New Methods for Cell Cycle Analysis

Predicting cyclins based on key features and machine learning methods

Cyclins are a group of proteins that regulate the cell cycle process by modulating various stages of cell division to ensure correct cell proliferation, differentiation, and apoptosis. Research on cyclins is crucial for understanding the biological functions and pathological states of cells. However, current research on cyclin identification based on machine learning only focuses on accuracy ignoring the interpretability of features. Therefore, in this study, we pay more attention to the interpretation and analysis of key features associated with cyclins. Firstly, we developed an SVM-based model for identifying cyclins with an accuracy of 92.8% through 5-fold. Then we analyzed the physicochemical properties of the 14 key features used in the model construction and identified the G and charged C1 features that are critical for distinguishing cyclins from non-cyclins. Furthermore, we constructed an SVM-based model using only these two features with an accuracy of 81.3% through the leave-one-out cross-validation. Our study shows that cyclins differ from non-cyclins in their physicochemical properties and that using only two features can achieve good prediction accuracy.

Role of Galectin-3 in intervertebral disc degeneration: an experimental study

BACKGROUND

This study investigated the role of Galectin-3 in the degeneration of intervertebral disc cartilage.

METHODS

The patients who underwent lumbar spine surgery due to degenerative disc disease were recruited and divided into Modic I, Modic II, and Modic III; groups. HE staining was used to detect the pathological changes in endplates. The changes of Galectin-3, MMP3, Aggrecan, CCL3, and Col II were detected by immunohistochemistry, RT-PCR, and Western blot. MTT and flow cytometry were used to detect cartilage endplate cell proliferation, cell cycle, and apoptosis.

RESULTS

With the progression of degeneration (from Modic I to III), the chondrocytes and density of the cartilage endplate of the intervertebral disc decreased, and the collagen arrangement of the cartilage endplate of the intervertebral disc was broken and calcified. Meanwhile, the expressions of Aggrecan, Col II, Galectin-3, Aggrecan, and CCL3 gradually decreased. After treatment with Galectin-3 inhibitor GB1107, the proliferation of rat cartilage end plate cells was significantly reduced (P < 0.05). GB1107 (25 µmol/L) also significantly promoted the apoptosis of cartilage endplate cells (P < 0.05). Moreover, the percentage of cartilage endplate cells in the G1 phase was significantly higher, while that in the G2 and S phases was significantly lower (P < 0.05). Additionally, the mRNA and protein expression levels of MMP3, CCL3, and Aggrecan in rat cartilage end plate cells were lower than those in the control group.

CONCLUSIONS

Galectin-3 decreases with the progression of the cartilage endplate degeneration of the intervertebral disc. Galectin-3 may affect intervertebral disc degeneration by regulating the degradation of the extracellular matrix.

Basic Methods of Cell Cycle Analysis

Cellular growth and the preparation of cells for division between two successive cell divisions is called the cell cycle. The cell cycle is divided into several phases; the length of these particular cell cycle phases is an important characteristic of cell life. The progression of cells through these phases is a highly orchestrated process governed by endogenous and exogenous factors. For the elucidation of the role of these factors, including pathological aspects, various methods have been developed. Among these methods, those focused on the analysis of the duration of distinct cell cycle phases play important role. The main aim of this review is to guide the readers through the basic methods of the determination of cell cycle phases and estimation of their length, with a focus on the effectiveness and reproducibility of the described methods.

Hohenbuehelia serotina polysaccharides self-assembled nanoparticles for delivery of quercetin and their anti-proliferative activities during gastrointestinal digestion in vitro

In this study, the self-assembled nanoparticles based on Hohenbuehelia serotina polysaccharides (QC-HSP NPs) were fabricated to encapsulate quercetin for improving its bioavailability. The structural characteristics, physicochemical properties as well as the cytotoxicity activities of QC-HSP NPs during gastrointestinal digestion in vitro were respectively investigated. The results showed that QC-HSP NPs possessed the spherical and smooth surface morphology, with the average particle size of 360 nm and zeta potential of -38.8 mV. Moreover, QC-HSP NPs had excellent physiochemical stabilities, and presented sustained-release characteristics during gastrointestinal digestion in vitro. Compared with undigested ones, QC-HSP NPs after gastrointestinal digestion exhibited the more significant anti-proliferative activity on HeLa cells through accumulation of intracellular ROS, arrest of cell cycle at G2/M phase by regulation of cyclin B1, CDK1, p53 and p21 and induction of apoptosis by ER apoptosis pathway. This study provides a new strategy for designing quercetin-loaded nanoparticles based on natural polysaccharides to improve the bioavailability of quercetin.

Hyperthermia Enhances Doxorubicin Therapeutic Efficacy against A375 and MNT-1 Melanoma Cells

Melanoma is the deadliest form of skin cancer, and its incidence has alarmingly increased in the last few decades, creating a need for novel treatment approaches. Thus, we evaluated the combinatorial effect of doxorubicin (DOX) and hyperthermia on A375 and MNT-1 human melanoma cell lines. Cells were treated with DOX for 24, 48, and 72 h and their viabilities were assessed. The effect of DOX IC10 and IC20 (combined at 43 °C for 30, 60, and 120 min) on cell viability was further analyzed. Interference on cell cycle dynamics, reactive oxygen species (ROS) production, and apoptosis upon treatment (with 30 min at 43 °C and DOX at the IC20 for 48 h) were analyzed by flow cytometry. Combined treatment significantly decreased cell viability, but not in all tested conditions, suggesting that the effect depends on the drug concentration and heat treatment duration. Combined treatment also mediated a G2/M phase arrest in both cell lines, as well as increasing ROS levels. Additionally, it induced early apoptosis in MNT-1 cells, while in A375 cells this effect was similar to the one caused by hyperthermia alone. These findings demonstrate that hyperthermia enhances DOX effect through cell cycle arrest, oxidative stress, and apoptotic cell death.

An Imbalanced Image Classification Method for the Cell Cycle Phase

The cell cycle is an important process in cellular life. In recent years, some image processing methods have been developed to determine the cell cycle stages of individual cells. However, in most of these methods, cells have to be segmented, and their features need to be extracted. During feature extraction, some important information may be lost, resulting in lower classification accuracy. Thus, we used a deep learning method to retain all cell features. In order to solve the problems surrounding insufficient numbers of original images and the imbalanced distribution of original images, we used the Wasserstein generative adversarial network-gradient penalty (WGAN-GP) for data augmentation. At the same time, a residual network (ResNet) was used for image classification. ResNet is one of the most used deep learning classification networks. The classification accuracy of cell cycle images was achieved more effectively with our method, reaching 83.88%. Compared with an accuracy of 79.40% in previous experiments, our accuracy increased by 4.48%. Another dataset was used to verify the effect of our model and, compared with the accuracy from previous results, our accuracy increased by 12.52%. The results showed that our new cell cycle image classification system based on WGAN-GP and ResNet is useful for the classification of imbalanced images. Moreover, our method could potentially solve the low classification accuracy in biomedical images caused by insufficient numbers of original images and the imbalanced distribution of original images.

Robust classification of cell cycle phase and biological feature extraction by image-based deep learning

Across the cell cycle, the subcellular organization undergoes major spatiotemporal changes that could in principle contain biological features that could potentially represent cell cycle phase. We applied convolutional neural network-based classifiers to extract such putative features from the fluorescence microscope images of cells stained for the nucleus, the Golgi apparatus, and the microtubule cytoskeleton. We demonstrate that cell images can be robustly classified according to G1/S and G2 cell cycle phases without the need for specific cell cycle markers. Grad-CAM analysis of the classification models enabled us to extract several pairs of quantitative parameters of specific subcellular features as good classifiers for the cell cycle phase. These results collectively demonstrate that machine learning-based image processing is useful to extract biological features underlying cellular phenomena of interest in an unbiased and data-driven manner.