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Measuring Energy Balance in Mice from VO2/VCO2, Food Intake and Activity Data
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Activity
Feeding Drinking
Body Mass
Running Wheel
Sleep Detection
Food Access Control
Environmental Control
Calorimetric Assessment
Environmental Monitoring
Temperature & Heart Rate
Oxymax/CLAMS
Marta Fiorotto Baylor College of Medicine
Chris Adams Columbus Instruments
Presenters:
Energy Expenditure Essentials
Chris Adams
Sales Manager,
Columbus Instruments International Corporation
Indirect Calorimetry Concept
“Nitrogen” In (VNi)
Oxygen In (VO2i)
Carbon Dioxide In (VCO2i)
“Nitrogen” Out (VNo)
Oxygen Out (VO2o)
Carbon Dioxide Out (VCO2o)
Air In (Vi) Air Out (Vo)
“Nitrogen” In (VNi)
Oxygen In (VO2i)
Carbon Dioxide In (VCO2i)
“Nitrogen” Out (VNo)
Oxygen Out (VO2o)
Carbon Dioxide Out (VCO2o)
Air In (Vi) Air Out (Vo)
Metabolic Equations - Basics
Vi and Vo are the input and output ventilation rates (LPM) O2i and O2o are the oxygen fractions at the input and output…
VO2 = ViO2i – VoO2o
VCO2 = VoCO2o – ViCO2i
VCO2
VO2
RER = AND
Respiratory Exchange Ratio:(RER): RER is simply the ration between the carbon dioxide production and the oxygen consumption. This value is a ration and thus does not have a unit. The RER ratio is calculated before any units conversion, weight normalization, or effective mass correction.
Metabolic Equations - Basics
Vi and Vo are the input and output ventilation rates (LPM) O2i and O2o are the oxygen fractions at the input and output…
VO2 = ViO2i – VoO2o Because O2 is exchanged for CO2 during the process, Vi ≠ Vo
And because this exchange also displaces N2, Ni ≠ No
The Haldane Transform
Nx = 1 – O2 – CO2
Ni
No
Vo = Vi x
Given: O2in = 20.9180% O2out = 20.4992% Flow-in set at 0.500 LPM (Vin)
CO2in = 0.0433% CO2out = 0.4282%
Metabolic Equations – Example
Metabolic Equations – Example (Haldane)
N2in = 1 – O2in – CO2in N2in = 1 - 0.209180 - 0.000433 = 0.790387 N2out = 1 – O2out – CO2out N2out = 1 - 0.204992 – 0.004282 = 0.790726
Vout = Vi * (N2in / N2out) = 0.500 * (0.790387/ 0.790726) 0.499786 LPM
Given: O2in = 20.9180% O2out = 20.4992% Flow-in set at 0.500 LPM (Vin)
CO2in = 0.0433% CO2out = 0.4282%
VO2 = (Vin * O2in) – (Vout * O2out) = (0.500 * 20.918%) – (0.499 * 20.4992%) = 0.002137 LPM of O2
VCO2 = (Vout * CO2out) – (Vin * CO2in) = (0.499 * 0.4282%) – (0.500 * 0.0433%) = 0.001923 LPM of CO2
Metabolic Equations – Example (VO2, VCO2, RER)
Given: O2in = 20.9180% O2out = 20.4992% Flow-in set at 0.500 LPM (Vin)
CO2in = 0.0433% CO2out = 0.4282% Vout at 0.499786 LPM
= 0.001923 / 0.002137 0.8999
Given: O2in = 20.9180% O2out = 20.4992% Flow-in set at 0.500 LPM (Vin)
CO2in = 0.0433% CO2out = 0.4282% Vout at 0.499786 LPM
And… VO2 = 0.002137 LPM VCO2 = 0.001923 LPM
VCO2
VO2
RER =
Metabolic Equations – Example (VO2, VCO2, RER)
normVO2 = (VO2 in ml per hour / Mass in kg)
= 128.22 / 0.0256 5008.6 ml/hr/kg
Therefore, if VO2 = 0.002137 LPM, and the mouse weighs 25.6g…
Metabolic Equations – Normalization
Normalization – allows for better comparison of metabolic rates between subjects of the same species but of varying sizes and composition
Metabolic Equations – Lusk Table
RER % of Total O2 Consumed By: % of Total Heat Produced By: Heat / Liter of O2:
Carb Fat Carb Fat (kJ) (kcal)
0.707 0.0 100.0 0.0 100.0 19.62 4.6862
0.750 14.7 85.3 15.6 84.4 19.84 4.7387
0.800 31.7 68.3 33.4 66.6 20.10 4.8008
0.850 48.8 51.2 50.7 49.3 20.35 4.8605
0.900 65.9 34.1 67.5 32.5 20.61 4.9226
0.950 82.9 17.1 84.0 16.0 20.87 4.9847
1.000 100.0 0.0 100.0 0.0 21.13 5.0468
Metabolic Equations – Heat per Liter of O2 vs. RER
From the Lusk Table, kcal per liter of O2 over RER The graph is nearly linear, best fit line slope yields an equation by which to calculate Calorific Value
Metabolic Equations – Calorific Value (CV) & Heat
CV (kcal/liter of O2) = 3.815 + (1.232 * RER)
= 3.815 + (1.232 * 0.8999) 4.9237 kcal/LO2
Given: RER = 0.8999 AND VO2 = 0.002137 LPM
Metabolic Equations – Calorific Value (CV) & Heat
CV (kcal/liter of O2) = 3.815 + (1.232 * RER)
= 3.815 + (1.232 * 0.8999) 4.9237 kcal/LO2
Given: RER = 0.8999 AND VO2 = 0.002137 LPM
Heat (kcal/hr) = CV * VO2
= 4.9237 * 0.002137 * 60 minutes 0.63132 kcal/hr
Metabolic Equations – Calorific Value (CV) & Heat
CV (kcal/liter of O2) = 3.815 + 1.232x
= 3.815 + (1.232 * RER) 4.9237 kcal/LO2
Given: RER = 0.8999 AND VO2 = 0.002137 LPM
Heat (kcal/hr) = CV * VO2
= 4.9237 * 0.002137 * 60 minutes 0.63132 kcal/hr
normHeat (kcal/kg/hr) = Heat / Body Mass in kg / Time in hours
= 0.63132 / .0256 24.6609 kcal/kg/hr
Metabolic Equations – Weir Heat Equation
Alternate equations (“user defined”) where: CV = Calorific Value in kcal/liter Heat = (CV1 * VO2) + (CV2*VCO2)
Heat = (CV1 * VO2) + (CV2 * VCO2) Default Weir Constants: 3.815 * VO2 1.232 * VCO2 VO2= 0.002137 LPM VCO2 = 0.001923 LPM
Heat = (3.815 * 0.002137 * 60 min) + (1.232 * 0.001923 * 60) 0.63131
Metabolic Equations – Weir Heat Equation
Heat = (CV1 * VO2) + (CV2 * VCO2) Common Weir Constants: 3.9 * VO2 1.1 * VCO2 VO2= 0.002137 LPM VCO2 = 0.001923 LPM
Heat = (3.9 * 0.002137 * 60 min) + (1.1 * 0.001923 * 60) 0.62798
0.62798 vs. 0.63131
Metabolic Equations – Weir Heat Equation
Simplified Calorimeter Diagram
Fresh Air Pump
Filter
Manifold
Valved Manifold
Sample Pump
Flow Controller
Flow Controller
CO2 Sensor
Tube Drier
O2 Sensor
Ammonia Filters
Metabolic Measurement Cycle
1. Cages are measured sequentially
2. Each measurement consists of a SETTLE and MEASURE time
• The SETTLE time allows for purging old gas sample and for O2 and CO2 sensor response.
• The gas concentrations are averaged over the MEASURE time.
MEA
SUR
E
SETTLE SETTLE SETTLE SETTLE
MEA
SUR
E
MEA
SUR
E
MEA
SUR
E
MEA
SUR
E SETTLE
MEA
SUR
E
SETTLE
MEA
SUR
E
1 2 3 REF 1 2
CYCLE TIME
CYCLE TIME
SETTLE
3
SETTLE
REF
1. Electrochemical – best suited for small economical systems
2. Paramagnetic – most common, maintenance free, and reasonably fast performance
3. Zirconia (High Speed) – fastest scan rate, ideal for large number of subjects
Three Most common Oxygen Sensor Technologies
• Dumbbell suspended in a magnetic field twists as its exposed to oxygen
• A light source bounces off a mirror on the dumbbell to a light detector to monitor the twisting
• Electric current passes through the dumbbell to counter the twist and re-center the dumbbell, the amount of current necessary is proportional to the [O2]
Paramagnetic O2
Paramagnetic O2
Pros
• Maintenance free!
• No consumable element
• User programmable between 0-100%
• Scans at 90 seconds per chamber
Cons
• Fast, but not the fastest
• Sample air diffuses through a membrane
• Oxygen drives the chemical reaction in the fuel cell and creates a voltage
• Voltage is proportional to the [O2]
Electrochemical O2 Sensor
Electrochemical O2 Sensor
Pros
• High accuracy at a low cost
Cons
• Requires periodic replacement
• A bit on the slow side at a scan rate of 3 min. per cage
• Zirconium Dioxide conducts electricity at high temperatures
• Oxygen ions pass through Zirconia which generate a “Nernst Voltage”
• Voltage proportional to natural log of ratio between concentrations
• Fast response
Zirconia Oxygen Sensor
Zirconia Oxygen Sensor
Pros
• Very fast scan rate at 50 seconds per chamber
Cons
• Expensive to buy
• Consumable element is also expensive to replace
• CO2 absorbs infrared light (4.255µM)
• IR light emitted at the target wavelength, through the sample gas, and the absorption is recorded by the detector
CO2 Sensor
• Nafion plastic selectively removes water vapor
• Sample air is never in contact with chemical drying agent
Hydroscopic Tube Dryer
Calorimeter Data
• Real time gas readings & differentials
• RER & heat calculations in real time
• Export as CSV
Fresh Air (Vi)(0.50-0.60 LPM)
Sample Out(0.400 LPM)
Overflow
Chamber Body
Validation Gas (Vval) (0.01 LPM)
O2o = (O2i * Vi) / (Vi + Vval)CO2o = ((CO2i * Vi ) + (CO2val * Vval)) / (Vi + Vval)
O2o = (O2i * Vi) / (Vi + Vval)CO2o = ((CO2i * Vi ) + (CO2val * Vval)) / (Vi + Vval)O2o = (O2i * Vi) / (Vi + Vval)
CO2o = ((CO2i * Vi ) + (CO2val * Vval)) / (Vi + Vval)
Calorimeter Validation
• CO2 infusion technique
• Precise injections of 20% CO2 simulates CO2 production
• 80% N2 displaces O2 to simulate O2 consumption
Calorimeter Validation
• CO2 infusion technique
• Precise injections of 20% CO2 simulates CO2 production
• 80% N2 displaces O2 to simulate O2 consumption
• Target is ~0.4% Δ and RER of 0.943
Food Intake: Center Feeder vs Overhead
Over-head Feeder
Pros
• Food in a familiar location
• Allows use of standard shoebox cage with bedding
• Low stress
• Uses pelleted food
• Easy maintenance
Food Intake: Center Feeder vs Overhead
Over-head Feeder
Cons
• Vulnerable to foraging
• Limited spillage collection
• Feeder takes longer to settle
Food Intake: Center Feeder vs Overhead
Center Feeder
Pros
• Minimal foraging
• Larger spillage reservoir
• Weight stabilizes faster
Food Intake: Center Feeder vs Overhead
Center Feeder
Cons
• Food presented at the floor
• Food must be powdered or crushed
• No bedding
• Requires acclimation
• Based on time
• Based on mass
• Based on time and mass
Automated Food Access
• Based on time
• Based on mass
• Based on time and mass
• Enter Energy value and limit based on “calories.”
Automated Food Access
• Based on time
• Based on mass
• Based on time and mass
• Enter Energy value and limit based on “calories.”
• Yoked/Paired feeding
Automated Food Access
• Synchronous with metabolic data (.CDTA)
Feeding Data
• Synchronous with metabolic data (.CDTA)
• Asynchronous feeding event log (.BDTA)
• Periodic user-defined intervals (.FTDA)
Feeding Data
• Synced with calorimetry data
• XTOT = all beam breaks in the X axis
• XAMB = strictly locomotor beam breaks
• ZTOT = rearing events
• Wheel = revolutions
Activity Data (low-res)
• Author: Allan Pack
• Sleeping bouts from inactivity data
• Validated by EEG/EMG
Inactivity (sleeping bouts)
• Allan Pack
• Sleeping Bouts
• Sleep bouts time stamped
• Sleep threshold is adjustable in 10s epochs
• Define light/dark cycle
Activity Data (high-res)
Oxymax Sleep Detection
• Allan Pack
• Sleeping Bouts
• XTOT = all beam breaks (ambulatory + stereotypic)
• XAMB = strictly ambulatory beam breaks
• ZTOT = rearing event
Activity Data (high-res)
• Spontaneous Exercise Activity
• Provides “Infinite floor space” for lower stress
• Revolutions synced with calorimeter
• High Res activity data
Running Wheel Activity
• Compare Data sets
• Rapid Screening
• Sharing
• Graph prep
• Quick Screening
CLAX Data Review & Stat program
• Compare Data sets
• Rapid Screening
• Sharing
• Graph prep
CLAX Data Review & Stat program
• Quick Screening
• Compare multiple data sets
• Share data with collaborators
• Compare Data sets
• Rapid Screening
• Sharing
• Graph prep
CLAX Data Review & Stat program
• Quick Screening
• Compare multiple data sets
• Share data with collaborators
• Stat analysis
How to Measure and Interpret Energy Expenditure and Metabolism in Mice
Marta Fiorotto, PhD
Associate Professor,
Pediatrics-Nutrition Baylor College of Medicine
1. Basics of energy metabolism and how to assess it
2. Examples of data generated with the CLAMS
3. Tips, best-practices, and suggestions
What are we going to cover today?
Basics of Energy Metabolism & How To Assess It
First Law of Thermodynamics
Energy can be neither created nor destroyed…
• However, energy can change forms, and energy can flow from one place to another.
• The total energy of an isolated system always remains the same.
Energy Consumed = Energy Expended + Energy Deposited
Metabolizable Energy Intake
Energy Deposited
Total Energy Expenditure
Energy Consumption
Metabolizable Energy Intake = Total Energy
Expenditure
Energy In
Δ BW = 0
Energy Balance
• There is no weight gain when energy intake and expenditure are equal
Energy Out
When energy intake is greater than energy expenditure, the excess is deposited as fat (and lean) resulting in weight gain.
Positive Balance
Energy In
Δ
Δ
>
When energy intake is greater than energy expenditure, the excess is deposited as fat (and lean) resulting in weight gain.
Positive Balance
Energy In
Total Energy Expenditure
Δ Δ Negative Balance
When energy intake is less than energy expenditure , the body mobilizes its energy reserves to meet the energy deficit and results in weight loss.
Negative Balance
When energy intake is less than energy expenditure , the body mobilizes its energy reserves to meet the energy deficit and results in weight loss.
<
CLAMS DATA
Metabolizable Energy Intake
ENERGY BALANCE
NEED
Food Intake
ME density of diet
(kcal or kJ/g diet)
Weight of food eaten Δ Metabolizable Energy density = Metabolizable Energy Intake
Metabolizable Energy
Metabolizable Energy content of a food is the “energy available for heat production (i.e., energy expenditure) and body gains”
Gross Energy Δ (Fecal Energy + Gaseous Energy + Urinary energy)
Nutrient Atwater Factors
kcal (kJ) /g
Protein 4 (17)
Fat 9 (13)
Carbohydrate
digestible 4 (17)
fermentable fiber 2.6 (11)
Alcohol 7 (29)
Energy Intake Food Intake ME content of diet
Energy Expenditure VO2 ,VCO2, RER, Heat
Body weight, fat and lean mass
Normalization Total and /kg BW
Body weight, fat and lean mass
Energy Deposited or Mobilized
Total Ein & Eexp
Consider contribution of Pox
For further discussion and instructions see: Tschöp MH et al. A guide to analysis of mouse energy metabolism. Nature Methods (2011) Dec 28;9(1):57-63.
CLAMS DATA
ENERGY BALANCE
NEED
Components of Total Energy Expenditure
• Basal metabolic rate ~ energy needed to sustain the metabolic activities of cells and tissues, and to maintain blood circulation, respiration, GI, and renal function.
Basal metabolism
TEF
Thermogenesis
Spontaneous and voluntary
physical activity
• Thermogenesis ~ energy expended to maintain body temperature.
• Thermic effect of food (TEF) ~ energy expenditure associated with the digestion and assimilation of food, ~ 10% TEE
• Physical Activity ~ energy expended for physical activity
Measuring Components of Total Energy Expenditure
• Basal metabolic rate ~ energy needed to sustain the metabolic activities of cells and tissues, and to maintain blood circulation, respiration, GI, and renal function.
Basal metabolism
TEF
Spontaneous and voluntary
physical activity
thermogenesis • RMR: Resting metabolic rate
Cannon and Nedergaard J Exp Biol 2011;214: 242
Thermogenesis
Protocol for Estimating RMR
• Acclimate mice to CLAMS
• Continue to monitor EE and activity for 7-9 hrs.
• Close feeders at LIGHTS ON (resting phase)
• Average lowest 2 values after 4-5 hrs of fasting
For greater discussion refer to: Speakman JR, Frontiers in Physiol 2013, 4: 34
Estimation of Activity Energy Expenditure
For greater discussion refer to: Speakman JR, Frontiers in Physiol 2013, 4: 34 Van Klinken et al., PLoS ONE 2012, 7: e36162.
• Activities vary in their energy cost and duration
• This is not straightforward with a multiplexed system!
• Compare treatment effects on spontaneous cage and running wheel activity
• Energy cost of locomotion can be estimated with a metabolic treadmill.
Estimation of Thermic Effect of Food
EE following a standardized meal consumed after a fast
minus
(RMR+ Activity Energy Expenditure)
Even & Nadkarni, Am J Physiol Regul Integr Comp Physiol 303: R459, 2012 Speakman JR, Frontiers in Physiol 2013, 4: 34
Review examples of data from CLAMS experiments
Differences in weight gain… are not due to lower food intake
Ghsr -/-, Ghrelin Receptor null
younger older 0
25
50
younger older
Fat
(% b
od
y w
eig
ht)
Ma X, et al. (2011) Ablations of Ghrelin and Ghrelin Receptor Exhibit Differential Metabolic Phenotypes and Thermogenic Capacity during Aging. PLoS ONE 6(1): e16391.
younger older
younger older
Ma X, et al. (2011) PLoS ONE 6: e16391. Lin, et al. (2011) Aging Cell 10: 996
RM
R (
kcal/
(kg
lean
.hr)
…but to greater energy expenditure
Assessing feeding behaviour
• Determine feeding bouts from high frequency food intake data
• Parameters that can be extracted: • Number of feeding bouts (meals) / day • Amount of food eaten per meal (and per day) • Duration of feeding bouts • Inter-meal interval length • Diurnal eating pattern
No difference in total intake
0
10
20
30
40
50
** *
Young Older
meals
/da
y
Meal frequency
0
50
100
150
200
250
*
**
Young Older
se
co
nds
Meal duration
* **
min
Young Older 0
10
20
30
40
50
60
Inter-meal interval
min
ute
s
From Lin et al., J Nutr. 2014 144:1349-55.
Altered feeding behaviour in Ghsr -/- mice • Define feeding bout: intake of 0.02g; balance stable for 10 sec
Young Older 0
1
2
3
4
g
/day
WT Ghsr-/-
Food intake
0.00
0.05
0.10
0.15 *
g/m
eal
Young Older
Meal size
?
Diurnal pattern of food consumption
Stashi et al., Cell Rep. 2014, 6:633.
• Peak feeding in Src2 -/- mice was advanced by approx. 85 min.
• This mirrored the phase-advanced shift in wheel running.
• SRC-2 is a critical positive regulator of the mammalian circadian clock.
Food Intake (g/h)
40-d-old 140-d-old
Cage Activity (counts/h) (X and Z dimensions)
Energy Expenditure (kcal/h) (normalized for lean and fat)
Fetal growth restricted Control
Fetal growth restriction promotes physical inactivity and obesity in female mice
Baker, et al., Int. J. Ob. (2015) 39: 98
Fetal growth restricted Control
Food Intake (g/h)
40-d-old 140-d-old
Cage Activity (counts/h) (X and Z dimensions)
Energy Expenditure (kcal/h) (normalized for lean and fat)
Fetal growth restriction promotes physical inactivity and obesity in female mice
Baker, et al., Int. J. Ob. (2015) 39: 98 Fetal growth restricted Control
counts/h
mdx Control P
Body Weight (g) 16.5 + 0.7 20.7 + 0.7 0.03
Energy Intake (kcal/d)* 11.0 + 0.7 11.3 + 0.4 NS
Activity (counts x 103/d) 26.32 + 3.89 54.39 + 3.89 <0.001
Total Energy Expenditure* (kcal/d)
11.0 + 0.5
10.5 + 0.3
0.03
Resting Energy Expenditure* (kcal/d)
9.0 + 0.4
8.4 + 0.2
0.01
Energy Balance (kcal/d) 0.44 + 0.16 1.03 + 0.16 0.05
*values are least square means adjusted for differences in FFM and fat mass +SE
Energy Expenditure in the Juvenile Mdx Mouse Model of Duchenne Muscular Dystrophy
Radley-Crabb, et al. (2014) PLoS ONE 9: e89277.
0
20
40
ave
rage
co
un
ts x
10
3/
d
X-ambulatory
Cage Activity
0 5 10 15 20 25 0
2
4
6
8
10
km/d
Days
P<0.001
Distance Run on Wheel
Control Treatment
Z-rearing
†
†
†, P<0.05 Exercise Effect
Running wheel and cage activity need not change in parallel
Mismatch between food Intake and energy expenditure observed in the composition of weight gain
0 5 10 15 20 25 0
2
4
6
8
10
km/d
Days
P<0.001
Distance Run on Wheel
Control Treatment Running wheel Sedentary
g/d
week 1 week 2 week 3 0.0
0.5 2.5
3.0
3.5
4.0 †
†
†
Daily Food Intake adjusted for BW + SE
†, P<0.05 Exercise Effect
Fat Gained
Control Treatment 0.0
0.5
1.0
1.5
2.0
g fa
t
P<0.05, Treatment x Exercise
†
ns
ns
Wheel Running Parameters
Why are the mice not running?
*, Treatment, P = 0.03
0
40
80
120
160
200
Bout length
*
revs
/bo
ut
Values are means + SE n=8-14/group
Control Treatment
0
50
100
150
200
250
bo
uts
/d
Bout frequency
* †, P<0.05 Control Wheel
Wheel
†
Data collected in 10 sec bins; 1 bout = 4> revs/10 sec
“Sleeping”
Sedentary 0
10
20
30
40
% t
ime
"as
leep
"
* 50
*, P<0.01 Treatment
Pack AI et al., Novel method for high-throughput phenotyping of sleep in mice. Physiol. Genomics 2007 28:232
Automated Food Access Control
• Meal feeding
• Restricted feeding
• Pair-feeding • Reduces manpower need (and mistakes)
Benefits:
• Less interference with mouse cages
• Enables feeding at times that are biologically relevant to the mouse
• Multiple meals with restricted feeding protocols reduces stress
• Diurnal feeding patterns are equivalent in both groups in pair-feeding protocols.
Tips, best-practices, and suggestions
Acclimation
1. Single housing
2. No or different bedding
3. Powdered diet
4. Different feeders
Factors that change for the mouse:
Recommendation:
1. One week prior to start, singly house and feed powdered diet
2. Three days before start place in CLAMS cages
3. Collect data for 48 hours
Variability in data decreases with acclimation
1 2 3 4 5
40 60 80
100 120 140 160 180 200
tota
l co
un
ts x
10
3/d
Total Daily Activity
Days in CLAMS
1 2 3 4 5 6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
kcal
/d
Total Daily EE
Days in CLAMS 21.5 21.0 17.8 18.0 17.8 7.5 6.1 5.3 4.3 2.6
Days in CLAMS
Daily Food Intake
CV 1 2 3 4 5
2.0
2.5
3.0
3.5
gra
ms/
d
16.2 13.8 8.7 8.3 5.1
Male, C57BL6, n=6, Body weight = 25.7 + 1 g (SD)
Means + 1SD Means + 1SD Means + 1SD
Animal-To-Animal Variability
• Some mice love to dig in their food and empty out their hoppers v. quickly
• Some mice never adapt and become very depressed, sleep, and don’t eat
• Small mice can squeeze under certain types of floors and get stuck
• Small mice sometimes can’t reach water; need long sipper tubes
• Some mice on restricted intakes learn to defeat the food access control
Important to recognize normal variability vs. “CLAMS-induced” variability
Recommend: 1. Monitor mice closely during measurements
2. Weigh before and after CLAMS
Environmental Variables:
1. Temperature should be monitored and constant (thermogenesis)
2. Inappropriate light exposure and noise can alter behaviour (activity, EE, etc)
Considerations for Experimental Designs
• When you have a multiplexed system, the frequency of EE measurements on individual mice will depend on the total number of mice.
• When doing pair-feeding studies, ensure “a priori” that the intakes of “master” mice (determinant of food intake for the pairing) are within the expected norm.
• Decide if you will need high frequency measurements, e.g., for feeding behaviour assessment. You can’t go back after the fact and get the data.
Considerations for Experimental Designs (cont.)
• To enable mice to have access to the feeder, but not so much that they sleep on top of it, the spacers should be customized for the size of the mouse.
• Sieve your diet to ensure that it has no lumps in it.
Considerations for Experimental Designs (cont.)
• The timing of the measurements relative to the appearance of the phenotype should be given careful consideration. Measurements after the phenotype has occurred could reflect an adaptation to the change rather than the cause.
• As it is likely that you may need to repeat an experiment in order to have adequate replicates, it’s important that all experimental groups are included in each batch. Animal, diet, environmental, and instrument variability can introduce batch-to-batch variability, and this can be statistically controlled for.
Thank You!
For additional information on the CLAMS for Rats and Mice and Oxymax Systems please visit:
http://www.colinst.com
Marta Fiorotto Baylor College of Medicine
Chris Adams Columbus Instruments
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