Lens-free shadow image based high-throughput continuous cell monitoring technique

Innovative imaging methods are continuously developed to investigate the function of biological systems at the microscopic scale. As an alternative to advanced cell microscopy techniques, we are developing lensfree video microscopy that opens new ranges of capabilities, in particular at the mesoscopic level. Lensfree video microscopy allows the observation of a cell culture in an incubator over a very large field of view (24 mm 2 ) for extended periods of time. As a result, a large set of comprehensive data can be gathered with strong statistics, both in space and time. Video lensfree microscopy can capture images of cells cultured in various physical environments. We emphasize on two different case studies: the quantitative analysis of the spontaneous network formation of HUVEC endothelial cells, and by coupling lensfree microscopy with 3D cell culture in the study of epithelial tissue morphogenesis. In summary, we demonstrate that lensfree video microscopy is a powerful tool to conduct cell assays in 2D and 3D culture experiments. The applications are in the realms of fundamental biology, tissue regeneration, drug development and toxicology studies.

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“…Lensfree video microscopy is a powerful tool to conduct experiments using 2D and 3D assays. Similar works can be found in [11][12][13][14].…”

Section: Iëì CMsupporting

confidence: 63%

Video lensfree microscopy of 2D and 3D culture of cells

et al. 2014

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“…As analyzed in [6,7,8,9,10,11], the spatial resolution can be improved by increasing D ls , or decreasing D obj and D PIX . D ls can be as long as several centimeters, which is mainly determined by the size of the POCT system.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Based Single-Frame Super-Resolution Processing for Lensless Blood Cell Counting

Huang

Jiang

Liu

et al. 2016

Sensors

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A lensless blood cell counting system integrating microfluidic channel and a complementary metal oxide semiconductor (CMOS) image sensor is a promising technique to miniaturize the conventional optical lens based imaging system for point-of-care testing (POCT). However, such a system has limited resolution, making it imperative to improve resolution from the system-level using super-resolution (SR) processing. Yet, how to improve resolution towards better cell detection and recognition with low cost of processing resources and without degrading system throughput is still a challenge. In this article, two machine learning based single-frame SR processing types are proposed and compared for lensless blood cell counting, namely the Extreme Learning Machine based SR (ELMSR) and Convolutional Neural Network based SR (CNNSR). Moreover, lensless blood cell counting prototypes using commercial CMOS image sensors and custom designed backside-illuminated CMOS image sensors are demonstrated with ELMSR and CNNSR. When one captured low-resolution lensless cell image is input, an improved high-resolution cell image will be output. The experimental results show that the cell resolution is improved by 4×, and CNNSR has 9.5% improvement over the ELMSR on resolution enhancing performance. The cell counting results also match well with a commercial flow cytometer. Such ELMSR and CNNSR therefore have the potential for efficient resolution improvement in lensless blood cell counting systems towards POCT applications.

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“…Third, the cells are not visualized which represents a huge loss of information in the era of HCA. As a recent alternative, owing to its simplicity, lensfree imaging is being assessed to perform live cell imaging891011. Using our lensfree video microscopy platform (methods, Fig.…”

mentioning

confidence: 99%

High-throughput monitoring of major cell functions by means of lensfree video microscopy

Kesavan

Momey

Cioni

et al. 2014

Sci Rep

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Quantification of basic cell functions is a preliminary step to understand complex cellular mechanisms, for e.g., to test compatibility of biomaterials, to assess the effectiveness of drugs and siRNAs, and to control cell behavior. However, commonly used quantification methods are label-dependent, and end-point assays. As an alternative, using our lensfree video microscopy platform to perform high-throughput real-time monitoring of cell culture, we introduce specifically devised metrics that are capable of non-invasive quantification of cell functions such as cell-substrate adhesion, cell spreading, cell division, cell division orientation and cell death. Unlike existing methods, our platform and associated metrics embrace entire population of thousands of cells whilst monitoring the fate of every single cell within the population. This results in a high content description of cell functions that typically contains 25,000 – 900,000 measurements per experiment depending on cell density and period of observation. As proof of concept, we monitored cell-substrate adhesion and spreading kinetics of human Mesenchymal Stem Cells (hMSCs) and primary human fibroblasts, we determined the cell division orientation of hMSCs, and we observed the effect of transfection of siCellDeath (siRNA known to induce cell death) on hMSCs and human Osteo Sarcoma (U2OS) Cells.

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Lens-free shadow image based high-throughput continuous cell monitoring technique

Cited by 50 publications

References 16 publications

Video lensfree microscopy of 2D and 3D culture of cells

Video lensfree microscopy of 2D and 3D culture of cells

Machine Learning Based Single-Frame Super-Resolution Processing for Lensless Blood Cell Counting

High-throughput monitoring of major cell functions by means of lensfree video microscopy

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