Rapid, portable and cost-effective yeast cell viability and concentration analysis using lensfree on-chip microscopy and machine learning

Rapid, portable and cost-effective yeast cell viability and concentration analysis using lensfree on-chip microscopy and machine learning†

Alborz Feizi,^abc Yibo Zhang,^a Alon Greenbaum,^ad Alex Guziak,^e Michelle Luong,^f Raymond Yan Lok Chan,^a Brandon Berg,^eg Haydar Ozkan,^a Wei Luo,^a Michael Wu,^a Yichen Wu^a and Aydogan Ozcan*^abch

Author affiliations

* Corresponding authors

^a Department of Electrical Engineering, University of California Los Angeles (UCLA), USA
E-mail: ozcan@ucla.edu
Web: http://www.innovate.ee.ucla.edu, http://org.ee.ucla.edu

^b Department of Bioengineering, University of California Los Angeles (UCLA), USA

^c California Nanosystems Institute (CNSI), University of California Los Angeles (UCLA), USA

^d Division of Biology and Biological Engineering, California Institute of Technology, USA

^e Physics and Astronomy Department, University of California Los Angeles (UCLA), USA

^f Department of Microbiology, Immunology, and Molecular Genetics, University of California (UCLA), USA

^g Physics Department, University of Michigan, USA

^h Department of Surgery, David Geffen School of Medicine, University of California (UCLA), USA

Abstract

Monitoring yeast cell viability and concentration is important in brewing, baking and biofuel production. However, existing methods of measuring viability and concentration are relatively bulky, tedious and expensive. Here we demonstrate a compact and cost-effective automatic yeast analysis platform (AYAP), which can rapidly measure cell concentration and viability. AYAP is based on digital in-line holography and on-chip microscopy and rapidly images a large field-of-view of 22.5 mm². This lens-free microscope weighs 70 g and utilizes a partially-coherent illumination source and an opto-electronic image sensor chip. A touch-screen user interface based on a tablet-PC is developed to reconstruct the holographic shadows captured by the image sensor chip and use a support vector machine (SVM) model to automatically classify live and dead cells in a yeast sample stained with methylene blue. In order to quantify its accuracy, we varied the viability and concentration of the cells and compared AYAP's performance with a fluorescence exclusion staining based gold-standard using regression analysis. The results agree very well with this gold-standard method and no significant difference was observed between the two methods within a concentration range of 1.4 × 10⁵ to 1.4 × 10⁶ cells per mL, providing a dynamic range suitable for various applications. This lensfree computational imaging technology that is coupled with machine learning algorithms would be useful for cost-effective and rapid quantification of cell viability and density even in field and resource-poor settings.