Using Computer Vision Libraries to Streamline Nuclei Quantification.

Abstract

Live cell assays and image-based cell analyses require data normalization for accurate interpretation. A commonly used method is to stain and quantify nuclei, followed by data normalization to nuclei count. This nuclei count is often expressed as cell count for uninucleate cells. While manual quantification can be laborious and time-consuming, available automated methods may not be preferred by all users, may lack validation for this specific application, or may be cost-prohibitive. Here, we provide step-by-step instructions for capturing quantifiable images of nuclei stained with fluorescent DNA stains and subsequently quantifying the nuclei using an automated object counting software program developed using Python computer vision libraries. We also validate this program across a range of cell densities. Although the exact time for program execution varies based on the number of images and computer hardware, this program consolidates hours of work counting nuclei into seconds for the program to run. While this protocol was developed using images of fixed, stained cells, images of stained nuclei in live cells and immunofluorescence applications can also be quantified using this program. Ultimately, this program provides an option that does not require a high degree of technological skill and is a validated, open-source alternative to aid cell and molecular biologists in streamlining their workflows, automating the tedious and time-consuming task of nuclei quantification.