Using flow cytometry and multistage machine learning to discover label-free signatures of algal lipid accumulation
| dc.contributor.author | Tanhaemami, Mohammad, author | |
| dc.contributor.author | Munsky, Brian, advisor | |
| dc.contributor.author | Prasad, Ashok, committee member | |
| dc.contributor.author | Chitsaz, Hamidreza, committee member | |
| dc.date.accessioned | 2021-01-11T11:20:08Z | |
| dc.date.available | 2021-01-11T11:20:08Z | |
| dc.date.issued | 2020 | |
| dc.description.abstract | Most applications of flow cytometry or cell sorting rely on the conjugation of fluorescent dyes to specific biomarkers. However, labeled biomarkers are not always available, they can be costly, and they may disrupt natural cell behavior. Label-free quantification based upon machine-learning approaches could help correct these issues, but label replacement strategies can be very difficult to discover when applied labels or other modifications in measurements inadvertently modify intrinsic cell properties. Here we demonstrate a new, but simple approach based upon feature selection and linear regression analyses to integrate statistical information collected from both labeled and unlabeled cell populations and to identify models for accurate label-free single-cell quantification. We verify the method's accuracy to predict lipid content in algal cells Picochlorum soloecismus during a nitrogen starvation and lipid accumulation time course. Our general approach is expected to improve label-free single-cell analysis for other organisms or pathways, where biomarkers are inconvenient, expensive, or disruptive to downstream cellular processes. | |
| dc.format.medium | born digital | |
| dc.format.medium | masters theses | |
| dc.identifier | Tanhaemami_colostate_0053N_16300.pdf | |
| dc.identifier.uri | https://hdl.handle.net/10217/219520 | |
| dc.language | English | |
| dc.language.iso | eng | |
| dc.publisher | Colorado State University. Libraries | |
| dc.relation.ispartof | 2020- | |
| dc.rights | Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright. | |
| dc.subject | label-free quantification | |
| dc.subject | microalgae | |
| dc.subject | flow cytometry | |
| dc.subject | single cell | |
| dc.subject | machine learning | |
| dc.title | Using flow cytometry and multistage machine learning to discover label-free signatures of algal lipid accumulation | |
| dc.type | Text | |
| dcterms.rights.dpla | This Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). | |
| thesis.degree.discipline | Chemical and Biological Engineering | |
| thesis.degree.grantor | Colorado State University | |
| thesis.degree.level | Masters | |
| thesis.degree.name | Master of Science (M.S.) |
Files
Original bundle
1 - 1 of 1
Loading...
- Name:
- Tanhaemami_colostate_0053N_16300.pdf
- Size:
- 4.57 MB
- Format:
- Adobe Portable Document Format