Sorting cells by their density

Abstract

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.

Label-Free Density-Based Detection of Adipocytes of Bone Marrow Origin Using Magnetic Levitation

“…Contrary to these methods that measure single cell density by the help of cell mass and volume analyzed with complex and expensive instrumentations, our system offers a fast and cost-effective way to measure densities by using a specific equation via magnetic levitation system 57,58,72 . Besides, most of the microfluidic systems for density measurement such as SMR, multiphase stems, usually need two or more liquids with different density 49,54,57 . Magnetic levitation technique does not need multiple phases with different liquids and thus offer ease of application.…”

“…However, nozzles in these systems limit the application to pre-adipocytes, as mature adipocytes are fragile and cannot withstand the flow [43][44][45] . Alternative cell separation platforms are available based on fluorescent or magnetic markers [46][47][48] , or utilization of complex microfluidic platforms that either use fluid interfaces where cells are stratified on different fluids based on their densities 49 . Furthermore, sensors that can track changes in electric cell-substrate or using Coherent anti-Stokes Raman scattering (CARS) imaging can be used as label-free methods [50][51][52] , however, these techniques are relatively expensive, require complex instrumentation and cannot perform single cell detection.…”

“…The conventional method to determine cell density is via density gradient centrifugation 58,59 . Even though this technique is relatively simple and cost-effective, it may cause cell damage due to high centrifugal forces, can only yield the average density of target cell population and cannot discriminate small differences in cell densities 49,60 . Density measurements based on a single cell are possible through microfluidic technology, such as suspended microchannel resonator (SMR) system 57,58 and optically induced electrokinetics (OEK) microfluidic platform 61 .…”

Systems-level physiology of the human red blood cell is computed from metabolic and macromolecular mechanisms

“…For the baseline simulations shown in Figure 2, we selected K eq values from a normal distribution using a 15% standard error 50 simulations and report the 95% confidence intervals for the 50 simulated curves, showing the mean trajectory; for the 2,3-DPG simulations, we selected K eq values from a normal distribution using a 5% standard error for 50 simulations and report the 95% confidence intervals, showing the mean trajectory. The 2,3-DPG concentrations from [16] were converted into units of mmol/L RBC using the reported density of RBC as 1.110 g/mL [37]. Pseudo-first-order elementary rate constant (kPERC) values [40] are used to help sample the kinetically feasible flux solution space through setting constraints on reactions.…”

On-chip density-based sorting of supercooled droplets and frozen droplets in continuous flow

“…Several examples exist of on-chip continuous densitybased sorting of particles and cells in the vertical direction due to gravity, 19,20 including the use of split flow thin-cell fractionation (SPLITT), 21 3D-printed devices, 22 diamagnetic levitation, 23 and in numerical simulations. 24 Density-based separations have also been performed in a horizontal orientation via the use of acoustic forces, [25][26][27][28] centrifugal platforms, [29][30][31] sedimentation pinched-flow fractionation in a centrifugal device, 32 and could potentially also be achieved in inertial microfluidic devices.…”