Multiscale Assay of Unlabeled Neurite Dynamics Using Phase Imaging with Computational Specificity

Abstract

Primary neuronal cultures have been widely used to study neuronal morphology, neurophysiology, neurodegenerative processes, and molecular mechanism of synaptic plasticity underlying learning and memory. Yet, the unique behavioral properties of neurons make them challenging to study -with phenotypic differences expressed as subtle changes in neuronal arborization rather than easy to assay features such as cell count. The need to analyze morphology, growth, and intracellular transport has motivated the development of increasingly sophisticated microscopes and image analysis techniques. Due to its high-contrast, highspecificity output, many assays rely on confocal fluorescence microscopy, genetic methods, or antibody staining techniques. These approaches often limit the ability to measure quantitatively dynamic activity such as intracellular transport and growth. In this work, we describe a method for label-free live-cell cell imaging with antibody staining specificity by estimating the associated fluorescent signals via quantitative phase imaging and deep convolutional neural networks. This computationally inferred fluorescence image is then used to generate a semantic segmentation map, annotating subcellular compartments of live unlabeled neural cultures. These synthetic fluorescence maps were further applied to study the time-lapse development of hippocampal neurons, highlighting the relationships between the cellular dry mass production and the dynamic transport activity within the nucleus and neurites. Our implementation provides a high-throughput strategy to analyze neural network arborization dynamically, with high specificity and without the typical phototoxicity and photobleaching limitations associated with fluorescent markers.

Molecularly Specific and Functional Live Cell Imaging by Label-Free Interference Microscopy

“…Moreover, with the computational specificity, label-free QPI makes it possible to continuously monitor the growths of the cell nucleus and cytoplasm over several days noninvasively . Based on a similar method, antibody staining specificity against the cultured neurons is also realized . Remarkably, the accuracy of digital staining is enhanced by acquiring label-free phase images with rich subcellular structural information by a sensitive interference reflection microscopy .…”

“…56 Based on a similar method, antibody staining specificity against the cultured neurons is also realized. 57 Remarkably, the accuracy of digital staining is enhanced by acquiring label-free phase images with rich subcellular structural information by a sensitive interference reflection microscopy. 58 The general connections between the refractive index and fluorescence intensity of several subcellular structures (actins, mitochondria, lipid droplets, plasma membranes, nuclei, and nucleoli) are closely examined with a high-resolution, three-dimensional refractive index tomographic mapping.…”

Advanced Optogenetic-Based Biosensing and Related Biomaterials

“…Central to the platform is an image segmentation system based on convolutional neural networks supplemented with functionality to count, characterize and control (bacterial and mammalian) cells’ growth and protein expression over time. Moreover, a combination of quantitative phase imaging and AI was demonstrated [ 113 , 114 ] to provide information about unlabeled live cells with high specificity suitable for detection for instance of neurite dynamics or intracellular mass changes.…”

Topography of pleural epithelial structure enabled by en face isolation and machine learning

“…Single‐cell segmentation of epithelium, the process that identifies and outlines individual cells in an image, is one of the most difficult tasks in biological image analysis (Akram et al, 2016; Van Valen et al, 2016). Conventional image segmentation uses watershed algorithms (Jo et al, 2021; Kandel et al, 2021; Wang et al, 2019). Watershed algorithms identify gray‐level inflection as a boundary even if the change is subtle.…”