PATTY corrects open-chromatin bias for improved bulk and single-cell CUT&Tag profiling.

Area of Science:

• Epigenetics and Genomics
• Computational Biology
• Molecular Biology

Background:

• Epigenomic profiling is crucial for understanding gene regulation and chromatin biology.
• Cleavage Under Targets & Tagmentation (CUT&Tag) is a powerful technique for epigenomic profiling, applicable to low cell numbers and single cells.
• Tn5 transposase used in CUT&Tag introduces open-chromatin bias, complicating data analysis, especially in sparse single-cell datasets.

Purpose of the Study:

• To address the challenge of open-chromatin bias in CUT&Tag data.
• To develop a computational method for correcting this bias.
• To improve the accuracy and robustness of CUT&Tag data analysis, particularly for single-cell applications.

Main Methods:

• Developed PATTY (Propensity Analyzer for Tn5 Transposase Yielded bias), a computational method leveraging ATAC-seq data.
• Integrated transcriptomic and epigenomic data using machine learning and integrative modeling.
• Created a single-cell CUT&Tag analysis framework built upon PATTY.

Main Results:

• PATTY effectively corrects open-chromatin bias in CUT&Tag datasets, including those from high-salt protocols.
• Demonstrated accurate and robust detection of active (H3K27ac) and repressive (H3K27me3, H3K9me3) histone modification sites.
• Showed improved cell clustering in single-cell CUT&Tag analysis using bias-corrected data.

Conclusions:

• PATTY provides a comprehensive solution for open-chromatin bias in CUT&Tag data.
• The method enhances the reliability of epigenomic profiling, facilitating deeper insights into chromatin biology.
• PATTY lays the groundwork for bias correction in other Tn5-based high-throughput assays.