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Object detection networks and augmented reality for cellular detection in fluorescence microscopy
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.
Computational tissue staining of non-linear multimodal imaging using supervised and unsupervised deep learning
Hematoxylin and Eosin (H&E) staining is the 'gold-standard' method in histopathology. However, standard H&E staining of high-quality tissue sections requires long sample preparation times including sample embedding, which restricts its application for 'real-time' disease diagnosis. Due to this reason, a label-free alternative technique like non-linear multimodal (NLM) imaging, which is the combination of three non-linear optical modalities including coherent anti-Stokes Raman scattering, two-photon excitation fluorescence and second-harmonic generation, is proposed in this work. To correlate the information of the NLM images with H&E images, this work proposes computational staining of NLM images using deep learning models in a supervised and an unsupervised approach. In the supervised and the unsupervised approach, conditional generative adversarial networks (CGANs) and cycle conditional generative adversarial networks (cycle CGANs) are used, respectively. Both CGAN and cycle CGAN models generate pseudo H&E images, which are quantitatively analyzed based on mean squared error, structure similarity index and color shading similarity index. The mean of the three metrics calculated for the computationally generated H&E images indicate significant performance. Thus, utilizing CGAN and cycle CGAN models for computational staining is beneficial for diagnostic applications without performing a laboratory-based staining procedure. To the author's best knowledge, it is the first time that NLM images are computationally stained to H&E images using GANs in an unsupervised manner.
Eosinophils and Neutrophils-Molecular Differences Revealed by Spontaneous Raman, CARS and Fluorescence Microscopy
Leukocytes are a part of the immune system that plays an important role in the host's defense against viral, bacterial, and fungal infections. Among the human leukocytes, two granulocytes, neutrophils (Ne) and eosinophils (EOS) play an important role in the innate immune system. For that purpose, eosinophils and neutrophils contain specific granules containing protoporphyrin-type proteins such as eosinophil peroxidase (EPO) and myeloperoxidase (MPO), respectively, which contribute directly to their anti-infection activity. Since both proteins are structurally and functionally different, they could potentially be a marker of both cells' types. To prove this hypothesis, UV-Vis absorption spectroscopy and Raman imaging were applied to analyze EPO and MPO and their content in leukocytes isolated from the whole blood. Moreover, leukocytes can contain lipidic structures, called lipid bodies (LBs), which are linked to the regulation of immune responses and are considered to be a marker of cell inflammation. In this work, we showed how to determine the number of LBs in two types of granulocytes, EOS and Ne, using fluorescence and coherent anti-Stokes Raman scattering (CARS) microscopy. Spectroscopic differences of EPO and MPO can be used to identify these cells in blood samples, while the detection of LBs can indicate the cell inflammation process.
Aggregation and mobility of membrane proteins interplay with local lipid order in the plasma membrane of T cells
To disentangle the elusive lipid-protein interactions in T-cell activation, we investigate how externally imposed variations in mobility of key membrane proteins (T-cell receptor [TCR], kinase Lck, and phosphatase CD45) affect the local lipid order and protein colocalisation. Using spectral imaging with polarity-sensitive membrane probes in model membranes and live Jurkat T cells, we find that partial immobilisation of proteins (including TCR) by aggregation or ligand binding changes their preference towards a more ordered lipid environment, which can recruit Lck. Our data suggest that the cellular membrane is poised to modulate the frequency of protein encounters upon alterations of their mobility, for example in ligand binding, which offers new mechanistic insight into the involvement of lipid-mediated interactions in membrane-hosted signalling events.
A new human adipocyte model with PTEN haploinsufficiency
Few human cell strains are suitable and readily available as in vitro adipocyte models. We used resected lipoma tissue from a patient with germline phosphatase and tensin homolog (PTEN) haploinsufficiency to establish a preadipocyte cell strain termed LipPD1 and aimed to characterize cellular functions and signalling pathway alterations in comparison to the established adipocyte model Simpson-Golabi-Behmel-Syndrome (SGBS) and to primary stromal-vascular fraction cells. We found that both cellular life span and the capacity for adipocyte differentiation as well as adipocyte-specific functions were preserved in LipPD1 and comparable to SGBS adipocytes. Basal and growth factor-stimulated activation of the PI3 K/AKT signalling pathway was increased in LipPD1 preadipocytes, corresponding to reduced PTEN levels in comparison to SGBS cells. Altogether, LipPD1 cells are a novel primary cell model with a defined genetic lesion suitable for the study of adipocyte biology. © 2020, © 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.