Optically amplified detection for biomedical sensing and imaging

Dispersion-managed technique in temporal-frequency measurement for MoTe2-based ultrafast laser

Ultrafast phenomena exist widely in modern scientific research. The time scale of ultrafast phenomena is mostly in the order of picosecond, femtosecond, or even attosecond. Nowadays, it is still a major challenge to study these nonrepetitive transient processes. Here, a temporal-frequency measurement based on a dispersion-managed technique has been proposed for an MoTe₂-based ultrafast laser. The temporal-frequency measurement comprises a laser diode, an optical switch, a section of tunable dispersion compensation fiber, and a three-port beam splitter. Resolution of the proposed measurement can be tuned in a wide range; further, the upper and lower resolution limits are numerically simulated. The proposed measurement is expected to be applied in ultrafast pulse detection due to its application in real-time measurement of ultrafast nonrepetitive signals.

Multi-pronged approach to human mesenchymal stromal cells senescence quantification with a focus on label-free methods

Human mesenchymal stromal cells (hMSCs) have demonstrated, in various preclinical settings, consistent ability in promoting tissue healing and improving outcomes in animal disease models. However, translation from the preclinical model into clinical practice has proven to be considerably more difficult. One key challenge being the inability to perform in situ assessment of the hMSCs in continuous culture, where the accumulation of the senescent cells impairs the culture’s viability, differentiation potential and ultimately leads to reduced therapeutic efficacies. Histochemical \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\upbeta $$\end{document}β-galactosidase staining is the current standard for measuring hMSC senescence, but this method is destructive and not label-free. In this study, we have investigated alternatives in quantification of hMSCs senescence, which included flow cytometry methods that are based on a combination of cell size measurements and fluorescence detection of SA-\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\upbeta $$\end{document}β-galactosidase activity using the fluorogenic substrate, C\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${_{12}}$$\end{document}12FDG; and autofluorescence methods that measure fluorescence output from endogenous fluorophores including lipopigments. For identification of senescent cells in the hMSC batches produced, the non-destructive and label-free methods could be a better way forward as they involve minimum manipulations of the cells of interest, increasing the final output of the therapeutic-grade hMSC cultures. In this work, we have grown hMSC cultures over a period of 7 months and compared early and senescent hMSC passages using the advanced flow cytometry and autofluorescence methods, which were benchmarked with the current standard in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\upbeta $$\end{document}β-galactosidase staining. Both the advanced methods demonstrated statistically significant values, (r = 0.76, p \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\le $$\end{document}≤ 0.001 for the fluorogenic C\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${_{12}}$$\end{document}12FDG method, and r = 0.72, p \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\le $$\end{document}≤ 0.05 for the forward scatter method), and good fold difference ranges (1.120–4.436 for total autofluorescence mean and 1.082–6.362 for lipopigment autofluorescence mean) between early and senescent passage hMSCs. Our autofluroescence imaging and spectra decomposition platform offers additional benefit in label-free characterisation of senescent hMSC cells and could be further developed for adoption for future in situ cellular senescence evaluation by the cell manufacturers.

Deep Cytometry: Deep learning with Real-time Inference in Cell Sorting and Flow Cytometry

Deep learning has achieved spectacular performance in image and speech recognition and synthesis. It outperforms other machine learning algorithms in problems where large amounts of data are available. In the area of measurement technology, instruments based on the photonic time stretch have established record real-time measurement throughput in spectroscopy, optical coherence tomography, and imaging flow cytometry. These extreme-throughput instruments generate approximately 1 Tbit/s of continuous measurement data and have led to the discovery of rare phenomena in nonlinear and complex systems as well as new types of biomedical instruments. Owing to the abundance of data they generate, time-stretch instruments are a natural fit to deep learning classification. Previously we had shown that high-throughput label-free cell classification with high accuracy can be achieved through a combination of time-stretch microscopy, image processing and feature extraction, followed by deep learning for finding cancer cells in the blood. Such a technology holds promise for early detection of primary cancer or metastasis. Here we describe a new deep learning pipeline, which entirely avoids the slow and computationally costly signal processing and feature extraction steps by a convolutional neural network that directly operates on the measured signals. The improvement in computational efficiency enables low-latency inference and makes this pipeline suitable for cell sorting via deep learning. Our neural network takes less than a few milliseconds to classify the cells, fast enough to provide a decision to a cell sorter for real-time separation of individual target cells. We demonstrate the applicability of our new method in the classification of OT-II white blood cells and SW-480 epithelial cancer cells with more than 95% accuracy in a label-free fashion.

Sensitivity-enhanced ultrafast optical tomography by parametric- and Raman-amplified temporal imaging

To overcome the speed limitation of conventional optical tomography, a temporal imaging technique has been integrated with optical time-domain reflectometry to realize ultrafast temporally magnified (TM) tomography. In this Letter, the sensitivity of TM tomography has been further enhanced using optical parametric amplification and distributed Raman amplification, and this technique is named temporally encoded amplified and magnified (TEAM) tomography. As a result, a 78-dB sensitivity has been realized, comparable to ultrafast optical coherence tomography systems. In addition, an 86.7-μm axial resolution can be realized across a 67.5-mm imaging range. To demonstrate the significance of sensitivity improvement, tomographic imaging of a centimeter-thick phantom is provided at an A-scan rate of 44 MHz.

Deep Learning in Label-free Cell Classification

Label-free cell analysis is essential to personalized genomics, cancer diagnostics, and drug development as it avoids adverse effects of staining reagents on cellular viability and cell signaling. However, currently available label-free cell assays mostly rely only on a single feature and lack sufficient differentiation. Also, the sample size analyzed by these assays is limited due to their low throughput. Here, we integrate feature extraction and deep learning with high-throughput quantitative imaging enabled by photonic time stretch, achieving record high accuracy in label-free cell classification. Our system captures quantitative optical phase and intensity images and extracts multiple biophysical features of individual cells. These biophysical measurements form a hyperdimensional feature space in which supervised learning is performed for cell classification. We compare various learning algorithms including artificial neural network, support vector machine, logistic regression, and a novel deep learning pipeline, which adopts global optimization of receiver operating characteristics. As a validation of the enhanced sensitivity and specificity of our system, we show classification of white blood T-cells against colon cancer cells, as well as lipid accumulating algal strains for biofuel production. This system opens up a new path to data-driven phenotypic diagnosis and better understanding of the heterogeneous gene expressions in cells.

Optical data compression in time stretch imaging

Time stretch imaging offers real-time image acquisition at millions of frames per second and subnanosecond shutter speed, and has enabled detection of rare cancer cells in blood with record throughput and specificity. An unintended consequence of high throughput image acquisition is the massive amount of digital data generated by the instrument. Here we report the first experimental demonstration of real-time optical image compression applied to time stretch imaging. By exploiting the sparsity of the image, we reduce the number of samples and the amount of data generated by the time stretch camera in our proof-of-concept experiments by about three times. Optical data compression addresses the big data predicament in such systems.

Broadband fiber-optical parametric amplification for ultrafast time-stretch imaging at 1.0 μm

We demonstrate a broadband all-fiber-optical parametric amplifier for ultrafast time-stretch imaging at 1.0 μm, featured by its compact design, alignment-free, high efficiency, and flexible gain spectrum through fiber nonlinearity- and dispersion-engineering: specifically on a dispersion-stabilized photonic-crystal fiber (PCF) to achieve a net gain over 20 THz (75 nm) and a highest gain of ~6000 (37.5 dB). Another unique feature of the parametric amplifier, over other optical amplifiers, is the coherent generation of a synchronized signal replica (called idler) that can be exploited to offer an extra 3-dB gain by optically superposing the signal and idler. It further enhances signal contrast and temporal stability. For proof-of-concept purpose, ultrahigh speed and diffraction-limited time-stretch microscopy is demonstrated with a single-shot line-scan rate of 13 MHz based on the dual-band (signal and idler) detection. Our scheme can be extended to other established bioimaging modalities, such as optical coherence tomography, near infrared fluorescence, and photoacoustic imaging, where weak signal detection at high speed is required.

Performance of megahertz amplified optical time-stretch optical coherence tomography (AOT-OCT)

Enabled by the ultrahigh-speed all-optical wavelength-swept mechanism and broadband optical amplification, amplified optical time-stretch optical coherence tomography (AOT-OCT) has recently been demonstrated as a practical alternative to achieve ultrafast A-scan rate of multi-MHz in OCT. With the aim of identifying the optimal scenarios for MHz operation in AOT-OCT, we here present a theoretical framework to evaluate its performance metric. In particular, the analysis discusses the unique features of AOT-OCT, such as its superior coherence length, and the relationship between the optical gain and the A-scan rate. More importantly, we evaluate the sensitivity of AOT-OCT in the MHz regime under the influence of the amplifier noise. Notably, the model shows that AOT-OCT is particularly promising when operated at the A-scan rate well beyond multi-MHz--not trivially achievable by any existing swept-source OCT platform. A sensitivity beyond 90 dB, close to the shot-noise limit, can be maintained in the range of 2 - 10 MHz with an optical net gain of ~10 dB. Experimental measurement also shows excellent agreement with the theoretical prediction. While distributed fiber Raman amplification is mainly considered in this paper, the theoretical model is generally applicable to any type of amplification schemes. As a result, our analysis serves as a useful tool for further optimization of AOT-OCT system--as a practical alternative to enable MHz OCT operation.

Optical signal to noise ratio improvement through unbalanced noise beating in phase-sensitive parametric amplifiers

We investigate the beating of signal and idler waves, which have imbalanced signal to noise ratios, in a phase-sensitive parametric amplifier. Imbalanced signal to noise ratios are achieved in two ways; first by imbalanced noise loading; second by varying idler to signal input power ratio. In the case of imbalanced noise loading the phase-sensitive amplifier improved the signal to noise ratio from 3 to 6 dB, and in the case of varying idler to signal input power ratio, the signal to noise ratio improved from 3 to in excess of 20 dB.