Machine Learning Based Real-Time Image-Guided Cell Sorting and Classification

Yi Gu; Alex Ce Zhang; Yuanyuan Han; Jie Li; Clark C Chen; Yu Hwa Lo

doi:10.1002/cyto.a.23764

Machine Learning Based Real-Time Image-Guided Cell Sorting and Classification

Yi Gu, Alex Ce Zhang, Yuanyuan Han, Jie Li, Clark C Chen, Yu Hwa Lo

Neurosurgery

Research output: Contribution to journal › Article › peer-review

35 Scopus citations

Abstract

Cell classification based on phenotypical, spatial, and genetic information greatly advances our understanding of the physiology and pathology of biological systems. Technologies derived from next generation sequencing and fluorescent activated cell sorting are cornerstones for cell- and genomic-based assays supporting cell classification and mapping. However, there exists a deficiency in technology space to rapidly isolate cells based on high content image information. Fluorescence-activated cell sorting can only resolve cell-to-cell variation in fluorescence and optical scattering. Utilizing microfluidics, photonics, computation microscopy, real-time image processing and machine learning, we demonstrate an image-guided cell sorting and classification system possessing the high throughput of flow cytometer and high information content of microscopy. We demonstrate the utility of this technology in cell sorting based on (1) nuclear localization of glucocorticoid receptors, (2) particle binding to the cell membrane, and (3) DNA damage induced γ-H2AX foci.

Original language	English (US)
Pages (from-to)	499-509
Number of pages	11
Journal	Cytometry Part A
Volume	95
Issue number	5
DOIs	https://doi.org/10.1002/cyto.a.23764
State	Published - May 2019

Bibliographical note

Funding Information:
Grant sponsor: National Institutes of Health, Grant numberR44DA042636; Grant sponsor: National Science Foun dation, Grant numberECCS-1542148 Additional Supporting Information may be found in the online version of this article.

Funding Information:
This work was performed in part at the San Diego Nanotechnology Infrastructure (SDNI) of UCSD, a member of the National Nanotechnology Coordinated Infrastructure (NNCI), which is supported by the National Science Foundation (Grant ECCS-1542148). Research reported in this publication was supported by the National Institutes of Health under award number R44DA042636. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Yuhwa Lo has an equity interest in NanoCellect Biomedical as a co-founder and a member of the company’s Scientific Advisory Board. NanoCellect may potentially benefit from the results of this research.

Publisher Copyright:
© 2019 International Society for Advancement of Cytometry

Keywords

image guided cell sorting
imaging flow cytometry
machine learning
microfluidic

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10.1002/cyto.a.23764

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abstract = "Cell classification based on phenotypical, spatial, and genetic information greatly advances our understanding of the physiology and pathology of biological systems. Technologies derived from next generation sequencing and fluorescent activated cell sorting are cornerstones for cell- and genomic-based assays supporting cell classification and mapping. However, there exists a deficiency in technology space to rapidly isolate cells based on high content image information. Fluorescence-activated cell sorting can only resolve cell-to-cell variation in fluorescence and optical scattering. Utilizing microfluidics, photonics, computation microscopy, real-time image processing and machine learning, we demonstrate an image-guided cell sorting and classification system possessing the high throughput of flow cytometer and high information content of microscopy. We demonstrate the utility of this technology in cell sorting based on (1) nuclear localization of glucocorticoid receptors, (2) particle binding to the cell membrane, and (3) DNA damage induced γ-H2AX foci.",

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Machine Learning Based Real-Time Image-Guided Cell Sorting and Classification

Abstract

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