Cell reagents used in biomedical analysis often change behavior of the cells that they are attached to, inhibiting their native signaling. On the other hand, label-free cell analysis techniques have long been viewed as challenging either due to insufficient accuracy by limited features, or because of low throughput as a sacrifice of improved precision. We present a recently developed artificial-intelligence augmented microscope, which builds upon high-throughput time stretch quantitative phase imaging (TS-QPI) and deep learning to perform label-free cell classification with record high-accuracy. Our system captures quantitative optical phase and intensity images simultaneously by frequency multiplexing, extracts multiple biophysical features of the individual cells from these images fused, and feeds these features into a supervised machine learning model for classification. The enhanced performance of our system compared to other label-free assays is demonstrated by classification of white blood T-cells versus colon cancer cells and lipid accumulating algal strains for biofuel production, which is as much as five-fold reduction in inaccuracy. This system obtains the accuracy required in practical applications such as personalized drug development, while the cells remain intact and the throughput is not sacrificed. Here, we introduce a data acquisition scheme based on quadrature phase demodulation that enables interruptionless storage of TS-QPI cell images. Our proof of principle demonstration is capable of saving 40 TB of cell images in about four hours, i.e. pictures of every single cell in 10 mL of a sample.
We show that blood cells can be classified with high accuracy and high throughput by combining machine learning with time stretch quantitative phase imaging. Our diagnostic system captures quantitative phase images in a flow microscope at millions of frames per second and extracts multiple biophysical features from individual cells including morphological characteristics, light absorption and scattering parameters, and protein concentration. These parameters form a hyperdimensional feature space in which supervised learning and cell classification is performed. We show binary classification of T-cells against colon cancer cells, as well classification of algae cell strains with high and low lipid content. The label-free screening averts the negative impact of staining reagents on cellular viability or cell signaling. The combination of time stretch machine vision and learning offers unprecedented cell analysis capabilities for cancer diagnostics, drug development and liquid biopsy for personalized genomics.
Flow cytometry is an optical method for studying cells based on their individual physical and chemical characteristics. It
is widely used in clinical diagnosis, medical research, and biotechnology for analysis of blood cells and other cells in
suspension. Conventional flow cytometers aim a laser beam at a stream of cells and measure the elastic scattering of
light at forward and side angles. They also perform single-point measurements of fluorescent emissions from labeled
cells. However, many reagents used in cell labeling reduce cellular viability or change the behavior of the target cells
through the activation of undesired cellular processes or inhibition of normal cellular activity. Therefore, labeled cells
are not completely representative of their unaltered form nor are they fully reliable for downstream studies. To remove
the requirement of cell labeling in flow cytometry, while still meeting the classification sensitivity and specificity goals,
measurement of additional biophysical parameters is essential. Here, we introduce an interferometric imaging flow
cytometer based on the world’s fastest continuous-time camera. Our system simultaneously measures cellular size,
scattering, and protein concentration as supplementary biophysical parameters for label-free cell classification. It
exploits the wide bandwidth of ultrafast laser pulses to perform blur-free quantitative phase and intensity imaging at flow
speeds as high as 10 meters per second and achieves nanometer-scale optical path length resolution for precise
measurements of cellular protein concentration.
Conference Committee Involvement (1)
Optical Data Science: Trends Shaping the Future of Photonics
30 January 2018 | San Francisco, California, United States