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      Smart computational light microscopes (SCLMs) of smart computational imaging laboratory (SCILab)

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          Abstract

          Computational microscopy, as a subfield of computational imaging, combines optical manipulation and image algorithmic reconstruction to recover multi-dimensional microscopic images or information of micro-objects. In recent years, the revolution in light-emitting diodes (LEDs), low-cost consumer image sensors, modern digital computers, and smartphones provide fertile opportunities for the rapid development of computational microscopy. Consequently, diverse forms of computational microscopy have been invented, including digital holographic microscopy (DHM), transport of intensity equation (TIE), differential phase contrast (DPC) microscopy, lens-free on-chip holography, and Fourier ptychographic microscopy (FPM). These computational microscopy techniques not only provide high-resolution, label-free, quantitative phase imaging capability but also decipher new and advanced biomedical research and industrial applications. Nevertheless, most computational microscopy techniques are still at an early stage of “proof of concept” or “proof of prototype” (based on commercially available microscope platforms). Translating those concepts to stand-alone optical instruments for practical use is an essential step for the promotion and adoption of computational microscopy by the wider bio-medicine, industry, and education community. In this paper, we present four smart computational light microscopes (SCLMs) developed by our laboratory, i.e., smart computational imaging laboratory (SCILab) of Nanjing University of Science and Technology (NJUST), China. These microscopes are empowered by advanced computational microscopy techniques, including digital holography, TIE, DPC, lensless holography, and FPM, which not only enables multi-modal contrast-enhanced observations for unstained specimens, but also can recover their three-dimensional profiles quantitatively. We introduce their basic principles, hardware configurations, reconstruction algorithms, and software design, quantify their imaging performance, and illustrate their typical applications for cell analysis, medical diagnosis, and microlens characterization.

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          Deep learning.

          Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.
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            Compressed sensing

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              Ror2 signaling regulates Golgi structure and transport through IFT20 for tumor invasiveness

              Signaling through the Ror2 receptor tyrosine kinase promotes invadopodia formation for tumor invasion. Here, we identify intraflagellar transport 20 (IFT20) as a new target of this signaling in tumors that lack primary cilia, and find that IFT20 mediates the ability of Ror2 signaling to induce the invasiveness of these tumors. We also find that IFT20 regulates the nucleation of Golgi-derived microtubules by affecting the GM130-AKAP450 complex, which promotes Golgi ribbon formation in achieving polarized secretion for cell migration and invasion. Furthermore, IFT20 promotes the efficiency of transport through the Golgi complex. These findings shed new insights into how Ror2 signaling promotes tumor invasiveness, and also advance the understanding of how Golgi structure and transport can be regulated.
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                Author and article information

                Contributors
                Journal
                PhotoniX
                PhotoniX
                Springer Science and Business Media LLC
                2662-1991
                December 2021
                September 03 2021
                : 2
                : 1
                Article
                10.1186/s43074-021-00040-2
                b07dda0f-2ba3-4bc6-9f92-81eb9c74592f
                © 2021

                https://creativecommons.org/licenses/by/4.0

                https://creativecommons.org/licenses/by/4.0

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