Remote immune processes revealed by immune-derived circulating cell-free DNA

Abstract

Blood cell counts often fail to report on immune processes occurring in remote tissues. Here we use immune cell type-specific methylation patterns in circulating cell-free DNA (cfDNA) for studying human immune cell dynamics. We characterized cfDNA released from specific immune cell types in healthy individuals (N=242), cross sectionally and longitudinally. Immune cfDNA levels had no individual steady state as opposed to blood cell counts, suggesting that cfDNA concentration reflects adjustment of cell survival to maintain homeostatic cell numbers. We also observed selective elevation of immune-derived cfDNA upon perturbations of immune homeostasis. Following influenza vaccination (N=92), B-cell-derived cfDNA levels increased prior to elevated B-cell counts and predicted efficacy of antibody production. Patients with Eosinophilic Esophagitis (N=21) and B-cell lymphoma (N=27) showed selective elevation of eosinophil and B-cell cfDNA respectively, which were undetectable by cell counts in blood. Immune-derived cfDNA provides a novel biomarker for monitoring immune responses to physiological and pathological processes that are not accessible using conventional methods.

Detecting cell-of-origin and cancer-specific methylation features of cell-free DNA from Nanopore sequencing

“…For our cfNano, we performed direct modification calling using the Megalodon software provided by ONT (https://github.com/nanoporetech/megalodon). To perform deconvolution, we used 1,000-2,000 marker CpGs per cell type based on a previously published atlas of purified cell types (“MethAtlas”, ( 5, 13 )), and estimated cell type fractions using Non-Negative Least Squares (NNLS) regression as described in ( 5 ). In order to better understand the impact of the relatively low sequencing depth of our cfNano samples (∼0.2x genome coverage), we first performed deconvolution of all samples using downsampling experiments starting with full sequence depth down to 0.0001x genome coverage (Figure 1A and Supplementary Figures 1-3).…”

“…In order to better understand the impact of the relatively low sequencing depth of our cfNano samples (∼0.2x genome coverage), we first performed deconvolution of all samples using downsampling experiments starting with full sequence depth down to 0.0001x genome coverage (Figure 1A and Supplementary Figures 1-3). Healthy plasma WGBS samples were taken from a recent study of 50-100x genomic coverage (( 13 ), Figure 1A left “Fox-Fisher” samples), and another WGBS study with 0.5-1x coverage (( 20 ) Figure 1A middle “Nguyen” samples). Finally, healthy cfNano samples were analyzed (Figure 1A right “this study”).…”

“…All methylation values are fold change relative to the flanking region (region from 0.8kb-1kb from the TFBS). From left to right, plots show 23 healthy plasma samples from ( 13 ), and 32 healthy plasma samples from ( 42 ), 3 healthy and 18 LuAd WGBS samples from ( 20 ), and 7 healthy and 6 LuAd cfNano samples from this study. (B) Average DNA methylation across chr16p, comparing lung tissue WGBS (top) to plasma cfNano samples from this study (bottom).…”

“…Due to their low cost, targeted bisulfite PCR (including multiplexed NGS versions ( 13 )) are also popular for clinical methylation sequencing, and these would not require the native modification calling capability of Nanopore. However, shallow whole-genome approaches that capture multiple genomic features could potentially be more informative, especially with regard to the course of the disease ( 8, 14, 15 ).…”

Cell-free, methylated DNA in blood samples reveals tissue-specific, cellular damage from radiation treatment

“…The short half-life of cfDNA (15 mins - 2 hours) is ideal for monitoring real-time changes in tissue homeostasis due to therapeutic interventions 27,28 . Also, few cfDNA analyses have taken advantage of CpG pattern analysis to increase sensitivity and specificity of cell type proportion estimates 22,23,29–31 . Since each cfDNA molecule has independent origins, pattern analysis of sequence reads allows for individual classification of each fragment as opposed to traditional methods that average the methylation status across a population of fragments aligned at single CpG sites 27,28 .…”