Single Cell Analysis I

Relevance of Gene Expression

  • Definition: Gene expression is the process by which information encoded in a gene is used to direct the assembly of a functional product, primarily proteins.

  • Cellular Responses: Gene expression allows cells to respond dynamically and change in their environment.

  • Protein Synthesis: Gene expression ensures specific proteins are created at the right time and in the right quantities in response to stimuli.

  • Cell Differentiation: Influences cell specialization, crucial for development, especially in stem cells.

  • Health and Disease: Dysregulation can lead to diseases, including cancer and genetic disorders.

  • Research and Medicine: Helps researchers study diseases, develop therapies, and personalize medicine.

Transcription and Translation Overview

  • Transcription: RNA polymerase reads a DNA segment (genome) to create complementary pre-RNA.

    • Pre-RNA is processed into mature mRNA by removing introns and adding a 5' cap and poly-A tail.

  • Translation: Ribosomes read the mature mRNA sequence; tRNA molecules bring specific amino acids to the ribosome based on the mRNA code.

    • Result: A protein that unfolds and forms the active protein.

RNA Sequencing (RNA-Seq)

  • RNA-Seq is a method to quickly sequence RNA on a massive scale, capturing almost all gene expression simultaneously.

  • Applications:

    • Identify gene expression changes.

    • Compare healthy and diseased tissue to see what is up-regulated or down-regulated (differentially expressed genes).

    • Identify novel genes related to disease or functional outcomes.

    • Identify aberrant genes that may influence diseases such as cancer.

  • Stages:

    • RNA preparation and sequencing library creation.

    • Data analysis: Compiling data, aligning reads to identify genes, quantifying reads, and data analysis.

Comparison of QPCR/QRTPCR vs. RNA-Seq

Feature

QPCR/QRTPCR

RNA-Seq

Process

Obtain and purify RNA, convert to cDNA, use specific primers for genes of interest

Obtain and purify RNA, convert to cDNA, massive parallel sequencing, sequence alignment, and statistics

Scope

Examines expression data based on specific genes of interest

Studies all genes that are up-regulated and down-regulated

Output

Expression data for a few genes

Expression data for all genes and ability to define new genes

Targeted vs Broad

Targeted

Broad

Single-Cell RNA Sequencing (scRNA-Seq)

  • RNA sequencing at the single-cell level.

  • Allows for RNA sequencing from an entire organ to understand cellular changes within every cell.

  • Provides cell specificity and diversity at unprecedented resolution.

  • Emerged over the past decade; early studies in 2009 used manual methods.

  • Advancements:

    • Automated techniques.

    • Multiplexing: Mixing different cells or samples and then identifying them.

    • Integration of fluidics to increase throughput.

    • Robotics and various capture methods to isolate cells and RNA.

  • 10X Genomics is the most widely used platform due to its automation.

scRNA-Seq vs. Bulk RNA-Seq

  • Scenario: Analyzing a heterogeneous organ (e.g., a tumor) with cancer cells, immune cells, and blood vessel cells.

  • Bulk RNA-Seq:

    • Isolates RNA from all cells mixed together.

    • Provides an average readout of gene expression across all cells.

    • Limitation: Cannot determine which specific cells are driving high or low expression of a gene.

  • scRNA-Seq:

    • Isolates each individual cell and barcodes the RNA.

    • Assigns gene expression changes to each individual cell.

    • Defines subpopulations (e.g., cancer cells, immune cells).

    • Provides cell-type-specific readout of gene expression.

  • Benefit: Clearer understanding of processes involved in diseases or conditions.

Strengths and Weaknesses Comparison

Feature

Bulk RNA-Seq

Single-Cell RNA-Seq

Measurement

Gene expression at the bulk level, averaging across all cells

Gene expression at a single-cell level, capturing individual cell heterogeneity

Resolution

Low resolution due to averaging across cells

High resolution, revealing cell-to-cell variation

Usefulness

Studying tissue-level changes, overall differential gene expression, pathway analysis

Characterizing cell types, cell states, and cell trajectories, understanding which cells drive specific pathways and gene expression

Data Analysis

Simpler data analysis

Complex data analysis, 10s to 100s thousands of cells, requires complex bioinformatics

Limitations

Cannot distinguish individual cell behaviors

Requires specialized protocols, handling, and computational tools

Cost

Lower Cost (e.g. 100−300100-300 per sample)

Higher Cost (e.g. ~16001600 per sample)

Single-Cell Sequencing Workflow (10X Chromium Genomics)

  • Input: Cells or nuclei.

  • Process:

    • Use kits and manufacturer instructions/equipment to create a library.

    • Sequence on platforms like Illumina or MySyk.

    • Data analysis and visualization (supported by companies but requires complex bioinformatics).

  • Focus: Sample preparation is critical.

Sample Preparation Workflow

  • Governed by planning, optimization, and running the sample.

  • Planning:

    • Consider the sample source (organ or tissue).

    • Review existing literature.

    • Develop a workflow.

    • Decide whether to digest the tissue or isolate specific cells like T cells from the spleen.

    • Design the experiment with relevant controls and consideration of factors like sex/gender.

  • Optimization:

    • Evaluate the protocol.

    • Consider dissociation or enrichment methods.

    • Assess cell suspension and viability.

    • Optimize to obtain the best cell viability and isolate metabolically active cells.

    • Include cleanup steps to improve the sample.

  • Running Samples:

    • Use the optimized protocol.

    • Treat samples gently and consistently.

    • Determine accurate cell stock concentration.

Sample Preparation Steps

  • Isolate samples.

  • Transport samples.

  • Dissociate or break down the tissue.

  • Enrich for specific cells.

  • Consider cell suspension and storage.

  • Quality control and final cell count.

  • Goal: Maximize the quality of the final cell suspension.

Cell Dissociation

  • Break down the tissue to understand cellular heterogeneity.

  • Use fresh tissue.

  • Mechanical and enzymatic dissociation (mincing, incubating with enzymes to break down connective tissue).

  • Cell enrichment using:

    • Kits with antibodies that bind to cells of interest (removed via magnetic beads).

    • Flow cytometry/FACS (fluorescence-activated cell sorting) to sort cells based on fluorescence.

  • Obtain a cell suspension.

Cell or Nuclear Approach

  • Enzymatic digestion and cell enrichment can result in cell death.

  • Example: In the kidney, epithelial cells may die during FACS.

  • Alternative: Use frozen tissue and mechanical disaggregation to isolate nuclei (single nuclei RNA-seq).

  • Considerations:

    • Enzymatic digestion can affect cell populations (e.g., removing immune cells).

    • Validate sample preparation protocols.

    • Single nuclear approaches yield less genetic material, so the read depth may be lower compared to single-cell approaches.

  • Examine Cellular Heterogeneity.

    • A dominant cell type in a particular tissue will result in imbalanced sequencing data.

    • Metabolically active nucleated cells should be isolated to have substantial genetic information.

10X Chromium Platform

  • Uses Next-Gen Technology with gel beads in emulsions.

  • Cells are barcoded.

  • Cells are mixed with barcoded gel beads, forming emulsions where cells attach to the beads in oil.

  • Beads have unique barcodes assigned to each cell.

  • Cells are lysed, and reverse transcription is performed.

  • Each color (barcode) is applied to the cells.

  • One GEM (gel bead in emulsion) represents one cell.

  • Unique molecular identifiers (UMIs) allow identifying each cell.

  • Results: 10X barcoded cDNAs.

  • Cells can be identified based on up-regulated genes.

Barcoding and Unique Molecular Identifiers (UMIs)

  • Each gel bead has a unique oligo sequence, including a 10X barcode and a UMI.

  • The gel bead combines with the genetic sequence of each attached cell.

  • Barcodes/indexes identify each individual cell.

Library Preparation

  • Mix gel beads and perform reverse transcription, releasing 10X barcoded cDNA.

  • Break the GEMs.

  • The resulting CDNA is barcoded.

  • Run bulk sequencing.

Data Analysis

  • After 10X Chromium platform processing and sequencing.

  • Data analysis uses barcoding and unique identifiers.

  • Identifies each individual cell based on the barcode and molecular identifier.

Comparison of Single-Cell RNA Sequencing Methods

  • 10X Genomics: droplet-based method.

  • Different companies have different technologies.

  • 10X Genomics is popular due to ease of use.