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SMART-seq/SMART-seq2

SMART-seq/SMART-seq2#

Check this GitHub page to see how SMART-seq/SMART-seq2 libraries are generated experimentally. Those are plate-based methods, where single cells are sorted into 96- or 384-well plates in a one-cell-per-well manner. The library from each well is prepared separately and indexed using Illumina Nextera Index primers.

For Your Own Experiments#

Your sequencing read configuration is like this:

Order

Read

Cycle

Description

1

Read 1

>50

This yields R1_001.fastq.gz, cDNA reads

2

Index 1 (i7)

8 or 10

This yields I1_001.fastq.gz, Cell barcodes

3

Index 2 (i5)

8 or 10

This yields I2_001.fastq.gz, Cell barcodes

4

Read 2

>50

This yields R2_001.fastq.gz, cDNA reads

If you sequence your data via your core facility or a company, you will need to provide the index sequence to them and they will demultiplex for you. You will get one (single-end) or two (pair-end) fastq files per cell.

If you sequence by yourself, you probably need to run bcl2fatq by yourself. Prepare a SampleSheet.csv with index information for each well (cell), and get the fastq for each cell by running bcl2fastq. Here is an example of SampleSheet.csv of a NextSeq run (only showing 5 random cells):

[Header],,,,,,,,,,,
IEMFileVersion,5,,,,,,,,,,
Date,17/12/2019,,,,,,,,,,
Workflow,GenerateFASTQ,,,,,,,,,,
Application,NextSeq FASTQ Only,,,,,,,,,,
Instrument Type,NextSeq/MiniSeq,,,,,,,,,,
Assay,AmpliSeq Library PLUS for Illumina,,,,,,,,,,
Index Adapters,AmpliSeq CD Indexes (384),,,,,,,,,,
Chemistry,Amplicon,,,,,,,,,,
,,,,,,,,,,,
[Reads],,,,,,,,,,,
75,,,,,,,,,,,
75,,,,,,,,,,,
,,,,,,,,,,,
[Settings],,,,,,,,,,,
,,,,,,,,,,,
[Data],,,,,,,,,,,
Sample_ID,Sample_Name,Sample_Plate,Sample_Well,Index_Plate,Index_Plate_Well,I7_Index_ID,index,I5_Index_ID,index2,Sample_Project,Description
Meso_D3_A01,,,,,,N701,TAAGGCGA,S502,ATAGAGAG,,
Meso_D3_B02,,,,,,N702,CGTACTAG,S503,AGAGGATA,,
Meso_D3_C03,,,,,,N703,AGGCAGAA,S505,CTCCTTAC,,
Meso_D3_D04,,,,,,N704,TCCTGAGC,S506,TATGCAGT,,
Meso_D3_E05,,,,,,N705,GGACTCCT,S507,TACTCCTT,,

Simply run bcl2fastq like this:

bcl2fastq --no-lane-splitting \
          --ignore-missing-positions \
          --ignore-missing-controls \
          --ignore-missing-filter \
          --ignore-missing-bcls \
          -r 4 -w 4 -p 4

After this, you will have R1_001.fastq.gz and R2_001.fastq.gz for each well:

Meso_D3_A01_S1_R1_001.fastq.gz
Meso_D3_A01_S1_R2_001.fastq.gz
Meso_D3_B02_S2_R1_001.fastq.gz
Meso_D3_B02_S2_R2_001.fastq.gz
Meso_D3_C03_S3_R1_001.fastq.gz
Meso_D3_C03_S3_R2_001.fastq.gz
Meso_D3_D04_S4_R1_001.fastq.gz
Meso_D3_D04_S4_R2_001.fastq.gz
Meso_D3_E05_S5_R1_001.fastq.gz
Meso_D3_E05_S5_R2_001.fastq.gz

You are ready to go from here

Public Data#

For the purpose of demonstration, we will use the data from the following paper:

Note

Hagai T, Chen X, Miragaia RJ, Rostom R, Gomes T, Kunowska N, Henriksson J, Park J- E, Proserpio V, Donati G, Bossini-Castillo L, Braga FAV, Naamati G, Fletcher J, Stephenson E, Vegh P, Trynka G, Kondova I, Dennis M, Haniffa M, Nourmohammad A, Lässig M, Teichmann SA (2018) Gene expression variability across cells and species shapes innate immunity. Nature 563:197–202. https://doi.org/10.1038/s41586-018-0657-2

where SMART-seq2 was used to investigate the cellular responses of dermal fibroblasts from various species after polyI:C transfection which mimics virus attack.

The whole data can be accessed from ArrayExpress under the accession code E-MTAB-5920. There are a lot of cells, and we only focused on the unstimulated human cells that passed quality control. To get the URL of the fastq files, we could do:

curl https://www.ebi.ac.uk/arrayexpress/files/E-MTAB-5920/E-MTAB-5920.sdrf.txt | \
    grep HS_unst | \
    grep -w pass | \
    cut -f 38 > fq_url.txt

The output fq_url.txt contain the links to the fastq files, two per cell. There are 114 files (57 cells) there. To download them, do

mkdir -p hagai2018/data
wget -P hagai2018/data -c -i fq_url.txt

From FastQ To Count Matrix#

After downloading the data, we are now ready for the data preprocessing. To use starsolo with SMART-seq2 data, we need to create a manifest, which is a tab-delimited file mapping the fastq files to cell IDs. To prepare the manifest, you can run the following command in bash:

for i in $(ls -l hagai2018/data/*.gz | cut -f 2 -d/ | cut -f 1 -d '_' | sort -u); \
    do echo -e "hagai2018/data/${i}_1.fastq.gz\thagai2018/data/${i}_2.fastq.gz\t${i}"; \
    done > hagai2018_manifest.tsv

These are the first 5 lines of hagai2018_manifest.tsv:

hagai2018/data/ERR2060664_1.fastq.gz    hagai2018/data/ERR2060664_2.fastq.gz    ERR2060664
hagai2018/data/ERR2060665_1.fastq.gz    hagai2018/data/ERR2060665_2.fastq.gz    ERR2060665
hagai2018/data/ERR2060666_1.fastq.gz    hagai2018/data/ERR2060666_2.fastq.gz    ERR2060666
hagai2018/data/ERR2060667_1.fastq.gz    hagai2018/data/ERR2060667_2.fastq.gz    ERR2060667
hagai2018/data/ERR2060668_1.fastq.gz    hagai2018/data/ERR2060668_2.fastq.gz    ERR2060668

Once we have the manifest, we could start the preprocessing by simply doing:

STAR --runThreadN 4 \
     --genomeDir hg38/star_index \
     --readFilesCommand zcat \
     --outFileNamePrefix hagai2018/star_outs/ \
     --soloType SmartSeq \
     --readFilesManifest hagai2018_manifest.tsv \
     --soloUMIdedup Exact NoDedup \
     --soloStrand Unstranded \
     --outSAMtype BAM SortedByCoordinate

It should run successfully.

Explanation#

If you understand the SMART-seq/SMART-seq2 experimental procedures described in this GitHub Page, the command above should be straightforward to understand.

--runThreadN 4

Use 4 cores for the preprocessing. Change accordingly if using more or less cores.

--genomeDir hg38/star_index

Pointing to the directory of the star index.

--readFilesCommand zcat

Since the fastq files are in .gz format, we need the zcat command to extract them on the fly.

--outFileNamePrefix hagai2018/star_outs/

We want to keep everything organised. This directs all output files inside the hagai2018/star_outs directory.

--soloType SmartSeq

The technology used to generate the data. The data generated by SMART-seq and SMART-seq2 are exactly the same. The differences are in the experimental conditions, not the sequencing data.

--readFilesManifest hagai2018_manifest.tsv

Apparently, the manifest file we just created.

--soloUMIdedup Exact NoDedup

The SMART-seq2 data do not have UMI in the reads. Exact means perform the deduplication using the genomic coordinates, that is, fragments with the exact same starts and ends will be treated as duplicates. NoDedup means do not perform deduplication. In ChIP-seq, deduplication is standard. In non-UMI RNA-seq, it seems deduplication is not always enforced (I might be wrong). I’m not sure if this makes a huge difference. Putting both options here will generated two versions of count matrices, one with and one without deduplication.

--soloStrand Unstranded

The choice of this parameter depends on where the cDNA reads come from. You need to check the experimental protocol. If they are from the same strand as the mRNA (the coding strand), this parameter will be Forward (this is the default). If they are from the opposite strand as the mRNA, which is often called the first strand, this parameter will be Reverse. In the case of SMART-seq2, we have pair-end mode, and both Read 1 and Read 2 come from cDNA, so we need to ask: which strand does Read 1 come from? The library is generated from full-length double-stranded cDNA. Very often, it is constructed using the Illumina Nextera Library Preparation Kit. Alternatively, Fragmentase + adaptor ligation can also be used. In both cases, the strand information is lost during the procedure: the reads can originate from either strand of the gene. Therefore, use Unstranded for SMART-seq and SMART-seq2 data.

--outSAMtype BAM SortedByCoordinate

We want sorted BAM for easy handling by other programs.

If everything goes well, your directory should look the same as the following:

scg_prep_test/hagai2018
├── data
│   ├── ERR2060664_1.fastq.gz
│   ├── ERR2060664_2.fastq.gz
│   ├── ERR2060665_1.fastq.gz
│   ├── ERR2060665_2.fastq.gz
│   ├── ERR2060666_1.fastq.gz
│   ├── ERR2060666_2.fastq.gz
│   ├── ERR2060667_1.fastq.gz
│   ├── ERR2060667_2.fastq.gz
│   ├── ERR2060668_1.fastq.gz
│   ├── ERR2060668_2.fastq.gz
│   ├── ERR2060669_1.fastq.gz
│   ├── ERR2060669_2.fastq.gz
│   ├── ERR2060670_1.fastq.gz
│   ├── ERR2060670_2.fastq.gz
│   ├── ERR2060671_1.fastq.gz
│   ├── ERR2060671_2.fastq.gz
│   ├── ERR2060672_1.fastq.gz
│   ├── ERR2060672_2.fastq.gz
│   ├── ERR2060673_1.fastq.gz
│   ├── ERR2060673_2.fastq.gz
│   ├── ERR2060674_1.fastq.gz
│   ├── ERR2060674_2.fastq.gz
│   ├── ERR2060675_1.fastq.gz
│   ├── ERR2060675_2.fastq.gz
│   ├── ERR2060676_1.fastq.gz
│   ├── ERR2060676_2.fastq.gz
│   ├── ERR2060677_1.fastq.gz
│   ├── ERR2060677_2.fastq.gz
│   ├── ERR2060678_1.fastq.gz
│   ├── ERR2060678_2.fastq.gz
│   ├── ERR2060679_1.fastq.gz
│   ├── ERR2060679_2.fastq.gz
│   ├── ERR2060680_1.fastq.gz
│   ├── ERR2060680_2.fastq.gz
│   ├── ERR2060681_1.fastq.gz
│   ├── ERR2060681_2.fastq.gz
│   ├── ERR2060682_1.fastq.gz
│   ├── ERR2060682_2.fastq.gz
│   ├── ERR2060683_1.fastq.gz
│   ├── ERR2060683_2.fastq.gz
│   ├── ERR2060684_1.fastq.gz
│   ├── ERR2060684_2.fastq.gz
│   ├── ERR2060685_1.fastq.gz
│   ├── ERR2060685_2.fastq.gz
│   ├── ERR2060686_1.fastq.gz
│   ├── ERR2060686_2.fastq.gz
│   ├── ERR2060687_1.fastq.gz
│   ├── ERR2060687_2.fastq.gz
│   ├── ERR2060688_1.fastq.gz
│   ├── ERR2060688_2.fastq.gz
│   ├── ERR2060689_1.fastq.gz
│   ├── ERR2060689_2.fastq.gz
│   ├── ERR2060690_1.fastq.gz
│   ├── ERR2060690_2.fastq.gz
│   ├── ERR2060691_1.fastq.gz
│   ├── ERR2060691_2.fastq.gz
│   ├── ERR2060692_1.fastq.gz
│   ├── ERR2060692_2.fastq.gz
│   ├── ERR2060693_1.fastq.gz
│   ├── ERR2060693_2.fastq.gz
│   ├── ERR2060694_1.fastq.gz
│   ├── ERR2060694_2.fastq.gz
│   ├── ERR2060695_1.fastq.gz
│   ├── ERR2060695_2.fastq.gz
│   ├── ERR2060696_1.fastq.gz
│   ├── ERR2060696_2.fastq.gz
│   ├── ERR2060697_1.fastq.gz
│   ├── ERR2060697_2.fastq.gz
│   ├── ERR2060698_1.fastq.gz
│   ├── ERR2060698_2.fastq.gz
│   ├── ERR2060699_1.fastq.gz
│   ├── ERR2060699_2.fastq.gz
│   ├── ERR2060700_1.fastq.gz
│   ├── ERR2060700_2.fastq.gz
│   ├── ERR2060701_1.fastq.gz
│   ├── ERR2060701_2.fastq.gz
│   ├── ERR2060702_1.fastq.gz
│   ├── ERR2060702_2.fastq.gz
│   ├── ERR2060703_1.fastq.gz
│   ├── ERR2060703_2.fastq.gz
│   ├── ERR2060704_1.fastq.gz
│   ├── ERR2060704_2.fastq.gz
│   ├── ERR2060705_1.fastq.gz
│   ├── ERR2060705_2.fastq.gz
│   ├── ERR2060706_1.fastq.gz
│   ├── ERR2060706_2.fastq.gz
│   ├── ERR2060707_1.fastq.gz
│   ├── ERR2060707_2.fastq.gz
│   ├── ERR2060708_1.fastq.gz
│   ├── ERR2060708_2.fastq.gz
│   ├── ERR2060709_1.fastq.gz
│   ├── ERR2060709_2.fastq.gz
│   ├── ERR2060710_1.fastq.gz
│   ├── ERR2060710_2.fastq.gz
│   ├── ERR2060711_1.fastq.gz
│   ├── ERR2060711_2.fastq.gz
│   ├── ERR2060712_1.fastq.gz
│   ├── ERR2060712_2.fastq.gz
│   ├── ERR2060713_1.fastq.gz
│   ├── ERR2060713_2.fastq.gz
│   ├── ERR2060714_1.fastq.gz
│   ├── ERR2060714_2.fastq.gz
│   ├── ERR2060715_1.fastq.gz
│   ├── ERR2060715_2.fastq.gz
│   ├── ERR2060716_1.fastq.gz
│   ├── ERR2060716_2.fastq.gz
│   ├── ERR2060717_1.fastq.gz
│   ├── ERR2060717_2.fastq.gz
│   ├── ERR2060718_1.fastq.gz
│   ├── ERR2060718_2.fastq.gz
│   ├── ERR2060719_1.fastq.gz
│   ├── ERR2060719_2.fastq.gz
│   ├── ERR2060720_1.fastq.gz
│   └── ERR2060720_2.fastq.gz
└── star_outs
    ├── Aligned.sortedByCoord.out.bam
    ├── Log.final.out
    ├── Log.out
    ├── Log.progress.out
    ├── SJ.out.tab
    └── Solo.out
        ├── Barcodes.stats
        └── Gene
            ├── Features.stats
            ├── filtered
            │   ├── barcodes.tsv
            │   ├── features.tsv
            │   └── umiDedup-Exact.mtx
            ├── raw
            │   ├── barcodes.tsv
            │   ├── features.tsv
            │   ├── umiDedup-Exact.mtx
            │   └── umiDedup-NoDedup.mtx
            ├── Summary.csv
            └── UMIperCellSorted.txt

6 directories, 130 files
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