CDSKNNXMBD: a novel clustering framework for large-scale single-cell data based on a stable graph structure.

BACKGROUND: Accurate and efficient cell grouping is essential for analyzing single-cell transcriptome sequencing (scRNA-seq) data. However, the existing clustering techniques often struggle to provide timely and accurate cell type groupings when dealing with datasets with large-scale or imbalanced cell types. Therefore, there is a need for improved methods that can handle the increasing size of scRNA-seq datasets while maintaining high accuracy and efficiency. METHODS: We propose CDSKNNXMBD (Community Detection based on a Stable K-Nearest Neighbor Graph Structure), a novel single-cell clustering framework integrating partition clustering algorithm and community detection algorithm, which achieves accurate and fast cell type grouping by finding a stable graph structure. RESULTS: We evaluated the effectiveness of our approach by analyzing 15 tissues from the human fetal atlas. Compared to existing methods, CDSKNN effectively counteracts the high imbalance in single-cell data, enabling effective clustering. Furthermore, we conducted comparisons across multiple single-cell datasets from different studies and sequencing techniques. CDSKNN is of high applicability and robustness, and capable of balancing the complexities of across diverse types of data. Most importantly, CDSKNN exhibits higher operational efficiency on datasets at the million-cell scale, requiring an average of only 6.33 min for clustering 1.46 million single cells, saving 33.3% to 99% of running time compared to those of existing methods. CONCLUSIONS: The CDSKNN is a flexible, resilient, and promising clustering tool that is particularly suitable for clustering imbalanced data and demonstrates high efficiency on large-scale scRNA-seq datasets.

A downsampling method enables robust clustering and integration of single-cell transcriptome data.

The random noises, sampling biases, and batch effects often confound true biological variations in single-cell RNA-sequencing (scRNA-seq) data. Adjusting such biases is key to the robust discoveries in downstream analyses, such as cell clustering, gene selection and data integration. Here we propose a model-based downsampling algorithm based on minimal unbiased representative points (MURPXMBD). MURPXMBD is designed to retrieve a set of representative points by reducing gene-wise random independent errors, while retaining the covariance structure of biological origin hence provide an unbiased representation of the cell population. Subsequent validation using benchmark datasets shows that MURPXMBD can improve the quality and accuracy of clustering algorithms, and thus facilitate the discovery of new cell types. Besides, MURPXMBD also improves the performance of dataset integration algorithms. In summary, MURPXMBD serves as a useful noise-reduction method for single-cell sequencing analysis in biomedical studies.

[Non-invasive electrical neuromodulation techniques: analgesic effects and neural mechanisms].

As non-pharmaceutical interventions, non-invasive electrical neuromodulation techniques are promising in pain management. With many advantages, such as low costs, high usability, and non-invasiveness, they have been exploited to treat multiple types of clinical pain. Proper use of these techniques requires a comprehensive understanding of how they work. In this article, we reviewed recent studies concerning non-invasive electrical peripheral nerve stimulation (transcutaneous electrical nerve stimulation and transcutaneous vagus/vagal nerve stimulation) as well as electrical central nerve stimulation (transcranial direct current stimulation and transcranial alternating current stimulation). Specifically, we discussed their analgesic effects on acute and chronic pain, and the neural mechanisms thereof. We then contrasted the four kinds of nerve stimulation techniques, pointing out limitations of existing studies and proposing directions for future research. With more extensive and in-depth research to overcome these limitations, we shall witness more clinical applications of non-invasive electrical nerve stimulations to alleviate patients' pain and ease the crippling medical and economic burden imposed on patients, their families, and the entire society.

Prenatal EGCG consumption impacts hepatic glycogen synthesis and lipid metabolism in adult mice.

In this study, the impact of prenatal exposure to Epigallocatechin gallate (EGCG) on the liver of adult offspring mice was investigated. While EGCG is known for its health benefits, its effects of prenatal exposure on the liver remain unclear. Pregnant C57BL/6 J mice were exposed to 1 mg/kg of EGCG for 16 days to assess hepatotoxicity effects of adult offspring. Transcriptomics and metabolomics were employed to elucidate the hepatotoxicity mechanisms. The findings revealed that prenatal EGCG exposure led to a decrease in liver somatic index, enhanced inflammatory responses and disrupted liver function through increased glycogen accumulation in adult mice. The integrated omics analysis revealed significant alterations in key pathways involved in liver glucose lipid metabolism, such as gluconeogenesis, dysregulation of insulin signaling, and induction of liver inflammation. Furthermore, the study found a negative correlation between the promoter methylation levels of Ppara and their mRNA levels, suggesting that EGCG could reduce hepatic lipid content through epigenetic modifications. The findings suggest that prenatal EGCG exposure can have detrimental impacts on the liver among adult individuals and emphasize the need for a comprehensive evaluation of the potential risks associated with EGCG consumption during pregnancy.

Gut microbiome interacts with pregnancy hormone metabolites in gestational diabetes mellitus.

Introduction: Change in the composition of intestinal microbiota is associated with metabolic disorders such as gestational diabetes mellitus (GDM). Methods: To understand how the microbiota impacts the development of gestational diabetes mellitus, we profiled the intestinal microbiome of 54 pregnant women, including 27 GDM subjects, by employing 16S rRNA gene sequencing. Additionally, we conducted targeted metabolomics assays to validate the identified pathways with overrepresented metabolites. Results: We evaluated the patterns of changing abundances of operational taxonomic units (OTU) between GDM and the healthy counterparts over three timepoints. Based on the significant OTUs, we inferred 132 significantly altered metabolic pathways in GDM. And identified two overrepresented metabolites of pregnancy hormone, butyrate and mevalonate, as potential intermediary metabolites of intestinal microbiota in GDM. Finally, we validated the impacts of the intestinal microbiota on GDM by demonstrating consistent changes of the serum levels of progesterone, estradiol, butyrate, and mevalonate in an independent cohort. Discussion: Our findings confirm that alterations in the microbiota play a role in the development of GDM by impacting the metabolism of pregnancy hormones. This provides a novel perspective on the pathogenesis of GDM and introduces potential biomarkers that can be used for early diagnosis and prevention of the disease.