Asset Details
MbrlCatalogueTitleDetail
Do you wish to reserve the book?
A single-cell Raman-based platform to identify developmental stages of human pluripotent stem cell-derived neurons
by
Xu, Jiabao
, Wang, Hui
, Cui, Zhanfeng
, Brinkhof, Bas
, Huang, Wei E.
, Hsu, Chia-Chen
, Ye, Hua
in
Applied Biological Sciences
/ Biological Sciences
/ Biomarkers
/ Cell differentiation
/ Cell therapy
/ Derivatives
/ Developmental stages
/ Drug screening
/ Embedding
/ Glycogen
/ Glycogens
/ Inhibitory postsynaptic potentials
/ Learning algorithms
/ Machine learning
/ Neural stem cells
/ Pluripotency
/ Raman spectra
/ Raman spectroscopy
/ Screening
/ Stem cells
/ Zebrafish
2020
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
You are currently in the queue to collect this book. You will be notified once it is your turn to collect the book.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
A single-cell Raman-based platform to identify developmental stages of human pluripotent stem cell-derived neurons
by
Xu, Jiabao
, Wang, Hui
, Cui, Zhanfeng
, Brinkhof, Bas
, Huang, Wei E.
, Hsu, Chia-Chen
, Ye, Hua
in
Applied Biological Sciences
/ Biological Sciences
/ Biomarkers
/ Cell differentiation
/ Cell therapy
/ Derivatives
/ Developmental stages
/ Drug screening
/ Embedding
/ Glycogen
/ Glycogens
/ Inhibitory postsynaptic potentials
/ Learning algorithms
/ Machine learning
/ Neural stem cells
/ Pluripotency
/ Raman spectra
/ Raman spectroscopy
/ Screening
/ Stem cells
/ Zebrafish
2020
Oops! Something went wrong.
While trying to remove the title from your shelf something went wrong :( Kindly try again later!
Do you wish to request the book?
A single-cell Raman-based platform to identify developmental stages of human pluripotent stem cell-derived neurons
by
Xu, Jiabao
, Wang, Hui
, Cui, Zhanfeng
, Brinkhof, Bas
, Huang, Wei E.
, Hsu, Chia-Chen
, Ye, Hua
in
Applied Biological Sciences
/ Biological Sciences
/ Biomarkers
/ Cell differentiation
/ Cell therapy
/ Derivatives
/ Developmental stages
/ Drug screening
/ Embedding
/ Glycogen
/ Glycogens
/ Inhibitory postsynaptic potentials
/ Learning algorithms
/ Machine learning
/ Neural stem cells
/ Pluripotency
/ Raman spectra
/ Raman spectroscopy
/ Screening
/ Stem cells
/ Zebrafish
2020
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
A single-cell Raman-based platform to identify developmental stages of human pluripotent stem cell-derived neurons
Journal Article
A single-cell Raman-based platform to identify developmental stages of human pluripotent stem cell-derived neurons
2020
Request Book From Autostore
and Choose the Collection Method
Overview
Stem cells with the capability to self-renew and differentiate into multiple cell derivatives provide platforms for drug screening and promising treatment options for a wide variety of neural diseases. Nevertheless, clinical applications of stem cells have been hindered partly owing to a lack of standardized techniques to characterize cell molecular profiles noninvasively and comprehensively. Here, we demonstrate that a label-free and noninvasive single-cell Raman microspectroscopy (SCRM) platform was able to identify neural cell lineages derived from clinically relevant human induced pluripotent stem cells (hiPSCs). By analyzing the intrinsic biochemical profiles of single cells at a large scale (8,774 Raman spectra in total), iPSCs and iPSC-derived neural cells can be distinguished by their intrinsic phenotypic Raman spectra. We identified a Raman biomarker from glycogen to distinguish iPSCs from their neural derivatives, and the result was verified by the conventional glycogen detection assays. Further analysis with a machine learning classification model, utilizing t-distributed stochastic neighbor embedding (t-SNE)-enhanced ensemble stacking, clearly categorized hiPSCs in different developmental stages with 97.5% accuracy. The present study demonstrates the capability of the SCRM-based platform to monitor cell development using high content screening with a noninvasive and label-free approach. This platform as well as our identified biomarker could be extensible to other cell types and can potentially have a high impact on neural stem cell therapy.
Publisher
National Academy of Sciences
Subject
This website uses cookies to ensure you get the best experience on our website.