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Subject: single cell analysis ×

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    Ultracompact three-dimensional tubular conductivity microsensors for ionic and biosensing applications
    (Washington, DC : American Chemical Society, 2014) Martinez-Cisneros, C.S.; Sanchez, S.; Xi, W.; Schmidt, O.G.
    We present ultracompact three-dimensional tubular structures integrating Au-based electrodes as impedimetric microsensors for the in-flow determination of mono- and divalent ionic species and HeLa cells. The microsensors show an improved performance of 2 orders of magnitude (limit of detection = 0.1 nM for KCl) compared to conventional planar conductivity detection systems integrated in microfluidic platforms and the capability to detect single HeLa cells in flowing phosphate buffered saline. These highly integrated conductivity tubular sensors thus open new possibilities for lab-in-a-tube devices for bioapplications such as biosensing and bioelectronics.
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    Single cell analysis in native tissue: Quantification of the retinoid content of hepatic stellate cells
    ([London] : Macmillan Publishers Limited, part of Springer Nature, 2016) Galler, Kerstin; Requardt, Robert Pascal; Glaser, Uwe; Markwart, Robby; Bocklitz, Thomas; Bauer, Michael; Popp, Jürgen; Neugebauer, Ute
    Hepatic stellate cells (HSCs) are retinoid storing cells in the liver: The retinoid content of those cells changes depending on nutrition and stress level. There are also differences with regard to a HSC’s anatomical position in the liver. Up to now, retinoid levels were only accessible from bulk measurements of tissue homogenates or cell extracts. Unfortunately, they do not account for the intercellular variability. Herein, Raman spectroscopy relying on excitation by the minimally destructive wavelength 785 nm is introduced for the assessment of the retinoid state of single HSCs in freshly isolated, unprocessed murine liver lobes. A quantitative estimation of the cellular retinoid content is derived. Implications of the retinoid content on hepatic health state are reported. The Raman-based results are integrated with histological assessments of the tissue samples. This spectroscopic approach enables single cell analysis regarding an important cellular feature in unharmed tissue.
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    Object detection networks and augmented reality for cellular detection in fluorescence microscopy
    (New York, NY : Rockefeller Univ. Press, 2020) Waithe, Dominic; Brown, Jill M.; Reglinski, Katharina; Diez-Sevilla, Isabel; Roberts, David; Eggeling, Christian
    Object detection networks are high-performance algorithms famously applied to the task of identifying and localizing objects in photography images. We demonstrate their application for the classification and localization of cells in fluorescence microscopy by benchmarking four leading object detection algorithms across multiple challenging 2D microscopy datasets. Furthermore we develop and demonstrate an algorithm that can localize and image cells in 3D, in close to real time, at the microscope using widely available and inexpensive hardware. Furthermore, we exploit the fast processing of these networks and develop a simple and effective augmented reality (AR) system for fluorescence microscopy systems using a display screen and back-projection onto the eyepiece. We show that it is possible to achieve very high classification accuracy using datasets with as few as 26 images present. Using our approach, it is possible for relatively nonskilled users to automate detection of cell classes with a variety of appearances and enable new avenues for automation of fluorescence microscopy acquisition pipelines. © 2020 Waithe et al.