respirometry.org

Tag: food intake

  • Measuring food uptake differentially

    Let’s say you need to measure the food uptake of an experimental animal, which of course could mean any creature, including you. For the sake of simplicity, imagine a mouse or a rat feeding intermittently from a food hopper.

    You’d think that all you needed to do was weigh the hopper periodically, such as at the start and end of each 24-hour cycle, and see how much its mass decreases. You’d be right, in a sense. That will indeed measure the change in food mass over that period. But if you think that the change in mass is an accurate representation of the amount of food the critter ate, you might be very wrong.

    This is because most food, including rat or mouse chow, is hygroscopic. It absorbs water from the water vapor in the air to an extent roughly proportional to relative humidity. And relative humidity is anything but constant, particularly inside a cage. As a result, neither is food mass.

    To get accurate food uptake figures, you need to measure differentially. In other words, food uptake must be calculated from the difference in food hopper masses just before and just after each feeding event. This figure* (where d is food hopper mass) illustrates the point.

    As you can see, a feeding event corresponds to a large increase in the variance of the measured food hopper mass. A good food uptake calculation algorithm, such as the one used by Promethion, searches for sections of stable mass readings immediately before and after each such event. Then it compares those readings and tests them for statistical significance. If a significant difference is found, the event is designated as a food uptake event. If not – and a surprising number of interactions with the food hopper don’t result in significant food uptake – then it’s ignored.

    As a result, slow changes in hopper mass resulting from fluctuations in relative humidity no longer distort food uptake data.

    True, but analyzing the problem at a deeper level, the mass of food that is eaten, however accurately it’s measured, still reflects the sum of two partitions:

    1. The dry weight of the food that is eaten
    2. The weight of water associated with the food

    The water content of typical mouse or rat chow is about 10-15%, so the error can be significant. Dry food mass would be a much better measure of food uptake.

    Funny you should say that. Because the Promethion system (unlike any other food uptake measurement or metabolic phenotyping system) measures water vapor partial pressure in the air pulled from the cage, it is possible, knowing this, to back-calculate food mass to its “dry” state, mathematically. All that is required is a good characterization of the chow’s mass versus ambient water vapor partial pressure.

    Not a single researcher anywhere in the world is yet doing this. But it’s possible (though only with Promethion). I wonder who will be the first to fill this vacuum?

    — John Lighton

     * Thanks to Thomas Förster, Ph.D., Sable Systems International’s expert in-house data analysis and data presentation consultant, for creating the graph.

  • Distinguishing individual food uptake in communally housed mice using RFID

    Mice are communal beasts, just like rats. They live in groups, and separating them – as required for measuring food intake / food uptake or energy expenditure – stresses them, elevating cortisol levels and leading to to a host of unwanted side-effects. Using the right technology, however, obtaining separate food uptake recordings from communally housed mice is straightforward. This short article demonstrates just such an application, combining a Promethion mass measurement module (2 mg resolution) with RFID.

    To identify an animal using RFID, a simple and quick injection of a subdermal PIT (Passive Integrated Transponder) tag, about the size of a grain of rice, is required. There are two broad types of PIT tags; half duplex (HDX) and full duplex (FDX). For a variety of reasons, HDX PIT tags are preferable. Any vet or trained animal care technician can insert the tag. (A number of people are experimenting with them too.)

    The principle of HDX RFID PIT tags is simple. A nearby coil periodically generates an EMF field at (typically) 134 kHz. The PIT tag contains a resonant circuit that charges a capacitor while the coil is generating its EMF field. Then the coil switches from transmitting to receiving mode; the PIT tag uses the energy stored in the capacitor to generate its own EMF field, which transmits a unique ID code back to the coil. And voilà, RFID! (Super-over-simplified, you understand.)

    So, we can separate individual mice easily. But what about food uptake? Well, Promethion has a unique mass sensor based, like a lab balance, on a load cell, that allows extremely precise food uptake measurements. You can read about the principle here.

    A little simple design work and a short spell with a lasercutter resulted in a box that held the mass sensor and food hopper, and restricted access to the food hopper to one mouse at a time via a tube just wide enough for a single mouse to enter. The tube that limited access was adjacent to an RFID reader of our own design. (Existing commercial RFID readers are limiting and cumbersome; I frown on them.)

    The graph to the left is worth a thousand words. Click to embiggen it. The red trace corresponds to the ID of the mouse; either absent (no mouse in the feeder) or at two different levels, one corresponding to the ID of one mouse, the other to the ID of her nestmate. The blue trace corresponds to the mass of the hopper, which clearly shows the disturbance caused by feeding, and the change in food hopper mass before and after each feeding event by each mouse.

    As you can see, separating food uptake / intake data for each mouse is easy. The precise uptake amount of food consumed during each feeding event is easily and automatically obtained, together with meal duration and unique-to-Promethion data such as the force that the mouse applied to the hopper during the feeding event. Using this RFID-based technique, subtle differences between mice can be teased apart from the stress of isolation. Better for the mouse, and thus – because an unstressed mouse is a better experimental subject – better for research too.

    What about separating the metabolic rates (energy expenditures) of individual mice in a communal setting? All I can say is, stay tuned.

  • Tiny Food Intake Events (Micro-Intakes) Can be Important!

    Many food intake events (= food uptake events) are too small for legacy “food intake measurement systems” or metabolic phenotyping systems to detect. Each of these feeding events corresponds to a neurological signal to feed, even if the actual amount is small. As such, they convey important behavioral information. I can easily imagine a gene knockout or a treatment that might affect micro-intake events while leaving macro-intake events unchanged – an important distinction to which most food intake measurement systems currently on the market are oblivious, but which Promethion can easily detect.

    The list to the left, which is a small section of a food intake analysis spreadsheet, shows 7 food intake events from a C57BL/6J mouse. (Parenthetically, it’s interesting to see the reactions of people who are used to mouse food intake amounts expressed to the nearest 0.01 g or even 0.1 g, when they see this level of precision.) You can see that two of the events, highlighted, are below 10 mg.

    This is where things get especially interesting. Legacy food or water intake measurement systems (which is to say, everything on the market except for Promethion) do not claim to detect food or water intake events less than about 10 to 20 mg. Promethion, on the other hand, was designed with fanatical attention to the highest possible resolution. In fact, intake events down to 2 mg can easily be detected. To achieve this level of resolution (about 1 part in 500.000) purely digital data transfer is essential. I blame my background in comparative physiology, concentrating on very hard-to-make measurements on small animals, for that emphasis on high resolution. Now, unexpectedly, it has opened a new window on the feeding behavior of laboratory mice. (Of course, this would also work for fluid or water intake.)

    The first question that pops to mind is – are these tiny events actually real,  or are they the result of random noise in the measurement equipment? Well, there is an easy way to test the random noise hypothesis. Each intake event is the result of comparing a stable mass before the intake event with a stable mass after the intake event. Each of those masses has a mean, a standard deviation and an N. Each is normally distributed. As a result, they can be compared using Student’s t statistic, which evaluates the probability of the two masses differing by chance.

    And here you see the result.  As you can see, the larger intake events have extremely high t values, corresponding to microscopically tiny probabilities (any probability below 0.001 is displayed as zero). But the micro-intake events also have a very respectable t values, demonstrating that there is no realistic probability that they are the result of random fluctuations in the measurement equipment. (This is also very obvious when looking at the raw data, which shows clear disturbances in the mass record during the micro-intake events; see below.)

    If you’re interested, you can look at an image of a more complete section of the spreadsheet, automatically generated by the Promethion data analysis program, here.

    But let’s do a belt-and-braces proof for you skeptics out there. To do so, let’s select the intake event denoted by the solid yellow bar at the bottom of the above image, and look at the raw data from which that data point was derived. This level of drill-down capability is, of course, unique to Promethion.

    To the left, you can see a graph of the food hopper mass vs. time. This is raw data – no smoothing or any other processing was applied.

    As the graph begins, the food hopper is untouched. Then the mouse starts to feed from the hopper, in the process exerting a downward force on the hopper that causes its measured mass to increase. (You might have noticed a column called UpF_g_min in the spreadsheet excerpt above; this is the integrated force that the mouse applied to the hopper during each feeding event, with the units g/min – which may be an indicator of motivational state, and is a measurement [again] unique to Promethion.) Finally the mouse leaves. The means and standard deviations of the hopper mass before and after the 7 mg feeding event are shown on the graph. For a larger version, click here. There’s no debating that the feeding event is real.

    The second question that pops to mind is, of course, who cares? The micro-intake events don’t contribute particularly significantly to total food intake! Why worry about them? Why not just ignore them? (Especially if you can’t measure them in the first place.)

    I respectfully disagree. As I covered briefly in the introductory paragraph:

    Each of these micro-intake events corresponds to a neurological signal to feed (even if the actual amount is small) and as such, it conveys important behavioral information. I can easily imagine a gene knockout or a treatment that might affect micro-intake events, perhaps by raising the satiation threshold, while leaving macro-intake events unchanged – an important distinction that traditional food intake measurement systems and metabolic phenotyping systems would miss. Promethion owes its ability to detect these intake events to Sable Systems‘ many years of experience with ultrahigh resolution circuitry, and the use of load cells (as in lab balances) as mass transducers combined with the archiving of the entire raw data stream, which provides maximum flexibility of analysis.

    I can think of many interesting research questions that arise from this. To take an easy example, are macro-intake events that follow multiple micro-intake events characterized by a slower intake rate? I see signs that that may be the case, but haven’t yet investigated this in detail. If you take the idea and run with it, good for you – I have more ideas than can ever be actually implemented.

    I welcome your input on this, and any questions you may have. I can be contacted here.