PUBLICATION
Systematic analysis of proximal midgut- and anorectal-originating contractions in larval zebrafish using event feature detection and supervised machine learning algorithms
- Authors
- Cassidy, R.M., Flores, E.M., Trinh Nguyen, A.K., Cheruvu, S.S., Uribe, R.A., Krachler, A.M., Odem, M.A.
- ID
- ZDB-PUB-231002-97
- Date
- 2023
- Source
- Neurogastroenterology and motility 35(12): e14675 (Journal)
- Registered Authors
- Uribe, Rosa
- Keywords
- fluorescence microscopy, gut motility, machine learning, reserpine, zebrafish
- MeSH Terms
-
- Algorithms
- Animals
- Larva/physiology
- Reserpine*
- Supervised Machine Learning
- Zebrafish*/physiology
- PubMed
- 37743702 Full text @ Neurogastroenterol. Motil.
Citation
Cassidy, R.M., Flores, E.M., Trinh Nguyen, A.K., Cheruvu, S.S., Uribe, R.A., Krachler, A.M., Odem, M.A. (2023) Systematic analysis of proximal midgut- and anorectal-originating contractions in larval zebrafish using event feature detection and supervised machine learning algorithms. Neurogastroenterology and motility. 35(12):e14675.
Abstract
Background Zebrafish larvae are translucent, allowing in vivo analysis of gut development and physiology, including gut motility. While recent progress has been made in measuring gut motility in larvae, challenges remain which can influence results, such as how data are interpreted, opportunities for technical user error, and inconsistencies in methods.
Methods To overcome these challenges, we noninvasively introduced Nile Red fluorescent dye to fill the intraluminal gut space in zebrafish larvae and collected serial confocal microscopic images of gut fluorescence. We automated the detection of fluorescent-contrasted contraction events against the median-subtracted signal and compared it to manually annotated gut contraction events across anatomically defined gut regions. Supervised machine learning (multiple logistic regression) was then used to discriminate between true contraction events and noise. To demonstrate, we analyzed motility in larvae under control and reserpine-treated conditions. We also used automated event detection analysis to compare unfed and fed larvae.
Key results Automated analysis retained event features for proximal midgut-originating retrograde and anterograde contractions and anorectal-originating retrograde contractions. While manual annotation showed reserpine disrupted gut motility, machine learning only achieved equivalent contraction discrimination in controls and failed to accurately identify contractions after reserpine due to insufficient intraluminal fluorescence. Automated analysis also showed feeding had no effect on the frequency of anorectal-originating contractions.
Conclusions & inferences Automated event detection analysis rapidly and accurately annotated contraction events, including the previously neglected phenomenon of anorectal contractions. However, challenges remain to discriminate contraction events based on intraluminal fluorescence under treatment conditions that disrupt functional motility.
Genes / Markers
Expression
Phenotype
Mutations / Transgenics
Human Disease / Model
Sequence Targeting Reagents
Fish
Orthology
Engineered Foreign Genes
Mapping