1k Human PBMCs Stained with a Panel of TotalSeq B Antibodies#

Dataset: 1k Human PBMCs Stained with a Panel of TotalSeq B Antibodies, Dual Indexed

The detailed description of this dataset can be found here.

Preparation#

Download fastq files.

$ wget https://cf.10xgenomics.com/samples/cell-exp/4.0.0/SC3_v3_NextGem_DI_PBMC_CSP_1K/SC3_v3_NextGem_DI_PBMC_CSP_1K_fastqs.tar

$ tar xvf SC3_v3_NextGem_DI_PBMC_CSP_1K/SC3_v3_NextGem_DI_PBMC_CSP_1K_fastqs.tar

Combine reads of different lanes.

$ cat SC3_v3_NextGem_DI_CSP-Labeled_PBMCs_1K_fastqs/SC3_v3_NextGem_DI_CSP-Labeled_PBMCs_1K_antibody_fastqs/SC3_v3_NextGem_DI_CSP-Labeled_PBMCs_1K_antibody_S1_L00?_R1_001.fastq.gz > SC3_v3_NextGem_DI_CSP-Labeled_PBMCs_1K_antibody_S1_combined_R1.fastq.gz

$ cat SC3_v3_NextGem_DI_CSP-Labeled_PBMCs_1K_fastqs/SC3_v3_NextGem_DI_CSP-Labeled_PBMCs_1K_antibody_fastqs/SC3_v3_NextGem_DI_CSP-Labeled_PBMCs_1K_antibody_S1_L00?_R2_001.fastq.gz > SC3_v3_NextGem_DI_CSP-Labeled_PBMCs_1K_antibody_S1_combined_R2.fastq.gz

Download cell barcode info. These are the cell-associated barcodes in this single cell RNA-Seq library.

$ wget https://cf.10xgenomics.com/samples/cell-exp/4.0.0/SC3_v3_NextGem_DI_PBMC_CSP_1K/SC3_v3_NextGem_DI_PBMC_CSP_1K_filtered_feature_bc_matrix.tar.gz

$ tar zxvf SC3_v3_NextGem_DI_PBMC_CSP_1K_filtered_feature_bc_matrix.tar.gz

Inspect cell barcodes.

$ gzip -dc filtered_feature_bc_matrix/barcodes.tsv.gz | head

AAACCCAGTACCAGAG-1
AAACGCTTCGGTCTGG-1
AAACGCTTCGTTGCCT-1
AAAGAACAGGAACTCG-1
AAAGGATAGGTCGAGT-1
AAAGGGCCAAGACGGT-1
AAAGGGCCATCCTCAC-1
AAAGGTATCGTGGACC-1
AAAGTCCGTCGCGTTG-1
AAAGTCCTCCTTATCA-1

Prepare feature barcodes.

$ wget https://cf.10xgenomics.com/samples/cell-exp/4.0.0/SC3_v3_NextGem_DI_PBMC_CSP_1K/SC3_v3_NextGem_DI_PBMC_CSP_1K_feature_ref.csv

Inspect feature barcode info.

$ head SC3_v3_NextGem_DI_PBMC_CSP_1K_feature_ref.csv

id,name,read,pattern,sequence,feature_type
CD3,CD3,R2,^NNNNNNNNNN(BC)NNNNNNNNN,CTCATTGTAACTCCT,Antibody Capture
CD4,CD4,R2,^NNNNNNNNNN(BC)NNNNNNNNN,TGTTCCCGCTCAACT,Antibody Capture
CD8a,CD8a,R2,^NNNNNNNNNN(BC)NNNNNNNNN,GCTGCGCTTTCCATT,Antibody Capture
CD11b,CD11b,R2,^NNNNNNNNNN(BC)NNNNNNNNN,GACAAGTGATCTGCA,Antibody Capture
CD14,CD14,R2,^NNNNNNNNNN(BC)NNNNNNNNN,TCTCAGACCTCCGTA,Antibody Capture
CD15,CD15,R2,^NNNNNNNNNN(BC)NNNNNNNNN,TCACCAGTACCTAGT,Antibody Capture
CD16,CD16,R2,^NNNNNNNNNN(BC)NNNNNNNNN,AAGTTCACTCTTTGC,Antibody Capture
CD19,CD19,R2,^NNNNNNNNNN(BC)NNNNNNNNN,CTGGGCAATTACTCG,Antibody Capture
CD20,CD20,R2,^NNNNNNNNNN(BC)NNNNNNNNN,TTCTGGGTCCCTAGA,Antibody Capture

Clean up.

$ wc -l SC3_v3_NextGem_DI_PBMC_CSP_1K_feature_ref.csv

33 SC3_v3_NextGem_DI_PBMC_CSP_1K_feature_ref.csv

$ cut -d',' -f1,5 SC3_v3_NextGem_DI_PBMC_CSP_1K_feature_ref.csv | tail -32 | sed 's/,/\t/g' > feature_barcode_ref.tsv

$ head feature_barcode_ref.tsv

CD3     CTCATTGTAACTCCT
CD4     TGTTCCCGCTCAACT
CD8a    GCTGCGCTTTCCATT
CD11b   GACAAGTGATCTGCA
CD14    TCTCAGACCTCCGTA
CD15    TCACCAGTACCTAGT
CD16    AAGTTCACTCTTTGC
CD19    CTGGGCAATTACTCG
CD20    TTCTGGGTCCCTAGA
CD25    TTTGTCCTGTACGCC

QC#

The first 20,000 read pairs are sampled (set by -n, default 100,000) for quality control. The -t option can be used to set the number of threads. By default, diagnostic results and plots are generated in the qc directory (set by --output_directory), and the full length of read 1 and read 2 are searched against reference cell and feature barcodes, respectively. The per base content of both read pairs and the distribution of matched barcode positions are summarized. Use -r1_c and/or -r2_c to limit the search range, and -cb_n and/or -fb_n to set the mismatch tolerance for cell and/or feature barcode matching (default 3).

$ fba qc \
    -1 SC3_v3_NextGem_DI_CSP-Labeled_PBMCs_1K_antibody_S1_combined_R1_001.fastq.gz \
    -2 SC3_v3_NextGem_DI_CSP-Labeled_PBMCs_1K_antibody_S1_combined_R2_001.fastq.gz \
    -w filtered_feature_bc_matrix/barcodes.tsv.gz \
    -f SC3_v3_NextGem_DI_PBMC_CSP_1K_feature_ref.tsv \
    --output_directory qc \
    -n 20000

This library was constructed using the Chromium Next GEM Single Cell 3ʹ Reagent Kits v3.1 (Dual Index) with Feature Barcode technology for Cell Surface Protein, and was sequenced on the Illumina NovaSeq 6000 platform. The first 16 bases of read 1 correspond to the cell barcodes, followed by 12 bases for UMIs. The base content plot reveals that the GC content of the cell barcodes is evenly distributed, while the UMIs show a slight enrichment for the nucleotide T.

../../../_images/Pyplot_read1_per_base_seq_content2.webp

Regarding read 2, the per base content analysis suggests that for the sampled reads, bases 0-9, 25-33, and 56-83 exhibit a balanced GC content, which indicates that their sequences are likely random at the library level. However, bases 34-55 and 84-89 appear to be constant sequences, and we can almost read the bases in these regions. The bases 10-24 are less random but also not constant, as they correspond to our feature barcodes, which is evident from the distribution of matched barcode positions on read 2.

../../../_images/Pyplot_read2_per_base_seq_content2.webp

../../../_images/Pyplot_read2_barcodes_starting_ending2.webp

The fragment structure inferred from the qc results of this feature barcoding library matches the design as specified on Page 3, Table 1 (Note that this is an example dataset from 10x Genomics). The sequence of bases 34-56 is referred to as “Capture Sequence 1” on the beads.

The detailed qc results are stored in the feature_barcoding_output.tsv.gz file. The matching_pos columns indicate the matched positions on reads, while the matching_description columns indicate mismatches in the format of substitutions:insertions:deletions.

$ gzip -dc qc/feature_barcoding_output.tsv.gz | head

read1_seq       cell_barcode    cb_matching_pos cb_matching_description read2_seq       feature_barcode fb_matching_pos fb_matching_description
GNAGGTTTCGTCGACACGGGTATGGCCA    GTAGGTTAGGTCGACA        0:16    3:0:0   GGAACGACGATCTCAGACCTCCGTAACGAACGTAGCTTTAAGGCCGGTCCTAGCAATGGCCATACCCGTGTCGACGAAACCTACCTGTCT      CD14_TCTCAGACCTCCGTA    10:25   0:0:0
TCGTGGGCAAGATTGAGTAAAAATTCAG    TCGTGGGCAAACTAGA        0:16    3:0:0   CAATCTACCGCTGGGCAATTACTCGCCCGATGTGGCTTTAAGGCCGGTCCTAGCAACTGAATTTTTACTCAATCTTGCCCACGACTGTCT      CD19_CTGGGCAATTACTCG    10:25   0:0:0
TCCGGGATCGTGGCTATGTGTTCGAGGG    no_match        NA      NA      TGGGTCAATTCTCATTGTAACTCCTTCCCTCAAAGCTTTAAGGCCGGTCCTAGCAACCCTCGAACACATAGCCACGATCCCGGACTGTCT      NA      NA      NA
GATTGGTGTGTCTCTCGGTGCCCAAAAT    no_match        NA      NA      TACACTGACAGCCGGACGACATTAAACGGAAGCCGCTTTAAGGCCGGTCCTAGCAAATTTTGGGCACCGAGAGACACACCAATCCTGTCT      NA      NA      NA
TGCTCGTAGTACAGGTAAGCGTGAAGCA    CATCGTCGTACAGGTG        2:17    2:0:1   GCCCACAACACTCCGAATCATGTTGACGGGGTTCGCTTTAAGGCCGGTCCTAGCAATGCTTCACGCTTACCTGTACTACGAGCACTGTCG      CD45RO_CTCCGAATCATGTTG  10:25   0:0:0
AGATGAAAGGGAGTAGGGTGCGGGTTAT    AGGGAGTAGGAGGGTG        7:22    2:0:1   TGCGTTTCTTCACCAGTACCTAGTCACACGTCAGCTTTAAGGCCGGCCCTAGCAAATAACCCGCACCCTACTCCCTTTCATCTCTGTCTC      CD15_TCACCAGTACCTAGT    9:24    0:0:0
CCTCCTCAGCTCTGCATATGAGCGAATT    no_match        NA      NA      AAGCAATGCTTCACCAGTACCTAGTCGTTCGTGTGCTTAAGGCCGGTCCTAGCAAAATTCGCTCATATGCAGAGCTGAGGAGGCTGTCTC      NA      NA      NA
TGATCTTAGAACACGTCAGGGTCCTGAA    TGATCTTTCAACACGT        0:16    2:0:0   GGGGGGGGGGGGGGGGAGGGGGCCGGAAAAGAACCCCGAGAGGCCAGCGCCAAACAAAAAAGAACAAAAAAGAGGAAAAAAAAAAAAAAA      no_match        NA      NA
GGGCTACAGGACGCTGGTTTCATTTTTT    CTGGTCTTCATTGTTC        13:28   2:0:1   CCTTAATCAACTCATTGTAACTCCTGTTCCCACAGCTTTAAGGCCGGTCCTAGCAAAAAAAATGAAACCAGCGTCCTGTAGCCCCTGTCT      CD3_CTCATTGTAACTCCT     10:25   0:0:0

Barcode extraction#

The lengths of cell and feature barcodes are all identical (16 and 15, respectively). And based on the qc results, the distributions of starting and ending positions of cell and feature barcodes are very uniform. Search ranges are set to 0,16 on read 1 and 10,25 on read 2. Two mismatches for cell and feature barcodes (-cb_m, -cf_m) are allowed. And by default, three ambiguous nucleotides (Ns) for read 1 and read2 (-cb_n, -cf_n) are allowed.

$ fba extract \
    -1 SC3_v3_NextGem_DI_CSP-Labeled_PBMCs_1K_antibody_S1_combined_R1_001.fastq.gz \
    -2 SC3_v3_NextGem_DI_CSP-Labeled_PBMCs_1K_antibody_S1_combined_R2_001.fastq.gz \
    -w filtered_feature_bc_matrix/barcodes.tsv.gz \
    -f SC3_v3_NextGem_DI_PBMC_CSP_1K_feature_ref.tsv \
    -o feature_barcoding_output.tsv.gz \
    -r1_c 0,16 \
    -r2_c 10,25 \
    -cb_m 2 \
    -fb_m 2

Preview of result.

$ gzip -dc feature_barcoding_output.tsv.gz | head

read1_seq       cell_barcode    cb_num_mismatches       read2_seq       feature_barcode fb_num_mismatches
TCGTGGGCAAGATTGAgtaaaaattcag    TCGTGGGGTAGATTGA        2       caatctaccgCTGGGCAATTACTCGcccgatgtggctttaaggccggtcctagcaactgaatttttactcaatcttgcccacgactgtct    CD19_CTGGGCAATTACTCG     0
TGTCCACTCTAGGGTCcagaaatcgcag    TGTCCACAGTAGGGTC        2       gtggccgtgtTCAATCCTTCCGCTTcgtcgttctgctttaaggccggtcctagcaagctgcgatttctggaccctagagtggacactgtc    CD45RA_TCAATCCTTCCGCTT   0
TTTCGATAGTGTCAGAtttacttatggt    TTTCGATTCTGTCAGA        2       caaagtcctaTCTCAGACCTCCGTAaagcatgtggctttaaggccggtcctagcaaaccataagtaaatctgacactatcgaaactgtct    CD14_TCTCAGACCTCCGTA     0
ACCATTTCAGAGTAGCaaaaccgttggg    ACCATTTGTGAGTAGC        2       acacgtggcgTGTTCCCGCTCAACTgtgcttcaagctttaaggccggtcctagcaacccaacggttttgctactctgaaatggtctgtct    CD4_TGTTCCCGCTCAACT      0
CATGCGGAGCACAGCGctagttacaaac    CATGCGGTCCACAGCG        2       ctgagcaggaTCTCAGACCTCCGTAatgggttaagctttaaggccggtcctagcaagtttgtaactagcgctgtgctccgcatgctgtct    CD14_TCTCAGACCTCCGTA     0
TGCCGAGCAACGTAGGgcgtaattagcg    TGCCGAGGTACGTAGG        2       aggagacccgTCTCAGACCTCCGTAagtacccgagctttaaggccggtcctagcaacgctaattacgccctacgttgctcggcactgtct    CD14_TCTCAGACCTCCGTA     0
ATTCCATGTCTCTCGTcgtctaactccc    ATTCCATCACTCTCGT        2       atgcagagtgCTCATTGTAACTCCTccgtttgacgctttaaggccggtcctagcaagggagttagacgacgagagacatggaatctgtct    CD3_CTCATTGTAACTCCT      0
GGCAGTCGTAAGGTTAtgcaccacacga    GGCAGTCCAAAGGTTA        2       gcaacatggtTCTCAGACCTCCGTAgcatgttaggctttaaggccggtcctagcaatcgtgtggtgcataaccttacgactgccctgtct    CD14_TCTCAGACCTCCGTA     0
GATGGAGGTGAGCTAGaaatgccaagtt    GATGGAGCAGAGCTAG        2       acacaatgaaTGTTCCCGCTCAACTtaccggggtgctttaaggccggtcctagcaaaacttggcatttctagctcacctccatcctgtct    CD4_TGTTCCCGCTCAACT      0

Result summary.

59.8% (4,607,787 out of 7,704,799) of total read pairs have valid cell and feature barcodes.

2021-02-17 23:37:41,353 - fba.__main__ - INFO - fba version: 0.0.7
2021-02-17 23:37:41,353 - fba.__main__ - INFO - Initiating logging ...
2021-02-17 23:37:41,353 - fba.__main__ - INFO - Python version: 3.7
2021-02-17 23:37:41,353 - fba.__main__ - INFO - Using extract subcommand ...
2021-02-17 23:37:41,356 - fba.levenshtein - INFO - Number of reference cell barcodes: 1,200
2021-02-17 23:37:41,356 - fba.levenshtein - INFO - Number of reference feature barcodes: 32
2021-02-17 23:37:41,356 - fba.levenshtein - INFO - Read 1 coordinates to search: [0, 16)
2021-02-17 23:37:41,356 - fba.levenshtein - INFO - Read 2 coordinates to search: [10, 25)
2021-02-17 23:37:41,356 - fba.levenshtein - INFO - Cell barcode maximum number of mismatches: 2
2021-02-17 23:37:41,356 - fba.levenshtein - INFO - Feature barcode maximum number of mismatches: 2
2021-02-17 23:37:41,356 - fba.levenshtein - INFO - Read 1 maximum number of N allowed: 3
2021-02-17 23:37:41,356 - fba.levenshtein - INFO - Read 2 maximum number of N allowed: 3
2021-02-17 23:37:41,634 - fba.levenshtein - INFO - Matching ...
2021-02-17 23:53:22,264 - fba.levenshtein - INFO - Number of read pairs processed: 7,704,799
2021-02-17 23:53:22,264 - fba.levenshtein - INFO - Number of read pairs w/ valid barcodes: 4,607,787
2021-02-17 23:53:22,279 - fba.__main__ - INFO - Done.

Matrix generation#

Only fragments with correctly matched cell and feature barcodes are included, while fragments with UMI lengths less than the specified value are discarded. UMI removal is performed using UMI-tools (Smith, T., et al. 2017. Genome Res. 27, 491–499.), with the starting position on read 1 set by -us (default 16) and the length set by -ul (default 12). The UMI deduplication method can be set using -ud (default directional), and the UMI deduplication mismatch threshold can be specified using -um (default 1).

The generated feature count matrix can be easily imported into well-established single cell analysis packages such as Seurat and Scanpy.

$ fba count \
    -i feature_barcoding_output.tsv.gz \
    -o matrix_featurecount.csv.gz \
    -us 16 \
    -ul 12 \
    -um 1 \
    -ud directional

Result summary.

69.8% (3,214,503 out of 4,607,787) of read pairs with valid cell and feature barcodes are unique fragments. 41.7% (3,214,503 out of 7,704,799) of total sequenced read pairs contribute to the final matrix.

2021-02-17 23:53:36,024 - fba.__main__ - INFO - fba version: 0.0.7
2021-02-17 23:53:36,024 - fba.__main__ - INFO - Initiating logging ...
2021-02-17 23:53:36,024 - fba.__main__ - INFO - Python version: 3.7
2021-02-17 23:53:36,024 - fba.__main__ - INFO - Using count subcommand ...
2021-02-17 23:53:36,024 - fba.count - INFO - UMI-tools version: 1.1.1
2021-02-17 23:53:36,027 - fba.count - INFO - UMI starting position on read 1: 16
2021-02-17 23:53:36,027 - fba.count - INFO - UMI length: 12
2021-02-17 23:53:36,027 - fba.count - INFO - UMI-tools deduplication threshold: 1
2021-02-17 23:53:36,027 - fba.count - INFO - UMI-tools deduplication method: directional
2021-02-17 23:53:36,027 - fba.count - INFO - Header line: read1_seq cell_barcode cb_num_mismatches read2_seq feature_barcode fb_num_mismatches
2021-02-17 23:53:49,419 - fba.count - INFO - Number of lines processed: 4,607,787
2021-02-17 23:53:49,422 - fba.count - INFO - Number of cell barcodes detected: 1,199
2021-02-17 23:53:49,422 - fba.count - INFO - Number of features detected: 30
2021-02-17 23:55:18,907 - fba.count - INFO - Total UMIs after deduplication: 3,214,503
2021-02-17 23:55:18,910 - fba.count - INFO - Median number of UMIs per cell: 2,564.0
2021-02-17 23:55:18,944 - fba.__main__ - INFO - Done.