Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Real-time deformability cytometry: on-the-fly cell mechanical phenotyping


We introduce real-time deformability cytometry (RT-DC) for continuous cell mechanical characterization of large populations (>100,000 cells) with analysis rates greater than 100 cells/s. RT-DC is sensitive to cytoskeletal alterations and can distinguish cell-cycle phases, track stem cell differentiation into distinct lineages and identify cell populations in whole blood by their mechanical fingerprints. This technique adds a new marker-free dimension to flow cytometry with diverse applications in biology, biotechnology and medicine.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Get just this article for as long as you need it


Prices may be subject to local taxes which are calculated during checkout

Figure 1: Real-time deformability cytometry (RT-DC).
Figure 2: Sensitivity to cytoskeletal drugs and cell-cycle progression.
Figure 3: Mechanical phenotyping of blood cells and their precursors.


  1. Elson, E.L. Annu. Rev. Biophys. Biophys. Chem. 17, 397–430 (1988).

    Article  CAS  Google Scholar 

  2. Titushkin, I. & Cho, M. Biophys. J. 93, 3693–3702 (2007).

    Article  CAS  Google Scholar 

  3. Lautenschläger, F. et al. Proc. Natl. Acad. Sci. USA 106, 15696–15701 (2009).

    Article  Google Scholar 

  4. Ekpenyong, A.E. et al. PLoS ONE 7, e45237 (2012).

    Article  CAS  Google Scholar 

  5. Remmerbach, T.W. et al. Cancer Res. 69, 1728–1732 (2009).

    Article  CAS  Google Scholar 

  6. Kumar, S. & Weaver, V.M. Cancer Metastasis Rev. 28, 113–127 (2009).

    Article  Google Scholar 

  7. Tse, H.T. et al. Sci. Transl. Med. 5, 212ra163 (2013).

    Article  Google Scholar 

  8. Van Vliet, K.J., Bao, G. & Suresh, S. Acta Mater. 51, 5881–5905 (2003).

    Article  CAS  Google Scholar 

  9. Gossett, D.R. et al. Proc. Natl. Acad. Sci. USA 109, 7630–7635 (2012).

    Article  CAS  Google Scholar 

  10. Skalak, R. & Branemark, P.I. Science 164, 717–719 (1969).

    Article  CAS  Google Scholar 

  11. Noguchi, H. & Gompper, G. Proc. Natl. Acad. Sci. USA 102, 14159–14164 (2005).

    Article  CAS  Google Scholar 

  12. Wan, J., Ristenpart, W.D. & Stone, H.A. Proc. Natl. Acad. Sci. USA 105, 16432–16437 (2008).

    Article  CAS  Google Scholar 

  13. Sawetzki, T., Eggleton, C.D., Desai, S.A. & Marr, D.W.M. Biophys. J. 105, 2281–2288 (2013).

    Article  CAS  Google Scholar 

  14. Dudani, J.S., Gossett, D.R., Tse, H.T. & Di Carlo, D. Lab Chip 13, 3728–3734 (2013).

    Article  CAS  Google Scholar 

  15. Matthews, H.K. et al. Dev. Cell 23, 371–383 (2012).

    Article  CAS  Google Scholar 

  16. Kunda, P. & Baum, B. Trends Cell Biol. 19, 174–179 (2009).

    Article  CAS  Google Scholar 

  17. Kline-Smith, S.L. & Walczak, C.E. Mol. Cell 15, 317–327 (2004).

    Article  CAS  Google Scholar 

  18. Stewart, M.P. et al. Nature 469, 226–230 (2011).

    Article  CAS  Google Scholar 

  19. Hur, S.C., Henderson-MacLennan, N.K., McCabe, E.R.B. & Di Carlo, D. Lab Chip 11, 912–920 (2011).

    Article  CAS  Google Scholar 

  20. Beech, J.P., Holm, S.H., Adolfsson, K. & Tegenfeldt, J.O. Lab Chip 12, 1048–1051 (2012).

    Article  CAS  Google Scholar 

  21. Suzuki, S. & Abe, K. Comput. Vis. Graph. Image Process. 30, 32–46 (1985).

    Article  Google Scholar 

  22. Haberman, W.L. & Sayre, R.M. Motion of rigid and fluid spheres in stationary and moving liquids inside cylindrical tubes (Research and Development Report 1143) (Hydromechanics Laboratory, Department of the Navy, 1958).

  23. Huebscher, R.G. ASHVE Trans. 54, 101–118 (1948).

    Google Scholar 

  24. Lurie, A.I. Theory of Elasticity (Springer, 2005).

  25. Ogawa, M., Parmley, R.T., Bank, H.L. & Spicer, S.S. Blood 48, 407–417 (1976).

    Article  CAS  Google Scholar 

  26. Goldblum, S., Bae, Y.K., Hink, W.F. & Chalmers, J. Biotechnol. Prog. 6, 383–390 (1990).

    Article  CAS  Google Scholar 

  27. Leung, M.F., Sokoloski, J.A. & Sartorelli, A.C. Cancer Res. 52, 949–954 (1992).

    CAS  PubMed  Google Scholar 

  28. Kiani, A. et al. Exp. Hematol. 35, 757–770 (2007).

    Article  CAS  Google Scholar 

  29. Ginhoux, F. & Jung, S. Nat. Rev. Immunol. 14, 392–404 (2014).

    Article  CAS  Google Scholar 

  30. Maloney, J.M. & Van Vliet, K.J. Soft Matter 10, 8031–8042 (2014).

    Article  CAS  Google Scholar 

  31. Mayer, T.U. et al. Science 286, 971–974 (1999).

    Article  CAS  Google Scholar 

Download references


We thank A. Taubenberger, M. Herbig, C. Liebers, L. Menschner, C. Klug, R. Berner, M. Bornhäuser, C. Bryant, E. Chilvers, B. Friedrich, A. Voigt, J. Tegenfeldt, M. Tschöp, F. Amblard, T. Hyman and S. Grill for technical support, advice and engaging discussions. The HL60/S4 cells were a generous gift of D. and A. Olins (University of New England). Financial support from the Alexander-von-Humboldt Stiftung (Humboldt-Professorship to J.G.), Sächsisches Ministerium für Wissenschaft und Kunst (TG70 grant to O.O. and J.G.), DFG-Center for Regenerative Medicine of the Technische Universität Dresden (seed grant to J.G.), Deutsche Forschungsgemeinschaft (DFG Emmy Noether Grant to J.M., MA 5831/1-1; KFO249 Gerok position to N.T.), Deutsche Gesellschaft für Pädiatrische Infektiologie (N.T.) and Studienstiftung des Deutschen Volkes (A.M.), Max Planck Society (E.F.-F.) and Leverhulme and Newton Trust (Early Career Fellowship to S.P.) is gratefully acknowledged.

Author information

Authors and Affiliations



J.G. conceived of the method; O.O. and J.G. designed most experiments and wrote the manuscript; O.O. and P.R. wrote the real-time analysis software; P.R. built the pulsed LED illumination and designed the lithography masters; O.O., P.R., D.K. and C.H. optimized the imaging; S. Girardo, S.P. and U.F.K. provided technical advice and help with soft lithography; A.M., O.O. and E.F.-F. developed the analytical model; S. Golfier performed and analyzed the cytoskeletal drug experiments; J.M. designed and interpreted the cell-cycle synchronization experiments; P.R. performed and analyzed the cell-cycle experiments; A.E. cultured and differentiated the HL60 cells; A.J. and M.W. designed the HSC separation and differentiation experiments; A.J. performed the HSC separation and differentiation; A.J. and O.O. measured and analyzed the HL60 and HSC cells; N.T., C.H. and O.O. performed and analyzed the whole blood and separated blood cell measurements.

Corresponding author

Correspondence to Jochen Guck.

Ethics declarations

Competing interests

O.O., P.R. and J.G. are named inventors on a patent application by the Technische Universität Dresden that covers the technology described.

Integrated supplementary information

Supplementary Figure 1 Real-time deformability cytometry: contour detection and theoretical modeling.

(a) Image of cell deformed in constriction; contour (red) according to image analysis algorithm. Scale bar, 5 µm. (b) Characteristic deformed bullet shape of an elastic sphere after application of the pressure and shear stress distributions shown in Figure 1d. Color code indicates displacement relative to non-deformed sphere.

Supplementary Figure 2 RT-DC sensitivity to cytoskeletal drugs.

In addition to the basic result of a dose response curve for cytochalasin D (cytoD) at a flow rate of 0.04 µl/s presented in the main paper (cf. Fig 2a), we carried out additional experiments presented here. Each scatter plot contains more than 2,000 cells measured. (a) RT-DC scatter plots of HL60 control cells (left column) and cells treated with 0.1 µM cytoD (middle column) for three different flow rates of 0.04, 0.08 and 0.12 µl/s in 20 μm × 20 μm channels. Right column shows 50%-density contour plots of the data shown in the two columns to the left plotted together for easier comparison. Thin lines are iso-elasticity lines (cf. Fig. 1f). As expected, cells treated with cytoD showed increased deformation, and a shift to lower elasticity bands, compared to controls at all three flow rates. As an additional basic performance check, both controls and treated cells each showed a consistent increase in deformation at increasing flow rates, which induce larger stresses. For all conditions, spherical shape of the cells before entering the channel was verified explicitly (Supplementary Fig. 7a,b). (b) The softening of HL60 cells with cytoD treatment is also in agreement with earlier reports of this effect on the same cell line1,2, and has been explicitly confirmed again here using an optical stretcher in creep compliance measurements. See Ekpenyong et al.1 for experimental details. Shown are averaged single-cell creep-compliance curves of untreated (N = 89, black curve) and cytoD-treated (N = 68, blue curve) HL60 cells. The error bars represent the standard error of the mean. Comparing the compliance of both populations shows a softening after cytoD treatment. (c) Dose-response curve showing the increasing ratio of cytoD-treated cell deformation relative to control cells as a function of different concentrations and for three different flow rates. All experiments were performed in 20 μm × 20 μm channels. Data points show the mean of three independent measurement sets, errors are standard error of mean. The data for the flow rate of 0.04 µl/s are the raw data presented in Fig. 2a. Of note, the relative increase in deformation of treated cells compared to controls is independent of the flow rate. Inset shows the effect of DMSO alone at different concentrations for a flow rate of 0.12 μl/s. The short measurement time per experimental condition of about 1 min enables the relatively simple acquisition of such dose-response curves (here a total of 60 experiments, each on a new chip, 150,000 cells in total), which would be tedious and time-consuming with previous low-throughput methods. However, the basic trend of increasing deformation with increasing concentration of cytoD has previously been reported with other techniques and on other cell types3,4. Similar dose-response curves were obtained for several other drugs affecting actin (jasplakinolide, blebbistatin) and microtubules (nocodazole, paclitaxel), which all showed the expected results (data not shown).

1. S. C. Erzurum, M. L. Kus, C. Bohse et al., Am. J. Respir. Cell Mol. Biol. 5 (3), 230 (1991).

2. A. E. Ekpenyong, G. Whyte, K. Chalut et al., PLoS One 7 (9), e45237 (2012).

3. C. Rotsch and M. Radmacher, Biophys. J. 78 (1), 520 (2000).

4. S. Kasas, X. Wang, H. Hirling et al., Cell Motil. Cytoskeleton 62 (2), 124 (2005).

Source data

Supplementary Figure 3 Confirmation of cytochalasin D effect on filamentous actin.

Confocal fluorescence images of a representative control (left) and cytoD-treated (right) cell stained for F-actin (red) and DNA (cyan). Top row shows x-y slices (top view), bottom row x-z reconstructions (side view; white line indicates microscope slide surface). Treated cells have a less pronounced actin cortex and are flatter after centrifugation onto the microscope slide for imaging. Scale bar is 10 µm. The greater deformation of cytoD treated cells (cf. Fig. 2a, Supplementary Fig. 1) is thus likely caused by the reduction of actin cortex thickness due to the known F-actin depolymerizing effect of cytoD5.

5. J. A. Cooper, The Journal of Cell Biology 105 (4), 1473 (1987).

Supplementary Figure 4 Progression through the cell cycle.

Change in deformation during progressing through the cell cycle. Each scatter plot contains more than 2,000 cells measured. (a) RT-DC scatter plots of HL60 cells chemically synchronized in G1 (gap phase 1), S (synthesis phase), G2 (gap phase 2) and M (mitosis) phases of the cell cycle. These are the raw data from the main text (cf. Fig. 2b). Thin lines are iso-elasticity lines (cf. Fig. 1f). Cells were measured in a 20 μm × 20 μm channel with a flow rate of 0.04 µl/s. From G1 to S phase cells showed both an increase in size and stiffness (as judged by the comparison with the iso-elasticity lines) at constant observed deformation; from S to G2 phase cells mainly increased in size with approximately constant stiffness (comparison with iso-elasticity lines) albeit an increase in observed deformation. However, with the transition from G2 into M phase cells stiffened significantly, based on both comparison with iso-elasticity lines and the resulting deformation, while their size remained identical. Please note that similar G2 results were obtained during a release from double thymidine block after 4 – 5 h instead of CDK1 inhibition (data not shown). (b) Fluorescence confocal microscopy images of G2 cells (top) and M cells (bottom) stained F-actin (red) and DNA (cyan) show that the increase in cell stiffness during mitosis is at least partly caused by an increase in the extent of the actin cortex. Scale bar is 10 µm. (c) Comparison of RT-DC data of HL-60 cells synchronized in M phase with those incubated with 1 μM cytoD shows a shift of the distribution towards iso-elasticity lines of lower stiffness and greater deformation at approximately the same size. Cells were measured in a 20 μm × 20 μm channel with a flow rate of 0.04 µl/s. (d) 50%-density contour plots of the cell populations in M phase (blue) and after treatment with cytoD (green) as shown in (c) for direct comparison. As described in the main text, this points to a contribution of F-actin in the stiffening of M cells. Such an increase in cell stiffness during mitosis has previously been reported from experiments with optical stretcher6 and atomic force microscopy7,8.

6. H. K. Matthews, U. Delabre, J. L. Rohn et al., Dev. Cell 23 (2), 371 (2012).

7. P. Kunda, A. E. Pelling, T. Liu et al., Curr. Biol. 18 (2), 91 (2008).

8. M. P. Stewart, J. Helenius, Y. Toyoda et al., Nature 469 (7329), 226 (2011).

Source data

Supplementary Figure 5 Mechanical phenotyping of blood precursor cells and their differentiated progeny.

Comparison of HL60-derived and bone marrow-derived CD34+ (BM-CD34+) cells. (a) RT-DC scatter plots of HL60 cells differentiated into granulocytes (HL60-gran), monocytes (HL60-mono) and macrophages (HL60-mac) measured at a flow rate of 0.04 µl/s in a 20 μm × 20 μm channel. Thin lines are iso-elasticity lines (cf. Fig. 1f). An increase in deformability of HL60 cells differentiated to granulocytes had also been found with other techniques9,10. (b) RT-DC scatter plots of BM-CD34+ cells differentiated in vitro into granulocytes (HSC-gran), monocytes (HSC-mono) and macrophages (HSC-mac) measured at a flow rate of 0.16 µl/s in a 30 µm x 30 µm channel (HSC-macs are larger than 20 µm in diameter, which requires a wider channel). Thin lines are iso-elasticity lines (cf. Fig. 1f). (c) FACS scatter plot of forward- and side-scattering, respectively indicative of size and granularity, of HSC-mac (red), HSC-mono (green) and HSC-gran cells (blue).

9. K. R. Hallows and R. S. Frank, Biorheology 29 (2–3), 295 (1992).

10. M. A. Tsai, R. E. Waugh, and P. C. Keng, Biorheology 33 (1), 1 (1996).

Source data

Supplementary Figure 6 FACS analysis of whole blood.

FACS of whole blood and of whole blood after red blood cell (RBC) depletion. (a) Forward vs. side scatter plot of whole blood showing a rather homogeneous distribution with no distinct peaks due to red blood cell scattering. (b) Depletion of red blood cells reveals three distinct subpopulations, which can be identified as lymphocytes (47.6 %, red solid line), monocytes (3.7 %, dashed line) and granulocytes (38.7 %, dotted line) based on gating.

Source data

Supplementary Figure 7 Mechanical phenotyping of cell types in whole blood.

In whole blood, in addition to white blood cells, also platelets and erythrocytes (red blood cells, RBC) can be found, with the latter being the largest fraction by far (RBC/other blood cells ~ 170:1). This poses the challenge whether any of the other cell types can be seen against the very large background of RBCs. To identify the remaining cells better, we reduced the number of RBCs by sedimentation in a dextran solution (see Online Methods for details) and measured the remaining sample. All samples shown in this figure were measured at a flow rate of 0.04 µl/s in a 20 μm × 20 μm channel. (a) RT-DC scatter plot of whole blood with most of the RBCs sedimented out. Here, the three remaining peaks are much more prominent (cf. Fig. 3d and Supplementary Fig. 6f,g). One of the remaining peaks can clearly be identified as platelets, based on their known very small size and separate RT-DC analysis (data not shown). Separation of (b) RBCs, (c) peripheral blood mono-nucleated cells (PBMCs; containing monocytes, lymphocytes, immature leukocytes) and (d) granulocytes (gran) by gradient density centrifugation, and subsequent RT-DC analysis clearly identified the remaining peaks. The correct relative numbers of these cells were confirmed with FACS analysis (see Supplementary Fig. 5b). (e) Isolated granulocytes were stimulated with phorbol-12-myristate-13-acetate (PMA), which resulted in a clear shift in their mechanical fingerprint. (f,g) RT-DC scatter plots of whole blood (diluted 1:50 in PBS – containing 0.5 % methylcellulose) from two different donors (in addition to the one in Fig. 3d) showing very little inter-donor variability of the relative positions of the four peaks.

Source data

Supplementary Figure 8 Verification of spherical cell shape before the cells enter the narrow channel.

The plots summarize RT-DC data of (a) HL60 cells, (b) HL60 cells treated with 0.1 µM cytochalasin D, and (c) CD43+ cells derived from peripheral blood (PB-CD34+) cells inside the reservoir before entering the channel where the measurements were carried out. Flow rates were 0.04 μl/s (HL60) and 0.08 μl/s (PB-CD34+), respectively. Fitting log-normal distributions to the 1-dimensional projection of the deformation in all populations reveals a mode of ≤ 0.01 close to the theoretical expected value of 0 for an ideal sphere. Results for other cell types and conditions appearing in the main text were similar (data not shown).

Source data

Supplementary Figure 9 Viability and rate of growth of HL60 cells after RT-DC analysis.

Experiments were carried out using a 20 μm x 20 μm channel and a flow rate of 0.12 μl/s. (a) After cell recovery the viability of post RT-DC cells was determined using a standard Annexin V-FITC/PI FACS assay and compared to a control sample (b) (see Online Methods). The FACS scatter plots show that the relative number of necrotic / apoptotic cells in the control sample (3.07 %) is very close to the value after RT-DC measurement (3.89 %). The viability test in (a) and (b) shows results from one measurement, but was repeated three times (viability post RT-DC: 94 ± 1 % and viability control 97 ± 1%). (c) In parallel the rate of growth of post RT-DC HL60 cells was observed for 5 days and compared to a control sample. Viability tests using Trypan Blue (see Online Methods) after 6, 24, 48, 72, 96 and 120 hours reveal a viability of 98% (control) and 97% (post RT-DC).

Source data

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–9 (PDF 5637 kb)

Screen movie of control PC monitor during RT-DC operation.

The movie shows at the top HL60 cells being flushed through a 20 μm x 20 μm channel at a flow rate of 0.04 μl/s. The frame rate was set to 2,000 fps. The bottom of the screen shows the deformation vs. cell size scatter plot building up on the fly. Our algorithm is capable of performing size gating in real-time, i.e. debris can automatically be excluded from the data. During the 10 s duration of the movie approximately 2,000 cells are analyzed. (MP4 332 kb)

Source data

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Otto, O., Rosendahl, P., Mietke, A. et al. Real-time deformability cytometry: on-the-fly cell mechanical phenotyping. Nat Methods 12, 199–202 (2015).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research