Machine Learning Based Real-Time Image-Guided Cell Sorting and Classification

Abstract

Cell classification based on phenotypical, spatial, and genetic information greatly advances our understanding of the physiology and pathology of biological systems. Technologies derived from next generation sequencing and fluorescent activated cell sorting are cornerstones for cell- and genomic-based assays supporting cell classification and mapping. However, there exists a deficiency in technology space to rapidly isolate cells based on high content image information. Fluorescence-activated cell sorting can only resolve cell-to-cell variation in fluorescence and optical scattering. Utilizing microfluidics, photonics, computation microscopy, real-time image processing and machine learning, we demonstrate an image-guided cell sorting and classification system possessing the high throughput of flow cytometer and high information content of microscopy. We demonstrate the utility of this technology in cell sorting based on (1) nuclear localization of glucocorticoid receptors, (2) particle binding to the cell membrane, and (3) DNA damage induced γ-H2AX foci. © 2019 International Society for Advancement of Cytometry.

Keywords: image guided cell sorting; imaging flow cytometry; machine learning; microfluidic.

Figure 1.

The Machine Learning Based Real-Time Image-Guided Cell Sorting and Classification system. (a) Schematic diagram of the image-guided cell sorting system. (Scale bar is 5µm). Bright field and fluorescence cell images are at first encoded into time domain waveforms and detected by PMTs. Cell images are then reconstructed from time domain waveforms. Next, image features are extracted and image-based gating criteria are generated. Finally, cell images are processed and sorting decision is made in real-time based on the image-guided gating criteria with supervised machine learning. DM, dichroic mirror; SF, spatial filter; EF, emission filter; PMT, photomultiplier tube. (b) Design of optical spatial filter having ten 100 μm by 50 μm slits positioned apart. (c) Microfluidic device with an on-chip piezoelectric PZT actuator to deflect selected cells in the microfluidic channel for image-guided cell sorting.

Figure 1.

The Machine Learning Based Real-Time Image-Guided Cell Sorting and Classification system. (a) Schematic diagram of the image-guided cell sorting system. (Scale bar is 5µm). Bright field and fluorescence cell images are at first encoded into time domain waveforms and detected by PMTs. Cell images are then reconstructed from time domain waveforms. Next, image features are extracted and image-based gating criteria are generated. Finally, cell images are processed and sorting decision is made in real-time based on the image-guided gating criteria with supervised machine learning. DM, dichroic mirror; SF, spatial filter; EF, emission filter; PMT, photomultiplier tube. (b) Design of optical spatial filter having ten 100 μm by 50 μm slits positioned apart. (c) Microfluidic device with an on-chip piezoelectric PZT actuator to deflect selected cells in the microfluidic channel for image-guided cell sorting.

Figure 2.

Sorting cells by spatial distribution of specific protein. (a) Example of microscope cell images. Row(1) shows translocated cells, and row(2) shows un-translocated cells. Column(1) shows fluorescent images, column(2) shows bright-field images, and column(3) shows overlaid images with their respective contours defined by the computer-generated red and white curves. (b) Example cell images generated by our system. Row(1) shows translocated cells, and row(2) shows un-translocated cells. Column(1) shows fluorescent images, column(2) shows bright-field images, and column(3) shows fluorescent images overlaid with bright-field images with their respective contours defined by the computer-generated red and white curves. (c) Hyperplane formed by SVM. A 5µm scale bar is shown in each row of micrographs.

Figure 3.

Sorting cells according to particle binding on cell membrane. (a) Example cell images generated by our system. Column(1) shows fluorescent images at 645nm, column(2) shows fluorescent images at 520nm, and column(3) shows overlaid images. (b) Histogram for normalized fluorescent area from beads. (c) Hyperplane formed by SVM. A 5µm scale bar is shown in each row of micrographs.

Figure 4.

Example images of irradiated cells. (a) Example cell images generated by real-time algorithm. Column(1) shows GFP images, column(2) shows gamma-h2ax images with background removed, column(3) shows overlaid images, and column(4) shows image contours, with green contour for GPF image and red contours for gamma-h2ax images. (b) Example cell images generated by off-line processing for human vision. Column(1) shows GFP images, column(2) shows gamma-h2ax images, and column(3) shows overlaid images. A 5µm scale bar is shown in each row of micrographs.

Figure 5.

Estimation of foci count based on image-derived parameters and total fluorescent intensity. (a) Scatter plot of predicted foci count based on total gamma-h2ax intensity versus actual foci count (b) Scatter plot of predicted foci count based on image-derived parameters versus actual foci count (c) Scatter plot of total gamma-h2ax intensity versus actual foci count including outliers (d) Histogram of predicted foci count based on total gamma-h2ax intensity of bin1 and bin2 (e) Histogram of predicted foci count based on image-derived parameters of bin1 and bin2 (f) Receiving Operating Characteristic(ROC) analysis of bin1 and bin2, showing superior performance of image-guided sorting than conventional intensity-based sorting.

Figure 6.

Sorting purity and yield. (a) Histogram of actual foci count for Gamma-ray irradiated cells. (b)Sorting yield versus sorting accuracy for isolation of cells with greater than 23 foci using image-guided and intensity-based methods.