Research Article: Sorting cells by their density

Date Published: July 19, 2017

Publisher: Public Library of Science

Author(s): Nazila Norouzi, Heran C. Bhakta, William H. Grover, Victor M Ugaz.


Sorting cells by their type is an important capability in biological research and medical diagnostics. However, most cell sorting techniques rely on labels or tags, which may have limited availability and specificity. Sorting different cell types by their different physical properties is an attractive alternative to labels because all cells intrinsically have these physical properties. But some physical properties, like cell size, vary significantly from cell to cell within a cell type; this makes it difficult to identify and sort cells based on their sizes alone. In this work we continuously sort different cells types by their density, a physical property with much lower cell-to-cell variation within a cell type (and therefore greater potential to discriminate different cell types) than other physical properties. We accomplish this using a 3D-printed microfluidic chip containing a horizontal flowing micron-scale density gradient. As cells flow through the chip, Earth’s gravity makes each cell move vertically to the point where the cell’s density matches the surrounding fluid’s density. When the horizontal channel then splits, cells with different densities are routed to different outlets. As a proof of concept, we use our density sorter chip to sort polymer microbeads by their material (polyethylene and polystyrene) and blood cells by their type (white blood cells and red blood cells). The chip enriches the fraction of white blood cells in a blood sample from 0.1% (in whole blood) to nearly 98% (in the output of the chip), a 1000x enrichment. Any researcher with access to a 3D printer can easily replicate our density sorter chip and use it in their own research using the design files provided as online Supporting Information. Additionally, researchers can simulate the performance of a density sorter chip in their own applications using the Python-based simulation software that accompanies this work. The simplicity, resolution, and throughput of this technique make it suitable for isolating even rare cell types in complex biological samples, in a wide variety of different research and clinical applications.

Partial Text

Biological and clinical samples are often heterogeneous populations of many different types of cells. Blood, for example, is a complex mixture of different cell types, only one of which may be needed for a given application. As a result, the ability to separate and sort cells by their type is fundamentally important in modern biological research and medical diagnostics.

The density-based cell sorter presented here offers several advantages over existing techniques like density gradient centrifugation. First, by using a miniaturized density gradient, our chip does not need hundreds or thousands of g’s of acceleration to separate cells by their density. Instead, Earth’s 1 g is adequate to separate samples with small (2%) differences in density in just a few seconds in the density sorter chip. Consequently, our technique eliminates the threat of g-force-induced damage to cells and also significantly reduces the cost of our technique and the amount of instrumentation required to perform it. Additionally, our chip uses simple laminar flow to automatically form the desired density gradient on-chip, a significant improvement over centrifugation techniques that require careful manual construction of the gradient. And by using chips with additional inlets supplied with fluids of different densities, continuously-flowing density gradients can be created with any desired density range and resolution. As a continuous-flow technique, our cell sorter eliminates virtually all of the manual labor associated with transferring samples into and collecting fractions out of density gradients. Finally, since our density sorter chips were fabricated using 3D printing, anyone with access to a 3D printer can download our design files and use our chips in their own research.




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