In situ Sub-Cellular Identification of Functional Amyloids in Bacteria and Archaea by Infrared Nanospectroscopy

Fast Nano-IR Hyperspectral Imaging Empowered by Large-Dataset-Free Miniaturized Spatial-Spectral Network

The emerging field of nanoscale infrared (nano-IR) offers label-free molecular contrast, yet its imaging speed is limited by point-by-point traverse acquisition of a three-dimensional (3D) data cube. Here, we develop a spatial-spectral network (SS-Net), a miniaturized deep-learning model, together with compressive sampling to accelerate the nano-IR imaging. The compressive sampling is performed in both the spatial and spectral domains to accelerate the imaging process. The SS-Net is trained to learn the mapping from small nano-IR image patches to the corresponding spectra. With this elaborated mapping strategy, the training can be finished quickly within several minutes using the subsampled data, eliminating the need for a large-labeled dataset of common deep learning methods. We also designed an efficient loss function, which incorporates the image and spectral similarity to enhance the training. We first validate the SS-Net on an open stimulated Raman-scattering dataset; the results exhibit the potential of 10-fold imaging speed improvement with state-of-the-art performance. We then demonstrate the versatility of this approach on atomic force microscopy infrared (AFM-IR) microscopy with 7-fold imaging speed improvement, even on nanoscale Fourier transform infrared (nano-FTIR) microscopy with up to 261.6 folds faster imaging speed. We further showcase the generalization of this method on AFM-force volume-based multiparametric nanoimaging. This method establishes a paradigm for rapid nano-IR imaging, opening new possibilities for cutting-edge research in materials, photonics, and beyond.

Amyloids, amorphous aggregates and assemblies of peptides - Assessing aggregation

Amyloid and amorphous aggregates represent the two major categories of aggregates associated with diseases, and although exhibiting distinct features, researchers often treat them as equivalent, which demonstrates the need for more thorough characterization. Here, we compare amyloid and amorphous aggregates based on their biochemical properties, kinetics, and morphological features. To further decipher this issue, we propose the use of peptide self-assemblies as minimalistic models for understanding the aggregation process. Peptide building blocks are significantly smaller than proteins that participate in aggregation, however, they make a plausible means to bridge the gap in discerning the aggregation process at the more complex, protein level. Additionally, we explore the potential use of peptide-inspired models to research the liquid-liquid phase separation as a feasible mechanism preceding amyloid formation. Connecting these concepts can help clarify our understanding of aggregation-related disorders and potentially provide novel drug targets to impede and reverse these serious illnesses.

Structural characterization of amyloid aggregates with spatially resolved infrared spectroscopy

The self-assembly of proteins and peptides into ordered structures called amyloid fibrils is a hallmark of numerous diseases, impacting the brain, heart, and other organs. The structure of amyloid aggregates is central to their function and thus has been extensively studied. However, the structural heterogeneities between aggregates as they evolve throughout the aggregation pathway are still not well understood. Conventional biophysical spectroscopic methods are bulk techniques and only report on the average structural parameters. Understanding the structure of individual aggregate species in a heterogeneous ensemble necessitates spatial resolution on the length scale of the aggregates. Recent technological advances have led to augmentation of infrared (IR) spectroscopy with imaging modalities, wherein the photothermal response of the sample upon vibrational excitation is leveraged to provide spatial resolution beyond the diffraction limit. These combined approaches are ideally suited to map out the structural heterogeneity of amyloid ensembles. AFM-IR, which integrates IR spectroscopy with atomic force microscopy enables identification of the structural facets the oligomers and fibrils at individual aggregate level with nanoscale resolution. These capabilities can be extended to chemical mapping in diseased tissue specimens with submicron resolution using optical photothermal microscopy, which combines IR spectroscopy with optical imaging. This book chapter provides the basic premise of these novel techniques and provides the typical methodology for using these approaches for amyloid structure determination. Detailed procedures pertaining to sample preparation and data acquisition and analysis are discussed and the aggregation of the amyloid β peptide is provided as a case study to provide the reader the experimental parameters necessary to use these techniques to complement their research efforts.

Mechanisms of Biomolecular Self-Assembly Investigated Through In Situ Observations of Structures and Dynamics

Biomolecular self-assembly of hierarchical materials is a precise and adaptable bottom-up approach to synthesizing across scales with considerable energy, health, environment, sustainability, and information technology applications. To achieve desired functions in biomaterials, it is essential to directly observe assembly dynamics and structural evolutions that reflect the underlying energy landscape and the assembly mechanism. This review will summarize the current understanding of biomolecular assembly mechanisms based on in situ characterization and discuss the broader significance and achievements of newly gained insights. In addition, we will also introduce how emerging deep learning/machine learning-based approaches, multiparametric characterization, and high-throughput methods can boost the development of biomolecular self-assembly. The objective of this review is to accelerate the development of in situ characterization approaches for biomolecular self-assembly and to inspire the next generation of biomimetic materials.

AFM-IR for Nanoscale Chemical Characterization in Life Sciences: Recent Developments and Future Directions

Despite the ubiquitous absorption of mid-infrared (IR) radiation by virtually all molecules that belong to the major biomolecules groups (proteins, lipids, carbohydrates, nucleic acids), the application of conventional IR microscopy to the life sciences remained somewhat limited, due to the restrictions on spatial resolution imposed by the diffraction limit (in the order of several micrometers). This issue is addressed by AFM-IR, a scanning probe-based technique that allows for chemical analysis at the nanoscale with resolutions down to 10 nm and thus has the potential to contribute to the investigation of nano and microscale biological processes. In this perspective, in addition to a concise description of the working principles and operating modes of AFM-IR, we present and evaluate the latest key applications of AFM-IR to the life sciences, summarizing what the technique has to offer to this field. Furthermore, we discuss the most relevant current limitations and point out potential future developments and areas for further application for fruitful interdisciplinary collaboration.

A guide to studying protein aggregation

Disrupted protein folding or decreased protein stability can lead to the accumulation of (partially) un- or misfolded proteins, which ultimately cause the formation of protein aggregates. Much of the interest in protein aggregation is associated with its involvement in a wide range of human diseases and the challenges it poses for large-scale biopharmaceutical manufacturing and formulation of therapeutic proteins and peptides. On the other hand, protein aggregates can also be functional, as observed in nature, which triggered its use in the development of biomaterials or therapeutics as well as for the improvement of food characteristics. Thus, unmasking the various steps involved in protein aggregation is critical to obtain a better understanding of the underlying mechanism of amyloid formation. This knowledge will allow a more tailored development of diagnostic methods and treatments for amyloid-associated diseases, as well as applications in the fields of new (bio)materials, food technology and therapeutics. However, the complex and dynamic nature of the aggregation process makes the study of protein aggregation challenging. To provide guidance on how to analyse protein aggregation, in this review we summarize the most commonly investigated aspects of protein aggregation with some popular corresponding methods.

Nanostructures and Nanotechnologies for the Detection of Extracellular Vesicle

Liquid biopsy has been taken as a minimally invasive examination and a promising surrogate to the clinically applied tissue-based test for the diagnosis and molecular analysis of cancer. Extracellular vesicles (EVs) carry complex molecular information from the tumor, allowing for the multicomponent analysis of cancer and would be beneficial to personalized medicine. In this review, the advanced nanomaterials and nanotechniques for the detection and molecular profiling of EVs, highlight the advantages of nanotechnology in the high-purity isolation and the high-sensitive and high-specific identification of EVs, are summarized. An outlook on the clinical application of nanotechnology-based liquid biopsy in the diagnosis, prognostication, and surveillance of cancer is also provided. It provides information for developing liquid biopsy based on EVs by discussing the advantages and challenges of functionalized nanomaterials and various nanotechnologies.

ECM-LSE: Prediction of Extracellular Matrix Proteins Using Deep Latent Space Encoding of k-Spaced Amino Acid Pairs

Extracelluar matrix (ECM) proteins create complex networks of macromolecules which fill-in the extracellular spaces of living tissues. They provide structural support and play an important role in maintaining cellular functions. Identification of ECM proteins can play a vital role in studying various types of diseases. Conventional wet lab-based methods are reliable; however, they are expensive and time consuming and are, therefore, not scalable. In this research, we propose a sequence-based novel machine learning approach for the prediction of ECM proteins. In the proposed method, composition of k-spaced amino acid pair (CKSAAP) features are encoded into a classifiable latent space (LS) with the help of deep latent space encoding (LSE). A comprehensive ablation analysis is conducted for performance evaluation of the proposed method. Results are compared with other state-of-the-art methods on the benchmark dataset, and the proposed ECM-LSE approach has shown to comprehensively outperform the contemporary methods.