Peeling back the layers of a cell’s epigenomic data

From the Henikoff and Setty labs, Basic Sciences and Public Health Sciences Divisions

Shrek: Ogres are like onions.

Donkey: They stink?

Shrek: Yes. No.

Donkey: Oh, they make you cry?

Shrek: No.

Donkey: Oh, you leave em out in the sun, they get all brown, start sproutin’ little white hairs.

Shrek: No. Layers. Onions have layers. Ogres have layers. Onions have layers. You get it. We both have layers.

-Shrek, 2001

The cell is an incredibly versatile biological machine, capable of swapping its myriad parts in and out to adopt many forms and play many roles in our bodies. The neuron, the blood cell, and the muscle cell, different though they may be, are all fundamentally composed of the same material and derived from the same progenitors. Understanding how cells adopt specific fates and functions is of paramount importance to biomedical research, as most diseases – cancer foremost among them – result from some form of malfunction of this process. There is an expectation that generating a sufficiently large catalogue of data quantifying the molecular state of a cell should inform us of what it is doing. Efforts to generate this “big data” began in earnest with the human genome project and the subsequent proliferation of technologies to read out genome-scale aspects of a cell’s identity. But as scientists have peeled back the so-called “-omic” layers within our cells, it has become clear just how many different types of information they contain. This includes transcriptomics – which genes are expressed and at what levels; proteomics – the abundance and molecular states of proteins; interactomics – which molecules form complexes with each other; and layer after layer of epigenomic information – DNA modifications, histone modifications, even the physical conformation of DNA. Recent technological advances have led to faster, cheaper, more precise methods for measuring more layers of information (aka “multi-omics”) from fewer cells, with the ultimate goal of being able to record and interpret the full suite of molecular details from the many individual cells within a body. Dr. Steve Henikoff, Professor in Fred Hutch’s Basic Sciences Division and member of the Fred Hutch/UW Cancer Consortium, has been at the leading edge of developing methods for epigenomic profiling, including the powerful CUT&Run and CUT&Tag techniques for identifying the locations of specific epigenetic marks within chromatin. In a new collaborative project with fellow Basic Sciences faculty member Dr. Manu Setty, led by postdocs  Dr. Derek Janssens (Henikoff lab) and Dr. Dominik Otto (Setty lab) and published in Genome Biology, the groups report a new method for multi-epigenomic profiling.

While all of an individual’s cells have the same genome, the epigenome – the ways in which our genome can be modified without changing its sequence – is highly variable and plays a key role in specifying cell state and function. The CUT&Tag method developed in the Henikoff lab is used to precisely measure which regions of our DNA are associated with a specific epigenetic mark. But we have so far been limited to measuring only one mark within a cell, leaving us with an incomplete picture. For example, we could measure which regions are associated with markers of active transcription, or we could measure which regions are associated with markers of transcriptional repression. Unsatisfied with this limitation, the groups asked “why not do both?”

CUT&Tag works by introducing an antibody that binds to a certain epigenomic mark, introducing an enzyme that cuts and labels the DNA in close proximity to that antibody (a process known as tagmentation), and then sequencing to identify those labeled fragments of DNA. The challenge of profiling multiple marks is that, while it’s simple enough to introduce two antibodies, after sequencing it can be difficult to impossible to determine which DNA fragment was associated with which antibody.

This project began with a serendipitous observation. While performing CUT&Tag against active genomic regions using an antibody against the initiated form of RNA polymerase II (Pol2S5p), the group noticed that many of the identified DNA fragments were smaller than 120 base pairs (bp) in size. This was a marked contrast from previous CUT&Tag experiments against repressive genomic regions using an antibody to the histone mark H3K27me3, which tended to generate DNA fragments larger than 120bp. If the DNA fragments themselves had distinguishing features, they reasoned, they could potentially introduce both antibodies simultaneously and deconvolve the data after sequencing. Indeed, they found that this approached worked extremely well to separate the signals after simultaneous profiling of the PolS5p and M3K27me3 marks in bulk samples and single cells.

While there are established techniques for multi-omic profiling using different modalities (i.e. transcriptomics and epigenomics), “methods to examine multiple chromatin binding proteins in the same single cells are only very recently starting to appear,” explains Dr. Janssens. He expressed his excitement for the power of this technique to reveal new insights into cellular behavior. “Methods that simultaneously profile both the active and repressed epigenome could provide a more comprehensive understanding of cell fate regulation than can be obtained by profiling the active or repressive chromatin landscapes in isolation,” he wrote. “We are now looking forward to apply CUT&Tag2for1 to more tissues such as human patient samples in order to examine the interplay between active and repressive chromatin at an unprecedented resolution.”

CUT&Tag2for1 schematic
Schematic of CUT&Tag2for1 DNA fragment generation (top) and characteristic DNA fragment length distributions (bottom) for Pol2S5p and H3K27me3. Image provided by Dr. Derek Janssens

This work was supported by the National Institutes of Health and the Howard Hughes Medical Institute.

Fred Hutch/UW Cancer Consortium members Steve Henikoff and Manu Setty contributed to this work.

Janssens DH, Otto DJ, Meers MP, Setty M, Ahmad K, Henikoff S. CUT&Tag2for1: a modified method for simultaneous profiling of the accessible and silenced regulome in single cells. Genome Biol. 2022 Mar 17;23(1):81. doi: 10.1186/s13059-022-02642-w. PMID: 35300717; PMCID: PMC8928696