August 2016, Amsterdam
The interactive 3D model below shows a slice through an aggregate collected from lake Markermeer, the Netherlands (June 2016). Each color in this aggregate model represents a different aggregate constituent. Blue corresponds to algae, green to bacteria, red to exopolysaccharides ('extracellular sugars', EPS) and white to alkaline phosphatase activity. The slide is 140 µm wide, 140 µm long and 30 µm thick.
Algae are at the base of the food web, directly or indirectly feeding higher trophic levels like shellfish, fish and birds. Some algae affect water quality by producing toxins and/or smelly scums on top of the water column that could suffocate other aquatic life. For these and other reasons, it is important to understand what factors drive algal growth.
In lake Markermeer, we do not understand why algae are able to grow in the water column. Algae require both light and nutrients (including orthophosphate) to grow. High concentrations of suspended lake bed particles quickly attenuate incident light in lake Markermeer. Consequently, it is likely that only the top surface layer of the water column provides sufficient light intensities for algal growth. Yet, orthophosphate concentrations in the water column - including the top surface layer with sufficient light - are very low. So how do algae acquire both light and nutrients to grow?
As you can see in the 3D model, Markermeer aggregates exhibit alkaline phosphatase activity (white). Alkaline phosphatases are enzymes that can release orthophosphate from organic phosphorus-comprising compounds. Thus, these aggregates potentially provide algae (blue) with nutrients in the surface layer of the water column. The location of alkaline phosphatase matches that of bacteria (green). This could indicate that bacteria produced these enzymes. Furthermore, you can see that algae, bacteria and alkaline phosphatases are embedded in exopolysaccharides (red). We expect that these extracellular sugars promote aggregate constituents to stick together.
Image data to construct the 3D model were acquired using an inverted confocal laser scanning microscope (LSM510, Zeiss). Images were acquired at different 'depths' (distances from the top/ bottom) of the aggregate. These optical slices were stacked on top of each other in order to reconstruct a 3D image. The different aggregate constituents (algae, bacteria, exopolysaccharides, ALPase activity) were detected by their different fluorescent properties. Algae were identified by a fluorescent pigment, chlorophyll a. The other constituents (bacteria, exopolysaccharides and ALPase activity) were labeled with fluorescent compounds (Syto-9, TRITC labeled lectins and ELF-97 substrate, respectively) prior to image acquisition.Model construction
From the image stack, a 3D model was constructed. Raw stack data was converted to a 4-channel TIFF-image stack using ImageJ distribution Fiji. The Tiff-image stack was processed using the MATLAB toolbox DIPimage. The six image processing steps are shown in the codebox below.