A high-throughput technique to map cell images to cell positions using a 3D imaging flow cytometer

We develop a high-throughput technique to relate positions of individual cells to their three-dimensional (3D) imaging features with single-cell resolution. The technique is particularly suitable for nonadherent cells where existing spatial biology methodologies relating cell properties to their positions in a solid tissue do not apply. Our design consists of two parts, as follows: recording 3D cell images at high throughput (500 to 1,000 cells/s) using a custom 3D imaging flow cytometer (3D-IFC) and dispensing cells in a firstin-first-out (FIFO) manner using a robotic cell placement platform (CPP). To prevent errors due to violations of the FIFO principle, we invented a method that uses marker beads and DNA sequencing software to detect errors. Experiments with human cancer cell lines demonstrate the feasibility of mapping 3D side scattering and fluorescent images, as well as two-dimensional (2D) transmission images of cells to their locations on the membrane filter for around 100,000 cells in less than 10 min. While the current work uses our specially designed 3D imaging flow cytometer to produce 3D cell images, our methodology can support other imaging modalities. The technology and method form a bridge between single-cell image analysis and single-cell molecular analysis.

Automated summary

We have developed a high-throughput technique that can relate the positions of individual cells to their three-dimensional imaging features. This technique is particularly useful for nonadherent cells and overcomes the limitations of existing spatial biology methodologies. Our approach involves recording 3D cell images at high throughput using a custom 3D imaging flow cytometer and dispensing cells in a FIFO manner using a robotic cell placement platform. Experiments with human cancer cell lines have demonstrated the feasibility of mapping 3D cell images to their locations on a membrane filter for a large number of cells in a short time. This technology and method bridge the gap between single-cell image analysis and single-cell molecular analysis.