InquirySingle-cell transcriptome sequencing explores gene regulatory networks on a genome-wide scale, especially for stem cells, tumor cells, and early embryonic developmental cell populations of high heterogeneity. Single-cell transcriptome analysis provides insight into cell differentiation, cell reprogramming, and transdifferentiation processes associated with the gene regulatory networks.
CD Genomics provides single-cell transcriptome sequencing services based on the 10x Genomics and Smart-Seq2 technology platforms. Every step has been carefully designed and scientifically optimized to ensure high-quality research results, covering material selection, library construction, sequencing, and data analysis.
Smart-seq2 single-cell transcriptome sequencing services utilize the SMART amplification technology to reversely transcribe and amplify through PCR for mRNA in single cells, combined with transposase library construction technology and high-throughput sequencing to quantify gene expression, functional enrichment, and metabolic pathways in single cells. Moreover, the Smart-seq2 single-cell transcriptome sequencing technique can be applied to samples with low volume or cell heterogeneity, which has become an emerging tool in the study of early embryonic development, stem cells, cancer, immunity, and other research fields. As a result, this powerful tool for studying gene expression at the single-cell level greatly expands the application of RNA-Seq.
Applications of Single-cell Smart-seq
Accessing the complete transcriptome of the single-cell sample;
Gene annotation and screening;
Gene structure analysis.
Flowchart for the Smart-seq2 library preparation (Simone Picelli et al. Nature Protocols 2014)
Smart-seq Workflow
Advantages of SMART-seq Technology
Low initial volume: suitable for samples that cannot meet volume requirements of platforms such as 10X Genomics
High coverage: sequencing covers the full-length sequence of cDNA, enabling the expression of tens of thousands of genes per cell
Comprehensive information: in addition to gene expression, the data can be used for variable splicing, cSNP analysis, and lncRNA studies with poly-A structures
Not strand-specific
Bioinformatics Analysis
| Standard analysis (reference gene sequence, reference genome sequence and gene annotation results are required) | Advanced Analysis |
|---|---|
| 1. Sequencing quality assessment and raw data filtering to remove adapter sequences and low-quality reads 2. Comparison of reference genomes or reference gene sequences 3. Sequencing and alignment assessment: data alignment statistics, sequencing saturation analysis, sequencing randomness analysis 4. Gene expression statistics: gene coverage, expression, expression abundance distribution 5. Transcript prediction and annotation of new genes (reference genome required) 6. Sample relationship analysis: principal component analysis (PCA), correlation test, sample clustering map 7. Differentially expressed gene analysis: differentially expressed gene screening, clustering analysis of differential gene expression patterns (heat map), differential gene GO function enrichment analysis, KEGG pathway enrichment analysis, DO gene-disease enrichment analysis (human), Reactome pathway enrichment analysis (human, rat, mouse) 8. String database protein interaction network analysis 9. GSEA gene set enrichment analysis |
1. SNP analysis 2. Gene structure optimization (reference genome required) 3. Gene splicing identification (reference genes required) |
Sample Requirements
Sample type: living intact cells, unsuitable for samples processed by fixation, staining, etc.; mammalian cells or other eukaryotic cells without cell wall structure, mRNA with poly-A structure
Sample volume: cell suspension <1 μL; 1~500 single cells/sample is recommended; no less than 3 sample replicates
