Grey Frequency-Based Methodology for Assessing HeLa Cell Damage

doi:10.21203/rs.3.rs-5250802/v1

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Grey Frequency-Based Methodology for Assessing HeLa Cell Damage

https://doi.org/10.21203/rs.3.rs-5250802/v1

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Cell biology techniques offer a solid foundation for evaluating and forecasting the danger of pollutants in the investigations of environmental toxicology. Studies on ecological toxicity, medication development, and illness diagnosis depend on evaluating cellular damage. The morphology of stimulated cells can alter the light scattering and reflection, and the brightness of microscopic images of the cells. This study demonstrated that stimulation-damaged and normal cells had distinct grey value distributions which led to the proposal of a novel theory to measure cellular damage by image brightness. Additionally, an evaluation technique based on grey frequency analysis can be created to determine the extent of cellular damage. This approach provides an effective and helpful tool for cellular damage visualization and quantitative evaluation in environmental toxicity assessment.

Cell damage

Greyscale frequency

Image brightness

Pollutant toxicity assessment

With global industrialization and urbanization, the extensive spread of environmental contaminants endangers ecosystems and human health and is a leading cause of sickness and death (Chowdhury et al. 2023; Fuller et al. 2022; Mou et al. 2023). Cell biology methodologies provide a scientific foundation for analyzing and forecasting contaminant risk in environmental toxicology research. Cell damage assessment is an important component of cell biology methods, which, in addition to being important in disease diagnosis and monitoring (Burton and Stolzing 2018; Kamisawa et al. 2015; Maria and Davidson 2020) and drug development and safety assessment (Fucikova et al. 2020; Ingber 2022; Weaver et al. 2020), is also important in assessing the toxicity of environmental pollutants. Park and Choi discovered that cytotoxicity, genotoxicity, and ecotoxicity tests may accurately assess the possible impacts of environmental contaminants on human health and ecosystems (Park and Choi 2007). Rubio et al. found that polystyrene nanoparticles (PSNPs) induce oxidative stress and genetic damage in various human hematopoietic cell lines. The result underscores the importance of conducting cellular damage assays to assess the biological impacts of pollutants, including microplastics accurately (Rubio et al. 2020). According to Santos et al., the cytotoxicity and genotoxicity of chlorinated disinfection by-products of 1,3-diphenyl guanidine (DPG) confirmed the usefulness of cellular damage assays in the toxicity assessment of environmental pollutants (Marques dos Santos et al. 2022). These findings imply that cellular damage assays not only aid in the rapid screening and identification of harmful compounds but also provide valuable information for understanding the mechanism of action of pollutants.

The most frequent methods for determining the level of cell damage fall into three categories: cell viability assays, biochemical index tests, and morphological observations. Cell viability assays are important for assessing cell survival and function. Kamiloglu et al. discussed a variety of cell viability assays in detail, including staining exclusion (e.g., Taipan blue staining), colorimetric assays (e.g., MTT and MTS assays), fluorescence (e.g., resazurin and 5-CFDA-AM assays), luminescence (e.g., ATP assay), and flow cytometry. (e.g., membrane asymmetry and permeability measurements) (Kamiloglu et al. 2020). Changes in cell viability can directly reflect a cell's response to external stimuli and provide useful information about the level of cellular damage.

Biochemical index assays evaluate various biomarkers and are typically used to estimate cellular damage. Apoptosis tests are an essential part of this category, and the presence of apoptosis is typically determined using methods such as Annexin V-FITC labeling. Apoptosis markers include the production or activation of certain proteins such as cysteinyl asparaginases (Caspases) (Sahoo et al. 2023), cytochrome C (Morse et al. 2024), Bcl-2 family proteins (Czabotar and Garcia-Saez 2023), and p53 (Liu and Gu 2022), which can indicate whether a cell is undergoing programmed death. Furthermore, biochemical index testing relies heavily on the detection of oxidative stress indicators. Oxidative stress is a major cause of cellular injury, defined as the breakdown of the equilibrium between oxidants and antioxidants in cells in response to damaging stimuli from the internal and external environments, resulting in cell and tissue damage (Sinha et al. 2015). This imbalance could be induced by metabolic processes, exposure to toxic chemicals, or a weakened antioxidant system (Gu et al. 2020; Hodjat et al. 2015; Zheng et al. 2020). The state of cells under oxidative stress can be determined by measuring intracellular levels of antioxidants (e.g., glutathione GSH) and oxidation products (e.g., malondialdehyde MDA). Changes in these biomarkers can indicate the severity of cellular damage caused by environmental toxins.

Morphological observation assesses cell damage by directly examining changes in cell morphology under a microscope. If there are evident changes in the morphology of the cells following stimulation, such as cell swelling, cell membrane crumpling, and so on, this intuitive information can assist researchers in making a preliminary assessment of the degree of damage. The advantage is that it is intuitive and non-invasive, and morphological changes are typically one of the first responses to hazardous stimuli. Images can reveal changes in cell morphology, making them a valuable tool for analyzing cell damage. Soltanian-Zadeh et al. created an automated approach for segmenting retinal ganglion cells using adaptive optics OCT images that use poorly supervised deep learning. This method increases the accuracy and efficiency of cell damage assessment (Soltanian-Zadeh et al. 2021). Rendeiro et al. used high-parameter imaging mass spectrometry to study the effect of SARS-CoV-2 infection on lung pathology, demonstrating the disordered structure of infected and injured lungs as well as the widespread dispersion of immune infiltrates (Rendeiro et al. 2021). This research demonstrated that processing and analyzing cellular pictures can extract damage-related morphological characteristics, which can then be used to measure and quantify cellular damage.

In our investigation, we discovered that microscopic images of normal and stimulated injured cells had different grey scale distributions, indicating a change in brightness between them. Based on this observation, we suggest a novel hypothesis: picture brightness can measure the degree of cellular damage, and a grey-scale frequency analysis approach can be created to assess the degree of cellular damage.

2.1 Cell Culture and Exposure Conditions

HeLa cells were grown in a high-sugar culture medium (DMEM) supplemented with 1% penicillin-streptomycin and 10% (v/v) heat-inactivated fetal bovine serum (FBS). After that, they were put in a 37°C cell culture incubator with 5% CO₂ concentration. After being isolated and suspended in fresh media, HeLa cells in the logarithmic growth phase were subjected to varying doses of diniconazole (2.5, 5, 10, and 20 µmol/L) for a full day.

2.2 Cell Morphology Observation and Photography

HeLa cells in the logarithmic growth phase were collected and transferred to six-well plates with 1 × 10⁶ cells/mL per well. After 24 h of adherence culture, the cells were exposed to 0, 2.5, 5, 10, and 20 µmol/L of diniconazole, and incubated for 24 h. The cell morphology was observed and photographed under an inverted microscope. One hundred photographs were randomly taken for each concentration, and the criteria for taking photographs were that the cells were clearly outlined and that 80% of the area in the photograph was the cellular area.

2.3 Cell Microscopy Image Acquisition and Processing

The treated cells were viewed under a microscope, and high-resolution cell micrographs were taken, which must display the morphological changes and structural properties of the cells under various stimuli. Background segmentation and feature extraction were performed on all acquired micro-images to ensure the accuracy of the subsequent analysis, which is briefly detailed below.

Original picture capture. Cell samples with varying degrees of damage were prepared and placed in an optical microscope for observation and photography. The magnification of the objective lens was set to 20 times, and the intensity and focal length of the light source were adjusted to ensure that the outlines of the cells were clearly visible in the images, avoiding interference from blurring or shadows. The same light source intensity was used for all cell concentrations, and microscopic photographs of the cells were eventually acquired.

Image background segmentation and feature extraction. Cell picture segmentation seeks to separate cells from their surroundings for easier examination. This method employed Cellpose software to boost segmentation efficiency and accuracy. Cellpose is an open-source deep-learning technology developed by Stanford University that can rapidly interpret a wide range of cell pictures (Stringer et al. 2021). After importing cell micrographs using Cellpose software and its parameter modification tools, the majority of the cells can be segmented automatically, except for a tiny portion that must be segmented manually. Finally, the compressed file can be saved as ROI and loaded into FIJI software for processing, yielding the image after cell segmentation. Finally, the grey value of the target region and the number of associated pixel points were extracted, allowing us to generate the target area's grey curve map.

2.4 Cell Viability Assay

Cell viability was measured using the CCK-8 kit by the manufacturer's instructions. To conduct cytotoxicity tests, HeLa cells were injected in 96-well plates at a density of 100 µL (5000 cells per well). Diniconazole was added to a final concentration of 0, 2.5, 5, 10, and 20 µmol/L after 24 h of incubation in a cell incubator. After 24 h of incubation, each well received 10 µL of CCK8 solution and was incubated for 1 h at 37°C in the dark. Finally, the absorbance was measured at 450 nm using a microplate reader.

2.5 Enzyme and Lipid Peroxidation Assays

The cells were injected onto 6-well plates with 1 × 10⁶ cells per well and incubated for 24 h. The cells were treated with diniconazole at various concentrations (0, 2.5, 5, 10, 20 µmol/L) and incubated for 24 h. Following incubation, the cells were analyzed for relevant indexes using MDA assay kits, SOD assay kits, and GSH assay kits, as specified by the commercial kit makers. The MDA, SOD, and GSH assay kits should be used according to the manufacturer's instructions. The protein concentration in the cells was measured using the BCA technique.

2.6 Apoptosis Detection

The Annexin V-FITC/PI Apoptosis Assay was used to assess cell death caused by the apoptotic or necrotic pathways. Cells were cultivated in 6-well plates for 24 h before being treated with diniconazole at various concentrations (0, 2.5, 5, 10, and 20 µmol/L). HeLa cells were trypsinized, washed three times with ice-cold PBS, and re-suspended in diluted Binding Buffer at a concentration of 1 × 10⁶ cells per mL. Flow cytometry was used to analyze membrane-bound protein V-FITC and propidium iodide (PI) solutions (Beckman Coulter CytoFlex S).

2.7 Quantifying the Toxicity Severity of Five Triazole Fungicides

Five triazole fungicides, difenoconazole (DIF), penconazole (PEN), bitertanol (BIT), hexaconazole (HEX), and epoxiconazole (EPO), were chosen and tested for toxicity using this technique. Briefly, cells were injected into 6-well plates and cultivated for 24 h. Cells were treated with various triazole concentrations (0, 12.5, 25, 50, 100 µmol/L) for 24 h. The corresponding microscopic images of the cells were gathered and analyzed, and the degree of damage was assessed.

3.1 Effects of Diniconazole on Cell Morphology

After 24 h of exposure to various doses of diniconazole, the HeLa cells were placed under an optical microscope (OLYMPUS CKX53) for cell observation and photography. The morphological alterations of the HeLa cells were depicted in Fig. 1. In the control group of HeLa cells, the cells had an oval shape with uniform size, smooth borders, no abnormal protrusions, no evident concave or granular structure, and the interior structure of the cells was visible, with the organelles distributed uniformly. Diniconazole caused the HeLa cells to transform from a uniform oval shape to a round shape, reducing the length-to-diameter ratio and weakening the adhesion force. The number of cells reduced steadily as the quantity of diniconazole increased. Under the microscope, these round cells were brighter than the surrounding oval or elongated cells. This suggests that diniconazole can cause cell shrinkage by affecting the cytoskeleton, resulting in a reduction in cell size.

3.2 Principle and Construction of Assays

Here we suggest a novel approach to assessing the level of HeLa cell damage. This method is built around the frequency at which each grey value appears in the cell photos. The block diagram displayed in Fig. 2 depicted the general structure of this method, which can be broken down into two sections: the processing of cell microscope images and the acquisition of the degree of cell damage in two parts.

After 24 h of diniconazole stimulation, cell samples varying in degree of damage were captured on camera and photographed using an optical microscope. The background segmentation of the microscopic images was utilized to identify the region of interest (ROI), from which feature extraction was conducted to ascertain the grey value distribution of the target region's pixels, ultimately yielding corresponding greyscale graphs.

The obtained grey-scale graph can be split into two sections, 200–255 and 0-200, as given in Fig. 2B. Normal HeLa cells are represented by the maximum number of pixel points in the grey-scale value 0-200 region, while aberrant HeLa cells are represented by the maximum number of pixel points in the grey-scale value 200–255 region. We can determine the extent of pollution-induced damage to HeLa cells by calculating the ratio of the maximum number of pixel dots in the abnormal region to the total number of maximum pixel dots in the two regions. The percentage of HeLa cells with abnormal morphology indicates the extent of cell damage.

This assay's basic method of determining the extent of cell damage is based on the contrast in brightness between normal and aberrant cells in microscopic pictures. In microscopic images, abnormal cells appear brighter than normal cells. This can be attributed to that drug-stimulated abnormal cells are undergoing apoptosis, which causes changes to the internal structure of the cells. Specifically, the nucleus is fragmented and the cytoplasm is condensed, increasing the number of scattering centers within the cells and increasing the amount of light that is scattered back toward the microscope's detector. These changes also significantly increase the attenuation coefficients of the tissues and the scattering of light (Chalut et al. 2009; Freek et al. 2001; Mulvey et al. 2007; van der Meer et al. 2010). On the other hand, light travels and scatters more equally within a normal cell due to its relatively stable internal structure, which results in a lower brightness. Normal cells seem darker overall than aberrant cells because their nuclei are not consolidated, their organelle distribution is rather regular, and they have fewer scattering centers. The ratio of normal to aberrant cells can be measured, allowing for the determination of the extent of cell damage, by analyzing the frequency distribution of the grey values of the two types of cells.

3.3 Effect of Diniconazole on the Rate of Abnormal Cells

Cell microscope photos of varied diniconazole concentrations were acquired after 24 h of exposure. One hundred photos were randomly selected for each concentration, and the grey value and number of pixel points of the target region after segmentation were extracted, and the associated grey curves were calculated to produce the graphs displayed in Fig. 3. Figure 3A shows that the number of pixel points for each curve is concentrated in the ranges of 0-200 and 200–255. The darker part of the image's target region is represented by the area ranging from 0 to 200, and the greyscale curves for different concentrations of diniconazole are reasonably close to the greyscale values corresponding to the maximum number of pixel points in this region. As a result, the maximum number of pixel points in the grey value range of 0-200 was selected to determine the number of normal HeLa cells, whereas the maximum number of pixel points in the grey value range of 200–255 was used to determine the number of abnormal HeLa cells. The aberrant cell ratio was calculated by dividing the number of pixel dots in abnormal HeLa cells by the total number of pixels. Each concentration of diniconazole corresponded to one abnormal HeLa cell ratio, and a trend plot of diniconazole concentration versus abnormal cell ratio was created (Fig. 3B), revealing that the abnormal cell ratio increased with diniconazole concentration.

3.4 Feasibility Analysis of Our Method

3.4.1 Effect of Diniconazole on Cell Viability

Cell viability is an important indicator for determining the degree of cell damage, which refers to the number of cells surviving in a given population, and it is also used to measure the survival or death of cells activated by medications or chemicals (Johansen et al. 2010). The vitality of HeLa cells was assessed using the CCK-8 cytotoxicity test 24 h after diniconazole was administered, and the findings were presented in Fig. 4. The viability of HeLa cells decreased as the concentration of diniconazole rose. Diniconazole has a high cytotoxic effect on HeLa cells and is prone to cell damage, as demonstrated by the findings of Xu et al. (Xu et al. 2020).

3.4.2 Diniconazole Induces Oxidative Stress in HeLa Cells

When oxidants outnumber antioxidants, they disrupt redox signaling and regulation, resulting in oxidative stress (Sies 2020). Changes in oxidative stress indicators are intimately related to cell membrane integrity, cellular metabolic activities, and the activation of apoptotic signaling pathways, all of which are significant factors for studying cell injury causes.

As a result, we measured the levels of common oxidative stress indicators in HeLa cells, including MDA, GSH, and SOD. MDA is a common sign of oxidative stress and lipid peroxidation. It is often generated through peroxidation caused by enzymes or free radicals, and it occurs in polyunsaturated fatty acids with at least three double bonds (Demirci-Çekiç et al. 2022). The body has two major strategies for defending itself against oxidative damage. The first is the scavenging of free radicals by electron donors like GSH, ascorbic acid, and thioredoxin, while the second is the scavenging of free radicals and reactive compounds by enzymes like SOD, catalase (CAT), and glutathione peroxidase (GPx) (Devasagayam et al. 2004). GSH, for example, acts as a protective barrier against hydrogen peroxide-induced oxidation and is an effective exogenous detoxifier that aids in the reduction of oxidative stress, the maintenance of redox equilibrium, and immune system regulation (Ali et al. 2020). SOD is found in many organisms, and its primary function is to catalyze the disproportionation reaction of the superoxide anion (O²⁻) and convert it into oxygen (O₂) and hydrogen peroxide (H₂O₂), reducing oxidative stress and free radical damage in cells and maintaining the REDOX balance (Halliwell 2024).

We examined the MDA content of HeLa cells after 24 h of exposure to diniconazole at various doses. Figure 5A shows substantial variations in MDA content between the control group and the other three concentrations (except 2.5 µmol/L). Diniconazole can stimulate lipid peroxidation in HeLa cells, promoting an increase in ROS.

Xu et al. (Xu et al. 2020) discovered that diniconazole can cause ROS in HeLa cells, confirming that diniconazole might cause oxidative stress in these cells. In addition, we investigated SOD enzymatic antioxidants and GSH non-enzymatic antioxidants, both of which play significant roles in controlling oxygen metabolism and free radical harmful effects (Almeida et al. 2002). In this study, the activities of SOD and GSH in HeLa cells dropped dramatically after 24 h of diniconazole treatment, showing that diniconazole impairs HeLa's ability to remove ROS, resulting in an imbalance between ROS production and breakdown and producing cellular oxidative stress (Fig. 5B&C). These findings imply that diniconazole causes excessive ROS, which the cell's antioxidant system cannot adequately eliminate, resulting in oxidative stress and cell damage.

3.4.3 Effect of Diniconazole on HeLa Cells Apoptosis

Apoptosis is a gene-regulated and controlled process of cell death that is required for the maintenance of normal embryonic development and tissue homeostasis in multicellular animals. It is also a crucial physiological mechanism for tissue creation throughout embryogenesis and after birth. Apoptosis is distinguished by cell shrinkage, chromatin concentration, and disintegration into minute membrane-bound apoptotic bodies that are absorbed by adjacent parenchymal cells, tumor cells, or macrophages (Bertheloot et al. 2021; Park et al. 2023). Apoptosis is an important way for cells to deal with injury, therefore it can be used to determine the extent of cell damage. The apoptosis generated by diniconazole was assessed by annexin V/PI staining. The results showed that when the concentration of diniconazole increased, the number of normal cells decreased somewhat (Fig. 6).

3.4.4 Correlation Analysis using Multidimensional Indicators for Cell Damage

After damage, HeLa cells treated with diniconazole showed notable changes in morphology and metabolic markers. To assess the efficiency of this method, it is required to compare the cell damage degree acquired by this method, i.e., the fraction of HeLa cells with abnormal morphology, as well as the changes in relevant indicators during the HeLa cell damage process. Figure 7 shows the findings of the comparison analysis. As the proportion of HeLa cells with aberrant shapes grew, the lipid peroxide MDA level increased (Fig. 7A), indicating a substantial positive connection. However, cell viability, GSH, and SOD levels, and the ratio of viable cells detected by apoptosis, all decreased as the ratio of HeLa cells with aberrant shapes increased (Fig. 7B, C, D, and E), indicating a clear negative association. To determine the degree of association between the outcomes determined by the new method and changes in associated markers during HeLa cell damage, linear regression was employed to fit the data. The results revealed that all of the correlation coefficients were greater than 0.8, suggesting that a high degree of fit between the extent of cell damage achieved by this approach and the alterations in prevalent cell damage indicators, thereby confirming the method's reliability.

3.5 Triazole Fungicide Toxicity Detection

We investigated the cellular damage caused by five triazole fungicides: DIF, EPO, HEX, PEN, and BIT. The toxicities of five triazole fungicides were determined using this technique, and the results are shown in Fig. 8. Figure 8 displays the five triazole fungicides in descending order of toxicity: DIF, BIT, PEN, HEX, and EPO. The toxicity ranking of the five fungicides produced using this method is similar to the results obtained by Kun et al. (Qiao et al. 2020) using the toxicological index LC50. By evaluating these five fungicides, we assessed the method's reliability in detecting and differentiating between different toxicity levels, proving its utility and value in the field of environmental toxicology.

In this study, we found that the normal and stimulation-damaged cells had distinct distributions of grey values after the drug stimulation. Based on this, a novel theory was put up, according to which the brightness of an image can indicate the extent of cellular damage. Furthermore, a technique based on grey-scale frequency analysis can be created to determine the extent of cellular damage.

By comparing the degree of cellular damage determined by the approach with the outcomes of conventional damage indicator tests, we were able to confirm the validity of the hypothesis in this work. The technique was also applied to evaluate the degree of cellular damage caused by five different triazole fungicides and to calculate the amount of their toxicity; the outcomes were in line with the findings documented in the literature. This technique offers an effective and promising approach for the toxicity assessment of environmental pollutants by combining the visualization and quantitative assessment of cellular damage. We demonstrated the potential for this method's broad applicability in the field of environmental toxicology by achieving improved detection efficiency and lower costs in the toxicity assessment of environmental toxins.

Author Contribution

Corresponding author: Xiaolong Xu ([email protected]) and Jianbo Jia ([email protected])Anqi Li: Writing – original draft, Methodology, Investigation. Linying Zhao: Formal analysis, Data curation. Changyu Liu: Project administration, Methodology, Formal analysis. Xiaolong Xu: Supervision, Project administration, Conceptualization. Jianbo Jia: Project administration, revision, review & editing. All authors reviewed the manuscript.

Acknowledgments

We acknowledge financial support from the National Natural Science Foundation of China (Grant Nos. 21974097 and 22274117), Education Department of Guangdong Province (Grant No. 2022ZDJS027).

Data Availability

Data is provided within the manuscript.

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No competing interests reported.

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Grey Frequency-Based Methodology for Assessing HeLa Cell Damage

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Version 1

Abstract

Figures

1 Introduction

2 Experimental Section

2.1 Cell Culture and Exposure Conditions

2.2 Cell Morphology Observation and Photography

2.3 Cell Microscopy Image Acquisition and Processing

2.4 Cell Viability Assay

2.5 Enzyme and Lipid Peroxidation Assays

2.6 Apoptosis Detection

2.7 Quantifying the Toxicity Severity of Five Triazole Fungicides

3 Results and Discussion

3.1 Effects of Diniconazole on Cell Morphology

3.2 Principle and Construction of Assays

3.3 Effect of Diniconazole on the Rate of Abnormal Cells

3.4 Feasibility Analysis of Our Method

3.4.1 Effect of Diniconazole on Cell Viability

3.4.2 Diniconazole Induces Oxidative Stress in HeLa Cells

3.4.3 Effect of Diniconazole on HeLa Cells Apoptosis

3.4.4 Correlation Analysis using Multidimensional Indicators for Cell Damage

3.5 Triazole Fungicide Toxicity Detection

4 Conclusion

Declarations

Author Contribution

Acknowledgments

Data Availability

References

Additional Declarations

Status:

Version 1