Home › Genetics / Genomics › Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
Genetics / Genomics JoVE (Open Access) Citable · DOI

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

DOI: 10.3791/58709-v
What you'll learn
  • ✓ Prepare adult mammalian tissues for single-cell RNA-Seq via enzymatic dissociation and cell isolation
  • ✓ Perform quality control checks to ensure viable, healthy cell populations before sequencing
  • ✓ Generate barcoded GEM partitions and execute droplet-based transcriptomics workflows
  • ✓ Analyze single-cell transcriptomic data using bioinformatic pipelines and interpret results
Protocol

This protocol describes the general processes and quality control checks necessary for preparing healthy adult mammalian single cells for droplet-based, high throughput single cell RNA-Seq preparations. Sequencing parameters, read alignment, and downstream single-cell bioinformatic analysis are also provided.

Difficulty
advanced
Total time
~2 days (tissue dissociation and cell preparation Day 1; GEM generation and library prep Day 1–2; sequencing and bioinformatics 3–7 days)
Model organism
Adult mammalian tissues (mice, rats, or other mammals)
Biosafety
BSL-1

Steps

1
Dissociate adult mammalian tissue into single cells

Enzymatically digest tissue using established protocols to break down extracellular matrix and generate single-cell suspensions. This foundational step prepares tissue samples for downstream analysis.

▶ 00:56
2
Isolate viable and healthy cells via sorting

Remove dead cells and debris using viability staining and fluorescence-activated cell sorting (FACS) or magnetic enrichment. Ensures high-quality input for transcriptomic analysis.

▶ 03:46
3
Generate barcoded GEM partitions for RNA capture

Encapsulate single cells, barcoded gel beads, and reagents into water-in-oil emulsions (GEMs). Each partition contains a unique barcode to tag transcripts from individual cells.

▶ 04:46
4
Submit sequencing data to NCBI GEO and SRA

Prepare and upload raw sequencing reads and metadata to public repositories for reproducibility and community access. Follows FAIR data principles.

▶ 06:17
5
Analyze single-cell transcriptomics via bioinformatics

Align reads to reference genome, perform quality control, clustering, and differential expression analysis. Interpret cell-type identity and transcriptional state from single-cell gene expression data.

▶ 07:34
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