S. H.
Holm
a,
J. P.
Beech
a,
M. P.
Barrett
b and
J. O.
Tegenfeldt
*a
aDivision of Solid State Physics, NanoLund, Lund University, PO Box 118, S-221 00 Lund, Sweden. E-mail: jonas.tegenfeldt@ftf.lth.se; Fax: +46 46 222 3637; Tel: +46 46 222 8063
bWelcome Trust Centre for Medical Parasitology, Institute of Infection, Immunity and Inflammation, College of Medical, Veterinary and Life Sciences, University of Glasgow, UK
First published on 8th March 2016
With our sights set on a simple and inexpensive diagnostics device based on extraction and enrichment of parasites from human blood, we present a device design that relies on a combination of multiple different deterministic lateral displacement arrays. Our end goal is a microfluidic device that will be easy to use in the rural, resource-deprived areas where simple-to-use medical tools are crucially needed for rapid and accurate diagnosis. Here, we exemplify this in the application of blood parasite enrichment from a sample of blood. With trypanosomes as a model system we show a combination of functionalities designed into a single device based on several deterministic lateral displacement arrays of different depths arranged in series. With only one inlet and no expensive or complicated pumping mechanisms to run separations we ensure the level of simplicity necessary for field use.
Endemic to sub-Saharan Africa, human African trypanosomiasis (HAT) is a fatal vector-borne parasitic disease caused by the protozoan parasite Trypanosoma brucei, transmitted by tsetse flies.9 Its diagnosis relies to a great extent on tedious and laborious microscopic detection in blood smears or lymph node aspirates.10 The key bottleneck with conventional microscopic diagnosis of HAT is the relatively low number of parasites against the overwhelming background of blood cells. Consequently the detection threshold for normal microscopic diagnosis is relatively high (∼10000 ml−1)11 in comparison to the parasitaemia (parasite number density in blood). This varies periodically during the course of the disease but can at times be as low as 100 ml−1, and consequently, there could be more than 50 million blood cells (predominantly erythrocytes) per parasite.11 Today, expensive and power-consuming technologies, such as ion exchange chromatography and centrifugation, are available at most modern facilities, and can be used for relatively efficient parasite detection (via enrichment relative to blood cell concentration). However, it is challenging to employ these tools in the geographical areas where the disease is prevalent, due to the lack of reliable power and storage facilities. Our aim is to use a microfluidic sorting scheme to extract the parasites for visual inspection, thus addressing the limitations of current technologies.
The method has been shown capable of size-fractionating a wide range of samples including blood components,17,18 cancer cells19 and synthetic particles such as droplets in two-phase flow.20 A recent review21 by Bridle et al. gives an expansive overview of this particle separation technique.
By controlling and exploiting shear forces our group recently added deformability22 to the list of parameters by which particles can be separated using DLD devices. Also, by controlling the orientation of particles as they pass through the device, we have been able to separate particles based on shape. As a proof of principle we showed that channel depth could be used to control the orientation of particles, and used this effect to accentuate the differences in shape of parasites and human blood cells8 making otherwise very difficult separations possible.
The work presented herein builds on these findings and involves a complete redesign of the device. The outcome is a more robust, easier to use device, with a significantly increased throughput. Our method provides a dramatically simpler and cheaper alternative to existing methods with the potential of enabling a fast and cheap point-of-care device that will have significant impact on field diagnosis.
Section (1) is designed to function like a traditional size-based DLD with a depth larger than the diameter of all blood cells. As we have previously shown,8 in deep devices particles rotate due to shear forces and are consequently sorted according to their smallest dimension. Here, the spherical leukocytes, being in the range 8–15 μm, are displaced and removed from the sample via a side channel. Erythrocytes and trypanosomes, on the other hand, have a smallest dimension of ∼2.5 μm which is below the critical size in this section. As a consequence, they are not laterally displaced and mainly follow the flow to the subsequent section.
Section (2) is shallow (3.5 μm) to minimize the rotational freedom of the cells and consequently maximize their effective size. The array is designed so that all cells are laterally displaced towards the channel walls creating a cell-free stream in the centre. A mirrored design23 is employed to minimize the migration distance of cells to the nearest wall which results in a higher throughput.
Section (3) has the same post size, post gap and row shift as section (2) but is deeper, (9 μm from previous optimization8). The row shift direction is also mirrored such that displaced particles are focused into the centre of the channel. The key idea is that the depth of this section is sufficient for erythrocytes to rotate and behave like 2.5 μm particles, as decided by their thickness, and they consequently remain near the walls. At the same time the longer parasites are still partially confined and behave as effectively larger particles being displaced back into the cell-free plasma and into a separate outlet where a dense array of posts (8 μm in diameter with a separation of 2 μm while keeping the array wide, 4 mm, in order to minimize the effect of clogged particles) serves as a sieving structure to capture and concentrate the cells of interest once they have been sorted. The parasites can thereafter be detected (future work will focus on detection schemes for the enriched parasites).
To maximize the throughput, the device has been kept as wide as possible. This means however that, together with the relatively small angle of displacement, the device needs to be long. In fact, the final device is around 14 cm. To fit this onto a standard glass slide the device is split up into its three different sections with multiple channels connecting the end of each array with the start of the proceeding array, see Fig. 5A–B. To ensure that the particles maintain their relative lateral position while moving between the different sections, careful calculations and CFD simulations (COMSOL Multiphysics 4.3, Comsol AB, Stockholm, Sweden) have been conducted, the key being to keep a constant resistance across the width of the device such that the flow and particle profiles are preserved around the 180° bends. More info on this optimization can be found in ESI section (3).†
In order to obtain good statistics on the device performance, measurements were often conducted with the same device for several hours. Consequently, to counteract particle sedimentation and ensure a homogenous sample, a small magnetic stir bar (length 5 mm, diameter 2 mm) was placed in the inlet reservoir and controlled by a magnet connected to a small electric motor. With a stir rate of ∼1 Hz and the dimensions of the stir bar and inlet reservoir as given above, the shear rate is estimated to be on the order of 10 s−1 to 100 s−1, which is less than the physiological shear rates of up to ∼104 s−1.27,28 Further, microscopic examination confirmed that the stir bar did not induce any morphological changes in the sample. For actual field use of the final device, the short time of analysis would eliminate the need for any stirring.
Particle tracking software, based on available MATLAB code, was written in MATLAB R2014b (The MathWorks Inc, Natick, MA, USA) and optimized for accurate and efficient particle recognition. Detailed information together with the code can be found in ESI section (4).†
Blood and trypanosomes were analysed at concentrations, which allowed for automated particle tracking while the addition of anticoagulants opened up for measurements over several hours per device without any blood clotting affecting the flow behaviour.
For all measurements, the morphologically similar but much less dangerous (and less restricted) parasite T. cyclops was used instead of the human parasites T. brucei rhodesiense or T. brucei gambiense.8
Blood and parasites were initially analysed separately in order to better characterize how the output of the device varied with flow rate. Subsequently, with the optimum flow rate determined, spiked samples were separated. For the initial samples where blood was run separately, it was diluted 10× in autoMACS Running Buffer (Miltenyi Biotec GmbH, Bergisch Gladbach, Germany). This is an isotonic PBS solution containing 2 mM EDTA, 0.5% BSA and 0.09% azide. In addition to preventing blood clotting, a lower concentration decreases the particle–particle interaction. For field-diagnostics we envision the collection of blood in pre-filled vials (e.g. BD Vacutainer® 366450, BD Biosciences, Franklin Lakes, NJ, USA). For the spiked blood samples, the blood was washed twice in Cunningham's buffer to remove the lytic factors causing natural lysis of T. cyclops, which is not adapted to human physiology (this step would not be necessary for the T. brucei). The 10× diluted blood sample was then spiked with trypanosomes at a concentration of approximately 1000 μl−1. This is in most cases a higher concentration than what occurs in normal rare cell detection. However, it allows us to accurately determine the device performance in terms of sorting efficiency. Detailed protocols are available in the ESI section (7).†
The results from the calibration of the flow speed in relation to the syringe compression are given in Fig. 4D. Here, it can be seen that the hydrodynamic resistance of the device decreases with increasing pressure, which is expected due to the elastic PDMS device becoming deformed, and inflated, at high pressures. Consequently, the flow rate is not linearly proportional to the applied driving pressure, instead it varies between 2.5 and 3.8 μl (min bar)−1. The volumetric flow rate here is given as the sample input rate. From this, approximately 20% of the total flow is separated out to outlet 1, enriched with leukocytes, while the sorting between erythrocytes and trypanosomes takes place in the remaining volume.
The erythrocytes and parasites were initially measured separately at various flow rates to allow for automated particle tracking of a large number of cells. Thereafter, spiked blood samples were analysed at the most promising flow rates.
The results from the separate measurements of erythrocytes and T. cyclops are presented in Fig. 6, with the sorting efficiency further analysed in Fig. 7.
In Fig. 7 the output of each section is given for both erythrocytes and T. cyclops at several different flow rates as a fraction of the total number of that cell type in that specific section. It can be seen that in section (1), Fig. 6B, the fraction of cells separated out are larger at low flow rates but converges to ∼20% with increasing flow rates (and consequently ∼80% of the cells continue to section (2)). Section (1) is deeper than the largest dimensions of both cell types, the length of the T. cyclops and the diameter of the erythrocytes. As a result the cells can rotate freely as discussed previously, resulting in a decreased effective size. With the section designed with a critical size of 7.1 μm, the cells are not expected to be laterally displaced. However, at low flow rates the shear stress exerted on the particles by the fluid is limited which leads to a decrease in both the orientation of the cells and also the cell deformation. As a consequence, the fraction of cells sorted out into outlet 1 can be higher than the expected 20%.
In section (2), Fig. 6C, the cells are steered towards the sides of the channel to open up a cell-free stream in the centre. Due to the low depth of this section, the cells are restricted from rotating which leads them to display a larger effective size and consequently they are more easily laterally displaced into the side streams along the channel walls. Here, the side streams are defined as the outermost 25% on both sides of the channel while the centre stream constitutes the remaining 50%. The size of these streams is defined by how the end of section (3) is divided into outlet 2 and 3, meaning that any cells residing in the centre stream at the end of section (2) are likely to also be sorted into outlet 3. In the same way, any cells residing in the side streams will end up in outlet 2 unless they are laterally displaced in section (3).
At lower flow rates, around 95% of cells entering section (2) will at the end reside in the side streams. However, at high flow rates the soft erythrocytes will, by deforming due to shear forces, avoid being laterally displaced and will end up as a contaminant among the parasites in outlet 3. At 3.8 μl min−1 around 20% of the erythrocytes exiting this section do so in the centre stream and are expected to end up as a contamination in outlet 3, leading to poor separation sensitivity.
Section (3) is identical to section (2) except for the direction of displacement and the depth, Fig. 6D. By the increase in depth to 9.0 μm, larger than the diameter of the erythrocytes, there is no steric hindrance to their orientation, leading to a decrease in their effective size as previously discussed. They are therefore not laterally displaced in this section and, if focused into the side streams in section (2), are sorted into outlet 2. Similar to the situation in section (1), it can be seen that if the flow rate is too low, the shear forces are not high enough to allow the cells to rotate and adopt the smaller effective size. Instead they have a maximum effective size equal to their diameter leading to a significant lateral displacement. At low flow rates (0.2 μl min−1) 12% of the erythrocytes entering this section are sorted into outlet 3 even though they exit section (2) well-focused in the side streams. Consequently, the erythrocyte contamination in outlet 3 is here a result of unwanted lateral displacement in section (3). An increase of the flow rate leads to a decrease in the fraction of erythrocytes sorted out into outlet 3, however at too high pressure, the fraction once again increases. This is however believed to be an effect of erythrocytes not being well focused into the side streams when they exit section (2). This could potentially be avoided by increasing the width of section (2), leading to a decrease in the flow velocity (while maintaining the volumetric flow rate) and consequently the shear stress exerted on cells in this section.
For the T. cyclops the highest fraction of extracted cells is acquired at low pressures while the fraction decreases with increasing pressures. This shows how adjusting the depth of the channel can have a large impact on the behaviour of the cells, allowing for separation based on their differences in shape.
The measurements of T. cyclops and erythrocytes carried out separately at various flow rates are summarized in Fig. 7. Firstly, the fraction of the two cells types which ends up in outlet 3 is shown, here calculated based on the input sample. This figure is similar to Fig. 6D, but the cells removed in section (1) have also been taken into account. The most notable difference is the decreased capture rate of T. cyclops at low flow rates which is due to the increase in fraction of T. cyclops being sorted out into outlet 1. As a result, the separation efficiency is decreased at these flow rates. The largest enrichment (32 times) is reached at 1.9 μl min−1. At this flow rate the fraction of erythrocytes entering outlet 3 is kept at a minimum (2.1%) while a large fraction of the trypanosomes are retained (66.5%). As a consequence, this was chosen as the flow rate for all the remaining measurements. However, it can be noted that even though the enrichment is the highest at this flow rate, the number of collected T. cyclops per minute increases with increasing flow rate. At a flow rate of 3.8 μl min−1 the number of collected T. cyclops is almost twice that of 1.9 μl min−1. The optimum trade-off between throughput and purity will be determined by the choice of detection scheme in the specific application.
On the whole, the outcome, given in Fig. 8, agrees with the previous measurements. For the erythrocytes 21.7% are diverted to outlet 1 (leukocyte outlet). For the remaining erythrocytes which continue into section (2) the fraction successfully focused into the side stream amounts to 97.6%. From this number, a small fraction (0.6%) is not retained along the sides of the channel in section (3) but instead end up in outlet 3 (T. cyclops outlet) as a contamination. The remaining 97.0% which still remain in the side streams exit into the correct outlet which is close to the result of the separate measurements.
Fig. 8 Distribution of the three different cell types at the end of each section. The numbers are given as the fraction of that cell type in that section. |
For the trypanosomes 18.3% is laterally displaced into outlet 1 while 92.8% of the remaining parasites are successfully focused into the side streams. This is, in agreement with previous measurements a smaller fraction than for the erythrocytes. But as the T. cyclops should exit into outlet 3 from the centre of section (3), this would lead to an increased sorting efficiency. In the end, 83.1% of the remaining T. cyclops exit into outlet 3, equivalent to a 30-fold enrichment in the number concentration. The leukocytes are, contrary to the other cells, laterally displaced to a great extent in section (1). The majority of them, 94.1%, are sorted out into outlet 1 while the remaining fraction continues to the shallower section (2). A large fraction of these leucocytes are not able to enter this shallow section, however over the time scale of our experiments the number of leucocytes filtered out was too small to adversely affect the device performance. This is believed to be an effect of the device's robustness to pressure changes, with section (2) being aimed at focusing all cells towards the wall. The result of this is that any smaller pressure variations will not have an effect on the device performance.
The total output from the device is summarized in Fig. 9, here we can see that 100% of the extracted leukocytes are found in outlet 1 owing to the large size and shape difference between leukocytes and the other cell types. There is a relatively large number of background cells present, around 20% of each cell type, as expected due to the design of section (1). The addition of a focusing section, similar to section (2), positioned before the sorting-out of leukocytes takes place, the number of background cells in outlet 1 could be greatly reduced. In outlet 2 the fraction of erythrocytes amounts to 76.0% (77.4%), while 67.9% (66.5%) of the T. cyclops are retained in outlet 3 (results of the earlier measurements on individual cell types are given in parenthesis). As can be seen, these fractions are in close agreement with the earlier measurements with a marginally smaller number of erythrocytes retained in outlet 2 while a slightly larger fraction of T. cyclops can be steered into outlet 3. It should however be noted that these numbers rely on the bifurcation of the flow into the outlets. The current device was designed to have, as described earlier, half the flow at the end of section (3) guided to outlet 2 and the other half to outlet 3. Changing these fractions would potentially lead to a better performance of the device. For example, decreasing the fraction of the flow in section (3) bifurcated into outlet 3 would remove more erythrocyte and would likely increase the specificity of the device.
Fig. 9 The output of the device given as the fractional distribution of the three measured cell types. |
We believe the approach that we have presented here shows great promise as a point-of-care test for a multitude of diseases. In addition to our test system with African trypanosomiasis other protozoan parasites could be considered such as, but not limited to, leishmaniasis, Chagas disease and malaria, where particle size, shape and deformability can be leveraged for the enrichment of hard to find objects. These diseases affect millions worldwide, not only via direct infection of human populations but also via the socioeconomic burden of livestock infection.29
While we believe the current device offers several advantages over current methods there is still work to be done towards a fully functioning field-ready diagnostic device. Most notably lacking is portable detection. One solution may be the devices developed by Ozcan et al.30 that have shown great promise for field-detection of pathogens combined with telemedicine. Another would be integrated electronic cell counting based on impedance spectroscopy.31
Further, the current experiments were performed in devices made using soft lithography. This is a rapid prototyping technique that is suitable for research, but for field applications large numbers of devices would be needed at lower cost. Foil-based fluidics32,33 and injection moulding approaches would provide the necessary basis for mass-produced devices and be fully compatible with our method.
Footnote |
† Electronic supplementary information (ESI) available. See DOI: 10.1039/c6ay00443a |
This journal is © The Royal Society of Chemistry 2016 |