Spectral imaging showing the reflectance variation of a crust surface before (A) and 60 minutes after addition of water (B) in vertically and horizontally positioned crust pieces.

<p>Chl <i>a</i> and PC indicate the spectral signals for chlorophyll <i>a</i> and phycocynin absorption, respectively at different time intervals (indicated in hours in the insert) after addition of water (C). The change of these two pigments during a wetting period of 180 minutes is shown (D). The concentration of Chl <i>a</i> showed an increase with time in crust pieces monitored from the top and from the sides (E).</p

Net oxygen production and recovery of respiration and photosynthesis was monitored in the wet crust piece using oxygen microsensor measurements.

<p>The time after wetting is indicated at the bottom of each profile. Note that respiration started within 4 minutes and increased up to 13 minutes, after which oxygen was produced via photosynthesis and net oxygen production reached a maximum after 118 minutes. The oxygen profiles did not shift upward and oxygen maxima stayed at the same depth, indicating the absence of upward migration of cyanobacteria.</p

Photographs showing the soil surface colour in crust pieces before (A) and 20 minutes after addition of water (B).

<p>Photographs showing the soil surface colour in crust pieces before (A) and 20 minutes after addition of water (B).</p

Pigment concentrations (mg g⁻¹ crust) from the top (<1 mm) and bottom (1–10 mm) layers of crusts (n = 3) after addition of water at different time intervals as analyzed using high performance liquid chromatography (HPLC).

<p>Note that the pigment concentrations at T0 were obtained from extracts of crust pieces directly after wetting.</p

The percentage of ¹³C label in the newly synthesized Chl <i>a</i> determined using MALDI-TOF mass spectrometry and the decrease of ¹³C labeled bicarbonate in the medium (n = 3).

<p>The percentage of ¹³C label in the newly synthesized Chl <i>a</i> determined using MALDI-TOF mass spectrometry and the decrease of ¹³C labeled bicarbonate in the medium (n = 3).</p

Exploring Three-Dimensional Matrix-Assisted Laser Desorption/Ionization Imaging Mass Spectrometry Data: Three-Dimensional Spatial Segmentation of Mouse Kidney

Three-dimensional (3D) imaging has a significant impact on many challenges of life sciences. Three-dimensional matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS) is an emerging label-free bioanalytical technique capturing the spatial distribution of hundreds of molecular compounds in 3D by providing a MALDI mass spectrum for each spatial point of a 3D sample. Currently, 3D MALDI-IMS cannot tap its full potential due to the lack efficient computational methods for constructing, processing, and visualizing large and complex 3D MALDI-IMS data. We present a new pipeline of efficient computational methods, which enables analysis and interpretation of a 3D MALDI-IMS data set. Construction of a MALDI-IMS data set was done according to the state-of-the-art protocols and involved sample preparation, spectra acquisition, spectra preprocessing, and registration of serial sections. For analysis and interpretation of 3D MALDI-IMS data, we applied the spatial segmentation approach which is well-accepted in analysis of two-dimensional (2D) MALDI-IMS data. In line with 2D data analysis, we used edge-preserving 3D image denoising prior to segmentation to reduce strong and chaotic spectrum-to-spectrum variation. For segmentation, we used an efficient clustering method, called bisecting <i>k</i>-means, which is optimized for hierarchical clustering of a large 3D MALDI-IMS data set. Using the proposed pipeline, we analyzed a central part of a mouse kidney using 33 serial sections of 3.5 μm thickness after the PAXgene tissue fixation and paraffin embedding. For each serial section, a 2D MALDI-IMS data set was acquired following the standard protocols with the high spatial resolution of 50 μm. Altogether, 512 495 mass spectra were acquired that corresponds to approximately 50 gigabytes of data. After registration of serial sections into a 3D data set, our computational pipeline allowed us to reveal the 3D kidney anatomical structure based on mass spectrometry data only. Finally, automated analysis discovered molecular masses colocalized with major anatomical regions. In the same way, the proposed pipeline can be used for analysis and interpretation of any 3D MALDI-IMS data set in particular of pathological cases