Smart and Fast Blood Counting of Trace Volumes ... - ACS Publications

Oct 23, 2015 - Center for Biophotonics, University of California, Davis, Davis, ... UC Davis William R. Pritchard Veterinary Medical Teaching Hospital...
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Smart and Fast Blood Counting of Trace Volumes of Body Fluids from Various Mammalian Species Using a Compact, Custom-Built Microscope Cytometer Tingjuan Gao,†,¶ Zachary J. Smith,†,¶ Tzu-yin Lin,‡ Danielle Carrade Holt,§ Stephen M. Lane,†,∥ Dennis L. Matthews,†,∥ Denis M. Dwyre,⊥ James Hood,# and Sebastian Wachsmann-Hogiu*,†,⊥ †

Center for Biophotonics, University of California, Davis, Davis, California 95817, United States Division of Hematology and Oncology, Department of Internal Medicine, University of California, Davis, Davis, California 95817, United States § UC Davis William R. Pritchard Veterinary Medical Teaching Hospital, Clinical Diagnostic Laboratories, University of California, Davis, Davis, California 95616, United States ∥ Department of Neurological Surgery, University of California, Davis, Davis, California 95817, United States ⊥ Department of Pathology and Laboratory Medicine, University of California, Davis, Davis, California 95817, United States # Tahoe Institute for Rural Health Research, Truckee, California 96160, United States ‡

S Supporting Information *

ABSTRACT: We report an accurate method to count red blood cells, platelets, and white blood cells, as well as to determine hemoglobin in the blood of humans, horses, dogs, cats, and cows. Red and white blood cell counts can also be performed on human body fluids such as cerebrospinal fluid, synovial fluid, and peritoneal fluid. The approach consists of using a compact, custom-built microscope to record large field-of-view, bright-field, and fluorescence images of samples that are stained with a single dye and using automatic algorithms to count blood cells and detect hemoglobin. The total process takes about 15 min, including 5 min for sample preparation, and 10 min for data collection and analysis. The minimum volume of blood needed for the test is 0.5 μL, which allows for minimally invasive sample collection such as using a finger prick rather than a venous draw. Blood counts were compared to gold-standard automated clinical instruments, with excellent agreement between the two methods as determined by a Bland−Altman analysis. Accuracy of counts on body fluids was consistent with hand counting by a trained clinical lab scientist, where our instrument demonstrated an approximately 100-fold lower limit of detection compared to current automated methods. The combination of a compact, custom-built instrument, simple sample collection and preparation, and automated analysis demonstrates that this approach could benefit global health through use in low-resource settings where central hematology laboratories are not accessible.

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by critical care physicians is typically 5 to 15 min.3,6 These benefits are often accomplished through the use of a portable or hand-held instrument.7,8 Transportable small bench equipment can also be used when truly portable or hand-held devices are not available. POCT increases the likelihood that the patient, physician, and care team will receive the results quicker, and be able to make quicker clinical decisions.9−12 Thus, clinical care is improved and moreover, cheaper, faster, and smarter POCT devices have found strong adoption by clinicians to make health care more cost-effective for many diseases.13−15

urrently there is a strong desire to decrease the turnaround time for medical diagnosis and reduce costs of health care without sacrificing the quality of diagnosis and care. To accomplish this goal, many companies and researchers are investigating bringing tests conveniently and immediately to patients, through the development of point-of-care testing (POCT) technologies.1−3 The goal is to collect specimens and obtain results in a short period of time at or near the location of the patient, so that the treatment plan can be necessarily adjusted before the patient leaves the medical facility.4 POCT is also advantageous because of the small sample volume and fast turnaround time required to perform a test. For example, the sample volume for a cluster of tests with minimal blood loss can be as low as 40 μL, depending on the instrument used and the tests performed,3,5 and the average turnaround time expected © 2015 American Chemical Society

Received: September 2, 2015 Accepted: October 23, 2015 Published: October 23, 2015 11854

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Figure 1. Experimental schematic of the microscope cytometry method. (A) Blood collection and preparation from a finger prick ①. The collection volume is determined by the capillary tubes, with all volumes being easily obtained from a finger prick. Minimum invasive effect (pain level) is expected for 0.5 and 1 μL collection. Blood is then diluted in an SDS/PBS mixture ② with a 30-s shake in reagents and further stained with AO ③. (B) Schematic of the custom-built microscope cytometer. The major components include a green LED and a blue LED as bright field and fluorescence sources, a 4x objective for large field of view imaging, an automated sample translation stage, an automatic emission filter wheel, and a CCD camera. (C) Overlay image (bright-field + fluorescence) of the center area of a sample chamber on a 2-chamber slide. The bottom and top ends in the large-field image (C, middle panel) correspond to the inlet and outlet of the chamber, respectively. The small region of interest shown by the blue box is presented to give a sense of scale. This small region of interest is also presented in the top row of Figure 3.

preparation method and obtaining counts accurately of as many cell types as possible, while the instrumentation was still based on a standard, bulky microscope, and the challenge of obtaining other parameters, such as hemoglobin, was not much discussed, either. In addition to blood, there is also a strong clinical need for automated counting of blood cells in human body fluids other than blood. For example, cerebrospinal fluid (CSF) is monitored for invasion of blood cells that are indicative of infection or disease. The majority of CSF samples are counted manually due to their low cell counts and limited sample volumes. These manual tests are typically performed by a trained clinical lab scientist, who examines a drop of the sample under a microscope and counts a few squares with a hemocytometer chamber. This process is labor intensive so that it is desirable for the manual methods to be replaced by automated analysis. However, when analyzed by automatic methods, the majority of samples seen clinically (as many as 80% in some reports) have cell count values below the limit of detection for the automated instrument.33 Taking all these factors into account, there is still an unmet need, even within standard clinical laboratories, for an automated cell counter that strikes an acceptable balance between maintaining clinical reliability and improving efficiency of the laboratory. Furthermore, assessing blood count parameters is not only important for humans but also for companion and farm animals, when it is needed to form a clinical diagnosis, screen for changes in patient health and monitor disease progression and treatment. M. Becker et al. compared several small in-clinic and laboratory veterinary hematology analyzers, in terms of the ease of clinical use, accuracy, advantages, and limitations of the various hematology systems.34 Although for most cell counts and RBC parameters, the in-clinic analyzers evaluated in this study performed acceptably compared to their laboratory counterparts, discrepancies >25−50% compared with the

One of the most active areas of POCT is to obtain clinical parameters from whole blood. In standard, central laboratorybased clinical practice and clinical trials, flow cytometry has been widely used for this purpose, due to its powerful ability of counting cells and detecting biomarkers from whole blood.16−19 It is a laser-based biophysical technology that allows simultaneous analysis of multiple parameters relating to the physical and chemical characteristics of up to thousands of particles per second flowing through a detection apparatus in a stream of fluid.17,19 As routinely used in clinical practice for a complete blood count (CBC), flow cytometry is a major player because it is fully automated in terms of the sample preparation, data collection, and quantitative analysis.20−23 However, the instrumentation is a large and complex system including the fluidic, optical, electronic, and data processing components. The reagents used may involve multiple fluorescent dyes and buffering solutions.20 This results in the requirement of central medical infrastructure and dependence on medical professionals to operate and maintain the instruments. In order to explore the possibility of the use of a portable POCT blood counter for humans, a few groups have studied ways to distinguish red blood cells (RBCs), white blood cells (WBCs) and their differentials, and other parameters. While promising, these techniques require either complex sample preparation such as purifying different cell types and handling multiple reagents24−27 and/or measurement of only single parameter28 or multiple parameters with separate instrumentation.29 Our group previously demonstrated a proof-of-concept using a simple sample preparation scheme and a commercial microscope to accurately count minute volumes of human blood and to obtain counts of RBCs, platelets, and 3-part differential of WBCs. It utilizes a simple, single-dye staining strategy and differentiates WBCs by decoding information on two-color (red and green) fluorescence intensity.30−32 In that work, the emphasis was on developing an easy-to-use sample 11855

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field, red fluorescence, and green fluorescence images were recorded. While samples from finger pricks and venous draws do not show significant difference, a comparison of fluorescence measurements from a venous draw and a finger prick from the same healthy subject is provided in Supporting Information (Figure S2). For hemoglobin determination, we used a modified version of the hemoglobin analysis method reported by Oshiro et al.35 Blood was diluted 10× in a solution of PBS and SDS, such that the final concentration of SDS was 5.2 mM. This was sufficient to fully lyse the RBCs, as well as to convert the two forms of hemoglobin (oxy- and deoxy-hemoglobin) into a single, oxidized form (methemoglobin). This simplifies the measurement and analysis by requiring only a single absorption measurement at a single wavelength. Based on the well-known Beer−Lambert law, there is a linear relationship between the measured optical density of the blood solution and the hemoglobin concentration, whose slope can be determined either from first-principles with accurate knowledge of the absorption spectrum of methemoglobin, or through simple empirical analysis, as will be discussed below in the section on image analysis, and Supporting Information (Figure S3) as well. Following the sample preparation, 10 μL of the prepared mixture was placed in the 100 μm-thick imaging chamber. To analyze other body fluids, we stained fluid samples with AO (12.5 μM, volume ratio 1:1), such that the final dilution factor is 2×. Ten microliters of the prepared mixture was then placed in the counting chamber for imaging. Because the fluid samples had widely varying concentrations of RBCs, it was not possible to sphere RBCs using our previously reported SDS sphering protocol, as the SDS will lyse RBCs if there is an overabundance of SDS compared with RBCs. Furthermore, because many body fluids are not environments designed to sustain cells, WBCs in fluids may have an altered appearance. Therefore, the cells in the fluid images have nonuniform appearance and orientation potentially different from those in blood. To identify all of the RBCs and WBCs in the fluid images, we therefore developed a new analysis method specially designed for these samples, described below in the section on image analysis. Instrument and Large Field-of-View Imaging. A compact, custom-built microscope cytometer was used for measurement of all blood and fluid samples. A schematic of this instrument is shown in Figure 1B. It consists of a green LED (China Young Sun LED Technology Co., Shenzhen, Guangdong, China 518 ± 18 nm) as a bright-field source and a blue LED (OD469L, Optodiode Inc., Newbury Park, CA, filtered to 470 ± 20 nm) as a fluorescence source. A computercontrolled one-dimensional translation stage (VT-50L, Micronix USA, Irvine, CA) is used to step the field of view along the length of the chamber. A 4× objective (Nikon CFI Plan Apo) is used for large-field imaging to allow high-throughput and accurate counting of blood cells, with the images being recorded by a cooled 8.3MP camera (QSI683, QSI Imaging). We use an automatic filter wheel (Thorlabs) to switch between two fluorescence filters, allowing for 2-channel fluorescence imaging (red: 685 ± 20 nm, green: 528 ± 19 nm). The entire system is controlled automatically via LabVIEW. The counting chamber was placed on the stage of the custom-built microscope cytometer. By translating the stage in one dimension, we imaged 5 continuous areas at the center of the chamber. For blood and fluid images, for each area, three sequential images were acquired automatically, including a

clinical gold standard were routinely observed. Thus, there is still need for developing new hematology instruments or improving existing instrument and software technologies, making automated blood counts more accurate and valuable for clinical veterinary practice. In this paper, we present a method for analyzing blood and other body fluids that expands upon our previous work to utilize a custom-built, large field of view microscope, and a finger-prick sample preparation protocol, combined with automated image analysis. The dimension of the microscope is small (∼0.2 cubic feet) and designed to be portable. The capillary pipettes used for blood collection allow volumes as low as 0.5 μL and therefore are negligibly invasive. The entire process takes ∼15 min including sample preparation, automated imaging, and data analysis. We measured clinical samples from humans, horses, dogs, cats, and cows. The sample types included blood, cerebrospinal fluid (CSF), synovial fluid and peritoneal fluid (from ascites). Our results provide many of the parameters of a current state-of-art clinical test, including RBC, WBC, platelet counts, and hemoglobin concentration with accuracy comparable to the current clinical instrumentation.

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EXPERIMENTAL SECTION Materials and Sample Collection. Phosphate-buffered saline (PBS) and the Countess cell counting chamber slides were purchased from Life Technologies. Acridine orange (AO) was obtained from ICN K&K Laboratories Inc. Sodium dodecyl sulfate (SDS) was purchased from Sigma-Aldrich. Glass capillary pipettes were purchased from Bioanalytic GmbH (10 μL) and Sigma-Aldrich (1 and 0.5 μL). Samples were obtained as EDTA-treated tubes of discarded, same-day blood or untreated tubes of discarded, same-day body fluids in the UC Davis Department of Pathology and Laboratory Medicine clinical lab. Several human blood samples were also obtained from healthy volunteers through a venous draw and/or a finger prick. Same-day, EDTA-treated animal blood samples were obtained from the Clinical Diagnostic Laboratories of UC Davis Veterinary Medical Teaching Hospital. For blood samples, we ran CBCs using automated hematology analyzers. A Coulter LH500 Hematology Analyzer (Beckman Coulter) was used to run CBCs for human blood samples, whereas an ADVIA 120 Hematology System (Siemens) was used to run blood counts for animal blood samples. Because commercial automated analyzers have a relatively poor limit of detection, they cannot be used on routine body fluid samples. Therefore, standard manual counts by a trained clinical laboratory scientist were used as a gold-standard metric for our fluid measurements. Sample Preparation. Figure 1A demonstrates the measurement schematic of blood collected from a finger prick. We used glass capillary pipettes with defined volumes of 0.5, 1, or 10 μL to collect whole blood from fingers or from tubes of blood described above. Details of the preparation, measurement, and analysis procedure were reported previously from our group.30 Briefly, we dropped the capillary pipet into an Eppendorf vial (1 mL) with defined amounts of SDS and PBS to sphere red cells and dilute the blood. Then we mixed them thoroughly by manually inverting the vial up and down for 30 s. Following that, we added AO to highlight WBCs and platelets, and allow for a 3-part WBC differential (see Supporting Information, Figure S1). Prepared blood was then placed into a 100 μm-thick imaging chamber, where bright11856

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Figure 2. Image analysis of fluid samples. (A) The full-field image (overlay of bright-field and fluorescence) of a CSF sample. (B) The enlarged image of a small region of interest shown by the white box in A. (C) A standard deviation image computed from taking the standard deviation of a 3 × 3 pixel neighborhood of each pixel in B. (D) A binarized version of C overlaid with object markers (green) determined by template matching as described in the text. (E) A segmented version of D by marker-controlled watershed segmentation. (F) A plot of the mean standard deviation image value and object size for each object in the full-field image. The green line separates noise objects from true cell objects. (G) Green and red fluorescence of each true cell object. Green lines represent fluorescence intensity thresholds that separate RBCs (blue dots) from WBCs (red dots).

bright-field image (0.05 s exposure time) and two fluorescence images (red: 0.8 s exposure time, and green: 0.4 s exposure time). In total, 15 images were automatically saved after the measurements were finished. We stitched them together to form a full-field view of the whole center area of the chamber. An example of the full-field view with overlay of three colors (bright field + red + green) is shown in Figure 1C. For hemoglobin measurements, only bright-field images were acquired. Image Analysis. By utilizing automated data analysis techniques reported previously from our group,30 we are able to obtain accurate RBC, platelet, and WBC counts. Briefly, this approach uses template-matching for RBC counting (brightfield images), threshold-based platelet counting and colorcoded WBC counting (fluorescence images). In our previous paper, we used a commercial microscope platform for recording images, although in this manuscript we use a dedicated, custombuilt microscope for this purpose. However, after accounting for slight differences in the two systems (pixel size, magnification, and sensor noise) the details of the algorithm are identical. For the hemoglobin measurements, we take bright field images only and use those images to compute an optical density, which has a linear relationship to hemoglobin concentration, as described in Supporting Information, Figure

S3. To measure the optical density, bright-field images of the chambers were recorded, and the average intensity of each image was computed. The optical density is the logarithm of this intensity divided by the average intensity of a reference image recorded using an identical chamber filled only with SDS and PBS solution. For fluid measurements, a new image analysis routine was developed specifically to extract RBC and WBC counts from the fluid samples. These samples had widely varying concentrations of RBCs and WBCs, and these cells had varying morphology, necessitating a different analysis procedure. This procedure is shown in Figure 2. Figure 2A shows a full field-ofview image of a fluid sample, with the white box representing a region of interest enlarged in Figure 2B, with several RBCs and WBCs present. To identify all of the objects in the image, we started by computing a standard deviation image, shown in Figure 2C. This image was generated by calculating, for each pixel in the original image, the standard deviation within a 3 × 3 neighborhood of that pixel. This has the effect of highlighting structured objects on a flat background. The standard deviation image was then thresholded, which creates a binary image as shown in Figure 2D. Then, marker-controlled watershed segmentation was used to segment this binary image into discrete objects. The foreground markers were chosen using template matching, where the template was a 7-pixel radius 11857

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Figure 3. Example images corresponding to a region of interest in a full-field image demonstrated in Figure 1C (middle panel, blue box)examples of human blood (top row), canine blood (middle row), and human CSF (bottom row). Columns 1, 2, 3, and 4 show bright-field, red fluorescence, green fluorescence and overlay of the previous three images, respectively.

determination, we measured samples from 28 humans, 6 horses, 6 dogs, 2 cats, and 4 cows (total n = 46). For the cell counts, to compare differences of blood cell appearance in those samples, Figure 3 demonstrates three typical raw images of human blood, dog (canine) blood and human CSF. All three samples were stained with the same concentration of AO, except that their dilution factors varied. Human and dog blood were diluted at 80×, while human CSF was diluted at only 2×. We selected different dilution factors because normal CSF counts give very few blood cells (0−5 WBCs, 0 RBC per μL), and even abnormal CSF counts are considerably low, compared with normal blood counts (4−10k WBCs and 4−7 million RBCs per μL). Taking the different dilution factors into account, we see a wide dynamic range of cell density across these three different samples. Typically, animals show similar WBC and platelet counts to humans, whereas have RBCs with higher counts and generally smaller sizes, as seen in Figure 3, Canine blood. In terms of body fluids other than blood, some types such as CSF have either no cells or very few cells, where even modest numbers of WBCs (>10 per μL) are typically an indicator of disease (infection, bleeding, inflammation, and tumor).36 The bottom images in Figure 3 (human CSF) are an example from a diseased patient with clinical values of 21 WBCs per microliter, and 6950 RBCs per microliter. It is worth noting, current automated cell counting systems used in the clinic have great difficulty running fluid samples with few cells, as they have relatively poor limits of detection. Due to the use of a robust imaging platform rather than flow cytometry and the ability to freely choose an appropriate dilution factor for each fluid type, our system has a much lower limit of detection. (The lowest counts we obtained for fluid samples using our method were around ∼2 cells per μL, corresponding to ∼6 cells in a full-field image: 4.5 mm × 13 mm in dimension.) In fact, counting few cells can even present an advantage in our system because a sample with fewer cells is typically easier to image and analyze due to the lack of crowding in the imaging chamber. Determining Cell Counts and Hemoglobin Concentrations in Human and Animal Blood. In order to validate

disk. A cross-correlation map was computed between the standard deviation image and the template, resulting in peaks in the correlation function centered on disk-like objects in the standard deviation image. MATLAB’s built-in “imextendedmax” function was applied to this correlation map to generate a binary image composed of object markers. These markers are shown in green in Figure 2C. The final segmentation result is shown in Figure 2E, where cells have been clearly identified, as well as several larger “noise” regions, which have been erroneously identified as cells. The separation of the “true” cells from these false objects can be easily made by plotting, for each object, the mean standard deviation image value within that object and the size of that object in a two-dimensional space, as shown in Figure 2F. As can be clearly seen, the noise objects vary widely in size, and tend to have much lower standard deviation values compared with true cells. Therefore, we defined a threshold (shown as the green line in Figure 2F) to separate true and false cells, with all objects with a standard deviation value greater than the threshold being defined as cells. RBCs were nonfluorescent under AO staining, whereas WBCs had significant fluorescence. Therefore, plotting the average green and red fluorescence for all cell objects separated WBCs and RBCs, as shown in Figure 2G. By creating threshold red and green fluorescence values, RBCs can be defined as those objects with red and green fluorescence below the threshold values, while WBCs are those with large fluorescence values. In theory, the WBCs could be further quantified as lymphocytes, monocytes, and granulocytes using our previously reported analysis method. However, the very low numbers of WBCs present in most fluids (generally ∼1000× less than in blood for healthy CSF, for example) made this determination difficult in the samples we tested in these experiments.

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RESULTS AND DISCUSSION Imaging Cells in Blood and Other Body Fluids. We measured blood counts in clinical samples from 39 subjects, including blood from 10 humans, 11 horses, 8 dogs, 6 cats, and 4 cows. We measured other body fluids (CSF, synovial fluid and peritoneal fluid) from 13 humans. For hemoglobin 11858

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Figure 4. Comparison of microscope cytometry determination of blood counts with clinical results from an automated hematology analyzer. Column 1: The red lines show the lines of perfect prediction and the green lines are the reported 95% CI for the automated analyzer. Column 2: Bland− Altman analysis of the microscope cytometry counting compared to a clinical instrument. Red lines show the bias between the two methods, while green lines show the calculated 95% CI for differences between the two methods.

with blood samples and have different clinical requirements, so we will discuss them separately in the next section. For blood cell counting, each preparation was repeated on three separate aliquots of the same sample to allow assessment of the measurement repeatability. Figure 4 shows the comparison of microscope cytometry of blood counts with

our microscope cytometer and data analysis, we performed a study of 39 samples for blood cell counting and 46 samples for hemoglobin determination, comparing our method with the commercial automated hematology analyzers currently in use in our human and animal pathology laboratories. Counts of the fluid samples are out of scale if plotted in the same linear graph 11859

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hemoglobin concentration, our method should provide a robust enough value for this clinical purpose. Counting Cells in Human Body Fluids Other than Blood. Counts of RBCs and WBCs of 13 fluid samples, including 9 CSFs, 2 synovial fluids, and 2 peritoneal fluids, were calculated from images recorded and processed as described above in the Experimental Section. These values are plotted in a logarithmic scale in Figure 5, as the samples present a wide

clinical results from an automated hematology analyzer. The error bar of each data point represents the repeatability of the three runs. Column 1 shows a correlation plot between the clinical result and the microscope cytometry method. The red lines represent the lines of perfect agreement, whereas the green lines above and below the red lines represent the 95% confidence interval (CI) of the clinical measurement, defined as ±1.96 standard deviations (SD), as reported by the manufacturer. It is seen that majority of our data lie within the range between the two green lines, confirming the consistency of our method with clinical results. There are a few data points, particularly evident in the platelet measurements, which fall out of this range but with acceptable errors. It is noticed WBCs show the largest error bars between the three aliquots, compared with RBCs, platelets, and hemoglobin. This is reasonable due to the fact that the counts of WBCs in blood are significantly lower (4−10k per μL) than RBCs (4−7 million per μL) and platelets (150−400k per μL) and therefore should produce larger statistical errors when the same amount of blood is measured. Hemoglobin concentrations show the tightest correlation with the clinical method, which evidence that our simple hemoglobin sample preparation protocol effectively releases hemoglobin into solution from lysed RBCs and provides an accurate absorbance test. We can also assess the agreement of our method with the clinical standard using a Bland−Altman analysis, as shown in Column 2 of Figure 4. Here the x-axis represents the mean of the two methods (clinical standard and our microscope cytometry method), whereas the y-axis represents their difference. The red line shows the average difference between these two methods, while the green lines represent ±1.96 SD of the difference data. This has the statistical meaning that assuming the data are normally distributed, 95% of future measurements should have differences lying between the green lines. Thus, we can use where these lines intercept the y-axis to determine whether our method produces acceptable agreement with the clinical gold standard. For example, our agreement with the ADVIA 120 analyzer for animal blood compares very favorably with the reports of agreement among other automated analyzers studied in the literature.34 In all cases, the 95% CI demonstrates that our error is small compared with clinically significant fluctuations in these values. The largest percent error is seen with platelets. However, this is heavily influenced by the presence of two or three outlier results. Because the blood samples we obtained from the animal hospital were often several hours old, there may be morphological changes in the blood of some samples due to age. In the future, we hope to examine blood taken directly from the animal by a skin prick to evaluate whether this can improve our method’s precision in measuring platelet values. However, even this relatively large error is still small compared to clinically significant variations given the large range of values that are considered to be “normal” among each of the different animal species, and this error is actually better than the agreement between other commercial analyzers and the ADVIA 120.34 For the hemoglobin determination, we see that we have an error that appears to be slightly dependent on the mean, with larger mean values leading to larger absolute errors. However, because the critical use of hemoglobin is in detecting anemia, and our system actually becomes more accurate with lower

Figure 5. Comparison of microscope cytometry determination of blood counts in body fluids with clinical results from a professional hand count technique. Blue circle points represent data of nine CSFs. Red triangle points represent data of two synovial fluids. Black square points represent data of two peritoneal fluids. Each data point includes an error bar showing repeatability of three runs. The red lines show the lines of perfect prediction. The green lines represent the minimum thresholds a clinical automated hematology analyzer can measure.

dynamic range of cell counts that are difficult to visualize accurately on a linear scale. Due to the relatively small number of fluid samples compared to blood samples processed by our hospital’s clinical lab, and due to the fact that fluid samples degrade quickly, these samples were more challenging to obtain. However, even with the relatively few number of samples collected in this study, our data show consistent correlation with clinical results. Our system tends to underestimate the number of WBCs compared with the hand counts. Although this discrepancy is not clinically significant, it may be due to the fact that the fluid samples we obtained must first be collected, prepared, and counted by the clinical staff before it was ready to be discarded, and we were permitted to use it for research purposes. Because the samples degraded over time, the samples that we collected may have some slight difference in counts compared with the clinical measurements made as close as possible to the collection time. Furthermore, inconsistency between our results and clinical results become more significant for samples with very low cell counts, such as the samples reported by clinical results with zero WBCs or zero RBCs per microliter (Figure 5). There are two effects that may account for this. The first is that our analysis method is imperfect, and there will always be some “noise” objects that are counted erroneously as cells. We quantified this in a control experiment where only PBS was loaded into the sample chamber, we correctly identified zero cells in both the WBC and RBC counts. However, when AO was added, insoluble impurities in the dye led to false identification of RBCs and WBCs (∼0.5 per μL on average). The second possible cause, however, is that all of these samples must be hand counted by a trained professional. Typically this is done with a high magnification microscope with relatively small fields of view. Given that our system counts a very large field of view, it is possible that there are rare cells being missed 11860

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potentially resolve a large majority of fluid counts without the need for manual examination. While this work demonstrates the promise of our system and method for rapid and inexpensive blood counting of a wide range of blood-containing fluids, more work remains to be done before this system could be transferred to clinical or field use. Future work will involve designing a microfluidic slide chamber to automate sample preparation. Further reduction in the size of the microscope is also possible with a custom optical design (we currently rely on off-the-shelf microscope optics). The data analysis algorithms we use can also be improved, for example, to further separate noise objects and reagent impurities from true cells in the fluid counting method. As all these improvements are combined together, we hope our method can be successfully translated to clinical use to provide faster, less invasive blood tests outside of central facilities.

with a hand count yet recognized by our system. We confirmed this by hand-counting the cells in one of our large-field images of a CSF sample that was clinically measured to have no WBCs. Yet for our hand count of this large-field image, we unambiguously identified several WBCs, calling into question the accuracy of clinical manual counts of zero cells per microliter. In addition to counting a large field of view, our system can also easily repeat measurements on a single sample. This ability to quickly perform multiple measurements on our automated system, compared with hand counting, provides a significant advantage in assessing the confidence of the results by allowing a user to run multiple volumes of fluid from a single sample. Especially in the case of a sample with extremely low cell counts, imaging multiple aliquots from a patient sample can increase confidence in low count values. In Figure 5, we also plotted green lines, which present a typical minimum limit of detection for a clinical automated hematology analyzer. It is reported that a clinical automated hematology analyzer can not accurately count RBCs fewer than 10k and WBCs fewer than 200 per microliter.33 Because ∼80− 90% of fluid samples are below those thresholds, currently hand-counting of cells is required within clinical pathology laboratories. Our method is based on large field-of-view imaging and automatic computing algorithm, and our dilution factor can be freely chosen, so there is an approximately 100− 1000-fold reduction in the limit of detection for our method compared with flow-cytometry-based hematology analyzers used in clinical laboratories, with the lowest counts we measure as 2 RBCs and 2 WBCs per microliter. To summarize, compared with the current hematology instruments in central laboratories and professional manual counts, our method seems promising to allow faster and more accurate analysis for body fluid samples. However, this potential must be further confirmed on a wider range of fluid samples and clinical conditions.

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.analchem.5b03384. Measurement of 3-part WBC differential for human blood; comparison of blood tests from a finger prick and from a venous draw; determination of Hemoglobin concentration in blood of human and animals (PDF)

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Author Contributions ¶

These authors contributed equally to this work (T.G. and Z.J.S.).

Notes

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The authors declare the following competing financial interest(s): All authors, except Tzu-yin Lin and Danielle Carrade Holt, are listed on two patent applications related to this work.

CONCLUSIONS We have built a compact microscope cytometer capable of bright-field and two-color fluorescence imaging of large field of view. It is designed to be the portable analog of a clinical automated hematology analyzer. By utilizing this microscope cytometer and combining with automatic analysis algorithms, we developed a method to accurately count RBCs, platelets and WBCs in blood and other body fluids. If blood is collected from a finger prick, as low as 0.5 μL is required for the test of blood counts, allowing for minimally invasive testing for patients. In addition, a similar imaging concept enables us to easily determine hemoglobin concentrations in blood samples as well. We have implemented this method to measure cell counts of 39 clinical blood samples, including blood from 10 humans, 11 horses, 8 dogs, 6 cats, 4 cows, as well as cell counts from 13 human body fluid samples. Hemoglobin measurements were made on a total of 46 clinical samples from both humans and animals. The data demonstrated consistency with clinical results and showed repeatability across three independent measurements. As clinical CBCs normally require central facilities, our microscope cytometer may provide an advantage as a POCT device in a small hospital or clinic. While testing other body fluids in laboratories currently requires a pathology professional to perform manual counts, our technique has the potential to provide automatic counting with a limit of detection that is 100−1000 fold lower than current clinical systems. This could

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ACKNOWLEDGMENTS We thank Leslie Freeman (CLS) and Lisa Gandy (CLS) from the UC Davis Department of Pathology and Laboratory Medicine clinical laboratories for their assistance in measuring clinical values for blood and body fluid samples. This work was supported in part by the NSF Program, Accelerating Innovation Research: Creation of an Ecosystem for Biophotonics Innovation, award number 1127888.

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