عنوان
پدید آورنده
موضوع
رده
کتابخانه
محل استقرار
TL56217
انگلیسی
Using Flow Cytometry and Multistage Machine Learning to Discover Label-Free Signatures of Algal Lipid Accumulation
[Thesis]
Tanhaemami, Mohammad
Munsky, Brian E.
Colorado State University
2020
66 p.
M.S.
Colorado State University
2020
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.
Bioengineering
Biomedical engineering
Chemical engineering
Tanhaemami, Mohammad
Munsky, Brian E.
Colorado State University
مطالعه متن کتاب p
[Thesis]
276903
a
Y
