Real-Time Analysis of Lipid Droplet Morpho-Chemical Dynamics in Living Human Hepatocytes via Phase-Guided Raman Sampling.

Lipid droplets (LDs) are highly dynamic organelles, undertaking many important functions such as maintaining lipid metabolism and cellular homeostasis. Traditional methods to analyze LD dynamics focus on morphological changes, while chemical dynamics cannot be easily probed with traditional analytical chemistry techniques. To overcome this challenge, we show here how our phase-guided Raman sampling method, where high-resolution phase microscopy images direct a Raman sampling beam, can perform label-free, multimodal characterization of LD dynamics in living cells at both the single-cell and single-LD levels with submicron accuracy and high temporal resolution.

Organelle-specific phase contrast microscopy (OS-PCM) enables facile correlation study of organelles and proteins.

Current methods for studying organelle and protein interactions and correlations depend on multiplex fluorescent labeling, which is experimentally complex and harmful to cells. Here we propose to solve this challenge via OS-PCM, where organelles are imaged and segmented without labels, and combined with standard fluorescence microscopy of protein distributions. In this work, we develop new neural networks to obtain unlabeled organelle, nucleus and membrane predictions from a single 2D image.

Deep learning-based size prediction for optical trapped nanoparticles and extracellular vesicles from limited bandwidth camera detection.

Due to its ability to record position, intensity, and intensity distribution information, camera-based monitoring of nanoparticles in optical traps can enable multi-parametric morpho-optical characterization at the single-particle level. However, blurring due to the relatively long (10s of microsecond) integration times and aliasing from the resulting limited temporal bandwidth affect the detected particle position when considering nanoparticles in traps with strong stiffness, leading to inaccurate size predictions. Here, we propose a ResNet-based method for accurate size characterization of trapped nanoparticles, which is trained by considering only simulated time series data of nanoparticles' constrained Brownian motion.

Highly Sensitive, Portable Detection System for Multiplex Chemiluminescence Analysis.

Chemiluminescence (CL) has emerged as a critical tool for the sensing and quantification of various bioanalytes in virtually all clinical fields. However, the rapid nature of many CL reactions raises challenges for typical low-cost optical sensors such as cameras to achieve accurate and sensitive detection. Meanwhile, classic sensors such as photomultiplier tubes are highly sensitive but lack spatial multiplexing capabilities and are generally not suited for point-of-care applications outside a standard laboratory setting.

Image reconstruction for low cost spatial light interference microscopy with fixed and arbitrary phase modulation.

During the past decade, spatial light interference microscopy (SLIM) has undergone rapid development, evidenced by its broadening applications in biology and medicine. However, the need for an expensive spatial light modulator (SLM) may limit its adoption, and the requirement for multiple images per plane limits its speed in volumetric imaging. Here we propose to address these issues by replacing the SLM with a mask fabricated from a low cost optical density (OD) filter, and recover high contrast images computationally rather than through phase-shifting.

Rapid Intracellular Detection and Analysis of Lipid Droplets' Morpho-Chemical Composition by Phase-Guided Raman Sampling.

Intracellular lipid droplets (LDs) are dynamic, complex organelles involved in nearly all aspects of cellular metabolism. In situ characterization methods are primarily limited to fluorescence imaging, which yields limited chemical information, or Raman spectroscopy, which provides excellent chemical profiling but very low throughput. Here, we propose a new paradigm where locations of both large and small droplets are obtained automatically from high-resolution phase images and fed into a galvomirror-controlled Raman sampling arm to obtain the full spectrum of each LD efficiently.

Fast Raman imaging through the combination of context-aware matrix completion and low spectral resolution.

Raman hyperspectral imaging is an effective method for label-free imaging with chemical specificity, yet the weak signals and correspondingly long integration times have hindered its wide adoption as a routine analytical method. Recently, low resolution Raman imaging has been proposed to improve the spectral signal-to-noise ratio, which significantly improves the speed of Raman imaging. In this paper, low resolution Raman spectroscopy is combined with "context-aware" matrix completion, where regions of the sample that are not of interest are skipped, and the regions that are measured are under-sampled, then reconstructed with a low-rank constraint.

A critical evaluation of compressed line-scan Raman imaging.

The weak signal strength of Raman imaging leads to long imaging times. To increase the speed of Raman imaging, line scanning and compressed Raman imaging methods have been proposed. Here we combine both line scanning and compressed sensing to further increase the speed.

Super-resolved Raman imaging via galvo-painted structured line illumination.

Traditional line-scan Raman imaging features a rapid imaging speed while preserving complete spectral information, yet has diffraction-limited resolution. Sinusoidally structured line excitation can yield an improvement in the lateral resolution of the Raman image along the line's direction. However, given the need for the line and spectrometer slit to be aligned, the resolution in the perpendicular direction remains diffraction limited.

Hybrid Principal Component Analysis Denoising Enables Rapid, Label-Free Morpho-Chemical Quantification of Individual Nanoliposomes.

Laser tweezers Raman spectroscopy enables multiplexed, quantitative chemical and morphological analysis of individual bionanoparticles such as drug-loaded nanoliposomes, yet it requires minutes-scale acquisition times per particle, leading to a lack of statistical power in typical small-sized data sets. The long acquisition times present a bottleneck not only in measurement time but also in the analytical throughput, as particle concentration (and thus throughput) must be kept low enough to avoid swarm measurement. The only effective way to improve this situation is to reduce the exposure time, which comes at the expense of increased noise.

Multicomponent Raman spectral regression using complete and incomplete models and convolutional neural networks.

With the advent of hyperspectral Raman imaging technology, especially the rapid and high-resolution imaging schemes, datasets with thousands to millions of spectra are now commonplace. Standard preprocessing and regression methods such as least squares approaches are time consuming and require input from highly trained operators. Here we propose a solution to this analytic bottleneck through a convolutional neural network trained fully on synthetic data and then applied to experimental measurements, including cases where complete spectral information is missing ( an underdetermined model).

High-Resolution Low-Power Hyperspectral Line-Scan Imaging of Fast Cellular Dynamics Using Azo-Enhanced Raman Scattering Probes.

Small-molecule Raman probes for cellular imaging have attracted great attention owing to their sharp peaks that are sensitive to environmental changes. The small cross section of molecular Raman scattering limits dynamic cellular Raman imaging to expensive and complex coherent approaches that acquire single-channel images and lose hyperspectral Raman information. We introduce a new method, dynamic azo-enhanced Raman imaging (DAERI), to couple the new class of azo-enhanced Raman probes with a high-speed line-scan Raman imaging system.

A Drop-in, Focus-Extending Phase Mask Simplifies Microscopic and Microfluidic Imaging Systems for Cost-Effective Point-of-Care Diagnostics.

Microscopic imaging and imaging flow cytometry have wide potential in point-of-care assays; however, their narrow depth of focus necessitates precise mechanical or fluidic focus control of a sample in order to acquire high-quality images that can be used for downstream analysis, increasing the cost and complexity of the imaging system. This complexity represents a barrier to miniaturization and translation of point-of-care assays based on microscopic imaging or imaging flow cytometry. To address this challenge, we present a simple drop-in phase mask with a physics-informed, circularly symmetric asphere phase profile that extends the depth of focus by >5-fold while largely preserving the image quality compared to other depth extending methods.

Label-free imaging of intracellular organelle dynamics using flat-fielding quantitative phase contrast microscopy (FF-QPCM).

Panoramic and long-term observation of nanosized organelle dynamics and interactions with high spatiotemporal resolution still hold great challenge for current imaging platforms. In this study, we propose a live-organelle imaging platform, where a flat-fielding quantitative phase contrast microscope (FF-QPCM) visualizes all the membrane-bound subcellular organelles, and an intermittent fluorescence channel assists in specific organelle identification. FF-QPCM features a high spatiotemporal resolution of 245 nm and 250 Hz and strong immunity against external disturbance.

Simultaneous 3D deconvolution and halo removal for spatial light interference microscopy through a two-edge apodized Wiener filter.

As one of the most sensitive quantitative phase microscopy techniques, spatial light interference microscopy (SLIM) has undergone rapid development in the past decade and has seen wide application in both basic science and clinical studies. However, as with any other traditional microscope, the axial resolution is the worst among the three dimensions. This leads to lower contrast in the thicker regions of cell samples.

Epi-illumination dark-field microscopy enables direct visualization of unlabeled small organisms with high spatial and temporal resolution.

Dark-field microscopy is known to offer both high resolution and direct visualization of thin samples. However, its performance and optimization on thick samples is under-explored and so far, only meso-scale information from whole organisms has been demonstrated. In this work, we carefully investigate the difference between trans- and epi-illumination configurations.

Organelle-specific phase contrast microscopy enables gentle monitoring and analysis of mitochondrial network dynamics.

Mitochondria are delicate organelles that play a key role in cell fate. Current research methods rely on fluorescence labeling that introduces stress due to photobleaching and phototoxicity. Here we propose a new, gentle method to study mitochondrial dynamics, where organelle-specific three-dimensional information is obtained in a label-free manner at high resolution, high specificity, and without detrimental effects associated with staining.

A sample-preparation-free, automated, sample-to-answer system for cell counting in human body fluids.

While many clinical laboratory tests are now highly automated, body fluid cell counting, particularly in low-cellularity samples such as cerebral spinal fluid (CSF), is often performed manually. Here, we report a simple, cost-effective method to obtain white and red blood cell counts from human body fluids such as CSF. The method consists of a compact, automated, and low-cost fluorescence microscope system, coupled to a sample chamber containing all of the necessary reagents in dry form to stain and prepare the sample.

Fast confocal Raman imaging via context-aware compressive sensing.

Raman hyperspectral imaging is a powerful method to obtain detailed chemical information about a wide variety of organic and inorganic samples noninvasively and without labels. However, due to the weak, nonresonant nature of spontaneous Raman scattering, acquiring a Raman imaging dataset is time-consuming and inefficient. In this paper we utilize a compressive imaging strategy coupled with a context-aware image prior to improve Raman imaging speed by 5- to 10-fold compared to classic point-scanning Raman imaging, while maintaining the traditional benefits of point scanning imaging, such as isotropic resolution and confocality.

Nanometer precise red blood cell sizing using a cost-effective quantitative dark field imaging system.

Because of the bulk, complexity, calibration requirements, and need for operator training, most current flow-based blood counting devices are not appropriate for field use. Standard imaging methods could be much more compact, inexpensive, and with minimal calibration requirements. However, due to the diffraction limit, imaging lacks the nanometer precision required to measure red blood cell volumes.

Optical volumetric projection with large NA objectives for fast high-resolution 3D imaging of neural signals.

One critical challenge in studying neural circuits of freely behaving model organisms is to record neural signals distributed within the whole brain, yet simultaneously maintaining cellular resolution. However, due to the dense packing of neuron cells in animal brains, high numerical aperture (NA) objectives are often required to differentiate neighboring neurons with the consequent need for axial scanning for whole brain imaging. Extending the depth of focus (EDoF) will be beneficial for fast 3D imaging of those neurons.

Low resolution Raman: the impact of spectral resolution on limit of detection and imaging speed in hyperspectral imaging.

The majority of problems in analytical Raman spectroscopy are mathematically over-determined, where many more spectral variables are measured than analytic outputs (such as chemical concentrations) are calculated. Thus, to improve spectral throughput and simplify system design, some researchers have explored the use of low resolution Raman systems for cell or tissue classification, achieving accuracy independent of spectral resolution. However, the tradeoffs inherent in this approach have not been systematically studied.

Combined Morpho-Chemical Profiling of Individual Extracellular Vesicles and Functional Nanoparticles without Labels.

Biological nanoparticles are important targets of study, yet their small size and tendency to aggregate makes their heterogeneity difficult to profile on a truly single-particle basis. Here we present a label-free system called 'Raman-enabled nanoparticle trapping analysis' (R-NTA) that optically traps individual nanoparticles, records Raman spectra and tracks particle motion to identify chemical composition, size, and refractive index. R-NTA has the unique capacity to characterize aggregation status and absolute chemical concentration at the single-particle level.

Droplet digital PCR enabled by microfluidic impact printing for absolute gene quantification.

Digital PCR enabled high-sensitivity and quantitative measurements of rare biological variants. A new digital droplet-enabled PCR technology was introduced in this paper, which partitioned genetic targets into a planar nanoliter droplet array by using a microfluidic impact printer (MIP) with a disposable microfluidic chip. The accuracy of this MIP-enabled PCR technology was verified by detecting a series of concentration gradients of GAPDH gene across spanning four orders of magnitude (from 0.

Improving the limit of detection in portable luminescent assay readers through smart optical design.

Critical biomarkers of disease are increasingly being detected by point-of-care assays. Chemiluminescence (CL) and electrochemiluminescence (ECL) are often used in such assays due to their convenience and that they do not require light sources or other components that could complicate or add cost to the system. Reports of these assays often include readers built on a cellphone platform or constructed from low-cost components.