Cell cultures as well as cells in tissue always display a certain degree of variability,and measurements based on cell averages will miss important information contained in a heterogeneous population. These differences among cells in a population may be essential to quantify when looking at, e.g., protein expression and mutations in tumor cells which often show high degree of heterogeneity.
Single nucleotide mutations in the mithochondrial DNA (mtDNA) can accumulate and later be present in large proportions of the mithocondria causing devastating diseases. To study mtDNA accumulation and segregation one needs to measure the amount of mtDNA mutations in each cell in multiple serial cell culture passages. The different degrees of mutation in a cell culture can be quantified by making measurements on individual cells as an alternative to looking at an average of a population. Fluorescence microscopy in combination with automated digital image analysis provides an efficient approach to this type of single cell analysis.
Image analysis software for these types of applications are often complicated and not easy to use for persons lacking extensive knowledge in image analysis, e.g., laboratory personnel. This paper presents a user friendly implementation of an automated method for image based measurements of mtDNA mutations in individual cells detected with padlock probes and rolling-circle amplification (RCA). The mitochondria are present in the cell’s cytoplasm, and here each cytoplasm has to be delineated without the presence of a cytoplasmic stain. Three different methods for segmentation of cytoplasms are compared and it is shown that automated cytoplasmic delineation can be performed 30 times faster than manual delineation, with an accuracy as high as 87%. The final image based measurements of mitochondrial mutation load are also compared to, and show high agreement with, measurements made using biochemical techniques.