Deep learning predicts function of live retinal pigment epithelium from quantitative microscopy.

Journal: The Journal of clinical investigation
Published Date: Feb 3, 2020

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.

Authors

  • Nicholas J Schaub
    Materials Measurement Laboratory, Biosystems and Biomaterials Division, National Institute of Standards and Technology, Gaithersburg, Maryland, USA.
  • Nathan A Hotaling
    Ocular and Stem Cell Translational Research Section, National Eye Institute, NIH, Bethesda, Maryland, USA.
  • Petre Manescu
    Information Technology Laboratory, Information Systems Group, National Institute of Standards and Technology, Gaithersburg, Maryland, USA.
  • Sarala Padi
    Information Technology Laboratory, Information Systems Group, National Institute of Standards and Technology, Gaithersburg, Maryland, USA.
  • Qin Wan
    Ocular and Stem Cell Translational Research Section, National Eye Institute, NIH, Bethesda, Maryland, USA.
  • Ruchi Sharma
    University of Victoria Faculty of Engineering & Computer Science, 3800 Finnerty Road, Victoria, British Columbia, V8W 3P6, CANADA.
  • Aman George
    Ocular and Stem Cell Translational Research Section, National Eye Institute, NIH, Bethesda, Maryland, USA.
  • Joe Chalfoun
    Information Technology Laboratory, Information Systems Group, National Institute of Standards and Technology, Gaithersburg, Maryland, USA.
  • Mylene Simon
    Information Technology Laboratory, Information Systems Group, National Institute of Standards and Technology, Gaithersburg, Maryland, USA.
  • Mohamed Ouladi
    Information Technology Laboratory, Information Systems Group, National Institute of Standards and Technology, Gaithersburg, Maryland, USA.
  • Carl G Simon
    Biosystems & Biomaterials Division, National Institute of Standards & Technology, Gaithersburg, MD, United States.
  • Peter Bajcsy
    National Institute of Standards and Technology, Gaithersburg, MD 20877, USA.
  • Kapil Bharti
    Ocular and Stem Cell Translational Research Section, National Eye Institute, NIH, Bethesda, Maryland, USA.

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