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

Author:

Cassidy Ryan M.1,Flores Erika M.2,Trinh Nguyen Anh K.2,Cheruvu Sai S.3,Uribe Rosa A.4,Krachler Anne Marie2,Odem Max A.2ORCID

Affiliation:

1. Brown Foundation Institute of Molecular Medicine McGovern Medical School at UTHealth Houston Texas USA

2. Department of Microbiology and Molecular Genetics McGovern Medical School at UTHealth Houston Texas USA

3. Department of Integrative Biology and Pharmacology McGovern Medical School at UTHealth Houston Texas USA

4. Department of Biosciences Rice University Houston Texas USA

Abstract

AbstractBackgroundZebrafish 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.

Funder

Cancer Prevention and Research Institute of Texas

John S. Dunn Foundation

National Institute of Allergy and Infectious Diseases

National Institute on Drug Abuse

National Institutes of Health

National Science Foundation

Publisher

Wiley

Subject

Gastroenterology,Endocrine and Autonomic Systems,Physiology

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