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[scRNAseq 논문] 싱글셀 분석 전체 흐름 "A practical guide to scRNAseq for biomedical research and clinical applications" 본문

논문

[scRNAseq 논문] 싱글셀 분석 전체 흐름 "A practical guide to scRNAseq for biomedical research and clinical applications"

바이오 대표 2023. 2. 11. 10:36

"A practical guide to scRNAseq for biomedical research and clinical applications"

 

Abstract

Biological sample 의 RNAseq을 이용해서 우리는 mRNA을 발견하고 정략분석을 진행 할 수 있고 이를 이용하여, cellular response를 연구 할 수 있다. 2009년, 첫 scRNAseq 연구가 진행된뒤로, 해당 필드의 많은 발전이 있었다. 해당 논문에서는 scRNAseq 연구를 디자인하기 위한 기본 정보들을 소개한다: hardware, protocol choice, QC, data analysis, biological interpretation.

*cellular response: Any process that results in a change in the state or activity of a cell (in terms of movement, secretion, enzyme production, gene expression, etc.)  as a result of a stimulus

 

Background

DNAseq, chromatin structure, mRNA, non-coding RNA, protein expression, protein modification, metabolites 등을 이용해서 CELL as molecule을 공부 할 수 있다.

  • Protein expression
  • Gene expression
    • RNAseq → GWAS (SNPs 발견)
    • expression correlates with cellular traits/changes

Transcriptomics

  • Microarray → bulk RNAseq → scRNAseq (cell unit)
  • Microarray: tag에 flourescent 를 붙여서 상대적으로 비교

 

Why consider performing scRNAseq scRNAseq?

  • Heterogeneity 확인 가능
    • embryonic and immune cells

*cell population: given area 에서 같은 특성을 갖는 group of cells

 

 

Basic steps in conducting scRNA-seq?

Step1. 원하는 Tissu 에서 single cell Isolation + Indexing

Step2. mRNA 뽑기 (using T-primers)

Step3. mRNA → cDNA (+ adaptor & UMIs)

Step4. cDNA Amplification (PCR)

Step5. Sequencing

Droplet-based platform (Chromium from 10X)

  1. Encapsulate thousands of single-cell individually
  2. Each droplet contains all the necessary reagents for cell lysis, RT, molecular tagging, and eliminating need for sc isolation

 

What type of material can be assessed by scRNAseq?

  • Any eukaryotic cells
    • 종종, mouse/human primary cells, tumors, nervous system, haematopoietically cells
  • (-) 콜라겐처럼 붙어있는/neighboring cell은 얻기 어렵다.
    • (+) cell 보다 nuclei 형태로 얻는 것이 less bias 해서 사용 증가 중
  • (-) scRNAseq 은 보통 isolation 하자마자 cell lysis / mRNA capture 필요
    • 10X 는 8 sample 제한
    • (+) cryopreservation (bank sample)

 

Which protocol should be employed?

  • Research question 에 따라 Protocol 다름
    • full-length transcript
      • (+) low expression transcripts 확인 가능
    • 3’ end transcripts
  • Issue
    • technical variation
      • (solve) spike-in
        • ERCC controls: set of 92 highly expressed RNA transcripts with known concentration
          • can normalize the sequencing depth

 

How many cells must i sequence / what depth?

*depth = # of transcripts detected from each cell

  • (-) 세포의 cDNA libraries 가 증가하면, depth 감소

 

How do single-cell data differ from bulk RNA-seq?

  • TPM 사용
    • 이유 1: TPM accounts for differences in gene length. (RPKM - only gene length)
    • 이유 2: TPM more robust to zero counts

 

Once i have sequenced my sc cDNA libraries, how do i analyse the data?

  • scRNAseq
  • QC
    • library size, number of detected genes, a fraction of reads mapping to mitochondria encoded genㄷs
    • synthetic spike-in RNAs
  • Dimension reduction (PCA, t-SNE, GPLVM)

 

Conclusion

Research question 에따라 이유 있는 experimental design을 만들고 진행해야 한다.

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