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Author: Lily
Introduction : The accessible chromatin regions () are enriched in human genome regulatory elements. Through studies of accessible chromatin regions, scientists have identified many genetic variants associated with human diseases. At present, a mature method for detecting open chromatin regions in a single cell is to use sequencing technology to perform single-cell assay (i.e. scATAC-seq) on transposase accessible chromatin using sequencing technology.
However, this method still faces the challenge: For data from scATAC-seq, it is still difficult to perform haplotype phasing or detect large-scale structural variations in it - such as insertions, deletions, duplications, inversions, and translocations - for data from scATAC-seq. These challenges can be solved by single-molecule long-read sequencing based on the third generation sequencing (TGS) platform.
In order to integrate the advantages of long-read sequencing into scATAC-seq, the team of Professor Tang Fuchu of the Center for Frontier Innovation in Biomedical Sciences, Peking University School of Life Sciences, developed a transposase-accessible chromatin single-cell assay method (scNanoATAC-seq) based on the nanopore sequencing platform. This method is a microplate-based scATAC-seq method that can be compatible with the TGS sequencing platform. The relevant research results were published in the journal Cell Research on October 11.
https://www.nature.com/articles/s41422-022-00730-x
Evaluation of scNanoATAC-seq
01
To evaluate the performance of scNanoATAC-seq in capturing chromatin accessibility in single cells, the research team tested the scNanoATAC-seq method in five human cell lines (GM12878, eHAP1, HEK293T, HFF-1 and K562) and human peripheral blood monocyte (PBMC). In this study, the median read length of the scNanoATAC-seq library ranges from 4000 bp to 4900 bp. The researchers used transcription start site (TSS) enrichment, fragment number and read-end fraction (FRIP) to evaluate the quality of data generated by the scNanoATAC-seq method. The scNanoATAC-seq read end of
GM12878 cells showed a strong enrichment pattern around the TSS and a footprint surrounding the CCCCTC binding factor (CTCF) binding site, similar to those in the NGS-based scATAC-seq data. The research team used coding candidate cis regulatory elements (cCRE) to analyze the peak distribution of GM12878 cells called from NGS-based scATAC-seq data and TGS-based SCNANOATAC-seq data. In the scNanoATAC-seq peak, the proportion of CTCF pure elements and distal enhancer-like features (dELS) was found to increase, while the proportion of promoter-like features (PLS) and proximal enhancer-like features (pELS) were relatively lower than that of NGS-based scATAC-seq, similar to the enrichment spectrum of gene region annotation. Consistent annotation results were found in peaks of K562 cells and HEK293T cells. When researchers used the cCRE set as a benchmark for regulatory elements to evaluate the accuracy and recall of peak calls, the performance between NGS-based scATAC-seq and TGS-based scNanoATAC-seq was comparable. The
research team conducted two batches of species mixing experiments using four equally mixed cell lines, including two human cell lines (K562 and GM12878) and two mouse cell lines (mESC and MEF). Of the two technical replicates, 2.2% (5 of 223) and 1.6% (4 of 248) single cells were identified as double cells, which is acceptable for single-cell chromatin accessibility sequencing.
Next, the researchers projected scNanoATAC-seq data from five human cell lines into uniform manifold approximation and projection (UMAP) space. Each cell line was well distinguished by unsupervised clustering without significant batch effects.
To estimate the optimal flux of single cells of scNanoATAC-seq, the researchers simulated single cell data by sampling the reads without replacing the pseudo-volume of each cell line to a specific number of reads per cell. 10,000 reads per cell (2,000 cells per sequencing run) is the maximum throughput of the scNanoATAC-seq method.
researchers found strong footprints at the binding sites of cell type-specific transcription factors, consistent with those in the 10×scATAC-seq data. To evaluate whether the scNanoATAC-seq method is suitable for in vivo samples, the investigators performed scNanoATAC-seq on sorted and unsorted PBMCs from individual donors. CD4 T cells, CD8 T cells, B cells, and monocytes were clearly identified in sorted and unclassified PBMCs without significant batch effects. To compare the peak call quality of scNanoATAC-seq data with the peak call quality of 10× scATAC-seq data, a cCREs-based benchmark was also performed on PBMC. test results show that scNanoATAC-seq performs well in identifying different cell populations and revealing key regulatory features for chromatin accessibility in each cell population.
A transposase-accessible chromatin single-cell assay method based on the nanopore sequencing platform (scNanoATAC-seq)—a microplate-based scATAC-seq method that is compatible with the TGS sequencing platform.
Research significance
02
scNanoATAC-seq is a long-read single-cell ATAC sequencing method based on the TGS platform, which can be applied in various biological fields. It can simultaneously detect chromatin accessibility and genetic variation in a single cell (including SV, SNPs and CNV). This study reveals allele-specific peaks (ASPs)—even if there is no heterozygous single nucleotide polymorphism in a certain peak. This is not possible for short-read scATAC-seq based on the NGS platform.
study provides direct evidence of commonality between adjacent peaks from scNanoATAC-seq, where chromatin accessibility at two sites in the same single cell and in fact chromatin accessibility on the same allele are simultaneously detected by long reads.
At the current sequencing depth, the cost of scNanoATAC-seq library is less than USD 2.5 per cell. Since the cost of ONT (Oxford Nanopore Technologies) sequencing at the same sequencing depth is still higher than that of the NGS platform, the sequencing method needs to be selected according to different needs - using more fragments to enrich the stronger epigenetic signals, or detecting long read specific features (such as structural variant SVs) through long reads etc.
Reference:
https://www.nature.com/articles/s41422-022-00730-x
Note: This article aims to introduce the progress of medical research and cannot be used as a reference for treatment plans. If you need health guidance, please go to a regular hospital for treatment.
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