Ferdinand Jamitzky

Label-Free Live-Cell Imaging with Confocal Raman Microscopy

Confocal Raman spectroscopy is a noninvasive alternative to established cell imaging methods because it does not require chemical fixation, the use of fluorescent markers, or genetic engineering. In particular, single live-cell, high-resolution imaging by confocal Raman microscopy is desirable because it allows further experiments concerning the individually investigated cells. However, to derive meaningful images from the spectroscopic data, one must identify cell components within the dataset. Using immunofluorescence images as a reference, we derive Raman spectral signatures by means of information measures to identify cell components such as the nucleus, the endoplasmic reticulum, the Golgi apparatus, and mitochondria. The extracted signatures allow us to generate representations equivalent to conventional (immuno)fluorescence images with more than three cell components at a time, exploiting the Raman spectral information alone.

The effect of low-temperature plasma on bacteria as observed by repeated AFM imaging

Research on low-temperature atmospheric plasma sources (LTAPS) has grown strongly over the last few years, in part driven by possible medical inxa0vivo applications. LTAPS offer new technology for medicine and biomedical engineering. Important application examples include in situ production of reactive molecules and ions, delivery at the molecular level, contact-free and self-sterilizing devices. An important issue is the efficient bactericidal effect of LTAPS, which has already been studied widely in vitro. In spite of the many investigations, details of the plasma effect on bacteria are still largely unknown. To contribute to a better understanding of the sterilization process, we investigated the morphological changes of bacteria using atomic force microscopy before and after plasma treatment at high resolution. We examined both gram-positive and gram-negative bacteria at different plasma exposure times. Additionally, the effect of UV radiation as one agent in the plasma was investigated separately. Our results suggest that several sterilizing mechanisms exist and they proceed at different timescales.

Homology modeling of human Toll-like receptors TLR7, 8, and 9 ligand-binding domains.

Toll‐like receptors (TLRs) play a key role in the innate immune system. The TLR7, 8, and 9 compose a family of intracellularly localized TLRs that signal in response to pathogen‐derived nucleic acids. So far, there are no crystallographic structures for TLR7, 8, and 9. For this reason, their ligand‐binding mechanisms are poorly understood. To enable first predictions of the receptor–ligand interaction sites, we developed three‐dimensional structures for the leucine‐rich repeat ectodomains of human TLR7, 8, and 9 based on homology modeling. To achieve a high sequence similarity between targets and templates, structural segments from all known TLR ectodomain structures (human TLR1/2/3/4 and mouse TLR3/4) were used as candidate templates for the modeling. The resulting models support previously reported essential ligand‐binding residues. They also provide a basis to identify three potential receptor dimerization mechanisms. Additionally, potential ligand‐binding residues are identified using combined procedures. We suggest further investigations of these residues through mutation experiments. Our modeling approach can be extended to other members of the TLR family or other repetitive proteins.

Chaos in dynamic atomic force microscopy

In tapping mode atomic force microscopy (AFM) the highly nonlinear tip-sample interaction gives rise to a complicated dynamics of the microcantilever. Apart from the well-known bistability under typical imaging conditions the system exhibits a complex dynamics at small average tip-sample distances, which are typical operation conditions for mechanical dynamic nanomanipulation. In order to investigate the dynamics at small average tip sample gaps experimental time series data are analysed employing nonlinear analysis tools and spectral analysis. The correlation dimension is computed together with a bifurcation diagram. By using statistical correlation measures such as the Kullback-Leibler distance, cross-correlation and mutual information the dataset can be segmented into different regimes. The analysis reveals period-3, period-2 and period-4 behaviour, as well as a weakly chaotic regime.

Inhibition of Toll-like receptors TLR4 and 7 signaling pathways by SIGIRR: A computational approach

Toll-like receptors (TLRs) belong to the Toll-like receptor/interleukin-1 receptor (TLR/IL-1R) superfamily which is defined by a common cytoplasmic Toll/interleukin-1 receptor (TIR) domain. TLRs recognize pathogen-associated molecular patterns and initiate an intracellular kinase cascade to trigger an immediate defensive response. SIGIRR (single immunoglobulin interleukin-1 receptor-related molecule), another member of the TLR/IL-1R superfamily, acts as a negative regulator of MyD88-dependent TLR signaling. It attenuates the recruitment of MyD88 adaptors to the receptors with its intracellular TIR domain. Thus, SIGIRR is a highly important molecule for the therapy of autoimmune diseases caused by TLRs. So far, the structural mechanism of interactions between SIGIRR, TLRs and adaptor molecules is unclear. To develop a working hypothesis for this interaction, we constructed three-dimensional models for the TIR domains of TLR4, TLR7, MyD88 and SIGIRR based on computational modeling. Through protein-protein docking analysis, we developed models of essential complexes involved in the TLR4 and 7 signaling and the SIGIRR inhibiting processes. We suggest that SIGIRR may exert its inhibitory effect through blocking the molecular interface of TLR4, TLR7 and the MyD88 adaptor mainly via its BB-loop region.

Scaling-index method as an image processing tool in scanning-probe microscopy

The scaling-index method (SIM) is a novel tool for image processing in scanning-probe microscopy. Originating from the theory of complex systems, the SIM can be used in order to extract structural information from arbitrary data sets. This method can readily be applied to the analysis of digital atomic-force microscopy (AFM) images. Especially for biomedical diagnostics, where genetic material is investigated by various microscopic methods, a reliable image segmentation based on the SIM algorithm is helpful. As a first application, AFM-images of GTG-banded human metaphase chromosomes (with G bands obtained by Trypsin using Giemsa) are compared with micrographs from conventional light microscopy by means of a scaling-index analysis. While the grey-level distributions of the optical and the AFM-images are largely different from each other, the scaling-index images are remarkably similar. Using this method, a fingerprint of an image can be produced which helps in the classification and interpretation of the measured data.

LRRML: a conformational database and an XML description of leucine-rich repeats (LRRs)

BackgroundLeucine-rich repeats (LRRs) are present in more than 6000 proteins. They are found in organisms ranging from viruses to eukaryotes and play an important role in protein-ligand interactions. To date, more than one hundred crystal structures of LRR containing proteins have been determined. This knowledge has increased our ability to use the crystal structures as templates to model LRR proteins with unknown structures. Since the individual three-dimensional LRR structures are not directly available from the established databases and since there are only a few detailed annotations for them, a conformational LRR database useful for homology modeling of LRR proteins is desirable.DescriptionWe developed LRRML, a conformational database and an extensible markup language (XML) description of LRRs. The release 0.2 contains 1261 individual LRR structures, which were identified from 112 PDB structures and annotated manually. An XML structure was defined to exchange and store the LRRs. LRRML provides a source for homology modeling and structural analysis of LRR proteins. In order to demonstrate the capabilities of the database we modeled the mouse Toll-like receptor 3 (TLR3) by multiple templates homology modeling and compared the result with the crystal structure.ConclusionLRRML is an information source for investigators involved in both theoretical and applied research on LRR proteins. It is available at http://zeus.krist.geo.uni-muenchen.de/~lrrml.

A leucine-rich repeat assembly approach for homology modeling of the human TLR5-10 and mouse TLR11-13 ectodomains

So far, 13 groups of mammalian Toll-like receptors (TLRs) have been identified. Most TLRs have been shown to recognize pathogen-associated molecular patterns from a wide range of invading agents and initiate both innate and adaptive immune responses. The TLR ectodomains are composed of varying numbers and types of leucine-rich repeats (LRRs). As the crystal structures are currently missing for most TLR ligand-binding ectodomains, homology modeling enables first predictions of their three-dimensional structures on the basis of the determined crystal structures of TLR ectodomains. However, the quality of the predicted models that are generated from full-length templates can be limited due to low sequence identity between the target and templates. To obtain better templates for modeling, we have developed an LRR template assembly approach. Individual LRR templates that are locally optimal for the target sequence are assembled into multiple templates. This method was validated through the comparison of a predicted model with the crystal structure of mouse TLR3. With this method, we also constructed ectodomain models of human TLR5, TLR6, TLR7, TLR8, TLR9, and TLR10 and mouse TLR11, TLR12, and TLR13 that can be used as first passes for a computational simulation of ligand docking or to design mutation experiments. This template assembly approach can be extended to other repetitive proteins.

Characterizing synchronization in time series using information measures extracted from symbolic representations

We present a methodology to characterize synchronization in time series based on symbolic representations. Each time series is mapped onto a sequence of p -dimensional delay vectors that are subsequently transformed into symbols by means of a rank-ordering of their values. Based on these representations, we propose a transcription scheme between symbols of the respective time series to study synchronization properties. Group-theoretical considerations and the use of information measures allow us to classify regimes of synchronization and to assess its strength. We apply our method to a prototype nonlinear system, which reveals a rich variety of coupled dynamics. We investigate in detail the robustness of the derived synchronization measure against noise and compare its value with that of the established measures.

TollML: a database of toll-like receptor structural motifs

Toll-like receptors (TLRs) play a key role in the innate immune system. TLRs recognize pathogen-associated molecular patterns and initiate an intracellular kinase cascade to induce an immediate defensive response. During recent years TLRs have become the focus of tremendous research interest. A central repository for the growing amount of relevant TLR sequence information has been created. Nevertheless, structural motifs of most sequenced TLR proteins, such as leucine-rich repeats (LRRs), are poorly annotated in the established databases. A database that organizes the structural motifs of TLRs could be useful for developing pattern recognition programs, structural modeling and understanding functional mechanisms of TLRs. We describe TollML, a database that integrates all of the TLR sequencing data from the NCBI protein database. Entries were first divided into TLR families (TLR1-23) and then semi-automatically subdivided into three levels of structural motif categories: (1) signal peptide (SP), ectodomain (ECD), transmembrane domain (TD) and Toll/IL-1 receptor (TIR) domain of each TLR; (2) LRRs of each ECD; (3) highly conserved segment (HCS), variable segment (VS) and insertions of each LRR. These categories can be searched quickly using an easy-to-use web interface and dynamically displayed by graphics. Additionally, all entries have hyperlinks to various sources including NCBI, Swiss-Prot, PDB, LRRML and PubMed in order to provide broad external information for users. The TollML database is available at http://tollml.lrz.de.