CellBouncer, a unified toolkit for single-cell demultiplexing and ambient RNA analysis, reveals hominid mitochondrial incompatibilities
5 affiliations
- 1The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA 94143, USA; Department of Neurology, University of California, San Francisco, San Francisco, CA 94143, USA; Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA. Electronic address: [email protected]
- 2University of California, San Francisco
- 3The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA 94143, USA; Department of Neurology, University of California, San Francisco, San Francisco, CA 94143, USA; Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
- 4Allen Institute for Brain Science, Seattle, WA 98109, USA
- 5The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA 94143, USA; Department of Neurology, University of California, San Francisco, San Francisco, CA 94143, USA; Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA. Electronic address: [email protected]
Abstract
Pooled processing, in which cells from multiple sources are cultured or captured together, is increasingly popular for droplet-based single-cell sequencing studies. This design allows efficient scaling of experiments, isolation of cell-intrinsic differences, and mitigation of batch effects. We present CellBouncer, a computational toolkit for demultiplexing and analyzing single-cell sequencing data from pooled experiments. We demonstrate that CellBouncer can separate and quantify multi-species and multi-individual cell mixtures, identify unknown mitochondrial haplotypes in cells, assign treatments from lipid-conjugated barcodes or CRISPR single-guide RNAs, and infer pool composition, outperforming existing methods. We introduce methods to quantify ambient RNA contamination per cell, infer individual donors' contributions to the ambient RNA pool, and determine a consensus doublet rate harmonized across data types. Applying these tools to tetraploid composite cells, we identify a competitive advantage of human over chimpanzee mitochondria across ten cell fusion lines and provide evidence for inter-mitochondrial incompatibility and mito-nuclear incompatibility between species.
Related
- MDigest: A comprehensive toolkit for detection of allosteric communication from molecular dynamics simulations of biochemical systems
- Tangermeme: a toolkit for understanding cis-regulatory logic using deep learning models
- MeXpose 2.0: A Comprehensive Toolkit for Single-Cell Spatial (Metall-)omicsCovering All Steps from Image Reconstruction to Integrative Analysis.
- CRISPR-HAWK: haplotype- and variant-aware guide design toolkit for CRISPR-Cas.
- The PERL toolkit: Using sand flies to identify leishmaniasis reservoirs
BibTeX
@article{schaefer2026cellbouncer,
title = {{CellBouncer, a unified toolkit for single-cell demultiplexing and ambient RNA analysis, reveals hominid mitochondrial incompatibilities}},
author = {Nathan K. Schaefer and Bryan J. Pavlovic and Matthew T. Schmitz and Alex A. Pollen},
journal = {Cell Genomics},
year = {2026},
month = jun,
doi = {10.1016/j.xgen.2026.101275},
url = {https://doi.org/10.1016/j.xgen.2026.101275},
note = {CC BY},
}
RIS
TY - JOUR
TI - CellBouncer, a unified toolkit for single-cell demultiplexing and ambient RNA analysis, reveals hominid mitochondrial incompatibilities
AU - Nathan K. Schaefer
AU - Bryan J. Pavlovic
AU - Matthew T. Schmitz
AU - Alex A. Pollen
PY - 2026
DA - 2026/06/10
JO - Cell Genomics
DO - 10.1016/j.xgen.2026.101275
UR - https://doi.org/10.1016/j.xgen.2026.101275
AB - Pooled processing, in which cells from multiple sources are cultured or captured together, is increasingly popular for droplet-based single-cell sequencing studies. This design allows efficient scaling of experiments, isolation of cell-intrinsic differences, and mitigation of batch effects. We present CellBouncer, a computational toolkit for demultiplexing and analyzing single-cell sequencing data from pooled experiments. We demonstrate that CellBouncer can separate and quantify multi-species and multi-individual cell mixtures, identify unknown mitochondrial haplotypes in cells, assign treatments from lipid-conjugated barcodes or CRISPR single-guide RNAs, and infer pool composition, outperforming existing methods. We introduce methods to quantify ambient RNA contamination per cell, infer individual donors' contributions to the ambient RNA pool, and determine a consensus doublet rate harmonized across data types. Applying these tools to tetraploid composite cells, we identify a competitive advantage of human over chimpanzee mitochondria across ten cell fusion lines and provide evidence for inter-mitochondrial incompatibility and mito-nuclear incompatibility between species.
SN - 2666-979X
ER -