Abstract
Copy number variations (CNVs), a common genomic mutation associated with various diseases, are important in research and clinical applications. Whole genome amplification (WGA) and massively parallel sequencing have been applied to single cell CNVs analysis, which provides new insight for the fields of biology and medicine. However, the WGA-induced bias significantly limits sensitivity and specificity for CNVs detection. Addressing these limitations, we developed a practical bioinformatic methodology for CNVs detection at the single cell level using low coverage massively parallel sequencing. This method consists of GC correction for WGA-induced bias removal, binary segmentation algorithm for locating CNVs breakpoints, and dynamic threshold determination for final signals filtering. Afterwards, we evaluated our method with seven test samples using low coverage sequencing (4∼9.5%). Four single-cell samples from peripheral blood, whose karyotypes were confirmed by whole genome sequencing analysis, were acquired. Three other test samples derived from blastocysts whose karyotypes were confirmed by SNP-array analysis were also recruited. The detection results for CNVs of larger than 1 Mb were highly consistent with confirmed results reaching 99.63% sensitivity and 97.71% specificity at base-pair level. Our study demonstrates the potential to overcome WGA-bias and to detect CNVs (>1 Mb) at the single cell level through low coverage massively parallel sequencing. It highlights the potential for CNVs research on single cells or limited DNA samples and may prove as a promising tool for research and clinical applications, such as pre-implantation genetic diagnosis/screening, fetal nucleated red blood cells research and cancer heterogeneity analysis.
Highlights
Copy number variations (CNVs) are known to be associated with various diseases, such as 22q11.2 deletion/duplication syndrome [1,2,3], Cri-du-Chat (5p deletion) [4] and even cancers [5]
We developed a Whole genome amplification (WGA)-induced bias correction strategy based on GC bias description using two single cells isolated from the peripheral blood (PB) of YH, a healthy Chinese individual with a normal karyotype
In order to observe and correct GC biases in WGA, we isolated, amplified, and sequenced two single cells from the PB of YH, a healthy Chinese individual with normal karyotype [24], generating an average of 13.04 million single-end (SE) 50 bp reads of each cell (Table 1).We defined the quotient between the reads number of each observation window and the average reads number as relative read number (RRN), which ideally would be equal to one in diploid genome
Summary
Copy number variations (CNVs) are known to be associated with various diseases, such as 22q11.2 deletion/duplication syndrome [1,2,3], Cri-du-Chat (5p deletion) [4] and even cancers [5]. Rather than cell-population research, single cell analysis provides insights into research of disease aetiology and diagnosis. It is especially useful for cancer heterogeneity research, since it has been shown to detect a single-nucleotide mutation that could result in a kidney tumor [6]. For the clinical purposes, preimplantation genetic diagnosis/screening (PGD/PGS) was used for disease scanning since 1990s [9,10,11]. This application allows CNVs analysis of single cell isolated from polar body, blastomere or blastocyst. Single-cell analysis may open up new opportunities for noninvasive prenatal genetic diagnosis by only needing a single fetal nucleated red blood cell (NRBC) [12,13]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
