Data-driven detection of age-related arbitrary monotonic changes in single-cell gene expression distributions.

Identification of genes whose expression increases or decreases with age is central to understanding the mechanisms behind aging. Recent scRNA-seq studies have shown that changes in single-cell expression profiles with aging are complex and diverse. In this study, we introduce a novel workflow to detect changes in the distribution of arbitrary monotonic age-related changes in single-cell expression profiles. Since single-cell gene expression profiles can be analyzed as probability distributions, our approach uses information theory to quantify the differences between distributions and employs distance matrices for association analysis. We tested this technique on simulated data and confirmed that potential parameter changes could be detected in a set of probability distributions. Application of the technique to a public scRNA-seq dataset demonstrated its potential utility as a straightforward screening method for identifying aging-related cellular features.

Visualization of the landscape of the read alignment shape of ATAC-seq data using Hellinger distance metric.

Assay for Transposase-Accessible Chromatin using high-throughput sequencing (ATAC-seq) is the popular technique using next-generation sequencing to measure chromatin accessibility and identify open chromatin regions. While read alignment shape information of next-generation sequencing data with intensity information has been used in various bioinformatics methods, few studies have focused on pure shape information alone. In this study, we investigated what types of ATAC-seq read alignment shapes are observed for the promoter region and whether the pure shape information was related or unrelated to other gene features. We introduced a novel concept and pipeline for handling the pure shape information of NGS data as probability distributions and quantifying their dissimilarities by information theory. Based on this concept, we demonstrate that the pure shape information of ATAC-seq data is correlated with chromatin openness and some gene characteristics. On the other hand, it is suggested that the pure information of ATAC-seq read alignment shape is unlikely to contain additional information to explain differences in RNA expression. Our study suggests that viewing the read alignment shape of NGS data as probability distributions enables us to capture the characteristics of the genome-wide landscape of such data in a non-parametric manner.

Data-driven identification and classification of nonlinear aging patterns reveals the landscape of associations between DNA methylation and aging.

BACKGROUND: Aging affects the incidence of diseases such as cancer and dementia, so the development of biomarkers for aging is an important research topic in medical science. While such biomarkers have been mainly identified based on the assumption of a linear relationship between phenotypic parameters, including molecular markers, and chronological age, numerous nonlinear changes between markers and aging have been identified. However, the overall landscape of the patterns in nonlinear changes that exist in aging is unknown. RESULT: We propose a novel computational method, Data-driven Identification and Classification of Nonlinear Aging Patterns (DICNAP), that is based on functional data analysis to identify biomarkers for aging and potential patterns of change during aging in a data-driven manner. We applied the proposed method to large-scale, public DNA methylation data to explore the potential patterns of age-related changes in methylation intensity. The results showed that not only linear, but also nonlinear changes in DNA methylation patterns exist. A monotonous demethylation pattern during aging, with its rate decreasing at around age 60, was identified as the candidate stable nonlinear pattern. We also analyzed the age-related changes in methylation variability. The results showed that the variability of methylation intensity tends to increase with age at age-associated sites. The representative variability pattern is a monotonically increasing pattern that accelerates after middle age. CONCLUSION: DICNAP was able to identify the potential patterns of the changes in the landscape of DNA methylation during aging. It contributes to an improvement in our theoretical understanding of the aging process.

Mathematical model for the relationship between single-cell and bulk gene expression to clarify the interpretation of bulk gene expression data.

BACKGROUND: Differential expression analysis is a standard approach in molecular biology. For example, genes whose expression levels differ between diseased and non-diseased samples are considered to be associated with that disease. On the other hand, differential variability analysis focuses on the differences of the variances of gene expression between sample groups. Although differential variability is also known to capture biological information, its interpretation remains unclear and controversial. Recent single-cell analyses have revealed that differences between sample groups can affect gene expression in a cellular subset-specific manner or by altering the proportion of a particular cellular subset. The aim of this study is to clarify the interpretation of mean and variance of bulk gene expression data. METHOD: We developed a mathematical model in which the bulk gene expression value is proportional to the mean value of the single-cell gene expression profile. Based on this model, we performed theoretical, simulated and real single-cell RNA-seq data analyses. RESULT AND CONCLUSION: We identified how differences in single-cell gene expression profiles affect the differences in the mean and the variance of bulk gene expression. It is shown that differential expression analysis of bulk expression data can overlook significant changes in gene expression at the single-cell level. Further, differential variability analysis capture the complex feature affected by different gene expression shifts for each subset, changes in the proportions of cellular subsets, and variation in single-cell distribution parameters among samples.

Serum Periostin Level and Genetic Polymorphisms Are Associated with Vertebral Fracture in Chinese Postmenopausal Women.

PURPOSES: In order to investigate the association between serum periostin levels and the variation of its encoding gene POSTN and the prevalence of vertebral fractures and bone mineral density (BMD) in Chinese postmenopausal women, an association study was performed. MATERIALS AND METHODS: 385 postmenopausal women were recruited. For participants without a history of vertebral fracture, lateral X-rays of the spine covering the fourth thoracic spine to the fifth lumbar spine were performed to detect any asymptomatic vertebral fractures. Ten tag-single nucleotide polymorphisms (SNP) of POSTN were genotyped. Serum periostin levels, biochemical parameters, and BMD were measured individually. RESULTS: rs9603226 was significantly associated with vertebral fractures. Compared to allele G, the minor allele A carriers of rs9603226 had a 1.722-fold higher prevalence of vertebral fracture (p = 0.037). rs3923854 was significantly associated with the serum periostin level. G/G genotype of rs3923854 had a higher serum periostin level than C/C and C/G (67.26 ± 19.90 ng/mL vs. 54.57 ± 21.44 ng/mL and 54.34 ± 18.23 ng/mL). Furthermore, there was a negative correlation between the serum level of periostin and BMD at trochanter and total hip. CONCLUSION: Our study suggested that genetic variation of POSTN could be a predicting factor for the risk of vertebral fractures. The serum level of periostin could be a potential biochemical parameter for osteoporosis in Chinese postmenopausal women.

Data-driven comparison of multiple high-dimensional single-cell expression profiles.

Comparing multiple single-cell expression datasets such as cytometry and scRNA-seq data between case and control donors provides information to elucidate the mechanisms of disease. We propose a completely data-driven computational biological method for this task. This overcomes the challenges of conventional cellular subset-based comparisons and facilitates further analyses such as machine learning and gene set analysis of single-cell expression datasets.

Cell population-based framework of genetic epidemiology in the single-cell omics era.

Genetic epidemiology is a rapidly advancing field due to the recent availability of large amounts of omics data. In recent years, it has become possible to obtain omics information at the single-cell level, so genetic epidemiological models need to be updated to integrate with single-cell expression data. In this perspective paper, we propose a cell population-based framework for genetic epidemiology in the single-cell era. In this framework, genetic diversity influences phenotypic diversity through the diversity of cell population profiles, which are defined as high-dimensional probability distributions of the state spaces of biomolecules of each omics layer. We discuss how biomolecular experimental measurement data can capture the different properties of this distribution. In particular, single-cell data constitute a sample from this population distribution where only some coordinate values are observable. From a data analysis standpoint, we introduce methodology for feature extraction from cell population profiles. Finally, we discuss how this framework can be applied not only to genetic epidemiology but also to systems biology.

Effects of uninterrupted sinusoidal LF-EMF stimulation on LTP induced by different combinations of TBS/HFS at the Schaffer collateral-CA1 of synapses.

Long-term potentiation (LTP) is an important aspect of synaptic plasticity and is one of the main mechanisms involved in memory. Low-frequency electromagnetic fields (LF-EMFs) such as transcranial magnetic stimulation are emerging neuromodulation tools for the regulation of LTP. However, whether LF-EMFs have different effects on different types of LTP has not yet been verified. Herein, we studied the regulatory effects of 15 Hz/2 mT sinusoidal magnetic field as pre-magnetic stimulation on several types of LTP, which were induced by theta-burst(TBS) or high-frequency stimulation (HFS) or some combination of them, and applied N-methyl-D-aspartate receptor(NMDAR) antagonists to observe the relationship between the regulation of LTP by LF-EMFs and NMDAR in the Schaffer collateral pathway of rat brain slices in vitro. The results presented in this paper are the performance of TBS and HFS was not exactly the same and the recovery speed of TBS-LTP was faster than HFS-LTP after receiving the regulation of LF-EMFs; moreover, the LTP level was affected by the order of combination and the effect of pre-magnetic stimulation could maintain the entire process of the combined induction experiment, while NMDAR antagonists could not completely offset the influence of LF-EMFs. The memory patterns are diverse, and this study has shown LF-EMFs can regulate LTP such as TBS-LTP and HFS-LTP and can continuously affect multiple LTP induction processes. However, different memory processes may have different performance in the face of LF-EMFs regulation. In terms of the mechanism of LF-EMFs-induced LTP regulation, NMDARs may be involved in the process of LF-EMF regulation of LTP, but are not the only factor.