Comparison of two automatic cell‐counting solutions for fluorescent microscopic images

Abstract

Cell counting in microscopic images is one of the fundamental analysis tools in life sciences, but is usually tedious, time consuming and prone to human error. Several programs for automatic cell counting have been developed so far, but most of them demand additional training or data input from the user. Most of them do not allow the users to online monitor the counting results, either. Therefore, we designed two straightforward, simple-to-use cell-counting programs that also allow users to correct the detection results. In this paper, we present the Cellcounter and Learn123 programs for automatic and semiautomatic counting of objects in fluorescent microscopic images (cells or cell nuclei) with a user-friendly interface. Although Cellcounter is based on predefined and fine-tuned set of filters optimized on sets of chosen experiments, Learn123 uses an evolutionary algorithm to determine the adapt filter parameters based on a learning set of images. Cellcounter also includes an extension for analysis of overlaying images. The efficiency of both programs was assessed on images of cells stained with different fluorescent dyes by comparing automatically obtained results with results that were manually annotated by an expert. With both programs, the correlation between automatic and manual counting was very high (R(2) < 0.9), although Cellcounter had some difficulties processing images with no cells or weakly stained cells, where sometimes the background noise was recognized as an object of interest. Nevertheless, the differences between manual and automatic counting were small compared to variations between experimental repeats. Both programs significantly reduced the time required to process the acquired images from hours to minutes. The programs enable consistent, robust, fast and accurate detection of fluorescent objects and can therefore be applied to a range of different applications in different fields of life sciences where fluorescent labelling is used for quantification of various phenomena. Moreover, Cellcounter overlay extension also enables fast analysis of related images that would otherwise require image merging for accurate analysis, whereas Learn123's evolutionary algorithm can adapt counting parameters to specific sets of images of different experimental settings.

Proposing Urothelial and Muscle In Vitro Cell Models as a Novel Approach for Assessment of Long-Term Toxicity of Nanoparticles

“…Magnetic PAA NPs are negatively charged and highly stable in physiological conditions, but they are less stable at high calcium concentration ( Table 1 ) [ 3 ]. As already described, they are short-term nontoxic and can be internalized in high quantities [ 56 , 60 ]. On the other hand, due to their highly positive zeta potential, [ 61 , 62 ] magnetic PEI NPs aggregated, and their size increased up to 10 times when dissolved in cell culture media due to the formation of biocorona and aggregation [ 4 , 62 ].…”

“…Hoechst/propidium iodide (PI) viability assay was used to determine the viability of L6 cells [ 60 , 67 ]. Following NPs exposure, cells were washed and labeled with Hoechst 33,342 (2 μg/mL, 15 min) (Thermo Fisher Scientific, US; Life Technologies, USA) and PI (0.15 mM, 5 min) (Sigma, St. Louis, USA) and analyzed using Zeiss 200 (Axiovert, Oberkochen, Germany).…”

“…Phase-contrast and at least 20 fluorescent images at 10× objective magnification were recorded for each fluorescent dye. Cells (stained nuclei) were counted using CellCounter software [ 60 ]. Thus, cell viability was estimated based on the number of Hoechst-positive (all) cells and PI-positive (dead) cells detected in 20 random fluorescent images.…”

Analysis of the Direct and Indirect Effects of Nanoparticle Exposure on Microglial and Neuronal Cells In Vitro

“…Briefly, following NP exposure, cells were stained with 2 μg/mL Hoechst 33342 (Life Technologies, Carlsbad, CA, USA) to obtain the total cell number and with 0.15 mM Propidium iodide (PI; Sigma-Aldrich) for 5 min to stain dead cells. Cells on obtained fluorescent images were counted using CellCounter software [ 60 ]. The number of viable cells ( N S ) was calculated by subtracting dead (PI positive) from all counted cells (Hoechst positive) for each sample.…”

Bayesian model for detection and classification of meningioma nuclei in microscopic images

“…A recent study by Lojk et al . () have compared a segmentation‐based to a learning‐based method for counting nuclei in images. Our present study is similar to theirs in that it employs machine learning to microscopic nucleus images.…”