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Burns, T. J.

Publications and source records attributed to Burns, T. J..

2 recordsLinked to original sources

Environmental DNA sampling in a terrestrial environment: methods to detect a critically endangered frog and a global pathogen

Environmental DNA techniques have become established as a useful tool for biological monitoring and are used extensively to determine species presence in aquatic systems. However, their application in terrestrial systems has been more limited, likely in part due to difficulties in choosing where to sample and ensuring that collected DNA reflects current species presence. We developed methods to sample eDNA in the terrestrial environment and trialled them under controlled and field conditions. We targeted three species, an elusive critically endangered frog, an abundant non-threatened frog, and the globally distributed amphibian skin pathogen chytrid fungus, which has been implicated in the decline of over 500 amphibian species. We used a sandpaper-sampling surface to trap DNA. After sampling, we washed the surface and filtered the wash water to gather material for DNA extraction and subsequent qPCR. Our controlled condition experiments demonstrated that frog and chytrid fungus DNA was detectable after as few as five contacts between a frog and the sampling surface. Furthermore, this DNA remained detectable after two weeks in cool, shaded, outdoor conditions. Our field experiments demonstrated that these techniques were transferable to natural habitats, where we detected both the common and rare amphibian target species, as well as chytrid fungus. Field sampling eDNA results were broadly consistent with those derived from conventional survey methods. Our methods have potential application in non-invasive sampling of amphibians and other species in terrestrial systems, broadening the applicability of eDNA techniques for species detection and monitoring.

ecology↗

Continuous visualization of differences between biological conditions in single-cell data

In high-dimensional single cell data, comparing changes in functional markers between conditions is typically done across manual or algorithm-derived partitions based on population-defining markers. Visualizations of these partitions is commonly done on low-dimensional embeddings (eg. t-SNE), colored by per-partition changes. Here, we provide an analysis and visualization tool that performs these comparisons across overlapping k-nearest neighbor (KNN) groupings. This allows one to color low-dimensional embeddings by marker changes without hard boundaries imposed by partitioning. We devised an objective optimization of k based on minimizing functional marker KNN imputation error. Proof-of-concept work visualized the exact location of an IL-7 responsive subset in a B cell developmental trajectory on a t-SNE map independent of clustering. Per-condition cell frequency analysis revealed that KNN is sensitive to detecting artifacts due to marker shift, and therefore can also be valuable in a quality control pipeline. Overall, we found that KNN groupings lead to useful multiple condition visualizations and efficiently extract a large amount of information from mass cytometry data. Our software is publicly available through the Bioconductor package Sconify.

systems biology↗