Lens-free computational imaging of capillary morphogenesis within three-dimensional substrates
University of California Irvine, Biomedical Engineering Department, Irvine, California.Journal of Biomedical Optics (Impact Factor: 2.86). 12/2012; 17(12):126018. DOI: 10.1117/1.JBO.17.12.126018
Endothelial cells cultured in three-dimensional (3-D) extracellular matrices spontaneously form microvessels in response to soluble and matrix-bound factors. Such cultures are common for the study of angiogenesis and may find widespread use in drug discovery. Vascular networks are imaged over weeks to measure the distribution of vessel morphogenic parameters. Measurements require micron-scale spatial resolution, which for light microscopy comes at the cost of limited field-of-view (FOV) and shallow depth-of-focus (DOF). Small FOVs and DOFs necessitate lateral and axial mechanical scanning, thus limiting imaging throughput. We present a lens-free holographic on-chip microscopy technique to rapidly image microvessels within a Petri dish over a large volume without any mechanical scanning. This on-chip method uses partially coherent illumination and a CMOS sensor to record in-line holographic images of the sample. For digital reconstruction of the measured holograms, we implement a multiheight phase recovery method to obtain phase images of capillary morphogenesis over a large FOV (24 mm2) with ∼1.5 μm spatial resolution. On average, measured capillary length in our method was within approximately 2% of lengths measured using a 10× microscope objective. These results suggest lens-free on-chip imaging is a useful toolset for high-throughput monitoring and quantitative analysis of microvascular 3-D networks.
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ABSTRACT: Lensless fluorescence imaging (LFI) is the imaging of fluorescence from cells or microspheres using an image sensor with no external lenses or filters. The simplicity of the hardware makes it well suited to replace fluorescence microscopes and flow cytometers in lab-on-a-chip applications, but the images captured by LFI are highly dependent on the distance between the sample and the sensor. This work demonstrates that not only can samples be accurately detected across a range of sample-sensor separations using LFI, but also that the separation can be accurately estimated based on the shape of fluorescence in the LFI image. First, a theoretical model that accurately predicts LFI images of microspheres is presented. Then, the experimental results are compared to the model and an image processing method for accurately predicting sample-sensor separation from LFI images is presented. Finally, LFI images of microspheres and cells passing through a microfluidic channel are presented.Journal of Biomedical Optics 01/2014; 19(1):16002. DOI:10.1117/1.JBO.19.1.016002 · 2.86 Impact Factor
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ABSTRACT: 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.Scientific Reports 08/2014; 4:5942. DOI:10.1038/srep05942 · 5.58 Impact Factor
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