Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures

Summary

We present a method, which utilizes a generalizable area-based image analysis approach to identify cell counts. Analysis of different cell populations exploited the significant cell height and structure differences between distinct cell types within an adaptive algorithm.

Abstract

Quantification of cells is necessary for a wide range of biological and biochemical studies. Conventional image analysis of cells typically employs either fluorescence detection approaches, such as immunofluorescent staining or transfection with fluorescent proteins or edge detection techniques, which are often error-prone due to noise and other non-idealities in the image background.

We designed a new algorithm that could accurately count and distinguish macrophages and fibroblasts, cells of different phenotypes that often colocalize during tissue regeneration. MATLAB was used to implement the algorithm, which differentiated distinct cell types based on differences in height from the background. A primary algorithm was developed using an area-based method to account for variations in cell size/structure and high-density seeding conditions.

Non-idealities in cell structures were accounted for with a secondary, iterative algorithm utilizing internal parameters such as cell coverage computed using experimental data for a given cell type. Finally, an analysis of coculture environments was carried out using an isolation algorithm in which various cell types were selectively excluded based on the evaluation of relative height differences within the image. This approach was found to accurately count cells within a 5% error margin for monocultured cells and within a 10% error margin for cocultured cells.

Introduction

Software is routinely implemented during image analysis techniques to ensure that the results are accurate, efficient, and unbiased. For cell-based assays, a common problem is the misidentification of cells. Images with improper focal and contrast settings may lead to cell blurring, in which the boundary of individual cells becomes hard to identify¹. The presence of extraneous image features such as pores, bubbles, or other undesired objects can hamper counting procedures by slowing the counting process and leading to misidentification. Furthermore, cell counting can be onerous, and counting hundreds of replicates can be extremely time-consumin....

Protocol

1. Cell culture and image acquisition

1. Culture RAW264.7 macrophages at 37 °C and 5% CO₂ in Dulbecco's Modified Eagle Medium (DMEM) supplemented with 10% fetal bovine serum (FBS), 1% penicillin-streptomycin, 1.5 g/L sodium bicarbonate, and 5 µM β-mercaptoethanol.
   1. For monoculture imaging, culture RAW264.7 cells at a density of 25,000 cells/cm² in a 5 mL cell culture flask with 1 mL of medium.
2. Culture NIH/3T3 cells at 37 °C and 5% CO₂ in DMEM supplemented with 10% fetal bovine serum and 1% penicillin-streptomycin¹¹.
3. Fo....

Results

Analysis of non-bulbous RAW264.7 macrophages was conducted in a monoculture setting at 25,000 cells/cm². Representative images were taken of the cell culture and processed in MATLAB following conversion to 8-bit tiff in ImageJ. Algorithm outputs throughout the process were recorded and documented in Figure 2 for the representative image. In this image, the algorithm counted 226 cells, and this image count was verified by comparison with a manual count that identified 241 cells (6........

Discussion

We designed a general area-based procedure that accurately and efficiently counted cells on the basis of cell height, allowing for stain-free quantitation of cells even in coculture systems. Critical steps for this procedure included the implementation of a relative intensity system by which cells could be differentiated. The use of a relative height analysis served two purposes: the need for external parameters was rendered unnecessary, as relative parameters were constant for the given cell type and parameter, and abso.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Axio Observer 7 Inverted Microscope	Zeiss	1028290770
β-mercaptoethanol	Life Technologies	21985023
Cell Scrapers	CellTreat	229310
Dublecco's Modified Eagle Medium	Fisher Scientific	12430047
Dublecco's PBS	Fisher Scientific	14190144
MATLAB Software	MathWorks	2021A
NIH/3T3 Cells	ATCC	ATCC CRL - 1658
Penicillin–Streptomycin	Sigma Aldrich	P4333-20ML
RAW264.7 Cells	ATCC	ATCC TIB - 71
Sodium Bicarbonate	Sigma Aldrich	S6014-25G
T75 Cell Culture Flask	Corning	CLS3814-24EA