seededclustR: Using Cluster Analysis of Mitochondria to Improve Cell Segmentation and Automated Quantification of mtDNA in Smooth Muscle Cells.

Intro Segmentation of fluorescently labelled cells is essential to pipelines of automated image analysis. Identifying regions of interest (ROIs) around labelled nuclei is relatively easy using tools available in ImageJ/Fiji, but determining the bounds of cells can be much harder. We asked if fluorescently labelled mitochondria could be used to improve the automated generation of cellular ROIs. Method The perimeter coordinates of nuclear ROIs and the centroid coordinates, area, mean intensity, angle, and solidity of ROIs around MitoTracker Orange (MTO)‐labelled mitochondria were generated in ImageJ and exported to R. Using the nuclei as seeds for a recursive k‐Nearest Neighbour search extending from each nucleus, MTO particles were assigned to their nearest‐neighbouring cells. A concave hull was created around MTO particles assigned to each cell using the concaveman package. We determined the sensitivity of this algorithm by comparing the accuracy of the generated concave hulls to the corresponding hand‐drawn ROIs, and ROIs from Voronoi expanded distance maps Results When we compared seededClustR ROIs to those drawn by hand, the Intersection‐Over‐Union (IOU) was 68 ± 8% (mean ± SD), Precision was 88 ± 6% and Recall 95 ± 3%. In contrast, Voronoi expansion of nuclei resulted in the IOU of 46 ± 18%, Precision of 83 ± 17%, and Recall of 91 ± 11%. Optimal IOU and Recall were observed when each MTO particle was compared to its 7–10 nearest neighbours. IOU and Recall were maximized using recursive steps of 40–200 μm, while Precision decreased from 94% to 88% as step size increased from 7–40 μm. Unexpectedly, extending seededClustR to an n‐dimensional kd‐tree calculation that included varying weights of MTO particle mean intensity, area, standard deviation, angle and solidity reduced cell tracing accuracy. Similarly, using Wilks’‐test to identify outliers decreased IOU by 17 percentage points (pp) and Recall by 20 pp, though it increased Precision by 6 pp. Finally, we tested whether if the more accurate cell ROI from seededClustR versus Voronoi ROIs could improve our ability to detect changes in optical measures of mtDNA in A7r5 smooth muscle cells treated with angiotensin II, 5,6‐dideoxycytidine, and H2O2, where results showed promising gains. Conclusion seededClustR is a generalizable algorithm that can use simple XY coordinates of puncta‐like features of cells to generate cellular ROIs around labelled nuclei with measures of accuracy that exceed simple dilation of nuclear ROIs. This approach should facilitate improved precision in automated screens of cellular phenotypes. Support or Funding Information Natural Sciences and Engineering Research Council of Canada

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