ChromaFold: Predicting 3D Contact Maps from Single-Cell Data

Understanding Chromatin Interactions Through Single-Cell Genomics

Unraveling the intricate 3D organization of the genome is crucial for deciphering gene regulation and interpreting the functional implications of non-coding genetic variants. Traditional chromosome conformation capture (3C) techniques, such as Hi-C and ChIA-PET, have provided valuable insights into chromatin interactions. However, these methods often require substantial input material, making them challenging to apply when studying rare cell populations.

Enter ChromaFold, a revolutionary deep learning model that can accurately predict 3D contact maps and regulatory interactions directly from single-cell ATAC sequencing (scATAC-seq) data. By leveraging the wealth of information encoded in the chromatin accessibility landscape, ChromaFold overcomes the limitations of existing technologies, enabling researchers to explore cell-type-specific chromatin interactions even in settings where 3C-based assays are infeasible.

Harnessing the Power of Single-Cell Chromatin Accessibility

Single-cell chromatin accessibility profiles, obtained through scATAC-seq, provide a window into the physical interactions between DNA and various nuclear molecules, including transcription factors, chromatin remodelers, and histones. This wealth of information, when properly harnessed, can unveil the intricate relationships between regulatory elements and their target genes.

The Key Advantages of ChromaFold:

1. Input Data Requirement: ChromaFold requires only scATAC-seq data as input, eliminating the need for additional experimental data, such as bulk ATAC-seq, DNA sequence, or CTCF ChIP-seq, which are often required by other predictive models.

2. Cell-Type Specificity: By training on paired scATAC-seq and Hi-C data from multiple human and mouse cell types, ChromaFold can accurately predict cell-type-specific 3D contact maps and regulatory interactions across diverse test cell populations.

3. Computational Efficiency: ChromaFold employs a lightweight neural network architecture, allowing it to be trained on standard GPUs, making it accessible to a wide range of researchers and laboratories.

4. Deconvolution Capabilities: By fine-tuning ChromaFold on paired scATAC-seq and Hi-C data from complex tissues, the model can deconvolve chromatin interactions across constituent cell subpopulations, providing valuable insights into cell-type-specific genome organization.

The ChromaFold Approach

At the heart of ChromaFold lies a deep learning model that leverages the inherent relationship between chromatin accessibility and 3D genome structure. The model takes three key input features:

1. Pseudobulk Chromatin Accessibility: Aggregated chromatin accessibility profiles across single cells, capturing the overall accessibility landscape.

2. Co-Accessibility Profiles: Measures of the correlation in accessibility between genomic regions, reflecting the likelihood of chromatin looping events.

3. Predicted CTCF Motif Tracks: An estimation of CTCF binding sites, given the crucial role of this architectural protein in shaping 3D chromatin structure.

The ChromaFold architecture consists of two feature extractors that process these inputs and a linear predictor that forecasts the 3D contact map between a focal genomic region and its neighboring bins. This lightweight design enables efficient training and deployment, even on standard computing hardware.

Benchmarking ChromaFold’s Performance

The researchers evaluated ChromaFold’s performance using both human and mouse test cell types, assessing its ability to predict the overall 3D contact map as well as significant chromatin interactions.

Key Findings:

1. Accurate 3D Contact Map Prediction: ChromaFold achieved an average distance-stratified Pearson correlation of 0.45-0.47 between the predicted and experimental contact maps in held-out cell types, demonstrating its ability to capture the spatial organization of the genome.

2. Significant Interaction Prediction: When identifying the top 10% of interactions based on their Z-score, ChromaFold attained an average area under the ROC curve (AUROC) of 0.77-0.79 in new cell types, showcasing its accuracy in pinpointing critical regulatory contacts.

3. Generalization Across Species: ChromaFold was able to generalize its predictive capabilities from human to mouse cell types, underscoring its robust and adaptable nature.

4. Improved Performance over Existing Methods: When compared to a recent deep learning method that utilizes bulk ATAC-seq, DNA sequence, and CTCF ChIP-seq, ChromaFold demonstrated superior performance in predicting cell-type-specific chromatin interactions, especially when CTCF ChIP-seq data was included as an input.

Deconvolving Chromatin Interactions in Complex Tissues

One of the remarkable capabilities of ChromaFold is its ability to deconvolve chromatin interactions in complex tissues, where multiple cell types coexist. By fine-tuning the model on paired scATAC-seq and Hi-C data from human pancreatic islets, the researchers were able to accurately resolve the cell-type-specific chromatin interactions of alpha cells and beta cells.

This powerful feature of ChromaFold enables researchers to unravel the intricate 3D genome organization within heterogeneous tissue samples, where traditional 3C-based techniques often fall short due to the dilution of cell-type-specific signals.

Unlocking the Potential of Single-Cell Genomics

ChromaFold’s ability to accurately predict 3D contact maps and regulatory interactions using scATAC-seq data alone represents a significant advancement in the field of single-cell genomics. By overcoming the limitations of existing 3C-based technologies, ChromaFold empowers researchers to explore the cell-type-specific organization of the genome, even in settings where obtaining sufficient input material for traditional experiments is challenging.

This innovative deep learning approach opens new avenues for understanding gene regulation, interpreting the functional impact of non-coding genetic variants, and investigating the role of chromatin architecture in cellular differentiation and disease progression. As single-cell genomics continues to evolve, tools like ChromaFold will be instrumental in unlocking the full potential of this transformative field.

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Key Takeaways

• Overcoming Experimental Limitations: ChromaFold’s ability to predict 3D contact maps and regulatory interactions from scATAC-seq data alone enables accurate exploration of chromatin architecture in rare cell populations and complex tissue samples.

• Accurate Predictions Across Cell Types and Species: ChromaFold demonstrated state-of-the-art performance in predicting cell-type-specific chromatin interactions, with the ability to generalize from human to mouse cell types.

• Efficient and Accessible Design: The lightweight architecture of ChromaFold allows for training on standard GPUs, making the tool widely accessible to researchers and laboratories.

• Deconvolution Capabilities: By fine-tuning on paired scATAC-seq and Hi-C data, ChromaFold can deconvolve cell-type-specific chromatin interactions within complex tissue samples, providing valuable insights into genome organization.

• Advancing Single-Cell Genomics: ChromaFold represents a significant step forward in leveraging the power of single-cell data to unravel the intricate 3D architecture of the genome, opening new possibilities for understanding gene regulation and disease mechanisms.