Quantitative comparison of single-cell sequencing methods using hippocampal neurons

Single-cell genomic analysis has grown rapidly in recent years and will find widespread applications in various fields of biology, including cancer biology, development, immunology, pre-implantation genetic diagnosis, and neurobiology. In this study, we amplified genomic DNA from individual hippocampal neurons using one of three single-cell DNA amplification methods (multiple annealing and looping-based amplification cycles (MALBAC), multiple displacement amplification (MDA), and GenomePlex whole genome amplification (WGA4)). We then systematically evaluated the genome coverage, GC-bias, reproducibility, and copy number variations among individual neurons. Our results showed that single-cell genome sequencing results obtained from the MALBAC and WGA4 methods are highly reproducible and have a high success rate. Chromosome-level and subchromosomal-level copy number variations among individual neurons can be detected.

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Accurate and sensitive single-cell-level detection of copy number variations by micro-channel multiple displacement amplification (μcMDA)

Nanoscale ◽

10.1039/c8nr04917c ◽

2018 ◽

Vol 10 (37) ◽

pp. 17933-17941 ◽

Cited By ~ 3

Author(s):

Junji Li ◽

Na Lu ◽

Yuhan Tao ◽

Mengqin Duan ◽

Yi Qiao ◽

...

Keyword(s):

Single Cell ◽

Copy Number ◽

High Performance ◽

Multiple Displacement Amplification ◽

Copy Number Variations ◽

Reaction Vessel ◽

Single Cell Level ◽

Micro Channel ◽

Cell Level ◽

Cnv Detection

An improved multiple displacement amplification (MDA) approach realized by compressing the geometry of the reaction vessel exhibits high performance for single-cell-level CNV detection.

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Uniform and accurate single-cell sequencing based on emulsion whole-genome amplification

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1513988112 ◽

2015 ◽

Vol 112 (38) ◽

pp. 11923-11928 ◽

Cited By ~ 122

Author(s):

Yusi Fu ◽

Chunmei Li ◽

Sijia Lu ◽

Wenxiong Zhou ◽

Fuchou Tang ◽

...

Keyword(s):

Single Cell ◽

Whole Genome Amplification ◽

Multiple Displacement Amplification ◽

Simultaneous Detection ◽

Dna Amplification ◽

Copy Number Variations ◽

Whole Genome ◽

Genome Amplification ◽

High Uniformity

Whole-genome amplification (WGA) for next-generation sequencing has seen wide applications in biology and medicine when characterization of the genome of a single cell is required. High uniformity and fidelity of WGA is needed to accurately determine genomic variations, such as copy number variations (CNVs) and single-nucleotide variations (SNVs). Prevailing WGA methods have been limited by fluctuation of the amplification yield along the genome, as well as false-positive and -negative errors for SNV identification. Here, we report emulsion WGA (eWGA) to overcome these problems. We divide single-cell genomic DNA into a large number (105) of picoliter aqueous droplets in oil. Containing only a few DNA fragments, each droplet is led to reach saturation of DNA amplification before demulsification such that the differences in amplification gain among the fragments are minimized. We demonstrate the proof-of-principle of eWGA with multiple displacement amplification (MDA), a popular WGA method. This easy-to-operate approach enables simultaneous detection of CNVs and SNVs in an individual human cell, exhibiting significantly improved amplification evenness and accuracy.

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Comprehensive Analysis of Copy Number Variations in Kidney Cancer by Single-Cell Exome Sequencing

Frontiers in Genetics ◽

10.3389/fgene.2019.01379 ◽

2020 ◽

Vol 10 ◽

Cited By ~ 1

Author(s):

Wenyang Zhou ◽

Fan Yang ◽

Zhaochun Xu ◽

Meng Luo ◽

Pingping Wang ◽

...

Keyword(s):

Single Cell ◽

Exome Sequencing ◽

Kidney Cancer ◽

Copy Number ◽

Copy Number Variations ◽

Comprehensive Analysis

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Integrated Computational Pipeline for Single-Cell Genomic Profiling

JCO Clinical Cancer Informatics ◽

10.1200/cci.19.00171 ◽

2020 ◽

pp. 464-471 ◽

Cited By ~ 1

Author(s):

Lubomir Chorbadjiev ◽

Jude Kendall ◽

Joan Alexander ◽

Viacheslav Zhygulin ◽

Junyan Song ◽

...

Keyword(s):

Single Cell ◽

Open Source ◽

Copy Number ◽

Cancer Diagnostics ◽

Massachusetts Institute Of Technology ◽

Computational Pipeline ◽

Single Cell Genomics ◽

Single Nucleus ◽

Institute Of Technology ◽

Cell Genome

PURPOSE Copy-number profiling of multiple individual cells from sparse sequencing may be used to reveal a detailed picture of genomic heterogeneity and clonal organization in a tissue biopsy specimen. We sought to provide a comprehensive computational pipeline for single-cell genomics, to facilitate adoption of this molecular technology for basic and translational research. MATERIALS AND METHODS The pipeline comprises software tools programmed in Python and in R and depends on Bowtie, HISAT2, Matplotlib, and Qt. It is installed and used with Anaconda. RESULTS Here we describe a complete pipeline for sparse single-cell genomic data, encompassing all steps of single-nucleus DNA copy-number profiling, from raw sequence processing to clonal structure analysis and visualization. For the latter, a specialized graphical user interface termed the single-cell genome viewer (SCGV) is provided. With applications to cancer diagnostics in mind, the SCGV allows for zooming and linkage to the University of California at Santa Cruz Genome Browser from each of the multiple integrated views of single-cell copy-number profiles. The latter can be organized by clonal substructure or by any of the associated metadata such as anatomic location and histologic characterization. CONCLUSION The pipeline is available as open-source software for Linux and OS X. Its modular structure, extensive documentation, and ease of deployment using Anaconda facilitate its adoption by researchers and practitioners of single-cell genomics. With open-source availability and Massachusetts Institute of Technology licensing, it provides a basis for additional development by the cancer bioinformatics community.

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