菜鸟电竞平台

Service hotline 400-065-1811

Product Brief

Single cell sequencing technology ( Single cell sequencing is a new technology for high throughput sequencing analysis of genome, transcriptome and apparent group at a single cell level. It can make up for the limitations of traditional high-throughput sequencing, reveal the gene structure and gene expression status of single cells, and reflect the heterogeneity between cells. Two thousand and thirteen In 2008, single cell sequencing technology was " Science listed it as the top six most noteworthy areas of the year; 2015 Year again Science translational medicine cover. At present, single cell sequencing technology plays an important role in cancer, developmental biology, microbiology, neuroscience, botany and other fields. It is becoming the focus of life science research and has broad application prospects.


Single cell transcriptome sequencing process -- I know more about it.

Our advantages

One Recognized by the industry. 10X Genomics and BD Rhapsody single cell platform to achieve true single cell sequencing.

Two It has rich experience in preparation of single cell suspension for different sample types, such as peripheral blood, cell lines, and freshness. Cryopreservation of organ tissue and tumor tissue to ensure cell viability to meet sequencing requirements.

Three Mature single cell sequencing library construction technology, completed at one time. A library of 1000-10000 cells was constructed to truly measure all cell types in the whole tissue, so that all types of cells in the sample could be analyzed comprehensively.

4. perfect single cell sequencing and experimental quality control process, as well as rich experience in single cell sequencing and data analysis, to achieve personalized and customized data analysis services.



Sample requirements

Sample type:

Tissue, blood, cultured cell lines, and prepared single cell suspension.

Note: if the customer samples are organized, and are unable to organize the dissociation to obtain single cell suspension, the strong ice will provide technical and experimental assistance as far as possible. However, due to the specificity of different types of samples, the experimental method can not be guaranteed to apply to all types of tissues.

Quality requirements:

One Cell activity is greater than 70%

Two Concentration is 500-2000 cells / mu L

Three Volume not less than 200 mu L

Four Cell culture medium and buffer can not contain Ca 2+ And Mg 2+

Five Cell volume less than 40 mu m



Experiment flow


1. single cell suspension preparation: select suitable single cell suspension preparation method according to sample characteristics, pay attention to red cell lysis; if customer samples have prepared single cell suspension, this step can be omitted.

2. cell activity detection: Cell activity needs more than 70%;

Three Single cell capture: through sorting platform ( Each cell was captured by BD, 10X and Drop-seq.

Four Cells Transcript tag addition: reverse transcription of CB and UMI to RNA combined with magnetic bead tag;

Five Library Construction: Yes. CDNA was amplified by random primer PCR.

Six Machine sequencing: strong ice recommended Illumina Hiseq or NovaSeq sequencing platform Data volume 100G/ samples.


Data analysis process


Result example

1. Preparation of single cell suspension (additional charges).
According to the purpose and object of the study, fresh samples were obtained, including different types of tumor samples and their corresponding normal samples. According to the characteristics of the tissue samples, the appropriate digestion conditions (enzyme solution, digestion temperature and time) were selected, and the required single cell suspension was obtained after sieving.

Preparation process of single cell suspension



2. Single cell count and activity detection.
Replacing cells from culture medium to Sample Buffer, Countstar or BD Rhapsody Scanner can accurately determine cell number and active state. (Note: when the proportion of red blood cells / dead cells is too high, it is recommended to remove such cells to ensure effective data volume.

BD Rhapsody Scanner test chart

Note: clear field map (left), green fluorescence (medium), and red fluorescence (right) were detected in living cells.



3. Single cell capture
10X Genomics or BD Rhapsody single cell capture platform was used to label each cell and its mRNA tag. The RNA collected with magnetic beads label is reverse transcribed, and the product can be brought back to the company for follow-up operation at 4 degree.


Figure 1: Drop-Seq

Harvard Medical School Team led by Steven McCarroll Introducing microfluidics into single cells In the RNA-seq method, Drop-seq is developed. Technology and technology in Two thousand and fifteen Published in Cell magazine. Macosko Et al., 2015 ) Drop-seq technology uses microfluidic devices to load microbeads and cells with cell barcode together into droplets. These droplets are generated on a small device and flow along a wide channel. Bar code is attached to some genes of each cell, so scientists can sequence all genes at once and track the source cells of each gene.


Figure two: 10X Genomics

Two thousand and sixteen Year, 10X Genomics Company launched for the first time Chromium TM system Comprehensive docking The Illumina sequencer can automate the large-scale single cell research. 10X Genomics single cell capture platform originates from Drop-Seq technology, forming a cell and gel beads through a "double cross" microfluidic system (gel bead). Oil droplets of oil in water ( The core technology of GEMs is the primer sequence on the surface of gel beads, marked by Barcode and mRNA in labeled cells. Of UMI and capture mRNA Of Poly dT is made up of. 10X Genomics Chromium TM The system can realize thousands or even tens of thousands of single cell analysis, solve routine. ScRNA-seq is insufficient in terms of flux or scalability.


Figure three: BD Rhapsody

BD Rhapsody Gamma The birth of the single cell analysis system is based on BD's 40 years of expertise in the field of cell biology. Use CytoSeq unique honeycomb panel technology for single cell capture. The technology uses 20W+ micropores (which are much larger than Input cells) to ensure single cell capture in single hole. At the same time, it avoids the problem of the probability impact impact capture efficiency in the traditional microfluidic system. The acquisition of microholes will have better capture efficiency and ensure the full use of Input cells.






4. High throughput sequencing
After PCR amplification, the library was constructed, and the sequencing Library of each sample was obtained. Then, the single cell transcriptome library was sequenced by Illumina Hiseq or NovaSeq high throughput sequencing instrument, and the sequencing data of each sample were obtained.

NovaSeq 6000 sequencer

5. Cell number judgement
Fastp software was used to control the raw data of the original machine. The cell barcode information and corresponding counts numbers in the sequencing data after quality control were statistically analyzed, the number of cells detected in the sequencing samples was determined, the number of sequencing cells was obtained, and the corresponding reads was extracted according to the final cell barcode information.

Note: abscissa is cell number and ordinate is the average of each cell. Counts number, according to the slope of the curve to determine the number of cells actually detected.



6. Genome comparison and expression statistics.
Taking cell barcode corresponding reads as the research object, STAR algorithm was used to compare the sequencing data to the corresponding genome of the species and obtain the BAM files for genome alignment. Then the BAM files and genome annotation files were taken as the research objects. The UMI was compared to the same gene, and the repetitive UMI sequences were removed. The number of UMI of each gene was obtained, the number of genes detected in each cell and the number of transcripts were counted, and the list of the table of the quantity of the expression was obtained.

Note: the left picture is the total number of genes detected in the cell, and the right picture is the elimination of duplication. The number of genes counted after UMI



7. Cell filtration and data standardization
Using genomic comparison results and expression results, the cells detected by sequencing were filtered, and the cells with less gene detection and larger mitochondrial genes were removed, and the number of filtered cells was counted and the corresponding expression matrix was obtained. Data normalization method (CPM/RLE/UQ/TMM/scran/Downsampling, etc.) was used to standardize the expression of cell genes in different samples, and standardized expression matrix was obtained.

Note: abscissa represents each cell. The number of UMI indicates the proportion of mitochondrial genes.



8. Analysis of cell subsets
Based on the data of gene expression in each cell, clustering analysis was applied to analyze the cell subsets, and t-SNE analysis was used to visualize the clustering results of cells. At the same time, we can make a statistical analysis of the proportion of cell subsets in different samples.

Azizi E et al., Cell. 2018

Note: the above image is the identification of immune cell subsets in breast cancer tissue, normal tissue, blood and lymph node samples.



9. Marker gene identification
The Marker gene of different cell subsets was identified, and the expression and distribution of Marker gene was visualized.

Dick S A et al., Nature immunology. 2019

Note: Feature Plot of marker gene Figure (above) and Violin diagram (2)



10. Screening of differentially expressed genes
For all or specific cell subsets, differentially expressed genes between cell subsets were screened to obtain differentially expressed genes among cell subsets.

Dick S A et al., Nature immunology. 2019

Note: the map is a cluster analysis map of different cell subsets. Heatmap)



11. Functional analysis (GO Analysis) and signal pathway analysis (Pathway Analysis)
Functional analysis and signal pathway analysis of Marker genes / differential genes were carried out by using GO database and KEGG database, such as NCBI/UNIPROT/SWISSPROT/AMIGO, respectively, so as to get GO entries and Pathway entries that were significantly enriched by these gene groups.

Dick S A et al., Nature immunology. 2019

Note: the graph is different. Pathway entries with significant difference between cluster genes



Advanced data analysis
1, RNA velocity analysis
The velocyto algorithm was used to predict the direction of change of individual cells and obtain the process of cell transformation.

Manno G L et al., Nature, 2018

Note: the figure is The result of RNA velocity analysis shows that the arrow direction represents the direction of cell evolution predicted by the algorithm.



2, Pesudotime analysis
Taking cell expression data as the research object, using TSCAN/monocle/SLICER/Ouija and other algorithms, we analyzed the change pattern of cells in the virtual time axis, simulated the dynamic process of cell reconstruction, obtained the state transition relationship among cells, and the differential gene expression in different state cells.

Dick S A et al., Nature immunology. 2019

Note: intercellular state transition Pesudotime Track graph (right) sum Heatmap diagram (left)



3. Intercellular communication analysis
The gene expression data of cell subsets were used to obtain the expression information of ligands and receptor genes in the cells, and the signal communication relationship between cell subsets was obtained.

Vento-Tormo R et al., Nature. 2018

Note: abscissa represents cell type, and ordinate represents intercellular communication. The size of the circle represents a significant difference. The more red the circle, the stronger the intercellular communication.



4. TCGA combined prognosis analysis.
Based on the clinical information of TCGA and the key basis selected from the study, combined with TCGA clinical data, the prognosis was analyzed, and the relationship between the gene / gene set and clinical prognosis was obtained.

Guo X, et al. Nature Medicine, 2018

Note: abscissa indicates survival time, ordinate represents proportion, and different color curves represent different groups.



Examples of literature

[1] Mickelsen LE, Bolisetty M, Chimileski BR, et al. Single-cell transcriptomic, al. 2019, 22 (4):


[2] Pijuan-Sala B, Griffiths JA, Guibentif C, et al. A single-cell A, 2019 B; 566 (7745):


[3] Cao J, Spielmann M, Qiu X, et al. The single-cell, al. 2019 J; 566 (7745):


[4] Tiklov Tan K, Bj rklund rklund K, Lahti L, et al. Single-cell, et 2019, 4, 10 (1): (581.)


[5] Bartoschek M, Oskolkov N, Bocci M, et al. Spatially and, al. 2018, 4, 9 (1): (5150.)


[6] Guo X, Zhang Y, Zheng L, et al. Global characterization Global, 2018 X; 24 (7):


[7] Azizi E, Carr AJ, Plitas G, et al. Single-Cell Map Single-Cell, 2018 E 23, 174 (5):


[8] Fujii M, Matano M, Toshimitsu K, et al. Human Intestinal Human, 2018 M 6, 23 (6):


[9] Dick SA, Macklin JA, Nejat S, et al. Self-renewing resident Self-renewing, 2019 SA; 20 (1):


[10] Kumar M P, Du J, Lagoudas G, et al. Analysis Analysis, M 2018, 6, 25 (6):



亚博体育【yabo7.in】←输入域名访问独家合作伙伴 _光明网亚博体育【yabo7.in】-海外商业版图-中新网亚博体育【yabo7.in】← 输入域名 _ 新浪财经_新浪网亚博体育【yabo9.in】-新华网365体育【yabo9.in】_体育_腾讯网365体育【yabo9.in】←输入域名-长城网365体育【yabo9.in】_凤凰网365体育【yabo9.in】←输入域名--浙江频道--人民网365体育【yabo9.in】_游戏_腾讯网uedbet体育(yabo7.in)_凤凰网资讯_凤凰网uedbet体育(yabo7.in)←输入域名_凤凰资讯亚博体育【yabo7.in】←输入域名-成为俱乐部全球合作伙伴|曼联_新浪财经_新浪网亚博体育【yabo7.in】←输入域名访问独家合作伙伴 _光明网亚博体育【yabo7.in】-海外商业版图-中新网亚博体育【yabo7.in】← 输入域名 _ 新浪财经_新浪网亚博体育【yabo9.in】-新华网365体育【yabo9.in】_体育_腾讯网365体育【yabo9.in】←输入域名-长城网365体育【yabo9.in】_凤凰网365体育【yabo9.in】←输入域名--浙江频道--人民网365体育【yabo9.in】_游戏_腾讯网uedbet体育(yabo7.in)_凤凰网资讯_凤凰网uedbet体育(yabo7.in)←输入域名_凤凰资讯