ArticleFrontiers in microbiology2022
SymbiQuant: A Machine Learning Object Detection Tool for Polyploid Independent Estimates of Endosymbiont Population Size.
Article in Frontiers in microbiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 4 citations in OpenAlex.
- Host plant quality reshapes symbiotic organ architecture without altering symbiont density.iScience · 2026Article
- In vivo interference of pea aphid endosymbiont Buchnera groEL gene by synthetic peptide nucleic acids.Scientific reports · 2024Article
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4 authors at 1 institution in 1 country.
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Abstract
Quantifying the size of endosymbiont populations is challenging because endosymbionts are typically difficult or impossible to culture and commonly polyploid. Current approaches to estimating endosymbiont population sizes include quantitative PCR (qPCR) targeting endosymbiont genomic DNA and flow-cytometry. While qPCR captures genome copy number data, it does not capture the number of bacterial cells in polyploid endosymbiont populations. In contrast, flow cytometry can capture accurate estimates of whole host-level endosymbiont population size, but it is not readily able to capture data at the level of endosymbiotic host cells. To complement these existing approaches for estimating endosymbiont population size, we designed and implemented an object detection/segmentation tool for counting the number of endosymbiont cells in micrographs of host tissues. The tool, called SymbiQuant, which makes use of recent advances in deep neural networks includes a graphic user interface that allows for human curation of tool output. We trained SymbiQuant for use in the model aphid/
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