Highlights d Robust method automatically adapting to various unseen experimental scenarios d Deep learning solution for accurate nucleus segmentation without user interaction d Accelerates, improves quality, and reduces complexity of bioimage analysis tasks
Single cell segmentation is typically one of the first and most crucial tasks of image-based cellular analysis. We present a deep learning approach aiming towards a truly general method for localizing nuclei across a diverse range of assays and light microscopy modalities. We outperform the 739 methods submitted to the 2018 Data Science Bowl on images representing a variety of realistic conditions, some of which were not represented in the training data. The key to our approach is to adapt our model to unseen and unlabeled data using image style transfer to generate augmented training samples. This allows the model to recognize nuclei in new and different experiments without requiring expert annotations.Identifying nuclei is the starting point for many microscopy-based cellular analyses. Accurate localization of the nucleus is the basis of a variety of quantitative measurements, but is also a first step for identifying individual cell borders, which enables a multitude of further analyses. Until recently, the dominant approaches for this task have been based on classic image processing algorithms (e.g. CellProfiler 1 ) which were sometimes guided by shape and spatial priors 2 . A drawback of these methods is the need for expert knowledge to properly adjust the parameters, which typically must be re-tuned when experimental conditions change.Recently, deep learning has revolutionized an assortment of tasks in image analysis, from image classification 3 to face recognition 4 , and scene segmentation 5 . It is also responsible for breakthroughs in diagnosing retinal images 6 , classifying skin lesions with superhuman performance 7 , as well as incredible advances in 3D fluorescence image analysis 8 . However, aside from initial works from Caicedo et al. 9 and Van Valen et al. 10 , deep learning has yet to significantly advance nucleus segmentation performance.
A cell elasticity measurement method is introduced that uses polymer microtools actuated by holographic optical tweezers. The microtools were prepared with two-photon polymerization. Their shape enables the approach of the cells in any lateral direction. In the presented case, endothelial cells grown on vertical polymer walls were probed by the tools in a lateral direction. The use of specially shaped microtools prevents the target cells from photodamage that may arise during optical trapping. The position of the tools was recorded simply with video microscopy and analyzed with image processing methods. We critically compare the resulting Young’s modulus values to those in the literature obtained by other methods. The application of optical tweezers extends the force range available for cell indentations measurements down to the fN regime. Our approach demonstrates a feasible alternative to the usual vertical indentation experiments.
Fluorescent observation of cells generally suffers from the limited axial resolution due to the elongated point spread function of the microscope optics. Consequently, three-dimensional imaging results in axial resolution being several times worse than the transversal. The optical solutions to this problem usually require complicated optics and extreme spatial stability. A straightforward way to eliminate anisotropic resolution is to fuse images recorded from multiple viewing directions achieved mostly by the mechanical rotation of the entire sample. In the presented approach, multiview imaging of single cells is implemented by rotating them around an axis perpendicular to the optical axis by means of holographic optical tweezers. For this, the cells are indirectly trapped and manipulated with special microtools made with two-photon polymerization. The cell is firmly attached to the microtool and is precisely manipulated with 6 degrees of freedom. The total control over the cells position allows for its multiview fluorescence imaging from arbitrarily selected directions. The image stacks obtained this way are combined into one 3D image array with a special image processing algorithm resulting in isotropic optical resolution. The presented tool and manipulation scheme can be readily applied in various microscope platforms.
Recent statistics report that more than 3.7 million new cases of cancer occur in Europe yearly, and the disease accounts for approximately 20% of all deaths. High-throughput screening of cancer cell cultures has dominated the search for novel, effective anticancer therapies in the past decades. Recently, functional assays with patient-derived ex vivo 3D cell culture have gained importance for drug discovery and precision medicine. We recently evaluated the major advancements and needs for the 3D cell culture screening, and concluded that strictly standardized and robust sample preparation is the most desired development. Here we propose an artificial intelligence-guided low-cost 3D cell culture delivery system. It consists of a light microscope, a micromanipulator, a syringe pump, and a controller computer. The system performs morphology-based feature analysis on spheroids and can select uniform sized or shaped spheroids to transfer them between various sample holders. It can select the samples from standard sample holders, including Petri dishes and microwell plates, and then transfer them to a variety of holders up to 384 well plates. The device performs reliable semi- and fully automated spheroid transfer. This results in highly controlled experimental conditions and eliminates non-trivial side effects of sample variability that is a key aspect towards next-generation precision medicine.
Optical micro manipulation of live cells has been extensively used to study a wide range of cellular phenomena with relevance in basic research or in diagnostics. The approaches span from manipulation of many cells for high throughput measurement or sorting, to more elaborated studies of intracellular events on trapped single cells when coupled with modern imaging techniques. In case of direct cell trapping the damaging effects of light-cell interaction must be minimized, for instance with the choice of proper laser wavelength. Microbeads have already been used for trapping cells indirectly thereby reducing the irradiation damage and increasing trapping efficiency with their high refractive index contrast. We show here that such intermediate objects can be tailor-made for indirect cell trapping to further increase cell-to-focal spot distance while maintaining their free and fast maneuverability. Carefully designed structures were produced with two-photon polymerization with shapes optimized for effective manipulation and cell attachment. Functionalization of the microstructures is also presented that enables cell attachment to them within a few seconds with strength much higher that the optical forces. Fast cell actuation in 6 degrees of freedom is demonstrated with the outlook to possible applications in cell imaging.
Recent statistics report that more than 3.7 million new cases of cancer occur in Europe yearly, and the disease accounts for approximately 20 % of all deaths. High-throughput screening of cancer cell cultures has dominated the search for novel, effective anticancer therapies in the past decades. Recently, ex vivo 3D cell cultures from the patient’s own cancer cells have gained importance. We recently evaluated the major advancements and needs of the 3D cell cultures screening field, and we concluded that strictly standardized sample preparation is the most desired development. Here we propose an artificial intelligence-guided low-cost 3D cell culture delivery system. It consists of a light microscope, a micromanipulator, a syringe pump, and a controller computer. The system performs morphology-based feature analysis on spheroids and transfers the most appropriate ones between various sample holders. It can select the samples from standard sample holders, including Petri dishes and microwell plates, and then transfer them to a variety of holders up to 384 well plates. The device performs reliable semi- and fully automated spheroid transfer. This results in highly controlled experimental conditions and eliminates non-trivial side effects of sample variability that is a key aspect towards next-generation precision medicine.
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