10x Genomics Support/Cell Ranger 7.2/Tutorials/

Analyzing Barcode Enabled Antigen Capture for T Cells (BEAM-T) with Cell Ranger multi

To follow along, you must:

  • Have basic UNIX command line experience
  • Fulfill these system requirements
  • Download and install the Cell Ranger software
  • Choose a compute platform
  • Have access to a UNIX command prompt

We will work with the 5k Human A0201 | B0702 PBMCs (BEAM-T) dataset.

Open up a terminal window. You may log in to a remote server or choose to perform the compute on your local machine. Refer to the System Requirements page for details.

In the working directory, create a new folder called beam-t and cd into that folder:

mkdir beam-t cd beam-t

Download the input FASTQ files:

curl -O https://cf.10xgenomics.com/samples/cell-vdj/7.1.0/5k_BEAM-T_Human_A0201_B0702_PBMC_5pv2_Multiplex/5k_BEAM-T_Human_A0201_B0702_PBMC_5pv2_Multiplex_fastqs.tar

A file named 5k_BEAM-T_Human_A0201_B0702_PBMC_5pv2_Multiplex_fastqs.tar should appear in your directory when you list files with the ls -lt command.

Uncompress the FASTQs:

tar -xf 5k_BEAM-T_Human_A0201_B0702_PBMC_5pv2_Multiplex_fastqs.tar

You should now see a folder called 5k_BEAM-T_Human_A0201_B0702_PBMC_5pv2_fastq

cd 5k_BEAM-T_Human_A0201_B0702_PBMC_5pv2_fastq ls

The folder contains three subfolders with library-specific FASTQS files: antigen_capture, gex, and vdj.

Navigate back to the working directory:

cd ..

Double check you are in the correct directory by running the ls command; the working directory should have the FASTQs 5k_BEAM-T_Human_A0201_B0702_PBMC_5pv2_fastqs folder.

Download the Feature Reference CSV available for this example dataset.

curl -O https://cf.10xgenomics.com/samples/cell-vdj/7.1.0/5k_BEAM-T_Human_A0201_B0702_PBMC_5pv2_Multiplex/5k_BEAM-T_Human_A0201_B0702_PBMC_5pv2_Multiplex_count_feature_reference.csv

To view the contents of the Feature Reference CSV, open it in your text editor of choice (e.g., nano)

nano 5k_BEAM-T_Human_A0201_B0702_PBMC_5pv2_Multiplex/5k_BEAM-T_Human_A0201_B0702_PBMC_5pv2_Multiplex_count_feature_reference.csv

The contents should look like this:

id,name,read,pattern,sequence,feature_type,mhc_allele Flu_A0201,Flu_A0201,R2,^(BC),GATTGGCTACTCAAT,Antigen Capture,HLA-A*02:01 CMV_B0702,CMV_B0702,R2,^(BC),CGGCTCACCGCGTCT,Antigen Capture,HLA-B*07:02 negative_control_A0201,negative_control_A0201,R2,^(BC),CTATCTACCGGCTCG,Antigen Capture,HLA-A*02:01 negative_control_B0702,negative_control_B0702,R2,^(BC),CATGTCTACGTTAAG,Antigen Capture,HLA-B*07:02

Since this is a BEAM-T (TCR Antigen Capture) dataset, the Feature Reference CSV contains the additional mhc_allele column. The BEAM-Ab tutorial guides you through analyzing a BCR Antigen Capture dataset.

When working with your own dataset, you must customize this file for your experiment. Learn more about the Feature Reference CSV.

Download the pre-built human reference transcriptome to the working directory (beam-t) and uncompress it:

curl -O https://cf.10xgenomics.com/supp/cell-exp/refdata-gex-GRCh38-2020-A.tar.gz tar -xf refdata-gex-GRCh38-2020-A.tar.gz

Next, download the pre-built V(D)J reference to the working directory and uncompress it:

curl -O https://cf.10xgenomics.com/supp/cell-vdj/refdata-cellranger-vdj-GRCh38-alts-ensembl-7.1.0.tar.gz tar -xf refdata-cellranger-vdj-GRCh38-alts-ensembl-7.1.0.tar.gz

In your working directory, create a new CSV file called 5k_BEAM-T_Human_A0201_B0702_PBMC_5pv2_Multiplex_config.csv using your text editor of choice. For example, you can create a file with nano using this command:

nano 5k_BEAM-T_Human_A0201_B0702_PBMC_5pv2_Multiplex_config.csv

Copy and paste this text into the newly created file and customize the /path/to/... part of file paths:

[gene-expression] ref,/path/to/references/refdata-gex-GRCh38-2020-A [feature] ref,/path/to/feature_references/5k_BEAM-T_Human_A0201_B0702_PBMC_5pv2_Multiplex_count_feature_reference.csv [vdj] ref,/path/to/references/vdj/refdata-cellranger-vdj-GRCh38-alts-ensembl-7.1.0 [libraries] fastq_id,fastqs,lanes,feature_types beamt_human_A0201_B0702_pbmc_ag,/path/to/fastqs/5k_BEAM-T_Human_A0201_B0702_PBMC_5pv2_fastqs/antigen_capture,1|2,Antigen Capture beamt_human_A0201_B0702_pbmc_vdj,/path/to/fastqs/5k_BEAM-T_Human_A0201_B0702_PBMC_5pv2_fastqs/vdj,1|2,VDJ-T beamt_human_A0201_B0702_pbmc_gex,/path/to/fastqs/5k_BEAM-T_Human_A0201_B0702_PBMC_5pv2_fastqs/gex,1|2,Gene Expression [antigen-specificity] control_id,mhc_allele negative_control_A0201,HLA-A*02:01 negative_control_B0702,HLA-B*07:02

Use your text editor's save command to save the file. In nano, save by typing CTRL+XyENTER.

A customizable multi config CSV template is available for download on the example dataset page, under the Input Files tab.

Once you have all the necessary files, make a new directory called runs/ in your beam-t/ working directory:

mkdir runs/ cd runs/

You will run cellranger multi in the runs/ directory.

After downloading/creating the FASTQ files, Feature Reference CSV, reference transcriptome, and V(D)J reference, you are ready to run cellranger multi.

Print the usage statement to get a list of all the options:

cellranger multi --help

The output should look similar to:

user_prompt$ cellranger multi --help cellranger-multi Analyze multiplexed data or combined gene expression/immune profiling/feature barcode data USAGE: cellranger multi [FLAGS] [OPTIONS] --id --csv FLAGS: --dry Do not execute the pipeline. Generate a pipeline invocation (.mro) file and stop --disable-ui Do not serve the web UI --noexit Keep web UI running after pipestance completes or fails --nopreflight Skip preflight checks -h, --help Prints help information OPTIONS: --id A unique run id and output folder name [a-zA-Z0- 9_-]+ --description Sample description to embed in output files [default: ] --csv Path of CSV file enumerating input libraries and analysis parameters --jobmode Job manager to use. Valid options: local (default), sge, lsf, slurm or path to a .template file. Search for help on "Cluster Mode" at support.10xgenomics.com for more details on configuring the pipeline to use a compute cluster [default: local] --localcores Set max cores the pipeline may request at one time. Only applies to local jobs ....

Options used in this tutorial

--idThe id argument must be a unique run ID. We will call this run HumanB_Cell_multi based on the sample type in the example dataset.
--csvPath to the multi config CSV file enumerating input libraries and analysis parameters. Your multi_config.csv file is in the working directory. When executing cellranger multi from the runs directory, the relative path should be: ../multi_config.csv

From within the beam-t/runs/ directory, run cellranger multi

/path/to/cellranger-7.1.0/cellranger multi --id=beam-t-run --csv=../5k_BEAM-T_Human_A0201_B0702_PBMC_5pv2_Multiplex_config.csv

The run begins similarly to this:

user_prompt$ cellranger multi --id=beam-t-run --csv=/jane.doe/beam-t/multi_config.csv Martian Runtime - v4.0.10 2023-06-15 11:44:24 [jobmngr] WARNING: configured to use 334GB of local memory, but only 194.9GB is currently available. Serving UI at http://bespin3.fuzzplex.com:34513?auth=-Sm5gsg6_G8FjcUX0_YD5J8SYoBODz4IWoVIK9ec0jg Running preflight checks (please wait)... 2023-06-15 11:44:33 [runtime] (ready) ID.beam-t-run.SC_MULTI_CS.PARSE_MULTI_CONFIG 2023-06-15 11:44:33 [runtime] (run:local) ID.beam-t-run.SC_MULTI_CS.PARSE_MULTI_CONFIG.fork0.chnk0.main 2023-06-15 11:44:56 [runtime] (chunks_complete) ID.beam-t-run.SC_MULTI_CS.PARSE_MULTI_CONFIG 2023-06-15 11:44:56 [runtime] (ready) ID.beam-t-run.SC_MULTI_CS.FULL_COUNT_INPUTS.WRITE_GENE_INDEX 2023-06-15 11:44:56 [runtime] (run:local) ID.beam-t-run.SC_MULTI_CS.FULL_COUNT_INPUTS.WRITE_GENE_INDEX.fork0.chnk0.main ....

When the output of the cellranger multi command says, “Pipestance completed successfully!”, the job is done:

web_summary: /jane.doe/beam-t/runs/beam-t-run/outs/per_sample_outs/beam-t/web_summary.html metrics_summary: /jane.doe/beam-t/runs/beam-t-run/runs/beam-t/outs/per_sample_outs/beam-t/metrics_summary$ } Waiting 6 seconds for UI to do final refresh. Pipestance completed successfully!

A successful cellranger multi run produces a new directory called beam-t-run (based on the --id flag specified during the run). The contents of the beam-t-run/ directory:

. ├── beam-t-run │ ├── beam-t.mri.tgz │ ├── _cmdline │ ├── _filelist │ ├── _finalstate │ ├── _invocation │ ├── _jobmode │ ├── _log │ ├── _mrosource │ ├── outs │ ├── _perf │ ├── _perf._truncated_ │ ├── SC_MULTI_CS │ ├── _sitecheck │ ├── _tags │ ├── _timestamp │ ├── _uuid │ ├── _vdrkill │ └── _versions

The outs/ directory contains all important output files generated by the cellranger multi pipeline:

── runs └── beam-t-run └──outs ├── config.csv ├── multi │ ├── count │ │ ├── feature_reference.csv │ │ ├── raw_cloupe.cloupe │ ├── raw_feature_bc_matrix │ │ ├── raw_feature_bc_matrix.h5 │ │ ├── raw_molecule_info.h5 │ │ ├── unassigned_alignments.bam │ │ └── unassigned_alignments.bam.bai │ └── vdj_t │ ├── all_contig_annotations.bed │ ├── all_contig_annotations.csv │ ├── all_contig_annotations.json │ ├── all_contig.bam │ ├── all_contig.bam.bai │ ├── all_contig.fasta │ ├── all_contig.fasta.fai │ └── all_contig.fastq ├── per_sample_outs │ └── beam-t │ ├── antigen_analysis │ ├── count │ ├── metrics_summary.csv │ ├── vdj_t │ └── web_summary.html └── vdj_reference ├── fasta │ ├── donor_regions.fa │ └── regions.fa └── reference.json