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What is single cell sequencing?

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 state of single cells, and reflect the heterogeneity between cells. In 2013, single cell sequencing technology was listed as the top six most noteworthy areas in the year by Science, and again on the cover of Science translational medicine in 2015. 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. It has broad application prospects.

Strong ice single cell sequencing advantage

In order to accurately and rapidly carry out single cell sequencing research, the ice based single cell sequencing is based on cutting-edge research literature for multiple optimization, truly accurate, reliable and comprehensive single cell sequencing analysis.

  • 10X Genomics
  • BD Rhapsody
  • Drop-Seq
  • BD FACSMelody

In 2016, 10X Genomics launched the ChromiumTM system for the first time. It fully connected with the Illumina sequencer and was able to automate the large-scale single cell research. 10X Genomics single cell capture platform originates from Drop-Seq technology. Through the "double cross" microfluidic system, a droplet of GEMs (GEMs) containing oil and water droplets containing cells and gel beads (gel bead) is formed. Its core technology is the primer sequence on the surface of gel beads, which is composed of Barcode labeled cells, UMI of mRNA labeled cells and Poly Poly of capturing mRNA. 10X Genomics ChromiumTM system can achieve thousands or even tens of thousands of single cell analysis, to solve the shortcomings of conventional scRNA-seq in terms of flux or scalability.

BD Rhapsody Gamma The birth of the single cell analysis system is based on BD's 40 year expertise in the field of cell biology, using CytoSeq specific honeycomb panel technology to capture single cells. 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.

The team led by Steven McCarroll of Harvard Medical School introduced microfluidics into single cell RNA-seq and developed Drop-seq technology, which was published in Cell in 2015 (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.

The BD FACSMelody cell sorting instrument combines the patent technology of BD high-end separator and intelligent automation software to push the simple and easy sorting instrument to a new height. Convenient operation can save the time of debugging and provide quality repeatable test results. BD FACSMelody cell sorting instrument allows more researchers to use complex flow cytometry and sorting technology to obtain more desirable cells and improve laboratory efficiency.

Sample requirements

Sample type

Tissue, blood, cultured cell lines, and prepared single cell suspension.
Note: if the customer samples are organized and the tissue samples have not been successfully digested into single cell suspension, 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 requirement

The cell activity was more than 70%, and the concentration was 500-2000 cells / L.
The volume is not less than 200 L, the cell culture medium and buffer can not contain Ca2+ and Mg2+, and the cell volume is less than 40 m.


Experiment flow

01
Customer samples

02
Cell counting and activity
Testing

03
Single cell capture

04
Cell / Transcript
Tag adding

05
Library Construction

06
Sequencing

Result example

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: the abscissa is the number of cells, the ordinate is the average counts number corresponding to each cell. According to the slope of the curve, the number of cells actually detected is determined.

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 statistics after removing duplicate UMI.

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 indicates the number of UMI in each cell, and the ordinate represents the proportion of mitochondrial genes.

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.

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

Identification of Marker gene

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

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

Differential gene screening

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

Note: the map is a cluster analysis map of differential gene among different cell subsets (Heatmap).

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.

Note: the map is a Pathway entry with significant difference between different cluster genes.

Advanced data analysis

RNA velocity analysis

The velocyto algorithm was used to predict the direction of change of individual cells and obtain the process of cell transformation.

Note: the graph is the result of RNA velocity analysis, where the arrow direction represents the direction of cell evolution predicted by the algorithm.

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.

Note: pesudotime trajectories of intercellular state transitions (I) and Heatmap diagrams (2)

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.

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.

Combined analysis of TCGA prognosis

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.

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

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