An ultra-stable gold-coordinated protein cage displaying reversible assembly.

Symmetrical protein cages have evolved to fulfil diverse roles in nature, including compartmentalization and cargo delivery1, and have inspired synthetic biologists to create novel protein assemblies via the precise manipulation of protein-protein interfaces. Despite the impressive array of protein cages produced in the laboratory, the design of inducible assemblies remains challenging2,3. Here we demonstrate an ultra-stable artificial protein cage, the assembly and disassembly of which can be controlled by metal coordination at the protein-protein interfaces. The addition of a gold (I)-triphenylphosphine compound to a cysteine-substituted, 11-mer protein ring triggers supramolecular self-assembly, which generates monodisperse cage structures with masses greater than 2 MDa. The geometry of these structures is based on the Archimedean snub cube and is, to our knowledge, unprecedented. Cryo-electron microscopy confirms that the assemblies are held together by 120 S-Aui-S staples between the protein oligomers, and exist in two chiral forms. The cage shows extreme chemical and thermal stability, yet it readily disassembles upon exposure to reducing agents. As well as gold, mercury(II) is also found to enable formation of the protein cage. This work establishes an approach for linking protein components into robust, higher-order structures, and expands the design space available for supramolecular assemblies to include previously unexplored geometries.

Fast cancer imaging in pancreatic biopsies using infrared imaging.

Pancreatic cancer, particularly Pancreatic ductal adenocarcinoma, remains a highly lethal form of cancer with limited early diagnosis and treatment options. Infrared (IR) spectroscopy, combined with machine learning, has demonstrated great potential in detecting various cancers. This study explores the translation of a diagnostic model from Fourier Transform Infrared to Quantum Cascade Laser (QCL) microscopy for pancreatic cancer classification. Furthermore, QCL microscopy offers faster measurements with selected frequencies, improving clinical feasibility. Thus, the goals of the study include establishing a QCL-based model for pancreatic cancer classification and creating a fast surgical margin detection model using reduced spectral information. The research involves preprocessing QCL data, training Random Forest (RF) classifiers, and optimizing the selection of spectral features for the models. Results demonstrate successful translation of the diagnostic model to QCL microscopy, achieving high predictive power (AUC = 98%) in detecting cancerous tissues. Moreover, a model for rapid surgical margin recognition, based on only a few spectral frequencies, is developed with promising differentiation between benign and cancerous regions. The findings highlight the potential of QCL microscopy for efficient pancreatic cancer diagnosis and surgical margin detection within clinical timeframes of minutes per surgical resection tissue.

Super-Resolved 3D Mapping of Molecular Orientation Using Vibrational Techniques.

When a sample has an anisotropic structure, it is possible to obtain additional information controlling the polarization of incident light. With their straightforward instrumentation approaches, infrared (IR) and Raman spectroscopies are widely popular in this area. Single-band-based determination of molecular in-plane orientation, typically used in materials science, is here extended by the concurrent use of two vibration bands, revealing the orientational ordering in three dimension. The concurrent analysis was applied to IR spectromicroscopic data to obtain orientation angles of a model polycaprolactone spherulite sample. The applicability of this method spans from high-resolution, diffraction-limited Fourier transform infrared (FT-IR) and Raman imaging to super-resolved optical photothermal infrared (O-PTIR) imaging. Due to the nontomographic experimental approach, no image distortion is visible and nanometer scale orientation domains can be observed. Three-dimensional (3D) bond orientation maps enable in-depth characterization and consequently precise control of the sample's physicochemical properties and functions.

In search of the correlation between nanomechanical and biomolecular properties of prostate cancer cells with different metastatic potential.

Nanomechanical properties of living cells, as measured with atomic force microscopy (AFM), are increasingly recognized as criteria that differentiate normal and pathologically altered cells. Locally measured cell elastic properties, described by the parameter known as Young's modulus, are currently proposed as a new diagnostic parameter that can be used at the early stage of cancer detection. In this study, local mechanical properties of normal human prostate (RWPE-1) cells and a range of malignant (22Rv1) and metastatic prostate cells (LNCaP, Du145 and PC3) were investigated. It was found that non-malignant prostate cells are stiffer than cancer cells while the metastatic cells are much softer than malignant cells from the primary tumor site. Next, the biochemical properties of the cells were measured using confocal Raman (RS) and Fourier-transform infrared (FT-IR) spectroscopies to reveal these cells' biochemical composition as malignant transformation proceeds. Nanomechanical and biochemical profiles of five different prostate cell lines were subsequently analyzed using partial least squares regression (PLSR) in order to identify which spectral features of the RS and FT-IR spectra correlate with the cell's elastic properties. The PLSR-based model could predict Young's modulus values based on both RS and FT-IR spectral information. These outcomes show not only that AFM, RS and FT-IR techniques can be used for discrimination between normal and cancer cells, but also that a linear correlation between mechanical response and biomolecular composition of the cells that undergo malignant transformation can be found. This knowledge broadens our understanding of how prostate cancer cells evolve thorough the multistep process of tumor pathogenesis.

Influence of interference effects on the spectral quality and histological classification by FT-IR imaging in transflection geometry.

Infrared (IR) imaging can be used for fast, accurate and non-destructive pathology recognition of biopsies when supported by machine learning algorithms. Transflection mode of measurements has the potential to be translated into the clinic due to economic reasons of large-scale imaging with the need for inexpensive substrates. Unfortunately, in this mode spectral distortions originating from light interference appear. Due to this fact transmission measurement mode is more frequently used in pathology recognition. Nevertheless, this measurement mode also is not devoid of spectral distortion effects like scattering. However, this effect is better understood and there are preprocessing algorithms to minimize it. In this work, we investigated the influence of interference effects on spectral quality of pancreatic tissues measured in transmission and transflection mode with Fourier tranform IR (FT-IR) microscopy using samples embedded with and without paraffin. The removal of paraffin leads to an altered magnitude of interference in transflection and provides a platform for a detailed analysis of its effect on the spectra of biological material, since the same sample is measured with different interference conditions. Moreover, the potential of transflection mode measurements in histological classification of analyzed samples was investigated and compared with classification results for transmission mode.

Macromolecular Orientation in Biological Tissues Using a Four-Polarization Method in FT-IR Imaging.

Fourier transform infrared spectroscopy has emerged as a powerful tool for tissue specimen investigation. Its nondestructive and label-free character enables direct determination of biochemical composition of samples. Furthermore, the introduction of polarization enriches this technique by the possibility of molecular orientation study apart from purely quantitative analysis. Most of the molecular orientation studies focused on polymer samples with a well-defined molecular axis. Here, a four-polarization approach for Herman's in-plane orientation function and azimuthal angle determination was applied to a human tissue sample investigation for the first time. Attention was focused on fibrous tissues rich in collagen because of their cylindrical shape and established amide bond vibrations. Despite the fact that the tissue specimen contains a variety of molecules, the presented results of molecular ordering and orientation agree with the theoretical prediction based on sample composition and vibration directions.

Nanoscale AFM-IR spectroscopic imaging of lipid heterogeneity and effect of irradiation in prostate cancer cells.

The recent development of the AFM-IR technique, which combines nanoscale imaging with chemical contrast through infrared spectroscopy, opened up new fields for exploration, which were out of reach for other modalities, e.g. Raman spectroscopy. Lipid droplets (LDs) are key organelles, which are associated with stress response mechanisms in cells and their size falls into that niche. LDs composition is heterogeneous and varies depending on cancer cell type and the tumor microenvironment. Prostate cancer cells show a unique lipid metabolism manifested by an increased requirement for lipid accumulation in cytosolic LDs. In the current work, AFM-IR nanoimaging was undertaken to analyze lipids in untreated and x-ray irradiated PC-3 prostate cancer cells. Cells poor in LDs showed slightly increased lipid signal in cytoplasm close to the nucleus. On the other hand, high lipid signal coming from LDs accumulation could be found in any part of the cytoplasmic region. The observed behavior was found to be independent from irradiation and its dose. According to the band assignment of the collected AFM-IR spectra, the main components of LDs were assigned to cholesteryl esters. The size of LDs present in cells poor in lipids was found to be of less than 1 µm, whereas LDs aggregates spread out over a few microns. Analysis of AFM-IR spectra shows relative homogeneity of LDs composition in single cells and heterogeneity of LDs content within the PC-3 cell population.

Measuring and Predicting the Internal Structure of Semiconductor Nanocrystals through Raman Spectroscopy.

Nanocrystals composed of mixed chemical domains have diverse properties that are driving their integration in next-generation electronics, light sources, and biosensors. However, the precise spatial distribution of elements within these particles is difficult to measure and control, yet profoundly impacts their quality and performance. Here we synthesized a unique series of 42 different quantum dot nanocrystals, composed of two chemical domains (CdS:CdSe), arranged in 7 alloy and (core)shell structural classes. Chemometric analyses of far-field Raman spectra accurately classified their internal structures from their vibrational signatures. These classifications provide direct insight into the elemental arrangement of the alloy as well as an independent prediction of fluorescence quantum yield. This nondestructive, rapid approach can be broadly applied to greatly enhance our capacity to measure, predict and monitor multicomponent nanomaterials for precise tuning of their structures and properties.

Rapid visualization of macromolecular orientation by discrete frequency mid-infrared spectroscopic imaging.

Infrared (IR) spectroscopic imaging has been used to measure the composition and orientation of polymeric systems for decades. IR microscopy can provide detailed views of microscopic regions, allowing the observation of both morphology and molecular properties of a sample, but involves a trade-off between the spatial extent and details of molecular content. Here we describe an approximately two orders of magnitude faster approach to measure the spherulitic structure and molecular orientation in large semi-crystalline polymer samples compared to extant Fourier transform infrared (FT-IR) spectroscopic imaging. This discrete frequency approach utilizes individual narrowband emission lines of a quantum cascade laser (QCL) source to spectrally image large areas rapidly. The inherent polarization of the laser beam is employed to measure orientation, enabling calculation of Hermans in-plane orientation function along with molecular chain angles distribution.

Pancreatic intraepithelial neoplasia detection and duct pathology grading using FT-IR imaging and machine learning.

Pancreatic intraepithelial neoplasia (PanIN) is manifested by noninvasive lesions in the epithelium of smaller pancreatic ducts. Generally, cancer development risk from low-grade PanIN is minor, whereas, invasive pancreatic ductal adenocarcinoma (PDAC) development is highly related to high-grade PanINs. Therefore, in the case of high-grade PanIN detection, additional surgical resection may be recommended. However, even the low-grade PanINs can indicate possible progression to PDAC. The definition of PanIN is constantly changing and there is a need for new tools to better characterize and understand its behavior. We have recently developed a comprehensive pancreatic cancer classification model with biopsies collected from over 600 biopsies from 250 patients. Here, we take the next step and employ Infrared (IR) spectroscopy to build the first classification model for PanINs detection. Furthermore, we created a Partial Least Squares Regression (PLSR) model to characterize ducts from benign to cancerous. This model was then used to predict and grade PanINs accordingly to their malignancy level.

High-definition FT-IR reveals a synergistic effect on lipid accumulation in prostate cancer cells induced by a combination of X-rays and radiosensitizing drugs.

Radiotherapy is one of the most commonly used cancer therapies with many benefits including low toxicity to healthy tissues. However, a major problem in radiotherapy is cancer radioresistance. To enhance the effect of this kind of therapy several approaches have been proposed such as the use of radiosensitizers. A combined treatment of radiotherapy and radiosensitizing drugs leads to a greater effect on cancer cells than anticipated from the addition of both responses (synergism). In this study, high-definition FT-IR imaging was applied to follow lipid accumulation in prostate cancer cells as a response to X-ray irradiation, radiosensitizing drugs, and a combined treatment of X-rays and the drugs. Lipid accumulation induced in the cells by an increasing X-ray dose and the presence of the drugs was analyzed using Principal Component Analysis and lipid staining. Finally, the synergistic effect of the combined therapy (X-rays and radiosensitizers) was confirmed by calculations of the integral intensity of the 2850 cm-1 band.

Quantification of plaque area and characterization of plaque biochemical composition with atherosclerosis progression in ApoE/LDLR(-/-) mice by FT-IR imaging.

In this work the quantitative determination of atherosclerotic lesion area (ApoE/LDLR(-/-) mice) by FT-IR imaging is presented and validated by comparison with atherosclerotic lesion area determination by classic Oil Red O staining. Cluster analysis of FT-IR-based measurements in the 2800-3025 cm(-1) range allowed for quantitative analysis of the atherosclerosis plaque area, the results of which were highly correlated with those of Oil Red O histological staining (R(2) = 0.935). Moreover, a specific class obtained from a second cluster analysis of the aortic cross-section samples at different stages of disease progression (3, 4 and 6 months old) seemed to represent the macrophages (CD68) area within the atherosclerotic plaque.

Secondary structure of proteins analyzed ex vivo in vascular wall in diabetic animals using FT-IR spectroscopy.

In recent years many methods for ex vivo tissue analysis or diagnosis of diseases have been applied, including infrared absorption spectroscopy. Fourier-transform infrared (FT-IR) absorption microspectroscopy allows the simultaneous monitoring of the content of various chemical compounds in tissues with both high selectivity and resolution. Imaging of tissue samples in very short time can be performed using a spectrometer equipped with a Focal Plane Array (FPA) detector. Additionally, a detection of minor components or subtle changes associated with the functional status of a tissue sample is possible when advanced methods of data analysis, such as chemometric techniques, are applied. Monitoring of secondary structures of proteins has already proved to be useful in the analysis of animal tissues in disease states. The aim of this work was to build a mathematical model based on FT-IR measurements for the prediction of alterations in the content of secondary structures of proteins analyzed by FT-IR in the vascular wall of diabetic animals. For that purpose a spectral database of proteins of known crystallography and secondary structures was assembled. Thirty-seven proteins were measured by means of two FT-IR techniques: transflection and Attenuated Total Reflectance (ATR). The obtained model was tested on cross-sections of rat tail, for which the content of proteins and their secondary structures was well characterized. Then, the model was applied for the detection of possible alterations in the secondary structures of proteins in the vascular wall of diabetic rats and mice. The obtained results suggest a prominent increase in E- and S-structures and a decrease in the content of H-structures in the vascular wall from diabetic mice and rats. FT-IR-based studies of secondary structures of proteins may be a novel approach to study complex processes ongoing in the vascular wall. The obtained results are satisfactory; however, the existing limitations of the method are also discussed.

Degradation of glycine and alanine on irradiated quartz.

Recent researches suggest participation of minerals in the formation of life under primordial conditions. Among all of the minerals, quartz seems to be one of the most probable to take part in such processes. However, an external source of energy is needed, e.g. electric discharge. A device simulating the proposed conditions was designed and was used to simulate prebiotic conditions. Investigation of processes occurring during the stimulation of quartz with electric discharge was studied by means of Ultraviolet-visible (UV-VIS) spectroscopy, in order to monitor the generation kinetics of free radicals. Additionally, infrared spectroscopy was applied to identify chemical reaction products created in a solution of alanine or glycine, in the presence of quartz treated with electric discharge. Formation of increased amounts of free radicals, compared to experiments performed without quartz and/or amino acid, is reported, along with identification of possible degradation products of alanine. No synthetic reactions were observed.

Scattering correction for samples with cylindrical domains measured with polarized infrared spectroscopy.

Scattering artifacts are one of the most common effects distorting transmission spectra in Fourier-Transform Infrared spectroscopy. Their increased impact, strongly diminishing the quantitative and qualitative power of IR spectroscopy, is especially observed for structures with a size comparable to the radiation wavelength. To tackle this problem, a wide range of preprocessing techniques based on the Extended Multiplicative Scattering Correction method was developed, using physical properties to remove scattering presence in the spectra. However, until recently those algorithms were mostly focused on spherically shaped samples, for example, cells. Here, an algorithm for samples with cylindrical domains is described, with additional implementation of a linearly polarized light case, which is crucial for the growing field of polarized IR imaging and spectroscopy. An open-source code with GPU based implementation is provided, with a calculation time of several seconds per spectrum. Optimizations done to improve the throughput of this algorithm allow the application of this method into the standard preprocessing pipeline of small datasets.

Comprehensive Histopathology Imaging in Pancreatic Biopsies: High Definition Infrared Imaging with Machine Learning Approach.

Infrared (IR) based histopathology offers a new paradigm in looking at tissues and can provide a complimentary information source for more classical histopathology, which makes it a noteworthy tool given possible clinical application. This study aims to build a robust, pixel level machine learning model for pancreatic cancer detection using IR imaging. In this article, we report a pancreatic cancer classification model based on data from over 600 biopsies (coming from 250 patients) imaged with IR diffraction-limited spatial resolution. To fully research model's classification ability, we measured tissues using two optical setups, resulting in Standard and High Definitions data. This forms one of the largest IR datasets analyzed up to now, with almost 700 million spectra of different tissue types. The first six-class model created for comprehensive histopathology achieved pixel (tissue) level AUC values above 0.95, giving a successful technique for digital staining with biochemical information extracted from IR spectra.

Attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy of a single endothelial cell.

Attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR) with the use of a slide-on germanium accessory followed by chemometric analysis allowed for providing meaningful information about the biochemical composition of a single endothelial cell. In this work, the methodology of the ATR-FTIR measurements of dried cells and dried cells immersed in water solution is presented. The contact of the cell and Ge crystal was set up manually and monitored through the integration of the amide I band. Additionally, the cell imaging in transreflection mode was tested, but the spectral differences between sub-cellular structures were not prominent in the registered spectra. It has been shown that the ATR-FTIR method gives better results due to the increased spatial resolution and S/R ratio as well as small contribution of the optical artifacts.

Application of FT-Raman spectroscopy for in situ detection of microorganisms on the surface of textiles.

In this work we present the usefulness of FT-Raman spectroscopy for microbiological analysis of textiles. This technique was used for non-destructive identification of Escherichia coli bacteria on cotton and polyester fabrics. It was possible to discriminate between infected and non-infected materials. Moreover, this technique allowed detection of detergent traces as well as investigation of the influence of microorganisms on different textiles. Raman analysis supported by chemometrics (cluster analysis and principal component analysis) was shown to be a method for identification of textiles with inoculum of microorganisms in a short time. The results can be potentially used in the fabric industry and related areas.

Exploring subcellular responses of prostate cancer cells to clinical doses of X-rays by Raman microspectroscopy.

Modern techniques of radiotherapy such as fractioned radiotherapy require applications of low doses of ionizing radiation (up to 10 Gy) for effective patient treatment. It is, therefore, crucial to understand the response mechanisms in cancer cells irradiated with low (clinical) doses. The cell's response to irradiation depends on a dose and post-irradiation time. Both factors should be considered when studying the influence of ionizing radiation on cancer cells. Thus, in the present study, PC-3 prostate cancer cells were irradiated with clinical doses of X-rays to determine dose- and time-dependent response to the irradiation. Raman spectroscopy and biological methods (MTT and comet assays) were applied for the analysis of biochemical changes in the cells induced by low doses of X-ray irradiation at 0 h and 24 h post-irradiation timepoints. Due to a limited view of the biochemical changes at the subcellular level given by single spectrum Raman measurements, Raman mapping of the whole cell area was performed. The results were compared with those obtained for cell irradiation with high doses. The analysis was based on the Partial Least Squares Regression (PLSR) method for the cytoplasmic and nuclear regions separately. Additionally, for the first time, irradiation classification was performed to confirm Raman spectroscopy as a powerful tool for studies on cancer cells treated with clinical doses of ionizing radiation.