AutoComet: A fully automated algorithm to quickly and accurately analyze comet assays.

DNA damage is a common cellular feature seen in cancer and neurodegenerative disease, but fast and accurate methods for quantifying DNA damage are lacking. Comet assays are a biochemical tool to measure DNA damage based on the migration of broken DNA strands towards a positive electrode, which creates a quantifiable 'tail' behind the cell. However, a major limitation of this approach is the time needed for analysis of comets in the images with available open-source algorithms. The requirement for manual curation and the laborious pre- and post-processing steps can take hours to days. To overcome these limitations, we developed AutoComet, a new open-source algorithm for comet analysis that utilizes automated comet segmentation and quantification of tail parameters. AutoComet first segments and filters comets based on size and intensity and then filters out comets without a well-connected head and tail, which significantly increases segmentation accuracy. Because AutoComet is fully automated, it minimizes curator bias and is scalable, decreasing analysis time over ten-fold, to less than 3 s per comet. AutoComet successfully detected statistically significant differences in tail parameters between cells with and without induced DNA damage, and was more comparable to the results of manual curation than other open-source software analysis programs. We conclude that the AutoComet algorithm provides a fast, unbiased and accurate method to quantify DNA damage that avoids the inherent limitations of manual curation and will significantly improve the ability to detect DNA damage.

Assessing Advantages and Drawbacks of Rapidly Generated Ultra-Large 3D Breast Cancer Spheroids: Studies with Chemotherapeutics and Nanoparticles.

Traditionally, two-dimensional (2D) monolayer cell culture models have been used to study in vitro conditions for their ease of use, simplicity and low cost. However, recently, three-dimensional (3D) cell culture models have been heavily investigated as they provide better physiological relevance for studying various disease behaviors, cellular activity and pharmaceutical interactions. Typically, small-sized tumor spheroid models (100-500 µm) are used to study various biological and physicochemical activities. Larger, millimetric spheroid models are becoming more desirable for simulating native tumor microenvironments (TMEs). Here, we assess the use of ultra-large spheroid models (~2000 µm) generated from scaffolds made from a nozzle-free, ultra-high resolution printer; these models are explored for assessing chemotherapeutic responses with molecular doxorubicin (DOX) and two analogues of DoxilⓇ (Dox-NPⓇ, DoxovesTM) on MDA-MB-231 and MCF-7 breast cancer cell lines. To provide a comparative baseline, small spheroid models (~500 µm) were developed using a self-aggregation method of MCF-7 breast cancer cell lines, and underwent similar drug treatments. Analysis of both large and small MCF-7 spheroids revealed that Dox-NP tends to have the highest level of inhibition, followed by molecular doxorubicin and then Doxoves. The experimental advantages and drawbacks of using these types of ultra-large spheroids for cancer research are discussed.