Researchers at the University of Pittsburgh have developed an innovative framework called HALO that reveals how changes in gene activity and chromatin structure interact over time. While open chromatin often signals active transcription, these two processes don’t always move in lockstep. HALO helps scientists understand when and how they do.

The main framework of HALO

A Top: The causal diagram of individual gene expression and its corresponding peaks, in both coupled (left) and decoupled (right) cases. Bottom: the causal diagram of relations between scRNA-seq and scATAC-seq data on the representation level, for decoupled (left) and coupled (right) cases. θ are functional parameters as a function of time for RNA (R) or ATAC (A) data, as well as coupled (c) or decoupled (d). B Architecture for representation learning within a causal regularized variational autoencoder (VAE) framework. From the jointly profiled scRNA-seq (blue) and scATAC-seq (red) data, latent representations are learned and can then be used via interpretable decoder to determine important genes and peaks. For the ATAC modality, the latent Z^A is divided into , representing the decoupled and coupled latent representations, respectively. Similarly, the RNA modality’s latent representations comprise  (decoupled) and  (coupled). The decoupled representations  and  adhere to the decoupled causal constraints Δ_decouple, whereas the coupled representations,  conform to the coupled causal constraints Δ_couple. C Illustration of the gene-peak level analysis process. Initially, genes and ATAC peaks within specified proximities are matched using non-negative binomial regression, linking gene expression to neighboring ATAC peaks. Subsequently, we compute decouple and couple scores to categorize gene-peaks as either decoupled or coupled. Finally, we employ the Granger causality test to identify distal regulatory relationships between peaks and genes, uncovering potential mechanisms of genetic regulation. 

By using a causal modeling approach, HALO separates gene expression and chromatin accessibility data into two parts, those that are coupled, meaning they change together, and those that are decoupled, meaning they vary independently. This allows scientists to see how genes and their regulatory regions communicate dynamically across time.

HALO not only highlights shared biological functions between different molecular layers but also identifies epigenetic factors that guide how cells specialize. Importantly, it uncovers time-dependent regulatory interactions that play roles in cellular differentiation and disease development. By applying this framework to single-cell RNA sequencing and chromatin accessibility data, researchers gain a clearer picture of how molecular events drive health and disease at the cellular level.

Availability – The code used to develop the model, perform the analyses and generate results in this study is publicly available and has been deposited in GitHub at https://github.com/benoslab/HALO