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Peroxisomes are crucial organelles that occur in almost all eukaryotes. Well known are their roles in various metabolic processes, such as hydrogen peroxide detoxification and lipid metabolism. Recent studies indicated that peroxisomes also have several non-metabolic functions, for instance, in stress response, signaling, and cellular ageing. In mammalian cells, the small size of peroxisomes (~200 nm, near the diffraction limit) hinders unveiling peroxisomal structures by conventional light microscopy. However, in the yeast Hansenula polymorpha, they can reach up to 1.5 μm in diameter, depending on the carbon source. To study the localization of peroxisomal proteins in cells in more detail, super-resolution imaging techniques such as stimulated emission depletion (STED) microscopy can be used. STED enables fast (live-cell) imaging well beyond the diffraction limit of light (30-40 nm in cells), without further data processing. Here, we present optimized protocols for the fluorescent labeling of specific peroxisomal proteins in fixed and living cells. Moreover, detailed measurement protocols for successful STED imaging of human and yeast peroxisomes (using antibodies or genetic tags labeled with dyes) are described, extended with suggestions for individual optimizations.

Subjective expectations are known to be associated with clinical outcomes. However, expectations exist about different aspects of recovery, and few studies have focused on expectations about specific treatments. Here, we present results from a prospective observational study of patients receiving lumbar steroid injections against low back pain (N = 252). Patients completed questionnaires directly before (T1), directly after (T2), and 2 weeks after (T3) the injection. In addition to pain intensity, we assessed expectations (and certainty therein) about treatment effects, using both numerical rating scale (NRS) and the Expectation for Treatment Scale (ETS). Regression models were used to explain (within-sample) treatment outcome (pain intensity at T3) based on pain levels, expectations, and certainty at T1 and T2. Using cross-validation, we examined the models' ability to predict (out-of-sample) treatment outcome. Pain intensity significantly decreased (P < 10−15) 2 weeks after injections, with a reduction of the median NRS score from 6 to 3. Numerical Rating Scale measures of pain, expectation, and certainty from T1 jointly explained treatment outcome (P < 10−15, R2 = 0.31). Expectations at T1 explained outcome on its own (P < 10−10,f2=0.19) and enabled out-of-sample predictions about outcome (P < 10−4), with a median error of 1.36 on a 0 to 10 NRS. Including measures from T2 did not significantly improve models. Using the ETS as an alternative measurement of treatment expectations (sensitivity analysis) gave consistent results. Our results demonstrate that treatment expectations play an important role for clinical outcome after lumbar injections and may represent targets for concomitant cognitive interventions. Predicting outcomes based on simple questionnaires might be useful to support treatment selection.

Correlative light and electron microscopy (CLEM) combines the advantages of protein localization by fluorescence microscopy with the high resolution of electron microscopy. Here, we describe a protocol that we developed for yeast peroxisome research. First, cells are fixed, using conditions that preserve the properties of fluorescent proteins and avoid the introduction of autofluorescence. Next, cryosections are prepared and imaged by fluorescence microscopy. The same sections are used for electron microscopy. Both images are aligned and merged, allowing to localize fluorescent proteins in electron microscopy images. This method was successfully used for peroxisomal membrane contact site research and allows to precisely localize contact site resident proteins at regions where membranes are closely associated at distances far below the resolution of conventional fluorescence microscopy.

Raw data for the Publication "Recycling of rare earth containing waste with peptide-functionalized floating glass bubbles in a phage mimicking approach"

Psychiatry faces fundamental challenges with regard to mechanistically guided differential diagnosis, as well as prediction of clinical trajectories and treatment response of individual patients. This has motivated the genesis of two closely intertwined fields: (i) Translational Neuromodeling (TN), which develops “computational assays” for inferring patient-specific disease processes from neuroimaging, electrophysiological, and behavioral data; and (ii) Computational Psychiatry (CP), with the goal of incorporating computational assays into clinical decision making in everyday practice. In order to serve as objective and reliable tools for clinical routine, computational assays require end-to-end pipelines from raw data (input) to clinically useful information (output). While these are yet to be established in clinical practice, individual components of this general end-to-end pipeline are being developed and made openly available for community use. In this paper, we present the Translational Algorithms for Psychiatry-Advancing Science (TAPAS) software package, an open-source collection of building blocks for computational assays in psychiatry. Collectively, the tools in TAPAS presently cover several important aspects of the desired end-to-end pipeline, including: (i) tailored experimental designs and optimization of measurement strategy prior to data acquisition, (ii) quality control during data acquisition, and (iii) artifact correction, statistical inference, and clinical application after data acquisition. Here, we review the different tools within TAPAS and illustrate how these may help provide a deeper understanding of neural and cognitive mechanisms of disease, with the ultimate goal of establishing automatized pipelines for predictions about individual patients. We hope that the openly available tools in TAPAS will contribute to the further development of TN/CP and facilitate the translation of advances in computational neuroscience into clinically relevant computational assays., Frontiers in Psychiatry, 12, ISSN:1664-0640

Metalliferous process wastewater not only represents a major ecotoxicological burden but can also serve as a secondary raw material source for the recovery of critical raw materials (CRM) like gallium. Smart, innovative strategies are needed for the economic recovery of industrial metals from such CRM sources. Biotechnological approaches are powerful tools to develop effective, selective and eco-friendly strategies in resource recovery. A particularly promising approach utilizes tailor-made biomolecules (such as peptides), that can be engineered to aid in the targeted extraction of individual metals. The application of phage Surface Display technology allows the directed molecular evolution of peptide ligands. This method has been used to identify the Ga-binding peptides TMHHAAIAHPPH, NYLPHQSSSPSR, SQALSTSRQDLR, HTQHIQSDDHLA and NDLQRHRLTAGP. In this study, the metal-binding properties of these peptides were further characterized. The peptides differed decisively in their interaction with gallium; in some cases, complex formation with gallium was strongly dependent on the surrounding buffer conditions. The peptide with the amino acid sequence NYLPHQSSSPSR showed the most promising gallium-binding properties. The site-selective and covalent immobilization of the gallium-binding peptide on polystyrene beads resulted in a robust and efficient material. It is suitable for the selective adsorption and desorption of gallium from industrial wastewater utilizing citric acid as environmentally friendly eluent. Biosorption studies performed with model and real water samples showed an up to ten-fold better adsorption of gallium as well as its effective separation from other contaminants like arsenic. Computer modeling suggests the probable structure of the peptide in aqueous solution and postulate a possible binding site for gallium.

The Junior Research Group BioKollekt works on the development of novel peptide-based separation processes for the recycling of strategically important metals. In earlier studies, peptides with high selectivity and affinity for particles of the fluorescent powders LaPO4:Ce,Tb and CeMgAl11O19:Tb were identified using phage surface display (PSD). Nonetheless, phages are no option to be a peptide carrier in a classical industry applied separation process. However, the transitioning from phage bound peptides to free peptides proved challenging. Since most analytics are developed mainly for solution phase-chemistry, they are not fully applicable to work with fast sedimenting particles and/or within the used concentration range. The focus of this study is the introduction of a method for testing and comparing particle-binding peptides by immobilization on glass supports. While the method itself is not dependent on fluorescence, exploiting the fluorescent properties of the target materials, as shown in Fig. 1, enables selective fluorescent scanning methods. This method, in general, allows analyzing and visualizing trends in binding efficiency, affinity and selectivity. It also helps to identify structures relevant for binding. Achieved by varying the peptide sequence this method furthermore enables relatively fast screening routines for key factors like peptide concentrations, elution methods and evaluation of binding capacities. Another part of this study is the evaluation of suitable peptide carriers for further transitioning to a working separation process. Suitable carriers under investigation are (nanocrystalline) celluloses and functionalized glass supports in various geometries. However, our current focus are superparamagnetic iron oxide nanoparticles with a bifunctional and therefore fine-tunable amphiphilic surface coating. Their unique behaviour, cheap and easy synthesis and possible recyclability during the process highlighting them as a promising type of carrier. The rather easy adjustment of the polarity to the needs of the separation process can furthermore enhance peptide-particle interactions in terms of selectivity and accessibility. While the research is still ongoing, preliminary results show promising behaviour and flexibility of the chosen systems.

This set contains the raw data of the fluorescence scanning experiments used in the publication of "Quantification of peptide bound particles: A phage mimicking approach via site-selective immobilization on glass" by Schrader et al..