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SymbiQuant: A Machine Learning Object Detection Tool for Polyploid Independent Estimates of Endosymbiont Population
Edward B James1, Xu Pan2, Odelia Schwartz2
1Department of Biology, University of Miami, Coral Gables, FL, United States.
Frontiers in Microbiology
|June 6, 2022
Summary
SymbiQuant, a new deep learning tool, accurately counts bacterial endosymbiont cells in host tissues. This method enhances understanding of endosymbiont population dynamics and phenotypes in various symbiotic relationships.
Area of Science:
- Microbiology
- Computational Biology
- Genomics
Background:
- Estimating endosymbiont population size is difficult due to culturing challenges and polyploidy.
- Existing methods like qPCR and flow cytometry have limitations in quantifying bacterial cell numbers or host-level data.
Purpose of the Study:
- To develop an accurate and versatile tool for quantifying endosymbiont cell populations within host tissues.
- To complement existing methods for studying endosymbiont population dynamics and phenotypes.
Main Methods:
- Designed and implemented SymbiQuant, an object detection/segmentation tool using deep neural networks.
- Trained SymbiQuant on aphid/Buchnera endosymbiosis micrographs and incorporated a graphical user interface for human curation.
- Validated SymbiQuant's accuracy in counting endosymbiont cells and capturing bacterial phenotype.
Main Results:
- SymbiQuant provides accurate endosymbiont cell counts, overcoming limitations of qPCR and flow cytometry.
- The tool effectively captures bacterial phenotype alongside population size.
- Demonstrated utility in studying Buchnera population dynamics during aphid development.
Conclusions:
- SymbiQuant offers a novel approach for quantifying endosymbiont populations at the cellular level.
- The tool has broad applicability across different endosymbiosis models with adaptable training data.
- SymbiQuant enhances research capabilities for studying symbiosis from organismal to cellular levels.
