Imaging the invisible—Bioorthogonal Raman probes for imaging of cells and tissues

A revolutionary avenue for vibrational imaging with super-multiplexing capability can be seen in the recent development of Raman-active bioortogonal tags or labels. These tags and isotopic labels represent groups of chemically inert and small modifications, which can be introduced to any biomolecule of interest and then supplied to single cells or entire organisms. Recent developments in the field of spontaneous Raman spectroscopy and stimulated Raman spectroscopy in combination with targeted imaging of biomolecules within living systems are the main focus of this review. After having introduced common strategies for bioorthogonal labeling, we present applications thereof for profiling of resistance patterns in bacterial cells, investigations of pharmaceutical drug-cell interactions in eukaryotic cells and cancer diagnosis in whole tissue samples. Ultimately, this approach proves to be a flexible and robust tool for in vivo imaging on several length scales and provides comparable information as fluorescence-based imaging without the need of bulky fluorescent tags. © 2020 The Authors. Journal of Biophotonics published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinhei

New perspectives for viability studies with high-content analysis Raman spectroscopy (HCA-RS)

Raman spectroscopy has been widely used in clinical and molecular biological studies, providing high chemical specificity without the necessity of labels and with little-to-no sample preparation. However, currently performed Raman-based studies of eukaryotic cells are still very laborious and time-consuming, resulting in a low number of sampled cells and questionable statistical validations. Furthermore, the approach requires a trained specialist to perform and analyze the experiments, rendering the method less attractive for most laboratories. In this work, we present a new high-content analysis Raman spectroscopy (HCA-RS) platform that overcomes the current challenges of conventional Raman spectroscopy implementations. HCA-RS allows sampling of a large number of cells under different physiological conditions without any user interaction. The performance of the approach is successfully demonstrated by the development of a Raman-based cell viability assay, i.e., the effect of doxorubicin concentration on monocytic THP-1 cells. A statistical model, principal component analysis combined with support vector machine (PCA-SVM), was found to successfully predict the percentage of viable cells in a mixed population and is in good agreement to results obtained by a standard cell viability assay. This study demonstrates the potential of Raman spectroscopy as a standard high-throughput tool for clinical and biological applications

Organic Agriculture in Africa. Statistical Yearbook 2023

Data on organic agriculture in Africa showing the sector’s relevance are scarce, even though much needed. Data are essential for policymakers and donors to monitor and evaluate policy measures on organic production. Furthermore, they are necessary by market actors for informed decision-making. To make existing data on organic farming in Africa better accessible for African stakeholders, FiBL, based on its annual data collection on organic agriculture worldwide (Willer et al. 2023), provides several tools showing the current status of organic agriculture in Africa as a whole, by its five regions and by country

Application of High-Throughput Screening Raman Spectroscopy (HTS-RS) for Label-Free Identification and Molecular Characterization of Pollen

Pollen studies play a critical role in various fields of science. In the last couple of decades, replacement of manual identification of pollen by image-based methods using pollen morphological features was a great leap forward, but challenges for pollen with similar morphology remain, and additional approaches are required. Spectroscopy approaches for identification of pollen, such as Raman spectroscopy has potential benefits over traditional methods, due to the investigation of the intrinsic molecular composition of a sample. However, current Raman-based characterization of pollen is complex and time-consuming, resulting in low throughput and limiting the statistical significance of the acquired data. Previously demonstrated high-throughput screening Raman spectroscopy (HTS-RS) eliminates the complexity as well as human interaction by incorporation full automation of the data acquisition process. Here, we present a customization of HTS-RS for pollen identification, enabling sampling of a large number of pollen in comparison to other state-of-the-art Raman pollen investigations. We show that using Raman spectra we are able to provide a preliminary estimation of pollen types based on growth habits using hierarchical cluster analysis (HCA) as well as good taxonomy of 37 different Pollen using principal component analysis-support vector machine (PCA-SVM) with good accuracy even for the pollen specimens sharing similar morphological features. Our results suggest that HTS-RS platform meets the demands for automated pollen detection making it an alternative method for research concerning pollen

TRY plant trait database – enhanced coverage and open access

Plant traits - the morphological, anatomical, physiological, biochemical and phenological characteristics of plants - determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits - almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Fortschritte in schwingungsspektroskopischen Methoden zur Untersuchung von Diatomeen und Humanzellen

Diatomeen sind einzellige, eukaryotische Mikroorganismen, die den Hauptbestandteil des marinen Phytoplanktons ausmachen. Sie stellen aufgrund ihrer vorwiegend autotrophen Lebensweise sowohl wichtige Primär- als auch Sauerstoffproduzenten dar und assimilieren täglich etwa 20 % des atmosphärischen Treibhausgases CO2, wodurch sie entscheidend zur Klimaregulation beitragen. Die hohe Verbreitung von Diatomeen in Süßwassern veranlasste Wissenschaftler zu dem Vorschlag, Diatomeen als Bioindikatoren für die Eutrophierung von Seen und Flüssen zu nutzen. Auch in wirtschaftlicher Hinsicht könnten Diatomeen in Zukunft eine entscheidende Rolle auf dem Gebiet der Biokraftstoffproduktion spielen. Um hierbei optimale Wachstumsbedingungen in den Bioreaktoren sicherzustellen, ist es erforderlich, zellphysiologische Änderungen in Abhängigkeit der Kultivierungsbedingungen nachzuvollziehen. Konventionelle Methoden wie Hochleistungsflüssigkeitschromatographie (HPLC) oder Gaschromatographie (GC) erlauben quantitative Aussagen über die mittlere makromolekulare Zusammensetzung der Kultur, haben aber den Nachteil, dass sie schwerlich in kontinuierliche Prozesskontrollverfahren einzubinden sind und aufwendige sowie invasive Probenvorbereitungsschritte erforderlich machen. Das Ziel der vorliegenden Arbeit ist es, die Eignung schwingungsspektroskopischer Methoden, insbesondere der Raman-Spektroskopie, für die in vivo Untersuchung der Zellphysiologie von Diatomeen zu überprüfen. In diesem Zusammenhang wird eine Bandbreite Raman-spektroskopischer Messmodalitäten entwickelt und angewandt, die von der konventionelle Raman- Mikrospektroskopie mit punkt- sowie linienförmigem Laserfokus über eine Hochdurchsatzvariante mit automatisierter Zellerkennung bis hin zur Kombination von Raman-Spektroskopie mit Mikrofluidik reicht

Imaging the invisible—Bioorthogonal Raman probes for imaging of cells and tissues

A revolutionary avenue for vibrational imaging with super-multiplexing capability can be seen in the recent development of Raman-active bioortogonal tags or labels. These tags and isotopic labels represent groups of chemically inert and small modifications, which can be introduced to any biomolecule of interest and then supplied to single cells or entire organisms. Recent developments in the field of spontaneous Raman spectroscopy and stimulated Raman spectroscopy in combination with targeted imaging of biomolecules within living systems are the main focus of this review. After having introduced common strategies for bioorthogonal labeling, we present applications thereof for profiling of resistance patterns in bacterial cells, investigations of pharmaceutical drug-cell interactions in eukaryotic cells and cancer diagnosis in whole tissue samples. Ultimately, this approach proves to be a flexible and robust tool for in vivo imaging on several length scales and provides comparable information as fluorescence-based imaging without the need of bulky fluorescent tags. © 2020 The Authors. Journal of Biophotonics published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinhei

Application of High-Throughput Screening Raman Spectroscopy (HTS-RS) for Label-Free Identification and Molecular Characterization of Pollen

Pollen studies play a critical role in various fields of science. In the last couple of decades, replacement of manual identification of pollen by image-based methods using pollen morphological features was a great leap forward, but challenges for pollen with similar morphology remain, and additional approaches are required. Spectroscopy approaches for identification of pollen, such as Raman spectroscopy has potential benefits over traditional methods, due to the investigation of the intrinsic molecular composition of a sample. However, current Raman-based characterization of pollen is complex and time-consuming, resulting in low throughput and limiting the statistical significance of the acquired data. Previously demonstrated high-throughput screening Raman spectroscopy (HTS-RS) eliminates the complexity as well as human interaction by incorporation full automation of the data acquisition process. Here, we present a customization of HTS-RS for pollen identification, enabling sampling of a large number of pollen in comparison to other state-of-the-art Raman pollen investigations. We show that using Raman spectra we are able to provide a preliminary estimation of pollen types based on growth habits using hierarchical cluster analysis (HCA) as well as good taxonomy of 37 different Pollen using principal component analysis-support vector machine (PCA-SVM) with good accuracy even for the pollen specimens sharing similar morphological features. Our results suggest that HTS-RS platform meets the demands for automated pollen detection making it an alternative method for research concerning pollen

Systematic Computational Design and Optimization of Light Absorbing Dyes

We present a workflow to aid the discovery of new dyes for the role of a photosensitive unit in the dye-sensitized photo-electrochemical cells (DS-PECs). New structures are generated in a fully automated way using the Compound Attachment Tool (CAT) introduced in this work. These structures are characterized with efficient approximate density functional theory (DFT) methods, and molecules with favorable optical properties are suggested for possible further use in DS-PECs. As around 2500 structures are generated in this work, and as we aim for still larger volumes of compounds to screen in subsequent applications, we have assessed the reliability of low-cost screening methods and show that simplified time-dependent density functional theory (sTDDFT) provides a satisfying accuracy/cost ratio. From the dyes considered, we propose a set that can be suitable for panchromatic sensitization of the photoelectrode in DS-PECs to further increase DS-PEC efficiency

Bladder tissue characterization using probe-based Raman spectroscopy: Evaluation of tissue heterogeneity and influence on the model prediction

Existing approaches for early-stage bladder tumor diagnosis largely depend on invasive and time-consuming procedures, resulting in hospitalization, bleeding, bladder perforation, infection and other health risks for the patient. The reduction of current risk factors, while maintaining or even improving the diagnostic precision, is an underlying factor in clinical instrumentation research. For example, for clinic surveillance of patients with a history of noninvasive bladder tumors real-time tumor diagnosis can enable immediate laser-based removal of tumors using flexible cystoscopes in the outpatient clinic. Therefore, novel diagnostic modalities are required that can provide real-time in vivo tumor diagnosis. Raman spectroscopy provides biochemical information of tissue samples ex vivo and in vivo and without the need for complicated sample preparation and staining procedures. For the past decade there has been a rise in applications to diagnose and characterize early cancer in different organs, such as in head and neck, colon and stomach, but also different pathologies, for example, inflammation and atherosclerotic plaques. Bladder pathology has also been studied but only with little attention to aspects that can influence the diagnosis, such as tissue heterogeneity, data preprocessing and model development. The present study presents a clinical investigative study on bladder biopsies to characterize the tumor grading ex vivo, using a compact fiber probe-based imaging Raman system, as a crucial step towards in vivo Raman endoscopy. Furthermore, this study presents an evaluation of the tissue heterogeneity of highly fluorescent bladder tissues, and the multivariate statistical analysis for discrimination between nontumor tissue, and low- and high-grade tumor. © 2019 The Authors. Journal of Biophotonics published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinhei