High-throughput, label-free, single-cell, microalgal lipid screening by machine-learning-equipped optofluidic time-stretch quantitative phase microscopy

Cytometry Part A

Volumn 91, Issue 5, 2017, Pages 494-502

  Guo, Baoshan ^a   Lei, Cheng ^a,b   Kobayashi, Hirofumi ^a   Ito, Takuro ^c   Yalikun, Yaxiaer ^d   Jiang, Yiyue ^a   Tanaka, Yo ^d   Ozeki, Yasuyuki ^a   Goda, Keisuke ^a,c,e  

^a UNIVERSITY OF TOKYO   (Japan)

^b TSINGHUA UNIVERSITY   (China)

^c JAPAN SCIENCE AND TECHNOLOGY AGENCY   (Japan)

^d RIKEN   (Japan)

^e UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

biofuel; Euglena gracilis; global warming; high throughput screening; machine learning; microfluidics; optofluidics; quantitative phase imaging; single cell analysis

Indexed keywords

CELL COUNT; CYTOLOGY; HIGH THROUGHPUT SCREENING; MACHINE LEARNING; MICROALGA; MICROFLUIDIC ANALYSIS; MICROSCOPY; PROCEDURES; SINGLE CELL ANALYSIS;

CELL COUNT; HIGH-THROUGHPUT SCREENING ASSAYS; MACHINE LEARNING; MICROALGAE; MICROFLUIDIC ANALYTICAL TECHNIQUES; MICROSCOPY; SINGLE-CELL ANALYSIS;

EID: 85017546094     PISSN: 15524922     EISSN: 15524930     Source Type: Journal    
DOI: 10.1002/cyto.a.23084     Document Type: Article

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