Stiffness Dependent Separation of Cells in a Microfluidic Device
Citation: Wang G, Mao W, Byler R, Patel K, Henegar C, et al. (
Stiffness Dependent Separation of Cells in a Microfluidic Device
Gonghao Wang 0
Wenbin Mao 0
Rebecca Byler 0
Krishna Patel 0
Caitlin Henegar 0
Alexander Alexeev 0
Todd Sulchek 0
Alexandre J. Kabla, University of Cambridge, United Kingdom
0 1 Woodruff School of Mechanical Engineering, Georgia Institute of Technology , Atlanta , Georgia , United States of America, 2 Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology , Atlanta , Georgia , United States of America, 3 Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology , Atlanta, Georgia , United States of America
Abnormal cell mechanical stiffness can point to the development of various diseases including cancers and infections. We report a new microfluidic technique for continuous cell separation utilizing variation in cell stiffness. We use a microfluidic channel decorated by periodic diagonal ridges that compress the flowing cells in rapid succession. The compression in combination with secondary flows in the ridged microfluidic channel translates each cell perpendicular to the channel axis in proportion to its stiffness. We demonstrate the physical principle of the cell sorting mechanism and show that our microfluidic approach can be effectively used to separate a variety of cell types which are similar in size but of different stiffnesses, spanning a range from 210 Pa to 23 kPa. Atomic force microscopy is used to directly measure the stiffness of the separated cells and we found that the trajectories in the microchannel correlated to stiffness. We have demonstrated that the current processing throughput is 250 cells per second. This microfluidic separation technique opens new ways for conducting rapid and low-cost cell analysis and disease diagnostics through biophysical markers.
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Funding: The authors thank National Science Foundation (project number CBET-0932510) and TI:GER program at Scheller College of Business at Georgia Institute
of Technology for financial support of this project. The authors also thank the Presidents Undergraduate Research Award (PURA) program at Georgia Tech for
providing funding to CH and the Petit Fellowship for support to RB. The funders had no role in study design, data collection and analysis, decision to publish, or
preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
Rapidly sorting and separating cells are critical for detecting
diseases such as cancers and infections and can enable a great
number of applications in biosciences and biotechnology. For
example, diseased cells have been identified through morphological
differences with healthy cells, and fluorescent molecular markers are
routinely used to separate specific subpopulations of cells [1,2].
However, the morphological overlap between the diseased and
healthy cells often poses a significant problem to accurate
identification of cell populations. New molecular and biophysical
markers which can be readily detected and used to rapidly sort cells
are vital for improving separation of different cell subpopulations
and accurately detecting specific disease conditions.
A variety of different physical mechanisms have been used to
separate cells, including magnetic fields [35], electric fields [69],
optical forces [1012] and acoustic fields [1315]. However, these
active separation methods require an external field which adds to
the complexity and increases the cost. Alternatively, labeling of
cells through specific binding of fluorescent antibodies [16] is
expensive, requires highly-trained personnel, and hampers the
downstream analysis of separated cells. Additionally, the
separation executed by these techniques occurs only after individual
readout of the labeling differentiation which limits the throughput.
Consequently, a label-free method that can separate cells
continuously by biophysical properties would greatly complement
existing separation technologies. While a variety of techniques
demonstrate separation by physical parameters such as size [17],
mass [18], and adhesion [19], a straightforward method to
separate cells by mechanical stiffness would benefit biomedical
capabilities. A number of pathophysiological states of individual
cells result in drastic changes in stiffness in comparison with
healthy counterparts. Mechanical stiffness has been utilized to
identify abnormal cell populations in detecting cancer [2022] and
identifying infectious disease [23]. For example, several studies
have shown a reduction in cell stiffness with increasing metastatic
efficiency in human cancer cell lines [2325]. Recently,
microfluidic methods were developed to classify and enrich cell
populations utilizing mechanical stiffness [2631]. One problem
with these methods is an overlap between the natural variations of
different biophysical properties that can influence stiffness-based
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