Single Cell Transcriptomics

Kerry Cobb

Objectives & Learning Goals for Workshop

  • HPC Usage
  • Singe Cell Transcriptome Concepts
  • Technical Details

Objectives & Learning Goals for Workshop

  • HPC Usage
    • Accessing remote HPC
    • Submitting jobs
    • Basic linux usage

Objectives & Learning Goals for Workshop

  • Singe Cell Transcriptome Concepts
    • Sample preparation
    • Read mapping
    • Quality control
    • Analysis

Objectives & Learning Goals for Workshop

  • Technical Details
    • Available software
    • Scripts to run analyses

Objectives & Learning Goals for today

  • Introduction to single cell transcriptomics
  • Overview of sample preparation, library construction, and sequencing
  • Initial data processing

Single Cell Transcriptomics

  • Why do single cell transcriptomics?
    • Characterize heterogeneity among cells within cell population
    • Identify rare cell types
    • Explore interaction and communication among cells
    • Trace cell lineages during development

Bulk vs Single Cell

  • Bulk transcriptomics
    • Measures average expression across all cells in a sample
    • Cannot detect heterogeneity among cells
    • Higher gene coverage
  • Single cell transcriptomics
    • Measures expression in individual cells
    • Can detect heterogeneity among cells
    • Lower gene coverage

Single Cell Transcriptomics Workflow

  1. Tissue preparation

  1. Single cell isolation

  1. Library preparation

  1. Sequencing

  1. Data processing & analysis

1. Tissue Preparation

  • Critical Step (Garbage in, garbage out)!
  • Many different methods
    • Details beyond the scope of this workshop
  • Cell selction / enrichment
  • Sample clean-up
  • Considerations for each method w/ regards to analysis
    • Expected cells
    • Quality of cells
    • Batch effects

1. Tissue Preparation

  • Take steps to remove batch effects & confounding variables

Baran-Gale et al. 2017
  • Consider replication if feasible

2. Single Cell Isolation

Svensson et al. 2017

2. Single Cell Isolation

Multiple methods broadly categorized:

  1. Emulsion based
  2. Plate based

Single Cell Isolation - Trade off between cells and genes

  • Depends on:
    • Method used
    • Choices made in implementation of method
  • Required number of cells increases with complexity of sample
  • Sample can often be resequenced

Single Cell Isolation - 10X Genomics

  • By far the most popular approach
  • Source of all workshop data

Single Cell Isolation - 10X Genomics

10X Genomics

Single Cell Isolation - Illumina single cell 3’ RNA

  • Very recent offering
  • Formerly known as PIPSeq
  • Looks very promising, several UConn & UCHC labs planning to adopt

Clark et al. 2023

Single Cell Isolation - Combinatorial Barcoding

Parse Biosciences Evercode

  • Cells serve as reacion vessel
  • Serial splitting, barcoding, and pooling results in each cell having a unique combination of barcodes
  • C(96,4) = 3,321,960

Tran et al. 2022

Single Cell Isolation - Combinatorial Barcoding

Scale Biosciences Quantum Barcoding

Scale Biosciences

Single Cell Isolation - SMART-SEQ

  • Plate-based
  • Much greater cost per cell
  • Can assay full transcripts

Macosko 2020

Single Cell Isolation - Others

  • Honeycomb HIVE
  • Singleron
  • Asteria
  • Fluent

3. Library Preparation

  1. Convert RNA to cDNA
    • Reverse transcription
  2. Fragmentation
  3. Barcode ligation
    • Unique molecular identifiers (UMIs)
    • Cell barcodes
  4. Sequencing adapter ligation
  5. Amplification
  6. Remove contimating RNA and cell debris

Illumina

10X Library Preparation

10X RNA Capture

Adapted from 10X Genomics

10X 3` vs 5` libraries

  • Refers to the end of an RNA molecule that is sequenced
  • 3` came first
  • 5` permits identification of T-cell and B-cell receptor sequence
  • Sensitivity is comparable according to 10X

10X 3` Capture

10X Genomics

10X 5` Capture

10X Genomics

10X 3’ Library construction

10X Genomics

10X 5’ Library construction

10X Genomics

Constructing Libraries from mRNA

  • 10X is most common
    • Multiple versions
  • Other methods exist
  • Method used can have important implications for analysis
    • Index hopping
    • PCR duplicates
    • Biased amplification
    • Biased capture?

Unique Molecular Identifiers (UMIs)

Adapted from 10X Genomics
  • Permit identification of PCR duplicates
  • Results in more accurate estimate of expression
  • 10X Genomics uses a random 12 nt sequence on each oligo

4. Sequencing

  • Typically done with Illumina short reads
    • Focus of workshop
  • Could be done with any sequencing platform
    • Each may require special considerations
      • Read length
      • Error rate
      • Technical artifacts/biases

Multiplexing samples

  • Samples can be multiplexed
  • Two approaches
    1. Cell labelling
    2. SNP variation
  • Can reduce impact of batch effects
  • Cell labelling permits overloading
    • Reduces empty droplets, doublets easy to identify

Stoeckius & Smibert 2018

Probe capture

  • Generally
  • RNA captured by hybridization with probes rather than poly A tail
  • Permits sequencing of formalin fixed tissue and low quality tissues
  • Adds additional cost
  • Limited to humans and mice

10X Genomics

Single-cell vs Single-nuclei

Single Cell

  • Can detect transcripts in the cytoplasm as well as the nucleus
  • Typically want to use fresh cells
  • Signal more prone to perturbation caused by tissue processing

Single Nuclei

  • Can detect more unprocessed mRNA containing introns
  • Cannot detect transcripts in the cytoplasm making it unsuitable for some investigations
  • Can be use with preserved cells and difficult to dissociate cells

5. Data Processing

  • Up next!