Two ways to read a genome: Scientists reveal the first body-wide, single-cell atlas that maps DNA folding and epigenetics together

July 23, 2026

Two ways to read a genome: Scientists reveal the first body-wide, single-cell atlas that maps DNA folding and epigenetics together

Salk Institute researchers profile 86,689 cells across 16 human tissues, linking cell-type-specific epigenetic features to disease risk and revealing that a cell’s 3D genome and DNA methylation don’t always tell the same story

July 23, 2026

• Lo más destacado
• Salk scientists and collaborators publish the first single-cell atlas to simultaneously map 3D genome architecture and DNA methylation across the human body—86,689 cells spanning 16 tissues, 35 major cell types, and 206 subtypes
• The atlas pinpoints cell types where epigenetic features overlap with genetic risk for conditions including atrial fibrillation, bipolar disorder, schizophrenia, and blood-glucose regulation, helping explain how non-coding DNA variants may contribute to disease
• All data are freely available through an interactive browser, providing a reference for gene-regulation research and a training resource for AI models that predict the impact of genetic variants

LA JOLLA—Every cell in the human body carries the same three billion letters of DNA, yet a neuron, a heart muscle cell, and a pancreatic beta cell each use that shared code to do entirely different jobs. How cells pull off this trick comes down to epigenetics—chemical tags and three-dimensional structures that govern which genes are active and which stay silent.

In a study published in Ciencia on July 23, 2026, researchers at the Salk Institute and Arc Institute and their collaborators unveiled the first body-wide single-cell atlas of two major epigenetic systems: three-dimensional genome folding and DNA methylation—precise patterns of methyl chemical groups tacked onto DNA—measured simultaneously in the same cells.

The atlas spans 86,689 cells from 16 human tissues, revealing 35 major cell types and 206 subtypes, and is freely available online. The work is part of the National Institutes of Health’s 4D Nucleome (NIH 4DN) program, which aims to understand how the genome is organized in space and time to regulate gene expression in health and disease.

Because the two epigenetic layers were measured together, the researchers could compare what each layer says about a cell’s identity. And while often the two pictures agree, they found sometimes they do not. The Salk paper is published alongside five other NIH 4DN papers in Science, and three others in Avances en Ciencia.

 

Why map epigenetics cell by cell?

The Human Genome Project, completed in 2003, produced a linear read of the three billion DNA letters in the human body. But the letters alone don’t explain how a single genome produces hundreds of different cell types. That information lives in the epigenome in the form of chemical modifications and structural folds layered on top of the DNA sequence, where they can switch genes “on” and “off” in patterns specific to each cell type.

Two of the most consequential epigenetic features are, 1) DNA methylation, where small chemical groups called methyl groups are attached to specific DNA bases, and 2) 3D genome organization, where intricate loops, folds, and compartments bring distant stretches of DNA into contact. Both influence gene expression, but they had never been measured together in single cells across the human body.

“There has been an appreciation for trying to understand, at the individual cell level, how the genome is organized, so that we can get a better idea of how genetic variants impact disease,” says co-corresponding author Joseph Ecker, doctor, a professor and Salk International Council Chair in Genetics at Salk and a Howard Hughes Medical Institute investigator. “Some cell types may be more vulnerable than others to genetic variants, because the genome is organized differently in different cell types—and whether a variant matters can depend on that organization.”

 

Why is non-coding DNA relevant in disease?

Most disease-associated genetic variants fall in the non-coding regions of the genome—the vast majority of DNA that doesn’t encode proteins. That has made it notoriously difficult to figure out how a variant contributes to disease, which cell type it acts in, and what gene it ultimately affects.

The new atlas takes a direct swing at that problem. The team identified more than 1.36 million differentially methylated regions and 283,606 differential chromatin loops across the human body’s cell types, using tissues from the heart, brain, lungs, stomach, skin, and more.

When they overlaid genetic variants known to raise disease risk, specific pairings emerged like variants for blood-glucose regulation concentrated in endocrine cells, atrial fibrillation variants in heart muscle cells, balding variants in skin fibroblasts, and bipolar disorder and schizophrenia variants in excitatory and inhibitory neurons.

“A lot of the genetic variation that predisposes someone to disease is in non-coding parts of the genome,” says co-corresponding author Jesse Dixon, MD, PhD, associate professor and Helen McLoraine Developmental Chair at Salk. “By adding in the 3D genome aspect, we can potentially bridge that gap—connecting noncoding variations with the genes they affect in specific cells and tissues.”

 

 

What happens when two epigenetic lenses disagree?

One of the study’s most surprising findings is that DNA methylation and 3D genome structure don’t always tell the same story about a cell. In skeletal muscle, the team found fibers that look like mature, differentiated muscle cells by their 3D genome folding, but still carry the methylation signature of muscle stem cells. The reverse almost never happens. The most plausible explanation, they explain, is that these cells are caught mid-differentiation, with 3D architecture updating first and methylation catching up.

Similar mismatches appeared in Schwann cells of the peripheral nervous system and in placental trophoblasts. The pattern suggests that different epigenetic features update on different time scales during cell state transitions—a finding that could reshape how researchers define “cell type” in adult tissues and how they track cells moving between states in disease.

The atlas also revises a long-standing assumption about “non-CG methylation,” an unusual form of methylation previously thought to be largely confined to brain cells and stem cells. The study shows that it carries cell-identity information across many human tissues, including muscle, pancreas, and immune cell types, at lower but biologically meaningful levels.

“The inconsistency between modalities may be further used to determine what cell populations are switching between each other in adult tissues and diseases, which could, for example, expand our understanding of cancer cell dynamics,” says co-first and co-corresponding author Jingtian Zhou, PhD, a former graduate researcher in Ecker’s lab who now leads his own lab at the Arc Institute.

 

A public resource for scientists and artificial intelligence

To make the atlas broadly usable, the team built an interactive web browser that lets researchers visualize DNA methylation and 3D chromatin contacts across every tissue, cell type, and subtype in the study. The underlying data, including 195 billion methylation measurements and 18 billion chromatin contacts, are freely available.

The resource arrives as artificial intelligence tools are increasingly used to predict the functional impact of genetic variants. Atlases like this one provide the labeled, cell-type-resolved training data that models need to make accurate predictions—a bottleneck that has historically limited the field.

For example, in a companion paper in the same issue of Ciencia, a study led by Bing Ren, PhD, from the New York Genome Center and Columbia University used the atlas’ cross-tissue methylation data to show that a substantial fraction of the brain’s resident immune cells, called microglia, are replaced by cells resembling blood monocytes between roughly ages 50 and 75. The finding challenges the long-held view that microglia persist from embryonic development throughout the life span.

“DNA methylation patterns are specific to each cell type and analogous to a cellular barcode,” says Ren, who also co-authored the Salk-led study. “The comprehensive cross-tissue DNA methylation atlases show that the aging microglia in human hippocampus more closely match the monocytes from peripheral blood than microglia from young adults, providing a crucial clue for the biological identity of these cells.”

The NIH 4D Nucleome consortium, of which this study is a part, aims to extend this kind of mapping into the fourth dimension: time. A 4D understanding of the genome—how its structure and chemistry change as cells develop, age, and respond to disease—remains a major goal, and the cross-tissue atlas provides reference scaffolding that future time-course studies will build on.

 

Otros autores y financiación

Other authors include co-first author Yue Wu of Salk and UC San Diego; Wei Tian, Rosa Castanon, Anna Bartlett, Dengxiaoyu Shi, Ben Clock, Samantha Marcotte, Joseph Nery, Michelle Liem, Naomi Claffey, Lara Boggeman, Cesar Barragan, and Carolyn O’Connor of Salk; Hanqing Liu of Salk and Harvard University; Zuolong Zhang and Guocong Yao of Henan University in China; Rafael Arrojo e Drigo of Vanderbilt University; Annika Weimer of Stanford University and the Broad Institute; Minyi Shi and Michael Snyder of Stanford University; Johnathan Cooper-Knock of the University of Sheffield in the UK; Sai Zhang of Yale University and University of Florida; Sebastian Preissl of UC San Diego, University of Freiburg in Germany, and University of Graz in Austria; Bing Ren of UC San Diego; Shengbo Chen of Nanchang University in China; and Chongyuan Luo of UC Los Angeles.

The work was supported by the National Institutes of Health (NHGRI 5R01HG010634, NCI 5U01CA260700, P30 014195, S10-OD023689, S10 OD034268), Wellcome Trust (216596/Z/19/Z), Arc Institute, Pew Charitable Trusts, Rita Allen Foundation, and Howard Hughes Medical Institute.

DOI: 10.1126/science.adx0673