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Updated: Sep 17, 2025

Author Spotlight: Automated Lifespan Monitoring – Discovering Aging Dynamics with the Lifespan Machine

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Published on: January 26, 2024

Profiling Epigenetic Aging at Cell-Type Resolution Through Long-Read Sequencing.

Alec Eames¹, Mahdi Moqri¹, Jesse R Poganik¹

¹Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.

|July 2, 2025

Summary

This summary is machine-generated.

This study introduces LongReadAge, a new method using long-read sequencing to measure cell type-specific DNA methylation aging. It reveals distinct aging patterns in different cell types from blood and cell-free DNA.

Keywords:

aging epigenetics long‐read sequencing

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TChIP-Seq: Cell-Type-Specific Epigenome Profiling

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Published on: January 23, 2019

Ultra-long Read Sequencing for Whole Genomic DNA Analysis

Ultra-long Read Sequencing for Whole Genomic DNA Analysis

Published on: March 15, 2019

Area of Science:

Epigenetics and Genomics
Computational Biology
Aging Research

Background:

DNA methylation is a key biomarker for aging, but bulk tissue analysis obscures cell-specific aging differences.
Existing methods lack the resolution to track aging trajectories within individual cell types.
Long-read sequencing offers potential for cell type deconvolution of methylation profiles.

Purpose of the Study:

To develop a framework for cell type-specific epigenetic age prediction using long-read sequencing.
To introduce a probabilistic model, LongReadAge, for analyzing cell type-level methylation data.
To investigate aging dynamics in myeloid cells and lymphocytes without cell sorting.

Main Methods:

Utilized long-read sequencing for DNA methylation profiling of extended fragments.
Developed a computational framework to map fragments to cell types based on methylation patterns.
Applied the LongReadAge model to cell type-specific methylation profiles from bulk leukocytes and cell-free DNA.

Main Results:

Successfully generated cell type-specific methylation profiles from bulk and cell-free DNA.
LongReadAge accurately predicted epigenetic age at the cell type level.
Demonstrated robust age prediction for myeloid cells and lymphocytes, even with limited shared features.

Conclusions:

The developed framework enables cell type-specific epigenetic age profiling without cell sorting.
LongReadAge provides a novel computational approach to study aging dynamics at cellular resolution.
This method opens new avenues for understanding heterogeneity in biological aging processes.