10x Genomics
Chromium Single Cell Gene Expression

Cell Ranger7.1 (latest), printed on 12/23/2024

Antibody Capture and CRISPR Guide Capture Analysis

Table of Contents

• Overview
• Feature and library types
• Libraries CSV file
• Feature Reference CSV file
• Feature Barcode extraction pattern
• Antibody Capture with TotalSeq-B
• Antibody Capture with TotalSeq-C
• Antibody Capture with TotalSeq-A
• CRISPR Guide Capture

Overview

Cell Ranger processes all Feature Barcode data through a counting pipeline that quantifies each feature in each cell. This analysis is done by the cellranger count pipeline. The pipeline outputs a unified feature-barcode matrix that contains gene expression counts alongside Feature Barcode counts for each cell barcode. The feature-barcode matrix replaces the gene-barcode matrix emitted by older versions of Cell Ranger.

The pipeline first extracts and corrects the cell barcode and UMI from the feature library using the same methods as gene expression read processing. It then matches the Feature Barcode read against the list of features declared in the Feature Barcode Reference. The counts for each feature are available in the feature-barcode matrix output files and in the Loupe Browser output file.

To enable Feature Barcode analysis, cellranger count needs two new inputs:

• Libraries CSV is passed to cellranger count with the --libraries flag, and declares the FASTQ files and library type for each input dataset. In a typical Feature Barcode analysis, there are two input libraries: one for Single Cell Gene Expression reads, and one for Feature Barcode reads. This argument replaces the --fastqs argument.
• Feature Reference CSV is passed to cellranger count with the --feature-ref flag and declares the set of Feature Barcode reagents in use in the experiment. For each unique Feature Barcode used, this file declares a feature name and identifier, the unique Feature Barcode sequence associated with this reagent, and a pattern indicating how to extract the Feature Barcode sequence from the read sequence. See Feature Barcode Reference for details on how to construct the feature reference.

After creating these two CSV files and customizing the code in red, run cellranger count:

cd /home/jdoe/runs
cellranger count --id=sample345 \
                   --libraries=library.csv \
                   --transcriptome=/opt/refdata-gex-GRCh38-2020-A \
                   --feature-ref=feature_ref.csv

The complete set of arguments to cellranger count are covered in Single-Library Analysis.

Feature and library types

When inputting Feature Barcode data to Cell Ranger via the Libraries CSV file, you must declare the library_type of each library. Specific values for library_type will enable additional downstream processing, specifically for CRISPR Guide Capture and Antibody Capture. The following table outlines the types of libraries that can be specified and what they mean for downstream processing.

library_type	Description
Antibody Capture	For use with experiments measuring cell surface protein expression levels via an antibody and/or antigen-multimer staining assay. Enables a t-SNE projection of the cells using only the Antibody Capture / Cell Surface Protein feature counts. This projection is available in an output file and in Loupe Browser. See the Antibody Algorithms page for more details.
CRISPR Guide Capture	Enables analysis of gene expression changes caused by the presence of CRISPR perturbations, in a Perturb-Seq style assay. See the CRISPR Overview page for more details. This mode also creates a t-SNE projection using only the CRISPR guide counts. This projection is available in an output file and in Loupe Browser.
Antigen Capture	Only applicable to Barcode Enabled Antigen Mapping (BEAM) libraries. Described on the 5' Immune Profiling section of the software support documentation
Custom	Provides processing of the Feature Barcode reads and a basic summary of the sequencing quality and library quality, but performs no special processing of the Feature Barcode counts.

Libraries CSV file

The Libraries CSV file declares the input FASTQ data for the libraries that make up a Feature Barcode experiment. This will include one library containing Single Cell Gene Expression reads, and one or more libraries containing Feature Barcode reads. To use cellranger count in Feature Barcode mode, you must create a Libraries CSV file and pass it with the --libraries flag. The following table describes what should be in the Libraries CSV file.

Column Name	Description
fastqs	A fully qualified path to the directory containing the demultiplexed FASTQ files for this sample. Analogous to the --fastqs arg to cellranger count. This field does not accept comma-delimited paths. If you have multiple sets of FASTQs for this library, add a row and use the same library_type value.
sample	Same as the --sample arg to cellranger count. Sample name assigned in the bcl2fastq sample sheet.
library_type	Must match a valid library type as described in the Library/Feature Types section. FASTQ data is interpreted from rows in the Feature Reference file. The algorithm matches feature_type from the Feature Reference CSV with library_type. This field is case-sensitive. Must be Gene Expression for the Single Cell Gene Expression libraries (same for Targeted Gene Expression). For Feature Barcode libraries, must be one of Custom, Antibody Capture (for Cell Surface Protein), or CRISPR Guide Capture. Use Antibody Capture (for TotalSeq™-C).

Example Libraries CSV files

This section has a few example Libraries CSVs. Copy+Paste the most relevant example into a file, customize it for your experiment, and save it as a CSV. Alternatively, you may download this Libraries CSV template and customize it. Be sure to use the correct full path to your FASTQ files.

Gene expression + CRISPR Guide Capture libraries. In this example, we have demultiplexed sequencing data from two libraries named GEX_sample1 and CRISPR_sample1 on the bcl2fastq/mkfastq sample sheet. This generated two FASTQ files named GEX_sample1_S0_L001_R1_001.fastq.gz and CRISPR_sample1_S0_L001_R1_001.fastq.gz in the path /opt/foo (be sure to use the correct full path to your FASTQ files). We pass the FASTQ sample names and paths to Cell Ranger with the appropriate library types:

fastqs,sample,library_type,
/opt/foo/,GEX_sample1,Gene Expression,
/opt/foo/,CRISPR_sample1,CRISPR Guide Capture,

Gene Expression + Antibody Capture. In this example we have demultiplexed sequencing data from two libraries named GEX_sample2 and Ab_sample2 on the bcl2fastq / mkfastq sample sheet. This generated two FASTQ files named GEX_sample2_S0_L001_R1_001.fastq.gz and Ab_sample2_S0_L001_R1_001.fastq.gz in the path /opt/foo (be sure to use the correct full path to your FASTQ files). We pass the FASTQ sample names to Cell Ranger with the appropriate library types:

fastqs,sample,library_type,
/opt/foo/,GEX_sample2,Gene Expression,
/opt/foo/,Ab_sample2,Antibody Capture,

Gene Expression + Antibody Capture + CRISPR Guide Capture libraries. In this example, we have demultiplexed sequencing data from three libraries named GEX_sample3, Ab_sample3, and CRISPR_sample3 on the bcl2fastq / mkfastq sample sheet. The result is three FASTQ files named GEX_sample3_S0_L001_R1_001.fastq.gz, Ab_sample3_S0_L001_R1_001.fastq.gz, and CRISPR_sample3_S0_L001_R1_001.fastq.gz in the path /opt/foo (be sure to use the correct full path to your FASTQ files). We pass the FASTQ sample names to Cell Ranger with the appropriate library types:

fastqs,sample,library_type,
/opt/foo/,GEX_sample3,Gene Expression,
/opt/foo/,Ab_sample3,Antibody Capture,
/opt/foo/,CRISPR_sample3,CRISPR Guide Capture,

Gene Expression + Antibody Capture + Antigen-multimer staining (TotalSeq™-C). In this example, we have demultiplexed sequencing data from three libraries named GEX_sample4, Ab_sample4, and Ag_sample4 on the bcl2fastq / mkfastq sample sheet. The result is three FASTQ files named GEX_sample4_S0_L001_R1_001.fastq.gz, Ab_sample4_S0_L001_R1_001.fastq.gz, and Ag_sample4_S0_L001_R1_001.fastq.gz in the path /opt/foo (be sure to use the correct full path to your FASTQ files). We pass the FASTQ sample names to Cell Ranger with the appropriate library types:

fastqs,sample,library_type,
/opt/foo/,GEX_sample4,Gene Expression,
/opt/foo/,Ab_sample4,Antibody Capture,
/opt/foo/,Ag_sample3,Antibody Capture,

Feature Reference CSV file

A Feature Reference CSV file is required when processing Feature Barcode data. It declares the molecule structure and unique Feature Barcode sequence of each feature present in your experiment. Each line of the CSV declares one unique Feature Barcode. The Feature Reference CSV file is passed to cellranger count with the --feature-ref flag or to cellranger multi in the [feature] section of the multi config CSV file. Please note that the CSV may not contain characters outside of the ASCII range.

Targeted Gene Expression data is compatible with Feature Barcode analysis. However, if Targeted Gene Expression data is analyzed in conjunction with CRISPR-based Feature Barcode data, there are additional requirements imposed for the Feature Reference CSV file. Specifically, any CRISPR guide RNA target genes (in the target_gene_id column of the Feature Reference CSV file) must correspond to genes that are also included in the targeted gene expression Target Panel CSV file (in the gene_id column).

This table describes the columns in the Feature Reference CSV file. Example files can be found below.

Column Name	Description
id	Unique ID used to track feature counts. May only include ASCII characters and must not use whitespace, slash, quote, or comma characters. Each ID must be unique and must not collide with a gene identifier from the transcriptome.
name	Human-readable name for this feature. May only include ASCII characters and must not use whitespace, slash, quote, or comma characters. This name will be displayed in the Loupe Browser Active Feature list.
read	Specifies which RNA sequencing read contains the Feature Barcode sequence. Must be R1 or R2. Note: in most cases R2 is the correct read.
pattern	Specifies how to extract the Feature Barcode sequence from the read. See the Barcode Extraction Pattern section below for details.
sequence	Nucleotide barcode sequence associated with this feature. E.g., antibody barcode or sgRNA protospacer sequence.
feature_type	Type of the feature. See the Library/Feature Types section for details on the allowed values for this field. FASTQ data specified in the Library CSV file with a library_type that matches the feature_type will be scanned for occurrences of this feature. Each feature type in the feature reference must match a library_type entry in the Libraries CSV file. This field is case-sensitive.
target_gene_id	Optional. Reference gene identifier of the target gene of a CRISPR guide RNA. A gene with this id must exist in the reference transcriptome. Providing target_gene_id and target_gene_name will enable the pipeline to perform differential expression analysis, assuming that control ("Non-Targeting") guides are also specified. Non-targeting guides must contain the value "Non-Targeting" in the target_gene_id and target_gene_name fields. See the CRISPR Overview section for more details.
target_gene_name	Optional. Gene name of the target gene of a CRISPR guide RNA. The gene name corresponding to the gene referenced in the target_gene_id field must match the gene name given here. See the CRISPR Overview section for more details.
mhc_allele	Only relevant for BEAM-T (TCR Antigen Capture). Defines the MHC allele associated with each antigen included in the experiment. See the Feature Reference section on the Antigen Capture page for more details.

Feature Barcode extraction pattern

The pattern field of the feature reference defines how to locate the Feature Barcode within a read. The Feature Barcode may appear at a known offset with respect to the start or end of the read or may appear at a fixed position relative to a known anchor sequence. The pattern column can be made up of a combination of these elements:

• 5P: denotes the beginning of the read sequence. May appear zero or one time, and must be at the beginning of the pattern. Only 5P or 3P may appear, not both (^ may be used instead of 5P).
• 3P: denotes the end of the read sequence. May appear zero or one times, and must be at the end of the pattern ($ may be used instead of 3P).
• N: denotes an arbitrary base.
• A, C, G, T: denotes a fixed base that must match the read sequence exactly.
• (BC): denotes the Feature Barcode sequence as specified in the sequence column of the feature reference. Must appear exactly once in the pattern.

Any constant sequences made up of A, C, G, and T in the pattern must match exactly in the read sequence. Any N in the pattern is allowed to match a single arbitrary base. A modest number of fixed bases should be used to minimize the chance of a sequencing error disrupting the match. The fixed sequence should also be long enough to uniquely identify the position of the Feature Barcode. For feature types that require a non-N anchor, we recommend 12bp-20bp of constant sequence.

The extracted Feature Barcode sequence is aligned to the feature reference and up to one base mismatch is allowed. The extracted Feature Barcode sequences are corrected up to a Hamming distance of one base with the 10x Genomics barcode correction algorithm for correcting cell barcodes.

Antibody Capture with TotalSeq™-B

TotalSeq™-B is a line of antibody-oligonucleotide conjugates supplied by BioLegend that are compatible with the Single Cell 3' v3 assay. The Feature Barcode sequence appears at a fixed position (10th base) in the R2 read.

read	pattern
R2	5PNNNNNNNNNN(BC)

• Example dataset
• Example TotalSeq™-B Feature Reference CSV (Please refer to BioLegend for the latest conjugated Feature Barcode information.)

Antibody Capture with TotalSeq™-C

TotalSeq™-C is a line of antibody-oligonucleotide conjugates supplied by BioLegend that are compatible with the Single Cell 5' assay. The Feature Barcode sequence appears at a fixed position (10th base) in the R2 read.

read	pattern
R2	5PNNNNNNNNNN(BC)

• Example dataset
  
• Example TotalSeq™-C Feature Reference CSV (Please refer to BioLegend for the latest conjugated Feature Barcode information.)

The feature reference for Immudex's dMHC Dextramer® libraries with dCODE Dextramers has the same feature barcode pattern as TotalSeq™-C. Use "Antibody Capture" in the feature_type column for dextramer or multimer reagents. Therefore, the same feature reference example for TotalSeq™-C can also be used for MHC Dextramer® libraries.

To analyze Barcode Enabled Antigen Mapping (BEAM) libraries, visit the corresponding 5' Immune Profiling page.

Antibody Capture for TotalSeq™-A

TotalSeq™-A is a line of antibody-oligonucleotide conjugates supplied by BioLegend that are compatible with the Single Cell 3' v2 and Single Cell 3' v3 kits. The Feature Barcode sequence appears at the start of the R2 read.

Although TotalSeq™-A can be used with the CITE-Seq assay, CITE-Seq is not a 10x Genomics-supported assay. Please contact New York Genome Center or BioLegend for assistance with the assay or software.

read	pattern
R2	5P(BC)

• Example TotalSeq™-A Feature Reference CSV (Please refer to BioLegend for the latest conjugated Feature Barcode information.)

CRISPR Guide Capture

In CRISPR Guide Capture assays, the Feature Barcode sequence is the CRISPR protospacer sequence. The protospacer is followed by a downstream constant sequence in the guide RNA which is used as an anchor to identify the location of the protospacer. We recommend using a 12bp-20bp constant sequence that can be uniquely identified but is short enough that it is unlikely to be disrupted by a sequencing error.

The example Feature Reference CSV files list six guide RNA features, each with six distinct barcode/protospacer sequences (sequence column). The pattern column has the same pattern for all six features. Please note that the pattern field is specific to your experiment and must be customized. Remember to replace the example pattern sequence provided here when running your own analysis.

We use the target_gene_id and target_gene_name columns to declare the target gene of each guide RNA, for use in downstream CRISPR perturbation analysis. Two guides are declared with target_gene_id as Non-Targeting. Cells containing Non-Targeting guides will be used as controls for CRISPR perturbation analysis. The four remaining guides target two genes.

Read	Pattern	Assay	Example
R2	(BC)GTTTAAGAGCTAAGCTGGAA	3’ Gene Expression with Feature Barcode	Download 3' CSV
R2	TTCCAGCATAGCTCTTAAAC(BC)	5’ Gene Expression with Feature Barcode	Download 5' CSV