rna seq and chip

• ChIP-seq


  Method to determine the locations at which a specific protein is bound to DNA on a genome-wide scale
• ChIP-seq experimental method

  1. Crosslink proteins and DNA using formaldehyde

  2. Fragment the DNA (with bound proteins) into small fragments (150 to 300 bp)

  3. Incubate the fragmented DNA with antibodies that recognize your protein of interest

  4. Immunoprecipitation: Use the antibody as a "handle" to purify DNA fragments that are attached to your protein of interest

  5. Reverse the crosslinks (high salt) and digest the proteins (proteases)

  6. Purify the DNA

  7. Sequence the DNA using NGS (obtain millions of short reads between 50 and 250 base pairs)

  8. Align the reads to the genomic DNA sequence to determine the sites that were bound by your protein of interest (computational analysis)
• ChIP-seq results


  • Histone H3 is present in all nucleosomes, which are located at random positions in the genome from cell to cell
  • Nucleosomes need to be cleared from the transcription start sites of actively transcribed genes, so the nucleosomes in these regions exhibit a specific pattern across all cells in the population
• ChIP-seq using an antibody that specifically recognizes H3 histones with a particular modification


  • SPDR1 ChIP-Seq Example
• H3K4me3


  A modification of histone which is present at transcriptionally active chromatin
• IgG antibody


  A negative control that should not specifically bind any chromatin, and so no specific DNA sequences should be enriched in DNA sequencing
• Peaks


  Represent the # of reads found with sequencing, so higher peaks mean the sequence is present more frequently in sequencing, indicating H3K4me3 is present at high levels in this spot of the genome
• Level of peak


  Level of H3K4me3
• ChIP-seq is done in two different tissues for both WT and mutant mice
• Presence of peaks near the TSS of SPDR1


  SPDR1 is transcriptionally active
• Lack of peaks near the TSS of SPDR1


  SPDR1 is transcriptionally silent
• Tissue 1 has one sample active and one sample silent, hypothesized to be muscle<b>
• Tissue 2 has both samples active, hypothesized to be brain
• ChIP-seq


  Chromatin-Immunpräzipitations-Sequenzierung
• RNA-sequencing (RNA-seq)


  Measures the presence and quantity of mRNA molecules in a biological sample, revealing which genes are being expressed and the level of their expression (transcriptome)
• Types of RNA-seq


  • Bulk RNA-seq
  • Single-cell RNA-sequencing (scRNA-seq)
• Bulk RNA-seq


  The RNA is collected from a sample that includes many cells, representing an average of the gene expression across the entire sample
• Single-cell RNA-sequencing (scRNA-seq)


  The transcriptomes of individual cells are measured, revealing the different types of cells in a complex sample and/or the heterogeneity in gene expression within similar cell types
• Although scRNA-seq produces a dataset with more information, bulk RNA-seq is cheaper and often sufficient to answer the question at hand
• Steps of Bulk RNA-seq

  1. Isolate total RNA

  2. Break RNA into fragments

  3. Convert RNA to cDNA

  4. Add oligonucleotide adaptors

  5. PCR amplify the sample
• Sequence the library using Illumina sequencing


  Obtain millions of short reads (50-150 base pairs) that represent the transcriptome of the sample
• Analyze the data


  1. Map (align) the reads to the genome reference

  2. Perform Differential Gene Expression (DGE) analysis
• Differential Gene Expression (DGE) analysis


  A method used to identify and quantify differences in the expression levels (mRNA levels) of genes between two or more biological conditions or sample groups
• DGE analysis results


  • Gene 1: no change in gene expression
  • Gene 2: increased gene expression in the mutant
  • Gene 3: decreased gene expression in the mutant
• scRNA-seq (10X Genomics platform)


  Unique DNA barcodes are assigned to the transcripts that arise from a single cell, allowing mRNAs to be grouped together based on their barcodes to identify the transcriptome of each cell
• Advantages of scRNA-seq over RNA-seq


  • Can identify the complete cellular composition of the sample, including new cell types
  • Can quantify gene expression levels on a cell-type-specific basis
• UMAP (Uniform Manifold Approximation and Projection)


  A method for representing complex scRNA-seq data in two dimensions, where each dot represents the transcriptome of a single cell and dots that cluster together likely correspond to the same or similar cell types
• scRNA-seq data can be used to identify previously unknown cell types, as demonstrated by the Juliano lab's discovery of a new type of neuron in Hydra