Upstream Notes 4: A Quick Guide to BD Single-Cell Upstream Analysis

February 20, 2026 · 6 min read · Analysis Notes

📢 What's in this post:

Why choose BD Rhapsody? A deep dive into its unique advantages

Starting from scratch: a complete environment setup guide

Hands-on walkthrough: running the full analysis pipeline step by step

Hello everyone! If you work in the biomedical field, you're probably already familiar with BD — the flow analyzers and cell sorters in most labs are BD instruments. As a leader in flow cytometry, BD has naturally made its move into single-cell genomics. Today, let's take a detailed look at the BD Rhapsody platform and its upstream analysis pipeline.

🤔 Why Choose BD Rhapsody?

1️⃣ How It Works

Unlike 10x Genomics' "microfluidics + oil droplet" approach, BD uses a microwell plate strategy to isolate individual cells. The workflow goes like this:

A cell suspension and barcoded magnetic beads are loaded onto a microwell plate
Under gravity, cells and beads settle to the bottom of each well
After cell lysis, mRNA is captured by oligo(dT) sequences on the bead surface
Beads are collected for downstream library construction

Source: BD Biosciences

2️⃣ Platform Comparison

Both platforms have their strengths — here's a head-to-head comparison:

	BD Rhapsody	10x Genomics
Cell isolation method	Microwell plate	Microfluidics (droplets)
Cell capture rate	>80%	~65%
Minimum starting cell number	As few as hundreds	Typically thousands
Sample compatibility	Supports FFPE	Primarily fresh samples
Cost	Higher	Relatively lower
Throughput	High	High

In summary, BD's key advantages are:

Higher cell capture efficiency (>80%)
Compatible with low-input samples
Able to process FFPE samples
Supports multi-omics (e.g., AbSeq, scRNA+ATAC, scTCR/BCR)

So if your sample is precious (limited cell numbers) or fragile (e.g., neutrophils), BD is a strong choice. Of course, these advantages come at a price — BD costs more and has somewhat lower scalability, since the microwell plate design is harder to scale up compared to droplet-based approaches.

3️⃣ Library Structure

The BD Rhapsody library structure is quite similar to 10x Genomics. It includes:

Cell Barcode (CB): identifies the cell of origin
Molecular Index (MI): analogous to a UMI; used for PCR duplicate removal
Gene sequence: the actual cDNA sequence from the transcript

Source: BD Biosciences

💻 Setup and Prerequisites

⚠️ System requirements:

OS: Linux or macOS

RAM: at least 96 GB

Storage: at least 256 GB

CPU: 8 cores or more

Required software: Docker, Python 3, CWL-runner

Installing Docker Engine

The instructions below are for Ubuntu. For other operating systems or alternative installation methods, please refer to the official Docker documentation. Installing as root is the simplest route; if you don't have root access, contact your system administrator for assistance. If root access is truly unavailable, the official docs describe a rootless Docker setup — we won't cover that here.

# Check if Docker is already installed
which docker

# If it's installed, skip ahead to the next section.
# Otherwise, follow the steps below.

# Remove any conflicting packages
for pkg in docker.io docker-doc docker-compose docker-compose-v2 podman-docker containerd runc; do
    sudo apt-get remove $pkg
done

# Add Docker's official GPG key
sudo apt-get update
sudo apt-get install ca-certificates curl
sudo install -m 0755 -d /etc/apt/keyrings
sudo curl -fsSL https://download.docker.com/linux/ubuntu/gpg -o /etc/apt/keyrings/docker.asc
sudo chmod a+r /etc/apt/keyrings/docker.asc

# Add the Docker repository
echo \
  "deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/docker.asc] https://download.docker.com/linux/ubuntu \
  $(. /etc/os-release && echo "$VERSION_CODENAME") stable" | \
  sudo tee /etc/apt/sources.list.d/docker.list > /dev/null
sudo apt-get update

# Install Docker
sudo apt-get install docker-ce docker-ce-cli containerd.io docker-buildx-plugin docker-compose-plugin

Test your installation by running a test container:

sudo docker run hello-world
# A successful installation should print:
# Hello from Docker!
# This message shows that your installation appears to be working correctly.

To allow non-root users to run Docker containers, perform this one-time configuration (requires root):

# Create the docker group
sudo groupadd docker

# Add the target user (or current user) to the docker group
sudo usermod -aG docker $USER

# Test without sudo
docker run hello-world

Security note: The configuration above grants the user elevated Docker privileges. If security is a concern, follow the rootless Docker setup instructions in the official documentation instead (some functionality will be limited).

Installing Dependencies and the Pipeline

# 1. Create a conda environment
conda create -n bd-rhapsody python=3.11
conda activate bd-rhapsody

# 2. Install required packages
pip install -U pip
pip install cwlref-runner

# 3. Download CWL and YML configuration files
# The link below may not point to the latest version.
# Check https://bitbucket.org/CRSwDev/cwl/downloads/ for the current repo.
wget https://bitbucket.org/CRSwDev/cwl/get/8d252728f875.zip

# Inspect the downloaded configuration files
ls CRSwDev-cwl-8d252728f875/
# README.md  v1.0  v1.02  v1.10  v1.10.1  v1.11  v1.11.1  v1.12  v1.12.1
# v1.3  v1.5  v1.6  v1.6.1  v1.7.1  v1.8  v1.9  v1.9.1  v2.0  v2.2  v2.2.1

# Inspect the latest v2.2.1 configuration
tree CRSwDev-cwl-8d252728f875/v2.2.1
# ├── Extra_Utilities
# │   ├── AnnotateCellLabelUMI_2.2.1.cwl
# │   ├── make_rhap_reference_2.2.1.cwl
# │   ├── PhiXContamination_2.2.1.cwl
# │   └── README.md
# ├── pipeline_inputs_template_2.2.1.yml
# └── rhapsody_pipeline_2.2.1.cwl

# 4. Pull the BD Rhapsody Docker image
docker pull bdgenomics/rhapsody

# Verify the image
docker images
# REPOSITORY            TAG       IMAGE ID       CREATED         SIZE
# bdgenomics/rhapsody   latest    6d2c53b1b712   6 months ago    3.53GB
# hello-world           latest    d2c94e258dcb   19 months ago   13.3kB

Preparing the Reference Genome

BD provides pre-built reference genomes for human and mouse.

# Transcriptome only (WTA)
# Human GRCh38 + Mouse GRCm38
wget https://bd-rhapsody-public.s3.amazonaws.com/Rhapsody-WTA/Pipeline-version2.x_WTA_references/RhapRef_Human42Mouse24_WTA_2023-02.tar.gz

# Human GRCh38 + Mouse GRCm39
wget https://bd-rhapsody-public.s3.amazonaws.com/Rhapsody-WTA/Pipeline-version2.x_WTA_references/RhapRef_Human42Mouse31_WTA_2023-02.tar.gz

# Human only
wget https://bd-rhapsody-public.s3.amazonaws.com/Rhapsody-WTA/Pipeline-version2.x_WTA_references/RhapRef_Human_WTA_2023-02.tar.gz

# Mouse only
wget https://bd-rhapsody-public.s3.amazonaws.com/Rhapsody-WTA/Pipeline-version2.x_WTA_references/RhapRef_Mouse_WTA_2023-02.tar.gz

# Transcriptome + ATAC (multiome)
# Human + Mouse
wget https://bd-rhapsody-public.s3.amazonaws.com/Rhapsody-WTA-ATAC/Pipeline-version2.x_WTA_ATAC_references/RhapRef_Human-Mouse_WTA-ATAC_2023-11.tar.gz

# Human only
wget https://bd-rhapsody-public.s3.amazonaws.com/Rhapsody-WTA-ATAC/Pipeline-version2.x_WTA_ATAC_references/RhapRef_Human_WTA-ATAC_2023-08.tar.gz

# Mouse only
wget https://bd-rhapsody-public.s3.amazonaws.com/Rhapsody-WTA-ATAC/Pipeline-version2.x_WTA_ATAC_references/RhapRef_Mouse_WTA-ATAC_2023-09.tar.gz

You can also build a custom reference genome. For example:

cd CRSwDev-cwl-8d252728f875/v2.2.1

cwl-runner Extra_Utilities/make_rhap_reference_2.2.1.cwl \
    --Genome_fasta GRCh38.primary_assembly.genome.fa \      # replace with your FASTA
    --Gtf gencode.v43.primary_assembly.annotation.gtf \    # replace with your GTF
    --Archive_prefix testrefhuman43 \                       # custom name for the reference archive
    --WTA_only                                              # flag to build transcriptome only

🚀 Running the Pipeline

Demo Dataset

We'll use the PBMC 2k dataset released by Illumina for this walkthrough. Download instructions are linked in the article. See the article for download instructions.

1️⃣ Prepare the Configuration File

Edit the template pipeline_inputs_template_2.2.1.yml and save it as pbmc2k.yml:

#!/usr/bin/env cwl-runner
cwl:tool: rhapsody

Reads:
  - class: File
    location: "pbmc2k/BD-Bio-NS2K-P2-600c-Sym-FL-10-2K-TD-mRNA_S1_L001_R1_001.fastq.gz"
  - class: File
    location: "pbmc2k/BD-Bio-NS2K-P2-600c-Sym-FL-10-2K-TD-mRNA_S1_L001_R2_001.fastq.gz"

Reference_Archive:
  class: File
  location: "ref/RhapRef_Human_WTA_2023-02.tar.gz"

Run_Name: pbmc2k
Maximum_Threads: 32
Generate_Bam: true

2️⃣ Launch the Analysis

mkdir results_folder

cwl-runner --parallel \
    --outdir results_folder \
    rhapsody_pipeline_2.2.1.cwl \
    pbmc2k.yml

3️⃣ Understanding the Output Files

After the pipeline completes, you'll find the following key output files:

pbmc2k_Pipeline_Report.html — summary analysis report
pbmc2k_Seurat.rds — Seurat object (for R-based downstream analysis)
pbmc2k.h5mu — MuData object (for Python/Scanpy-based downstream analysis)
pbmc2k_RSEC_MolsPerCell_MEX.zip — count matrix in MEX format
pbmc2k_Bioproduct.bam — BAM alignment file

Source: BD Biosciences

References

BD Rhapsody Bioinformatics Official Documentation

#bioinformatics #single-cell sequencing #BD_Rhapsody #data analysis

Originally posted on WeChat Official Account: bioinfo_studyroom

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