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

Fast In Vitro Synthesis and Direct Labeling of Nanobodies for Prototyping in Microscopy Applications

Small antigen binders, such as nanobodies, have become widely used in biomedical research and pharmaceutical development. However, the pipeline for the generation of functional conjugated probes and drugs from identified binders remains a major time-consuming bottleneck. Here, we developed a method for fast nanobody production and conjugation based on an in vitro synthesis platform. Our system allows for small batch synthesis of nanobodies with the inclusion of a noncanonical amino acid (NCAA). This NCAA can then be used for direct conjugation of molecules to the synthesized nanobody using click-chemistry, reducing the time from binder-encoding DNA to a conjugated probe tremendously. In this study, we conjugated a fluorescent dye to an anti-Green fluorescent protein (GFP) nanobody and attained a fully functional probe suitable for advanced super-resolution microscopy within a short time frame of 2 days. Our work illustrates that an in vitro synthesis platform in combination with click-chemistry can be successfully employed to produce conjugated small antigen binding probes. The fast production and conjugation, combined with the possibility for parallelization as well as precise analysis by microscopy, forms an excellent platform for nanobody prototyping. The here-illustrated method can be used for quick selection and benchmarking of obtained nanobody sequences/clones, e. g., from a phage-display, for use as conjugated small-molecule carriers. This procedure can accelerate the bioengineering of nanobodies for research and pharmaceutical applications.

AI analysis of super-resolution microscopy: Biological discovery in the absence of ground truth

Super-resolution microscopy, or nanoscopy, enables the use of fluorescent-based molecular localization tools to study molecular structure at the nanoscale level in the intact cell, bridging the mesoscale gap to classical structural biology methodologies. Analysis of super-resolution data by artificial intelligence (AI), such as machine learning, offers tremendous potential for the discovery of new biology, that, by definition, is not known and lacks ground truth. Herein, we describe the application of weakly supervised paradigms to super-resolution microscopy and its potential to enable the accelerated exploration of the nanoscale architecture of subcellular macromolecules and organelles.

Quantifying morphologies of developing neuronal cells using deep learning with imperfect annotations

The functionality of human intelligence relies on the interaction and health of neurons, hence, quantifying neuronal morphologies can be crucial for investigating the functionality of the human brain. This paper proposes a deep learning (DL) based method for segmenting and quantifying neuronal structures in fluorescence microscopy images of developing neuronal cells cultured in vitro. Compared to the majority of supervised DL-based segmentation methods that heavily rely on creating exact corresponding masks of neuronal structures for the preparation of training samples, the proposed approach allows for imperfect annotation of neurons, as it only requires tracing the centrelines of the neurites. This ability accelerates the preparation of training data by several folds. Our proposed framework is built on a modified version of PSPNet with an EfficientNet backbone pre-trained on the CityScapes dataset. To handle the imperfectness of training samples, we incorporated a weighted combination of two loss functions, namely the Dice loss and Lovász loss functions, into our network. We evaluated the proposed framework and several other state-of-the-art methods on a published dataset of approximately 900 manually quantified cultured mouse neurons. Our results indicate a close correlation between the proposed method and manual quantification in terms of neuron length and the number of branches while demonstrating improved analysis speed. Furthermore, the proposed method achieved high accuracy in neuron segmentation, as evidenced by the evaluation of the neurons' length and number of branches.

Mapping molecular complexes with super-resolution microscopy and single-particle analysis

Understanding the structure of supramolecular complexes provides insight into their functional capabilities and how they can be modulated in the context of disease. Super-resolution microscopy (SRM) excels in performing this task by resolving ultrastructural details at the nanoscale with molecular specificity. However, technical limitations, such as underlabelling, preclude its ability to provide complete structures. Single-particle analysis (SPA) overcomes this limitation by combining information from multiple images of identical structures and producing an averaged model, effectively enhancing the resolution and coverage of image reconstructions. This review highlights important studies using SRM-SPA, demonstrating how it broadens our knowledge by elucidating features of key biological structures with unprecedented detail.