Digital Holographic Microscopy in Veterinary Medicine-A Feasibility Study to Analyze Label-Free Leukocytes in Blood and Milk of Dairy Cows

Abstract

For several years, the determination of a differential cell count of a raw milk sample has been proposed as a more accurate tool for monitoring the udder health of dairy cows compared with using the absolute somatic cell count. However, the required sample preparation and staining process can be labor- and cost-intensive. Therefore, the aim of our study was to demonstrate the feasibility of analyzing unlabeled blood and milk leukocytes from dairy cows by means of digital holographic microscopy (DHM). For this, we trained three different machine learning methods, i.e., k-Nearest Neighbor, Random Forests, and Support Vector Machine, on sorted leukocyte populations (granulocytes, lymphocytes, and monocytes/macrophages) isolated from blood and milk samples of three dairy cows by using fluorescence-activated cell sorting. Afterward, those classifiers were applied to differentiate unlabeled blood and milk samples analyzed by DHM. A total of 70 blood and 70 milk samples were used. Those samples were collected from five clinically healthy cows at 14-time points within a study period of 26 days. The outcome was compared with the results of the same samples analyzed by flow cytometry and (in the case of blood samples) also to routine analysis in an external laboratory. Moreover, a standard vaccination was used as an immune stimulus during the study to check for changes in cell morphology or cell counts. When applied to isolated leukocytes, Random Forests performed best, with a specificity of 0.93 for blood and 0.84 for milk cells and a sensitivity of 0.90 and 0.81, respectively. Although the results of the three analytical methods differed, it could be demonstrated that a DHM analysis is applicable for blood and milk leukocyte samples with high reliability. Compared with the flow cytometric results, Random Forests showed an MAE of 0.11 (SD = 0.04), an RMSE of 0.13 (SD = 0.14), and an MRE of 1.00 (SD = 1.11) for all blood leukocyte counts and an MAE of 0.20 (SD = 0.11), an RMSE of 0.21 (SD = 0.11) and an MRE of 1.95 (SD = 2.17) for all milk cell populations. Further studies with larger sample sizes and varying immune cell compositions are required to establish method-specific reference ranges.

Keywords: dairy cow; differential cell count; differential leukocyte count; digital holographic microscopy; immunomonitoring; immunophenotyping; somatic cell count; udder health.

Figure 1

Schematic overview of the workflow.

Figure 2

Sampling scheme of blood and milk samples. Blood and milk samples were collected from each of the five cows at 14 time points. All cows were vaccinated on day 8 after sampling.

Figure 3

Representative false-color phase images of different populations of unlabeled leukocytes, analyzed in DHM. (A) Blood granulocyte; (B) Blood lymphocyte; (C) Blood monocyte; (D) Milk granulocyte; (E) Milk lymphocyte; (F) Milk macrophage.

Figure 4

Exemplary scatter plots of flow cytometric and digital holographic microscopy analyses. (A) Blood leukocyte populations analyzed by FACS; (B) Blood leukocyte populations analyzed by DHM; (C) Milk leukocyte populations analyzed by FACS; (D) Milk leukocyte populations analyzed by DHM.

Figure 5

Confusion matrix showing the results of Random Forest classification of sorted blood cells.

Figure 6

Confusion matrix showing the results of Random Forest classification of sorted milk cells.

Figure 7

Cell count progression over time, DHM results obtained using k-Nearest Neighbor. (A) Blood cells of cow #963, analyzed by DHM, FACS and external laboratory; (B) Milk cells of cow #963, analyzed by DHM and FACS.