Imaging of dense cell cultures by multiwavelength lens-free video microscopy.

They present results for lens-free microscopy for the imaging of dense cell culture. With this aim, they use a multiwavelength LED illumination with well separated wavelengths, together with the implementation of an appropriate holographic reconstruction algorithm. This allows for a fast and efficient reconstruction of the phase image of densely packed cells (up to 700 cells/mm2 ) over a large field of view of 29.4 mm2 . Combined with the compactness of the system which fits altogether inside an incubator, lens-free microscopy becomes a unique tool to monitor cell cultures over several days. The high contrast phase shift images provide robust cell segmentation and tracking, and enable high throughput monitoring of individual cell dimensions, dry mass, and motility. They tested the multiwavelength lens-free video-microscope over a broad range of cell lines, including mesenchymal, endothelial, and epithelial cells. © 2017 International Society for Advancement of Cytometry.

Alternation of inverse problem approach and deep learning for lens-free microscopy image reconstruction.

A lens-free microscope is a simple imaging device performing in-line holographic measurements. In the absence of focusing optics, a reconstruction algorithm is used to retrieve the sample image by solving the inverse problem. This is usually performed by optimization algorithms relying on gradient computation. However the presence of local minima leads to unsatisfactory convergence when phase wrapping errors occur. This is particularly the case in large optical thickness samples, for example cells in suspension and cells undergoing mitosis. To date, the occurrence of phase wrapping errors in the holographic reconstruction limits the application of lens-free microscopy in live cell imaging. To overcome this issue, we propose a novel approach in which the reconstruction alternates between two approaches, an inverse problem optimization and deep learning. The computation starts with a first reconstruction guess of the cell sample image. The result is then fed into a neural network, which is trained to correct phase wrapping errors. The neural network prediction is next used as the initialization of a second and last reconstruction step, which corrects to a certain extent the neural network prediction errors. We demonstrate the applicability of this approach in solving the phase wrapping problem occurring with cells in suspension at large densities. This is a challenging sample that typically cannot be reconstructed without phase wrapping errors, when using inverse problem optimization alone.

Quantitative phase imaging of adherent mammalian cells: a comparative study.

The Quantitative phase imaging methods have several advantages when it comes to monitoring cultures of adherent mammalian cells. Because of low photo-toxicity and no need for staining, we can follow cells in a minimally invasive way over a long period of time. The ability to measure the optical path difference in a quantitative manner allows the measurement of the cell dry mass, an important metric for studying the growth kinetics of mammalian cells. Here we present and compare cell measurements obtained with three different techniques: digital holographic microscopy, lens-free microscopy and quadriwave lateral sheering interferometry. We report a linear relationship between optical volume density values measured with these different techniques and estimate the precisions of this measurement for the different individual instruments.

Multispectral total-variation reconstruction applied to lens-free microscopy.

Lens-free microscopy multispectral acquisitions are processed with an inverse problem approach: a multispectral total variation criterion is defined and minimized with the conjugate gradients method. Reconstruction results show that the method is efficient to recover the phase image of densely packed cells.

Dynamics of cell and tissue growth acquired by means of extended field of view lensfree microscopy.

In this paper, we discuss a new methodology based on lensfree imaging to perform wound healing assay with unprecedented statistics. Our video lensfree microscopy setup is a simple device featuring only a CMOS sensor and a semi coherent illumination system. Yet it is a powerful mean for the real-time monitoring of cultivated cells. It presents several key advantages, e.g. integration into standard incubator, compatibility with standard cell culture protocol, simplicity and ease of use. It can perform the follow-up in a large field of view (25 mm(2)) of several crucial parameters during the culture of cells i.e. their motility, their proliferation rate or their death. Consequently the setup can gather large statistics both in space and time. Here we uses this facility in the context of wound healing assay to perform label-free measurements of the velocities of the fronts of proliferation of the cell layer as a function of time by means of particle image velocimetry (PIV) processing. However, for such tissue growth experiments, the field of view of 25 mm(2) remains not sufficient and results can be biased depending on the position of the device with respect to the recipient of the cell culture. Hence, to conduct exhaustive wound healing assays, we propose to enlarge the field of view up to 10 cm(2) through a raster scan, by moving the source/sensor with respect to the Petri dish. We have performed acquisitions of wound healing assay (keratinocytes HaCaT) both in real-time (25 mm(2)) and in final point (10 cm(2)) to assess the combination of velocimetry measurements and final point wide field imaging. In the future, we aim at combining directly our extended field of view acquisitions (>10 cm(2)) with real time ability inside the incubator.

Lensfree diffractive tomography for the imaging of 3D cell cultures.

New microscopes are needed to help realize the full potential of 3D organoid culture studies. In order to image large volumes of 3D organoid cultures while preserving the ability to catch every single cell, we propose a new imaging platform based on lensfree microscopy. We have built a lensfree diffractive tomography setup performing multi-angle acquisitions of 3D organoid culture embedded in Matrigel and developed a dedicated 3D holographic reconstruction algorithm based on the Fourier diffraction theorem. With this new imaging platform, we have been able to reconstruct a 3D volume as large as 21.5 mm (3) of a 3D organoid culture of prostatic RWPE1 cells showing the ability of these cells to assemble in 3D intricate cellular network at the mesoscopic scale. Importantly, comparisons with 2D images show that it is possible to resolve single cells isolated from the main cellular structure with our lensfree diffractive tomography setup.

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

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.

Bacteria detection with thin wetting film lensless imaging.

Lensless on-chip imaging is a promising technique to count and monitor cells and micro-objects in liquid sample. In this paper we apply this technique to the observation of µL sample containing bacteria evaporated onto a microscope slide. Compared with previously reported techniques, a large improvement in signal to noise ratio is obtained due to the presence of a few µm thick wetting film creating a micro-lens on top of each bacteria. In these conditions, standard CMOS sensor are able to detect micro-objects as small as few µm, e.g. E.coli and Bacillus subtilis bacteria and 1 µm polymer beads with a large signal to noise ratio of 45 ± 10. An overall detection efficiency of 85 ± 7% and a co-localization error of σ(1D) = 1.1µm compared with reference fluorescence microscopy images are achieved. This novel technique will be used as a pre-positioning tool prior to other optical identification methods, e.g. Raman spectroscopy.