Using Computer Vision Libraries to Streamline Nuclei Quantification

Summary

This article describes step-by-step methods to automate image-based nuclei quantification using an open-source executable program validated across a range of cell densities. This program provides an alternative that addresses barriers related to cost, accessibility for users with limited technological skillsets, and application-specific validation that may limit utility of existing technologies.

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.

Introduction

Functional and image-based experiments are critical for understanding the impacts of experimental treatments on whole-cell biochemistry and physiology. Valid interpretation of data from cell biology experiments depends on the accuracy and reproducibility of the experimental protocol, including data normalization. For example, analyses of oxygen consumption and extracellular acidification rates in live cells at baseline and after treatment with specific drugs allow for the assessment of various aspects of energy metabolism^1,2. Measuring the activity of enzymes such as lactate dehydrogenase in the supernatant of....

Protocol

NOTE: Supplemental files can be found at the following link https://osf.io/a2s4d/?view_only=2d1042eb8f7c4c4a84579fe4e84fb03c

1. Capturing and saving images using fluorescence microscopy

1. Prepare cell or tissue samples to be imaged, including staining with desired DNA dye. To obtain the images used here, C2C12 myoblasts (CRL-1772, American Type Culture Collection) were grown in 6-well plates for 48-72 h under standard culture conditions (5% CO₂, 37 °C, humidified), with or without 50 mM EtOH⁶, and fixed in ice-cold methanol as previously described¹³. Fixed cells were m....

Results

Each batch image run produces: 1) a set of image files with contours applied showing the outlines of identified nuclei (Figure 5), and 2) a .csv file (spreadsheet) linking image file names and the associated counts. Viewing the contours will allow the user to visually assess the count quality. Specifically, images obtained according to section 1 should have all (or nearly all) nuclei surrounded by a solid green line indicating that the nucleus was counted by .......

Discussion

Our nuclei quantification program has several advantages over existing options: it requires only minimal technological skills, is validated for the specific task of nuclei quantification, and is open-source; the latter overcomes cost-related barriers. Ultimately, this program provides cell and molecular biologists with an additional option to quickly and accurately quantify nuclei in images captured using fluorescence microscopy. Currently available automated nuclei or cell counting programs are not preferred by all user.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Computer with access to results file from method 2 or 3	-	-	See step 2.6 (for Method 2) or step 3.3.9 or 3.4.9 (for Method 3)
Computer with internet access, modern browser	-	-	e.g., Google Chrome
Computer with internet access, modern browser, and Windows OS	Varies	Varies	For Mac, Linux, or other OS, use Method 3
Computer with software for image capture	Zeiss	AxioVision	Other software is acceptable; must be compatible with the fluorescence microscope
File location for output (results spreadsheet and image contours)	-	-	Can be a new, empty folder
Fluorescence microscope	Zeiss	Axiovert 200M	Other fluorescence microscopes are acceptable; must be equipped with appropriate filter cubes, desired objective, and camera
Folder containing all images to be quantified	-	-	See step 1.12
Python version 3.10 or higher	Python	-	Available for free download and installation at https://www.python.org/downloads/
Samples to be imaged	-	-	Fixed or live, stained or counterstained with fluorescent DNA dyes
Spreadsheet software	Microsoft	Excel	Similar spreadsheet software is also acceptable