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1. scifi-ATAC-seq: massive-scale single-cell chromatin accessibility sequencing using combinatorial fluidic indexing

   https://doi.org/10.1186/s13059-024-03235-5  

   Zhang, Xuan; Marand, Alexandre P.; Yan, Haidong; Schmitz, Robert J. (December 2024, Genome Biology)

   Abstract Single-cell ATAC-seq has emerged as a powerful approach for revealing candidate cis-regulatory elements genome-wide at cell-type resolution. However, current single-cell methods suffer from limited throughput and high costs. Here, we present a novel technique called scifi-ATAC-seq, single-cell combinatorial fluidic indexing ATAC-sequencing, which combines a barcoded Tn5 pre-indexing step with droplet-based single-cell ATAC-seq using the 10X Genomics platform. With scifi-ATAC-seq, up to 200,000 nuclei across multiple samples can be indexed in a single emulsion reaction, representing an approximately 20-fold increase in throughput compared to the standard 10X Genomics workflow. 

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2. Intrinsic bias estimation for improved analysis of bulk and single-cell chromatin accessibility profiles using SELMA

   https://doi.org/10.1038/s41467-022-33194-z  

   Hu, Shengen Shawn; Liu, Lin; Li, Qi; Ma, Wenjing; Guertin, Michael J.; Meyer, Clifford A.; Deng, Ke; Zhang, Tingting; Zang, Chongzhi (December 2022, Nature Communications)

   Abstract Genome-wide profiling of chromatin accessibility by DNase-seq or ATAC-seq has been widely used to identify regulatory DNA elements and transcription factor binding sites. However, enzymatic DNA cleavage exhibits intrinsic sequence biases that confound chromatin accessibility profiling data analysis. Existing computational tools are limited in their ability to account for such intrinsic biases and not designed for analyzing single-cell data. Here, we present Simplex Encoded Linear Model for Accessible Chromatin (SELMA), a computational method for systematic estimation of intrinsic cleavage biases from genomic chromatin accessibility profiling data. We demonstrate that SELMA yields accurate and robust bias estimation from both bulk and single-cell DNase-seq and ATAC-seq data. SELMA can utilize internal mitochondrial DNA data to improve bias estimation. We show that transcription factor binding inference from DNase footprints can be improved by incorporating estimated biases using SELMA. Furthermore, we show strong effects of intrinsic biases in single-cell ATAC-seq data, and develop the first single-cell ATAC-seq intrinsic bias correction model to improve cell clustering. SELMA can enhance the performance of existing bioinformatics tools and improve the analysis of both bulk and single-cell chromatin accessibility sequencing data. 

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3. scMC learns biological variation through the alignment of multiple single-cell genomics datasets

   https://doi.org/10.1186/s13059-020-02238-2  

   Zhang, Lihua; Nie, Qing (December 2021, Genome Biology)

   null (Ed.)

   Abstract Distinguishing biological from technical variation is crucial when integrating and comparing single-cell genomics datasets across different experiments. Existing methods lack the capability in explicitly distinguishing these two variations, often leading to the removal of both variations. Here, we present an integration method scMC to remove the technical variation while preserving the intrinsic biological variation. scMC learns biological variation via variance analysis to subtract technical variation inferred in an unsupervised manner. Application of scMC to both simulated and real datasets from single-cell RNA-seq and ATAC-seq experiments demonstrates its capability of detecting context-shared and context-specific biological signals via accurate alignment. 

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4. Multilocus basis of incipient reproductive isolation in hybridizing populations is revealed by pangenomic and epigenetic divergence

   Ruggieri, A A; Cicconardi, F; Bellin, N; Montgomery, S H; Mallet, J; Van_Belleghem, S M; McMillan, W O; Counterman, B A; Papa, R (August 2026, NCBI)

   ATAC-seq data are available through NCBI BioProject at www.ncbi.nlm.nih.gov/bioproject/PRJNA795145, and 10x data are available at www.ncbi.nlm.nih.gov/bioproject/PRJNA1240918. 

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5. Predicting transcription factor binding in single cells through deep learning

   https://doi.org/10.1126/sciadv.aba9031  

   Fu, Laiyi; Zhang, Lihua; Dollinger, Emmanuel; Peng, Qinke; Nie, Qing; Xie, Xiaohui (December 2020, Science Advances)

   null (Ed.)

   Characterizing genome-wide binding profiles of transcription factors (TFs) is essential for understanding biological processes. Although techniques have been developed to assess binding profiles within a population of cells, determining them at a single-cell level remains elusive. Here, we report scFAN (single-cell factor analysis network), a deep learning model that predicts genome-wide TF binding profiles in individual cells. scFAN is pretrained on genome-wide bulk assay for transposase-accessible chromatin sequencing (ATAC-seq), DNA sequence, and chromatin immunoprecipitation sequencing (ChIP-seq) data and uses single-cell ATAC-seq to predict TF binding in individual cells. We demonstrate the efficacy of scFAN by both studying sequence motifs enriched within predicted binding peaks and using predicted TFs for discovering cell types. We develop a new metric “TF activity score” to characterize each cell and show that activity scores can reliably capture cell identities. scFAN allows us to discover and study cellular identities and heterogeneity based on chromatin accessibility profiles. 

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