To obtain a background-corrected image, it is as straightforward as: import skimage.data If you use this software, please cite that pre-print. To learn more about the theory behind SMO, you can read the pre-print in BioRxiv. We provide an easy to use Python package and plugins for some of the major image processing softwares: napari, CellProfiler, and ImageJ / FIJI. (I) Differentiated adipocytes were overlaid with corresponding nuclei using the “ OverlayObjects” module.SMO is a Python package that implements the Silver Mountain Operator (SMO), which allows to recover an unbiased estimation of the background intensity distribution in a robust way. (H) Objects below the area threshold were excluded from the final analysis using the “ FilterObjects” module. (G) Adipocytes were identified by size, shape, and intensity from the binary image using the “ IdentifyPrimaryObjects” module. (F) Identified objects from the “ SplitOrMergeObjects” module were converted to a binary image using the “ ConvertObjectsToImage” module. (E) Touching lipid droplets were grouped together using the “ SplitOrMergeObjects” module. (D) Identified lipid droplets using the “ IdentifyPrimaryObjects” and “ FilterObjects” modules. (C) Corresponding image of DAPI stained nuclei. (B) Conversion of original image to grayscale using the “ ColorToGray” module. Image also contains a magnified picture of a differentiated adipocyte to model the diversity in lipid droplet sizes within a single cell. (A) Original image of differentiated adipocytes with a high density of BODIPY stained lipid droplets. In conclusion, this novel image analysis tool can provide a more precise evaluation of lipid droplet and adipogenesis dysregulation, a critical need in the understanding of metabolic disorders.Īdipocyte image segmentation lipid droplet quantitative analyses. CellProfiler streamlines the lipid droplet phenotypic analysis of adipocytes compared to more traditional analysis methods. A clustering analysis is also possible using CellProfiler which allows for the quantification of total lipid content per individual adipocyte to provide insight into single-cell responsiveness to adipogenic stimuli. Our results show that CellProfiler is able to accurately identify a greater number of lipid droplets compared to ImageJ. For ImageJ, we used an already developed macro designed to identify particles and quantify their area, and for CellProfiler, we developed a new analysis pipeline. For the lipid droplet analysis, we used two approaches, the free online computer software of reference, ImageJ, and another free online computer software, CellProfiler. Therefore, the aims of this study were to develop an accurate, standardized approach to quantify lipid droplet size of mature adipocytes and a clustering approach to analyze the total lipid content per adipocyte. Nutrition, stress, or chemical exposure can dysregulate adipogenic differentiation and lipid metabolism. However, imaging tools for evaluating intracellular lipid droplets remain at their infancy. During differentiation, neutral lipids that accumulate in adipocytes can be detected using stains and used as an index of cell differentiation. Adipogenic differentiation is the process by which preadipocytes become mature adipocytes, cells that store energy and regulate metabolic homeostasis.
0 Comments
Leave a Reply. |