Bifurcating networks are commonly found in nature. One example is the microvascular system, composed of blood vessels consecutively branching into daughter vessels, driving the blood into the capillaries, where the red blood cells (RBCs) are responsible for delivering O 2 and up taking cell waste and CO 2 . In this preliminary study, we explore a microfluidic bifurcating geometry inspired by such biological models, for investigating RBC partitioning as well as RBC-plasma separation favored by the consecutive bifurcating channels. A biomimetic design rule [1] based on Murray’s law [2] was used to set the channels’ dimensions along the network, which consists of consecutive bifurcating channels of reducing diameter. The ability to apply differential flow resistances by controlling the flow rates at the end of the network allowed us to monitor the formation of a cell-free layer (CFL) for different flow conditions at haematocrits of 1% and 5%. We have also compared the values of CFL thickness determined directly by the measurement on the projection image created from a stack of images or indirectly by analyzing the intensity profile in the same projection. The results obtained from this study confirm the potential to study RBC partitioning along bifurcating networks, which could be of particular interest for the separation of RBCs from plasma in point-of-care devices.JF would like to thank Professor Graça Minas and her coworkers for
providing the laboratory facilities and technical help during the experiments.info:eu-repo/semantics/publishedVersio

AS Popel

DA Fedosov

J Zhang

K Zografos

RT Yen

S Sharma

S Tripathi

T Krüger

Joana Fidalgo

Diana Pinho

Rui Lima

Mónica S. N. Oliveira

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Red Blood Cells (RBCs) Visualisation in Bifurcations and Bends

Fidalgo, Joana

Pinho, Diana

Lima, Rui A.

Oliveira, Mónica S.N.

Biblioteca Digital do IPB

Red Blood Cells (RBCs) Visualisationin Bifurcations and BendsJoana Fidalgo1(&), Diana Pinho2,3, Rui Lima2,4,and Mónica S.N. Oliveira11 James Weir Fluids Laboratory, Department of Mechanical and AerospaceEngineering, University of Strathclyde, Glasgow G1 1XJ, UK{joana.fidalgo,monica.oliveira}@strath.ac.uk2 Department of Chemical Engineering, Engineering Faculty, TransportPhenomena Research Center, R. Dr. Roberto Frias, 4200-465 Porto, Portugal3 Polytechnic Institute of Bragança, ESTiG/IPB, C. Sta. Apolonia, 5301-857Bragança, Portugal4 MEtRiCS, DME School of Engineering, University of Minho, Campus deAzurém, 4800-058 Guimarães, PortugalAbstract. Bifurcating networks are commonly found in nature. One example isthe microvascular system, composed of blood vessels consecutively branchinginto daughter vessels, driving the blood into the capillaries, where the red bloodcells (RBCs) are responsible for delivering O2 and up taking cell waste and CO2.In this preliminary study, we explore a microfluidic bifurcating geometryinspired by such biological models, for investigating RBC partitioning as well asRBC-plasma separation favored by the consecutive bifurcating channels.A biomimetic design rule [1] based on Murray’s law [2] was used to set thechannels’ dimensions along the network, which consists of consecutive bifur-cating channels of reducing diameter. The ability to apply differential flowresistances by controlling the flow rates at the end of the network allowed us tomonitor the formation of a cell-free layer (CFL) for different flow conditions athaematocrits of 1% and 5%. We have also compared the values of CFLthickness determined directly by the measurement on the projection imagecreated from a stack of images or indirectly by analyzing the intensity proﬁle inthe same projection.The results obtained from this study conﬁrm the potential to study RBCpartitioning along bifurcating networks, which could be of particular interest forthe separation of RBCs from plasma in point-of-care devices.Keywords: RBC  Bifurcating networks  Cell-free layer  Microcirculation1 IntroductionMicrofluidic devices have been widely studied for applications in medical diagnostics[3]. In particular, they present a good alternative for the process of plasma separationfrom whole blood since it requires a small volume of sample taking advantage of thegeometry design and complex blood rheology at the microscale [4]. Ideally, the© Springer International Publishing AG 2018J.M.R.S. Tavares and R.M. Natal Jorge (eds.), VipIMAGE 2017,Lecture Notes in Computational Vision and Biomechanics 27,DOI 10.1007/978-3-319-68195-5_103efﬁciency of this process should be high enough to allow the pure plasma to beanalysed in-line, on the same platform device, usually for protein or other analytedetection. Even though several distinct approaches have been explored so far, thepassive separation methods are always preferred since they require simpler setup, arecost effective and easier to integrate with biosensors. In addition, these methods usuallysubject the cells to smaller stresses ensuring the integrity of the original sample [5].In this preliminary study, we investigate the use of bifurcating networks for pur-poses of plasma separation by testing suspensions of RBC in dextran 40 solution.Taking advantage of the typical blood flow effects such as the plasma skimming andZweifach-Fung effect (bifurcation law), the cell-depleted layer (CFL) formed close tothe channel walls continues to flow into the channels of the forward generations.Taking into consideration that the CFL thickness is regularly used to quantify theseparation efﬁciency and that there is no unique way to deﬁne it since the boundaryouter cell-plasma is diffuse [6], we propose a systematic method for measuring the CFLalong the channels based on the intensity proﬁles obtained after image processing.2 Materials and MethodsThe preliminary results presented in this paper were obtained from microfluidicexperiments using animal blood. The procedures for RBC sample preparation andPDMS device production are described below.Microfluidic geometry: The microfluidic geometry consists of a series of 180° bifur-cations, with each channel dividing into two equal daughter branches, followed by a90° bend. Figure 1 illustrates the schematics of the network, which is symmetric on y-axis and composed by 4 generations of channels (i = 0, 1, 2 and 3). This geometry wasoriginally designed to impose uniform average wall shear stress distribution along thenetwork to mimic the blood flow conditions found in healthy vessels. The channels’widths determined by a biomimetic design rule [1] are given in Table 1 for a constantdepth of *100 lm.Fig. 1. Schematics of the symetric microfluidic geometry used. The bifurcating network iscomposed of 4 generations (i = 0, 1, 2 and 3) and the fluid is pulled from the 4 outlets; blackarrows indicate the flow direction. To study the effect of the flow rate ratio, Q2 was varied in twoof the streams, while Q1 in the two remaining streams is kept constant.946 J. Fidalgo et al.The SU-8 mould and PDMS microchannels were produced using standard micro-fluidic technics (photo and soft-lithography, respectively) and the channels were sealedto a glass slide using oxygen plasma treatment (ZEPTO, Diener) for a faster andstronger surface bonding.Blood sample preparation: The blood sample was collected from healthy sheep andstored at 4 °C until further use. Anticoagulant ethylenediaminetetraacetic acid (EDTA)was used. All procedures for the collection of blood were carried out in compliancewith the Ethics Committee for Animal Experimentation of IPB (Bragança, Portugal).The sample was centrifuged at 2000 rpm for 15 min and the supernatant (includingplasma, white blood cells, platelets, proteins and other molecules) was discarded. Redblood cells (RBCs) were washed twice using physiological saline solution (NaCl 0.9%w/v) using the same centrifuging conditions. The cells were then suspended in Dextran40 (Dx40) according to the desired haematocrit (Hct) as deﬁned by:Hct ¼ VRBCV RBCþDx40ð Þð1ÞIn this work we used Hct of 1% and 5%.Experimental setup and image analysis: The microfluidic device was connected to 4glass syringes of 100 lL (SGE) using flexible tubbing and the flow rates Q1 and Q2were independently manipulated using a precise syringe pump (neMESYS, Cetoni)with 2 modules, one for each flow rate.The images and videos were obtained using an inverted microscope OLYMPUSCKX41 and a high-speed camera (i-speed LT, Olympus). Image analysis was per-formed using ImageJ open software, based on the Z–projection of the stack of frames,using different intensity criteria (median or standard deviation) depending of theparameters to be analysed. The projections using the median intensity were used forCFL measurement as well as to investigate the RBC distribution inside the channels byexamining the correspondent intensity proﬁles, as will be discussed further in theresults. Figure 2 presents typical images obtained during experiments and after imageprocessing. Initially, a stack of bright ﬁeld images from the region of interest isobtained (see Fig. 2a for bifurcation i = 0 ! 1, including branches 11 and 12). UsingImageJ, a median intensity projection using Z-project is created from a stack of at least80 images and the two regions of interest are deﬁned (Fig. 2b) and analysed in detailusing intensity proﬁles.Table 1. Channels dimensions.Dimension (lm) Channel generation (i)0 1 2 3Width, w 202.5 120.0 80.0 55.0Depth, h 97.2Red Blood Cells (RBCs) Visualisation 9473 Results and DiscussionFigure 3 presents the effect of flow rate ratio Q2/Q1, in the development and evolutionof a CFL downstream of the ﬁrst bifurcation of the network (for i = 0 ! 1). As it canbe observed, the CFL formed in the smaller flow rate channel (named channel 11)increases as a function of the flow rate ratio. This RBC distribution at the bifurcation isa result of the combination of the flow conditions imposed and the blood complexrheology. This behaviour has been well documented in previous blood flow studies indifferent bifurcation geometries [4, 7].Fig. 2. Typical bright ﬁeld microscopy image obtained in these experiments (a); image obtainedfrom the stack (80 frames) using the median intensity projection method (b); scale barcorresponds to 200 lm.Fig. 3. Effect of increasing flow rate ratio (Q2/Q1) on the CFL formed downstream of the ﬁrstbifurcation (i = 0 ! 1) for Hct = 5%, and Q2/Q1 of (a) 1, (b) 10, (c) 20, (d) 40. The black arrowrepresents the flow direction and the scale bar corresponds to 200 lm.948 J. Fidalgo et al.Due to the cell deformability, the velocity gradient across the channel width (as aresult of parabolic proﬁle in both y- and z-directions) and the effect of the wall, the cellstend to be displaced towards the centre of the channel [8, 9]. This particular effect isenhanced when conﬁnement becomes stronger; this is, with dimensions compared to afew RBC diameters. The cell migration has a strong effect on the suspension viscositysince the higher haematocrit at the center generates a larger bulk viscosity, while theregion depleted of RBC close to the walls (cell-free layer) has a smaller viscosity, actingas a lubricant for the remaining fluid [10, 11]. Even though this CFL is not easilydetectable in Fig. 2, it is present even along the main channel due to the small geometrydimensions (this can be seen clearly for 1% Hct using higher magniﬁcations, not shownhere due to space considerations). This effect of plasma skimming will influence the wayRBC divide when encountering a bifurcation. When the suspension of particles (RBCs)faces an asymmetric bifurcation, which in this case is determined by the unequal flowrates imposed in channels 11 (2  Q1) and 12 (2  Q2), the volume fraction of RBC islarger in the daughter branch with the larger flow rate (channel 12) than in the branchwith the lower flow rate (channel 11). This is the so called Zweifach-Fung effect whichis directly influenced by the cell distribution at the inlet [12]. We intend to explore thisphenomenon in our customised geometries for plasma separation, since each bifurcationallows the reduction of haematocrit and the formation of a large CFL in speciﬁcbranches along consecutive generations, as is demonstrated on Fig. 4.Providing accurate measurements of the thickness of the cell-free layer is always achallenge. First, there is no exclusive way to deﬁne the CFL as discussed by Kruger[6], since the threshold between the RBC region and the CFL is diffuse, which isfurther complicated by the fact that in experiments this diffuse layer is uneven. Here,we try to assess the CFL thickness using different methodologies and discuss its impacton the values measured. Figure 5 presents the CFL thickness measured on channel 11as a function of the flow rate ratio, for Hct = 1% and 5%. This measurement was madeby user inspection directly in the image obtained via the median intensity projectionmethod using the Z-project plugin from ImageJ. Qualitatively, it is clear that the CFLthickness is higher for lower Hct and that the size of the cell-free layer in channel 11Fig. 4. Real image of the channels on the latter generations (i = 2 and i = 3) corresponding tothe flow conditions presented in Fig. 2; 1% Hct; Q2/Q1 = 40; 200 lm scale bar.Red Blood Cells (RBCs) Visualisation 949increases as Q2/Q1 increases for the range of flow rate ratios considered. The right edgeof both curves seem to plateau for Q2/Q1 = 100 since after a certain flow rate ratio, thenumber of cells entering the smaller flow rate channel (11) continues to decrease, butthey tend to occupy a large section of the channel width. Because the cells are moredispersed, the CFL limit becomes highly diffuse and the measurement of its thickness,following this method, is not reliable anymore.Figure 6 presents the RBC diverging point for different flow rate ratios. ForQ2 = Q1, the cells divide in the position corresponding to the main channel centrelineand, as the flow rate ratio increases, the RBC diverging point displaces towards thechannel 11 (e.g. Q2/Q1 = 40). For low flow rate ratios the edge between the outermostRBCs and the CFL is relatively sharp (Fig. 6a), but for large flow rate ratios, the edgebecomes blurred (Fig. 6c) and the methodology is particularly user subjective.Fig. 5. Effect of the flow rate ratio (Q2/Q1) on the CFL thickness directly measured along thechannel 11 (generation i = 1) for Hct of 1% (squares) and 5% (circles) using a median intensityprojection method.Fig. 6. Evolution of RBC diverging point as a function of flow rate ratio Q2/Q1 of 1 (a), 10(b) and 40 (c) for sample 1% Hct. The images are a Z-Projection of the stack using the standarddeviation intensity; 200 lm scale bar.950 J. Fidalgo et al.In the second methodology adopted, we used the intensity proﬁles obtained fromthe Z-project to assess the CFL thickness. Figure 7a, b show the superposition of theprojection image and the intensity proﬁle obtained for channels 12 and 11, respec-tively. As it is easily observed, the projection for the channel with larger flow rate(12) is very homogeneous suggesting a similar distribution of cells along the channelwidth. For channel 11, however, there is a lateral peak close to the outer wall corre-sponding to the CFL. Several thresholds of intensity were considered to determine thelimit of the CFL. The ﬁnal CFL thickness would then correspond to the distance fromthe channel inner wall to the y-position where the intensity reaches the deﬁnedthreshold (expressed as % of variation of intensity between the maximum value and theaverage value within the centre of the bulk cell region). Table 2 shows the comparisonbetween the CFL measured directly in the image using the visual inspection procedure(Fig. 7b, dashed line) and the CFL determined using the intensity proﬁle consideringdifferent values for the threshold (100, 90, 80, 70, 60 and 50%), where 100% corre-sponds to the Dx40 solution flowing without cells.Fig. 7. Indirect measurement of CFL thickness using intensity proﬁles. Superposition of theprojection image and the corresponding intensity proﬁle for channels 12 and 11 (a and b,respectively) for 1% Hct and Q2/Q1 = 40. The colour symbols in part (b) identify the differentthresholds considered and the vertical dashed line corresponds to the CFL limit determined byvisual inspection.Table 2. Comparison between CFL thickness (lm) measured directly from visual inspection ofthe median intensity projection and using the intensity proﬁle for 1% Hct and Q2/Q1 = 40.Direct measurement fromZ-Projection image usingmedian intensityIndirect measurement using the intensity proﬁle(Z-Projection median intensity) considering differentpercentages of the maximum intensity as threshold20.04 lm % of maximumintensity50 60 70 80 90 100CFL (lm) 20.94 18.52 16.10 13.68 11.25 8.83Red Blood Cells (RBCs) Visualisation 951The value obtained by visual inspection (20.04 lm) is close to the value consid-ering the threshold of 50% using the intensity proﬁle. It is clear that there are still anumber of cells flowing inside the CFL and deciding which value of threshold is moremeaningful is still a subject of debate. The advantage in the case of this methodology isthat the CFL thickness would be systematically measured for all the cases, this is, forflow conditions for which the layer is well deﬁned and for the cases when the boundaryCFL-cell bulk is not perfectly clear. This would also avoid the personal subjectivitywhen deﬁning the CFL limits.4 ConclusionThe execution of preliminary experiments in bifurcating networks conﬁrmed thepotential of using this type of geometries for blood-plasma separation. Due to thebifurcation law, a CFL is easily formed in the ﬁrst bifurcation and observed along theconsecutive channels with smaller flow rates. In the present geometry with 4 genera-tions of narrowing channels, the relative thickness of the cell depleted layer increases inrelation to the total channel width, as the suspension moves along the network. Webelieve that by controlling the 4 outlets independently, for different ranges of total flowrate, overall separation efﬁciency could be enhanced. We have also demonstrated thatthe method for CFL measurement here proposed, based on the intensity proﬁles,establishes a reliable alternative to monitor the separation process and, at the same time,allows consistency between measurements under different flow conditions, avoidingpersonal subjectivity when deﬁning the limit between cell bulk and CFL.Acknowledgments. JF would like to thank Professor Graça Minas and her coworkers forproviding the laboratory facilities and technical help during the experiments.References1. Zografos, K., Barber, R.W., Emerson, D.R., Oliveira, M.S.N.: A design rule for constantdepth microfluidic networks for power-law fluids. Microfluid. Nanofluidics 19, 737–749(2015)2. Murray, C.D.: The physiological principle of minimum work. I. The vascular system and thecost of blood volume. Proc. Natl. Acad. Sci. USA 12, 207–14 (1926)3. Sharma, S., Zapatero-Rodríguez, J., Estrela, P., O’Kennedy, R.: Point-of-care diagnostics inlow resource settings: present status and future role of microfluidics. Biosensors 5(3), 577–601 (2015)4. Leble, V., et al.: Asymmetry of red blood cell motions in a microchannel with a divergingand converging bifurcation. Biomicrofluidics 5, 44120–44120-15 (2011)5. Tripathi, S., Kumar, Y.B.V., Prabhakar, A., Joshi, S.S., Agrawal, A.: Passive blood plasmaseparation at the microscale: a review of design principles and microdevices. J. Micromech.Microeng. 25(8), 083001 (2015)6. Krüger, T.: Effect of tube diameter and capillary number on platelet margination andnear-wall dynamics. Rheol. Acta 55(6), 511–526 (2016)952 J. Fidalgo et al.7. Yen, R.T., Fung, Y.C.: Effect of velocity distribution on red cell distribution in capillaryblood vessels. Am. Physiol. Soc. 235, 251–257 (1978)8. Di Carlo, D.: Inertial microfluidics. Lab. Chip. 9(21), 3038 (2009)9. Zhang, J., et al.: Fundamentals and applications of inertial microfluidics: a review. Lab.Chip. 16(1), 10–34 (201610. Popel, A.S., Johnson, P.C.: Microcirculation and Hemorheology. Annu. Rev. FluidMech.37, 43–69 (2005)11. Fedosov, D.A., Caswell, B., Popel, A.S., Karniadakis, G.E.M.: Blood flow and cell-freelayer in microvessels. Microcirculation 17(8), 615–628 (2010)12. Kumar, A., Graham, M.D.: Cell distribution and segregation phenomena during blood flow.In: Complex Fluids in Biological Systems, pp. 399–434. Springer, New York (2015)Red Blood Cells (RBCs) Visualisation 953

Red blood cells (RBCs) visualisation in bifurcations and bends

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Red blood cells (RBCs) visualisation in bifurcations and bends

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