CD Genomics proprietary GenSeqTM Technology provides comprehensive Single-Cell Sequencing services. These global gene expression patterns in single cells already have dramatically advanced cell biology.
The Introduction of Single-Cell Sequencing
Single-cell sequencing is a new technology for amplifying and sequencing the DNA/RNA at single cell level. The principle is to use MDA or MALBAC to efficiently amplify the DNA/RNA from isolated single cells, followed by deep sequencing. Powerful bioinformatics analyses enable us to obtain information of cell-cell heterogeneity, cell population difference and cell evolution. Single-cell genomics will help to uncover cell lineage relationships; single-cell transcriptomics will supplant the coarse notion of marker-based cell types; and single-cell epigenomics and proteomics will allow the functional states of individual cells to be analyzed. At CD Genomics, we are dedicated to offering high-quality single-cell RNA sequencing, single-cell DNA sequencing, single-cell DNA methylation sequencing and 10X Genomics single-cell Sequencing.
- Profiling scarce clinical samples
- Measuring intra-tumor heterogeneity and guiding chemotherapy
- Cancer cells evolution analysis during tumor progression
- Pre-implantation genetic diagnosis (PGD)
CD Genomics's Single-Cell kit produces amplified DNA fragments suitable for Copy Number Variation (CNV) analysis using oligonucleotide aCGH or qPCR; SNP genotyping, mutation detection and sequencing.
Advantages of Single-Cell Sequencing
- Complete: End-to-end workflow for whole transcriptome analysis of individual cells.
- Highest throughput: Unprecedented parallel processing of up to 96 single cells per run.
- Easiest to use: Less than three hours total hands-on time, working directly from single cells, with no RNA fragmentation and purification step.
- Affordable: One-eighth the cost of other library preparation system.
Single-Cell Sequencing Workflow
The advent of flow cytometry and laser capture microdissection has made it possible to capture single cells, and the DNA or RNA of single cells was amplified for single-cell sequencing. The general workflow for single-cell sequencing is outlined below.
|Sample requirements and preparation
- The original sequencing data
- Experimental results
- Data analysis report
- Details in Single-Cell Sequencing for your writing (customization)
CD Genomics's Single-Cell 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.
1. The principle, advantages and disadvantages of single-cell genome amplification.
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.
2. Sampling requirements for single-cell sequencing.
MDA amplification: the sample volume can not exceed 2 μL. The PCR tube that company supply contains 2 μL PBS. MALBAC amplification: the sample volume can not exceed 1 μL. Ensure that samples are free of Ca2+, Mg2+, the company provides a tube containing 4 μL lysate. Samples should be separated independently as far as possible, avoiding cell adhesion and cell fragments, affecting the quality of amplification.
ChIP-seq analysis of histone H3K9 trimethylation in peripheral blood mononuclear cells of membranous nephropathy patients
Journal: Brazilian Journal of Medical and Biological Research
Published: 12 December 2013
Membranous nephropathy (MN), characterized by the presence of diffuse thickening of the glomerular basement membrane and subepithelial in situ immune complex disposition, is the most common cause of idiopathic nephrotic syndrome in adults, with an incidence of 5-10 per million per year. A number of studies have confirmed the relevance of several experimental insights to the pathogenesis of human MN, but the specific biomarkers of MN have not been fully elucidated.
In this study, the authors used chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) to analyze the variations in a methylated histone (H3K9me3) in peripheral blood mononuclear cells from 10 MN patients and 10 healthy subjects. There were 108 genes with significantly different expression in the MN patients compared with the normal controls. In MN patients, significantly increased activity was seen in 75 H3K9me3 genes, and decreased activity was seen in 33, compared with healthy subjects. Five positive genes, DiGeorge syndrome critical region gene 6 (DGCR6), sorting nexin 16 (SNX16), contactin 4 (CNTN4), baculoviral IAP repeat containing 3 (BIRC3), and baculoviral IAP repeat containing 2 (BIRC2), were selected and quantified. There were alterations of H3K9me3 in MN patients.
Table.1 ChIP-seq and alignment results. Aligned reads: the number of reads refers to the reference genome; Aligned reads only: the number of reads only refers to the reference genome; % Aligned: aligned reads/total reads; % Only: aligned reads only/total reads.
Figure 1. ChIP-seq peak distribution (distance) of H3K9me3 from membranous nephropathy patients.
Figure 2. Genome-wide distribution of peaks relative to annotated genes.
Figure 3. Distribution of peaks relative genes by Gene Ontology (GO) analysis (AmiGO 1.8). The lateral axis represents the GO terminology. The left vertical axis represents the proportion of the related genes. The right vertical axis represents the number of the related genes.
Table 2. Basic information of motif of H3K9me3 from patients with membranous nephropathy. S: C+G; R: A+G; K: T+G; Y: C+T; M: A+C; V: A+C+G. Figure 4. ChIP-seq motif logo. The lateral axis indicates locus of motif. The total height of the vertical axis reflects the conservation of the motif. The height of each base represents probability of the base.
Table 3. Selected genes with H3K9me3 alterations between patients with membranous nephropathy and healthy controls identified by ChIP-seq.
In summary, the authors think these results may be candidates to help explain pathogenesis in MN patients. Such novel findings show that H3K9me3 may be a potential biomarker or promising target for epigenetic-based MN therapies.
Sui W G, et al. ChIP-seq analysis of histone H3K9 trimethylation inperipheral blood mononuclear cells of membranous nephropathy patients. Brazilian journal of medical and biological research , 2014, 47(1):42-9.