Systematic analysis of proximal midgut- and anorectal-originating contractions in larval zebrafish using event feature detection and supervised machine learning algorithms

BackgroundZebrafish 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.MethodsTo 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 ResultsAutomated 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 & InferencesAutomated 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. Label: Immerse larvae in embryo media with Nile Red dye to selectively label the intestinal tract. Acquire: Divide the intestinal tract into regions of interest and acquire serial fluorescence images. Extract: Use the FIBSI program to normalize regional fluorescence time series and extract contraction event features. Filter: Use multiple logistic regression to filter noise from contractions based on event features.image

Automated summary

Researchers successfully annotated contraction events of gut motility, including the previously neglected phenomenon of anorectal contractions, using automated event detection analysis. However, challenges remain in discriminating contraction events based on intraluminal fluorescence under treatment conditions that disrupt functional motility.