This multi-disciplinary in-vivo imaging project combines methods from x-ray physics, radio- and nano-chemistry, environmental & agricultural sciences, computer modelling and mathematics to visualize otherwise inseparable plants tissues to 1) determine their anatomy in 3D, 2) track tissue developmental patterning, 3) analyze transport rates of natural compounds around the plant body, and 4) study the effect of environmental conditions on plant development, compound transport and metabolism in 4D.
In order to see different anatomical features, biological samples are usually cut to thin 2D slices, stained with histological stains and imaged using light microscopy. However, only limited spatial information can be gained from 2D sections and samples represent single time points. This is problematic for developmental and temporal transport assays.
Material scientists routinely use x-ray µ-Computed Tomog-raphy (µCT) for 3D objects. Understandably, also plant researchers have found the benefits of µCT and synchrotron imaging modalities, which enable analysis of intact plant samples in 3D with high resolution. Imaging biological samples that are fixed or dried is relatively easy, but this is not the case with living subjects. Every living thing that contains water, DNA, RNA and proteins is susceptible for genotoxic radiation damage, cell death, tissue shrinkage and movement, which make in-vivo imaging exposure duration and radiation dose sensitive. For this reason temporal 4D x-ray microscopy analyses have not been conducted routinely.
Similarly to animal research, seemingly uniform plant tissues can be highlighted and differentiated by infusing them with contrast dyes (see image). In order to take the next big step forward, plant specific “natural-compound mimicking” custom-made radiolabelled contrast dyes will be synthetized and tested on multiple imaging modalities. To summarize, our aim to advance high resolution 4D x-ray in-vivo imaging by 1) generating high resolution datasets from living plants infiltrated with tissue specific contrast dyes, 2) developing new sparing imaging algorithms, and 3) enabling semi-automatic sample analysis and comparison of different 3D datasets via advanced computing.