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single cell transcriptome sequencing

Single-Cell Transcriptome Sequencing CapitalBio

Capitalbio technology uses the 10x genomics platform to detect mrna in each cell at the single-cell level. Through a large number of single-cell gene expression data, it can perform cell expression feature clustering, subgroup expression feature analysis, cell differentiation trajectory, cell-cell interactions, etc.

Application of Single-Cell Transcriptome Sequencing
Drug Research
Oncology Research
Stem Cell Research
Immune Research
Disease Mechanism
Aging Development
Plant Research
Method Advantages Analysis Display Examples1 Examples2

10x Genomics single-cell transcriptome technology is based on a microfluidic platform that encapsulates gel beads and single cells with barcodes and primers in oil droplets; within each oil droplet, the gel beads dissolve and cells are lysed to release mRNA, and barcoded cDNA is generated by reverse transcription. After the liquid oil layer is destroyed, the cDNA is subsequently used for library construction, and then the library is sequenced and detected by the Illumina sequencing platform, and a large number of single-cell gene expression data can be obtained at one time, so as to achieve the purpose of transcriptome sequencing at the single-cell level.


Platform and principle of single-cell transcriptome

  • High throughput: 20,000 cells per sample

  • Short TAT: Suspension preparation + Cell capture + Library preparation in 2 days

  • Low cost: Cost per cell is much lower than other single cell platforms

  • Rich experience: Completed 10K+ samples, 150+ tissue types

  • Certificated service: CapitalBio Technology's Single-Cell Transcriptome Sequencing Service has received official CSP certification from 10X Genomics

  • One-stop service:Provide one-stop service from sample processing to data visualization


Cell Grouping and Subpopulation Annotation


Differential Gene Clustering Heatmap






Pseudotime Analysis


RNA Velocity Analysis


Cell Association Analysis


Cellphone DB Cell–cell Communication Analysis


TCGA Survival Analysis

MET Amplification Attenuates Lung Tumor Response to Immunotherapy by Inhibiting STING

Journal: Cancer Discovery      IF:38.272


Immune checkpoint blockade (ICB) has demonstrated therapeutic efficacy in patients with non-small cell lung cancer (NSCLC), with significant benefits in terms of overall survival, durable responses, and favorable safety profiles. But most patients ultimately fail to respond to ICB therapy due to primary or secondary resistance. In this study, 81 NSCLC patients were compared before and after ICB treatment and found that patients with MET amplification were resistant to ICB. Using single-cell transcriptome sequencing, it was found that hepatocyte growth factor receptor (MET) amplification reduced the expression of STING through UPF1 protein, attenuates the IFN response and affects the generation of anti-tumor immunity. The combination of MET inhibitors and anti-PD-1 inhibitors can enhance anti-tumor immunity and help tumor regression. This study shows for the first time that MET copy number is a key factor influencing the response to ICB in lung cancer patients and holds promise for rapid translation into clinical applications. The single-cell sequencing service in this study was provided by CapitalBio Technology.


Main Conclusions

1. Non-responsive patients were found to be associated with high MET copy number in 81 NSCLC patients receiving anti-PD1 therapy; single-cell sequencing of PBMC samples from patients classified by MET revealed differences in immune cell composition and poor immune cell function in MET-amplified patients.



Survival curves of MET high copy number and WT patients


Differences in immune cell composition between MET high copy number and WT patients


Differences in gene expression of different patients

2. Using tumor transplantation mouse model validation, it was found that anti-PD-1 and anti-MET combination therapy slowed tumor growth; impairment of antitumor immunity by MET amplification is dependent on STING; cell line knockout experiments and mouse tumor model validation proved that MET reduces the expression of STING through UPF1, weakens the interferon response, and affects the generation of anti-tumor immunity.


Anti-PD-1 and anti-MET combination therapy slows tumor growth


STING knockout abolishes the antitumor effect of METi+Anti-PD1

Dissecting Transcriptional Heterogeneity in Primary Gastric Adenocarcinoma by Single-Cell RNA Sequencing

Journal: Gut      IF:31.793


Gastric cancer has a high degree of molecular and morphological heterogeneity. Analyzing the heterogeneity of gastric cancer at the molecular level is of great significance for precise diagnosis and treatment. In this study, a comprehensive expression profile of gastric cancer was constructed by single-cell sequencing technology, and a novel classification method for distinguishing benign and malignant epithelial cells was established. A computational method to quantify the degree of cell differentiation was constructed and revealed that the degree of differentiation is closely related to the prognosis of gastric cancer. A new type of gastric cancer was discovered, which provides a valuable data resource for dissecting gastric cancer heterogeneity, gastric cancer prognosis, and analyzing the metaplastic evolution process of gastric cancer. The single-cell sequencing service in this study was provided by CapitalBio Technology.


Main Conclusions

1. Single-cell sequencing constructs a comprehensive single-cell atlas of gastric cancer.


Cell clustering map 

 Marker gene display

Comparison of tumor and non-tumor tissue cell ratios


2. Using TCGA data and single-cell data to establish a computational method to quantify the degree of malignancy of epithelial cells, classify epithelial cells into malignant and non-malignant epithelia, and perform differential gene comparison and enrichment analysis.


Classification of malignant and non-malignant epithelial cells

Malignant and non-malignant epithelial differential genes


3. Non-malignant cells were further grouped into 4 subpopulations, and pseudotemporal analysis revealed their evolutionary relationships and the cellular origin of gastric metaplasia.


Non-malignant cell population Pseudotime analysis

Pseudotime analysis

Schematic diagram of the evolution of gastric metaplastic cells

4. The malignant cells were further divided into 5 subgroups C1-C5, and their differentiation values were calculated according to the gene expression data. It was found that the subgroups with high differentiation values corresponded to the shorter survival period; C4 highly expressed Chief cell marker gene consistent with the molecular characteristics of gastric adenocarcinoma of the fundic gland (GA-FG-CCP).


Malignant cell population    


Differentiation value heatmap


Survival analysis curve    


                        Chief cell markers expressed in the C4 subpopulation

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