Changyue Chen1, Jing Li1, JueFeng Wan2,3, Yuan Lu1, Zhen Zhang2,3, ZengHui Xu4,5. 1. Department of Medical Research, Shanghai MajorMed Diagnostics Company, Shanghai, China. 2. Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China. 3. Department of Oncology, Fudan University Shanghai Medical College, Shanghai, China. 4. Laboratory of Gene and Viral Therapy, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University of Chinese PLA, Shanghai, China. 5. ShangHai Cell Therapy Group Company, Shanghai, China.
Abstract
BACKGROUND: Single-cell whole-genome sequencing provides novel insights into the nature of genetic heterogeneity in normal and diseased cells. However, amplification of formalin-fixed tissues with low cell numbers is still problematic and multiple annealing, and looping-based amplification cycles (MALBAC) is a commonly used whole-genome amplification (WGA) method with low cell numbers. METHODS: We developed a low-input tailing method to evaluate the MALBAC-based WGA from sub-nanogram or less quantities of input DNA. The tailing method uses 2100 BioAnalyzer to evaluate the size distribution of MALBAC products, and comparing the tailing with 10380 bp. RESULTS: Compared with a 22 loci qPCR panel, the tailing method provided a similar WGA evaluation efficiency in 13 samples on one set of study, with lower input, cheaper cost, shorter manual time, and a clear filtering cut off. Later, we demonstrated a strong correlation between tailing size and coverage breadth in another 29 samples on two sets of assays. As a result, the tailing method showed that it could predict whether a sequence breadth achieved 70% or not with 100% accuracy on these three sets of assays. Although further studies are needed, this tailing method is expected to be used as an excellent tool to select high-quality WGA products before library construction. CONCLUSIONS: Our tailing method can provide a new WGA quality test to evaluate the WGA efficiency with 100% accuracy (42/42). Compared with qPCR panel, our tailing method needs lower input, cheaper cost, shorter manual time, a clear filtering cut off, and extendable high throughput as well as the same sensitivity.
BACKGROUND: Single-cell whole-genome sequencing provides novel insights into the nature of genetic heterogeneity in normal and diseased cells. However, amplification of formalin-fixed tissues with low cell numbers is still problematic and multiple annealing, and looping-based amplification cycles (MALBAC) is a commonly used whole-genome amplification (WGA) method with low cell numbers. METHODS: We developed a low-input tailing method to evaluate the MALBAC-based WGA from sub-nanogram or less quantities of input DNA. The tailing method uses 2100 BioAnalyzer to evaluate the size distribution of MALBAC products, and comparing the tailing with 10380 bp. RESULTS: Compared with a 22 loci qPCR panel, the tailing method provided a similar WGA evaluation efficiency in 13 samples on one set of study, with lower input, cheaper cost, shorter manual time, and a clear filtering cut off. Later, we demonstrated a strong correlation between tailing size and coverage breadth in another 29 samples on two sets of assays. As a result, the tailing method showed that it could predict whether a sequence breadth achieved 70% or not with 100% accuracy on these three sets of assays. Although further studies are needed, this tailing method is expected to be used as an excellent tool to select high-quality WGA products before library construction. CONCLUSIONS: Our tailing method can provide a new WGA quality test to evaluate the WGA efficiency with 100% accuracy (42/42). Compared with qPCR panel, our tailing method needs lower input, cheaper cost, shorter manual time, a clear filtering cut off, and extendable high throughput as well as the same sensitivity.
Authors: Maxwell A Sherman; Alison R Barton; Michael A Lodato; Carl Vitzthum; Michael E Coulter; Christopher A Walsh; Peter J Park Journal: Nucleic Acids Res Date: 2018-02-28 Impact factor: 16.971
Authors: Stephen Q Wong; Jason Li; Angela Y-C Tan; Ravikiran Vedururu; Jia-Min B Pang; Hongdo Do; Jason Ellul; Ken Doig; Anthony Bell; Grant A MacArthur; Stephen B Fox; David M Thomas; Andrew Fellowes; John P Parisot; Alexander Dobrovic Journal: BMC Med Genomics Date: 2014-05-13 Impact factor: 3.063