Using video microscopy to improve quantitative estimates of protozoal motility and cell volume

Abstract

The objective of this study was to apply digital imaging to improve quantification of rumen protozoal biomass and distinguish treatment differences in cell motility and volume among ruminal protozoa. Observations of protozoa in rumen fluid treated with essential oils (CinnaGar, CIN; Provimi North America, Brookville, OH) or an ionophore (monensin, MON) indicated possible cell shrinkage. We hypothesized that MON would decrease protozoal motility and interact with CIN on cell volume. In addition, we hypothesized that analysis of still frames from video of swimming protozoa would improve volume prediction accuracy. Flocculated rumen fluid was incubated in batch culture dosed with N-free feed only (control), MON, CIN, or a combination of MON+CIN. Samples were taken at 0, 3, or 6 h post-treatment and wet-mounted on a microscope fitted with a high-definition camera. At 3 h post-inoculation, there was a treatment interaction for average speed such that CIN attenuated the effect of MON, with treatment means of 243, 138, 211, and 183 µm/s for control, MON, CIN, and MON+CIN, respectively. At 6 h post-inoculation, MON decreased average speed by 79.2 µm/s compared with the main effect mean without MON. We measured both minimum and maximum diameters (depth and width, respectively) perpendicular to the longitudinal axis of swimming protozoa, yielding a 3-dimensional estimate of protozoal volume. The ellipsoid formula (4/3)πabc, where a = 1/2 length, b = 1/2 width, and c = 1/2 depth, was compared with previously published volume estimations using genera-specific coefficients (genera-specific coefficient × length × width). Residuals (genera-specific coefficients - ellipsoid) were plotted against predicted (ellipsoid) and centered to the mean (X-x¯) to evaluate both mean and slope biases. For Entodinium spp., Y = 0.248 (±0.037) (X - 7.98 × 10) + 1.97 × 10 (±1.48 × 10); n = 100; r [coefficient of determination (squared correlation coefficient)] = 0.31, with significant slope and mean biases. For family Isotrichidae, Y = -0.124 (±0.068) (X - 2.54 × 10) - 1.21 × 10 (±4.86 × 10); n = 32; r = 0.10, where slope tended to be different from zero but with no mean bias. For Epidinium spp., Y = 0.375 (±0.056) (X - 2.45 × 10) + 6.65 × 10 (±0.28 × 10); n = 64; r = 0.43, with both mean and slope biases. The present regression analyses demonstrate that the genera-specific coefficient-based method more likely overestimates volume for Entodinium and Epidinium than for the teardrop-shaped Isotrichidae. Based on simulations derived from previous literature reporting treatments that depress protozoal populations or among-animal changes in protozoal population structures, our proposed ellipsoid method offers potential to advance the prediction of treatment effects on protozoal volume and to shift focus from the number of cells present to the diversity, function, and biomass of protozoa under various treatment conditions.

Extending Burk Dehority’s Perspectives on the Role of Ciliate Protozoa in the Rumen

“…Dasytricha ruminantium images and Isotricha intestinalis videos were added. As discussed previously, Burk distinguished Charonina ventriculi, and images are now better represented in the library to help future researchers distinguish this atypical protozoan's unusual features (Wenner et al, 2018).…”

“…Very early expectation for such high contribution of protozoal biomass (at least 50% of the microbial biomass) were at least in part derived from calculations based on protozoal volume and converted to biomass. However, these prior protozoal volumes derived using the geometric formula for cylinders were probably overestimated by 25 to 40% (Wenner et al, 2018). Based on these arguments, rather than 50%, perhaps a more accurate expectation should be 25% of the microbial biomass being derived from protozoa in the rumen compared with 15% in the ruminal outflow for lactating dairy cattle (Ahvenjärvi et al, 2018).…”

“…Based on this work, one of our final exchanges with Burk was when he suggested to briefly decrease the temperature to eliminate protozoa from continuous cultures. Curiously, our continuous culture conditions sometimes greatly increased the relative abundance of Charonina (Wenner, 2016), which is often overlooked during typical counting and therefore might contribute more to protozoal ecology than currently known (Wenner et al, 2018). Dehority and Mattos (1978) noted that this genus had an ecology more closely resembling that of entodiniomorphids even though it was then classified with the isotrichids, perhaps envisaging its reclassification in Entodiniomorphida (Cedrola et al, 2015).…”

“…He reasoned that cell counts should be multiplied by ruminal volume to derive a rumen pool. The nutritional need to move from cell counts to ruminal and duodenal biomass led to our development of a qPCR technique that relies on collecting a protozoal standard (Sylvester et al, 2005) and our development of a videographic technique (Wenner et al, 2018) to adapt counts to volume. Both counts and protozoal 18S rRNA gene copies are useful (Kittelmann et al, 2015), but both also have pros and cons that will be described in a subsequent section.…”

“…Therefore, the absolute abundance (copies of 18S rRNA genes per unit of weight or volume of samples) and relative abundance (% of total protozoal sequences) of small entodinia can be underestimated by qPCR and amplicon sequencing, respectively, whereas those with a large cell, such as Polyplastron and Epidinium, are overestimated (Ishaq and Wright, 2014;Kittelmann et al, 2015). As such, 18S rRNA gene-targeting qPCR probably does not allow accurate quantification of protozoal abundance per se but might be associated more positively with activity if we can assume that larger cells have greater activity on a cell basis (Wenner et al, 2018). Kittelmann et al (2015) showed that microscopic counting was more accurate than high-throughput sequencing using primer set RP841F/Reg1302R in determining protozoal abundance.…”