Your browser doesn't support javascript.
loading
Show: 20 | 50 | 100
Results 1 - 1 de 1
Filter
Add more filters










Database
Language
Publication year range
1.
J Clin Invest ; 130(2): 1010-1023, 2020 02 03.
Article in English | MEDLINE | ID: mdl-31714897

ABSTRACT

Increases in the number of cell therapies in the preclinical and clinical phases have prompted the need for reliable and noninvasive assays to validate transplant function in clinical biomanufacturing. We developed a robust characterization methodology composed of quantitative bright-field absorbance microscopy (QBAM) and deep neural networks (DNNs) to noninvasively predict tissue function and cellular donor identity. The methodology was validated using clinical-grade induced pluripotent stem cell-derived retinal pigment epithelial cells (iPSC-RPE). QBAM images of iPSC-RPE were used to train DNNs that predicted iPSC-RPE monolayer transepithelial resistance, predicted polarized vascular endothelial growth factor (VEGF) secretion, and matched iPSC-RPE monolayers to the stem cell donors. DNN predictions were supplemented with traditional machine-learning algorithms that identified shape and texture features of single cells that were used to predict tissue function and iPSC donor identity. These results demonstrate noninvasive cell therapy characterization can be achieved with QBAM and machine learning.


Subject(s)
Cell Differentiation , Deep Learning , Image Processing, Computer-Assisted , Induced Pluripotent Stem Cells , Microscopy , Retinal Pigment Epithelium , Humans , Induced Pluripotent Stem Cells/cytology , Induced Pluripotent Stem Cells/metabolism , Retinal Pigment Epithelium/cytology , Retinal Pigment Epithelium/metabolism
SELECTION OF CITATIONS
SEARCH DETAIL
...