomposition analysis
Key to bouts:
High activity
Low-to-medium activity
Rest (short bouts)
Light grey: Rest (long bouts)
Average of 10 cages with two male mice in each
Single cages

Activity is context, strain, sex, and age dependent.
In the short time- perspective it correlates with number of animals in
the cage.
Number of animals in a cage (5-1 & 1-5)

Spatial aspects on bouts and different levels of activity
in cages with mice housed at different density
One example:

Husbandry entrained type 3 rhythmicity of in-cage behaviour:
Cage change

Slow (circannual) rhythmicity of in-cage behaviours
(seasonal-like rhythm)

Scientific Reports | (2021) 11:4961 | https://doi.org/10.1038/s41598-021-84141-9

Slow –season-like- oscillation of unknown origin. It is likely
driven by an endogenous oscillator.

Spectral analysis

Activity bouts by decomposition analysis

A bout duration is the time window of sustained activity
within a predefined activity range:
Rest, low intensity, medium intensity and high intensity activity.
The duration and frequency of, and transition between, bouts may
provide insights into in-cage life and how this changes over time,
across week days etc.
Bouts of activity
All bouts, Female B6x4, n=10
100
75
50
25
0
Cumulative
freq.
(%)
10 100 1000
Bout duration sec (log scale)
20
15
’
10
5
0
50
40
30
20
10
0
Red= high intensity activity
Green= low-to-medium activity
Blue= rest
10 100 1000

Using decomposition analyses to show differences between
female and male mice of day-time and night-time activities.
Female = grey dots
Male = dark and light blue dots
Rest
Low-to-
medium
activity
High activity
Lights-OFF
Rest
Low-to-
medium
activity
High activity
Lights-ON

Changes in the composition of bouts of rest and activity across
slow-oscillations in aging
Fraction
of
total
weekly
activity
Average
activitaions

Pro’s
• Is scalable and operates in real-
time/near real-time
• Can be fully automated
• Provides non-intrusive cumulative
24/7 data collections of in-cage
activity
• Useful complement to animal welfare
surveillance and behavioural
phenotyping
• Possible to intergrate or combine with
other recording devices
Con’s
• The basic system records only floor
activity and if animals are abscent
from the floor.
• It cannot provide individual data if
mice are group-housed.
• The signal from the electrode may
also be impacted by other factors
than movements of live animal.
The DVC Pro’s and Con’s

Added value
• Will improve our understanding of spontaneous home-cage behaviours of laboratory
mice.
• Help us to characterize behavioural responses to care and use
procedures/interventions and assist in experimental design and monitoring.
• May be used to screen behavioural phenotypes of different genotypes.
• DVC data collections will enable us to build-up libraries on strain, substrains, sex and
age-associated home-cage behaviors that can be very useful for future husbandry and
research efforts.
We are still in the infancy of home-cage monitoring and have yet much to learn how to best use this technique and about the
mice that live in the cage.
The DVC Pro’s and Con’s

This presentation is based on data published:
doi.org/10.1038/s41598-021-84141-9
doi.org/10.1371/journal.pone.0211063
and
unpublished data and preliminary observations from
ongoing studies that should not be redistributed and
that may differ form the data that will be published
once the studies have been completed.
Funding was provided by the Swedish research council
and Karolinska Institutet (KI)
Collaborators:
Dr Karin Pernold, Departmen of Laboratory medicine, KI
Dr Eric Rullman, Department of Laboratory medicine, KI
The Tecniplast SpA DVC team:
Giorgio Rosati
Mara Rigamonti
Fabio Iannello
Stefano Zordan
Dr M. Raspa, CNR-Campus, Monterotondo Scalo, Rome, IT
Dr M. V. Wiles, The Jackson Laboratory, Bar Harbor, US
Staff and consultants at:
The Wallenberg facility at KI, 2015-2019
The ECF facility at SU, 2019-
Disclaimer and acknowledgements
Thank you for listening!

Thank you for
participating!
Scientific Director
Tecniplast S.p.A.
Senior Professor
CLICK HERE to learn more and
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From “Artificial” to “Real”: What 24/7 Home Cage Monitoring Teaches Us In Pre-Clinical Neurodegenerative Disease Models

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