Single-Cell-Based Platform for Copy Number Variation Profiling through Digital Counting of Amplified Genomic DNA Fragments

Recent advances and application of whole genome amplification in molecular diagnosis and medicine

Whole genome amplification (WGA) is a technology for non-selective amplification of the whole genome sequence, first appearing in 1992. Its primary purpose is to amplify and reflect the whole genome of trace tissues and single cells without sequence bias and to provide sufficient DNA template for subsequent multigene and multilocus analysis, along with comprehensive genome research. WGA provides a method to obtain a large amount of genetic information from a small amount of DNA and provides a valuable tool for preserving limited samples in molecular biology. WGA technology is especially suitable for forensic identification and genetic disease research, along with new technologies such as next-generation sequencing (NGS). In addition, WGA is also widely used in single-cell sequencing. Due to the small amount of DNA in a single cell, it is often unable to meet the amount of samples needed for sequencing, so WGA is generally used to achieve the amplification of trace samples. This paper reviews WGA methods based on different principles, summarizes both amplification principle and amplification quality, and discusses the application prospects and challenges of WGA technology in molecular diagnosis and medicine.

Single-Cell Genomics-Based Molecular Algorithm for Early Cancer Detection

As one of the prime applications of liquid biopsy, the detection of tumor-derived whole cells and molecular markers is enabled in a noninvasive means before symptoms or hints from imaging procedures used for cancer screening. However, liquid biopsy is not a diagnostic test of malignant diseases per se because it fails to establish a definitive cancer diagnosis. Although single-cell genomics provides a genome-wide genetic alternation landscape, it is technologically challenging to confirm cell malignancy of a suspicious cell in body fluids due to unknown technical noise of single-cell sequencing and genomic variation among cancer cells, especially when tumor tissues are unavailable for sequencing as the reference. To address this challenge, we report a molecular algorithm, named scCancerDx, for confirming cell malignancy based on single-cell copy number alternation profiles of suspicious cells from body fluids, leading to a definitive cancer diagnosis. The scCancerDx algorithm has been trained with normal cells and cancer cell lines and validated with single tumor cells disassociated from clinical samples. The established scCancerDx algorithm then validates hexokinase 2 (HK2) as an efficient metabolic function-associated marker of identifying disseminated tumor cells in different body fluids across many cancer types. The HK2-based test, together with scCancerDx, has been investigated for the early detection of bladder cancer (BC) at a preclinical phase by detecting high glycolytic HK2^high tumor cells in urine. Early BC detection improves patient prognosis and avoids radical resection for enhancing life quality.

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

Whole genome amplification (WGA) has laid the foundation for investigating complex genomic alteration with single-cell or even single-molecule resolution. Coupled with sequencing-based copy number variation (CNV) analysis, it promotes understanding of the nature of commonly existing genetic heterogeneity by constructing the sequencing profiles for every single cell. However, prevailing methods only provide insights into limited aspects due to their intrinsic technical challenges. Their output data, as a result, fails to render comprehensive information (which is) concerned. Here, we describe the CNV detection analysis based on micro-channel multiple displacement amplification (μcMDA), a protocol able to provide optimized amplification uniformity while inheriting the advantages of MDA chemistry. We demonstrate the analysis of both the normal diploid YH-1 cell line and the aneuploid K562 cancer cell line. In the detection of simulated CNVs ranging from 300 kb to 2 Mb, μcMDA can respectively increase the detection rates of copy number loss and gain by 28.8% and 40.2% on average, using only 0.2× sequencing data. When detecting the inherent CNVs in tumor cells, the resolution of CNV recognition can be improved to 250 kb. Starting from either superabundant template copies or minute single-cell-level input, this easily accessible approach is capable of providing quantitatively reliable coverage as well as more robust GC-content regression for CNV detection.