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Alternative Title

New method to quantify 3D biological structures and identify differences in zebrafish forebrain development

Publication Date


First Advisor

Michael J.F. Barresi

Document Type

Honors Project

Degree Name

Bachelor of Arts


Biological Sciences


Computational biology, Developmental biology, Image analysis, Biological structures, Neurodevelopment, Zebrafish, Axon guidance, Brain-Growth, Axonal transport


Research in developmental biology has relied on the analysis of morphological phe notypes through qualitative examination of maximum intensity projections (MIPs) that surrenders the power of three dimensional data. We contend that the subtlety required for reverse genetics has surpassed the limits of qualitative analysis of MIP data. New statistical methods to analyze visual data are required to detect these subtle phenotypes. In addition, these methods would best serve the community if they could leverage all the data contained within the 3D datasets that are becom ing common with the advent of sophisticated microscopy techniques. One barrier to achieving statistical power in image analyses has been the misalignment of spatial relationships between different images. We have created ∆SCOPE to enable this type of analysis and overcome these challenges. ∆SCOPE is a program for biological image analysis that enables quantification and statistical analysis of 3D multichannel signals that are positioned around a well-defined structure. ∆SCOPE enables description of the biological structure using a mathematical model that aligns and compares dif ferent samples while also accounting for individual variation. We demonstrate the utility of this program by quantifying the phenotypes produced by abnormal axon guidance cues in the post-optic commissure of the zebrafish forebrain. Our method has successfully quantified a severe non-midline crossing phenotype in the you-too (gli2-DR) mutant as well as revealed more subtle previously uncharacterized defects in the associated glial cells at the midline. We are currently building this method into a user-friendly, open source program that the community at large can use to similarly quantify 3D, multichannel datasets, which will provide statistical rigor and novel insight often lost in the qualitative inspection of subtle phenotypic changes.


2018 Morgan Sarah Schwartz. Access limited to the Smith College community and other researchers while on campus. Smith College community members also may access from off-campus using a Smith College log-in. Other off-campus researchers may request a copy through Interlibrary Loan for personal use.




37 pages : illustrations (some color) Includes bibliographical references (pages 36-37)