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Single-Cell RNA Sequencing Q&A

  • General Questions

  • What are the characteristics of 10X Genomics single cell labeled RNA?
    • 10X Genomics single cell labels RNA with A-tail (10X reverse transcription primer is olig dT), including labeled mRNA and lncRNA.
  • Can I do circRNA sequencing by 10X Genomics single cell sequencing?
    • 10X Genomics single cells cannot label circRNA, so you cannot do circRNA of 10X single cells.
  • How do I obtain Marker genes in single cell transcriptome sequencing?
    • 1) cellmarker: including 13,605 Marker genes for 467 cell types from 158 groups (sub-tissues) in human, and 9,148 Marker genes for 389 cell types from 81 tissues (sub-tissues) in mouse
      2) literature review
      3) subpopulation upregulated expression gene speculation
      4) homologous genes of closely related species
      5) CD Genomics' own single-cell sequencing database
  • What single-cell transcriptome sequencing projects have you been involved in?
    • We have helped various clients with their projects, cell heterogeneity studies, development and differentiation, immune response, novel cell discovery, cellular level studies of targeted editing genes, disease typing, construction of cellular atlases, etc.
  • What is the difference between 5'-end and 3'-end capture for single cell sequencing library construction?
    • There are two main methods for 10x Genomics single cell transcriptome sequencing: 5' end and 3' end library construction. For 3' end library construction, the terminal sequence of Gel beads is poly(dT), which is used for complementary pairing with the polyA structure at the 3' end of the transcriptome. While in the 5' end of the library construction, the terminal sequences of Gel beads are switch oligo sequences with polyG structures at the end. The transcript will add the structure of polyC at its 5' end by the action of enzyme, thus completing the pairing.
  • Why is the 5' end capture method usually used for VDJ?
    • The capture method of VDJ library building is fixed by the 5' end capture method, in order to enrich to the variable VDJ region at the 5' end while keeping the sequence length short, instead of the more conservative C region at the 3' end, which is usually not the focus of research.
  • What association analysis can be done for single-cell transcriptome sequencing and single-cell ATAC sequencing?
    • 1) Association of subpopulations, directly mapping transcriptome subpopulations to ATAC cell subpopulations.
      2) Association of genomic open regions and downstream gene expression.
      3) The association of the temporal relationship of open regions and the temporal relationship of gene expression.
  • How to choose the depth of single-cell RNA sequencing?
    • The depth of single-cell transcriptome sequencing is not very different from normal transcriptome sequencing. There are about 10-14,000 gene expression species in a single cell, but the total number of all genes detected in a single-cell sequencing batch will often exceed 20,000. One study comparing data from the same cell at 180M reads and 50M reads depth showed a high degree of concordance. In addition, the depth of sequencing determines the "resolution", and it has been shown that single-cell sequencing can effectively distinguish a group of cells with close expression patterns at approximately 107 reads.
  • How many cells can be measured at a time by single-cell transcriptome sequencing?
    • Single-cell transcriptome sequencing is equipped with 8 channel microfluidic chip, which can measure 8 samples at the same time, each channel can measure 100-10000 cells, one chip can obtain 100-80000 cells at the same time.
  • How efficient is the single-cell capture of single-cell transcriptome? Will there be multiple cells captured?
    • Under normal experimental conditions, the single-cell capture efficiency is as high as 65%; the probability of capturing multiple cells is extremely low, about 0.9%/1000 cells.
  • What is the typical amount of data obtained from a single cell?
    • The official recommendation is 50K reads per cell. The number of genes expressed in a cell varies greatly from tissue to tissue, and the final amount of data measured will need to be analyzed on a case-by-case basis.
      For some tissues and cells, it can be increased to 100 K reads/cell; however, too high sequencing depth can also bring more noise and make subsequent data analysis difficult. When using Illumina PE150 (double-end sequencing) strategy, if the standard capture is about 8K cells, the amount of sequencing data for one sample is typically 120 Gbase/sample (8000 x 50K x 150 x 2 / 109 = 120 Gbase).
  • What kinds of analysis can be used from scRNA sequencing?
    • The standard analysis process includes sequencing data quality control and statistics of various indexes (cell number, gene detection number, data volume measured by individual cells, etc.), gene expression quantification, cell type classification, differentially expressed gene screening and functional annotation, etc. In addition, personalized analysis can be performed with project background and experimental design.
  • What preferences exist in single-cell RNA sequencing
    • - Amplification preferences
      - Drop-out rates: Some highly expressed mRNAs cannot be amplified
      - Transcriptional bursting
      - Background noise
      - Preferences due to cell cycle and cell size
      - Batch effect
  • What is important to note about the reverse transcription step in scRNA sequencing?
    • 1) The efficiency of reverse transcriptase is critical
      2) Drop-out rates are typically in the range of 60% to 90%
      3) Drop-out rates can vary greatly between two different libraries, if the same cell lines are treated with the same method
  • What to look for in amplification process steps in scRNA sequencing?
    • 1) Any amplification step can lead to preferences
      2) Many single-cell transcriptome sequencing methods have a UMI metric to help us correct for amplification preferences
      3) Full-length transcriptomes such as SmartSeq2 do not have a UMI and therefore cannot be corrected for amplification preferences using the UMI method
  • What are the quality control checkpoints in scRNA sequencing?
    • • Unique pairing rate
      • Proportion of matches to exonic regions
      • 3' preference in single-cell full-length transcripts
      • Reads paired to mRNA
      • UMI/reads ratio
      • Number of genes detected
      • Detection of Spike-in RNA
      • Mitochondrial, ribosomal RNA ratio
  • What is the problem if there are few spike-in RNA sequences?
    • If there are few spike-in RNA sequences, then it can be a direct indication of a library building failure. If the spike-in is normal, but the cell has few RNA sequences, it may be because the cell itself is very small or the cell is broken before the library is built.
  • What is the problem if the scRNA sequencing results find high mitochondrial RNA?
    • If the mitochondrial RNA is too high, it likewise indicates that there is a cell breakage. This is because when the cell is broken, cytoplasmic RNA will escape, but mitochondrial RNA will not spill out because it is wrapped by the mitochondrial membrane. Therefore, when there is a break in the cell membrane, the percentage of mitochondrial RNA will be high.
  • What is the problem if a high percentage of ribosomal RNA is found in scRNA sequencing result?
    • When the percentage of ribosomal RNA is high, it may be because there is a high amount of RNA degradation in the cell. In the full-length single-cell transcriptome, 3' preference can be used to detect the presence of a high amount of RNA degradation in the cell.

  • Sample Preparation

  • What should I do if I tend to form clumps during the preparation of single cell suspensions?
    • 1) Add a few drops of sterile DNAse (1mg/ml dissolved in water) to the cell suspension to break the DNA strands. Blow gently on the cells to prevent physical damage to the cell membrane.
      2) Find the appropriate time for enzymatic digestion.
      3) Wash the cells with calcium and magnesium free balanced salt solution, EDTA can be added and the concentration can be up to 2 mM.
      4) It is best not to shake vigorously during digestion, so that the cells are especially prone to fall off in a flake-like manner.
  • How do I prepare samples for single cell transcriptome sequencing?
    • The samples used for single cell transcriptome sequencing are different in animal cells and plant cells. Animal cells need to be treated with single cell suspensions and plant cells x need to be treated with protoplasts. Fresh samples generally require a cell viability of 90% or more, without the presence of reverse transcription inhibitors and non-cellular nucleic acid molecules.
  • Why is the cell viability required after single-cell suspension preparation so high?
    • Because RNA from dead cells will be released inside the extracellular fluid or mixed into the GEM of living cells. These free RNA may be wrapped with the cells and affect the subsequent reaction, which eventually leads to inaccurate analysis results. At the same time, the high number of dead cells will also lead to inaccurate cell number estimation and affect the capture rate.
For Research Use Only. Not for use in diagnostic procedures.
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Single-cell RNA Sequencing
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