Data-driven multiplexed microtomography of endogenous subcellular dynamics

Abstract

Simultaneous imaging of various facets of intact biological systems across multiple spatiotemporal scales would be an invaluable tool in biomedicine. However, conventional imaging modalities have stark tradeoffs precluding the fulfilment of all functional requirements. Here we propose the refractive index (RI), an intrinsic quantity governing light-matter interaction, as a means for such measurement. We show that major endogenous subcellular structures, which are conventionally accessed via exogenous fluorescence labeling, are encoded in 3D RI tomograms. We decode this information in a data-driven manner, thereby achieving multiplexed microtomography. This approach inherits the advantages of both high-specificity fluorescence imaging and label-free RI imaging. The performance, reliability, and scalability of this technology have been extensively characterized, and its application within single-cell profiling at unprecedented scales has been demonstrated.

Roadmap on Digital Holography-Based Quantitative Phase Imaging

“…The IDT system has more flexibility in controlling the free-floating sample, and it is possible to extend the spatial frequency coverage along the axial direction. The continuous development and progressive solutions by the researchers in computational, experimental, and artificial intelligence algorithms has brought enhanced measurement accuracy to technique, increased certainty of analysis [112][113][114][115][131][132][133], as well as access to new multimodal techniques and functionalities [134][135][136][137][138][139][140].…”

“…Reconstructed images have also been analyzed, with the aid of AI, for biological studies, including cell type classifications (Figure 8a) [113] and segmentation [114]. Recently, obtaining molecular information from unlabeled live cells has been realized by training a network architecture with RI and fluorescence image pairs in order to extract molecular information from unlabeled RI tomogram data (Figure 8b) [115].…”

Label-free monitoring of 3D cortical neuronal growth in vitro using optical diffraction tomography

“…Therefore, in addition to obtaining RI tomograms using ODT, measuring fluorescence images to provide cellular-level selectivity with the use of live-cell fluorescent dyes, such as NeuO and CDr10b, would provide even more affluent information about the sample over time. Furthermore, there have been recent advances in predicting additional information about biological samples using deep learning [61][62][63]. By extending these approaches into 3D neurons, the information conventionally obtained by labeling can be acquired from time-lapse label-free RI tomograms.…”

Label-Free White Blood Cell Classification Using Refractive Index Tomography and Deep Learning

“…Recent advances in artificial intelligence (AI) have suggested unexplored domains of QPI beyond simply characterizing biological samples [22]. As datasets obtained from QPI do not rely on the variability of staining quality, various machine learning and deep learning approaches can exploit uniform-quality and high-dimensional datasets to perform label-free image segmentation [23,24], classification [25][26][27][28][29][30][31][32], and inference [33][34][35][36][37][38][39]. Such synergetic approaches for label-free blood cell identification have also been demonstrated, which are of interest to this work [25,26,28,[40][41][42][43].…”