Automatic real time evaluation of red blood cell elasticity by optical tweezers Diógenes S. Moura, Diego C. N. Silva, Ajoke J. Williams, Marcos A. C. Bezerra, Adriana Fontes, and Renato E. de Araujo Citation: Review of Scientific Instruments 86, 053702 (2015); doi: 10.1063/1.4919010 View online: http://dx.doi.org/10.1063/1.4919010 View Table of Contents: http://scitation.aip.org/content/aip/journal/rsi/86/5?ver=pdfcov Published by the AIP Publishing Articles you may be interested in Biomechanical properties of red blood cells in health and disease towards microfluidics Biomicrofluidics 8, 051501 (2014); 10.1063/1.4895755 Probing the coupled adhesion and deformation characteristics of suspension cells Appl. Phys. Lett. 105, 073703 (2014); 10.1063/1.4893734 Total three-dimensional imaging of phase objects using defocusing microscopy: Application to red blood cells Appl. Phys. Lett. 104, 251107 (2014); 10.1063/1.4884420 Simulation of malaria-infected red blood cells in microfluidic channels: Passage and blockage Biomicrofluidics 7, 044115 (2013); 10.1063/1.4817959 Optical tweezers assisted quantitative phase imaging led to thickness mapping of red blood cells Appl. Phys. Lett. 103, 013703 (2013); 10.1063/1.4812985
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REVIEW OF SCIENTIFIC INSTRUMENTS 86, 053702 (2015)
Automatic real time evaluation of red blood cell elasticity by optical tweezers Diógenes S. Moura,1 Diego C. N. Silva,2,3 Ajoke J. Williams,1 Marcos A. C. Bezerra,3 Adriana Fontes,1,3,a) and Renato E. de Araujo1
1
Laboratory of Biomedical Optics and Imaging, Federal University of Pernambuco, Recife, Pernambuco, Brazil College of Biological Sciences, Federal University of Vale do São Francisco–UNIVASF, Petrolina, Pernambuco, Brazil 3 Department of Biophysics and Radiobiology, Federal University of Pernambuco, Recife, Pernambuco, Brazil 2
(Received 23 December 2014; accepted 14 April 2015; published online 6 May 2015) Optical tweezers have been used to trap, manipulate, and measure individual cell properties. In this work, we show that the association of a computer controlled optical tweezers system with image processing techniques allows rapid and reproducible evaluation of cell deformability. In particular, the deformability of red blood cells (RBCs) plays a key role in the transport of oxygen through the blood microcirculation. The automatic measurement processes consisted of three steps: acquisition, segmentation of images, and measurement of the elasticity of the cells. An optical tweezers system was setup on an upright microscope equipped with a CCD camera and a motorized XYZ stage, computer controlled by a Labview platform. On the optical tweezers setup, the deformation of the captured RBC was obtained by moving the motorized stage. The automatic real-time homemade system was evaluated by measuring RBCs elasticity from normal donors and patients with sickle cell anemia. Approximately 150 erythrocytes were examined, and the elasticity values obtained by using the developed system were compared to the values measured by two experts. With the automatic system, there was a significant time reduction (60×) of the erythrocytes elasticity evaluation. Automated system can help to expand the applications of optical tweezers in hematology and hemotherapy. C 2015 AIP Publishing LLC. [http://dx.doi.org/10.1063/1.4919010] I. INTRODUCTION
The deformability of red blood cells (RBCs) plays a key role in the transport of oxygen through the blood microcirculation. RBCs must withstand significant deformations during repeated passages through microvessels with fenestrated walls and sinusoids of the spleen.1 Several techniques have been used to assess the deformability of RBCs, among them are micropipette aspiration,2 filtration,3 magnetic twisting cytometry,4 and optical tweezers.5 In particular, optical tweezers are tools that, through the optical trapping, based on photon momentum transfer, can be used as a measuring and manipulation instrument.6–8 For the study of RBC biology, optical tweezers allow the determination of mechanical and electrical properties (such as adhesion, viscosity membrane, overall cell elasticity, and zeta potential) of normal erythrocytes and that ones altered by some extrinsic or intrinsic factors, for example, hematologic diseases, drugs, or storage in blood banks.5,9–11 In the literature, an increasing attention is being given for hematologic diseases, due to its high incidence in the population, the demand for new diagnostic procedures, and for the erythrocyte biology comprehension. In addition, new drugs have been proposed and their effects on RBCs biophysical properties are the key parameters to be evaluated.
a)Author to whom correspondence should be addressed. Electronic mail:
[email protected]. Telephone: (+55) 81 21267818.
The procedures for the use of optical tweezers for determining the elasticity of erythrocytes are accompanied by a laborious methodology of data/image analysis performed by a trained professional,5 imposing a long period of time to obtain quantitative description of the evaluation system. In this context, automatizing RBCs’ measurements performed by the optical tweezers associated with automatic image analysis can be a powerful tool for the characterization of erythrocytes. It is worth pointing out that image processing techniques have been applied in various areas of life sciences, enabling rapid and reproducible data analysis, such as the identification and classification of cells12 and the count of eggs of mosquitoes’ transmitters of diseases.13 This work presents the integration of an automatic image analysis in an optical tweezers system. The developed system allows, in real time, the quantification of RBCs elasticity during cell trapping. This association can help to expand the applications of optical tweezers for hematology and hemotheraphy purposes. II. METHODS A. Automatic measurement system
The measurement system allowed the evaluation of RBC elasticity in real time during the cell trapping. The automatic measurement processes were consisted of three steps: acquisition, segmentation of images, and measurement of the elasticity of the cells. The three-step procedures are presented in Figure 1.
0034-6748/2015/86(5)/053702/4/$30.00 86, 053702-1 © 2015 AIP Publishing LLC This article is copyrighted as indicated in the article. Reuse of AIP content is subject to the terms at: http://scitationnew.aip.org/termsconditions. Downloaded to IP: 137.30.242.61 On: Mon, 01 Jun 2015 21:30:05
053702-2
Moura et al.
Rev. Sci. Instrum. 86, 053702 (2015)
speeds (140 µm/s, 175 µm/s, 210 µm/s, 245 µm/s, 280 µm/s, and 315 µm/s) were used during the acquisition. The whole capture processes and microscope stage movements were done by using the Labview platform. Videos in AVI format of 24 bits, with images of 352 × 240 in size, were generated. 2. Segmentation
FIG. 1. The automatic measurement system processes for RBCs elasticity evaluation.
1. Acquisition
The optical tweezers system used a Nd:YAG laser (1064 nm—IPG Photonics, USA). The laser power at the sample was approximately 100 mW. The diameter and the divergence of the beam were controlled by a telescope, as shown in Figure 2. The optical tweezers system was setup on a microscope (ZeissAxiolab) equipped with a CCD camera and a motorized XYZ stage (Prior Scientific-Prior II), controlled by a joystick or by a computer via Labview platform. The system (Figure 2) used an oil immersion objective (100×, NA = 1.25, Zeiss). On the optical tweezers setup, the deformation of the captured RBC can be obtained by moving the motorized stage. For that process, different stage velocities were used. After trapping RBCs by optical tweezers, the capture of trapped cell videos was initiated. To acquire the videos, a CCD camera connected to a video capture card (Pinnacle System) was used. The beginning of the capture was synchronized with the start of the stage movement. Six different predetermined constant drag
Segmentation refers to the decomposition process of a digital image into multiple segments (regions) that form the image.14 To segment the data, it performed a histogram equalization increasing the image contrast. It used a 5 × 5 median filter to remove noise and smooth the image, and a Laplacian filter (kernel size 21 × 21) was explored to detect sharp transitions and highlights edge pixels. A morphological dilation operation was also used to remove smaller objects from the grayscale images. Then, images were binarized by using a global threshold, converting low intensity pixels to black and pixels with greater intensity to white. A morphological erosion operation was performed on the binary images improving the binary image quality. The segmentation procedure takes approximately 4 ms to process each frame. 3. Elasticity measurement
After segmentation, a Labview function called “Max Clamp” was used to distinguish cell edges and measure the distance between the furthest opposed points on the edges founded. The obtained cell size information (in pixel) was converted to micrometers by previous calibration. For each velocity, an average cell elongation length value was obtained from 10 frames for each trapped cell. The RBC elasticity was associated to its length deformation in accordance with the following expression:5 η L 02 + V, L = L0 + * , µZeq -
(1)
with L being the length of the cell after deformation, L0 is the initial length of the erythrocyte, η is the blood serum viscosity (1.65 cP), µ is the apparent overall elasticity of the cell, and V is the velocity of the cell. The cell was located at a distance Z1 from the bottom of a Neubauer chamber and Z2 from the cover
FIG. 2. The optical tweezers system configuration and zoom of cell in the cover slip. This article is copyrighted as indicated in the article. Reuse of AIP content is subject to the terms at: http://scitationnew.aip.org/termsconditions. Downloaded to IP: 137.30.242.61 On: Mon, 01 Jun 2015 21:30:05
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Moura et al.
Rev. Sci. Instrum. 86, 053702 (2015)
FIG. 3. Control panel of real time elasticity measurements program of RBCs.
slip, and 1/Zeq = 1/Z1 + 1/Z2. By automatically measuring the deformed cell length and exploring different drag speeds, one can determine, according to the Eq. (1), the elasticity (µ) of the cell.5,9 The depth Z1 was measured by focusing the bottom of the Neubauer chamber (100 µm of depth) and then lowering the chamber by the desired amount (in this case 50 µm) while keeping the cell fixed with the optical tweezers. All trapped cells were moved to a fixed height away from the Neubauer bottom surface to avoid changes on the hydrodynamic force due to the chamber’ interfaces.11 The data were analyzed by the Wilcoxon statistical test.
B. Sample preparation
All samples were collected in Hemope (Fundação Hemope at Recife, Brazil) in two 5 ml vacutainer tubes, one containing EDTA anticoagulant and another without anticoagulant solution. The study was approved by the Hemope Research Ethics Committee (Report No. 001/2011). Erythrocytes obtained from EDTA blood tube were diluted in serum of the patient
in proportion (0.5 µl/500 µl). It was evaluated approximately 100 cells for nine health control donors. Afterwards, it was evaluated around 10 RBCs of patients of sickle cell anemia (HbSS).
III. RESULTS AND DISCUSSION
Figure 3 shows the control panel of the automatic measuring system of RBCs elasticity. The program allows video recordings (30 fps). The system evaluates in real time the cell lengths and gives the value of the elasticity immediately after capturing the video. In Figure 3, in the top left, one can identify the image captured by the system. The cell axis elongation was determined by the stage motion direction. It is also presented to the user, the image of the cell after the segmentation process (Figure 3, top right). The control panel also displays, graphically, the determined cell length values according to the different dragging speeds, V . The value for the cell length is determined by the average of the top ten higher erythrocytes’ lengths of
FIG. 4. Stages of the segmentation processes: (a) original image, (b) histogram equalization, (c) median filter, (d) convolution-highlight details filter, (e) expansion, (f) thresholding, (g) erosion, and (h) segmented and screened cell. This article is copyrighted as indicated in the article. Reuse of AIP content is subject to the terms at: http://scitationnew.aip.org/termsconditions. Downloaded to IP:
137.30.242.61 On: Mon, 01 Jun 2015 21:30:05
053702-4
Moura et al.
each speed. The averages of the lengths, in microns, are shown in the lower right of Figure 3. A real time plot of the measured stretched cell length in function of the dragging speed and the elasticity value in dyne/cm is also presented on the system control panel (Figure 3-lower left). Image segmentation is an important procedure on the automatic elasticity measurements. Figure 4 shows the resulting images of each step of the segmentation process. Clear images of the cells are required for the automated evaluation. Poor image quality can induce errors of the cell length determination. The automatic system eliminates that type of errors by averaging 10 values of the cell lengths obtained for each speed. For a set of high quality images, we observed that the standard deviation of the cell elongation is much smaller than 0.5 µm. Therefore, for each speed, the system is set to consider only averaged elongation values no greater than 0.5 µm from the previous cell length. The results obtained by the automated system were evaluated by comparing them with the values of elasticity measured by two experts.9,10 With the automatic system, there was a significant reduction in time to determine the elasticity of erythrocytes. The determination of elasticity by a trained professional takes approximately 30 min for one cell and it is based on the evaluation of only 6 images (1 image for each velocity). In the automated system, the values for elasticity are obtained by analyzing a much larger amount of images (10 for each velocity), improving the reliability of the obtained results. The determination of elasticity by real time automatic system takes approximately 30 s, reducing in approximately 60× the time of analysis. In order to test the automatic system, it determined the elasticity of RBCs from healthy donors (control) and from patients with sickle cell anemia (HbSS). For control donors, the average elasticity obtained by the automatic system was (4.9 ± 0.3) × 10−4 dyn/cm, and for patients with sickle cell anemia, the average deformability was (7.4 ± 1) × 10−4 dyn/cm. The results obtained by the two experts (conventional method) correspond to an average elasticity of (4.5 ± 0.2) × 10−4 dyn/cm for control and (6.8 ± 1) × 10−4 dyn/cm for HbSS. Thus, it can be seen that the results obtained by the automatic system are in agreement with those obtained by the conventional method, with p ≥ 0.6. HbSS cells deformability presented a widespread distribution compared to control cell values, and in this case, p was smaller than 0.05 showing that HbSS and control cells are distinct population, despite the small number of cells of HbSS analyzed.
Rev. Sci. Instrum. 86, 053702 (2015)
IV. CONCLUSIONS
This work presents an optical system capable of automatically evaluating the elasticity of erythrocytes. Exploring a homemade optical tweezers system and techniques of image processing, real time measurements of cell deformability were performed. The association of computer controlled optical tweezers system and image segmentation techniques allows a rapid and reproducible analysis of RBC elasticity. The developed automatic system was evaluated by screening approximately 100 cells and comparing these results with the values of elasticity measured by two experts by the conventional method. With the automatic system, there was a significant reduction in time (∼ 60×) to determine the elasticity of erythrocytes. We also showed that the automated system was also able to detect differences between RBCs of normal donors and of patients with sickle cell anemia. This automated system as shown here could help to expand the applications of optical tweezers in hematology and hemotheraphy.
ACKNOWLEDGMENTS
The authors thank CNPq and FACEPE for funding. The authors are members of INCT-Photonics. 1N.
Mohandas and J. A. Chasis, Semin. Hematol. 30, 171 (1993). A. Evans and P. L. La Celle, Blood 45, 29 (1975). 3J. H. Jandl, R. L. Simmons, W. B. Castle, and G. R. Minot, Blood 18, 133 (1961). 4M. Puig-de-Morales-Marinkovic, K. T. Turner, and J. P. Butler, Am. J. Physiol.: Cell Physiol. 293, 597 (2007). 5A. Fontes, M. L. Barjas Castro, M. M. Brandão, H. P. Fernandes, A. A. Thomaz, R. R. Huruta, L. Y. Pozzo, L. C. Barbosa, F. F. Costa, S. T. O. Saad, and C. L. Cesar, J. Opt. 13, 044012 (2011). 6A. Ashkin, J. M. Dziedzic, J. E. Bjorkholm, and S. Chu, Opt. Lett. 11, 288 (1986). 7K. C. Neuman and S. M. Block, Rev. Sci. Instrum. 75, 2787 (2004). 8G. Pesce, A. Sasso, and S. Fusco, Rev. Sci. Instrum. 76, 115105 (2005). 9D. C. N. Silva, C. N. Jovino, C. A. L. Silva, H. P. Fernandes, M. M. Filho, S. C. Lucena, A. M. D. N. Costa, C. L. Cesar, M. L. Barjas-Castro, B. S. Santos, and A. Fontes, PLoS One 7, e31778 (2012). 10M. M. Brandão, M. L. Barjas-Castro, A. Fontes, C. L. Cesar, F. F. Costa, and S. T. Saad, Clin. Hemorheol. Microcirc. 43, 217 (2009). 11M. M. Brandão, A. Fontes, M. L. Barjas-Castro, L. C. Barbosa, F. F. Costa, C. L. Cesar, and S. T. Saad, Eur. J. Haematol. 70, 207 (2003). 12S. C. Grover, A. G. Skirtach, R. C. Gauthier, and C. P. Grover, J. Biomed. Opt. 6, 14 (2001). 13M. G. M. M. Silva, M. A. B. Rodrigues, and R. E. de Araujo, Rev. Bras. Eng. Biomed. 28, 364 (2012). 14A. K. Jain, Fundamentals of Digital Image Processing (Prentice-Hall, New Jersey, 1989). 2E.
This article is copyrighted as indicated in the article. Reuse of AIP content is subject to the terms at: http://scitationnew.aip.org/termsconditions. Downloaded to IP: 137.30.242.61 On: Mon, 01 Jun 2015 21:30:05
This article is copyrighted as indicated in the article. Reuse of AIP content is subject to the terms at: http://scitationnew.aip.org/termsconditions. Downloaded to IP: 137.30.242.61 On: Mon, 01 Jun 2015 21:30:05
REVIEW OF SCIENTIFIC INSTRUMENTS 86, 053702 (2015)
Automatic real time evaluation of red blood cell elasticity by optical tweezers Diógenes S. Moura,1 Diego C. N. Silva,2,3 Ajoke J. Williams,1 Marcos A. C. Bezerra,3 Adriana Fontes,1,3,a) and Renato E. de Araujo1
1
Laboratory of Biomedical Optics and Imaging, Federal University of Pernambuco, Recife, Pernambuco, Brazil College of Biological Sciences, Federal University of Vale do São Francisco–UNIVASF, Petrolina, Pernambuco, Brazil 3 Department of Biophysics and Radiobiology, Federal University of Pernambuco, Recife, Pernambuco, Brazil 2
(Received 23 December 2014; accepted 14 April 2015; published online 6 May 2015) Optical tweezers have been used to trap, manipulate, and measure individual cell properties. In this work, we show that the association of a computer controlled optical tweezers system with image processing techniques allows rapid and reproducible evaluation of cell deformability. In particular, the deformability of red blood cells (RBCs) plays a key role in the transport of oxygen through the blood microcirculation. The automatic measurement processes consisted of three steps: acquisition, segmentation of images, and measurement of the elasticity of the cells. An optical tweezers system was setup on an upright microscope equipped with a CCD camera and a motorized XYZ stage, computer controlled by a Labview platform. On the optical tweezers setup, the deformation of the captured RBC was obtained by moving the motorized stage. The automatic real-time homemade system was evaluated by measuring RBCs elasticity from normal donors and patients with sickle cell anemia. Approximately 150 erythrocytes were examined, and the elasticity values obtained by using the developed system were compared to the values measured by two experts. With the automatic system, there was a significant time reduction (60×) of the erythrocytes elasticity evaluation. Automated system can help to expand the applications of optical tweezers in hematology and hemotherapy. C 2015 AIP Publishing LLC. [http://dx.doi.org/10.1063/1.4919010] I. INTRODUCTION
The deformability of red blood cells (RBCs) plays a key role in the transport of oxygen through the blood microcirculation. RBCs must withstand significant deformations during repeated passages through microvessels with fenestrated walls and sinusoids of the spleen.1 Several techniques have been used to assess the deformability of RBCs, among them are micropipette aspiration,2 filtration,3 magnetic twisting cytometry,4 and optical tweezers.5 In particular, optical tweezers are tools that, through the optical trapping, based on photon momentum transfer, can be used as a measuring and manipulation instrument.6–8 For the study of RBC biology, optical tweezers allow the determination of mechanical and electrical properties (such as adhesion, viscosity membrane, overall cell elasticity, and zeta potential) of normal erythrocytes and that ones altered by some extrinsic or intrinsic factors, for example, hematologic diseases, drugs, or storage in blood banks.5,9–11 In the literature, an increasing attention is being given for hematologic diseases, due to its high incidence in the population, the demand for new diagnostic procedures, and for the erythrocyte biology comprehension. In addition, new drugs have been proposed and their effects on RBCs biophysical properties are the key parameters to be evaluated.
a)Author to whom correspondence should be addressed. Electronic mail:
[email protected]. Telephone: (+55) 81 21267818.
The procedures for the use of optical tweezers for determining the elasticity of erythrocytes are accompanied by a laborious methodology of data/image analysis performed by a trained professional,5 imposing a long period of time to obtain quantitative description of the evaluation system. In this context, automatizing RBCs’ measurements performed by the optical tweezers associated with automatic image analysis can be a powerful tool for the characterization of erythrocytes. It is worth pointing out that image processing techniques have been applied in various areas of life sciences, enabling rapid and reproducible data analysis, such as the identification and classification of cells12 and the count of eggs of mosquitoes’ transmitters of diseases.13 This work presents the integration of an automatic image analysis in an optical tweezers system. The developed system allows, in real time, the quantification of RBCs elasticity during cell trapping. This association can help to expand the applications of optical tweezers for hematology and hemotheraphy purposes. II. METHODS A. Automatic measurement system
The measurement system allowed the evaluation of RBC elasticity in real time during the cell trapping. The automatic measurement processes were consisted of three steps: acquisition, segmentation of images, and measurement of the elasticity of the cells. The three-step procedures are presented in Figure 1.
0034-6748/2015/86(5)/053702/4/$30.00 86, 053702-1 © 2015 AIP Publishing LLC This article is copyrighted as indicated in the article. Reuse of AIP content is subject to the terms at: http://scitationnew.aip.org/termsconditions. Downloaded to IP: 137.30.242.61 On: Mon, 01 Jun 2015 21:30:05
053702-2
Moura et al.
Rev. Sci. Instrum. 86, 053702 (2015)
speeds (140 µm/s, 175 µm/s, 210 µm/s, 245 µm/s, 280 µm/s, and 315 µm/s) were used during the acquisition. The whole capture processes and microscope stage movements were done by using the Labview platform. Videos in AVI format of 24 bits, with images of 352 × 240 in size, were generated. 2. Segmentation
FIG. 1. The automatic measurement system processes for RBCs elasticity evaluation.
1. Acquisition
The optical tweezers system used a Nd:YAG laser (1064 nm—IPG Photonics, USA). The laser power at the sample was approximately 100 mW. The diameter and the divergence of the beam were controlled by a telescope, as shown in Figure 2. The optical tweezers system was setup on a microscope (ZeissAxiolab) equipped with a CCD camera and a motorized XYZ stage (Prior Scientific-Prior II), controlled by a joystick or by a computer via Labview platform. The system (Figure 2) used an oil immersion objective (100×, NA = 1.25, Zeiss). On the optical tweezers setup, the deformation of the captured RBC can be obtained by moving the motorized stage. For that process, different stage velocities were used. After trapping RBCs by optical tweezers, the capture of trapped cell videos was initiated. To acquire the videos, a CCD camera connected to a video capture card (Pinnacle System) was used. The beginning of the capture was synchronized with the start of the stage movement. Six different predetermined constant drag
Segmentation refers to the decomposition process of a digital image into multiple segments (regions) that form the image.14 To segment the data, it performed a histogram equalization increasing the image contrast. It used a 5 × 5 median filter to remove noise and smooth the image, and a Laplacian filter (kernel size 21 × 21) was explored to detect sharp transitions and highlights edge pixels. A morphological dilation operation was also used to remove smaller objects from the grayscale images. Then, images were binarized by using a global threshold, converting low intensity pixels to black and pixels with greater intensity to white. A morphological erosion operation was performed on the binary images improving the binary image quality. The segmentation procedure takes approximately 4 ms to process each frame. 3. Elasticity measurement
After segmentation, a Labview function called “Max Clamp” was used to distinguish cell edges and measure the distance between the furthest opposed points on the edges founded. The obtained cell size information (in pixel) was converted to micrometers by previous calibration. For each velocity, an average cell elongation length value was obtained from 10 frames for each trapped cell. The RBC elasticity was associated to its length deformation in accordance with the following expression:5 η L 02 + V, L = L0 + * , µZeq -
(1)
with L being the length of the cell after deformation, L0 is the initial length of the erythrocyte, η is the blood serum viscosity (1.65 cP), µ is the apparent overall elasticity of the cell, and V is the velocity of the cell. The cell was located at a distance Z1 from the bottom of a Neubauer chamber and Z2 from the cover
FIG. 2. The optical tweezers system configuration and zoom of cell in the cover slip. This article is copyrighted as indicated in the article. Reuse of AIP content is subject to the terms at: http://scitationnew.aip.org/termsconditions. Downloaded to IP: 137.30.242.61 On: Mon, 01 Jun 2015 21:30:05
053702-3
Moura et al.
Rev. Sci. Instrum. 86, 053702 (2015)
FIG. 3. Control panel of real time elasticity measurements program of RBCs.
slip, and 1/Zeq = 1/Z1 + 1/Z2. By automatically measuring the deformed cell length and exploring different drag speeds, one can determine, according to the Eq. (1), the elasticity (µ) of the cell.5,9 The depth Z1 was measured by focusing the bottom of the Neubauer chamber (100 µm of depth) and then lowering the chamber by the desired amount (in this case 50 µm) while keeping the cell fixed with the optical tweezers. All trapped cells were moved to a fixed height away from the Neubauer bottom surface to avoid changes on the hydrodynamic force due to the chamber’ interfaces.11 The data were analyzed by the Wilcoxon statistical test.
B. Sample preparation
All samples were collected in Hemope (Fundação Hemope at Recife, Brazil) in two 5 ml vacutainer tubes, one containing EDTA anticoagulant and another without anticoagulant solution. The study was approved by the Hemope Research Ethics Committee (Report No. 001/2011). Erythrocytes obtained from EDTA blood tube were diluted in serum of the patient
in proportion (0.5 µl/500 µl). It was evaluated approximately 100 cells for nine health control donors. Afterwards, it was evaluated around 10 RBCs of patients of sickle cell anemia (HbSS).
III. RESULTS AND DISCUSSION
Figure 3 shows the control panel of the automatic measuring system of RBCs elasticity. The program allows video recordings (30 fps). The system evaluates in real time the cell lengths and gives the value of the elasticity immediately after capturing the video. In Figure 3, in the top left, one can identify the image captured by the system. The cell axis elongation was determined by the stage motion direction. It is also presented to the user, the image of the cell after the segmentation process (Figure 3, top right). The control panel also displays, graphically, the determined cell length values according to the different dragging speeds, V . The value for the cell length is determined by the average of the top ten higher erythrocytes’ lengths of
FIG. 4. Stages of the segmentation processes: (a) original image, (b) histogram equalization, (c) median filter, (d) convolution-highlight details filter, (e) expansion, (f) thresholding, (g) erosion, and (h) segmented and screened cell. This article is copyrighted as indicated in the article. Reuse of AIP content is subject to the terms at: http://scitationnew.aip.org/termsconditions. Downloaded to IP:
137.30.242.61 On: Mon, 01 Jun 2015 21:30:05
053702-4
Moura et al.
each speed. The averages of the lengths, in microns, are shown in the lower right of Figure 3. A real time plot of the measured stretched cell length in function of the dragging speed and the elasticity value in dyne/cm is also presented on the system control panel (Figure 3-lower left). Image segmentation is an important procedure on the automatic elasticity measurements. Figure 4 shows the resulting images of each step of the segmentation process. Clear images of the cells are required for the automated evaluation. Poor image quality can induce errors of the cell length determination. The automatic system eliminates that type of errors by averaging 10 values of the cell lengths obtained for each speed. For a set of high quality images, we observed that the standard deviation of the cell elongation is much smaller than 0.5 µm. Therefore, for each speed, the system is set to consider only averaged elongation values no greater than 0.5 µm from the previous cell length. The results obtained by the automated system were evaluated by comparing them with the values of elasticity measured by two experts.9,10 With the automatic system, there was a significant reduction in time to determine the elasticity of erythrocytes. The determination of elasticity by a trained professional takes approximately 30 min for one cell and it is based on the evaluation of only 6 images (1 image for each velocity). In the automated system, the values for elasticity are obtained by analyzing a much larger amount of images (10 for each velocity), improving the reliability of the obtained results. The determination of elasticity by real time automatic system takes approximately 30 s, reducing in approximately 60× the time of analysis. In order to test the automatic system, it determined the elasticity of RBCs from healthy donors (control) and from patients with sickle cell anemia (HbSS). For control donors, the average elasticity obtained by the automatic system was (4.9 ± 0.3) × 10−4 dyn/cm, and for patients with sickle cell anemia, the average deformability was (7.4 ± 1) × 10−4 dyn/cm. The results obtained by the two experts (conventional method) correspond to an average elasticity of (4.5 ± 0.2) × 10−4 dyn/cm for control and (6.8 ± 1) × 10−4 dyn/cm for HbSS. Thus, it can be seen that the results obtained by the automatic system are in agreement with those obtained by the conventional method, with p ≥ 0.6. HbSS cells deformability presented a widespread distribution compared to control cell values, and in this case, p was smaller than 0.05 showing that HbSS and control cells are distinct population, despite the small number of cells of HbSS analyzed.
Rev. Sci. Instrum. 86, 053702 (2015)
IV. CONCLUSIONS
This work presents an optical system capable of automatically evaluating the elasticity of erythrocytes. Exploring a homemade optical tweezers system and techniques of image processing, real time measurements of cell deformability were performed. The association of computer controlled optical tweezers system and image segmentation techniques allows a rapid and reproducible analysis of RBC elasticity. The developed automatic system was evaluated by screening approximately 100 cells and comparing these results with the values of elasticity measured by two experts by the conventional method. With the automatic system, there was a significant reduction in time (∼ 60×) to determine the elasticity of erythrocytes. We also showed that the automated system was also able to detect differences between RBCs of normal donors and of patients with sickle cell anemia. This automated system as shown here could help to expand the applications of optical tweezers in hematology and hemotheraphy.
ACKNOWLEDGMENTS
The authors thank CNPq and FACEPE for funding. The authors are members of INCT-Photonics. 1N.
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