Fluorescence- or Förster resonance energy transfer (FRET) is a measurable physical energy transfer phenomenon between appropriate chromophores, when they are in sufficient proximity, usually within 10 nm. This feature has made them incredibly useful tools for many biomedical studies on molecular interactions. Furthermore, this principle is increasingly exploited for the design of biosensors, where two chromophores are linked with a sensory domain controlling their distance and thus the degree of FRET. The versatility of these FRET-biosensors made it possible to assess a vast amount of biological variables in a fast and standardized manner, allowing not only high-throughput studies but also sub-cellular measurements of biological processes. In this review, we aim at giving an overview over the recent advances in genetically encoded, fluorescent-protein based FRET-biosensors, as these represent the largest and most vividly growing group of FRET-based sensors. For easy understanding, we are grouping them into four categories, depending on their molecular mechanism. These are based on: (a) cleavage; (b) conformational-change; (c) mechanical force and (d) changes in the micro-environment. We also address the many issues and considerations that come with the development of FRET-based biosensors, as well as the possibilities that are available to measure them.

The question whether two proteins interact with each other or whether a protein localizes to a certain region of the cell is often addressed with fluorescence microscopy and analysis of a potential colocalization of fluorescence markers. Since a mere visual estimation does not allow quantification of the degree of colocalization, different statistical methods of pixel‐intensity correlation are commonly used to score it. We observed that these correlation coefficients are prone to false positive results and tend to show high values even for molecules that reside in different organelles. Our aim was to improve this type of analysis and we developed a novel method combining object‐recognition based colocalization analysis with pixel‐intensity correlation to calculate an object‐corrected Pearson coefficient. We designed a macro for the Fiji‐version of the software ImageJ and tested the performance systematically with various organelle markers revealing an improved robustness of our approach over classical methods. In order to prove that colocalization does not necessarily mean a physical interaction, we performed FRET (fluorescence resonance energy transfer) microscopy. This confirmed that non‐interacting molecules can exhibit a nearly complete colocalization, but that they do not show any significant FRET signal in contrast to proteins that are bound to each other.

FRET (Fluorescence Resonance Energy Transfer) measurements are commonly applied to proof protein-protein interactions. However, standard methods of live cell FRET microscopy and signal normalization only allow a principle assessment of mutual binding and are unable to deduce quantitative information of the interaction. We present an evaluation and normalization procedure for 3-filter FRET measurements, which reflects the process of complex formation by plotting FRET-saturation curves. The advantage of this approach relative to traditional signal normalizations is demonstrated by mathematical simulations. Thereby, we also identify the contribution of critical parameters such as the total amount of donor and acceptor molecules and their molar ratio. When combined with a fitting procedure, this normalization facilitates the extraction of key properties of protein complexes such as the interaction stoichiometry or the apparent affinity of the binding partners. Finally, the feasibility of our method is verified by investigating three exemplary protein complexes. Altogether, our approach offers a novel method for a quantitative analysis of protein interactions by 3-filter FRET microscopy, as well as flow cytometry. To facilitate the application of this method, we created macros and routines for the programs ImageJ , R and MS-Excel , which we make publicly available.

Extracellular vesicles (EVs) being defined as lipid-bilayer encircled particles are released by almost all known mammalian cell types and represent a heterogenous set of cell fragments that are found in the blood circulation and all other known body fluids. The current nomenclature distinguishes mainly three forms: microvesicles, which are formed by budding from the plasma membrane; exosomes, which are released, when endosomes with intraluminal vesicles fuse with the plasma membrane; and apoptotic bodies representing fragments of apoptotic cells. Their importance for a great variety of biological processes became increasingly evident in the last decade when it was discovered that they contribute to intercellular communication by transferring nucleotides and proteins to recipient cells. In this review, we delineate several aspects of their isolation, purification, and analysis; and discuss some pitfalls that have to be considered therein. Further on, we describe various cellular sources of EVs and explain with different examples, how they link cancer and inflammatory conditions with thrombotic processes. In particular, we elaborate on the roles of EVs in cancer-associated thrombosis and COVID-19, representing two important paradigms, where local pathological processes have systemic effects in the whole organism at least in part via EVs. Finally, we also discuss possible developments of the field in the future and how EVs might be used as biomarkers for diagnosis, and as vehicles for therapeutics.

Measuring Förster–resonance–energy–transfer (FRET) efficiency allows the investigation of protein–protein interactions (PPI), but extracting quantitative measures of affinity necessitates highly advanced technical equipment or isolated proteins. We demonstrate the validity of a recently suggested novel approach to quantitatively analyze FRET-based experiments in living mammalian cells using standard equipment using the interaction between different type-1 peroxisomal targeting signals (PTS1) and their soluble receptor peroxin 5 (PEX5) as a model system. Large data sets were obtained by flow cytometry coupled FRET measurements of cells expressing PTS1-tagged EGFP together with mCherry fused to the PTS1-binding domain of PEX5, and were subjected to a fitting algorithm extracting a quantitative measure of the interaction strength. This measure correlates with results obtained by in vitro techniques and a two-hybrid assay, but is unaffected by the distance between the fluorophores. Moreover, we introduce a live cell competition assay based on this approach, capable of depicting dose- and affinity-dependent modulation of the PPI. Using this system, we demonstrate the relevance of a sequence element next to the core tripeptide in PTS1 motifs for the interaction strength between PTS1 and PEX5, which is supported by a structure-based computational prediction of the binding energy indicating a direct involvement of this sequence in the interaction.