This work was supported by the Natural Sciences and Engineering Research Council of Canada. Microfabrication was performed with the support of the McGill Nanotools Microfab facility at McGill University and the Department of Electronics at Carleton University.

Blood is collected from healthy individuals with the approval of the ethics committee of the University of Ottawa (H11-13-06).
1. Microchannel Fabrication
The microchannels are fabricated based on the standard photolithography methods19.
<ol>
	<li>Design the microchannel geometry using a Computer Aided Design (CAD) software and print the configuration on a transparency photo-mask. These masks are crucial since they dictate the light path during the fabrication process. In this case, the microchannel dimensions are 120 &#181;m in thickness, 60 &#181;m in depth and 7 mm in length.</li>
	<li>In a cleanroom, spin coat an epoxy based negative photo-resist on a silicon wafer at 2,000 rpm for 30 sec to obtain the desired microchannel depth (60 &#181;m).</li>
	<li>Expose the wafer and the photo-mask under a UV lamp at 650 mJ/cm2 for 70 sec. Ensure to expose only the transparent areas of the photo-mask to the UV-light. The exposure of the transparent areas allows for polymerization of the chains resulting in a hardening of the photo-resist. Immerse the silicon piece into a developer to remove excess of photo-resist.</li>
	<li>Once the mold is fabricated, mix a silicon based elastomer and a curing agent at a ratio of 10:1 to create a solution of poly-dimethyl-siloxane (PDMS). Put the PDMS solution in a vacuum chamber to degas. Pour it into the mold and heat it at 60 &#176;C for 90 min to create microchannels. Then bond the individual channels to a glass slide using oxygen plasma bonding for 90 sec. 
	NOTE: the PDMS degas process is performed in order to eliminate the air bubbles engendered by the mixing process.</li>
	<li>Ensure that the microchannel dimensions allow for successful testing. In this protocol, the microchannels have a rectangular cross-section of 120 x 60 &#181;m. In comparison, an average RBC has a diameter of 8 &#181;m and a thickness of 2 &#181;m. Therefore, the microchannel width is sufficient to observe proper aggregation and accurately determine the velocity profiles.</li>
</ol>
2. Blood Preparation
<ol>
	<li>Collect blood from healthy volunteer donors, after approval of an ethics committee. Treat the blood with ethylenediaminetetraacetic acid (EDTA) present in the collection tubes (7.2 mg EDTA in 4 ml of blood). 
	NOTE: The EDTA is used to avoid blood clotting.</li>
	<li>Centrifuge the blood samples three times for 10 min at 1,400 x g (3,000 rpm) in order to separate the blood constituents. As a result of the centrifugation, observe three distinct layers. 
	NOTE: The three layers are the RBC layer (located at the bottom of the tube), the buffy coat (consisting of white blood cells and platelets located on top of the RBC layer) and the plasma layer (located on top of all the other constituents).
	<ol>
		<li>After the first centrifugation, collect blood plasma using a blood pipette. Avoid any contact with the buffy coat.</li>
		<li>Collect and dispose of the buffy coat in a container with bleach diluted at 10%.</li>
		<li>Add 3 ml of PBS pH 7.4 to each of the tubes with the remaining RBC layer and ensure the proper balance of the centrifuge. Gently mix the tubes and centrifuge once again at 1,400 x g (3,000 rpm) for 10 min. 
		NOTE: Ensure that the PBS solution does not contain any Magnesium or Calcium since they could alter the cell adhesion.</li>
		<li>Dispose of the PBS in the tubes after the second centrifugation and remove the remaining buffy coat if present. Repeat steps 2.2.3 and 2.2.4 for the third centrifugation to yield clean RBCs.</li>
	</ol>
	</li>
	<li>Mix the required amount (0.05, 0.1 and 0.15 ml) of the clean RBCs with plasma (0.95, 0.9 and 0.85 ml) in a 1 ml tube in order to obtain RBC-suspensions with the corresponding (5%, 10% and 15%) hematocrit. Check hematocrits with a microcentrifuge.</li>
</ol>
3. Fluids Preparation
Introduce two fluids in the double Y-microchannel: RBC-suspensions and PBS.
<ol>
	<li>Add 60 &#181;l of the fluorescent tracer particles solution (1% solid, dparticle = 0.86 &#181;m, &#955;abs = 542 nm and &#955;emission = 612 nm) into the 1 ml RBC-suspension tubes. 
	NOTE: The fluorescent tracer particles (internally dyed polystyrene particles) are used to measure the velocity of the flow within the microchannel. These particles fluoresce when exposed to the corresponding wavelength of the laser beam (&#955; = 542 nm).</li>
	<li>Prepare a PBS pH 7.4 solution and add 60 &#181;l of the same fluorescent tracer particles solution in 1 ml of the PBS solution.</li>
</ol>
4. Aggregates Size Measurements
<ol>
	<li>To insert the fluids (RBC-suspensions and PBS) in the microchannel, use two glass syringes of 25 &#181;l and 100 &#181;l. This will help to reduce the compliance of the system. Connect the microchannel to the syringes via tubing and connectors fitting the entry holes. Fill the glass syringes using the pressure difference existing between the fluid container and the syringe. Ensure no bubbles are present in the system. As an alternative method, use a pressure controlled system to drive both fluids in the microchannel.</li>
	<li>Place the microchannel on an inverted microscope stage connected to a high speed camera and a &#181;PIV system. Program the syringe pump to the desired flow rates (e.g., Q = 2 &#181;l/hr for blood engendering a flowrate of Q = 8 &#181;l/hr for PBS). 
	NOTE: Only one syringe pump is used. Using two different glass syringes, with two different internal diameters, it is possible to vary to the flowrate entering each branch of the Y-microchannel.</li>
	<li>Insert both fluids in the double Y-microchannel each from a different entry branch and at different flowrates as shown in Figure 1. In order to test different shear rates, change the pump flowrate. This maintains the same appropriate ratio (4 in this case) between the two branches20 (Figures 2 and 3). Wait for the flow to reach steady state before acquiring the measurements: visually inspect the high speed camera images for a smooth flow. 
	NOTE: The ratio is crucial in obtaining a linear velocity profile within the blood layer.</li>
	<li>Visualize the RBCs using the high speed camera. Connect the camera to a computer to control, record and save the images of the fluid flow. Once the two fluid flows reach steady state, start recording.
	<ol>
		<li>Acquire several images for better processing results. Determine an optimal camera exposure time for a clear image of the aggregates. 
		NOTE: The camera exposure time for this case was 0.5 msec. The choice of time interval between each frame is based on the camera frame rate and the flow rate in the microchannel. The time between each frame processed in this procedure was 60 msec. Therefore a camera capable of recording 18 frames per seconds is sufficient. 
		NOTE: Avoid blood settling in the tube and syringe, which could lead to erroneous measurements.</li>
	</ol>
	</li>
	<li>Using image processing techniques (a MATLAB program in this case) to ameliorate the image quality, detect the aggregates in each of the images based on pixel intensities (illustrated in Figure 4) as follows:
	<ol>
		<li>Enhance the images contrast using the histogram equalizer method to obtain a better image quality for each frame.</li>
		<li>Convert the greyscale images into binary images. For this purpose, set a threshold value, ranging from 0 to 1, that dictates the conversion of each pixel. Any pixel in the image below the threshold value will be detected as a black pixel, while the pixels with values larger than the threshold will be detected as white pixels.</li>
		<li>Fill the holes (if present and necessary) corresponding to the gaps within one aggregate for better results.</li>
		<li>Detect the cells by determining neighboring white pixels in the binary image so that adjacent cells are associated as aggregates.</li>
		<li>Label the different aggregates detected and convert the binary image into a red-green-blue (RGB) image for better visualization. Combine the original frames with each of the corresponding processed images to verify the efficiency of the image processing technique and ensure that all the cells are taken into consideration, as shown in Figure 5. Perform steps 4.5.1, 4.5.2, 4.5.3, 4.5.4 and 4.5.5 for all the frames captured.</li>
		<li>Calculate the area of each aggregate detected in the frame based on the number of pixels detected. Based on the lens magnification used, calculate the conversion factor, using a calibration reticule, and convert the results to &#181;m2. Average the aggregate sizes detected in each frame and then average the results for all frames to obtain the average aggregate size for each recording of RBC-suspensions.</li>
		<li>Calculate the area of one RBC to determine a representative estimated number of RBCs within each detected aggregate. Using these results, calculate the distribution of the percentage of RBCs within each aggregate as a function of the aggregate sizes (represented by the estimated number of cells in each aggregate), as shown in Figure 6. 
		NOTE: To measure the RBC aggregates, ImageJ software could be used (instead of MATLAB) to perform the same tasks on each frame.</li>
	</ol>
	</li>
</ol>
5. Fluid Velocity and Shear Rate Measurements
Determine the fluid velocity and hence the shear rate using a &#181;PIV system.
<ol>
	<li>Once the data is acquired using the high speed camera, switch to the double pulsed camera used for the &#181;PIV system. Use an imaging software for image acquisition of the flow and the image processing for velocity field determination.</li>
	<li>Take all the precautions necessary, depending on the class of the laser, before turning on the laser. Then turn on the system, the camera and the laser.</li>
	<li>Calibrate the camera based on the lens magnification used. For this purpose, use a microscale of 10 &#181;m precision under the microscope and set the size of 1 pixel in the image.</li>
	<li>Start the laser and visualize the particles in both fluids. Find the middle plane of the microchannel by focusing on the fastest particles in the flow (i.e., the fastest particles should be the brightest).</li>
	<li>Set the dt (time interval between two consecutive frames) to ensure the proper displacement of the particle. The fastest particles should move about 5 to 10 pixels between both images.</li>
	<li>Start recording the flow. Set the software to acquire 100 pairs of images, 5 msec apart. Perform steps 5.4, 5.5 and 5.6 for all the different RBC-suspensions and for different shear rates. 
	NOTE: More details of the methodology are given in the study of Pitts and Fenech21.</li>
	<li>Process the recordings acquired using the cross-correlation method with the imaging software. 
	NOTE: This consists of discretizing the pair of images into small windows of predetermined size (based on the fluid flow and the time interval chosen by the user) called correlation windows and following the displacement of the particles in each window.
	<ol>
		<li>First, determine if the images acquired require pre-processing (including background subtraction, &#8220;base-clipping&#8221;22 or image overlapping23,24) for better data processing.</li>
		<li>Then choose the correlation window size and shape, as well of the percentage of images overlapping. A detailed study was performed by Pitts et al.25 to determine the optimal parameters and image pre-processing techniques for blood flow in different channel configurations. Express the results as an average velocity field calculated from the 100 image pairs and the Root Mean Square (RMS) error in velocity.</li>
	</ol>
	</li>
	<li>Extract a velocity profile at the channel outlet, from the processed data as shown in Figure 7. For a better profile, average the velocity field in space. 
	NOTE: The bars composing the velocity profile correspond to the correlation window size chosen relative to the channel width. The RMS error in velocity is also shown in Figure 7, representing the error in the experimental velocity estimation.</li>
	<li>Turn off the laser once all the measurements and processing are performed,. Shut down all the components of the system: software, cameras, moving stage, computers and laser.</li>
	<li>To calculate the shear rate, first detect and estimate the blood thickness in the microchannel based on the high speed camera recording. For this purpose, average all the frames in the specific recording to obtain a background image of the video. Delimit the blood layer (as shown in Figure 8 for the RBC-suspensions flowing at 10 &#181;l/hr when suspended at 5%, 10% and 15% hematocrit). Obtain the velocity value for the corresponding blood layer thickness and calculate the shear rate by dividing the velocity value by the blood layer thickness.</li>
</ol>

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The authors have nothing to disclose.

Using the present methodology, it is possible to analyze qualitatively and quantitatively the RBC aggregates under different flow conditions and hematocrits. For successful testing and aggregate detection, it is crucial to determine the appropriate velocity ratio between the two fluids at the microchannel entry. This ratio is very important to obtain an optimal blood layer thickness where the velocity profile is quasi-linear20.
Another key factor for successful testing is a good image quality. In fact, since the method is based on image processing, it is very important to have a contrast between the image background (the fluid inside the channel) and the particles to be detected. Here, as shown in Figures 2 and 3, the particles appear to be darker than the background, which helps in the aggregate detection. The most important parameter to consider for the image processing technique is the threshold value. Therefore, based on the contrast obtained it is crucial to choose the optimal threshold value where all the aggregates will be taken into consideration, as shown in Figure 5. The present image segmentation, used for this study, is widely used for cell detection and counting26- 28. If the quality of the image is not as desired and a global threshold value is not appropriate, one can use a different algorithm to determine the optimal threshold value such as Otsu&#8217;s method29 or an adaptive thresholding method (discretizing the image and obtaining a local threshold for each discretized window).
Another factor to take into account is the scale factor for a proper conversion from pixels to &#181;m.
Using the proper conversion factor, one can determine the diameter and thickness of one RBC that will serve to determine the aggregate size distribution (based on the estimated number of RBCs in each aggregate). Depending on the orientation of the aggregates, properly calculate the area on one RBC (frontal or side view). As mentioned in section 4 (step 4.4.1), it is important to determine the proper camera exposure time and the frame rate to ensure that the dynamic properties of the aggregates are captured between each consecutive frame. This value is based on the flow rate used when acquiring the measurements. Several other factors need to be taken into consideration when acquiring results using the &#181;PIV system and are discussed clearly in the study of Pitts and Fenech21.
The methodology was tested successfully on several RBC-suspensions flowing at different hematocrit. However, due to the limitation of the image processing technique and the lack of contrast between the image background and the aggregates, it is difficult to detect the RBC aggregates for higher hematocrits (from 20% to 45%). Indeed as mentioned previously, the image quality is crucial for this methodology.
Using this protocol, the RBC aggregate sizes can be measured in a controlled microfluidic device and hence it is possible to obtain information on RBC aggregation dynamically in microcirculation and obtain the RBC aggregate sizes for a range of physiological shear rates. It is also possible to complement numerical and experimental studies of blood rheology and relate the aggregate sizes and behavior to clinical and pathological studies.

An erratum was issue for <a href="https://www-jove-com.remotexs.ntu.edu.sg/video/52719">Controlled Microfluidic Environment for Dynamic Investigation of Red Blood Cell Aggregation</a>. The introduction section was updated.

An erratum was issued for <a href="https://www-jove-com.remotexs.ntu.edu.sg/video/52719">Controlled Microfluidic Environment for Dynamic Investigation of Red Blood Cell Aggregation</a>. The introduction section was updated.

Erratum: Controlled Microfluidic Environment for Dynamic Investigation of Red Blood Cell Aggregation

Red Blood Cells (RBCs) play a crucial role in determining the rheological behavior of blood. They are almost singularly responsible for the particular properties of blood in vitro and in vivo. Under physiological conditions, RBCs occupy 40% to 45% of the blood volume. In microcirculation, RBCs only occupy up to 20% of the blood volume due to smaller vessel diameters and the plasma skimming effect1. This phenomenon of plasma reduction in microcirculation is known as the F&#229;hr&#230;us effect. At low shear rates, RBCs are able to bridge together and form one dimensional or three dimensional structures called &#8220;rouleaux&#8221; or aggregates, hence contributing to the non-Newtonian behavior of blood. However, the mechanism of RBC aggregation is not completely understood. Two theories exist to model the aggregation of RBCs: the bridging of cells theory due to the cross-linking of the macromolecules2 and the force attraction theory caused by depletion of the molecules due to the osmotic gradient3.
Typically, for human blood, aggregates form at very low shear rates4 ranging from 1 to 10 sec-1. Above this range, RBCs tend to disaggregate and flow separately within the vessel.
Understanding the conditions of the aggregates formation is of a great importance to the hemorheology field in terms of defining blood&#8217;s rheological behavior. These aggregates are often seen at the macrocirculation level (&#62;300 &#181;m diameter)5. At this scale, blood is considered as a Newtonian fluid and a homogenous mixture. However, these aggregates are rarely seen in the capillary level (4-10 &#181;m in diameter) and are usually an indication of pathological conditions such as diabetes6 and obesity. Other pathological conditions that could change RBC aggregation include inflammatory or infectious conditions, cardiovascular diseases such as hypertension or atherosclerosis, genetic disorders and chronic diseases7. Therefore, understanding the RBC aggregation mechanism and analyzing these entities (by defining a relationship between the size of these aggregates and the flow conditions) could lead to the understanding of the microrheological behavior of blood and hence relate it to clinical applications.
RBC aggregates can be altered by several factors such as the hematocrit (volume of RBCs in blood), the shear rate, the vessel diameter, the RBC membrane stiffness and the suspending medium composition8-10. Therefore, controlled conditions are required in order to effectively analyze the RBC aggregates. Several methods are able to analyze aggregate formation by providing static aggregation measurements (aggregation index) that offers relevant information about blood behavior. These methods include, inter alia, the erythrocyte sedimentation rate method11, the light transmission method12, the light reflection method13 and the low shear viscosity method14.
Few studies have attempted to study RBC aggregation and determine the degree of aggregation in controlled flow conditions15-17. However, these studies indirectly investigate RBC aggregate sizes by determining the ratio of the occupied space in a shearing system measured based on microscopic blood images providing information on the degree of aggregation as well as the local viscosity. Chen et al.18 presented a direct measurement technique for RBC aggregate sizes and provided RBC aggregate size distribution for different shear stresses by varying the flowrate of the suspensions while monitoring the pressure drop across a flow chamber. The shear stresses are calculated based on the monitored pressure using Stokes equation18.
We therefore present a new procedure to directly quantify RBC aggregates in a controlled microfluidic environment, dynamically, under specific, constant and measurable shear rates. The blood flow in the shear system is directly observed (perpendicularly to the flow direction), providing a different angle on flow investigation compared to previous studies15,18 and a visualization of the full domain of interest. RBC-suspensions are entrained, in a double Y-microchannel (as illustrated in Figure 1), with a Phosphate Buffered Saline (PBS) solution hence creating a shear flow in the blood layer. Within this blood layer a constant shear rate can be obtained. The RBC-suspensions are tested at different hematocrit (H) levels (5%, 10% and 15%) and under different shear rates (2-11 sec-1). The blood velocity and shear rate are determined using a micro Particle Image Velocimetry (&#181;PIV) system while the flow is visualized using a high speed camera. The results obtained are then processed with a MATLAB code based on the image intensities in order to detect the RBCs and determine aggregate sizes.

<table><tbody><tr><td>SU8-50 epoxy based negative Photo-resist</td><td>MicroChem Corp.</td><td></td><td></td></tr><tr><td>SU8-50 developer</td><td>MicroChem Corp.</td><td></td><td></td></tr><tr><td>Poly(dimethylsiloxane) (PDMS) Sylgard-184</td><td>Dow-Corning</td><td>3097358-1004</td><td></td></tr><tr><td>PE-50 series Plasma system&amp;nbsp;</td><td>Plasma Etch</td><td>PE-50 series</td><td></td></tr><tr><td>Blood collection tubes with K&lt;sub&gt;2&lt;/sub&gt;-EDTA (ethylenediaminetetraacetic acid)</td><td>FisherSci</td><td>B367861</td><td></td></tr><tr><td>Centrifuge, &lt;em&gt;i.e.&lt;/em&gt; Thermo Scientific CL2</td><td>Thermo Scientific</td><td>004260F</td><td></td></tr><tr><td>Poshpate buffered saline (PBS)</td><td>Sigma Aldrich</td><td>P5368-10PAK</td><td></td></tr><tr><td>Tracer fluorescent particles solution (15 ml)</td><td>FisherSci</td><td>R800</td><td></td></tr><tr><td>Aggregometer</td><td>RheoMeditech</td><td>Rheo Scan AnD300</td><td></td></tr><tr><td>Glass syringes (50 &amp;micro;l)</td><td>Hamilton</td><td>80965</td><td></td></tr><tr><td>Tubing (Tygon)</td><td>FisherSci</td><td>AAA00001</td><td></td></tr><tr><td>High speed camera (Basler)</td><td>Graftek Imaging Inc.</td><td>basler acA2000-340km</td><td>A camera capable of recording 18 frames per second could be used.</td></tr><tr><td>Double pulsed camera&amp;nbsp;</td><td>LaVision</td><td>Imager Intense</td><td></td></tr><tr><td>Microscope MITAS</td><td>LaVision</td><td>MITAS</td><td></td></tr><tr><td>Nd:YAG laser</td><td>New Wave Research</td><td>Solo-II</td><td></td></tr><tr><td>Syringe pump (Nexus3000 and PicoPlus)</td><td>Chemyx Inc. and Harvard Apparatus</td><td>Nexus3000 and PicoPlus</td><td></td></tr><tr><td>DaVis software</td><td>LaVision</td><td>Davis</td><td></td></tr></tbody></table>

controlled microfluidic environment for dynamic investigation of red blood cell aggregation

Blood, as a non-Newtonian biofluid, represents the focus of numerous studies in the hemorheology field. Blood constituents include red blood cells, white blood cells and platelets that are suspended in blood plasma. Due to the abundance of the RBCs (40% to 45% of the blood volume), their behavior dictates the rheological behavior of blood especially in the microcirculation. At very low shear rates, RBCs are seen to assemble and form entities called aggregates, which causes the non-Newtonian behavior of blood. It is important to understand the conditions of the aggregates formation to comprehend the blood rheology in microcirculation. The protocol described here details the experimental procedure to determine quantitatively the RBC aggregates in microcirculation under constant shear rate, based on image processing. For this purpose, RBC-suspensions are tested and analyzed in 120 x 60 &#181;m poly-dimethyl-siloxane (PDMS) microchannels. The RBC-suspensions are entrained using a second fluid in order to obtain a linear velocity profile within the blood layer and thus achieve a wide range of constant shear rates. The shear rate is determined using a micro Particle Image Velocimetry (&#181;PIV) system, while RBC aggregates are visualized using a high speed camera. The videos captured of the RBC aggregates are analyzed using image processing techniques in order to determine the aggregate sizes based on the images intensities.

An example of the two-fluid flow in the double Y-microchannel is shown in Figure 2 for human RBCs suspended at 5%, 10% and 15% hematocrit and flowing at 10 &#181;l/hr. Figure 3 shows the difference in aggregate sizes when the flow in the channel is reduced from 10 &#181;l/hr to 5 &#181;l/hr for a hematocrit of 10%. This gives a qualitative notion of the sizes of the aggregates when varying the hematocrit and shear rate. Figure 5 follows the displacement of four human RBC aggregates, for three consecutive frames, providing a qualitative measure of the camera frame rate required and qualitative notion of the aggregates distribution within each frame. In Figure 5, the small aggregates (with 8 or less estimated RBCs) are shown in blue, while medium aggregates (ranging from 9 to 30 estimated RBCs) and large aggregates (greater than 30 estimated RBCs) are shown in green and red respectively.
The velocity profiles of the different RBC-suspensions in the channel are displayed in Figure 7, where the red, blue and green curves represent the velocity profiles of the RBCs suspended at 5%, 10% and 15% hematocrit respectively. The RMS errors of the velocity, also displayed in Figure 7 for each RBC-suspension, are relatively small compared to the velocity values, indicating the precision of the velocity measurements, and hence shear rates. The interface locations are denoted as &#8216;E&#8217; and shown as the solid lines in the same figure.
The corresponding shear rates for the different RBC-suspensions (based on the velocity profiles and the blood layer thickness) are shown in Table 1. The average aggregate sizes determined for each of the RBC-suspensions, based on the image processing method to detect the area of the RBC aggregates, are shown in Figure 9, as a function of the corresponding shear rate. An example of the distribution of the percentage of RBCs within each aggregate is shown in Figure 6. The aggregate sizes are represented as an estimated number of RBC in the aggregates.
<img alt="Figure 1" src="/files/ftp_upload/52719/52719fig1.jpg" /> 
Figure 1. Double Y-microchannel configuration and entry fluids. Blood enters the first branch at Q = 2 &#181;l/hr while PBS enters the second branch at Q = 8 &#181;l/hr. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/52719/52719fig1highres.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" src="/files/ftp_upload/52719/52719fig2.jpg" /> 
Figure 2. Human RBC-suspension at different hematocrit. The figure represents captured frames of the human RBC-suspensions flowing at Q = 10 &#181;l/hr at (A) 5% (B) 10% and (C) 15% hematocrit. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/52719/52719fig2highres.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" src="/files/ftp_upload/52719/52719fig3.jpg" /> 
Figure 3. Human RBC-suspension at different flowrates. The figure represents captured frames of the human RBCs suspended at 10% hematocrit H flowing (A) Q = 10 &#181;l/hr and (B) Q = 5 &#181;l/hr. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/52719/52719fig3highres.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" src="/files/ftp_upload/52719/52719fig4.jpg" /> 
Figure 4. Flow chart of the image processing program used for aggregate detection. The steps shown describe the basic methodology used. The quality of the image is enhanced to be converted to a binary image. The aggregates are detected and labeled based on their respective sizes. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/52719/52719fig4highres.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" src="/files/ftp_upload/52719/52719fig5.jpg" /> 
Figure 5. RGB coloring and net motion of the several human RBC aggregates for three consecutive frames. The figure shows the net motion of four aggregates detected in three consecutive frames at (A) t = 0 msec, (B) t = 60 msec and (C) t = 120 msec. The large aggregates (&#62; 30 estimated RBCs), medium aggregates (9-30 estimated RBCs) and small aggregates (&#60;8 estimated RBCs) are shown in red, green and blue respectively. Each of the aggregates detected are marked with a black circle. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/52719/52719fig5highres.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" src="/files/ftp_upload/52719/52719fig6.jpg" /> 
Figure 6. RBC aggregate size distribution for 10%H RBC-suspension for different flow rates. Aggregate size distribution for blood samples suspended at 10% H, flowing at Q = 10 and 5 &#181;l/hr. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/52719/52719fig6highres.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 7" src="/files/ftp_upload/52719/52719fig7.jpg" /> 
Figure 7. Velocity profile comparison for different hematocrit. The velocity profiles are shown for RBCs in plasma suspended at 5% H (Red), 10% H (blue), 15%H (green) and simulation20 (solid line) for the 120 x 60 &#181;m double Y-microchannel with a Q = 10 &#181;l/hr. The interface location is denoted by E for the experiments. The corresponding RMS errors of the different velocity profiles are displayed. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/52719/52719fig7highres.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 8" src="/files/ftp_upload/52719/52719fig8.jpg" /> 
Figure 8. Image background for the different RBC-suspensions and delimitation of the blood layer thickness. The figure shows the delimitation of the blood layer thickness of the RBC-suspensions flowing at 10 &#181;l/hr suspended at (A) 5%, (B) 10% and (C) 15% H. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/52719/52719fig8highres.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 9" src="/files/ftp_upload/52719/52719fig9.jpg" /> 
Figure 9. Average aggregate sizes as a function of the corresponding shear rates. The results are obtained for the different RBC-suspensions flowing at Q = 10 &#181;l/hr at 5% (A), 10% (B) and 15% H (C). <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/52719/52719fig9highres.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="0" cellpadding="0" cellspacing="0">
	<tbody>
		<tr>
			<td>Hematocrit</td>
			<td>Flow rate (&#181;l/hr) </td>
			<td>Shear rate (sec-1)</td>
		</tr>
		<tr>
			<td>5%</td>
			<td>10</td>
			<td>11.02</td>
		</tr>
		<tr>
			<td>5%</td>
			<td>5</td>
			<td>5.36</td>
		</tr>
		<tr>
			<td>10%</td>
			<td>10</td>
			<td>8.17</td>
		</tr>
		<tr>
			<td>10%</td>
			<td>5</td>
			<td>4.47</td>
		</tr>
		<tr>
			<td>15%</td>
			<td>10</td>
			<td>7.41</td>
		</tr>
		<tr>
			<td>15%</td>
			<td>5</td>
			<td>2.51</td>
		</tr>
	</tbody>
</table>
Table 1. Shear rate values for different blood flow cases. The shear rate values are obtained using the &#181;PIV data and image processing results for different RBC-suspensions with 5%, 10% and 15% H, flowing at Q = 10 &#181;l/hr and Q = 5 &#181;l/hr.

Watch this Scientific Journal Video about Controlled Microfluidic Environment for Dynamic Investigation of Red Blood Cell Aggregation at JoVE.com

Controlled Microfluidic Environment for Dynamic Investigation of Red Blood Cell Aggregation

The protocol described details an experimental procedure to quantify Red Blood Cell (RBC) aggregates under a controlled and constant shear rate, based on image processing techniques. The goal of this protocol is to relate RBC aggregate sizes to the corresponding shear rate in a controlled microfluidic environment.

Blood, as a non-Newtonian biofluid, represents the focus of numerous studies in the hemorheology field. Blood constituents include red blood cells, white ...

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Nanyang Technological University

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Bioengineering

11.7K Views.  University of Ottawa. The protocol described details an experimental procedure to quantify Red Blood Cell (RBC) aggregates under a controlled and constant shear rate, based on image processing techniques. The goal of this protocol is to relate RBC aggregate sizes to the corresponding shear rate in a controlled microfluidic environment.

Video: Controlled Microfluidic Environment for Dynamic Investigation of Red Blood Cell Aggregation

Endothelialized Microfluidics for Studying Microvascular Interactions in Hematologic Diseases

Advances in microfabrication techniques have enabled the production of inexpensive and reproducible microfluidic systems for conducting biological and biochemical experiments at the micro- and nanoscales 1,2. In addition, microfluidics have also been specifically used to quantitatively analyze hematologic and microvascular processes, because of their ability to easily control the dynamic fluidic environment and biological conditions3-6. As such, researchers have more recently used microfluidic systems to study blood cell deformability, blood cell aggregation, microvascular blood flow, and blood cell-endothelial cell interactions6-13.However, these microfluidic systems either did not include cultured endothelial cells or were larger than the sizescale relevant to microvascular pathologic processes. A microfluidic platform with cultured endothelial cells that accurately recapitulates the cellular, physical, and hemodynamic environment of the microcirculation is needed to further our understanding of the underlying biophysical pathophysiology of hematologic diseases that involve the microvasculature.
Here, we report a method to create an "endothelialized" in vitro model of the microvasculature, using a simple, single mask microfabrication process in conjunction with standard endothelial cell culture techniques, to study pathologic biophysical microvascular interactions that occur in hematologic disease. This "microvasculature-on-a-chip" provides the researcher with a robust assay that tightly controls biological as well as biophysical conditions and is operated using a standard syringe pump and brightfield/fluorescence microscopy. Parameters such as microcirculatory hemodynamic conditions, endothelial cell type, blood cell type(s) and concentration(s), drug/inhibitory concentration etc., can all be easily controlled. As such, our microsystem provides a method to quantitatively investigate disease processes in which microvascular flow is impaired due to alterations in cell adhesion, aggregation, and deformability, a capability unavailable with existing assays.

A method to culture an endothelial cell monolayer throughout the entire inner 3D surface of a microfluidic device with microvascular-sized channels (&lt;30 &mu;m) is described. This in vitro microvasculature model enables the study of biophysical interactions between blood cells, endothelial cells, and soluble factors in hematologic diseases.

Advances in  microfabrication techniques have enabled the production of inexpensive and  reproducible microfluidic systems for conducting biological and ...

Micro-particle Image Velocimetry for Velocity Profile Measurements of Micro Blood Flows

Micro-particle image velocimetry (&mu;PIV) is used to visualize paired images of micro particles seeded in blood flows. The images are cross-correlated to give an accurate velocity profile. A protocol is presented for &mu;PIV measurements of blood flows in microchannels. At the scale of the microcirculation, blood cannot be considered a homogeneous fluid, as it is a suspension of flexible particles suspended in plasma, a Newtonian fluid. Shear rate, maximum velocity, velocity profile shape, and flow rate can be derived from these measurements. Several key parameters such as focal depth, particle concentration, and system compliance, are presented in order to ensure accurate, useful data along with examples and representative results for various hematocrits and flow conditions.

Micro-particle image velocimetry (&mu;PIV) is used to visualize paired images of micro particles seeded in blood flows which are cross-correlated to give an accurate velocity profile. Shear rate, maximum velocity, velocity profile shape, and flow rate, each of which has clinical applications, can be derived from these measurements.

Micro-particle image velocimetry (&mu;PIV) is used to visualize paired images of micro particles seeded in blood flows. The images are cross-correlated to ...

Microfluidic Platform for Measuring Neutrophil Chemotaxis from Unprocessed Whole Blood

Neutrophils play an essential role in protection against infections and their numbers in the blood are frequently measured in the clinic. Higher neutrophil counts in the blood are usually an indicator of ongoing infections, while low neutrophil counts are a warning sign for higher risks for infections. To accomplish their functions, neutrophils also have to be able to move effectively from the blood where they spend most of their life, into tissues, where infections occur. Consequently, any defects in the ability of neutrophils to migrate can increase the risks for infections, even when neutrophils are present in appropriate numbers in the blood. However, measuring neutrophil migration ability in the clinic is a challenging task, which is time consuming, requires large volume of blood, and expert knowledge. To address these limitations, we designed a robust microfluidic assays for neutrophil migration, which requires a single droplet of unprocessed blood, circumvents the need for neutrophil separation, and is easy to quantify on a simple microscope. In this assay, neutrophils migrate directly from the blood droplet, through small channels, towards the source of chemoattractant. To prevent the granular flow of red blood cells through the same channels, we implemented mechanical filters with right angle turns that selectively block the advance of red blood cells. We validated the assay by comparing neutrophil migration from blood droplets collected from finger prick and venous blood. We also compared these whole blood (WB) sources with neutrophil migration from samples of purified neutrophils and found consistent speed and directionality between the three sources. This microfluidic platform will enable the study of human neutrophil migration in the clinic and the research setting to help advance our understanding of neutrophil functions in health and disease.

This protocol details an assay designed to measure human neutrophil chemotaxis from one droplet of whole blood with robust reproducibility. This approach circumvents the need for neutrophil separation and requires only a few minutes of assay preparation time. The microfluidic chip enables the repeated measure of neutrophil chemotaxis over time in infants or small mammals, where sample volume is limited.

Neutrophils play an essential role in protection against infections and their numbers in the blood are frequently measured in the clinic. Higher ...

A Microfluidic Technique to Probe Cell Deformability

Here we detail the design, fabrication, and use of a microfluidic device to evaluate the deformability of a large number of individual cells in an efficient manner. Typically, data for ~102 cells can be acquired within a 1 hr experiment. An automated image analysis program enables efficient post-experiment analysis of image data, enabling processing to be complete within a few hours. Our device geometry is unique in that cells must deform through a series of micron-scale constrictions, thereby enabling the initial deformation and time-dependent relaxation of individual cells to be assayed. The applicability of this method to human promyelocytic leukemia (HL-60) cells is demonstrated. Driving cells to deform through micron-scale constrictions using pressure-driven flow, we observe that human promyelocytic (HL-60) cells momentarily occlude the first constriction for a median time of 9.3 msec before passaging more quickly through the subsequent constrictions with a median transit time of 4.0 msec per constriction. By contrast, all-trans retinoic acid-treated (neutrophil-type) HL-60 cells occlude the first constriction for only 4.3 msec before passaging through the subsequent constrictions with a median transit time of 3.3 msec. This method can provide insight into the viscoelastic nature of cells, and ultimately reveal the molecular origins of this behavior.

We demonstrate a microfluidics-based assay to measure the timescale for cells to transit through a sequence of micron-scale constrictions.

Here we detail the design, fabrication, and use of a microfluidic device to evaluate the deformability of a large number of individual cells in an ...

Platelet Adhesion and Aggregation Under Flow using Microfluidic Flow Cells

Platelet aggregation occurs in response to vascular injury where the extracellular matrix below the endothelium has been exposed. The platelet adhesion cascade takes place in the presence of shear flow, a factor not accounted for in conventional (static) well-plate assays. This article reports on a platelet-aggregation assay utilizing a microfluidic well-plate format to emulate physiological shear flow conditions. Extracellular proteins, collagen I or von Willebrand factor are deposited within the microfluidic channel using active perfusion with a pneumatic pump. The matrix proteins are then washed with buffer and blocked to prepare the microfluidic channel for platelet interactions. Whole blood labeled with fluorescent dye is perfused through the channel at various flow rates in order to achieve platelet activation and aggregation. Inhibitors of platelet aggregation can be added prior to the flow cell experiment to generate IC50 dose response data.

The platelet adhesion cascade takes place in the presence of shear flow, a factor not accounted for in conventional (static) well-plate assays. This article reports on a platelet-aggregation assay utilizing a microfluidic well-plate format to emulate physiological shear flow conditions.

Platelet aggregation occurs in response to vascular injury where the extracellular matrix below the endothelium has been exposed. The platelet adhesion ...

Biology

Microfluidic Flow Chambers Using Reconstituted Blood to Model Hemostasis and Platelet Transfusion In Vitro

Blood platelets prepared for transfusion gradually lose hemostatic function during storage. Platelet function can be investigated using a variety of (indirect) in vitro experiments, but none of these is as comprehensive as microfluidic flow chambers. In this protocol, the reconstitution of thrombocytopenic fresh blood with stored blood bank platelets is used to simulate platelet transfusion. Next, the reconstituted sample is perfused in microfluidic flow chambers which mimic hemostasis on exposed subendothelial matrix proteins. Effects of blood donation, transport, component separation, storage and pathogen inactivation can be measured in paired experimental designs. This allows reliable comparison of the impact every manipulation in blood component preparation has on hemostasis. Our results demonstrate the impact of temperature cycling, shear rates, platelet concentration and storage duration on platelet function. In conclusion, this protocol analyzes the function of blood bank platelets and this ultimately aids in optimization of the processing chain including phlebotomy, transport, component preparation, storage and transfusion.

Microfluidic Flow Chambers Using Reconstituted Blood to Model Hemostasis and Platelet Transfusion In Vitro

Platelet transfusion and hemostasis was modeled using blood reconstitution and microfluidic flow chambers to investigate the function of blood banking platelets. The data demonstrate the consequences of platelet storage lesion on hemostasis, in vitro.

Blood platelets prepared for transfusion gradually lose hemostatic function during storage. Platelet function can be investigated using a variety of ...

A Microfluidic Flow Chamber Model for Platelet Transfusion and Hemostasis Measures Platelet Deposition and Fibrin Formation in Real-time

Microfluidic models of hemostasis assess platelet function under conditions of hydrodynamic shear, but in the presence of anticoagulants, this analysis is restricted to platelet deposition only. The intricate relationship between Ca2+-dependent coagulation and platelet function requires careful and controlled recalcification of blood prior to analysis. Our setup uses a Y-shaped mixing channel, which supplies concentrated Ca2+/Mg2+ buffer to flowing blood just prior to perfusion, enabling rapid recalcification without sample stasis. A ten-fold difference in flow velocity between both reservoirs minimizes dilution. The recalcified blood is then perfused in a collagen-coated analysis chamber, and differential labeling permits real-time imaging of both platelet and fibrin deposition using fluorescence video microscopy. The system uses only commercially available tools, increasing the chances of standardization. Reconstitution of thrombocytopenic blood with platelets from banked concentrates furthermore models platelet transfusion, proving its use in this research domain. Exemplary data demonstrated that coagulation onset and fibrin deposition were linearly dependent on the platelet concentration, confirming the relationship between primary and secondary hemostasis in our model. In a timeframe of 16 perfusion min, contact activation did not take place, despite recalcification to normal Ca2+ and Mg2+ levels. When coagulation factor XIIa was inhibited by corn trypsin inhibitor, this time frame was even longer, indicating a considerable dynamic range in which the changes in the procoagulant nature of the platelets can be assessed. Co-immobilization of tissue factor with collagen significantly reduced the time to onset of coagulation, but not its rate. The option to study the tissue factor and/or the contact pathway increases the versatility and utility of the assay.

This paper describes an experimental model of hemostasis that simultaneously measures platelet function and coagulation. Platelet and fibrin fluorescence is measured in real-time, and platelet adhesion rate, coagulation rate, and onset of coagulation are determined. The model is used to determine platelet procoagulant properties under flow in concentrates for transfusion.

Microfluidic models of hemostasis assess platelet function under conditions of hydrodynamic shear, but in the presence of anticoagulants, this analysis is ...

Traction Microscopy Integrated with Microfluidics for Chemotactic Collective Migration

Cells change migration patterns in response to chemical stimuli, including the gradients of the stimuli. Cellular migration in the direction of a chemical gradient, known as chemotaxis, plays an important role in development, the immune response, wound healing, and cancer metastasis. While chemotaxis modulates the migration of single cells as well as collections of cells in vivo, in vitro research focuses on single-cell chemotaxis, partly due to the lack of the proper experimental tools. To fill that gap, described here is a unique experimental system that combines microfluidics and micropatterning to demonstrate the effects of chemical gradients on collective cell migration. Furthermore, traction microscopy and monolayer stress microscopy are incorporated into the system to characterize changes in cellular force on the substrate as well as between neighboring cells. As proof-of-concept, the migration of micropatterned circular islands of Madin-Darby canine kidney (MDCK) cells is tested under a gradient of hepatocyte growth factor (HGF), a known scattering factor. It is found that cells located near the higher concentration of HGF migrate faster than those on the opposite side within a cell island. Within the same island, cellular traction is similar on both sides, but intercellular stress is much lower on the side of higher HGF concentration. This novel experimental system can provide new opportunities to studying the mechanics of chemotactic migration by cellular collectives.

Collective cell migration in development, wound healing, and cancer metastasis is often guided by the gradients of growth factors or signaling molecules. Described here is an experimental system combining traction microscopy with a microfluidic system and a demonstration of how to quantify the mechanics of collective migration under biochemical gradient.

Cells change migration patterns in response to chemical stimuli, including the gradients of the stimuli. Cellular migration in the direction of a chemical ...

Quantitative Analysis of Microbial Autoaggregation Using Imaging Flow Cytometry

Imaging Flow Cytometry to Study Microbial Autoaggregation

Beneficial and probiotic bacteria play essential roles in their hosts, providing various health benefits, including immunity to infectious diseases. The Lactobacillaceae family consists of Gram-positive bacteria with confirmed probiotic properties. This study utilizes Lactobacillaceae species as a model to demonstrate the effectiveness of single-cell high throughput analysis in studying cellular aggregation. The focus is on analyzing the response of these beneficial species to simple carbohydrates from the diet.
The study showcases how Imaging Flow Cytometry (IFC) can overcome the fundamental differences in the assembly of probiotic bacteria in the presence and absence of carbohydrates. IFC combines the power and speed of conventional flow cytometry with the spatial resolution of microscopy, enabling high-rate complex morphometric measurements in a phenotypically defined manner across a library of beneficial bacterial strains and conditions. This protocol provides insights into the autoaggregation of Lactobacillaceae species and sheds light on their response to dietary carbohydrates, contributing to understanding the mechanisms behind the beneficial effects of these probiotic bacteria.

Author Spotlight: Advancing Research in Microbial Autoaggregation Using Imaging Flow Cytometry

This protocol describes a quantitative approach to measure microbial autoaggregation using imaging flow cytometry.

Beneficial and probiotic bacteria play essential roles in their hosts, providing various health benefits, including immunity to infectious diseases. The ...

Single-Cell Mechanobiology Using Fluorescence Micropipette Aspiration

Fluorescence Micropipette Aspiration Assay to Investigate Red Blood Cell Mechanosensing

Micropipette aspiration assays have long been a cornerstone for the investigation of live-cell mechanics, offering insights into cellular responses to mechanical stress. This paper details an innovative adaptation of the fluorescence-coupled micropipette aspiration (fMPA) assay. The fMPA assay introduces the capability to administer precise mechanical forces while concurrently monitoring the live-cell mechanotransduction processes mediated by ion channels. The sophisticated setup incorporates a precision-engineered borosilicate glass micropipette connected to a finely regulated water reservoir and pneumatic aspiration system, facilitating controlled pressure application with increments as refined as &#177; 1 mmHg. A significant enhancement is the integration of epi-fluorescence imaging, allowing for the simultaneous observation and quantification of cell morphological changes and intracellular calcium fluxes during aspiration. The fMPA assay, through its synergistic combination of epi-fluorescence imaging with micropipette aspiration, sets a new standard for the study of cell mechanosensing within mechanically challenging environments. This multifaceted approach is adaptable to various experimental setups, providing critical insights into the single-cell mechanosensing mechanisms.

Author Spotlight: Advancing Live-Cell Mechanobiology Through Fluorescence Microaspiration

The exploration of cellular behavior under mechanical stress is pivotal for advances in cellular mechanics and mechanobiology. We introduce the Fluorescence Micropipette Aspiration (fMPA) technique, a novel method combining controlled mechanical stimulation with comprehensive analysis of intracellular signaling in single cells. This technique investigates new in-depth studies of live-cell mechanobiology.

Micropipette aspiration assays have long been a cornerstone for the investigation of live-cell mechanics, offering insights into cellular responses to ...

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Fluorescence Micropipette Aspiration Assay to Investigate Red Blood Cell Mechanosensing