Matched single-cell chromatin, transcriptome, and surface marker profiling captures in vivo epigenomic reprogramming during basal-to-luminal transition in the mammary gland

This paper introduces OneCell CUT&Tag, a user-friendly single-cell multi-omics method capable of profiling chromatin, transcriptomes, and surface markers from individual cells, which was used to reveal continuous epigenomic priming and a binary transcriptomic switch during basal-to-luminal transdifferentiation in the mammary gland.

The Gist

Imagine you are trying to understand how a city changes over time. You could look at the people living there (the transcriptome or RNA), you could look at the traffic lights and road signs (the epigenome or chromatin), or you could look at the uniforms people are wearing (the surface markers).

For a long time, scientists could only look at these things separately, or they had to look at a whole crowd of people at once, losing the unique story of each individual. It was like trying to understand a specific conversation in a noisy stadium by only listening to the average noise level of the whole crowd.

This paper introduces a new, super-powerful tool called OneCell CUT&Tag. Here is what they did, explained simply:

1. The Problem: The "Crowd" vs. The "Individual"

Previously, to study the "road signs" (epigenetics) inside a cell, scientists needed thousands of cells to get a clear signal. It was like trying to hear a whisper by shouting a crowd together. If you wanted to study rare cells (like a specific type of stem cell or a patient's tumor cell), you often didn't have enough of them to run these tests. Also, most methods couldn't look at the "people" (RNA) and the "road signs" (epigenetics) in the exact same cell at the same time.

2. The Solution: The "OneCell" Detective

The researchers built a new method called OneCell CUT&Tag. Think of this as a high-tech detective kit that can enter a single cell and take three different photos at once:

• Photo A: What genes are currently active? (The Transcriptome/RNA)
• Photo B: What are the "switches" on the DNA doing? Are they turned on or off? (The Epigenome/Histone marks)
• Photo C: What "uniform" is the cell wearing on its surface? (Surface markers)

The Magic: They can do this starting with just one single cell. They don't need a crowd. They can even do this on frozen tissue samples, like taking a photo of a crime scene that happened years ago.

3. The Experiment: The Mammary Gland "City"

To test their new detective kit, they looked at the mammary gland (breast tissue) in mice. This tissue has two main neighborhoods:

• The Basal Neighborhood: The "foundation" cells (like the construction workers).
• The Luminal Neighborhood: The "functional" cells that produce milk (like the delivery drivers).

Scientists knew that under certain conditions, the "construction workers" (Basal cells) could transform into "delivery drivers" (Luminal cells). But they didn't know how that transformation happened step-by-step.

4. The Big Discovery: The "Secret Training"

Using OneCell, they found something amazing that previous methods missed:

• The "Hidden" Priming: They found that some "construction workers" (Basal cells) were secretly wearing "delivery driver" road signs (epigenetic marks) before they actually changed their uniforms or started delivering milk.
  • Analogy: Imagine a construction worker who is still wearing their hard hat and boots, but their DNA has already put up "Delivery Driver" signs in their house. They haven't changed jobs yet, but they are primed and ready to switch instantly if needed.
• The "Binary Switch" vs. The "Slow Fade":
  • When they looked at the genes (the RNA), the cells seemed to switch jobs instantly. One minute they are a worker, the next they are a driver. It was a binary switch (On/Off).
  • But when they looked at the road signs (the Epigenome), the change was a slow, continuous slide. The cells were slowly reorganizing their internal map before they ever changed their job title.

5. Why This Matters

This is like discovering that a student isn't just "bad at math" or "good at math." You realize they have been studying math in secret for months (epigenetic priming) before they finally got an A on the test (gene expression).

In summary:
This paper gives us a new microscope that lets us see the secret preparation cells do before they change who they are. It shows that cells don't just flip a switch; they slowly rewire their internal instructions (epigenetics) first, which allows them to adapt quickly to new situations, like healing a wound or, unfortunately, helping cancer grow.

Because this tool works on single cells and rare samples, it opens the door to studying difficult-to-get samples, like tiny tumors from patients or early embryos, helping us understand life at its most fundamental, individual level.

Technical Summary

1. Problem Statement

Single-cell multi-omics methods have revolutionized the understanding of gene regulation by allowing simultaneous mapping of chromatin states and transcriptomes. However, existing technologies face significant limitations:

• High Input Requirements: Most current methods (e.g., droplet-based or combinatorial indexing) require thousands to hundreds of thousands of starting cells, making them unsuitable for rare cell populations, in vivo samples, or patient-derived material.
• Lack of True Single-Cell Resolution: Many low-input methods profile chromatin and RNA from different cells or require bioinformatic aggregation into "metacells" to achieve sufficient coverage, obscuring cell-to-cell heterogeneity.
• Incomplete Multi-modal Data: Few methods successfully integrate histone modification profiling, full-length transcriptomics, and surface marker quantification from the same individual cell without intermediate pooling.

2. Methodology: OneCell CUT&Tag

The authors introduce OneCell CUT&Tag, a user-friendly, plate-based workflow designed to profile matched epigenomes, transcriptomes, and surface markers from as few as one cell.

• Workflow Overview:

  1. Single-Cell Isolation: Cells are sorted individually (via FACS based on surface markers) or pipetted directly into 384-well plates.
  2. Cytoplasmic Split: Upon lysis, a fraction of cytoplasmic mRNA is transferred to a "mirror plate" for transcriptomic library preparation using an adapted FLASH-seq protocol (generating full-length mRNA libraries).
  3. Nuclear Processing: The remaining nuclei undergo CUT&Tag (Cleavage Under Targets and Tagmentation) using protein A-Tn5 fused to specific antibodies (e.g., H3K27me3, H3K4me1).
  4. Library Prep: Both RNA and DNA libraries are prepared in parallel within the same well, avoiding any transitory pooling of cells.
  5. Automation: The workflow has been adapted for liquid-handling robots (I.DOT and VIAFLO 384), increasing throughput and robustness while reducing hands-on time.

• Key Technical Optimizations:

  • Fine-tuned lysis buffers to maintain chromatin integrity while preserving cytoplasmic mRNA.
  • Use of carboxylic magnetic beads for serial solution exchange without material loss.
  • Optimization of NP-40 detergent concentration to ensure efficient nuclei lysis without DNA loss.

3. Key Contributions

• Low-Input Multi-omics: The first method to provide matched, high-resolution epigenomic, transcriptomic, and surface marker data from a single cell (input $\ge$ 1 cell).
• High Coverage: Unlike droplet methods that produce sparse data requiring pseudo-bulk analysis, OneCell generates high-coverage data (median ~26k unique reads/cell for H3K27me3) allowing for the resolution of single-cell coverage tracks at 1-Mb genomic resolution.
• Versatility: Successfully applied to diverse sample types, including cell lines (MDA-MB-468), fresh and frozen mouse tissues, human patient-derived xenografts (TNBC), and mouse zygotes.

4. Key Results

A. Benchmarking and Performance

• Comparison: OneCell was benchmarked against sortChIC, 10X droplet scCUT&Tag, and scChIP-seq using the MDA-MB-468 cell line.
• Metrics: OneCell achieved a median of 26,008 unique reads/cell and a FrIP (Fraction of Reads in Peaks) of 0.77, comparable to sortChIC but with significantly lower input requirements. It outperformed droplet methods, which yielded sparse data (~5k reads/cell).
• Correlation: High correlation was observed between OneCell data and bulk references, validating its accuracy.

B. Application to Mouse Zygotes (Early Development)

• Zygotic Reprogramming: The method was applied to single mouse zygotes to map H3K27me3 (repressive) and H3K4me1 (permissive) alongside transcriptomes.
• Findings:
  • H3K4me1: Accumulated genome-wide without clear promoter/enhancer specificity, showing no correlation with gene expression.
  • H3K27me3: Formed large megabase-scale domains and acted as a specific negative regulator of a subset of genes.
  • Conclusion: At the zygote stage, genes appear poised for activation, with H3K27me3 serving as the primary repressive mechanism modulating transcription.

C. Epigenomic Priming in the Mammary Gland

• Cell Fate Mapping: Profiling of the mouse mammary gland epithelium (Basal, Luminal ER-, Luminal ER+).
• Discordance Discovery: While RNA and cytometry profiles aligned perfectly, a subset of cells classified as Basal by RNA/cytometry exhibited Luminal-like epigenomes (specifically H3K4me1 enrichment and H3K27me3 depletion at luminal loci).
• Priming: This "epigenomic priming" was undetectable at the RNA or protein level, suggesting that basal cells possess a latent epigenetic "ready state" for luminal differentiation, likely mediated by H3K4me1.
• MOFA Analysis: Multi-Omics Factor Analysis revealed that specific latent factors (e.g., Factor 7) integrated RNA and epigenomic data to define basal cell fate, while others (Factor 2) captured epigenomic-only variations related to stemness factors (e.g., Zfx, Trp63).

D. Dynamics of Basal-to-Luminal Transdifferentiation

• In Vivo Model: Basal cells were transplanted into cleared fat pads of immunodeficient mice to induce regeneration in the absence of luminal cells.
• Continuous vs. Binary:
  • Epigenome: Showed a continuous progression from basal to luminal states, revealing a transitional cell population with intermediate epigenomic profiles absent in reference populations.
  • Transcriptome: Exhibited a binary switch between basal and luminal identities.
• Transitioning Cells: Cells in the intermediate epigenomic state showed:
  • Upregulation of stemness drivers (e.g., Axl).
  • Downregulation of TNF$\alpha$ and p53 signaling (suggesting temporary suppression of p53 facilitates plasticity).
  • Increased proliferation.

5. Significance

• Decoupling Regulatory Layers: OneCell demonstrates that epigenomic remodeling often precedes and poises cells for fate transitions, occurring before detectable changes in the transcriptome. This challenges the view of cell fate transitions as purely binary switches.
• Rare Cell Analysis: The ability to profile single cells from low-input, patient-derived, or rare in vivo samples opens new avenues for studying developmental biology, cancer heterogeneity, and drug resistance mechanisms that were previously inaccessible due to input constraints.
• Mechanistic Insight: By linking surface markers, chromatin states, and gene expression in the same cell, the study elucidates the molecular mechanisms (e.g., H3K27me3 repression, H3K4me1 priming) driving cellular plasticity and tissue regeneration.

In summary, OneCell CUT&Tag is a transformative tool that bridges the gap between low-input feasibility and high-resolution multi-omic profiling, revealing hidden layers of cellular identity and plasticity in complex biological systems.