Dr. John Lighton, PhD and InsideScientific discuss the importance of metabolism and its relation to behavioral studies in the mouse model. “Micro-intake events” can comprise between 20% to 50% of total feeding events in C57BL/6 mice on a 12H/12H diurnal cycle. Given that each event corresponds to a decision to initiate intake followed by rapid satiety and termination of feeding behavior, what is the relevance of these gustatory signals to the brain? And how does one measure the outcome? Animal behavior and metabolism are traditionally measured using very different techniques operating at divergent timescales that are often poorly, if at all, synchronized. This makes analyzing meaningful correlations between metabolic measurements, intake events and animal behavior difficult or virtually impossible. To address this challenge, Sable Systems thought out the design of an integrated metabolic and behavioral monitoring system that would no only provide researchers the collective measurement capabilities needed by also introduce a re-thinking of current best practices. In addition to presenting essential physiology concepts, Dr. Lighton demonstrates the power of synchronized data acquisition with temporal resolution and precision that can extract unprecedented detail from circadian cycles of behavior and metabolism.

1. Circadian Rhythms
of Food Intake:
Are You Seeing The
Whole Picture?

2. Circadian Rhythms of Food Intake:
Are You Seeing The Whole Picture?
Circadian Rhythms of Food Intake (John Lighton, PhD)
• What is your Aim?
• Essential Tech, Tools and Approach
• It’s All About Data Resolution (Resting EE, Activity, and Food Intake)
• Environmental Influences
• Some Additional Tech and Closing Thoughts

3. InsideScientific is an online educational environment
designed for life science researchers. Our goal is to aid in
the sharing and distribution of scientific information
regarding innovative technologies, protocols, research tools
and laboratory services.

4. Circadian Rhythms of Food Intake:
Are You Seeing The Whole Picture?
John Lighton, PhD
President & Chief Scientist,
Sable Systems International
Copyright 2015 InsideScientific & Sable Systems International. All Rights Reserved.

5. Question: What is Your Aim?
Ultimately, that’s up to you & your research questions!
But -
• Proximately, you probably need to obtain data…
• Now: Describing proximate ends to achieve your ultimate goals
• Also some interesting findings…
– On intake (food, water), and output (metabolic heat and activity)
– These data should be synchronized
– These data should have high temporal resolution
– The data resolution should be high
– Analytical flexibility should be maximal
– The more data modalities, the better…

6. THE CAGE…
• The ideal is…
• Home Cage
• Ambient RH
• Autoclavable
• BPA-free
• No seal required
• Metabolic
measurement
optional

8. MASS MONITORING
• Req’d for Gravimetric
Intake Monitoring
• Force Transducer =
Easy, Cheap
• Limited Range
• Poor Resolution
• OK for Student Labs
• The Alternative…

9. MASS MONITORING
• Real Load Cell (the
standard for accurate
mass measurement)
• Lab Balance
Resolution, Range
• Radical Innovation
(compact, intelligent,
retains individual
calibration)
Addresses Resolution
Challenges and issues
of “dead volume”

10. FOOD INTAKE
• Universal Load Cell
• Real-time measurement
• Recorded to disc at 1Hz
• 2 mg resolution,
1 kg range
• Crumb tray – no spillage
• Grille spacing reduces
caching = accurate intake
• Multiple grilles available
• Fine granularity gives
insight into behavioral
patterns

11. Raw Feeding Data
• Stores hopper mass vs. time
• Recorded to disc at 1Hz
One Feeding Episode
• Analyze behavior
• Determine individual
feeding episode intake
rates

12. Processed
Feeding Data
• Outputs cumulative
intake vs. time
• Shows rate of intake,
structure
• Can output binned data
• Integrates with other
cage data
• Fully traceable
FOOD INTAKE

13. Processed
Feeding Data
• Outputs cumulative
intake vs. time
• Shows rate of intake,
structure
• Can output binned data
• Integrates with other
cage data
• Fully traceable
Food Selection
• Determine food preference
• Control access to
either/both foods
• Integrates with other
cage data
FOOD INTAKE

14. FOOD INTAKE ANALYSIS
StartDate StartTime EndDate EndTime InterUp_minUp_min Up_g Up_g_min Stud_t Prob RFID
4/5/2013 18:22:47 4/5/2013 18:24:24 0 1.63 0.017 0.011 10.78 0 45779
4/5/2013 18:24:40 4/5/2013 18:27:02 0.27 2.38 0.004 0.002 2.43 0.029 37355
4/5/2013 18:27:23 4/5/2013 18:28:08 0.35 0.77 0.013 0.017 20.21 0 45779
4/5/2013 18:28:46 4/5/2013 18:29:10 0.63 0.42 0.003 0.008 8.21 0 45779
4/5/2013 18:30:55 4/5/2013 18:32:02 1.75 1.13 0.009 0.008 26.44 0 37355
4/5/2013 18:37:02 4/5/2013 18:41:06 5 4.08 0.089 0.022 8.79 0 37355
4/5/2013 18:41:26 4/5/2013 18:46:00 0.33 4.58 0.068 0.015 77.59 0 45779
4/5/2013 18:48:30 4/5/2013 18:50:40 2.5 2.18 0.06 0.027 302.49 0 37355
4/5/2013 18:58:57 4/5/2013 18:59:08 8.28 0.2 0.006 0.032 16.39 0 37355
4/5/2013 19:27:38 4/5/2013 19:30:36 7.62 2.98 0.083 0.028 164.49 0 37355
4/5/2013 19:31:00 4/5/2013 19:32:44 0.4 1.75 0.072 0.041 150.41 0 45779
4/5/2013 19:35:09 4/5/2013 19:36:23 2.42 1.25 0.114 0.092 11 0 37355
4/5/2013 19:36:53 4/5/2013 19:39:01 0.5 2.15 0.016 0.008 7.64 0 37355
4/5/2013 19:39:11 4/5/2013 19:40:12 0.17 1.03 0.027 0.026 7.89 0 45779
4/5/2013 19:40:50 4/5/2013 19:41:17 0.63 0.47 0.003 0.006 5.99 0 37355
4/5/2013 19:41:32 4/5/2013 19:43:05 0.25 1.57 0.015 0.01 40.12 0 45779
4/5/2013 19:44:50 4/5/2013 19:45:21 1.75 0.53 0.005 0.01 13.2 0 45779
4/5/2013 19:53:24 4/5/2013 19:53:39 6.15 0.27 0.01 0.039 29.99 0 45779
4/5/2013 19:54:15 4/5/2013 19:58:09 0.6 3.92 0.146 0.037 441.09 0 37355
4/5/2013 20:03:56 4/5/2013 20:07:18 5.78 3.38 0.11 0.032 270.45 0 45779
4/5/2013 20:07:27 4/5/2013 20:07:50 0.15 0.4 0.061 0.152 5.23 0 45779
4/5/2013 20:27:15 4/5/2013 20:30:20 19.42 3.1 0.142 0.046 397.8 0 45779
4/5/2013 20:34:13 4/5/2013 20:36:19 3.88 2.12 0.075 0.035 275.58 0 45779
4/5/2013 20:37:13 4/5/2013 20:39:12 0.9 2 0.117 0.058 368.39 0 45779
• Available Over Any
Desired Interval
• Full List Of All Intake
Events
• Resolution 2 mg
• 10-20x Finer Resolution
Than Legacy Systems
• (Highlighted: Events
Visible Only to
Promethion)
• Statistical Verification of
Each Intake Event

15. • Micro-Intake Events =
30%+ of All Ingestive
Behavior
• Proportion of Micro-
Intake Events is Higher
in the Photophase!
• Each = Initiation & Early
Termination of Ingestive
Behavior

16. FOOD ACCESS CONTROL
Access Control
Module
• Computer controlled
access to food
• Available for Mouse or
Rat food hoppers
• Intelligent Obstruction
Detection
• Access Control Door
• Connects to mass
sensor
• Light Source & Bedding
Temperature Sensor
• Customizable assays
(paired/yoked)

17. Food Access Control
• Select from 8 preset assays
• Customize time and duration
• Individual cage setup
Select
Access
Control
Type
Add to your
custom
assay
Unlimited
number of
variations

18. WATER INTAKE
Water Intake
Monitor
• 2 µL resolution –
accuracy!
• Real-time measurement
• Key is to reduce leakage
• Increased granularity
gives insight into
behavioral patterns

19. Raw Drinking Data
• Stores bottle mass vs. time
• Recorded to disc at 1Hz
Processed
Drinking Data
• Analyze
behavior
• Eliminate drift
• Determine
individual episode
intake rates

20. BODY MASS Body Mass
Monitor
• 2 mg resolution
• Real-time measurement
• Recorded to disc at 1Hz
• Highly accurate
• Provides in-cage
enrichment
• Reduced technician
interaction

21. Raw Body Mass Data
• Measure mass vs. time
• Ensure data is recorded to disc at sufficient
rate (1Hz) – typical log is every 15 min
• Is used about every 15 minutes
Track body
mass over
time

22. Processed
Body Mass
Data
• Stores most recent
body mass for each
timestamp
• Synchronized with
other data
• No handling stress
Circadian Cycle
Clearly Visible

23. VOLUNTARY EXERCISE
• Real-time measurement
• Magnetic Reed Switch
• Wheel Stop available
• Increased granularity
gives insight into
behavioral patterns
• Correlates with metabolic
data
Running
Wheel

24. Raw Running Wheel Data
• Stores RPS vs. time
• Recorded to disc at 1Hz
Processed Running
Wheel Data
• Shows cumulative
distance run
• Can be binned and
synchronized with
metabolic data

25. TOTAL ACTIVITY
• XY and Z IR arrays
• 1 cm beam spacing
• 0.25cm calculated centroid
• Real-time measurement
• Intelligent obstruction
detection
• Rearing captured with
Z-axis
• Highly accurate
• Not affected by static
obstructions
• Correlated with metabolic
data
• Fine & coarse motion are
separable

26. Activity Analysis
• Total Activity
• Total Distance Traveled
• Rearing
• Activity associated with in-cage devices
• In cage position vs. time
• Customizable assays
• Position histograms
Activity Comparison
• WT vs. ob/ob
• Interaction with FH-MB
• Time spent time spent near food hopper
• Time spent at cage perimeter vs. inside

28. CALORIMETRY
Flow Generators
& Gas Analyzers
• Automated calibration
• Sequential or continuous
monitoring
• Can group for 4, 8,16, 24,
32 etc. cages
• Expandable and modular
• Water vapor dilution
correction
• Integrated O2, CO2, WVP
analyzers
• Pull-mode (negative
pressure)

29. PULL MODE…

30. MULTIPLEXED CALORIMETRY
• Metabolic measurement of multiple animals in
sequence
• Economical – shares analyzers between animals
• We can reduce dwell time to < 15 sec, yielding a cycle
time of 2 minutes/8, 16 or 24 animals
• This is < 50% of the time constant of the cages!
• Excellent for determining mean, resting, and active EE
• Even possible to correlate EE with activity

31. CONTINUOUS CALORIMETRY
• Metabolic measurement of multiple animals
simultaneously
• One analyzer chain per animal
• Time resolution for metabolic signals = 1 second
• This allows mathematical removal of washout effects
• Excellent for determining even the most fleeting and
subtle metabolic signals

32. DATA ANALYSIS Data
Synchronization
• Overlay metabolic data
with other continuously
monitored parameters
• All data are perfectly
synchronized and can be
exported to other
programs if required
(open formats)

33. It’s All About Data Resolution

34. BEHAVIORAL ANALYSIS
Raw Data Synchronization - Zoomed
•Body mass (black), food intake (red), wheel (gold), and water intake (blue)

35. Automatic
Behavior
Extraction
EFODA Intake from food hopper A (significant intake found)
TFODA Interaction with food hopper A (no significant intake)
DWATR Intake from water dispenser (significant intake found)
TWATR Interaction with water dispenser (no significant intake)
WHEEL Interaction with wheel (>= 1 revolution)
IHOME Entered habitat (stable mass reading)
THOME Interaction with habitat (no stable mass reading)
LLNGE Long lounge (> 60 sec, no non-XY sensor interactions)
SLNGE Short lounge (5 - 60 sec, no non-XY sensor interactions)
EFODB Intake from food hopper B (significant intake found)
TFODB Interaction with food hopper B (no significant intake)
• Based On Sensor
Interactions
• Fully Automated
• No Video Analysis
Required
• All Animals
Simultaneously

36. EACH BEHAVIOR HAS:
1. Date and time of start, end of behavior
2. Seconds duration
3. Mean X, Y position during behavior
4. Total distance in cm moved during behavior
5. Percent of time spent rearing during behavior
6. Quantification of behavior (behavior-dependent), e.g.:
1. Mass of food or water intake
2. Meters run on wheel
3. Body mass (in habitat)
7. Optional parameters (e.g. EE, RQ, Tb, HR, etc. etc.) as specified

37. BEHAVIOR DATA LIST
Start_Date Start_Time End_Time Durat_Sec Activity Amount Rear% X_cm Y_cm S_cm
5/26/2012 18:33:33 18:33:53 22 IHOME 21.896 0 8.8 22.3 0
5/26/2012 18:33:54 18:34:05 13 SLNGE 17 100 8.8 18.4 17
5/26/2012 18:34:06 18:35:46 107 WHEEL 56 0 9.1 3.8 0
5/26/2012 18:35:47 18:36:07 23 SLNGE 38 100 8.6 12.3 38
5/26/2012 18:36:08 18:37:51 110 WHEEL 79 0 9.1 3.8 0
5/26/2012 18:37:52 18:38:00 10 SLNGE 8 100 9.3 4.6 8
5/26/2012 18:38:01 18:38:07 7 DWATR 0.013 85.7 8.7 24.2 8
5/26/2012 18:38:08 18:38:25 19 SLNGE 29 100 8.3 22 29
5/26/2012 18:38:26 18:42:52 284 WHEEL 184 0 9.1 4.1 0
5/26/2012 18:42:52 18:43:03 12 SLNGE 4 100 9 4.3 4
5/26/2012 18:43:04 18:43:08 5 DWATR 0.052 80 8.3 16 17
5/26/2012 18:43:08 18:43:15 8 SLNGE 0 12.5 8.1 22.2 0
5/26/2012 18:43:16 18:43:21 6 THOME 0 83.3 8.7 21.8 0
5/26/2012 18:43:22 18:43:42 23 SLNGE 18 65.2 10.2 17.5 18
5/26/2012 18:43:43 18:43:45 3 WHEEL 44 0 9.6 4.8 0
5/26/2012 18:43:46 18:43:51 6 TFODA 0 100 9.5 16.1 18
5/26/2012 18:43:52 18:44:54 67 WHEEL 44 0 9.1 4.1 0
5/26/2012 18:44:55 18:45:18 26 SLNGE 20 69.2 9.9 6.5 20
5/26/2012 18:45:19 19:59:49 4756 IHOME 21.971 0 8.8 22 0
5/26/2012 19:59:50 19:59:57 9 SLNGE 1 100 8.8 21.9 1
5/26/2012 19:59:58 20:03:39 236 EFODA 0.058 42.4 9 20.2 2

38. TIME BUDGETS
Behavior Minutes Percent
efoda 153.2 11.51
tfoda 3.6 0.27
dwatr 9.5 0.71
twatr 1.3 0.1
wheel 212.2 15.94
ihome 596.2 44.78
thome 5.6 0.42
llnge 281.4 21.13
slnge 68.3 5.13

39. LOCOMOTION BUDGET (-WHEEL)
Behavior Meters Percent
efoda 13.5 14.37
tfoda 3.3 3.56
dwatr 6.4 6.79
twatr 0.8 0.82
ihome 0 0
thome 2.4 2.55
llnge 36.2 38.63
slnge 31.2 33.28

40. LOCOMOTION BUDGET (+WHEEL)
Behavior Meters Percent
efoda 13.5 0.4
tfoda 3.3 0.1
dwatr 6.4 0.19
twatr 0.8 0.02
wheel 3241.2 97.19
ihome 0 0
thome 2.4 0.07
llnge 36.2 1.08
slnge 31.2 0.93

41. TRANSITION PROBABILITY MATRIX
efoda tfoda dwatr twatr wheel ihome thome llnge slnge
efoda 0 0 10.64 2.13 2.13 6.38 0 12.77 65.96
tfoda 0 0 7.14 3.57 14.29 0 0 7.14 67.86
dwatr 5.13 2.56 0 0 0 2.56 0 2.56 87.18
twatr 11.11 22.22 0 0 11.11 0 0 22.22 33.33
wheel 0 3.06 0 1.02 0 0 0 5.1 90.82
ihome 0 0 0 1.39 0 0 0 34.72 63.89
thome 0 2.44 0 0 2.44 0 0 26.83 68.29
llnge 13.21 20.75 1.89 1.89 11.32 20.75 30.19 0 0
slnge 14.8 4 12.4 1.6 34 22.8 10.4 0 0

42. EthoScan: Can Extract Behavior-Specific Data!
Activity Amount Rear% X_cm Y_cm S_cm temp light kcal_hr_2 RQ_2
LLNGE 28 14.8 8.4 7.6 28 21.10325 0.734515 0.34031 0.764798
EFODA 0.081 49.4 9.1 20.1 13 21.10951 0.739017 0.48767 0.800055
SLNGE 7 100 9.7 6.9 7 21.14026 0.738523 0.484761 0.790139
WHEEL 6 0 9.1 4 0 21.14551 2.797765 0.52275 0.817151
SLNGE 2 100 9 4.2 2 21.15659 3.756291 0.569091 0.818588
THOME 0 100 9 4.8 0 21.16365 3.747151 0.569289 0.80943
SLNGE 0 100 9 4.4 0 21.15832 3.740504 0.542788 0.822751
WHEEL 54 0 9.1 3.8 0 21.16305 3.725633 0.612379 0.826051
SLNGE 2 100 9.2 3.9 2 21.11449 3.715779 0.629385 0.832367
TFODA 0 100 12.3 17.3 5 21.11274 3.714397 0.642374 0.837089
SLNGE 29 77.8 7.6 10.7 29 21.10952 3.712675 0.642436 0.842363
DWATR 0.017 0 8.3 22 4 21.09149 3.712122 0.628371 0.843027

43. Resting EE…

44. Cause of this
variation?
• Short Visits to Habitat
Correlate with High EE
• Long Visits = Separate
Data Population
• Why?
• To Answer – Need High
Temporal Resolution of
Metabolic Signals
IN ENRICHMENT HABITAT

45. ACTH &
Cortisol spike?
DETAIL OF EE IN HABITAT
• Cool-Down Period Lasts
~15 Min
• Variable Low Activity
Duration Correlates with
Low EE
• ANY Movement is
Detectable in Habitat
• EE Rises PRIOR to
Activity (Leaving
Habitat)
• How Can You Apply
This?

46. CONSISTENT > of EE before activity begins!

47. It’s All About Data Resolution

48. Activity…

49. Variation
caused by
different
running
speeds
RUNNING WHEEL
• Most Energy-
Intensive
Behavior in Cage
• C57BL/6J Mice
Run ~6km in
Scotophase
• Variable
Metabolic
Signature

50. MEASURING RUNNING ENERGETICS
Steve Wickler & Horse Rosemary Gillespie & Ant

51. RUNNING MICE = DIFFICULT!
Frustrated mouse… Scared mouse…
CORTISOL!!

52. We had a thought! Instead of a Treadmill…

53. Mouse 1 of 8
C57BL/6J male
Body mass 24.89 g
Ambient 21.04 °C
100% voluntary, stress-free locomotion
• muscle function
• coordination
• cardiovascular condition
• free of stress bias
STRESSLESS LOCOMOTION ENERGETICS

54. … AND SELF-CONSISTENT (8 MICE, 1 NIGHT)

55. Data Also
Consistent
with
Literature
Values

56. Food Intake…

57. SHORT INTAKE DURATION = LOW INTAKE AMOUNT

58. FOOD INTAKE – HIGH EE AT SHORT INTAKES
• Micro-Intakes More
Common in Photophase
• Often Occur After Brief
High EE (e.g. Wheel
Running) Episodes
• Feedforward?

59. LEGACY INTAKE EVENT DETECTION…
• Adequate for Total Food
Intake Measurements
• Miss a Significant
Proportion (>30%) of
Ingestive Events

60. HIGH RESOLUTION INTAKE EVENT DETECTION
• Full Complexity of
Ingestive Behavior is
Captured
• Made Possible by
Advanced Data
Acquisition Techniques
(1:500,000 Resolution)
• Statistically Verified

61. ENVIRONMENTAL INFLUENCES
• Sound Level
• Light Level
• Occupancy
• Temperature
• RH%
• Barometric Pressure

62. “darkness” (not)
Office
light is on
Move
around
office
Lights off,
close door,
then open it
ENVIRONMENTAL EXAMPLE
• This is from my office
• Moving around
• Closing the door
• Opening the door
• Exit sign down the
hall very dimly
illuminates the room
(practically darkness)

63. OMG!
OMG!
OMG!
LOL
ENVIRONMENTAL EXAMPLE
• Environmental
factors influence
animal behavior and
metabolism
• Without recording
environmental
factors, such effects
add noise to the data
(= unexplained
variations)

64. ENVIRONMENT EXPLAINED
Result: Cleaner
data with fewer
outliers
• Possible to explain
otherwise mysterious
responses by
experimental animals

65. PROMETHION
Light Field Camera
= Raw Light Data

66. Light Field Camera
= Raw Light Data

67. Light Field Camera
= Raw Light Data

68. It’s All About Resolution

69. Worried about
Raw Data
Information
Overload?

70. The Exact Information
You Require
SCRIPT ACCESS…

71. Chapter 1. A Brief History of Metabolic Measurement
Chapter 2. Constant Volume and Constant Pressure Respirometry
Chapter 3. Coulometric Respirometry
Chapter 4. Constant Volume Techniques Using Gas Analysis
Chapter 5. Aquatic Oxygen Analysis
Chapter 6. Direct Calorimetry
Chapter 7. Measuring Field Metabolic Rates
Chapter 8. Flowthrough Respirometry: Overview
Chapter 9. Flowthrough Respirometry: The Equations
Chapter 10. Flowthrough Respirometry Using Incurrent Flow Measurement
Chapter 11. Flowthrough Respirometry Using Excurrent Flow Measurement
Chapter 12. Validating Flowthrough Respirometry
Chapter 13. Metabolic Data Analysis and Presentation
Chapter 14. The Varieties Of Gas Analyzers
Chapter 15. The Varieties Of Flow Meters
Chapter 16. The Varieties Of Activity Detectors
Chapter 17. The Varieties Of Scrubbers, Tubing And Tubing Connectors

72. Thank You!
For additional information on continuous or
multiplexed metabolic measurements and
behavioral systems please visit:
http://sablesys.com