6k Single-cell Multimodal Readout of NIH-3T3, MyLa, Sez4 and PBMCs

Dataset: ECCITE-seq

Mimitou, E.P., Cheng, A., Montalbano, A., Hao, S., Stoeckius, M., Legut, M., Roush, T., Herrera, A., Papalexi, E., Ouyang, Z., et al. (2019). Multiplexed detection of proteins, transcriptomes, clonotypes and CRISPR perturbations in single cells. Nat. Methods 16, 409–412.


Preparation

Download fastq files from European Nucleotide Archive.

Hashtag (Cell hashing):

$ curl -O ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR855/007/SRR8550947/SRR8550947_1.fastq.gz

$ curl -O ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR855/007/SRR8550947/SRR8550947_2.fastq.gz

Protein-tag (CITE-seq):

$ curl -O ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR855/006/SRR8550946/SRR8550946_1.fastq.gz

$ curl -O ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR855/006/SRR8550946/SRR8550946_2.fastq.gz

Guide-tag (sgRNAs):

$ curl -O ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR855/008/SRR8550948/SRR8550948_1.fastq.gz

$ curl -O ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR855/008/SRR8550948/SRR8550948_2.fastq.gz

Download pre-processed transcriptome matrix from Gene Expression Omnibus. We will need the cell-associated barcodes, which are determined by the transcriptomes.

$ wget https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM3596nnn/GSM3596084/suppl/GSM3596084_mx-cDNA.txt.gz

Inspect cell barcodes:

$ gzip -dc GSM3596084_mx-cDNA.txt.gz | head -1 | sed 's/"//g' | sed 's/ /\n/g' | sort | grep -B1 1

CAGATCACACGTAAGG
CAGATCACACGTAAGG.1
--
CGCTATCAGCCGCCTA
CGCTATCAGCCGCCTA.1
--
TTTGCGCCAGTTCATG
TTTGCGCCAGTTCATG.1

It seems there are 3 colliding barcodes. We will use the first ones.

$ gzip -dc GSM3596084_mx-cDNA.txt.gz | head -1 | sed 's/"//g' | sed 's/ /\n/g' | sort | grep -v 1 > cell_barcodes.txt

$ head cell_barcodes.txt

AAACCTGAGTGGTAGC
AAACCTGAGTGTTTGC
AAACCTGAGTTAGGTA
AAACCTGCAAGTTGTC
AAACCTGCAATTCCTT
AAACCTGCACAGACTT
AAACCTGCACATCCGG
AAACCTGCACGGTAAG
AAACCTGCAGACACTT
AAACCTGCATCCGCGA

Hashtag

Preparation

Prepare feature barcodes (hashtag-oligo sequences, from Supplementary Table 4 and 5, legend of Supplementary Figure 1):

NIH-3T3 cells were split into 7 tubes and stained with 7 barcoded hashing antibodies (Hashtag-A to Hashtag-G), followed by washing and pooling. MyLa, Sez4 and PBMCs were stained with Hashtag_1, Hashtag_2 and Hashtag_3 respectively.

$ cat feature_barcodes_hashtag.tsv

Hashtag_1       ACATGTTACCGT
Hashtag_2       AGCTTACTATCC
Hashtag_3       TATCACATCGGT
Hashtag_A       AGGACCATCCAA
Hashtag_B       TCGATAATGCGA
Hashtag_C       GAGGCTGAGCTA
Hashtag_D       GTGTGACGTATT
Hashtag_E       ACTGTCTAACGG
Hashtag_F       CACATAATGACG
Hashtag_G       TAACGACGTGGT

QC

The first 100,000 read pairs are sampled (default, set by -n) 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 SRR8550947_1.fastq.gz \
    -2 SRR8550947_2.fastq.gz \
    -w cell_barcodes.txt \
    -f feature_barcodes_hashtag.tsv \
    --output_directory qc

2022-01-08 16:32:43,465 - fba.__main__ - INFO - fba version: 0.0.x
2022-01-08 16:32:43,465 - fba.__main__ - INFO - Initiating logging ...
2022-01-08 16:32:43,465 - fba.__main__ - INFO - Python version: 3.7
2022-01-08 16:32:43,465 - fba.__main__ - INFO - Using qc subcommand ...
2022-01-08 16:32:44,108 - fba.qc - INFO - Summarizing per base read content ...
2022-01-08 16:32:44,108 - fba.qc - INFO - Number of read pairs to analyze: 100,000
2022-01-08 16:32:44,108 - fba.qc - INFO - Output directory: qc
2022-01-08 16:32:44,429 - fba.qc - INFO - Number of reads processed: 100,000
2022-01-08 16:32:47,187 - fba.regex - INFO - regex version: 2.5.91
2022-01-08 16:32:47,192 - fba.regex - INFO - Number of reference cell barcodes: 6,871
2022-01-08 16:32:47,192 - fba.regex - INFO - Number of reference feature barcodes: 10
2022-01-08 16:32:47,192 - fba.regex - INFO - Cell barcode maximum number of mismatches: 3
2022-01-08 16:32:47,192 - fba.regex - INFO - Feature barcode maximum number of mismatches: 3
2022-01-08 16:32:47,192 - fba.regex - INFO - Read 1 maximum number of N allowed: inf
2022-01-08 16:32:47,192 - fba.regex - INFO - Read 2 maximum number of N allowed: inf
2022-01-08 16:32:47,192 - fba.regex - INFO - Number of read pairs to analyze: 100,000
2022-01-08 16:32:48,448 - fba.regex - INFO - Number of threads: 72
2022-01-08 16:32:48,448 - fba.regex - INFO - Chunk size: 50,000
2022-01-08 16:32:48,449 - fba.regex - INFO - Matching ...
2022-01-08 16:33:55,391 - fba.regex - INFO - Read pairs processed: 50,000
2022-01-08 16:35:02,689 - fba.regex - INFO - Read pairs processed: 100,000
2022-01-08 16:35:04,179 - fba.qc - INFO - Summarizing barcode coordinates ...
2022-01-08 16:35:04,179 - fba.qc - INFO - Output directory: qc
2022-01-08 16:35:05,431 - fba.__main__ - INFO - Done.

For read 1, the first 16 bases represent cell barcodes, and the following 10 bases are UMIs (the read 1 length is 26). According to the base content plot, the GC content of cell barcodes is relatively uniform. However, UMIs are slightly enriched with T bases.

../../../_images/Pyplot_read1_per_base_seq_content_hashtag.webp

As for read 2, the per base content suggests that bases 0-12 correspond to our feature barcodes (refer to the distribution of matched barcode positions on read 2).

../../../_images/Pyplot_read2_per_base_seq_content_hashtag.webp ../../../_images/Pyplot_read2_barcodes_starting_ending_hashtag.webp

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
NGACGGCGTGTGACGAACGCGCGCCT      GACGCGTGTGCGAAAC        1:18    0:2:1   ACATGTTACCGTCCCATATAAGAAAAGGCGCGCGTTCGT Hashtag_1_ACATGTTACCGT  0:12    0:0:0
NCTACACCACGGTAGAGACCTAGGTC      CACCACTGTGAGTGAC        4:19    2:0:1   AGGACCATCCAACCCATATAAGAAAGACCTAGGTCTCTA Hashtag_A_AGGACCATCCAA  0:12    0:0:0
GCAAACTAGATGGCGTCGACGCTTAG      GCAAACTAGATGGCGT        0:16    0:0:0   AGCTTACTATCCCCCATATAGAAGCTAAGCGTCGACGCC Hashtag_2_AGCTTACTATCC  0:12    0:0:0
CCTTCGAAGTGCCATTCTTTCACTGG      CCTTCGAAGTGCCATT        0:16    0:0:0   TATCACATCGGTCCCATATAAGAAACCAGTGAAAGAATG Hashtag_3_TATCACATCGGT  0:12    0:0:0
NGATCTGGTATGAAACGATCAGGTCA      AGATCTGGTATGAAAC        0:16    1:0:0   AGCTTACTATCCCCCATATAAGAAATGACCTGATCGTTT Hashtag_2_AGCTTACTATCC  0:12    0:0:0
NTCGGGATCTGTGCAAATCGGGTAGT      CTCGGGATCTGTGCAA        0:16    1:0:0   AGCTTACTATCCCCCATATAAGAAACTACCCGATTTGCA Hashtag_2_AGCTTACTATCC  0:12    0:0:0
GCGCGATGTACTTAGCTGCGTAGGTG      GCGCGATGTACTTAGC        0:16    0:0:0   TATCACATCGGTCCCATATAAGAAACACCTACGCAGCTA Hashtag_3_TATCACATCGGT  0:12    0:0:0
AACCGCGCACACAGAGCGTTTGGCCG      AACCGCGCACACAGAG        0:16    0:0:0   ACATGTTACCGTCCCATATAAGAAACGGCCAAACGCTCT Hashtag_1_ACATGTTACCGT  0:12    0:0:0
NAATGAACATGCGCACACGATAGTTT      no_match        NA      NA      TATCACATCGGTCCCATATAAGAAAAAACTATCGTGTGC NA      NA      NA

Barcode extraction

Both the cell and feature barcodes have identical lengths of 16 and 12, respectively. The qc results show a very uniform distribution of the starting and ending positions of the barcodes. The search range for read 1 is set to 0,16, and for read 2, it is set to 0,12. A single mismatch (-cb_m, -cf_m) is permitted for the cell and feature barcodes. Additionally, three ambiguous nucleotides (Ns) are allowed by default for both read 1 and read 2 (-cb_n, -cf_n).

$ fba extract \
    -1 SRR8550947_1.fastq.gz \
    -2 SRR8550947_2.fastq.gz \
    -w cell_barcodes.txt \
    -f feature_barcodes_hashtag.tsv \
    -o feature_barcoding_output.tsv.gz \
    -r1_c 0,16 \
    -r2_c 0,12 \
    -cb_m 1 \
    -fb_m 1 \
    -cb_n 3 \
    -fb_n 3

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
NGACGGCGTGTGACGAacgcgcgcct      TGACGGCGTGTGACGA        1       ACATGTTACCGTcccatataagaaaaggcgcgcgttcgt Hashtag_1_ACATGTTACCGT  0
NCTACACCACGGTAGAgacctaggtc      CCTACACCACGGTAGA        1       AGGACCATCCAAcccatataagaaagacctaggtctcta Hashtag_A_AGGACCATCCAA  0
GCAAACTAGATGGCGTcgacgcttag      GCAAACTAGATGGCGT        0       AGCTTACTATCCcccatatagaagctaagcgtcgacgcc Hashtag_2_AGCTTACTATCC  0
CCTTCGAAGTGCCATTctttcactgg      CCTTCGAAGTGCCATT        0       TATCACATCGGTcccatataagaaaccagtgaaagaatg Hashtag_3_TATCACATCGGT  0
NGATCTGGTATGAAACgatcaggtca      AGATCTGGTATGAAAC        1       AGCTTACTATCCcccatataagaaatgacctgatcgttt Hashtag_2_AGCTTACTATCC  0
NTCGGGATCTGTGCAAatcgggtagt      CTCGGGATCTGTGCAA        1       AGCTTACTATCCcccatataagaaactacccgatttgca Hashtag_2_AGCTTACTATCC  0
GCGCGATGTACTTAGCtgcgtaggtg      GCGCGATGTACTTAGC        0       TATCACATCGGTcccatataagaaacacctacgcagcta Hashtag_3_TATCACATCGGT  0
AACCGCGCACACAGAGcgtttggccg      AACCGCGCACACAGAG        0       ACATGTTACCGTcccatataagaaacggccaaacgctct Hashtag_1_ACATGTTACCGT  0
TCAGATGAGAATGTTGgtggggcttc      TCAGATGAGAATGTTG        0       TATCACATCGGTcccatataagaaagaagccccaccaac Hashtag_3_TATCACATCGGT  0

Result summary.

57.0% (4,897,995 out of 8,591,807) of total read pairs have valid cell and feature barcodes.

2022-01-08 16:35:05,778 - fba.__main__ - INFO - fba version: 0.0.x
2022-01-08 16:35:05,778 - fba.__main__ - INFO - Initiating logging ...
2022-01-08 16:35:05,778 - fba.__main__ - INFO - Python version: 3.7
2022-01-08 16:35:05,778 - fba.__main__ - INFO - Using extract subcommand ...
2022-01-08 16:35:05,791 - fba.levenshtein - INFO - Number of reference cell barcodes: 6,871
2022-01-08 16:35:05,791 - fba.levenshtein - INFO - Number of reference feature barcodes: 10
2022-01-08 16:35:05,791 - fba.levenshtein - INFO - Read 1 coordinates to search: [0, 16)
2022-01-08 16:35:05,791 - fba.levenshtein - INFO - Read 2 coordinates to search: [0, 12)
2022-01-08 16:35:05,791 - fba.levenshtein - INFO - Cell barcode maximum number of mismatches: 1
2022-01-08 16:35:05,792 - fba.levenshtein - INFO - Feature barcode maximum number of mismatches: 1
2022-01-08 16:35:05,792 - fba.levenshtein - INFO - Read 1 maximum number of N allowed: 3
2022-01-08 16:35:05,792 - fba.levenshtein - INFO - Read 2 maximum number of N allowed: 3
2022-01-08 16:35:05,984 - fba.levenshtein - INFO - Matching ...
2022-01-08 16:38:39,570 - fba.levenshtein - INFO - Number of read pairs processed: 8,591,807
2022-01-08 16:38:39,572 - fba.levenshtein - INFO - Number of read pairs w/ valid barcodes: 4,897,995
2022-01-08 16:38:39,582 - 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: Seurat and Scanpy.

$ fba count \
    -i feature_barcoding_output.tsv.gz \
    -o matrix_featurecount.csv.gz \
    -us 16 \
    -ul 10 \
    -um 1 \
    -ud directional

Result summary.

31.3% (1,531,088 out of 4,897,995) of read pairs with valid cell and feature barcodes are unique fragments. 17.8% (1,531,088 out of 8,591,807) of total sequenced read pairs contribute to the final matrix with an average of 55 UMIs per cell.

2022-01-08 16:41:49,871 - fba.__main__ - INFO - fba version: 0.0.x
2022-01-08 16:41:49,871 - fba.__main__ - INFO - Initiating logging ...
2022-01-08 16:41:49,871 - fba.__main__ - INFO - Python version: 3.7
2022-01-08 16:41:49,871 - fba.__main__ - INFO - Using demultiplex subcommand ...
2022-01-08 16:42:01,202 - fba.__main__ - INFO - Skipping arguments: "-p/--prob"
2022-01-08 16:42:01,203 - fba.demultiplex - INFO - Output directory: demultiplexed
2022-01-08 16:42:01,203 - fba.demultiplex - INFO - Demultiplexing method: 1
2022-01-08 16:42:01,203 - fba.demultiplex - INFO - UMI normalization method: clr
2022-01-08 16:42:01,203 - fba.demultiplex - INFO - Visualization: On
2022-01-08 16:42:01,203 - fba.demultiplex - INFO - Visualization method: tsne
2022-01-08 16:42:01,203 - fba.demultiplex - INFO - Loading feature count matrix: matrix_featurecount.csv.gz ...
2022-01-08 16:42:01,479 - fba.demultiplex - INFO - Number of cells: 6,871
2022-01-08 16:42:01,479 - fba.demultiplex - INFO - Number of positive cells for a feature to be included: 200
2022-01-08 16:42:01,498 - fba.demultiplex - INFO - Number of features: 10 / 10 (after filtering / original in the matrix)
2022-01-08 16:42:01,498 - fba.demultiplex - INFO - Features: Hashtag_1 Hashtag_2 Hashtag_3 Hashtag_A Hashtag_B Hashtag_C Hashtag_D Hashtag_E Hashtag_F Hashtag_G
2022-01-08 16:42:01,499 - fba.demultiplex - INFO - Total UMIs: 1,531,088 / 1,531,088
2022-01-08 16:42:01,507 - fba.demultiplex - INFO - Median number of UMIs per cell: 55.0 / 55.0
2022-01-08 16:42:01,507 - fba.demultiplex - INFO - Demultiplexing ...
2022-01-08 16:44:11,296 - fba.demultiplex - INFO - Generating heatmap ...
2022-01-08 16:44:17,314 - fba.demultiplex - INFO - Embedding ...
2022-01-08 16:44:28,444 - fba.__main__ - INFO - Done.

Demultiplexing

Cells are classified based on the abundance of features (hashtags, no transcriptome information used). Demultiplexing method 1 (set by -dm) is implemented based on the method described in Stoeckius, M., et al. (2018) with some modifications. A cell identity matrix is generated in the output directory (default demultiplexed, set by --output_directory): 0 means negative, 1 means positive. To adjust the quantile threshold for demultiplexing, use -q (default 0.9999). To generate visualization plots, set -v.

$ fba demultiplex \
    -i matrix_featurecount.csv.gz \
    --output_directory demultiplexed \
    -dm 1 \
    -v

2022-01-08 16:41:49,871 - fba.__main__ - INFO - fba version: 0.0.x
2022-01-08 16:41:49,871 - fba.__main__ - INFO - Initiating logging ...
2022-01-08 16:41:49,871 - fba.__main__ - INFO - Python version: 3.7
2022-01-08 16:41:49,871 - fba.__main__ - INFO - Using demultiplex subcommand ...
2022-01-08 16:42:01,202 - fba.__main__ - INFO - Skipping arguments: "-p/--prob"
2022-01-08 16:42:01,203 - fba.demultiplex - INFO - Output directory: demultiplexed
2022-01-08 16:42:01,203 - fba.demultiplex - INFO - Demultiplexing method: 1
2022-01-08 16:42:01,203 - fba.demultiplex - INFO - UMI normalization method: clr
2022-01-08 16:42:01,203 - fba.demultiplex - INFO - Visualization: On
2022-01-08 16:42:01,203 - fba.demultiplex - INFO - Visualization method: tsne
2022-01-08 16:42:01,203 - fba.demultiplex - INFO - Loading feature count matrix: matrix_featurecount.csv.gz ...
2022-01-08 16:42:01,479 - fba.demultiplex - INFO - Number of cells: 6,871
2022-01-08 16:42:01,479 - fba.demultiplex - INFO - Number of positive cells for a feature to be included: 200
2022-01-08 16:42:01,498 - fba.demultiplex - INFO - Number of features: 10 / 10 (after filtering / original in the matrix)
2022-01-08 16:42:01,498 - fba.demultiplex - INFO - Features: Hashtag_1 Hashtag_2 Hashtag_3 Hashtag_A Hashtag_B Hashtag_C Hashtag_D Hashtag_E Hashtag_F Hashtag_G
2022-01-08 16:42:01,499 - fba.demultiplex - INFO - Total UMIs: 1,531,088 / 1,531,088
2022-01-08 16:42:01,507 - fba.demultiplex - INFO - Median number of UMIs per cell: 55.0 / 55.0
2022-01-08 16:42:01,507 - fba.demultiplex - INFO - Demultiplexing ...
2022-01-08 16:44:11,296 - fba.demultiplex - INFO - Generating heatmap ...
2022-01-08 16:44:17,314 - fba.demultiplex - INFO - Embedding ...
2022-01-08 16:44:28,444 - fba.__main__ - INFO - Done.

Heatmap of the relative abundance of features (hashtags) across all cells. Each column represents a single cell.

Heatmap

t-SNE embedding of cells based on the abundance of features (hashtags, no transcriptome information used). Colors indicate the hashtag status for each cell, as called by FBA.

t-SNE embedding

Preview the demultiplexing result: the numbers of singlets, multiplets and negative cells. In summary, the numbers of MyLa, Sez4, PBMCs and NIH-3T3 cells demultiplexed are 324, 283, 914 and 4,518 respectively.

In [1]: import pandas as pd

In [2]: m = pd.read_csv("demultiplexed/matrix_cell_identity.csv.gz", index_col=0)

In [3]: m.loc[:, m.sum(axis=0) == 1].sum(axis=1)
Out[3]:
Hashtag_1    324
Hashtag_2    283
Hashtag_3    914
Hashtag_A    673
Hashtag_B    771
Hashtag_C    709
Hashtag_D    603
Hashtag_E    707
Hashtag_F    764
Hashtag_G    291
dtype: int64

In [4]: sum(m.sum(axis=0) > 1)
Out[4]: 341

In [5]: sum(m.sum(axis=0) == 0)
Out[5]: 491

Protein-tag

Preparation

Prepare feature barcodes (protein-tag sequences, from Supplementary Table 3, legend of Supplementary Figure 1):

All cells were stained with a mix of anti-human CD29 and anti-mouse CD29 antibodies.

$ cat feature_barcodes_CD29.tsv

hCD29   AATAGCGGAGCC
mCD29   CGAAGACCAAGA

QC

$ fba qc \
    -1 SRR8550946_1.fastq.gz \
    -2 SRR8550946_2.fastq.gz \
    -w cell_barcodes.txt \
    -f feature_barcodes_CD29.tsv \
    --output_directory qc

2022-01-08 12:29:00,323 - fba.__main__ - INFO - fba version: 0.0.x
2022-01-08 12:29:00,323 - fba.__main__ - INFO - Initiating logging ...
2022-01-08 12:29:00,323 - fba.__main__ - INFO - Python version: 3.7
2022-01-08 12:29:00,323 - fba.__main__ - INFO - Using qc subcommand ...
2022-01-08 12:29:00,896 - fba.qc - INFO - Summarizing per base read content ...
2022-01-08 12:29:00,896 - fba.qc - INFO - Number of read pairs to analyze: 100,000
2022-01-08 12:29:00,896 - fba.qc - INFO - Output directory: qc
2022-01-08 12:29:01,119 - fba.qc - INFO - Number of reads processed: 100,000
2022-01-08 12:29:03,848 - fba.regex - INFO - regex version: 2.5.91
2022-01-08 12:29:03,852 - fba.regex - INFO - Number of reference cell barcodes: 6,871
2022-01-08 12:29:03,852 - fba.regex - INFO - Number of reference feature barcodes: 2
2022-01-08 12:29:03,852 - fba.regex - INFO - Cell barcode maximum number of mismatches: 3
2022-01-08 12:29:03,852 - fba.regex - INFO - Feature barcode maximum number of mismatches: 3
2022-01-08 12:29:03,852 - fba.regex - INFO - Read 1 maximum number of N allowed: inf
2022-01-08 12:29:03,852 - fba.regex - INFO - Read 2 maximum number of N allowed: inf
2022-01-08 12:29:03,852 - fba.regex - INFO - Number of read pairs to analyze: 100,000
2022-01-08 12:29:05,062 - fba.regex - INFO - Number of threads: 72
2022-01-08 12:29:05,062 - fba.regex - INFO - Chunk size: 50,000
2022-01-08 12:29:05,062 - fba.regex - INFO - Matching ...
2022-01-08 12:31:07,626 - fba.regex - INFO - Read pairs processed: 50,000
2022-01-08 12:33:11,971 - fba.regex - INFO - Read pairs processed: 100,000
2022-01-08 12:33:13,341 - fba.qc - INFO - Summarizing barcode coordinates ...
2022-01-08 12:33:13,341 - fba.qc - INFO - Output directory: qc
2022-01-08 12:33:14,203 - fba.__main__ - INFO - Done.

The per base content analysis of read 2 suggests that bases 0-12 correspond to our feature barcodes, as indicated by the distribution of matched barcode positions (see fba qc results).

../../../_images/Pyplot_read2_per_base_seq_content_protein-tag.webp ../../../_images/Pyplot_read2_barcodes_starting_ending_protein-tag.webp

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 feature_barcoding_output.tsv.gz | head -20

read1_seq       cell_barcode    cb_matching_pos cb_matching_description read2_seq       feature_barcode fb_matching_pos fb_matching_description
NAGCCGATCACGCGGTCTGGTGGGCA      CAGCCGATCACGCGGT        0:16    1:0:0   AATTCCGTCAGATGACCCATATAAGAAATGCCCACCAGA no_match        NA      NA
NACACAAGTCTCCCTAGGCCTGTGAC      CAAGATCTCCCTTGTG        4:18    1:0:2   AATTCCGTCAGATGACCCATATAAGAAAGTCACAGGCCT no_match        NA      NA
TGTATTCAGGAGTCTGAATTGTAATA      CTCGAGGAGATCTGAA        4:18    1:0:2   AATTCCGTCAGATGACCCATATAAGAAATATTACAATTC no_match        NA      NA
NCGTCAAGTGCCTGGTGCTCCTGTAT      ACGTCAAGTGCCTGGT        0:16    1:0:0   CGAAGACCAAGACCCATATAAGAAAATACAGGAGCACCA mCD29_CGAAGACCAAGA      0:12    0:0:0
CTAGAGTAGATCGATACGCGGATGGT      CTAGAGTAGATCTGAA        0:15    1:0:1   CGAAGACCAAGACCCATATAAGAAAACCATCCGCGTATC mCD29_CGAAGACCAAGA      0:12    0:0:0
NATCGGGGTCGAACAGGGAGCGTCAG      AACCGCGAGCGTCAAG        11:26   2:0:1   AATTCCGTCAGATGACCCATATAAGAAACTGACGCTCCC no_match        NA      NA
AAAGCAAAGACAAGCCAGTATTTACG      ACACCAAGTCCAGTAT        7:21    1:0:2   AATTCCGTCAGATGACCCATATAAGAAACGTAAATACTG no_match        NA      NA
AAGCCGCGTCTCAACAACAGACTACG      AAGCCGCGTCTCAACA        0:16    0:0:0   AATTCCGTCAGATGACCCATATAAGAAACGTAGTCTGTT no_match        NA      NA
ACGGAGAAGCGCCTCACTCTATCTTC      ATCCGAAAGCGCCTCA        0:16    1:1:1   AATTCCGTCAGATGACCCATATAAGAAAGAAGATAGAGT no_match        NA      NA
TCGTACCCACCATCCTACACCGGCAC      CGGAGTCCACCATCCT        1:16    2:0:1   AATTCCGTCAGATGACCCATATAAGAAAAGTGCCGGTGT no_match        NA      NA
ACAGCTACAGTATGCTTAAAAACAGG      ACAGCTAAGTACTTGC        0:15    0:1:2   AATTCCGTCAGATGACCCATATAAGAAACCTGTTTTTAA no_match        NA      NA
NTAAGACGTCTAAACCGAGCTGGCAC      CAGCAGCGTCTAAACC        2:16    1:0:2   AATTCCGTCAGATGACCCATATAAGAAAGTGCCAGCTCG no_match        NA      NA
TCTCTAATCCAGTATGCCTCTCTTGA      AATCCAGTCCGCATCT        5:21    3:0:0   AATTCCGTCAGATGACCCATATAAGAAATCAAGAGAGGC mCD29_CGAAGACCAAGA      23:34   2:0:1
GACAGAGTCGCATGATTAAAAATCAA      ACAGCTATCGCATGAT        1:16    2:0:1   AATTCCGTCAGATGACCCATATAAGAAATTGATTTTTAA no_match        NA      NA
NTGAGGGTCTCCAACCGCTTTCTAAT      GATCGCGTCTCCAACC        2:16    1:0:2   AATTCCGTCAGATGACCCATATAAGAAAATTAGAAAGCG no_match        NA      NA
NAGCTGGTCGCCAAATACGTATAACT      GCCAAATTCGATAGAA        9:24    2:0:1   AATTCCGTCAGATGACCCATATAAGAAAAGTTATACGTA no_match        NA      NA
GTGCGGTAGTGTTGAAGGTTTATAAT      AGTAGTCAGAAGGTTT        7:21    1:0:2   AATTCCGTCAGATGACCCATATAAGAAAATTATAAACCT no_match        NA      NA
NGAGCACGTGCCTGTGCTACTAGTAC      ACGCCAGGTGCCTGTG        2:16    1:0:2   AATTCCGTCAGATGACCCATATAAGAAAGTACTAGTAGC no_match        NA      NA
CGGGTCAAGACACTAAAAAACCTGCT      ACACTGACAAACTGCT        9:26    2:1:0   AATTCCGTCAGATGACCCATATAAGAAAAGCAGGTTTTT no_match        NA      NA

Barcode extraction

$ fba extract \
    -1 SRR8550946_1.fastq.gz \
    -2 SRR8550946_2.fastq.gz \
    -w cell_barcodes.txt \
    -f feature_barcodes_CD29.tsv \
    -o feature_barcoding_output.tsv.gz \
    -r1_c 0,16 \
    -r2_c 0,12 \
    -cb_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
NCGTCAAGTGCCTGGTgctcctgtat      ACGTCAAGTGCCTGGT        1       CGAAGACCAAGAcccatataagaaaatacaggagcacca mCD29_CGAAGACCAAGA      0
CTAGAGTAGATCGATAcgcggatggt      CTAGAGTAGATCTGAA        2       CGAAGACCAAGAcccatataagaaaaccatccgcgtatc mCD29_CGAAGACCAAGA      0
GGAAAGCCAATCCGATatcccgtatc      GGAAAGCCAATCCGAT        0       CGAAGACCAAGAcccatataagaaagatacgggatatcg mCD29_CGAAGACCAAGA      0
GCAAACTCAAACAACAaaccttaagg      GCAAACTCAAACAACA        0       CGAAGACCAAGAcccatataagaaaccttaaggtttgtt mCD29_CGAAGACCAAGA      0
GTTACAGGTCTCCACTaatagaaggg      GTTACAGGTCTCCACT        0       AATAGCGGAGCCcccatataagaaacccttctattagtg hCD29_AATAGCGGAGCC      0
CGGACACAGGGCTTCCaaagttttag      CGGACACAGGGCTTCC        0       AATAGCGGAGCCcccatataagaaactaaaactttggaa hCD29_AATAGCGGAGCC      0
TACGGATTCACCACCTcaccctcttg      TACGGATTCACCACCT        0       CGAAGACCAAGAcccatataagaaacaagagggtgaggt mCD29_CGAAGACCAAGA      0
GCTTCCAGTTCCCTTGcagacaagag      GCTTCCAGTTCCCTTG        0       CGAAGACCAAGAcccatataagaaactcttgtctgcaag mCD29_CGAAGACCAAGA      0
CTGCCTAGTGAAATCAatggggaggc      CTGCCTAGTGAAATCA        0       CGAAGACCAAGAcccatataagaaagcctccccattgat mCD29_CGAAGACCAAGA      0

Result summary.

5.9% (256,759 out of 4,372,604) of total read pairs have valid cell and feature barcodes.

2022-01-08 12:33:14,547 - fba.__main__ - INFO - fba version: 0.0.x
2022-01-08 12:33:14,547 - fba.__main__ - INFO - Initiating logging ...
2022-01-08 12:33:14,547 - fba.__main__ - INFO - Python version: 3.7
2022-01-08 12:33:14,547 - fba.__main__ - INFO - Using extract subcommand ...
2022-01-08 12:33:14,561 - fba.levenshtein - INFO - Number of reference cell barcodes: 6,871
2022-01-08 12:33:14,561 - fba.levenshtein - INFO - Number of reference feature barcodes: 2
2022-01-08 12:33:14,561 - fba.levenshtein - INFO - Read 1 coordinates to search: [0, 16)
2022-01-08 12:33:14,561 - fba.levenshtein - INFO - Read 2 coordinates to search: [0, 12)
2022-01-08 12:33:14,561 - fba.levenshtein - INFO - Cell barcode maximum number of mismatches: 2
2022-01-08 12:33:14,561 - fba.levenshtein - INFO - Feature barcode maximum number of mismatches: 1
2022-01-08 12:33:14,561 - fba.levenshtein - INFO - Read 1 maximum number of N allowed: 3
2022-01-08 12:33:14,561 - fba.levenshtein - INFO - Read 2 maximum number of N allowed: 3
2022-01-08 12:33:15,856 - fba.levenshtein - INFO - Matching ...
2022-01-08 12:37:37,930 - fba.levenshtein - INFO - Number of read pairs processed: 4,372,604
2022-01-08 12:37:37,931 - fba.levenshtein - INFO - Number of read pairs w/ valid barcodes: 256,759
2022-01-08 12:37:37,985 - fba.__main__ - INFO - Done.

Matrix generation

$ fba count \
    -i feature_barcoding_output.tsv.gz \
    -o matrix_featurecount.csv.gz \
    -us 16 \
    -ul 10 \
    -um 1 \
    -ud directional

Result summary.

96.2% (246,996 out of 256,759) of read pairs with valid cell and feature barcodes are unique fragments. 5.6% (246,996 out of 4,372,604) of total sequenced read pairs contribute to the final matrix with an average of 29 UMIs per cell.

2022-01-08 12:37:38,233 - fba.__main__ - INFO - fba version: 0.0.x
2022-01-08 12:37:38,233 - fba.__main__ - INFO - Initiating logging ...
2022-01-08 12:37:38,233 - fba.__main__ - INFO - Python version: 3.7
2022-01-08 12:37:38,233 - fba.__main__ - INFO - Using count subcommand ...
2022-01-08 12:37:39,087 - fba.count - INFO - UMI-tools version: 1.1.1
2022-01-08 12:37:39,090 - fba.count - INFO - UMI starting position on read 1: 16
2022-01-08 12:37:39,090 - fba.count - INFO - UMI length: 10
2022-01-08 12:37:39,090 - fba.count - INFO - UMI-tools deduplication threshold: 1
2022-01-08 12:37:39,090 - fba.count - INFO - UMI-tools deduplication method: directional
2022-01-08 12:37:39,090 - fba.count - INFO - Header line: read1_seq cell_barcode cb_num_mismatches read2_seq feature_barcode fb_num_mismatches
2022-01-08 12:37:39,587 - fba.count - INFO - Number of lines processed: 256,759
2022-01-08 12:37:39,590 - fba.count - INFO - Number of cell barcodes detected: 6,871
2022-01-08 12:37:39,590 - fba.count - INFO - Number of features detected: 2
2022-01-08 12:37:40,917 - fba.count - INFO - Total UMIs after deduplication: 246,996
2022-01-08 12:37:40,926 - fba.count - INFO - Median number of UMIs per cell: 29.0
2022-01-08 12:37:40,983 - fba.__main__ - INFO - Done.

t-SNE embedding of cells based on the abundance of features (hashtags, no transcriptome information used). Colors indicate the hashtag status for each cell, as called by FBA, and the abundance of protein tags. This is a re-creation of Fig. 1c in Mimitou, E.P., et al. (2019) (The embedding is based on hashtags, not the transcriptomes).

t-SNE embedding

Guide-tag

Preparation

Prepare feature barcodes (guide-tag sequences, from Supplementary Table 2, Supplementary Figure 1c).

$ cat feature_barcodes_guide-tag.tsv

mNT1    CGCGGAGCCGAATACCTCG
mNT2    CGTCGAACCTCCGTGAAAG
mNT3    ATCGAGCCGAACTGCAACT
mNT4    AAGGCGTTCGCCTTACACG
mNT5    GACATTTAGTACCCGGAGT
mNT6    CTCGTTCCCTAACGGCGCG
mNT7    CCCGTAGACGGTCGAACAA
mNT8    CCATATCGCACCCGATGGG
mNT9    TTACTAGCAGGTGACGCCC
mNT10   AATACGTTGCGAGTAGAAG

QC

$ fba qc \
    -1 SRR8550948_1.fastq.gz \
    -2 SRR8550948_2.fastq.gz \
    -w cell_barcodes.txt \
    -f feature_barcodes_guide-tag.tsv \
    --output_directory qc

2022-01-08 14:39:46,616 - fba.__main__ - INFO - fba version: 0.0.x
2022-01-08 14:39:46,616 - fba.__main__ - INFO - Initiating logging ...
2022-01-08 14:39:46,617 - fba.__main__ - INFO - Python version: 3.7
2022-01-08 14:39:46,617 - fba.__main__ - INFO - Using qc subcommand ...
2022-01-08 14:39:47,209 - fba.qc - INFO - Summarizing per base read content ...
2022-01-08 14:39:47,209 - fba.qc - INFO - Number of read pairs to analyze: 100,000
2022-01-08 14:39:47,209 - fba.qc - INFO - Output directory: qc
2022-01-08 14:39:47,428 - fba.qc - INFO - Number of reads processed: 100,000
2022-01-08 14:39:50,099 - fba.regex - INFO - regex version: 2.5.91
2022-01-08 14:39:50,103 - fba.regex - INFO - Number of reference cell barcodes: 6,871
2022-01-08 14:39:50,103 - fba.regex - INFO - Number of reference feature barcodes: 10
2022-01-08 14:39:50,103 - fba.regex - INFO - Cell barcode maximum number of mismatches: 3
2022-01-08 14:39:50,103 - fba.regex - INFO - Feature barcode maximum number of mismatches: 3
2022-01-08 14:39:50,103 - fba.regex - INFO - Read 1 maximum number of N allowed: inf
2022-01-08 14:39:50,103 - fba.regex - INFO - Read 2 maximum number of N allowed: inf
2022-01-08 14:39:50,103 - fba.regex - INFO - Number of read pairs to analyze: 100,000
2022-01-08 14:39:51,295 - fba.regex - INFO - Number of threads: 72
2022-01-08 14:39:51,296 - fba.regex - INFO - Chunk size: 50,000
2022-01-08 14:39:51,296 - fba.regex - INFO - Matching ...
2022-01-08 14:40:49,770 - fba.regex - INFO - Read pairs processed: 50,000
2022-01-08 14:41:50,088 - fba.regex - INFO - Read pairs processed: 100,000
2022-01-08 14:41:51,366 - fba.qc - INFO - Summarizing barcode coordinates ...
2022-01-08 14:41:51,366 - fba.qc - INFO - Output directory: qc
2022-01-08 14:41:52,542 - fba.__main__ - INFO - Done.

As for read 2, the per base content suggests that bases 15-34 correspond to our feature barcodes (see the distribution of matched barcode positions on read 2).

../../../_images/Pyplot_read2_per_base_seq_content_guide-tag.webp ../../../_images/Pyplot_read2_barcodes_starting_ending_guide-tag.webp

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 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
NTAAGAGGTCTGCAATCTATATGCAA      AGCGGTCCATGCAATC        3:17    1:0:2   TTCTAGCTCTAAAACCCCGTAGACGGTCGAACAATCCCC mNT7_CCCGTAGACGGTCGAACAA        15:34   0:0:0
ATCCACCGTCATATCGACATGCCACA      ATCCACCGTCATATCG        0:16    0:0:0   TTCTAGCTCTAAAACCCCGTAGACGGTCGAACAATCCCC mNT7_CCCGTAGACGGTCGAACAA        15:34   0:0:0
TGCCAAACACTGAAGGATGTCGCCAC      ACACTGAAGGATGTAT        6:22    2:0:0   TTCTAGCTCTAAAACGACATTTAGTACCCGGAGTCCCCA mNT5_GACATTTAGTACCCGGAGT        15:34   0:0:0
NTCGAAGAGGGTATCGTGAAGTGCTT      AGGGATGGTGAAGGCT        7:25    1:2:0   TTCTAGCTCTAAAACCCATATCGCACCCGATGGGTCCCC mNT8_CCATATCGCACCCGATGGG        15:34   0:0:0
CCTTACGAGTGGACGTGCAGTCAGGT      CCTTACGAGTGGACGT        0:16    0:0:0   TTCTAGCTCTAAAACCTCGTTCCCTAACGGCGCGGCCCA mNT6_CTCGTTCCCTAACGGCGCG        15:34   0:0:0
GGTACAGGATCGCAAACGCGCAAATT      ACACTGATCGCAAACT        3:18    2:0:1   AATTCCGTCAGATGACCCATATAAGAAATTTGCGCGTTT no_match        NA      NA
NAAATGATCCAAACTGTAAGGGAAGC      AAACCTGTCCAAACTG        1:16    0:1:2   TTCTAGCTCTAAAACGACATTTAGTACCCGGAGTCCCCA mNT5_GACATTTAGTACCCGGAGT        15:34   0:0:0
NCACATAAGGAGTTGCGCAACCGCGA      CACATAGAGTTCGCGC        1:18    0:2:1   TTCTAGCTCTAAAACCCATATCGCACCCGATGGGTCCCC mNT8_CCATATCGCACCCGATGGG        15:34   0:0:0
AGGGTGAAGCGCTTATTAATCGAAGG      AGGGTGAAGCGATGAC        0:16    3:0:0   AATTCCGTCAGATGACCCATATAAGAAACCTTCGATTAA no_match        NA      NA

Barcode extraction

$ fba extract \
    -1 SRR8550948_1.fastq.gz \
    -2 SRR8550948_2.fastq.gz \
    -w cell_barcodes.txt \
    -f feature_barcodes_guide-tag.tsv \
    -o feature_barcoding_output.tsv.gz \
    -r1_c 0,16 \
    -r2_c 15,34 \
    -cb_m 2

Result summary.

63.4% (24,425,023 out of 38,537,829) of total read pairs have valid cell and feature barcodes.

2022-01-08 14:41:52,877 - fba.__main__ - INFO - fba version: 0.0.x
2022-01-08 14:41:52,877 - fba.__main__ - INFO - Initiating logging ...
2022-01-08 14:41:52,877 - fba.__main__ - INFO - Python version: 3.7
2022-01-08 14:41:52,877 - fba.__main__ - INFO - Using extract subcommand ...
2022-01-08 14:41:52,894 - fba.levenshtein - INFO - Number of reference cell barcodes: 6,871
2022-01-08 14:41:52,895 - fba.levenshtein - INFO - Number of reference feature barcodes: 10
2022-01-08 14:41:52,895 - fba.levenshtein - INFO - Read 1 coordinates to search: [0, 16)
2022-01-08 14:41:52,895 - fba.levenshtein - INFO - Read 2 coordinates to search: [15, 34)
2022-01-08 14:41:52,895 - fba.levenshtein - INFO - Cell barcode maximum number of mismatches: 2
2022-01-08 14:41:52,895 - fba.levenshtein - INFO - Feature barcode maximum number of mismatches: 1
2022-01-08 14:41:52,895 - fba.levenshtein - INFO - Read 1 maximum number of N allowed: 3
2022-01-08 14:41:52,895 - fba.levenshtein - INFO - Read 2 maximum number of N allowed: 3
2022-01-08 14:41:54,222 - fba.levenshtein - INFO - Matching ...
2022-01-08 14:54:23,469 - fba.levenshtein - INFO - Read pairs processed: 10,000,000
2022-01-08 15:06:57,696 - fba.levenshtein - INFO - Read pairs processed: 20,000,000
2022-01-08 15:19:24,990 - fba.levenshtein - INFO - Read pairs processed: 30,000,000
2022-01-08 15:30:09,131 - fba.levenshtein - INFO - Number of read pairs processed: 38,537,829
2022-01-08 15:30:09,131 - fba.levenshtein - INFO - Number of read pairs w/ valid barcodes: 24,425,023
2022-01-08 15:30:09,188 - fba.__main__ - INFO - Done.

Matrix generation

$ fba count \
    -i feature_barcoding_output.tsv.gz \
    -o matrix_featurecount.csv.gz \
    -us 16 \
    -ul 10 \
    -um 1 \
    -ud directional

Result summary.

4.3% (1,050,888 out of 24,425,023) of read pairs with valid cell and feature barcodes are unique fragments. 2.7% (1,050,888 out of 38,537,829) of total sequenced read pairs contribute to the final matrix with an average of 102 UMIs per cell.

2022-01-08 15:30:09,447 - fba.__main__ - INFO - fba version: 0.0.x
2022-01-08 15:30:09,447 - fba.__main__ - INFO - Initiating logging ...
2022-01-08 15:30:09,447 - fba.__main__ - INFO - Python version: 3.7
2022-01-08 15:30:09,447 - fba.__main__ - INFO - Using count subcommand ...
2022-01-08 15:30:10,313 - fba.count - INFO - UMI-tools version: 1.1.1
2022-01-08 15:30:10,316 - fba.count - INFO - UMI starting position on read 1: 16
2022-01-08 15:30:10,316 - fba.count - INFO - UMI length: 10
2022-01-08 15:30:10,316 - fba.count - INFO - UMI-tools deduplication threshold: 1
2022-01-08 15:30:10,316 - fba.count - INFO - UMI-tools deduplication method: directional
2022-01-08 15:30:10,316 - fba.count - INFO - Header line: read1_seq cell_barcode cb_num_mismatches read2_seq feature_barcode fb_num_mismatches
2022-01-08 15:30:53,780 - fba.count - INFO - Number of lines processed: 24,425,023
2022-01-08 15:30:53,785 - fba.count - INFO - Number of cell barcodes detected: 6,867
2022-01-08 15:30:53,785 - fba.count - INFO - Number of features detected: 10
2022-01-08 15:31:18,853 - fba.count - INFO - Total UMIs after deduplication: 1,050,888
2022-01-08 15:31:18,862 - fba.count - INFO - Median number of UMIs per cell: 102.0
2022-01-08 15:31:19,003 - fba.__main__ - INFO - Done.

t-SNE embedding of cells based on the abundance of features (hashtags, no transcriptome information used). Colors indicate the guide tag abundance for each cell, as caculated by FBA. This is a re-creation of Fig. 1c (iv) in Mimitou, E.P., et al. (2019) (The embedding is based on hashtags, not the transcriptomes).

t-SNE embedding