Single-Cell DNA Sequencing

CD Genomics provides comprehensive whole genome amplification to discover of DNA mutations in single cells.

The Introduction of Single-Cell Sequencing

Random, low-abundance mutations in the genome of somatic cells are difficult to be qualified and characterized. To circumvent this problem and simultaneously account for the mutational heterogeneity within tissues, sequencing of a representative number of single cells is applied.

Whole genome amplification (WGA) technology has emerged as a tool to perform assessment DNA mutations in single, or a limited number of cells. To enable the discovery of true variation in single cells, the WGA of single-copy genomic DNA starting from a single cell is optimized to obtain sufficient amounts of DNA for sequencing. Faced with the problem of limited or insufficient DNA quantity for various downstream analysis, amplified DNA can be directly used for sequencing.

Various WGA techniques have been developed. We provide services that accurately copy the original source DNA with the lowest false positive rate and validated protocol well-suited for detection of copy number variations and single nucleotide variations across the genome from a single cell after whole genome amplification.

Applications:

• to discover of CNVs and single nucleotide variations (SNVs) across genome in single cells or ultra-low input
• to obtain a base-by-base view of a exome at single cell resolution

Single-Cell Sequencing Workflow

The advent of cell sorting/partitioning technologies, such as flow cytometry and microfluidic, has made it possible to capture single cells, and WGA is optimized to providing highly yields of whole genomic DNA for sequencing. The general workflow for single-cell DNA sequencing is outlined below.

Service Specifications

	Sample requirements 96-well plates of live cells suspension are recommended for this procedure. Frozen cells in specific frozen lysates can also be used as starting material. >1000 pg extracted DNA.
	Sequencing HiSeq X Ten Depth of coverage ≥ 30x More than 85% of bases with a ≥Q30 quality score
	Bioinformatics Analysis Raw data quality control Alignment with reference genome SNP/InDel/SV/CNV calling Annotation and statistics Advanced analysis: monogenic disorders, complex/multifactorial disorders, and cancer

Deliverables

• The original sequencing data
• Experimental results
• Data analysis report
• Details in Single-Cell Sequencing for your writing (customization)

CD Genomics’ Single-Cell DNA Sequencing conference focuses on the links between cell variation in tissues and organ function and further elucidates the origins of diseases. If you have additional requirements or questions, please feel free to contact us.

Reference
Deleye, L et al. Performance of four modern whole genome amplification methods for copy number variant detection in single cells. Scientific reports, 2017 Jun 13; 7(1): 3422.

Dong, X et al. Accurate identification of single nucleotide variants in whole genome amplified single cells. Natare Methods. 2017 May; 14(5): 491–493.

i. MDA（Multiple Displacement Amplification）

Invented by the Laskin et al. in 2001. Reacted using random six polymer primers and φ29 DNA polymerase, which had strong chain replacement properties and could amplify the DNA fragment of 50~100kb under isothermal conditions. At the same time, because of its 3 ‘-5’ exonuclease activity and proofreading activity, the φ29 DNA polymerase has high fidelity. The MDA method has a higher genome coverage.

ii. MALBAC（Multiple Annealing and Looping–Based Amplification Cycles）

The Quasilinear amplification process reduces the sequence preference of exponential amplification. The 5 ‘of amplified primers containing the common sequence of 27bp and 3’ is a random sequence of 8bp, which can be combined with the template at low temperature at 15~20 C, and then amplify these ring-shaped amplicons after the quasilinear amplification of 8~12 cycles.

The advantage of MALBAC method is that sequence preference is repeatable between different cells. Because of its better homogeneity of amplification, its data is more suitable for CNV analysis. The weakness of MALBAC is that the fidelity of polymerase it used is not as good as φ29 DNA polymerase, so MALBAC will have more false positives when detecting SNV; in addition, because of its repeatability sequence preference, the region of low amplification in the genome is sometimes lost in the process of amplification.