Obendorf, L. | bioRxiv Science

bioRxiv2022

A versatile Halo- and SNAP-tagged BMP/TGFβ receptor library for quantification of cell surface ligand binding

The TGF{beta} superfamily of secreted growth factors comprises more than 30 members including TGF{beta}s, BMPs and Activins. While all TGF{beta} superfamily members signal through heteromeric receptor complexes to regulate a plethora of developmental and homeostatic processes, each ligand possesses a unique affinity towards a subset of BMP and TGF{beta} type I and type II receptors. Whereas the Activin and TGF{beta} class display a higher affinity towards type II receptors, BMPs and GDFs preferentially bind to type I receptors. Sofar, the lack of specific antibodies and chemical biology tools hampered simultaneous testing of ligand binding towards all BMP and TGF{beta} receptors. Here we present a N-terminally Halo- and SNAP-tagged TGF{beta}/BMP receptor library to visualize the receptor complexes in dual color. In combination with novel fluorescently labeled TGF{beta} superfamily ligands, we established a Ligand Surface Binding Assay (LSBA) for optical quantification of receptor-dependent growth factor binding for Activin A, TGF{beta}1 and BMP9 in a cellular context. We confirm ligand-receptor interface specificity by identifying BMPR2- or ALK2-mutants that switch from a low-affinity Activin A- or BMP9-receptor to a high-affinity receptor, respectively.

cell biology↗

bioRxiv2021

Precise measurement of nanoscopic septin ring structures in deep learning-assisted quantitative superresolution microscopy

The combination of image analysis and fluorescence superresolution microscopy methods allows for unprecedented insight into the organization of macromolecular assemblies in cells. Advances in deep learning-based object recognition enables the automated processing of large amounts of data, resulting in high accuracy through averaging. However, while the analysis of highly symmetric structures of constant size allows for a resolution approaching the dimensions of structural biology, deep learning methods are prone to different forms of bias. A biased recognition of structures may prohibit the development of readouts for processes that involve significant changes in size or shape of amorphous macromolecular complexes. What is required to overcome this problem is a detailed investigation of potential sources of bias and the rigorous testing of trained models using real or simulated data covering a wide dynamic range of possible results. Here we combine single molecule localization-based superresolution microscopy of septin ring structures with the training of several different deep learning models for a quantitative investigation of bias resulting from different training approaches and finally quantitative changes in septin ring structures. We find that trade-off exists between measurement accuracy and the dynamic range of recognized phenotypes. Using our trained models, we furthermore find that septin ring size can be explained by the number of subunits they are assembled from alone. Our work provides a new experimental system for the investigation of septin polymerization.

cell biology↗