BMS8110复习（十二）：Lecture 12 - Single Cell Sequencing

Contents

• Introduction
• Single-cell RNA-seq data analysis
• Single-cell sequencing in cancer research
• Lineage tracing(谱系追踪)

Why Single Cell Studies?

Challenges for single-cell data analysis

• Gene dropout
• Confounding effects 
• High variability
  • Distinguishing between biological and technical variation

Lots of software avaiable to analyze single cell RNA-seq data: Seurat, VELOCYTO, Monocle

Differentiation Trajectory Reconstruction Algorithms

• Single-cell transcriptomics allows one to investigate the transcriptional state of thousands of individual single cells thereby reliably capturing cell-type diversity and their interrelations in heterogeneous samples.
• Algorithms
  • Dimensionality Reduction-Based Algorithms
  • Nearest Neighbour Graph-Based Algorithms
  • Other Lineage Reconstruction Algorithms

Monocle

Single cell transcriptome dynamics during myogenesis(心肌再生)

Application：

• Reconstructing lineage relationships in the fetal neocortex
• Predicting progression of cells through hematopoiesis(血细胞生成)
• Reconstruction of the lineage trajectories of the placenta 胎盘的谱系轨迹
• Reconstruction of the lineage trajectories of hair follicles(毛囊)
• Reconstruction of the lineage trajectories of cancer

RNA Velocity

• RNA velocity is the time derivative of the gene expression state. RNA速度是基因表达状态的时间导数。
• RNA velocity is determined by the balance between production of spliced mRNA from unspliced mRNA, and the mRNA degradation. RNA速度是由未剪接mRNA产生剪接mRNA与mRNA降解之间的平衡决定的。

The biological foundation

RNA velocity could predict the mature mRNA abundance into the future

Kinetics(动力学) of transcription during human embryonic(胚胎的) glutamatergic(谷氨酸能) neurogenesis(神经形成)(EGN)

Application:

• Computational and molecular examination of spermatogonial(精子发生的) plasticity(可塑性)
• Cell type atlas and lineage tree of Planarians