Juergen Schnekenburger

Multimodal label-free in vitro toxicity testing with digital holographic microscopy

Common in vitro toxicity tests of drugs, chemicals or nanomaterials involves the measurement of cellular endpoints like stress response, cell viability, proliferation or cell death. The assay systems determine enzyme activity or protein expression by optical read out of enzyme substrates or marker protein labeling. These standard procedures have several disadvantages. Cellular processes have to be stopped at a distinct time point for the read out, where usually only parts of the cells were affected by the treatment with substances. Typically, only one parameter is analyzed and detection of cellular processes requires several time consuming incubations and washing steps. Here we have applied digital holographic microscopy (DHM) for a multimodal label-free analysis of drug toxicity. NIH 3T3 cells were incubated with 1 μM Taxol for 24 h. The recorded quantitative phase images were analyzed for cell thickness, cell volume, dry mass and cell migration. Taxol treated cells showed rapidly decreasing cell motility as measure of cell viability. A short increase in cell thickness and dry mass indicated cell division and growth in control cells, whereas Taxol treatment resulted in a continuous increase in cell height followed by a rapid decrease and a decrease of dry mass as indicators of cell death. Multimodal DHM analysis of drug treatment by multiple parameters allows direct and label-free detection of several toxicity parameters in parallel. DHM can quantify cellular reactions minimally invasive over a long time period and analyze kinetics of delayed cellular responses. Our results demonstrate digital holographic microscopy as a valuable tool for multimodal toxicity testing.

Dynamic in vivo analysis of drug induced actin cytoskeleton degradation by digital holographic microscopy

The actin cytoskeleton mediates a variety of crucial cellular functions as migration, intracellular transport, exocytosis, endocytosis and force generation. The highly dynamic actin fibers are therefore targets for several drugs and toxins. However the study of actin interfering processes by standard microscopy techniques fails in the detailed resolution of dynamic spatial alterations required for a deeper understanding of toxic effects. Here we applied digital holographic microscopy in the online functional analysis of the actin cytoskeleton disrupting marine toxin Latrunculin B. SEM and fluorescence microscopy showed rapid Latrunculin B induced alterations in cell morphology and actin fiber degradation in pancreas tumor cells. The dynamic digital holographic in vivo analysis of the drug dependent cellular processes demonstrated differences in the actin cytoskeleton stability of highly differentiated and dedifferentiated pancreas tumor cell lines. The spatial resolution of the morphological alterations revealed unequal changes in cell morphology. While cells with a low metastatic potential showed Latrunculin B induced cell collapse within 4 h the metastatic tumor cells were increased in cell volume indicating Latrunculin B effects also on cell water content. These data demonstrate that marker free, non-destructive online analysis of cellular morphology and dynamic spatial processes in living cells by digital holography offers new insights in actin dependent cellular mechanisms. Digital holographic microscopy was shown to be a versatile tool in the screening of toxic drug effects and cancer cell biology.

Multivariate classification of fourier transform infrared hyperspectral images of skin cancer cells

A multilevel framework for the multiclass classification of spectra extracted from Fourier transform infrared images is described. This learning structure was employed to discriminate the spectra extracted from hyperspectral images of two batches of four different skin cultured cells (two normal and two tumor), where the cells of one batch had been stained with fluorescence live cell dyes. Different options were explored in each stage of the framework, specifically in the spectral pre-processing and the employed classification algorithm. Special care was taken to optimize the learning models and to objectively estimate the generalization performance by means of cross-validation. A very high discriminative performance was obtained for all the unstained skin cell types. However, the presence of the stains introduces spectral artifacts that worsen the class separation, as has been demonstrated in several classification experiments.

Discrimination of skin cancer cells using Fourier transform infrared spectroscopy

Fourier transform infrared (FTIR) spectroscopy is a highly versatile tool for cell and tissue analysis. Modern commercial FTIR microspectroscopes allow the acquisition of good-quality hyperspectral images from cytopathological samples within relatively short times. This study aims at assessing the abilities of FTIR spectra to discriminate different types of cultured skin cell lines by different computer analysis technologies. In particular, 22700 single skin cells, belonging to two non-tumoral and two tumoral cell lines, were analysed. These cells were prepared in three different batches that included each cell type. Different spectral preprocessing and classification strategies were considered, including the current standard approaches to reduce Mie scattering artefacts. Special care was taken for the optimisation, training and evaluation of the learning models in order to avoid possible overfitting. Excellent classification performance (balanced accuracy between 0.85 and 0.95) was achieved when the algorithms were trained and tested with the cells from the same batch. When cells from different batches were used for training and testing the balanced accuracy reached values between 0.35 and 0.6, demonstrating the strong influence of sample preparation on the results and comparability of cell FTIR spectra. A deep study of the most optimistic results was performed in order to identify perturbations that influenced the final classification.

New Spectral Representation and Dissimilarity Measures Assessment for FTIR-spectra using Unsupervised Classification

In this work, different combinations of dissimilarity coefficients and clustering algorithms are compared in order to separate FTIR data in different classes. For this purpose, a dataset of eighty five spectra of four types of sample cells acquired with two different protocols are used (fixed and unfixed). Five dissimilarity coefficients were assessed by using three types of unsupervised classifiers (K-means, K-medoids and Agglomerative Hierarchical Clustering). We introduce in particular a new spectral representation by detecting the signals´ peaks and their corresponding dynamics and widths. The motivation of this representation is to introduce invariant properties with respect to small spectra shifts or intensity variations. As main results, the dissimilarity measure called Spectral Information Divergence obtained the best classification performance for both treatment protocols when is used over the proposed spectral representation.