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Tag: activity metabolism

  • Activity Metabolism Measurements Made Easy in Metabolic Phenotyping

    One of the classic controversies in the field of metabolic science is the effect of activity on overall energy expenditure. As a comparative physiologist with a long-standing interest in energetics (and several papers published in that field), I find this controversy to be particularly strange, especially because some respectable and intelligent people will politely insist that activity has very little effect on overall energy expenditure. At the center of this controversy lies the beating heart of metabolic phenotyping.

    Analysis of data from what I’ll call “legacy” metabolic phenotyping systems can, indeed, show little if any relation between metabolic rates and activity. I’ve dealt in passing with this controversy in my blog entries “How multiplexing distorts metabolic phenotyping data” and “The future of metabolic phenotyping”. Simply put, it is difficult or impossible to correlate activity and energy expenditure in a conventional, legacy metabolic phenotyping system. However, in the new Promethion-C continuous metabolic phenotyping system, what was previously hidden or distorted is now revealed with startling clarity.

    Let me show you exactly what I mean. For this example, I will choose a measurement that most researchers would consider to be practically impossible in a metabolic phenotyping system – the accurate measurement of minimum cost of transport, which requires tracking energy expenditure with great precision and high temporal resolution, and synchronizing those data perfectly with the running speed of an animal. This is almost invariably performed one animal at a time, using a treadmill that forces the animal to run over a wide range of set, constant speeds. A metabolic measurement system measures the energy expenditure of the animal, and also records its running speed. For each animal, its running speed in meters per second is regressed against its energy expenditure in watts per kilogram. The slope of the resulting line has the units of Joules per meter per kilogram, and is known as the minimum cost of transport or MCOT. This is not an easy measurement to make, under the best of circumstances. But if it can be measured accurately in a metabolic phenotyping system, you can be certain that the data from that metabolic phenotyping system are trustworthy. Making it far less likely that you will obtain results that simply don’t make sense.

    At the Sable Systems metabolic phenotyping course in October 2-6 2012, we obtained a few nights’ worth of continuous, non-multiplexed data from several mice. We used C57BL/6J mice from Jackson Labs. Thanks to the second by second metabolic measurement capability of the Promethion-C system on multiple animals at once, and thanks to the fact that the running speed of each mouse in its running wheel was accurately measured second by second in synchrony with its metabolic data, we could correlate energy expenditure with voluntary running speed for each mouse. This process was greatly assisted by our ability to apply response correction to the metabolic data (see overview by Lighton, 2012), which was possible only because those data (a) are low in noise and (b) were acquired without multiplexing.

    And here is a typical graph of energy expenditure vs. voluntary running speed. The quality of the data is absolutely outstanding – at least as good as, if not better than, equivalent data obtained from animals under highly controlled conditions using a treadmill. The data were acquired over a night’s worth of running wheel activity. The MCOT of this particular 25 gram mouse, which is the slope of the line in the graph, was 29.0 J per meter per kilogram, and variation in its running speed explained over 98% of the variation in its energy expenditure. In the classic paper on minimum cost of transport (Taylor et al. 1982), only two of the 90+ cited studies met or exceeded that figure of merit for data quality.

    But here’s the $64,000 question. Sure, we measured the MCOTs of the mice, but are those figures believable? Are they accurate? Let’s be scientifically respectable and phrase this as a testable hypothesis. We know from Taylor at al. (1982) that there is an allometric relation between MCOT and body mass across a wide range of animal body masses. This relation, which has proven to be remarkably robust, allows us to predict the minimum cost of transport that should be measured for an animal with the body mass of our mice. Our null hypothesis is that the MCOT measured for our mice in our Promethion-C system will bear no relation to their predicted MCOT based on data in the scientific literature. This would cast serious doubt on the ability of our Promethion-C system to measure activity metabolism accurately. It may also cause such a system to show little if any correlation between activity and energy expenditure.

    This figure shows the allometric relation between MCOT and body mass in a wide variety of animals (data from Taylor et al. 1982). The gray zone shows the 95% prediction interval based on existing data. The red dot is the mean MCOT measured from our mice in the Promethion-C metabolic phenotyping system, and was calculated as the mean of nine slopes (MCOTs) each based on a night’s worth of data as shown in the previous graph. As you can see, it is solidly within the predicted range of the values measured under far more rigorous conditions – using a treadmill, at controlled speeds, under constant and highly trained human supervision, and generating a great deal of stress for the animal involved. In fact, it’s my suspicion that the stress of treadmill running leads to elevated cost of transport measurements, as I once showed in one of my far too infrequent Nature papers (Lighton and Feener, 1989). This makes the position of our mouse MCOT data, which is slightly but by no means significantly below the predicted line, all the more believable. As anyone who has run mice on a treadmill knows all too well, treadmill-running requires a lot of training for the mice and a lot of patience for the operator, and it also requires, unfortunately, incentivizing the mouse to run by placing a shock-grid at the back of the treadmill chamber. You can imagine what this does to the mouse’s stress and thus cortisol levels, which will inevitably elevate its metabolic rate. Yet here, we are obtaining superior data without requiring a treadmill, without stressing the mouse, without requiring a skilled operator’s constant attention, and we are doing so on many mice simultaneously. What’s not to love?

    Thus the null hypothesis is not only falsified, it is smashed into smithereens that daintily and prettily drift down to the floor like dandelion seeds, except that they sparkle in the moonlight.

    MCOT is an important parameter. Suddenly, it becomes practical to measure it in a metabolic phenotyping system en masse without constant, highly skilled human supervision and considerable animal stress. And if the system can measure MCOT, which is one of the most difficult metabolic parameters to measure, it can certainly make any other metabolic measurements demanded of it. Provided, of course, the metabolic phenotyping system in question is a Promethion-C. Friends don’t let friends depend on anything but Promethion for their critical data.

    If you have any questions, contact me.

     — John Lighton

     

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

    Citations:

    C.R. Taylor, N.C. Heglund and G.M. Maloiy (1982) Energetics and mechanics of terrestrial locomotion. I. Metabolic energy consumption as a function of speed and body size in birds and mammals.  J Exp Biol 97, 1-21.

    J.R.B. Lighton and D.F. Feener (1989) A comparison of energetics and ventilation of desert ants during voluntary and forced locomotion. Nature 342, 174-175.

    J.R.B. Lighton (2012) “Instantaneous” metabolic measurement.  J Exp Biol 215, 1605-1606.

     

     

  • The “Deep Data Field System” in Metabolic Phenotyping

    Data from a metabolic phenotyping system can be rigidly determined by the parameters entered at the time of the experiment (as with legacy systems), or the data can be a rich mine of information, with complete post-hoc analytical flexibility (as with Promethion). The latter approach allows extraction of experimental data, the need for which was not anticipated at the time of the study. This can save serious amounts of time and money – apart from producing better science.

    Browsing through a data set produced by a Promethion metabolic phenotyping system is a fascinating experience. This is for many reasons, but one of the most interesting to me personally is the richness of detail. Because the data from all of the sensors in all of the cages are recorded once a second (together with either multiplexed or continuous metabolic data, depending on the system used), every detail of the experimental animal’s interactions with them are recorded as well. This is in stark contrast to legacy metabolic phenotyping systems, which record only a pre-digested sub-set of processed data created using black-box algorithms. In such systems, you have to pre-set the analytical constraints that determine the type of data you will obtain – for example, binning metabolic or uptake data by certain time-periods.

    The result is that Promethion data are agnostic. I stress the word because the Promethion-equipped investigator has access to the entire set of raw data from which any analyses can be derived. Although a Promethion metabolic or behavioral phenotyping system gives you helpful intermediate feedback on processed variables such as EE and RQ, the raw data are intact and can be analyzed, and re-analyzed, in any way you choose. The Promethion system, unlike legacy systems, makes no assumptions or presumptions about the questions you are asking or the way in which you are trying to answer them with your experiment. An analysis script extracts the data you need, and analysis scripts are very versatile. The standard output of a legacy metabolic phenotyping system represents only a tiny sub-set of the data available from a Promethion system (of course you can use as little or as much as you want).

    Of course, it isn’t easy to record high-resolution data from hundreds of sensors every second. That’s one reason why the legacy systems can’t do it. The other reason is more basic – if everyone is happy with legacy systems, why bother to change? I won’t dignify that reason with a reply.

    I’ll be posting examples of unexpected treasures hidden in Promethion data in due course. But, searching for a metaphor to communicate the ease of extracting novel data from these rich data-sets, I came upon a technology that shares many features with Promethion, though in a different technological world, and the parallels are so interesting I decided to share them with you.

    That other technology is the light field camera, pioneered by Lytro. It captures all of the data in the light field at which it’s pointed, just as the Promethion system does with behavioral and metabolic data. So far, it sounds like a conventional camera, just as Promethion sounds superficially like a typical metabolic or behavioral phenotyping system. But in fact there is a radical difference. Let’s explore the metaphor by treating images from a Lytro camera* as if they were extracted from a data set in the course of a scientific investigation.

    So, let’s start. You have a single image from a light field camera, or, in our metaphor, a single data file from a “data field system”, i.e. a Promethion system. You want to answer a specific scientific question, symbolized by the fellow’s face in the foreground.

    All well and good. However, as you proceed with writing your paper, you come upon another paper bearing directly on your topic, and it forces you to change the analytical focus of your study slightly. In our metaphor, the focus shifts to the woman’s face. Not a problem for a light field camera or, of course, a data field system. Just change the focus (or the analytical script) post-hoc.


    Had you been using a conventional camera, or a legacy metabolic phenotyping system, you would have had to set up the shoot again, or deal with an intolerable loss of detail. But because you are using a light field camera, or a data field system, the day is saved. No need to go to the delay, trouble and expense of re-booking a fortnight at the local metabolic phenotyping facility.

    Now for a scenario familiar to everyone who has published. Your paper’s been submitted and reviewed, but two reviewers – though favorably inclined in general – are demanding, in light of other work just published, that certain additional measurements and analyses must be made. These will normally require you to repeat the entire experiment! Or, to use our light field camera metaphor, the focus has to change to the fellow in the background. Not a problem with a Lytro light field camera:

    …And also not a problem with a Promethion data field system. No re-booking an expensive facility, derailing other projects, or re-submitting the MS to a less-fussy, lower-impact journal. Just use a new analytical script. To give you a concrete example, let’s say the referee demands that the food uptake data must be accompanied by a full meal pattern analysis with one-second granularity. With Promethion, that isn’t a problem at all. Or a complete analysis of behavioral transition probabilities between midnight and 3 a.m. before and after treatment? Null problemo. Or time budgets? A no-brainer. I could go on, but you get the point.

    Any questions or comments? Join the site membership (totally spam-free; see the bar to your left) if you have a valid email address **, and post them here. Or contact me directly if you wish.

    * The images shown above are derivative works based on public content from the Lytro site in accordance with their published policies.

    ** By which I mean commercial, academic, or identifiably yours. This restriction is imposed by hordes of would-be posters with names like hgetrew (email address gfhgfjr5566@gmail.com) who are simply forum spammers, and whose applications are heartlessly deleted.