Biology subjects

Teschendorff, A. E.

Publications and source records attributed to Teschendorff, A. E..

13 recordsLinked to original sources

bioRxiv2026

OmniAge: a compendium of aging omic biomarkers links mitotic clocks to clonal hematopoiesis and causality

Interest in aging omic biomarkers has grown due to their ability to quantify biological age. Most of these biomarkers have been derived in blood and fall into many diverse categories, yet relatively little is known about their correlative patterns, especially between biomarkers from different categories. Here we present the OmniAge R and Python package, a collection of 413 aging omic biomarkers representing 12 different categories, including traditional epigenetic clocks, epigenetic mitotic clocks, DNA methylation-based proxies for clonal hematopoiesis and inflammaging, causal clocks, cell-type specific epigenetic clocks and single-cell transcriptomic clocks. By studying their inter-class correlations across large blood datasets, we reveal associations of mitotic age with clonal hematopoiesis subtypes and causal clocks, which are predictive of cancer risk. Using proxies of serum protein levels, we further dissect associations with mitotic clocks, clonal hematopoiesis and causal clocks into distinct biological processes mapping to key aging pathways. Applying OmniAge to multi-modal data of sorted immune cell-types reveals that age-acceleration derived from transcriptomic and epigenetic clocks correlate, but that this is driven by underlying cell-type heterogeneity. In summary, the OmniAge package is an exploratory tool for evaluating large numbers of aging omic biomarkers, and to aid discovery and generate new hypotheses.

bioinformatics↗

bioRxiv2025

LDscore: a scalable, Python 3-powered web platform for LD score regression analysis

Linkage disequilibrium score regression (LDSC) is an important analytical tool for quantifying heritability and estimating genetic correlations between complex traits. However, the LDSC original implementation relies on an outdated Python 2 framework and deploying the standard command-line tools requires significant setup, data access, and computational expertise, creating a barrier for many researchers. To overcome these limitations, we developed LDscore, a significant technical and accessibility upgraded version of LDSC that allows for rapid analysis of GWAS data. The core advancement is the recoding of the LDSC framework in Python 3, enabling computational optimization and ensuring long-term sustainability. Built on top of this improved foundation, LDscore is implemented as a free, publicly available web application integrated within the popular NCI LDlink framework. LDscore can accelerate scientific research by providing an intuitive graphical interface for heritability estimation, genetic correlation, and LD score calculation, including access to an expanded range of reference populations for online analysis. Notably, our results show that selecting the most appropriate reference population LD panel, even at the subcontinental ancestry group level, is essential for minimizing population stratification bias in heritability estimation. By leveraging cloud computing for superior scalability and eliminating the need for local installation, LDscore adheres to FAIR principles, improving access, traceability, and reproducibility across an expanded set of reference populations, and effectively widens access to researchers worldwide providing support for in-depth genetic analyses. Brief summaryLinkage disequilibrium score regression (LDSC), a widely-used method for quantifying heritability and genetic correlation, is limited by an outdated Python 2 framework and complex command-line deployment. We developed LDscore, a significant technical upgrade built on Python 3 for sustainability and computational optimization. LDscore is a free, cloud-based web application integrated into NCI LDlink. LDscore eliminates installation barriers, offering an intuitive interface for computing heritability estimates, LD scores, and genetic correlation. Crucially, LDscore expands the range of reference populations available in LDSC, which can reduce population-stratification-based bias. Leveraging cloud computing, LDscore accelerates and widens global researcher access to LDSC-based genetic computation. AvailabilityLDscore is freely available within LDlink at https://ldlink.nih.gov/ldscore. Source code for the updated LDSC Python3 framework is available at https://github.com/CBIIT/ldsc under the GNU General Public License v3.0 and the webtool code is at https://github.com/CBIIT/nci-webtools-dceg-linkage (webtool code) under the MIT license.

genomics↗

bioRxiv2024

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

BackgroundMost 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. MethodsTo 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. We illustrate this concept in the context of DNA methylation data of liver and kidney cancer, deriving in each case novel cancer subtypes and assessing their prognostic relevance against current state-of-the-art prognostic models. ResultsIn both liver and kidney cancer, we reveal improved cell-type specific prognostic models, not discoverable using standard methods. In the case of kidney cancer, we show how combinatorial indexing of epithelial and immune-cell clusters define improved prognostic models driven by synergy of high mitotic age and altered cytokine signaling. We validate the improved prognostic models in independent datasets, and identify underlying cytokine-immune-cell signatures driving poor outcome. ConclusionsIn summary, cell-type specific combinatorial clustering is a valuable strategy to help dissect and improve current prognostic classifications of cancer in terms of the underlying cell-type specific epigenetic and transcriptomic alterations.

bioinformatics↗

bioRxiv2024

Molecular Landscape of Modality-Specific Exercise Adaptation in Human Skeletal Muscle through Large-Scale Multi-OMICs Integration

We conducted a large-scale, statistically powered, meta-analysis of exercise adaptations in human skeletal muscles, integrating epigenetic, transcriptomic, transcription factors, and proteomic data across 12 independent cohorts comprising over 1000 participants and 2340 human muscle samples. Our study identified distinctive signatures associated with maximal oxygen consumption (VO2max), and identified five genes robustly intersecting multi-OMIC layers. Notably, transcription factors predominantly functioned as activators across these layers, regulating expression of target genes irrespective of whether DNA methylation levels were low or high, indicating a synergistic effect between TFs and the methylome. Analysis of distinct exercise modalities (aerobic and resistance exercise) revealed unique gene pathways, contrasting with patterns observed in inactivity (muscle disuse) studies. These findings offer a comprehensive understanding of exercise and modality-specific adaptations, shedding light on muscle health and the molecular mechanisms associated with cardiorespiratory fitness, aging, and disease prevention.

molecular biology↗

bioRxiv2023

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 (DA) patterns, yet this task can be statistically challenging due to the noise in single-cell data, inter-sample variability and because DA patterns are often of small effect size. Here we present a DA-testing paradigm called ELVAR that uses cell attribute aware clustering when inferring differentially enriched communities within the single-cell manifold. Using simulated and real single-cell and single-nucleus RNA-Seq datasets, we benchmark ELVAR against an analogous DA algorithm that uses Louvain for clustering, as well as local neighborhood-based DA-testing methods, demonstrating that ELVAR improves the sensitivity to detect DA-shifts in relation to aging, precancerous states and Covid-19 phenotypes. In effect, leveraging cell attribute information when inferring cell communities can denoise single-cell data, avoid the need for batch correction and help retrieve more robust cell states for subsequent DA-testing. ELVAR is available as an open-source R-package.

bioinformatics↗

bioRxiv2023

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

AbstractO_ST_ABSBackgroundC_ST_ABSChanges in cell-type composition of complex tissues are associated with a wide range of diseases, 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 these shifts in tissue composition. MethodsHere we derive a DNA methylation reference matrix for 12 immune cell-types in human blood and extensively validate it with flow-cytometric count data and in whole-genome bisulfite sequencing data of sorted cells. Using this reference matrix and Stouffers method, we perform a meta-analysis encompassing 25,629 blood samples from 22 different cohorts, to comprehensively map associations between the 12 immune-cell fractions and common phenotypes, including health outcomes. ResultsOur meta-analysis reveals many associations with age, sex, smoking and obesity, many of which we validate with single-cell RNA-sequencing. We discover that T-regulatory and naive T-cell subsets are higher in women compared to men, whilst the reverse is true for monocyte, natural killer, basophil and eosinophil fractions. In a large subset encompassing 5000 individuals we find associations with stress, exercise, sleep and health outcomes, revealing that naive T-cell and B-cell fractions are associated with a reduced risk of all-cause mortality independently of age, sex, race, smoking, obesity and alcohol consumption. We find that decreased natural killer cell counts are associated with smoking, obesity and stress levels, whilst an increased count correlates with exercise, sleep and a reduced risk of all-cause mortality. ConclusionsThis work derives and extensively validates a high resolution DNAm reference matrix for blood, and uses it to generate a comprehensive map of associations between immune cell fractions and common phenotypes, including health outcomes. AvailabilityThe 12 immune cell-type DNAm reference matrices for Illumina 850k and 450k beadarrays alongside tools for cell-type fraction estimation are freely available from our EpiDISH Bioconductor R-package http://www.bioconductor.org/packages/devel/bioc/html/EpiDISH.html

bioinformatics↗

bioRxiv2022

dbDEMC 3.0: Functional Exploration of Differentially Expressed miRNAs in Cancers of Human and Model Organisms

microRNAs (miRNAs) are important regulators in gene expression. The deregulation of miRNA expression is widely reported in the transformation from physiological to pathological state of cells. A large amount of differentially expressed miRNAs (DEMs) have been identified in various human cancers by using high-throughput technologies, such as microarray and miRNA-seq. Through mining of published researches with high-throughput experiment information, the database of differentially expressed miRNAs in human cancers (dbDEMC) was constructed with the aim of providing a systematic resource for the storage and query of the DEMs. Here we report an update of the dbDEMC to version 3.0, containing two-fold more data entries than the previous version, now including also data from mouse and rat. The dbDEMC 3.0 contains 3,268 unique DEMs in 40 different cancer types. The current datasets for differential expression analysis have expanded to 9 generalized categories. Moreover, the current release integrates functional annotations of DEMs obtained from experimentally validated targets. The annotations can greatly benefit integrative analysis of DEMs. In summary, dbDEMC 3.0 provides a valuable resource for characterizing molecular functions and regulatory mechanisms of DEMs in human cancers. The dbDEMC 3.0 is freely accessible at https://www.biosino.org/dbDEMC.

bioinformatics↗

bioRxiv2020

Improved detection of tumor suppressor events in single-cell RNA-Seq data

Tissue-specific transcription factors are frequently inactivated in cancer. To fully dissect the heterogeneity of such tumor suppressor events requires single-cell resolution, yet this is challenging because of the high dropout rate. Here we propose a simple yet effective computational strategy called SCIRA to infer regulatory activity of tissue-specific transcription factors at single-cell resolution and use this tool to identify tumor suppressor events in single-cell RNA-Seq cancer studies. We demonstrate that tissue-specific transcription factors are preferentially inactivated in the corresponding cancer cells, suggesting that these are driver events. For many known or suspected tumor suppressors, SCIRA predicts inactivation in single cancer cells where differential expression does not, indicating that SCIRA improves the sensitivity to detect changes in regulatory activity. We identify NKX2-1 and TBX4 inactivation as early tumor suppressor events in normal non-ciliated lung epithelial cells from smokers. In summary, SCIRA can help chart the heterogeneity of tumor suppressor events at single-cell resolution.Competing Interest StatementThe authors have declared no competing interest.View Full Text

bioinformatics↗

bioRxiv2019

Leveraging high-powered RNA-Seq datasets to improve inference of regulatory activity in single-cell RNA-Seq data

Inferring the activity of transcription factors in single cells is a key task to improve our understanding of development and complex genetic diseases. This task is, however, challenging due to the relatively large dropout rate and noisy nature of single-cell RNA-Seq data. Here we present a novel statistical inference framework called SCIRA (Single Cell Inference of Regulatory Activity), which leverages the power of large-scale bulk RNA-Seq datasets to infer high-quality tissue-specific regulatory networks, from which regulatory activity estimates in single cells can be subsequently obtained. We show that SCIRA can correctly infer regulatory activity of transcription factors affected by high technical dropouts. In particular, SCIRA can improve sensitivity by as much as 70% compared to differential expression analysis and current state-of-the-art methods. Importantly, SCIRA can reveal novel regulators of cell-fate in tissue-development, even for cell-types that only make up 5% of the tissue, and can identify key novel tumor suppressor genes in cancer at single cell resolution. In summary, SCIRA will be an invaluable tool for single-cell studies aiming to accurately map activity patterns of key transcription factors during development, and how these are altered in disease.

bioinformatics↗

bioRxiv2018

Integrated single-cell potency and expression landscape in mammary epithelium reveals novel bipotent-like cells associated with breast cancer risk

The identification of progenitor and stem like cells in epithelial tissues, as well as those that may serve as the cell of origin for epithelial cancers, is an outstanding challenge. Here we present a novel algorithm, called LandSCENT, which constructs a 3-dimensional integrated landscape of cell-states, encompassing cell-potency and expression subtypes, to facilitate the identification of progenitor and stem-like cells. Application to thousands of single-cell RNA-Seq profiles from the normal mammary epithelium reveals a rare 5% subpopulation of highly potent single-cells. The integrated landscape naturally predicts that these cells define a bi-potent-like state, a result not obtainable via standard methods or without invoking prior assumptions. The bi-potent-like cells are overrepresented within the basal compartment but also overlap with an immature luminal phenotype. We characterize the transcriptome of these cells and show that is enriched for a mammary stem-cell module. We further identify YBX1, a regulator of breast cancer risk identified from GWAS, as the key transcription factor defining this candidate bi-potent cellular phenotype. We validate the putative bi-potency of YBX1-marked cells using independent FACS-sorted bulk expression data. In addition, YBX1 is overexpressed in basal breast cancer and correlates with clinical outcome. In summary, we here provide a novel computational framework which may serve to identify and prioritize candidate normal or cancer progenitor/stem-like single-cell phenotypes, for subsequent functional studies.

bioinformatics↗

bioRxiv2018

Cell and tissue type independent age-associated DNA methylation changes are not rare but common

Age-associated DNA methylation changes have been widely reported across many different tissue and cell types. Epigenetic clocks that can predict chronological age with a surprisingly high degree of accuracy appear to do so independently of tissue and cell-type, suggesting that a component of epigenetic drift is cell-type independent. However, the relative amount of age-associated DNAm changes that are specific to a cell or tissue type versus the amount that occurs independently of cell or tissue type is unclear and a matter of debate, with a recent study concluding that most epigenetic drift is tissue-specific. Here, we perform a novel comprehensive statistical analysis, including matched multi cell-type and multi-tissue DNA methylation profiles from the same individuals and adjusting for cell-type heterogeneity, demonstrating that a substantial amount of epigenetic drift, possibly over 70%, is shared between significant numbers of different tissue/cell types. We further show that ELOVL2 is not unique and that many other CpG sites, some mapping to genes in the Wnt and glutamate receptor signaling pathways, are altered with age across at least 10 different cell/tissue types. We propose that while most age-associated DNAm changes are shared between cell-types that the putative functional effect is likely to be tissue-specific.

bioinformatics↗

bioRxiv2017

A comparison of reference-based algorithms for correcting cell-type heterogeneity in Epigenome-Wide Association Studies

BackgroundIntra-sample cellular heterogeneity presents numerous challenges to the identification of biomarkers in large Epigenome-Wide Association Studies (EWAS). While a number of reference-based deconvolution algorithms have emerged, their potential remains underexplored and a comparative evaluation of these algorithms beyond tissues such as blood is still lacking.\n\nResultsHere we present a novel framework for reference-based inference, which leverages cell-type specific DNAse Hypersensitive Site (DHS) information from the NIH Epigenomics Roadmap to construct an improved reference DNA methylation database. We show that this leads to a marginal but statistically significant improvement of cell-count estimates in whole blood as well as in mixtures involving epithelial cell-types. Using this framework we compare a widely used state-of-the-art reference-based algorithm (called constrained projection) to two non-constrained approaches including CIBERSORT and a method based on robust partial correlations. We conclude that the widely-used constrained projection technique may not always be optimal. Instead, we find that the method based on robust partial correlations is generally more robust across a range of different tissue types and for realistic noise levels. We call the combined algorithm which uses DHS data and robust partial correlations for inference, EpiDISH (Epigenetic Dissection of Intra-Sample Heterogeneity). Finally, we demonstrate the added value of EpiDISH in an EWAS of smoking.\n\nConclusionsEstimating cell-type fractions and subsequent inference in EWAS may benefit from the use of non-constrained reference-based cell-type deconvolution methods.

bioinformatics↗

bioRxiv2016

Single-cell entropy for accurate estimation of differentiation potency from a cell’s transcriptome

The ability to quantify differentiation potential of single cells is a task of critical importance for single-cell studies. So far however, there is no robust general molecular correlate of differentiation potential at the single cell level. Here we show that differentiation potency of a single cell can be approximated by computing the signaling promiscuity, or entropy, of a cells transcriptomic profile in the context of a cellular interaction network, without the need for model training or feature selection. We validate signaling entropy in over 7,000 single cell RNA-Seq profiles, representing all main differentiation stages, including time-course data. We develop a novel algorithm called Single Cell Entropy (SCENT), which correctly identifies known cell subpopulations of varying potency, enabling reconstruction of cell-lineage trajectories. By comparing bulk to single cell data, SCENT reveals that expression heterogeneity within single cell populations is regulated, pointing towards the importance of cell-cell interactions. In the context of cancer, SCENT can identify drug resistant cancer stem-cell phenotypes, including those obtained from circulating tumor cells. In summary, SCENT can directly estimate the differentiation potency and plasticity of single-cells, allowing unbiased quantification of intercellular heterogeneity, and providing a means to identify normal and cancer stem cell phenotypes.\n\nSoftware AvailabilitySCENT is freely available as an R-package from github: https://github.com/aet21/SCENT

systems biology↗