Variations in Innate Immune Cell Subtypes Correlate with Epigenetic Clocks, Inflammaging and Health Outcomes.

Epigenetic clocks in blood have shown promise as tools to quantify biological age, displaying robust associations with morbidity and all-cause mortality. Whilst the effect of cell-type heterogeneity on epigenetic clock estimates has been explored, such studies have been limited to studying heterogeneity within the adaptive immune system. Much less is known about whether heterogeneity within the innate immune system can impact epigenetic clock estimates and their associations with health outcomes.

An improved reference library and method for accurate cell-type deconvolution of bulk-tissue miRNA data.

MicroRNAs (miRNAs) play key roles in development and disease, and have great biomarker potential. However, because miRNA expression is highly cell-type specific, identifying miRNA biomarkers from complex tissues is hampered by the underlying cell-type heterogeneity. Due to that current single-cell RNA-Seq protocols are lagging behind for quantification of miRNA expression, and most miRNA profiling samples do not have matched mRNA expression or DNA methylation data for cell-type deconvolution, it is an urgent need to develop computational methods for cell-type proportion estimation of bulk-tissue miRNA data.

Molecular landscape of sex- and modality-specific exercise adaptation in human skeletal muscle through large-scale multi-omics integration.

We investigated the molecular mechanisms of exercise adaptations in human muscle by integrating genome, methylome, transcriptome, and proteome data from over 1,000 participants (2,340 muscle samples). We identified distinctive signatures associated with maximal oxygen consumption (VO), and multi-omics integration uncovered five key genes as robust exercise markers across layers, with transcription factors functioning as activators, synergizing with DNA methylation to regulate gene expression. Minimal sex differences were observed, while modality-specific analysis highlighted distinct pathways for aerobic and resistance exercise, contrasting with muscle disuse patterns.

Unified high-resolution immune cell fraction estimation in blood tissue from birth to old age.

Variations in immune-cell fractions can confound or hamper interpretation of DNAm-based biomarkers in blood. Although cell-type deconvolution can address this challenge for cord and adult blood, currently there is no method applicable to blood from other age groups, including infants and children. Here we construct and extensively validate a DNAm reference panel, called UniLIFE, for 19 immune cell-types, applicable to blood tissue of any age.

FastQTLmapping: an ultra-fast and memory efficient package for mQTL-like analysis.

Background: FastQTLmapping addresses the need for an ultra-fast and memory-efficient solver capable of handling exhaustive multiple regression analysis with a vast number of dependent and explanatory variables, including covariates. This challenge is especially pronounced in methylation quantitative trait loci (mQTL)-like analysis, which typically involves high-dimensional genetic and epigenetic data.

Results: FastQTLmapping is a precompiled C++ software solution accelerated by Intel MKL and GSL, freely available at https://github.

Cell-type-specific subtyping of epigenomes improves prognostic stratification of cancer.

Background: Most molecular classifications of cancer are based on bulk-tissue profiles that measure an average over many distinct cell types. As such, cancer subtypes inferred from transcriptomic or epigenetic data are strongly influenced by cell-type composition and do not necessarily reflect subtypes defined by cell-type-specific cancer-associated alterations, which could lead to suboptimal cancer classifications.

Methods: To address this problem, we here propose the novel concept of cell-type-specific combinatorial clustering (CELTYC), which aims to group cancer samples by the molecular alterations they display in specific cell types.

Single-cell multi-stage spatial evolutional map of esophageal carcinogenesis.

Cancer development involves the co-evolution of cancer cells and their surrounding microenvironment, yet the dynamics of this interaction within the physical architecture remains poorly understood. Here, we present a spatial transcriptomic map at single-cell resolution, encompassing 127 multi-stage fields of view from 43 patients, to chart the evolutionary trajectories of human esophageal squamous cell carcinoma (ESCC). By analyzing 6.

Interpretable deep learning of single-cell and epigenetic data reveals novel molecular insights in aging.

Deep learning (DL) and explainable artificial intelligence (XAI) have emerged as powerful machine-learning tools to identify complex predictive data patterns in a spatial or temporal domain. Here, we consider the application of DL and XAI to large omic datasets, in order to study biological aging at the molecular level. We develop an advanced multi-view graph-level representation learning (MGRL) framework that integrates prior biological network information, to build molecular aging clocks at cell-type resolution, which we subsequently interpret using XAI.

Epigenetic ageing clocks: statistical methods and emerging computational challenges.

Over the past decade, epigenetic clocks have emerged as powerful machine learning tools, not only to estimate chronological and biological age but also to assess the efficacy of anti-ageing, cellular rejuvenation and disease-preventive interventions. However, many computational and statistical challenges remain that limit our understanding, interpretation and application of epigenetic clocks. Here, we review these computational challenges, focusing on interpretation, cell-type heterogeneity and emerging single-cell methods, aiming to provide guidelines for the rigorous construction of interpretable epigenetic clocks at cell-type and single-cell resolution.

Cell-type specific epigenetic clocks to quantify biological age at cell-type resolution.

The ability to accurately quantify biological age could help monitor and control healthy aging. Epigenetic clocks have emerged as promising tools for estimating biological age, yet they have been developed from heterogeneous bulk tissues, and are thus composites of two aging processes, one reflecting the change of cell-type composition with age and another reflecting the aging of individual cell-types. There is thus a need to dissect and quantify these two components of epigenetic clocks, and to develop epigenetic clocks that can yield biological age estimates at cell-type resolution.

Universal penalized regression (Elastic-net) model with differentially methylated promoters for oral cancer prediction.

Background: DNA methylation showed notable potential to act as a diagnostic marker in many cancers. Many studies proposed DNA methylation biomarker in OSCC detection, while most of these studies are limited to specific cohorts or geographical location. However, the generalizability of DNA methylation as a diagnostic marker in oral cancer across different geographical locations is yet to be investigated.

Integrative analysis of genomic and epigenomic regulation reveals miRNA mediated tumor heterogeneity and immune evasion in lower grade glioma.

The expression dysregulation of microRNAs (miRNA) has been widely reported during cancer development, however, the underling mechanism remains largely unanswered. In the present work, we performed a systematic integrative study for genome-wide DNA methylation, copy number variation and miRNA expression data to identify mechanisms underlying miRNA dysregulation in lower grade glioma. We identify 719 miRNAs whose expression was associated with alterations of copy number variation or promoter methylation.

Computational single-cell methods for predicting cancer risk.

Despite recent biotechnological breakthroughs, cancer risk prediction remains a formidable computational and experimental challenge. Addressing it is critical in order to improve prevention, early detection and survival rates. Here, I briefly summarize some key emerging theoretical and computational challenges as well as recent computational advances that promise to help realize the goals of cancer-risk prediction.

Longitudinal analysis of epigenome-wide DNA methylation reveals novel loci associated with BMI change in East Asians.

Background: Obesity is a global public health concern linked to chronic diseases such as cardiovascular disease and type 2 diabetes (T2D). Emerging evidence suggests that epigenetic modifications, particularly DNA methylation, may contribute to obesity. However, the molecular mechanism underlying the longitudinal change of BMI has not been well-explored, especially in East Asian populations.

An improved epigenetic counter to track mitotic age in normal and precancerous tissues.

The cumulative number of stem cell divisions in a tissue, known as mitotic age, is thought to be a major determinant of cancer-risk. Somatic mutational and DNA methylation (DNAm) clocks are promising tools to molecularly track mitotic age, yet their relationship is underexplored and their potential for cancer risk prediction in normal tissues remains to be demonstrated. Here we build and validate an improved pan-tissue DNAm counter of total mitotic age called stemTOC.

Quantifying the stochastic component of epigenetic aging.

DNA methylation clocks can accurately estimate chronological age and, to some extent, also biological age, yet the process by which age-associated DNA methylation (DNAm) changes are acquired appears to be quasi-stochastic, raising a fundamental question: how much of an epigenetic clock's predictive accuracy could be explained by a stochastic process of DNAm change? Here, using DNAm data from sorted immune cells, we build realistic simulation models, subsequently demonstrating in over 22,770 sorted and whole-blood samples from 25 independent cohorts that approximately 66-75% of the accuracy underpinning Horvath's clock could be driven by a stochastic process. This fraction increases to 90% for the more accurate Zhang's clock, but is lower (63%) for the PhenoAge clock, suggesting that biological aging is reflected by nonstochastic processes. Confirming this, we demonstrate that Horvath's age acceleration in males and PhenoAge's age acceleration in severe coronavirus disease 2019 cases and smokers are not driven by an increased rate of stochastic change but by nonstochastic processes.

Analysis of blood methylation quantitative trait loci in East Asians reveals ancestry-specific impacts on complex traits.

Methylation quantitative trait loci (mQTLs) are essential for understanding the role of DNA methylation changes in genetic predisposition, yet they have not been fully characterized in East Asians (EAs). Here we identified mQTLs in whole blood from 3,523 Chinese individuals and replicated them in additional 1,858 Chinese individuals from two cohorts. Over 9% of mQTLs displayed specificity to EAs, facilitating the fine-mapping of EA-specific genetic associations, as shown for variants associated with height.

On epigenetic stochasticity, entropy and cancer risk.

Epigenetic changes are known to accrue in normal cells as a result of ageing and cumulative exposure to cancer risk factors. Increasing evidence points towards age-related epigenetic changes being acquired in a quasi-stochastic manner, and that they may play a causal role in cancer development. Here, I describe the quasi-stochastic nature of DNA methylation (DNAm) changes in ageing cells as well as in normal cells at risk of neoplastic transformation, discussing the implications of this stochasticity for developing cancer risk prediction strategies, and in particular, how it may require a conceptual paradigm shift in how we select cancer risk markers.

Challenges and perspectives in computational deconvolution of genomics data.

Deciphering cell-type heterogeneity is crucial for systematically understanding tissue homeostasis and its dysregulation in diseases. Computational deconvolution is an efficient approach for estimating cell-type abundances from a variety of omics data. Despite substantial methodological progress in computational deconvolution in recent years, challenges are still outstanding.

A meta-analysis of immune-cell fractions at high resolution reveals novel associations with common phenotypes and health outcomes.

Background: Changes in cell-type composition of tissues are associated with a wide range of diseases and environmental risk factors and may be causally implicated in disease development and progression. However, these shifts in cell-type fractions are often of a low magnitude, or involve similar cell subtypes, making their reliable identification challenging. DNA methylation profiling in a tissue like blood is a promising approach to discover shifts in cell-type abundance, yet studies have only been performed at a relatively low cellular resolution and in isolation, limiting their power to detect shifts in tissue composition.

Cell-attribute aware community detection improves differential abundance testing from single-cell RNA-Seq data.

Variations of cell-type proportions within tissues could be informative of biological aging and disease risk. Single-cell RNA-sequencing offers the opportunity to detect such differential abundance patterns, yet this task can be statistically challenging due to the noise in single-cell data, inter-sample variability and because such patterns are often of small effect size. Here we present a differential abundance testing paradigm called ELVAR that uses cell attribute aware clustering when inferring differentially enriched communities within the single-cell manifold.

Inference of age-associated transcription factor regulatory activity changes in single cells.

Transcription factors (TFs) control cell identity and function. How their activity is altered during healthy aging is critical for an improved understanding of aging and disease risk, yet relatively little is known about such changes at cell-type resolution. Here we present and validate a TF activity estimation method for single cells from the hematopoietic system that is based on TF regulons, and apply it to a mouse single-cell RNA-sequencing atlas, to infer age-associated differentiation activity changes in the immune cells of different organs.