Reconstructing cell cycle and disease progression using deep learning

We show that deep convolutional neural networks combined with nonlinear dimension reduction enable reconstructing biological processes based on raw image data. We demonstrate this by reconstructing the cell cycle of Jurkat cells and disease progression in diabetic retinopathy. In further analysis of Jurkat cells, we detect and separate a subpopulation of dead cells in an unsupervised manner and, in classifying discrete cell cycle stages, we reach a sixfold reduction in error rate compared to a recent approach based on boosting on image features. In contrast to previous methods, deep learning based predictions are fast enough for on-the-fly analysis in an imaging flow cytometer

Slanted channel microfluidic chip for 3D fluorescence imaging of cells in flow

Three-dimensional cellular imaging techniques have become indispensable tools in biological research and medical diagnostics. Conventional 3D imaging approaches employ focal stack collection to image different planes of the cell. In this work, we present the design and fabrication of a slanted channel microfluidic chip for 3D fluorescence imaging of cells in flow. The approach employs slanted microfluidic channels fabricated in glass using ultrafast laser inscription. The slanted nature of the microfluidic channels ensures that samples come into and go out of focus, as they pass through the microscope imaging field of view. This novel approach enables the collection of focal stacks in a straight-forward and automated manner, even with off-the-shelf microscopes that are not equipped with any motorized translation/rotation sample stages. The presented approach not only simplifies conventional focal stack collection, but also enhances the capabilities of a regular widefield fluorescence microscope to match the features of a sophisticated confocal microscope. We demonstrate the retrieval of sectioned slices of microspheres and cells, with the use of computational algorithms to enhance the signal-to-noise ratio (SNR) in the collected raw images. The retrieved sectioned images have been used to visualize fluorescent microspheres and bovine sperm cell nucleus in 3D while using a regular widefield fluorescence microscope. We have been able to achieve sectioning of approximately 200 slices per cell, which corresponds to a spatial translation of similar to 15 nm per slice along the optical axis of the microscope

Imaging Cells in Flow Cytometer Using Spatial-Temporal Transformation

Flow cytometers measure fluorescence and light scattering and analyze multiple physical characteristics of a large population of single cells as cells flow in a fluid stream through an excitation light beam. Although flow cytometers have massive statistical power due to their single cell resolution and high throughput, they produce no information about cell morphology or spatial resolution offered by microscopy, which is a much wanted feature missing in almost all flow cytometers. In this paper, we invent a method of spatial-temporal transformation to provide flow cytometers with cell imaging capabilities. The method uses mathematical algorithms and a spatial filter as the only hardware needed to give flow cytometers imaging capabilities. Instead of CCDs or any megapixel cameras found in any imaging systems, we obtain high quality image of fast moving cells in a flow cytometer using PMT detectors, thus obtaining high throughput in manners fully compatible with existing cytometers. To prove the concept, we demonstrate cell imaging for cells travelling at a velocity of 0.2 m/s in a microfluidic channel, corresponding to a throughput of approximately 1,000 cells per second