Soil collected from victims or suspects of a crime, or on evidentiary items associated with illegal activity, has long been recognized as forensically important, with the potential to tie evidence to a specific location. Unfortunately soil is generally regarded as class evidence due to the lack of individualizing features produced from conventional analyses of its chemical and physical characteristics. However, soil samples have other attributes that have the potential to help individualize them. Most notable of these are the enormous number and variety of microorganisms that exist in each gram of soil, the characterization of which could possibly act as a microbial fingerprint for a given soil sample, if techniques can be developed that thoroughly and reliably assay their presence and abundance. Multiple techniques for characterizing the microbiome of soil at the molecular level have been proposed, however none of these has resulted in dependable, reproducible datasets that can be used to reliably identify a soil sample, generally either because the diversity of microorganisms assayed is not well represented, or because the data produced lack the detail needed for individualization. In the research proposed here, the massive datasets that can quickly and accurately be produced via next generation sequencing will be used for soil identification. This methodology was unthinkable just a few years ago, but today it is widely used by microbial ecologists, including those who study bacterial populations in soils, although it has not been leveraged in a forensic context. Next generation sequencing results in huge amounts of specific data (well over 100,000 sequences of ca. 500 bp in a single run), and computer statistical methods have been developed to analyze them in an efficient and highly informative manner.
Importantly, the data are directly related to both the presence and abundance of different bacterial species/groups present, thus both species and abundance can be considered. The proposed study addresses four questions essential for any soil identification assay:
1. Can habitat types be reliably differentiated?
2. Can similar habitats from various sites be differentiated?
3. To what extent, if any, does bacterial content in a soil vary over small areas (spatial variability)?
4. To what extent, if any, does the bacterial makeup of a site change over time (temporal variability)?
In the series of experiments proposed we will use next generation sequencing of hypervariable regions of the bacterial 16S ribosomal RNA gene found in all bacteria to:
Measure the ability to differentiate soils from 10 habitat types common in our area,
including: agricultural (2 types) and non-agricultural fields, coniferous and deciduous woods, construction site, grass lawn, marsh edge, road edge, and sandy lakeshore
Measure the ability to differentiate soils from a single habitat type (lawn) from
spatially different sites
Examine temporal variability of bacteria in soil by sampling over time (daily, weekly,
monthly, quarterly, annually)
Examine spatial variability within a habitat
And finally, test the ability to distinguish specific soils on mock evidentiary items
The data produced will be analyzed using the software Mothur, which has specifically been developed by microbial ecologists to analyze and characterize these types of datasets. Principal component analysis and multidimensional scaling will then be used to see if, and the extent to which, soils from various habitats/sites are differentiated. In preliminary examination of a lawn, woodland, and marsh edge, soil samples from them could readily be distinguished, indicating that this strategy holds great promise.