Biology subjects

McKennan, C.

Publications and source records attributed to McKennan, C..

6 recordsLinked to original sources

bioRxiv2026

Re-evaluating the Need for Double Centrifugation in Plasma Cell-Free DNA Analysis

Plasma cell-free DNA (cfDNA) is a central analyte in liquid biopsy applications spanning prenatal testing, oncology, and epigenomic profiling. To minimize contamination by high-molecular-weight genomic DNA (gDNA) released from nucleated blood cells, standard pre-analytical workflows typically mandate a double-centrifugation protocol prior to cfDNA extraction. This requirement has limited the use of many existing plasma biorepositories that were prepared using only a single low-speed centrifugation step. In this study, we evaluated whether single-spun plasma is sufficient for accurate cfDNA analysis when samples are processed under controlled conditions. Using paired single- and double-spun plasma aliquots derived from the same early-pregnancy maternal blood samples collected in EDTA tubes, we performed whole-genome DNA methylation sequencing and assessed cfDNA integrity across multiple orthogonal dimensions. These included cell-type proportion deconvolution using large and small DNA methylation reference signatures, CpG-level methylation rate estimation with explicit variance modeling, beta-binomial-corrected correlation analyses across libraries, cfDNA fragment length profiling, and genotype-based fetal fraction estimation. Across all analyses, we found no evidence that a second high-speed centrifugation step improved accuracy, reduced technical variability, or enhanced analytical fidelity. Cell-type proportion estimates and CpG-level methylation rates were statistically indistinguishable between single- and double-spun plasma, fragment length distributions were nearly identical, and fetal fraction estimates showed near-perfect concordance. Together, these results demonstrate that a single low-speed centrifugation step is sufficient for high-fidelity cfDNA methylation, fragmentomic, and genotyping analyses. Our findings support the expanded use of legacy single-spun plasma collections for liquid biopsy research and assay development and motivate a re-evaluation of rigid double-centrifugation requirements in cfDNA workflows.

genomics↗

bioRxiv2024

Impact of early-life respiratory syncytial virus infection on cell type-specific airway DNA methylation

RationaleInfection with respiratory syncytial virus (RSV) in early-life is associated with subsequent childhood respiratory disorders, but the mechanism for this association is unknown. We hypothesized that RSV-mediated alteration in airway DNA methylation (DNAm) may play a role. ObjectivesInvestigate the impact of early-life RSV infection on airway DNAm. MethodsOur study population consisted of children from the INSPIRE population-based birth cohort. Early-life infection with RSV was defined as infection not requiring clinical illness before age one year, ascertained through active and passive surveillance. Methylation was measured in DNA from nasal airway epithelial cells (NAECs) at ages two years (n=88) and six years (n=539). Bulk gene expression in NAECs was available in a subset of participants at age two years (n=54). In silico cell type deconvolution was used to infer cell type-specific associations in suprabasal, ciliated, and "other" cell types. Measurements and Main ResultsWe identified 164 CpGs in ciliated cells and seven CpGs in suprabasal cells whose cell type-specific DNAm at age two years was associated with early-life RSV infection. The seven associations in suprabasal cells were still present at age six years and the directions of association replicated in in vitro infection of epithelial cells with RSV in air liquid interface cultures. DNAm levels at four of the seven CpGs identified in suprabasal cells correlated with the expression of their nearest genes in suprabasal cells, which included genes previously implicated in asthma-related diseases. The DNAm levels of these four CpGs and the expression of their nearest genes in suprabasal cells at age two years were also associated with recurrent wheezing at age four years. ConclusionsWe demonstrate epigenetic changes in airway progenitor cells of children who have had RSV infection in the first year of life are conserved over time, associated with subsequent wheeze, have functional relevance, and replicate in in vitro infection in ALI cultures. Our study is the first to show that early-life RSV infection, as opposed to severe illness, alters DNAm levels at functionally and clinically relevant loci.

genomics↗

bioRxiv2024

BLEND: Probabilistic Cellular Deconvolution with Automated Reference Selection

Cellular deconvolution aims to estimate cell type fractions from bulk transcriptomic and other omics data. Most existing deconvolution methods fail to account for the heterogeneity in cell type-specific (CTS) expression across bulk samples, ignore discrepancies between CTS expression in bulk and cell type reference data, and provide no guidance on cell type reference selection or integration. To address these issues, we introduce BLEND, a hierarchical Bayesian method that leverages multiple reference datasets. BLEND learns the most suitable references for each bulk sample by exploring the convex hulls of references and employs a "bag-of-words" representation for bulk count data for deconvolution. To speed up the computation, we provide an efficient EM algorithm for parameter estimation. Notably, BLEND requires no data transformation, normalization, cell type marker gene selection, or reference quality evaluation. Benchmarking studies on both simulated and real human brain data highlight BLENDs superior performance in various scenarios. The analysis of Alzheimers disease data illustrates BLENDs application in real data and reference resource integration.

bioinformatics↗

bioRxiv2023

scMD: cell type deconvolution using single-cell DNA methylation references

The proliferation of single-cell RNA sequencing data has led to the widespread use of cellular deconvolution, aiding the extraction of cell type-specific information from extensive bulk data. However, those advances have been mostly limited to transcriptomic data. With recent development in single-cell DNA methylation (scDNAm), new avenues have been opened for deconvolving bulk DNAm data, particularly for solid tissues like the brain that lack cell-type references. Due to technical limitations, current scDNAm sequences represent a small proportion of the whole genome for each single cell, and those detected regions differ across cells. This makes scDNAm data ultrahigh dimensional and ultra-sparse. To deal with these challenges, we introduce scMD (single cell Methylation Deconvolution), a cellular deconvolution framework to reliably estimate cell type fractions from tissue-level DNAm data. To analyze large-scale complex scDNAm data, scMD employs a statistical approach to aggregate scDNAm data at the cell cluster level, identify cell-type marker DNAm sites, and create a precise cell-type signature matrix that surpasses state-of-the-art sorted-cell or RNA-derived references. Through thorough benchmarking in several datasets, we demonstrate scMDs superior performance in estimating cellular fractions from bulk DNAm data. With scMD-estimated cellular fractions, we identify cell type fractions and cell type-specific differentially methylated cytosines associated with Alzheimers disease.

bioinformatics↗

bioRxiv2023

Accurate estimation of rare cell type fractions fromtissue omics data via hierarchical deconvolution

Bulk transcriptomics in tissue samples reflects the average expression levels across different cell types and is highly influenced by cellular fractions. As such, it is critical to estimate cellular fractions to both deconfound differential expression analyses and infer cell type-specific differential expression. Since experimentally counting cells is infeasible in most tissues and studies, in silico cellular deconvolution methods have been developed as an alternative. However, existing methods are designed for tissues consisting of clearly distinguishable cell types and have difficulties estimating highly correlated or rare cell types. To address this challenge, we propose Hierarchical Deconvolution (HiDecon) that uses single-cell RNA sequencing references and a hierarchical cell type tree, which models the similarities among cell types and cell differentiation relationships, to estimate cellular fractions in bulk data. By coordinating cell fractions across layers of the hierarchical tree, cellular fraction information is passed up and down the tree, which helps correct estimation biases by pooling information across related cell types. The flexible hierarchical tree structure also enables estimating rare cell fractions by splitting the tree to higher resolutions. Through simulations and real data applications with the ground truth of measured cellular fractions, we demonstrate that HiDecon significantly outperforms existing methods and accurately estimates cellular fractions.

bioinformatics↗

bioRxiv2018

Longitudinal studies at birth and age 7 reveal strong effects of genetic variation on ancestry-associated DNA methylation patterns in blood cells from ethnically admixed children

Epigenetic architecture is influenced by genetic and environmental factors, but little is known about their relative contributions or longitudinal dynamics. Here, we studied DNA methylation (DNAm) at over 750,000 CpG sites in mononuclear blood cells collected at birth and age 7 from 196 children of primarily self-reported Black and Hispanic ethnicities to study race-associated DNAm patterns. We developed a novel Bayesian method for high dimensional longitudinal data and showed that race-associated DNAm patterns at birth and age 7 are nearly identical. Additionally, we estimated that up to 51% of all self-reported race-associated CpGs had race-dependent DNAm levels that were mediated through local genotype and, quite surprisingly, found that genetic factors explained an overwhelming majority of the variation in DNAm levels at other, previously identified, environmentally-associated CpGs. These results not only indicate that race-associated DNAm patterns in blood are present at birth and are primarily genetically, and not environmentally, determined, but also that DNAm in blood cells overall is robust to many environmental exposures during the first 7 years of life.

genomics↗