Abstract

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 first-in-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.

Highlights

  • We develop a high-throughput technique to relate positions of individual cells to their three-dimensional (3D) imaging features with single-cell resolution

  • To address the first challenge, we introduced three types of marker beads of distinctive features that can be recognized by an off-the-shelf imager on the cell placement robot to match their sequence to the readout from the 3D imaging flow cytometer (3D-IFC)

  • When a cell or bead passes through the laser interrogation area, it is illuminated by a scanning light-sheet at a 200-kHz scanning rate

Read more

Summary

Introduction

We develop a high-throughput technique to relate positions of individual cells to their three-dimensional (3D) imaging features with single-cell resolution. Single-cell analysis can be broadly divided into two areas, namely, single-cell genomics and single-cell high-content microscopy [3,4,5] The former deciphers the genomic and phenotypical information by detecting gene expressions, mutations, and genetic aberrations in individual cells [6,7,8,9]. The emerging field of spatial biology aims to solve this issue via DNA barcoding technologies [11,12,13,14,15] Current methods such as the 10× Visium platform are unable to resolve single cells [16, 17].

Methods
Results
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call