Methods for robust measurements in low signal-to-noise systems and their application to nematode mechanoreceptors
Abstract/Contents
- Abstract
- Animals small and large, vertebrate and invertebrate alike, use specialized sensory cells to detect mechanical cues in their environment. These mechanoreceptors are tuned to specific stimulus profiles and often work as part of an ensemble of sensory cells that allows animals to detect multiple types of mechanical stimuli in concert. Experimental data and theoretical modeling predicts that mechanoreceptor function and form are tightly coupled, however testing this hypothesis has faced numerous technical challenges. These biological sensors are typically embedded in tissue that limits direct access to the fundamental sensing units (i.e., the Mechanoelectrical Transduction, MeT, ion channels). The nematode Caenorhabditis elegans is uniquely poised as an ideal system for probing the relationship between receptor form and function. Prior work has established the physiological relevance of C. elegans receptor shape, yet the genetic and molecular interactions leading to this relevance has not been elucidated. This is likely again due to technical limitations and a lack of tools available for (1) controlling receptor morphology and (2) reliably scoring morphology in various genetic backgrounds. In this thesis, by applying mechanical engineering principles to this biological system, I have developed novel experimental and analytical approaches for overcoming this technical gap. In early work I refined a previously established yet underutilized method for studying C. elegans mechanoreceptors in vitro. Using this approach I have demonstrated that mechanoreceptor shape can be synthetically controlled via contactless micropatterning of peanut lectin. To achieve a robust analysis of these experimental results I developed a suite of image processing code to conduct both automated and supervised morphological measurements. To validate these approaches I applied these methods to quantifying the effects of various genetic mutations to MeT channel localization and mechanoreceptor morphology in vitro. These mechanoreceptors often show significantly greater morphological heterogeneity than their in vivo correlates, effectively raising the noise floor and making it difficult to derive meaningful measurements. This decrease in signal-to-noise requires experimenters to exercise subjectivity when choosing representative samples and reduces confidence in drawing conclusions from these measurements. To circumvent this challenge I show that micropatterning can be used in vitro to reduce biological variance, and that image segmentation can be used in silico to reduce operator bias, increase robustness and reproducibility. Combined, these approaches allow for reliable measurements in otherwise noisy backgrounds. This thesis has generated a number of tools that will enable researchers to conduct robust morphological measurements in low signal-to-noise biological systems such as nematode mechanoreceptors. This work establishes patterning tools that can be used to generate controlled variations in neurite curvature in the future and further probe the relationship between ion channel localization, receptor shape, and mechanosensory function. The processes developed in this work can also be easily extended to cryo-electron tomography, atomic force microscopy, and real time imaging of organelle trafficking.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2021; ©2021 |
| Publication date | 2021; 2021 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Franco, Joy Ann |
|---|---|
| Degree supervisor | Goodman, Miriam Beth |
| Degree supervisor | Kenny, Thomas William |
| Thesis advisor | Goodman, Miriam Beth |
| Thesis advisor | Kenny, Thomas William |
| Thesis advisor | Chaudhuri, Ovijit |
| Thesis advisor | Pruitt, Beth |
| Degree committee member | Chaudhuri, Ovijit |
| Degree committee member | Pruitt, Beth |
| Associated with | Stanford University, Department of Mechanical Engineering |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Joy Ann Franco. |
|---|---|
| Note | Submitted to the Department of Mechanical Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2021. |
| Location | https://purl.stanford.edu/hy400rj1686 |
Access conditions
- Copyright
- © 2021 by Joy Ann Franco
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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