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Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
Published on: January 10, 2019
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Reference-free discovery with barcoded single-cell sequencing
Roozbeh Dehghannasiri1, Marek Kokot2, Alexander L Starr3
1Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
Nature Biotechnology
|April 22, 2026
Summary
sc-SPLASH enables reference-free discovery in single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics. This new method efficiently preprocesses barcoded data, revealing novel proteins in sponge and tunicate immune cells.
Area of Science:
- Genomics
- Bioinformatics
- Evolutionary Biology
Background:
- Single-cell RNA sequencing (scRNA-seq) typically focuses on gene expression, overlooking other transcriptome variations.
- Existing methods for barcoded data preprocessing are computationally intensive.
Purpose of the Study:
- Introduce sc-SPLASH, a novel statistics-first, reference-free method for scRNA-seq and spatial transcriptomics.
- Develop an optimized barcoded data preprocessing submodule (BKC) for enhanced efficiency.
Main Methods:
- sc-SPLASH employs a statistics-first approach for reference-free analysis.
- The BKC submodule accelerates barcoded data preprocessing, outperforming UMI-tools by approximately 50-fold.
- Applied to immune-like cells in sponge (Spongilla) and tunicate (Ciona).
Main Results:
- sc-SPLASH successfully identifies novel secreted repeat proteins.
- Discovered proteins were found in immune-like cells of Spongilla (a species absent from reference databases) and Ciona.
- BKC submodule significantly improves preprocessing speed.
Conclusions:
- sc-SPLASH offers a powerful new tool for transcriptome variation discovery in single-cell and spatial genomics.
- The BKC submodule provides a substantial speed improvement for barcoded data preprocessing.
- This approach can uncover novel biological insights, even in organisms with incomplete genomic references.
