10x Genomics Single Cell 5’

Check this GitHub page to see how 10x Genomics Single Cell 5’ V2 libraries are generated experimentally. The sequencing configuration of the V2 chemistry is the same as the V1 chemistry. In the 5’ kit, both gene expression library and V(D)J library can be generated. We focused on the gene expression preprocessing in this documentation. This is a droplet-based method, where cells are captured inside droplets. At the same time, gel beads with barcoded Templates Switch Oligo (TSO) primer containing UMIs are also captured inside the droplet. Reverse transcription happens inside the droplet, and the mRNA molecules are captured at the 5’ end by the TSO. The cells and gel beads are loaded on the microfluidic device at certain concentrations, such that a fraction of droplets contain only one cell AND one bead.

For Your Own Experiments

Your sequencing read configuration is like this:

Order	Read	Cycle	Description
1	Read 1	At least 26	R1_001.fastq.gz, 16 bp cell barcodes + 10 bp UMI + TTTCTTATATGGG + 5’ of cDNA
2	Index 1 (i7)	8 or 10	I1_001.fastq.gz, Sample index
3	Index 2 (i5)	8 or 10 or None	I2_001.fastq.gz, Sample index (if using dual index)
4	Read 2	>50	R2_001.fastq.gz, cDNA reads

Most people just do 26 cycles for Read 1, but sometimes it is good to sequence longer because you will get the transcription start sites information.

If you sequence your data via your core facility or a company, you will need to provide the sample index sequence, which is the primer (PN-1000213) taken from the commercial kit from 10x Genomics, to them and they will demultiplex for you. You will get two fastq files per sample. Read 1 contains the cell barcodes and UMI and Read 2 contains the reads from cDNA.

If you sequence by yourself, you need to run bcl2fastq by yourself with a SampleSheet.csv. Here is an example of SampleSheet.csv of a NextSeq run with two different samples using the indexing primers from the A1 and B1 wells, respectively:

[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],,,,,,,,,,,
26,,,,,,,,,,,
98,,,,,,,,,,,
,,,,,,,,,,,
[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
Sample01,,,,,,SI-GA-A1_1,GGTTTACT,,,,
Sample01,,,,,,SI-GA-A1_2,CTAAACGG,,,,
Sample01,,,,,,SI-GA-A1_3,TCGGCGTC,,,,
Sample01,,,,,,SI-GA-A1_4,AACCGTAA,,,,
Sample02,,,,,,SI-GA-B1_1,GTAATCTT,,,,
Sample02,,,,,,SI-GA-B1_2,TCCGGAAG,,,,
Sample02,,,,,,SI-GA-B1_3,AGTTCGGC,,,,
Sample02,,,,,,SI-GA-B1_4,CAGCATCA,,,,

You can see each sample actually has four different index sequences. This is because each well from the plate PN-1000213 actually contain four different indices for base balancing. You can also use dual index (PN-1000215), and you should add that to the SampleSheet.csv if you use that. 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 sample:

# sample01
Sample01_S1_R1_001.fastq.gz # 26 bp: cell barcode + UMI
Sample01_S1_R2_001.fastq.gz # cDNA reads

# sample02
Sample02_S2_R1_001.fastq.gz # 26 bp: cell barcode + UMI
Sample02_S2_R2_001.fastq.gz # cDNA reads

You are good to go from here.

Public Data

For the purpose of demonstration, we will use the 10x Genomics Single Cell 5’ V2 Gene Expression data from the following paper:

Note

Masuda K, Kornberg A, Miller J, Lin S, Suek N, Botella T, Secener KA, Bacarella AM, Cheng L, Ingham M, Rosario V, Al-Mazrou AM, Lee-Kong SA, Kiran RP, Stoeckius M, Smibert P, Portillo AD, Oberstein PE, Sims PA, Yan KS, Han A(2020) Multiplexed single-cell analysis reveals prognostic and nonprognostic T cell types in human colorectal cancer. JCI Insight 7:e154646. https://doi.org/10.1172/jci.insight.154646

where the authors investigated quite a lot of T cells from colorectal cancer. We are going to use the data from the Aug13_sample1 sample. You can download the fastq file from this ArrayExpress page.

mkdir -p masuda2022/10x5p
wget -P masuda2022/10x5p -c ftp://ftp.sra.ebi.ac.uk/vol1/fastq/ERR466/006/ERR4667456/ERR4667456_1.fastq.gz
wget -P masuda2022/10x5p -c ftp://ftp.sra.ebi.ac.uk/vol1/fastq/ERR466/006/ERR4667456/ERR4667456_2.fastq.gz

Prepare Whitelist

The barcodes on the gel beads of the 10x Genomics platform are well defined. We need the information for the V2 chemistry. If you have cellranger in your computer, you will find a file called 737K-august-2016.txt in the lib/python/cellranger/barcodes/ directory. If you don’t have cellranger, I have prepared the file for you:

# download the whitelist 
wget -P masuda2022/10x5p https://teichlab.github.io/scg_lib_structs/data/10X-Genomics/737K-august-2016.txt.gz
gunzip masuda2022/10x5p/737K-august-2016.txt.gz

From FastQ To Count Matrix

Now we could start the preprocessing by simply doing:

STAR --runThreadN 4 \
     --genomeDir hg38/star_index \
     --readFilesCommand zcat \
     --outFileNamePrefix masuda2022/star_outs/ \
     --readFilesIn masuda2022/10x5p/ERR4667456_2.fastq.gz masuda2022/10x5p/ERR4667456_1.fastq.gz \
     --soloType CB_UMI_Simple \
     --soloCBstart 1 --soloCBlen 16 --soloUMIstart 17 --soloUMIlen 10 \
     --soloBarcodeReadLength 0 \
     --soloCBwhitelist masuda2022/10x5p/737K-august-2016.txt \
     --soloCellFilter EmptyDrops_CR \
     --soloStrand Reverse \
     --outSAMattributes CB UB \
     --outSAMtype BAM SortedByCoordinate

Explanation

If you understand the 10x Genomics Single Cell 5’ V2 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. The data is from human samples.

--readFilesCommand zcat

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

--outFileNamePrefix masuda2022/star_outs/

We want to keep everything organised. This directs all output files inside the masuda2022/star_outs directory.

--readFilesIn masuda2022/10x5p/ERR4667456_2.fastq.gz masuda2022/10x5p/ERR4667456_1.fastq.gz

If you check the manual, we should put two files here. The first file is the reads that come from cDNA, and the second the file should contain cell barcode and UMI. In 10x Genomics Single Cell 5’ V2, cDNA reads come from Read 2, and the cell barcode and UMI come from Read 1. Check the 10x Genomics Single Cell 5’ V2 GitHub Page if you are not sure.

--soloType CB_UMI_Simple

Most of the time, you should use this option, and specify the configuration of cell barcodes and UMI in the command line (see immediately below). Sometimes, it is actually easier to prepare the cell barcode and UMI file upfront so that we could use this parameter.

--soloCBstart 1 --soloCBlen 16 --soloUMIstart 17 --soloUMIlen 10

The name of the parameter is pretty much self-explanatory. If using --soloType CB_UMI_Simple, we can specify where the cell barcode and UMI start and how long they are in the reads from the first file passed to --readFilesIn. Note the position is 1-based (the first base of the read is 1, NOT 0).

--soloBarcodeReadLength 0

The length of the cell barcode + UMI (Read 1) is 16 + 10 = 26 bp. Therefore, star by default makes sure that reads from the Read 1 file is 26-bp long. However, the data we are analysing have 28 bp in length. The last two bp of Read 1 is all TT. This option turns off the length check and make sure the program runs without throwing an error.

--soloCBwhitelist masuda2022/10x5p/737K-august-2016.txt

The plain text file containing all possible valid cell barcodes, one per line. 10x Genomics Single Cell 5’ V2 is a commercial platform. The whitelist is taken from their commercial software cellranger.

--soloCellFilter EmptyDrops_CR

Experiments are never perfect. Even for droplets that do not contain any cell, you may still get some reads. In general, the number of reads from those droplets should be much smaller, often orders of magnitude smaller, than those droplets with cells. In order to identify true cells from the background, you can apply different algorithms. Check the star manual for more information. We use EmptyDrops_CR which is the most frequently used parameter.

--soloStrand Reverse

The choice of this parameter depends on where the cDNA reads come from, i.e. the reads from the first file passed to --readFilesIn. You need to check the experimental protocol. If the cDNA reads 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 10x Genomics Single Cell 5’ V2, the cDNA reads are from the Read 2 file. During the experiment, the mRNA molecules are captured at the 5’ end by the TSO with an Illumina Read 1 sequence. Therefore, Read 1 consists of cell barcodes and UMI comes from the coding strand. Read 2 comes from the first strand, complementary to the coding strand. Therefore, use Reverse for 10x Genomics Single Cell 5’ V2 data. Check the 10x Genomics Single Cell 5’ V2 GitHub Page if you are not sure.

--outSAMattributes CB UB

We want the cell barcode and UMI sequences in the CB and UB attributes of the output, respectively. The information will be very helpful for downstream analysis.

--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/masuda2022/
├── 10x5p
│   ├── 737K-august-2016.txt
│   ├── ERR4667456_1.fastq.gz
│   └── ERR4667456_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
            │   └── matrix.mtx
            ├── raw
            │   ├── barcodes.tsv
            │   ├── features.tsv
            │   └── matrix.mtx
            ├── Summary.csv
            └── UMIperCellSorted.txt

6 directories, 18 files