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. Experiments prove the method outperforms state-of-art sizing algorithms such as adjusted Lorentzian fitting or CNN-based networks on both standard nanoparticles and extracellular vesicles (EVs), as well as maintains good accuracy even when measurement times are relatively short (<1s per particle). On samples of clinical EVs, our network demonstrates a well-generalized ability to accurately determine the EV size distribution, as confirmed by comparison with gold-standard nanoparticle tracking analysis (NTA). Furthermore, by combining the sizing network with still frame images from high-speed video, the camera-based optical tweezers have the unique capacity to quantify both the size and refractive index of bio-nanoparticles at the single-particle level. These experiments prove the proposed sizing network as an ideal path for predicting the morphological heterogeneity of bio-nanoparticles in optical potential trapping-related measurements.

Effect of Anisotropic Structural Depth on Orientation and Differentiation Behavior of Skeletal Muscle Cells.

Extensive research has been conducted to examine how substrate topological factors are involved in modulating the cell behavior. Among numerous topological factors, the vital influence of the touchable depth of substrates on cell behaviors has already been extensively characterized, but the response of cells to the topological structure at untouchable depth is still elusive. Herein, the influences of substrate depth on myoblast behaviors are systematically investigated using substrates with depths ranging from touchable depth (microgrooved) to untouchable depth (microbridges). The results show that an increase in microgroove depth is accompanied by an inhibited cell spreading, an enhanced elongation, and a more obvious orientation along microgrooves. Interestingly, myoblasts located on microbridges show a more pronounced elongation with increasing culture time but a position-dependent orientation. Myoblasts on the center and parallel boundary of microbridges orient along the bridges, while myoblasts on the vertical boundary align perpendicular to the microbridges. Moreover, the differentiation results of the myoblasts indicate that the differentiated myotubes can maintain this position-dependent orientation. The simulation of the stress field in cell monolayers suggests that the position-dependent orientation is caused by the comprehensive response of myoblasts to the substrate discontinuity and substrate depth. These findings provide valuable insights into the mechanism of cell depth sensing and could inform the design of tissue engineering scaffolds for skeletal muscle and biohybrid actuation.

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. Using this phase-guided Raman sampling, we can characterize hundreds of LDs within a single cell in minutes and easily acquire more than 40,000 high-quality spectra. The data set revealed strong, cell line-dependent, cell-dependent, and individual droplet-dependent composition changes to various culture conditions. In particular, we revealed a strong competitive relationship between mono- and polyunsaturated fatty acids, where supplementation with one led to a relative decrease in the other.

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. However, the direct combination leads to poor reconstruction results due to the missed coverage of the sample. To avoid this issue, "full-coverage" Compressed Line-scan Raman Imaging (FC-CLRI) is proposed, where line positions are random but constrained to measure each line position of the sample at least once. In proof-of-concept studies of polymer beads and yeast cells, FC-CLRI achieved reasonable image quality while making only 20-40% of the measurements of a fully-sampled line-scan image, achieving 640 µm2 FOV imaging in <2 min with 1.5 mW µm-2 laser power. Furthermore, we critically evaluate the CLRI method through comparison with simple downsampling, and have found that FC-CLRI preserves spatial resolution better while naïve downsampling provides an overall higher image quality for complex samples.

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. Here, we present a hybrid principal component analysis (PCA) denoising method, where a small number (∼30 spectra) of high signal-to-noise ratio (SNR) training data construct an effective principal component subspace into which low SNR test data are projected. Simulations and experiments prove the method outperforms traditional denoising methods such as the wavelet transform or traditional PCA. On experimental liposome samples, denoising accelerated data acquisition from 90 to 3 s, with an overall 4.5-fold improvement in particle throughput. The denoised data retained the ability to accurately determine complex morphochemical parameters such as lamellarity of individual nanoliposomes, as confirmed by comparison with cryo-EM imaging. We therefore show that hybrid PCA denoising is an efficient and effective tool for denoising spectral data sets with limited chemical variability and that the RR-NTA technique offers an ideal path for studying the multidimensional heterogeneity of nanoliposomes and other micro/nanoscale bioparticles.

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 (i.e. an underdetermined model). An advantage of the model is that it combines background correction and regression into a single step, and does not require user-selected parameters. We compare our results with traditional least squares methods, including the popular asymmetric least squares (AsLS) approach. Our results demonstrate that the proposed CNN model boasts less sensitivity to parameter selection, and with a rapid processing speed, with performance equal to or better than comparison methods. The performance is validated on synthetic spectral mixtures, as well as experimentally measured single-vesicle liposome data.

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. We validate the method on NIST standards and liposomes, demonstrating that R-NTA can accurately characterize size and chemical heterogeneity, including determining combined morpho-chemical properties such as the number of lamellae in individual liposomes. Applied to extracellular vesicles (EVs), we find distinct differences between EVs from cancerous and noncancerous cells, and that knockdown of the TRPP2 ion channel, which is pathologically highly expressed in laryngeal cancer cells, leads the EVs to more closely resemble EVs from normal epithelial cells. Intriguingly, the differences in EV content are found in small subpopulations of EVs, highlighting the importance of single-particle measurements. These experiments demonstrate the power of the R-NTA system to measure and characterize the morpho-chemical heterogeneity of bionanoparticles.

Maskless Surface Modification of Polyurethane Films by an Atmospheric Pressure He/O2 Plasma Microjet for Gelatin Immobilization.

A localized maskless modification method of polyurethane (PU) films through an atmospheric pressure He/O2 plasma microjet (APPµJ) was proposed. The APPµJ system combines an atmospheric pressure plasma jet (APPJ) with a microfabricated silicon micronozzle with dimension of 30 µm, which has advantages of simple structure and low cost. The possibility of APPµJ in functionalizing PU films with hydroxyl (⁻OH) groups and covalent grafting of gelatin for improving its biocompatibility was demonstrated. The morphologies and chemical compositions of the modified surface were analyzed by scanning electronic microscopy (SEM), Raman spectroscopy, and X-ray photoelectron spectroscopy (XPS). The fluorescent images show the modified surface can be divided into four areas with different fluorescence intensity from the center to the outside domain. The distribution of the rings could be controlled by plasma process parameters, such as the treatment time and the flow rate of O2. When the treatment time is 4 to 5 min with the oxygen percentage of 0.6%, the PU film can be effectively local functionalized with the diameter of 170 µm. In addition, the modification mechanism of PU films by the APPµJ is investigated. The localized polymer modified by APPµJ has potential applications in the field of tissue engineering.

Raman Plus X: Biomedical Applications of Multimodal Raman Spectroscopy.

Raman spectroscopy is a label-free method of obtaining detailed chemical information about samples. Its compatibility with living tissue makes it an attractive choice for biomedical analysis, yet its translation from a research tool to a clinical tool has been slow, hampered by fundamental Raman scattering issues such as long integration times and limited penetration depth. In this review we detail the how combining Raman spectroscopy with other techniques yields multimodal instruments that can help to surmount the translational barriers faced by Raman alone. We review Raman combined with several optical and non-optical methods, including fluorescence, elastic scattering, OCT, phase imaging, and mass spectrometry. In each section we highlight the power of each combination along with a brief history and presentation of representative results. Finally, we conclude with a perspective detailing both benefits and challenges for multimodal Raman measurements, and give thoughts on future directions in the field.

Fabrication of SiNx Thin Film of Micro Dielectric Barrier Discharge Reactor for Maskless Nanoscale Etching.

The prevention of glow-to-arc transition exhibited by micro dielectric barrier discharge (MDBD), as well as its long lifetime, has generated much excitement across a variety of applications. Silicon nitride (SiNx) is often used as a dielectric barrier layer in DBD due to its excellent chemical inertness and high electrical permittivity. However, during fabrication of the MDBD devices with multilayer films for maskless nano etching, the residual stress-induced deformation may bring cracks or wrinkles of the devices after depositing SiNx by plasma enhanced chemical vapor deposition (PECVD). Considering that the residual stress of SiNx can be tailored from compressive stress to tensile stress under different PECVD deposition parameters, in order to minimize the stress-induced deformation and avoid cracks or wrinkles of the MDBD device, we experimentally measured stress in each thin film of a MDBD device, then used numerical simulation to analyze and obtain the minimum deformation of multilayer films when the intrinsic stress of SiNx is -200 MPa compressive stress. The stress of SiNx can be tailored to the desired value by tuning the deposition parameters of the SiNx film, such as the silane (SiH4)⁻ammonia (NH3) flow ratio, radio frequency (RF) power, chamber pressure, and deposition temperature. Finally, we used the optimum PECVD process parameters to successfully fabricate a MDBD device with good quality.

[CT perfusion imaging evaluation on hemodynamic changes of acute spontaneous intracerebral hemorrhage surrounding tissues].

OBJECTIVE: To discuss the hemodynamic changes in patients with acute supratentorial spontaneous intracerebral hemorrhage (within 72 hours) by using 320-slice of low-dose volume CT perfusion imaging. METHODS: Twenty-six patients of The First Affiliated Hospital of Wenzhou Medical University during December 2012 to December 2013 with acute supratentorial SICH diagnosed by plain CT scanning and clinic were enrolled. With hematoma maximum level for reference, the hematoma volume, edema area and perfusion defect area were measured, and the perfusion parameters values of the marginal area and outer area of the intracerebral hematoma and contralateral mirror area were measured, including cerebral blood flow (CBF), cerebral blood volume (CBV), mean transit time (MTT) and time-to-peak (TTP), and rCBF, rCBV, rMTT and rTTP were calculated by ipsilateral/contralateral value. RESULTS: The CBF, CBV of the marginal area were lower than the contralateral mirror area (tCBF=-8.125, tCBV=-8.671, PCBF, CBV<0.01); the MTT of the marginal area was shorter than the contralateral mirror area (tMTT=-3.246, PMTT<0.05); the TTP of the marginal area was longer than the contralateral mirror area (tTTP=5.027, PTTP<0.01). The CBV of the outer area was lower than the contralateral mirror area (tCBV=-2.337, PCBV<0.05); the MTT of the outer area was shorter than the contralateral mirror area (tMTT=-2.421, PMTT<0.05); the TTP of the outer area was longer than the contralateral mirror area (tTTP=2.077, PTTP<0.05). There was a siginificant relationship between the volume of acute hematoma and rCBV, rMTT, rTTP of the marginal area (rrCBV=-0.412, PrCBV<0.05, rrMTT=-0.437, PrMTT<0.05, rrTTP=0.475, PrMTT<0.05). Perihematomal CBF perfusion defect area showed a positive linear relation with the volume of acute hematoma (r=0.440, P<0.05). There was a positive linear relationship between the maximum level edema area and the hematoma volume, perihematomal CBF perfusion defect area (r=0.400, r=0.81, P<0.05). CONCLUSIONS: 320-slice of low-dose and volume CT perfusion imaging can perfectly reflect the hemodynamic changes in brain tissuse after acute supratentorial SICH. Hypoperfusion was appeared in perihematomal area of acute supratentorial SICH. The perihematomal brain tissue may exists ischemic injury associated with the size of hematoma.The hematoma place holder effect, ischemic injury are the important cause of acute brain edema formation.