Field Comparisons of Three Biomarker Detection Methods in Icelandic Mars Analogue Environments

Abstract:

The ability to estimate the spatial and temporal distributions of biomarkers has been identified as a key need for planning life detection strategies. In a typical planetary exploration scenario, sampling site selection will be informed only by remote sensing data; however, if a difference of a few tens of meters, or centimeters, makes a significant difference in the results, science objectives may not be met.

We conducted an analogue planetary expedition to test the correlation of three common biomarker detection methods -- cell counts through fluorescence microscopy, ATP quantification, and quantitative PCR with universal primer sets (bacteria, archaea, and fungi) -- and their spatial scale representativeness. Sampling sites in recent Icelandic lava fields (Fimmvörđuháls and Eldfell) spanned four nested spatial scales: 1 m, 10 m, 100 m, and > 1 km. Each site was homogeneous at typical 'remote sampling' resolution (overall temperature, apparent moisture content, and regolith grain size).

No correlation between cell counts and either ATP or qPCR data was significant at any distance scale; ATP quantification and the archaeal and fungal qPCR data showed a marginal negative correlation at the 1 m level. Visible cell count data was statistically site-dependent for sites 10 m and 100 m apart, but not for sites > 1 km apart, whereas ATP results and qPCR data showed site dependence at all four scales. Distance had no significant effect on variability in cell counts and qPCR data, but was positively correlated with ATP variability.

These results highlight the difficulty of choosing a 'good' biomarker: not only may different methods yield conflicting results, but they may also be differentially representative of the overall area. We intend to expand on this work with a follow-up campaign using comprehensive assays of physicochemical site properties to better distinguish between effects of environmental variability and intrinsic biomarker variability.