We're interested in crosstalk between contracting muscle and the adipocytes that form between muscle fibers in injury and disease called intermuscular adipose tissue, or IMAT. Genetic engineering in cells and mice is a powerful tool for mechanistically dissecting fat muscle crosstalk. Analysis of signaling via transcriptomics and proteomics identifies mediators, and then biomaterial-based drug delivery can intervene in these pathways.
One of the primary challenges is isolating and quantifying IMAT adipocytes. Especially in small animal models, the IMAT signal is washed out by whole-muscle analysis like transcriptomics or lipidomics. And quantifying IMAT by non-invasive imaging suffers from similar washout as most voxels contain a mixture of muscle and IMAT.
We recently used this technique described in this article to demonstrate that IMAT directly impairs muscle contractility. With precise measures of IMAT deposition, we showed that it does not just replace contractile material, but impedes a contraction in the remaining lean muscle. This protocol addresses challenges with quantifying IMAT.
The current standard to measure IMAT in small animals is single-section histology, which is not-comprehensive or non-invasive imaging, which is expensive and low resolution. We believe this has limited our understanding of IMAT muscle crosstalk. This technique provides a comprehensive and inexpensive method for assessing IMAT by qualitative visualization and multiscale quantification.
We hope this technique's simplicity will encourage more investigators to measure IMAT and their animal models. Also, this technique will provide a higher precision tool for the investigators interested in IMAT to uncover more subtle relationships between IMAT adipocytes and other cells. To begin, inspect the muscles of interest dissected from the sacrificed mouse for the absence of bone chips or ragged edges and trim them away with sharp scissors, if needed.
Weigh the cleaned muscles using an analytical balance and record their weight. After placing the muscle in at least 0.1 milliliters of 1%SDS per milligram of weight, place the well plate on a rocking shaker set to 50 to 80 hertz. Visually inspect the SDS solution periodically.
When the solution becomes cloudy, remove the solution with a pipette without aspirating the muscles and replace it with an equal volume of fresh 1%SDS. When the solution remains clear for 24 hours, remove the final SDS solution from the decellularized muscles and replace it with an equal volume of PBS. Inspect the decellularized muscles under a stereo microscope and carefully remove any hair or debris stuck to the muscle using forceps.
Carefully remove the PBS and replace it with an equal volume of 3.7%formaldehyde before returning the plate to the rocking shaker for 24 hours. Prepare the ORO solution by dissolving 0.5 grams of ORO powder in 100 milliliters of isopropanol to generate a stock solution. Prepare the working solution for all muscles by combining the ORO stock solution and deionized water at a 60:40 ratio.
Cover the working solution for 10 minutes to allow the particulate to settle. Filter it through a 40-micrometer mesh, followed by a 0.22-micrometer syringe filter. Next, remove the formaldehyde solution from the decellularized muscles in the well plate and wash the muscles three times with an equal volume of PBS.
Then, replace the PBS with an equal volume of 60%isopropanol solution and incubate it on the rocking shaker for five minutes. Once done, replace the 60%isopropanol solution with ORO working solution before 10 minutes of incubation on the rocking shaker. Next, replace the ORO working solution with an equal volume of 1%SDS and inspect the SDS solution periodically.
Once the solution becomes noticeably pink, replace the SDS solution with fresh 1%SDS. When the solution remains clear for 24 hours, replace the 1%SDS with PBS. Under a stereo microscope at 4x magnification, remove any obvious debris or particulate stuck to the outside of the stained muscle.
If the staining is satisfactory, showing the bright red spheres floating in a transparent matrix, acquire the staining images using a camera attached to the stereo microscope. Properly decellularized muscles were white and semi-transparent. When decellularized muscles were stained with ORO to visualize intramuscular adipose tissue, or IMAT, IMAT lipid droplets were apparent within the clear muscle structures as red spheres.
Healthy mouse hindlimb muscles showed a little natural IMAT, evidenced by little to no red ORO-positive lipid, as compared to the hindlimb muscles injected with cardiotoxin or glycerol. Incomplete decellularization was identified immediately following initial SDS treatment or after the washout of the ORO staining as semi-opaque light pink fibers. Incomplete ORO clearance was identified, following ORO washout, as a pink or red uniform background, rather than distinct fiber lines.
Begin by transferring the decellularized skeletal muscles stained with ORO to 200 microliters of isopropanol in the individual wells of a 96-well plate. Agitate the solution by tapping the plate and mechanically crush the muscle with a pipette tip under a microscope until no red spheres can be seen in the decellularized muscle suspension. Again, mix the cell suspension in each well by pipetting up and down before transferring 75 microliters to two clean wells of the plate.
Cover the plate and read the absorbance of the duplicate 75-microliter wells using a spectrophotometer or plate reader. To begin the quantification of stained IMAT lipid droplet metrics, open confocal stacks in ImageJ. Open the threshold user interface by selecting Image, then Adjust, then Threshold.
In the user interface, select Intermodes as the thresholding type and ensure Dark background is selected. Then, click on Apply to run a thresholding algorithm. Run the watershed algorithm to separate touching lipid droplets by selecting Process, then Binary and Watershed.
Select Yes in the dialogue box to process all the images in the stack to add a thin black line dividing larger areas of solid white. To set the maximum and minimum particle sizes, open the original image and outline the smallest and largest lipid droplet in view, using the Oval tool. Then, add these shapes to the ROI Manager by typing T, select Analyze, then Set Measurements.
In the dialogue box, select the settings, check Area only, and click OK.Then, select Measure from the ROI Manager. Use the two area measures from this Results window as the size range in Analyze Particles. To identify the region of interest, or ROIs, with the Analyze Particles algorithm, select Analyze, then Analyze Particles.
A dialogue box opens to set the selection settings. To output the ROI measurements, select Analyze, then Set Measurements, and select Area, Centroid, Fit ellipse, and Stack position. Click on OK.Then, select Measure from the ROI Manager.
The data in the results table can be selected and copied into Excel for further analysis. For a detailed assessment of individual lipid droplet metrics and distribution, the BODIPY fluorescently-labeled lipid droplets were imaged via confocal microscopy. Thresholding and shape segmentation provided a good first pass for generating ROIs for each lipid droplet.
However, manual edits were needed to correct the errors. Deep lipid droplets, which appear dim on the image, can be added by hand using the Oval tool in ImageJ. A group of lipid droplets, mistakenly identified as a single ROI, can also be corrected by deleting and replacing the original ROI with multiple new ROIs.
Where a single lipid droplet was identified as a unique ROI in multiple slices, the duplicate ROIs were consolidated into a single ROI.
