The Dataset

The paper

Dorothée Selimoglu-Buet, et al. “A miR-150/TET3 pathway regulates the generation of mouse and human non-classical monocyte subset.” Nature Communications volume 9, Article number: 5455 (2018)

The project on the European Nucleotide Archive (ENA), PRJEB29201.

Relevant RNA sections of the paper

Mouse models

Wild-type CD45.1 and CD45.2, C57BL/6 animals expressing the GFP under human ubiquitin C promoter38 and miR-150−/− mice were purchased from Jackson Laboratories (Charles River France, L’Arbresle, France). Mice harboring Tet3 allele with the coding sequences of exon 11 flanked by two loxP, a strategy similar that described with the Tet2 allele69, were generated by the Plateforme Recombinaison homolog (Institut Cochin, Paris, France) and were intercrossed with mice expressing tamoxifen-inducible Cre (Cre-ERT) transgene under control of the Scl/Tal1 promoter/enhancer. To delete Tet3-floxed alleles, tamoxifen was solubilized at 20 mg/ml in sunflower oil (Sigma-Aldrich, Saint-Quentin Fallavier, France) and 8 mg tamoxifen were administered to mice once per day for 2 days via oral gavage. For competitive and rescue experiments, recipient mice were housed in a barrier facility under pathogen-free conditions after transplantation. Cell transfer experiments were performed in 8- to 12-week-old female mice.

RNA-sequencing

RNA integrity (RNA integrity score ≥7.0) was checked on the Agilent 2100 Bioanalyzer (Agilent) and quantity was determined using Qubit (Invitrogen). SureSelect Automated Strand Specific RNA Library Preparation Kit was used according to the manufacturer’s instructions with the Bravo Platform. Briefly, 100 ng of total RNA sample was used for poly-A mRNA selection using oligo(dT) beads and subjected to thermal mRNA fragmentation. The fragmented mRNA samples were subjected to cDNA synthesis and were further converted into double-stranded DNA using the reagents supplied in the kit, and the resulting double-stranded DNA was used for library preparation. The final libraries were sequenced on a NovaSeq 6000 for mice samples (Illumina) in paired-end 100 bp mode in order to reach at least 30 millions reads per sample at Gustave Roussy.

For mouse sample analysis, Fastq files quality have been analyzed with FastQC (v0.11.7) and aggregated with MultiQC (v1.5). The quantification was performed on Gencode mouse M18 (GRCm38p6) transcriptome and comprehensive gene annotation, with Salmon (v0.10.2). The index was build with the default k-mer length of 31, with the genecode flag on, the perfect hash option, and all 1569 sequence duplicates within the genome were kept. The quantification was done with the default expected maximization algorithm, verified through 100 boostrap rounds, sequence-specific bias correction, fragment GC-bias correction, and automatic library detection parameter. Clustered heatmaps were performs from normalized counts with pheatmap, an R package, using Pearson’s coefficient as distance metric for rows and column and the Ward.D2 method for the clustering. Volcano plot were built using ggplot package. The differential analysis was performed with Sleuth (v0.29.0), on data converted by wasabi (v0.2).

Statistical analysis

Student’s t test were performed using the Prism software.

Results

RNA-sequencing of classical and nonclassical monocyte subsets of four healthy donors identified 2176 differentially expressed genes with an adjusted P value <0.01 (Supplementary Table 3 and Supplementary Figure 10C), including TET3 whose expression was down-regulated in nonclassical monocytes (−1.6-fold, P = 0.001) (Fig. 8d). Finally, an abnormal repartition of monocyte subsets, with an increase in Ly6Clow monocytes at the expanse of Ly6Chigh cells, was detected in the blood of mice carrying inactivated Tet3 alleles compared to wild-type littermates (Fig. 8e, f and Supplementary Figure 10D–F), without any change in the mean fluorescence intensity of CD115 and CX3CR1 at the surface of Tet3−/− mouse monocytes (Supplementary Figure 10G). Hence, these experiments argued for TET3 as a target of miR-150 whose down-regulation is required for the differentiation of classical into nonclassical monocytes, in mice and in humans.

Project Setup

Let’s set up a project directory for the week, and talk a bit about project philosophy..

Creating a Project Directory

First, create a directory for you and the example project in the workshop share directory:

cd
mkdir -p /share/workshop/mrnaseq_workshop/$USER/rnaseq_example

Link raw fastq files

1. Next, go into that directory, create a raw data directory (we are going to call this 00-RawData) and cd into that directory. Lets then create symbolic links to the sample directories that contains the raw data.

    cd /share/workshop/mrnaseq_workshop/$USER/rnaseq_example
    mkdir 00-RawData
    cd 00-RawData/
    ln -s /share/biocore/workshops/2020_mRNAseq_July/00-RawData/* .
   

   This directory now contains a folder for each sample and the fastq files for each sample are in the sample folders.

2. Let’s create a sample sheet for the project and store sample names in a file called samples.txt

    ls > ../samples.txt
    cat ../samples.txt
   

Getting to know your data

1. Now, take a look at the raw data directory.

    ls /share/workshop/mrnaseq_workshop/$USER/rnaseq_example/00-RawData
   

   You will see a list of the contents of each directory.

    ls *
   

   Lets get a better look at all the files in all of the directories.

    ls -lah */*
   

2. Pick a directory and go into it. View the contents of the files using the ‘less’ command, when gzipped used ‘zless’ (which is just the ‘less’ command for gzipped files):

    cd mouse_110_WT_C/
    zless mouse_110_WT_C.R1.fastq.gz
   

   Make sure you can identify which lines correspond to a read and which lines are the header, sequence, and quality values. Press ‘q’ to exit this screen.

3. Then, let’s figure out the number of reads in this file. A simple way to do that is to count the number of lines and divide by 4 (because the record of each read uses 4 lines). In order to do this use cat to output the uncompressed file and pipe that to “wc” to count the number of lines:

    zcat mouse_110_WT_C.R1.fastq.gz | wc -l
   

   Divide this number by 4 and you have the number of reads in this file.

4. One more thing to try is to figure out the length of the reads without counting each nucleotide. First get the first 4 lines of the file (i.e. the first record):

    zcat mouse_110_WT_C.R1.fastq.gz  | head -2 | tail -1
   

   Note the header lines (1st and 3rd line) and sequence and quality lines (2nd and 4th) in each 4-line fastq block.

5. Then, copy and paste the DNA sequence line into the following command (replace [sequence] with the line):

    echo -n [sequence] | wc -c
   

   This will give you the length of the read. Also can do the bash one liner:

    echo -n $(zcat mouse_110_WT_C.R1.fastq.gz  | head -2 | tail -1) | wc -c
   

   See if you can figure out how this command works.

   This will give you the read count without doing any division. See if you can figure out how this command works:

    zcat mouse_110_WT_C.R1.fastq.gz | grep -c "^@A00461:28"
   

Prepare our experiment folder for analysis

Now go back to your ‘rnaseq_example’ directory and create two directories called ‘slurmout’ and ‘01-HTS_Preproc’:

cd /share/workshop/mrnaseq_workshop/$USER/rnaseq_example
mkdir References
mkdir slurmout
mkdir 01-HTS_Preproc

We’ll put reference sequence, genome, etc. in the References directory. The results of all our slurm script will output .out and .err files into the slurmout folder. The results of our preprocessing steps will be put into the 01-HTS_Preproc directory. The next step after that will go into a “02-…” directory, etc. You can collect scripts that perform each step, and notes and metadata relevant for each step, in the directory for that step. This way anyone looking to replicate your analysis has limited places to search for the commands you used. In addition, you may want to change the permissions on your original 00-RawData directory to “read only”, so that you can never accidentally corrupt (or delete) your raw data. We won’t worry about this here, because we’ve linked in sample folders.

Your directory should then look like the below:

$ ls
00-RawData  01-HTS_Preproc  References  samples.txt  slurmout

Questions you should now be able to answer.

1. How many reads are in the sample you checked?
2. How many basepairs is R1, how many is R2?
3. What is the name of the sequencer this dataset was run on?
4. Which run number is this for that sequencer?
5. What lane was this ran on?
6. Randomly check a few samples, were they all run on the same sequencer, run, and lane?