Using Flow Cytometry and Multistage Machine Learning to Discover Label-Free Signatures of Algal Lipid Accumulation
نام عام مواد
[Thesis]
نام نخستين پديدآور
Tanhaemami, Mohammad
نام ساير پديدآوران
Munsky, Brian E.
وضعیت نشر و پخش و غیره
نام ناشر، پخش کننده و غيره
Colorado State University
تاریخ نشرو بخش و غیره
2020
يادداشت کلی
متن يادداشت
66 p.
یادداشتهای مربوط به پایان نامه ها
جزئيات پايان نامه و نوع درجه آن
M.S.
کسي که مدرک را اعطا کرده
Colorado State University
امتياز متن
2020
یادداشتهای مربوط به خلاصه یا چکیده
متن يادداشت
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.
اصطلاحهای موضوعی کنترل نشده
اصطلاح موضوعی
Bioengineering
اصطلاح موضوعی
Biomedical engineering
اصطلاح موضوعی
Chemical engineering
نام شخص به منزله سر شناسه - (مسئولیت معنوی درجه اول )