Quantitative Monitoring of Lipid Accumulation Over Time in Cultured Adipocytes as Function of Culture Conditions: Toward Controlled Adipose Tissue Engineering

Abstract

Adipose tissue engineering is investigated for native fat substitutes and wound healing model systems. Research and clinical applications of bioartificial fat require a quantitative and objective method to continuously measure adipogenesis in living cultures as opposed to currently used culture-destructive techniques that stain lipid droplet (LD) accumulation. To allow standardization, automatic quantification of LD size is further needed, but currently LD size is measured mostly manually. We developed an image processing-based method that does not require staining to monitor adipose cell maturation in vitro nondestructively using optical micrographs taken consecutively during culturing. We employed our method to monitor LD accumulation in 3T3-L1 and mesenchymal stem cells over 37 days. For each cell type, percentage of lipid area, number of droplets per cell, and droplet diameter were obtained every 2-3 days. In 3T3-L1 cultures, high insulin concentration (10 microg/mL) yielded a significantly different (p < 0.01) time course of all three outcome measures. In mesenchymal stem cell cultures, high fetal bovine serum concentration (12.5%) produced significantly more lipid area (p < 0.01). Our method was able to successfully characterize time courses and extents of adipogenesis and is useful for a wide range of applications testing the effects of biochemical, mechanical, and thermal stimulations in tissue engineering of bioartificial fat constructs.

Large, but not Small Sustained Tensile Strains Stimulate Adipogenesis in Culture

“…We monitored the lipid production in the cultures using a previously developed, non-destructive image-processing-based method, 20 and compared the adipogenic outcomes across the different levels of sustained tensile strains. Eventually, we could identify a domain of critical strains above which adipogenesis in the statically stretched cultures was significantly accelerated.…”

Automated Image Processing for Spatially Resolved Analysis of Lipid Droplets in Cultured 3T3-L1 Adipocytes

“…Previous work on automated analysis of label-free images 8 identified LDs by applying a brightness threshold as a filter. While this approach is straightforward to implement, we found that the results can be sensitive to slight variations in the brightness and contrast of the acquired image, especially when the image contains a heterogeneous distribution of nonuniformly differentiated cells with a range of LD sizes.…”

“…After another 48 h, the second medium was replaced with the adipocyte basal medium. On days 4,8,12, and 16, images were recorded for six randomly selected wells, which were then sacrificed for enzymatic assays of total TG content.…”

“…To generate the high threshold image, all pixels with values greater (lighter) than the threshold are set to white, and pixels less (darker) than the threshold are set to black. Setting the threshold to a fixed value 8 was not desirable because the overall brightness and contrast could vary between images. Therefore, the threshold was recomputed automatically for each image based on the pixel value distribution of the image using a built-in function, which uses Otsu's method 10 to choose a threshold value that minimizes the intraclass variance of the black and white pixels.…”

“…This filter can be readily implemented for automated analysis, as recently demonstrated by Or-Tzadikario et al on images of cultured adipocytes treated with adipogenic or lipogenic factors. 8 However, relying solely on a fixed brightness threshold can confound the analysis due to uneven contrast arising from various factors unrelated to LD morphology, such as variations in cell density, location within the well, and shadows caused by cellular debris. Furthermore, LDs of different sizes may appear brighter than others.…”