We present artificial confocal microscopy (ACM) to achieve confocal-level depth sectioning, sensitivity, and chemical specificity non-destructively on unlabeled specimens. ACM is equipped with a laser scanning confocal microscopy with a quantitative phase imaging module, which provides optical path-length maps of the specimen colocalized with the fluorescence channel. Using pairs of phase and fluorescence images, a convolution neural network was trained to translate the former into the latter. The ACM images hold much stronger depth sectioning than the input (phase) images, enabling us to recover confocal-like tomographic volumes of microspheres, hippocampal neurons in culture, and three-dimensional liver cancer spheroids.
In this study, we use phase imaging with computational specificity (PICS) to detect unlabeled mitochondria in live cells and monitor their dynamics over time.This is a two-step study with first phase involving detection of mitochondria in phase images using deep learning. HCT116 cells with GFP tagged mitochondria were imaged with a correlative SLIM and fluorescence imaging instrument, resulting in pairs of registered phase and fluorescence images per field of view. A deep neural network, EfficientNetB2+U-Net, was trained on the phase - fluorescence image pairs. Our network can predict mitochondria from the SLIM images with a SSIM of 0.9. The second step involves monitoring the effects of anticancer drugs on the mitochondria network dynamic, dry mass of mitochondria content, and their correlation with the overall cell health and drug efficacy. This method can potentially be translated into a tool for label-free efficacy evaluation of mitochondria inhibiting drugs for cancer therapy.
We propose synthetic aperture gradient light interference microscopy (SA-GLIM) as a solution to avoid computational complexity in standard Fourier pytchographic microscopy. This new system combines direct phase measurements from GLIM with various illumination angles, and a synthetic aperture reconstruction method, to produce high resolution, large FOV quantitative phase maps. Using a 5× objective lens (NA = 0.15), SA-GLIM generates phase maps with a spatial resolution of 850 nm and FOV approximately 1.7×1.7 mm2. We tested the performance using a mixture of polystyrene beads (1 μm and 3 μm in diameter), and the smaller beads can be easily resolved in the final image. Compared with standard FPM, SA-GLIM records substantially fewer low-resolution images, which makes the data throughput highly efficient.
Quantitative phase imaging (QPI), with its capability to capture intrinsic contrast within transparent samples, has emerged as an important imaging method for biomedical research. However, due to its label-free nature, QPI lacks specificity and thus faces limitations in complex cellular systems. In our previous works, we have proposed phase imaging with computational specificity (PICS), a novel AI-enhanced imaging approach that advances QPI by utilizing deep learning for specificity. Here we present that PICS can be applied to study individual cell behavior and cellular dry mass change across different phases of the cell cycle. The cell cycle information is traditionally obtained by fluorescence microscopy with markers like Fluorescence Ubiquitin Cell Cycle Indicator (FUCCI). Our work showed that using deep learning, we can train a neural network to accurately predict the cell cycle phase (G1, S, or G2) for each individual cell.
We propose a multimodal imaging system, LS-GLIM, consisting of the light scanning microscope (LSM 900, Zeiss) and the gradient light interference microscope (GLIM) module [1], with photomultiplier tube (PMT) detection. GLIM upgrades a differential interference microscope with a liquid crystal variable retarder (LCVR), which introduces further controlled phase shifts between the x and y polarized light. The quantitative phase information can be retrieved from the four frames corresponding to each phase shift in the GLIM. We anticipate a broad range of applications that LS-GLIM will enable through phase imaging with computational specificity (PICS), especially in thick, highly-scattering samples.
We proposed a fast 3D RI construction method, based on the Wolf equations for propagating correlations of partially coherent light. This approach, referred to as Wolf phase tomography (WPT), involves minimal computational steps, renders high-resolution RI tomograms, without time-consuming deconvolution operations. WPT decouples the refractive index distribution from the thickness of the sample directly in the space-time domain, without the need for Fourier transformation. We demonstrate that, from three independent intensity measurements corresponding to each phase shift, the RI distribution is reconstructed directly from the Laplacian and second time derivative of the complex correlation functions.
We demonstrate that live-dead cell assay can be conducted in a label-free manner using quantitative phase imaging and deep learning. We apply the concept of our newly-developed phase imaging with computational specificity (PICS) to digitally stain for the live/dead markers. HeLa cultured mixed with viability fluorescent reagents (ReadyProbes, ThermoFisher) were imaged for 24 hours by spatial light interference microscopy (SLIM) and fluorescent microscopy. Based on the ratio of the two fluorescence signals, semantic segmentation maps were generated to label the state of the cell as either live, injured, or dead. We trained an EfficientNet to infer cell viability from SLIM images with semantic maps as ground truth. Validated on the testing dataset, the trained network reported an F1 score of 73.4%, 97.0%, and 94.3% in identifying live, injured, and dead cells, respectively.
Microscopic imaging modalities can be classified into two categories: those that form contrast from external agents such as dyes, and label-free methods that generate contrast from the object’s unmodified structure. While label-free methods such as brightfield, phase contrast, or quantitative phase imaging (QPI) are substantially easier to use, as well as non-toxic, their lack of specificity leads many researchers to turn to labels for insights into biological processes, despite limitations due to photobleaching and phototoxicity. The label-free image may contain the structures of interest, but it is often difficult or time-consuming to distinguish these structures from their surroundings. Here we summarize our recent progress in shattering this tradeoff, by using machine learning to perform automated segmentation on label-free, intrinsic contrast, quantitative phase images.
Although both neurons and oligodendrocytes have been well studied individually, very little is known about how they interact with each other. New methods are needed to further study the intricacies of this interplay in terms of cellular and molecular dynamics. Spatial Light Interference Microscopy (SLIM) is a quantitative phase imaging technique that generates phase maps related to the dry mass content of the sample. In this work, we study the ability of SLIM to quantify myelination at the axonal level. We imaged a series of cocultures comprising hippocampal neurons and oligodendrocytes, of varying densities, using SLIM, and evaluated dry mass formation and growth of myelin.
Optimal growth as well as branching of axons and dendrites is critical for the nervous system function. Neuritic length, arborization, and growth rate determine the innervation properties of neurons and define each cell’s computational capability. Thus, to investigate the nervous system function, we need to develop methods and instrumentation techniques capable of quantifying various aspects of neural network formation: neuron process extension, retraction, stability, and branching. During the last three decades, fluorescence microscopy has yielded enormous advances in our understanding of neurobiology. While fluorescent markers provide valuable specificity to imaging, photobleaching, and photoxicity often limit the duration of the investigation. Here, we used spatial light interference microscopy (SLIM) to measure quantitatively neurite outgrowth as a function of cell confluence. Because it is label-free and nondestructive, SLIM allows for long-term investigation over many hours. We found that neurons exhibit a higher growth rate of neurite length in low-confluence versus medium- and high-confluence conditions. We believe this methodology will aid investigators in performing unbiased, nondestructive analysis of morphometric neuronal parameters.
Traditionally the measurement of electrical activity in neurons has been carried out using microelectrode arrays that require the conducting elements to be in contact with the neuronal network. This method, also referred to as “electrophysiology”, while being excellent in terms of temporal resolution is limited in spatial resolution and is invasive. An optical microscopy method for measuring electrical activity is thus highly desired. Common-path quantitative phase imaging (QPI) systems are good candidates for such investigations as they provide high sensitivity (on the order of nanometers) to the plasma membrane fluctuations that can be linked to electrical activity in a neuronal circuit. In this work we measured electrical activity in a culture of rat cortical neurons using MISS microscopy, a high-speed common-path QPI technique having an axial resolution of around 1 nm in optical path-length, which we introduced at PW BIOS 2016. Specifically, we measured the vesicular cycling (endocytosis and exocytosis) occurring at axon terminals of the neurons due to electrical activity caused by adding a high K+ solution to the cell culture. The axon terminals were localized using a micro-fluidic device that separated them from the rest of the culture. Stacks of images of these terminals were acquired at 826 fps both before and after K+ excitation and the temporal standard deviation maps for the two cases were compared to measure the membrane fluctuations. Concurrently, the existence of vesicular cycling was confirmed through fluorescent tagging and imaging of the vesicles at and around the axon terminals.
The Hilbert-Huang transform (HHT) has been shown to be effective for characterizing a wide range of nonstationary
signals in terms of elemental components through what has been called the empirical mode decomposition. The HHT
has been utilized extensively despite the absence of a serious analytical foundation, as it provides a concise basis for the
analysis of strongly nonlinear systems. In this paper, we attempt to provide the missing link, showing the relationship
between the EMD and the slow-flow equations of the system. The slow-flow model is established by performing a
partition between slow and fast dynamics using the complexification-averaging technique, and a dynamical system
described by slowly-varying amplitudes and phases is obtained. These variables can also be extracted directly from the
experimental measurements using the Hilbert transform coupled with the EMD. The comparison between the
experimental and analytical results forms the basis of a nonlinear system identification method, termed the slow-flow
model identification method, which is demonstrated using numerical examples.
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