Automated enumeration of Eimeria oocysts in feces for rapid coccidiosis monitoring

Abstract

Coccidiosis represents a major driver in the economic performance of poultry operations, as coccidia control is expensive, and infections can result in increased feed conversion ratios, uneven growth rates, increased co-morbidities with pathogens such as Salmonella, and mortality within flocks. Shifts in broiler production to antibiotic-free strategies, increased attention on pre-harvest food safety, and growing incidence of anti-coccidial drug resistance has created a need for increased understanding of interventional efficacy and methods of coccidia control. Conventional methods to quantify coccidia oocysts in fecal samples involve manual microscopy processes that are time and labor intensive and subject to operator error, limiting their use as a diagnostic and monitoring tool in animal parasite control. To address the need for a high-throughput, robust, and reliable method to enumerate coccidia oocysts from poultry fecal samples, a novel diagnostic tool was developed. Utilizing the PIPER instrument and MagDrive technology, the diagnostic eliminates the requirement for extensive training and manual counting which currently limits the application of conventional microscopic methods of oocysts per gram (OPG) measurement. Automated microscopy to identify and count oocysts and report OPG simplifies analysis and removes potential sources of operator error. Morphometric analysis on identified oocysts allows for the oocyst counts to be separated into 3 size categories, which were shown to discriminate the 3 most common Eimeria species in commercial broilers, E. acervulina, E. tenella, and E. maxima. For 75% of the samples tested, the counts obtained by the PIPER and hemocytometer methods were within 2-fold of each other. Additionally, the PIPER method showed less variability than the hemocytometer counting method when OPG levels were below 100,000. By automated identification and counting of oocysts from 12 individual fecal samples in less than one hour, this tool could enable routine, noninvasive diagnostic monitoring of coccidia in poultry operations. This approach can generate large, uniform, and accurate data sets that create new opportunities for understanding the epidemiology and economics of coccidia infections and interventional efficacy.

Keywords: Eimeria enumeration; Eimeria oocyst; coccidia; coccidiosis monitoring.

Figure 1

Schematic of Assay Workflow. (A) Sample preparation. 1 g of a fecal sample is mixed with 5 mL of 1M NaOH in a filter bag (side A). The bag is massaged for 30 s to thoroughly mix the sample followed by incubation for 15 min at room temperature. Another 5 mL of a saturated sugar solution (Sample Additive) is subsequently added to prevent oocyst settling, and the sample is mixed again. A 280 µL aliquot of the slurry is then removed from the filtered side of the bag (side B) to avoid any solids that could clog the microfluidic device and transferred to a new tube. The sample is mixed with 20 µL of Ferrofluid and 3 µL of a nucleic acid intercalating dye (Detection Reagent), vortexed to mix, and loaded into a single well of a PIPER cartridge. (B) Separation of oocysts on PIPER. Sample is mixed with a biocompatible ferrofluid and loaded into a cartridge. Microvalves in the cartridge control a pumping layer in the cartridge which pulls the sample over the magnetic PCB, which pushes target cells up for imaging by a built-in epifluorescent microscope above the cartridge. Data can be transferred to a cloud-based system or to a connected laboratory information management system based on the needs of the end user.

Figure 2

Example of PIPER image detecting oocysts. Image is magnified 100%. Detected oocysts are indicated by circles. Color discriminates oocysts based on size: large (yellow), medium (blue), or small (green).

Figure 3

Accuracy of image recognition algorithm. Plot of counts obtained by manual review of 67 images (corresponding to replicates from 3 individual, fecal samples) to counts obtained for the same images by the image recognition algorithm. A plot of manual counts to algorithm counts for each of the images shows a linear regression line with a slope near 1 and coefficient of determination (R-squared) of 0.99.

Figure 4

Correlation to Hemocytometer. Paired average hemocytometer and average PIPER counts for 77 independent samples were plotted against each other to determine the calibration of PIPER counts to oocysts per gram (OPG). The calibration from PIPER count to OPG (as measured by hemocytometer) was determined by fitting a regression line through the origin. The slope of the regression line was 425, generating the following calibration equation: PIPER total OPG = 425 × [PIPER total oocyst count]. The coefficient of determination (R-squared) for the regression line was 0.9835.

Figure 5

Comparison of PIPER to hemocytometer variability. Graph of the average log hemocytometer OPG versus the CV of hemocytometer (circles) or PIPER (plus symbols) counts. The line is a LOESS local regression used to generate a smoothed curve representing each CV (smooth line: hemocytometer; dashed line: PIPER). The shadows represent the standard error (95% confidence interval) of the mean CV of each measurement. Note that the mean PIPER count CV is more consistent and less than the hemocytometer count CV below 100,000 (log = 5). Above 100,000 OPG, there is not an appreciable difference in method mean CVs in this data set.

Figure 6

Assay linearity. Plot of PIPER counts obtained for 3 different amounts of each of four, cleaned oocyst samples versus the predicted counts for each sample portion. The average count for replicates of the 1× portion of each sample was multiplied by 0.3 or 0.1, respectively, to calculate a predicted count for the smaller portions. The r² value for the line y = x was 0.9666.

Figure. 7

Relative percent difference between PIPER and hemocytometer. Ninety-six samples with hemocytometer OPG between 100,000 and 2 million were evaluated by paired hemocytometer and PIPER counts. 44 samples were evaluated at Site A (squares), 20 samples were evaluated at Site B (triangles), and 32 samples were evaluated at Site C (circles). Average counts for the replicates of each sample were calculated. The relative percent difference was computed using the formula RPD = hemocytometer OPG – average PIPER OPG//[(hemocytometer OPG + PIPER OPG)/2]. The dashed lines at ± 66.6% represent a two-fold difference.