FTIR-spektroskopische Untersuchungen an der kleinen GTPase Ras und kolorektalem Karzinom

Mit trFTIR wurde die Hydrolysereaktion der GTPase Ras untersucht. Für die Rolle interner Wassermoleküle konnten durch Optimierung der Messbedingungen 5 Kontinuumsabsorptionen zeitaufgelöst beobachtet werden. Ortsspezifische Mutationen (Q61A, E63D/Q) zeigten, dass die Banden dem H-brückennetzwerk in Front des $\gamma$-Phosphats zuzuordnen sind. So konnte zum ersten Mal der nukleophile Angriff direkt beobachtet werden. Für die Methode des FTIR-Imaging konnte eine Mess- und Datenbehandlungsmethodik etabliert werden, die als Grundlage für klinische Studien an Geweben dienen kann. Hierzu wurde ein umfangreiches $\it {Toolset}$ etabliert, mit dem die Schritte schnell und flexibel erledigt werden können. Die Messfläche konnte von $mm^{2}$ auf $cm^{2}$ erweitert werden. Der Random Forest konnte für spektrale Daten als schneller und robuster Klassifizierer mit einer Genauigkeit von 95% genutzt werden. Ähnlichkeitskarten konnten zudem als neue Methode der Datensortierung und Charakterisierung etabliert werden

Similarity maps and hierarchical clustering for annotating FT-IR spectral images

BACKGROUND: Unsupervised segmentation of multi-spectral images plays an important role in annotating infrared microscopic images and is an essential step in label-free spectral histopathology. In this context, diverse clustering approaches have been utilized and evaluated in order to achieve segmentations of Fourier Transform Infrared (FT-IR) microscopic images that agree with histopathological characterization. RESULTS: We introduce so-called interactive similarity maps as an alternative annotation strategy for annotating infrared microscopic images. We demonstrate that segmentations obtained from interactive similarity maps lead to similarly accurate segmentations as segmentations obtained from conventionally used hierarchical clustering approaches. In order to perform this comparison on quantitative grounds, we provide a scheme that allows to identify non-horizontal cuts in dendrograms. This yields a validation scheme for hierarchical clustering approaches commonly used in infrared microscopy. CONCLUSIONS: We demonstrate that interactive similarity maps may identify more accurate segmentations than hierarchical clustering based approaches, and thus are a viable and due to their interactive nature attractive alternative to hierarchical clustering. Our validation scheme furthermore shows that performance of hierarchical two-means is comparable to the traditionally used Ward’s clustering. As the former is much more efficient in time and memory, our results suggest another less resource demanding alternative for annotating large spectral images

A representation learning approach for recovering scatter-corrected spectra from Fourier-transform infrared spectra of tissue samples

Infrared spectra obtained from cell or tissue specimen have commonly been observed to involve a significant degree of scattering effects, often Mie scattering, which probably overshadows biochemically relevant spectral information by a nonlinear, nonadditive spectral component in Fourier transform infrared (FTIR) spectroscopic measurements. Correspondingly, many successful machine learning approaches for FTIR spectra have relied on preprocessing procedures that computationally remove the scattering components from an infrared spectrum. We propose an approach to approximate this complex preprocessing function using deep neural networks. As we demonstrate, the resulting model is not just several orders of magnitudes faster, which is important for real-time clinical applications, but also generalizes strongly across different tissue types. Using Bayesian machine learning approaches, our approach unveils model uncertainty that coincides with a band shift in the amide I region that occurs when scattering is removed computationally based on an established physical model. Furthermore, our proposed method overcomes the trade-off between computation time and the corrected spectrum being biased towards an artificial reference spectrum