A Caltech Library Service

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

Feizi, Alborz and Zhang, Yibo and Greenbaum, Alon and Guziak, Alex and Luong, Michelle and Chan, Raymond Yan Lok and Berg, Brandon and Ozkan, Haydar and Luo, Wei and Wu, Michael and Wu, Yichen and Ozcan, Aydogan (2016) Rapid, portable and cost-effective yeast cell viability and concentration analysis using lensfree on-chip microscopy and machine learning. Lab on a Chip, 16 (22). pp. 4350-4358. ISSN 1473-0197.

[img] PDF - Published Version
Creative Commons Attribution.

[img] PDF - Supplemental Material
Creative Commons Attribution.


Use this Persistent URL to link to this item:


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 mm^2. 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 × 10^5 to 1.4 × 10^6 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.

Item Type:Article
Related URLs:
URLURL TypeDescription Information
Additional Information:© 2016 The Royal Society of Chemistry. This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. Received 30th July 2016, Accepted 23rd September 2016, First published online 23 Sep 2016. The Ozcan Research Group at UCLA gratefully acknowledges the support of the Presidential Early Career Award for Scientists and Engineers (PECASE), the Army Research Office (ARO; W911NF-13-1-0419 and W911NF-13-1-0197), the ARO Life Sciences Division, the National Science Foundation (NSF) CBET Division Biophotonics Program, the NSF Emerging Frontiers in Research and Innovation (EFRI) Award, the NSF EAGER Award, NSF INSPIRE Award, NSF Partnerships for Innovation: Building Innovation Capacity (PFI:BIC) Program, Office of Naval Research (ONR), the Howard Hughes Medical Institute (HHMI), Vodafone Americas Foundation, and KAUST. This work is based upon research performed in a laboratory renovated by the National Science Foundation under Grant No. 0963183, which is an award funded under the American Recovery and Reinvestment Act of 2009 (ARRA). Author Contributions: A. F., A. G., and A. O. conceived the idea, A. F. and Y. Z. conducted the experiments and performed data analysis, A. G., M. L., R. Y. L. C., B. B., H. O., W. L., M. W. and Y. W. made contributions to experiments, microscope design, user interface design or data processing/analysis. A. F., Y. Z., and A. O. wrote the manuscript. A. O. supervised the project. Conflicts of interest: A. O. is the co-founder of a company (Cellmic LLC) that commercializes computational microscopy, sensing and diagnostics tools.
Funding AgencyGrant Number
Army Research Office (ARO)W911NF-13-1-0419
Army Research Office (ARO) W911NF-13-1-0197
Office of Naval Research (ONR)UNSPECIFIED
Howard Hughes Medical Institute (HHMI)UNSPECIFIED
Vodafone Americas FoundationUNSPECIFIED
King Abdullah University of Science and Technology (KAUST)UNSPECIFIED
American Recovery and Reinvestment Act of 2009UNSPECIFIED
Issue or Number:22
Record Number:CaltechAUTHORS:20161018-093042032
Persistent URL:
Official Citation:Rapid, portable and cost-effective yeast cell viability and concentration analysis using lensfree on-chip microscopy and machine learning Alborz Feizi, Yibo Zhang, Alon Greenbaum, Alex Guziak, Michelle Luong, Raymond Yan Lok Chan, Brandon Berg, Haydar Ozkan, Wei Luo, Michael Wu, Yichen Wua and Aydogan Ozcan Lab Chip, 2016,16, 4350-4358 DOI: 10.1039/C6LC00976J
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:71207
Deposited By: Tony Diaz
Deposited On:18 Oct 2016 16:43
Last Modified:03 Oct 2019 16:05

Repository Staff Only: item control page