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

Cellular hypertrophy of adipose tissue underlies many of the proposed proinflammatory mechanisms for obesity-related diseases. Adipose hypertrophy results from an accumulation of esterified lipids (triglycerides) into membrane-enclosed intracellular lipid droplets (LDs). The coupling between adipocyte metabolism and LD morphology could be exploited to investigate biochemical regulation of lipid pathways by monitoring the dynamics of LDs. This article describes an image processing method to identify LDs based on several distinctive optical and morphological characteristics of these cellular bodies as they appear under bright-field. The algorithm was developed against images of 3T3-L1 preadipocyte cultures induced to differentiate into adipocytes. We show that the calculated lipid volumes are in excellent agreement with enzymatic assay data on total intracellular triglyceride content. We also demonstrate that the image processing method can efficiently characterize the highly heterogeneous spatial distribution of LDs in a culture by showing that differentiation occurs in distinct clusters separated by regions of nearly undifferentiated cells. Prospectively, the LD detection method described in this work could be applied to time-lapse data collected with simple visible light microscopy equipment to quantitatively investigate LD dynamics.