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.

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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.

Laser scanning technology is one of the most integral parts of today's scientific research, manufacturing, defense, and biomedicine. In many applications, high-speed scanning capability is essential for scanning a large area in a short time and multi-dimensional sensing of moving objects and dynamical processes with fine temporal resolution. Unfortunately, conventional laser scanners are often too slow, resulting in limited precision and utility. Here we present a new type of laser scanner that offers ∼1,000 times higher scan rates than conventional state-of-the-art scanners. This method employs spatial dispersion of temporally stretched broadband optical pulses onto the target, enabling inertia-free laser scans at unprecedented scan rates of nearly 100 MHz at 800 nm. To show our scanner's broad utility, we use it to demonstrate unique and previously difficult-to-achieve capabilities in imaging, surface vibrometry, and flow cytometry at a record 2D raster scan rate of more than 100 kHz with 27,000 resolvable points.

Flow cytometry is a powerful tool for cell counting and biomarker detection in biotechnology and medicine especially with regards to blood analysis. Standard flow cytometers perform cell type classification both by estimating size and granularity of cells using forward-and sidescattered light signals and through the collection of emission spectra of fluorescently-labeled cells. However, cell surface labeling as a means of marking cells is often undesirable as many reagents negatively impact cellular viability or provide activating/inhibitory signals, which can alter the behavior of the desired cellular subtypes for downstream applications or analysis. To eliminate the need for labeling, we introduce a label-free imaging-based flow cytometer that measures size and cell protein concentration simultaneously either as a stand-alone instrument or as an add-on to conventional flow cytometers. Cell protein concentration adds a parameter to cell classification, which improves the specificity and sensitivity of flow cytometers without the requirement of cell labeling. This system uses coherent dispersive Fourier transform to perform phase imaging at flow speeds as high as a few meters per second. ©2013 Optical Society of America

Conventional laser vibrometers are incapable of performing multidimensional vibrometry at high speeds because they build on single-point measurements and rely on beam scanning, significantly limiting their utility and precision. Here we introduce a laser vibrometer that performs high-speed multidimensional imaging-based vibration and velocity measurements with nanometer-scale axial resolution without the need for beam scanning. As a proof-of-concept, we demonstrate real-time microscopic imaging of acoustic vibrations with 1 nm axial resolution, 1200 image pixels, and 30 ps dwell time at 36.7 MHz scan rate.

High-speed high-contrast imaging modalities that enable image acquisition of transparent media without the need for chemical staining are essential tools for a broad range of applications; from semiconductor process monitoring to blood screening. Here we introduce a method for contrast-enhanced imaging of unstained transparent objects that is capable of high-throughput imaging. This method combines the Nomarski phase contrast capability with the ultrahigh frame rate and shutter speed of serial time-encoded amplified microscopy. As a proof of concept, we show imaging of a transparent test structure and white blood cells in flow at a shutter speed of 33 ps and a frame rate of 36.1 MHz using a single-pixel photo-detector. This method is expected to be a valuable tool for high-throughput screening of unstained cells.

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.