Speaker
Description
Elemental imaging has become an important tool for investigating biological systems, providing spatially resolved measurements of elements in whole organisms, tissues and cells. While instrumentation continues to advance across multiple modalities including laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS), X-ray fluorescence microscopy (XFM), and particle-induced X-ray emission (PIXE), and laser induced breakdown spectroscopy (LIBS), software development has largely remained focused on instrument-specific data processing. As a result, many biological researchers lack access to elemental imaging data co-registration, interpretation and statistical tools designed around the common questions asked of elemental imaging data.
We present a suite of complementary open-source software tools developed through the Biomedical National Elemental Imaging Resource (BNEIR) that support modality-independent analysis of biological elemental imaging datasets. TRACE enables co-registration of elemental maps with histological whole-slide images, facilitating tissue annotation and quantitative comparison of elemental abundance across biological structures. TRACE further supports integration with other spatially resolved datasets through a unified tissue coordinate framework.
Muad’Data provides interactive visualization and quantitative exploration of elemental maps, including region-of-interest analysis, elemental overlays, ratio imaging, and extraction of summary statistics from user-defined tissue regions. ScaleBarOn supports standardized visualization and comparison of large collections of elemental images through common scaling approaches, publication-ready figure generation, and quantitative comparison across biological replicates.
Together, these tools demonstrate an alternative software paradigm for elemental imaging in biology, emphasizing biological interpretation rather than instrument-specific data processing. We discuss future opportunities for community software development, including implementation of spatial statistics, measures of heterogeneity and clustering (e.g., Moran’s I), cross-platform data standards, and interoperable workflows that enable quantitative comparison of elemental distributions across imaging modalities. Such capabilities will be increasingly important as elemental imaging datasets grow in size, complexity, and biological relevance.