Modeling fragment counts improves single-cell ATAC-seq analysis

The transcription factor NF-κB orchestrates nucleosome remodeling during the primary response to Toll-like receptor 4 signaling

Inducible nucleosome remodeling at hundreds of latent enhancers and several promoters shapes the transcriptional response to Toll-like receptor 4 (TLR4) signaling in macrophages. We aimed to define the identities of the transcription factors that promote TLR-induced remodeling. An analysis strategy based on ATAC-seq and single-cell ATAC-seq that enriched for genomic regions most likely to undergo remodeling revealed that the transcription factor nuclear factor κB (NF-κB) bound to all high-confidence peaks marking remodeling during the primary response to the TLR4 ligand, lipid A. Deletion of NF-κB subunits RelA and c-Rel resulted in the loss of remodeling at high-confidence ATAC-seq peaks, and CRISPR-Cas9 mutagenesis of NF-κB-binding motifs impaired remodeling. Remodeling selectivity at defined regions was conferred by collaboration with other inducible factors, including IRF3- and MAP-kinase-induced factors. Thus, NF-κB is unique among TLR4-activated transcription factors in its broad contribution to inducible nucleosome remodeling, alongside its ability to activate poised enhancers and promoters assembled into open chromatin.

Integrating single-cell multimodal epigenomic data using 1D-convolutional neural networks

Recent experimental developments enable single-cell multimodal epigenomic profiling, which measures multiple histone modifications and chromatin accessibility within the same cell. Such parallel measurements provide exciting new opportunities to investigate how epigenomic modalities vary together across cell types and states. A pivotal step in using this type of data is integrating the epigenomic modalities to learn a unified representation of each cell, but existing approaches are not designed to model the unique nature of this data type. Our key insight is to model single-cell multimodal epigenome data as a multi-channel sequential signal. Based on this insight, we developed ConvNet-VAEs, a novel framework that uses 1D-convolutional variational autoencoders (VAEs) for single-cell multimodal epigenomic data integration. We evaluated ConvNet-VAEs on nano-CT and scNTT-seq data generated from juvenile mouse brain and human bone marrow. We found that ConvNet-VAEs can perform dimension reduction and batch correction better than previous architectures while using significantly fewer parameters. Furthermore, the performance gap between convolutional and fully-connected architectures increases with the number of modalities, and deeper convolutional architectures can increase performance while performance degrades for deeper fully-connected architectures. Our results indicate that convolutional autoencoders are a promising method for integrating current and future single-cell multimodal epigenomic datasets.

Disentanglement of single-cell data with biolord

Biolord is a deep generative method for disentangling single-cell multi-omic data to known and unknown attributes, including spatial, temporal and disease states, used to reveal the decoupled biological signatures over diverse single-cell modalities and biological systems. By virtually shifting cells across states, biolord generates experimentally inaccessible samples, outperforming state-of-the-art methods in predictions of cellular response to unseen drugs and genetic perturbations. Biolord is available at https://github.com/nitzanlab/biolord .