Application of FTIR Spectroscopy to Analyze RNA Structure

INSTRAS: INfrared Spectroscopic imaging-based TRAnsformers for medical image Segmentation

Infrared (IR) spectroscopic imaging is of potentially wide use in medical imaging applications due to its ability to capture both chemical and spatial information. This complexity of the data both necessitates using machine intelligence as well as presents an opportunity to harness a high-dimensionality data set that offers far more information than today's manually-interpreted images. While convolutional neural networks (CNNs), including the well-known U-Net model, have demonstrated impressive performance in image segmentation, the inherent locality of convolution limits the effectiveness of these models for encoding IR data, resulting in suboptimal performance. In this work, we propose an INfrared Spectroscopic imaging-based TRAnsformers for medical image Segmentation (INSTRAS). This novel model leverages the strength of the transformer encoders to segment IR breast images effectively. Incorporating skip-connection and transformer encoders, INSTRAS overcomes the issue of pure convolution models, such as the difficulty of capturing long-range dependencies. To evaluate the performance of our model and existing convolutional models, we conducted training on various encoder-decoder models using a breast dataset of IR images. INSTRAS, utilizing 9 spectral bands for segmentation, achieved a remarkable AUC score of 0.9788, underscoring its superior capabilities compared to purely convolutional models. These experimental results attest to INSTRAS's advanced and improved segmentation abilities for IR imaging.

RNA: The Unsuspected Conductor in the Orchestra of Macromolecular Crowding

This comprehensive Review delves into the chemical principles governing RNA-mediated crowding events, commonly referred to as granules or biological condensates. We explore the pivotal role played by RNA sequence, structure, and chemical modifications in these processes, uncovering their correlation with crowding phenomena under physiological conditions. Additionally, we investigate instances where crowding deviates from its intended function, leading to pathological consequences. By deepening our understanding of the delicate balance that governs molecular crowding driven by RNA and its implications for cellular homeostasis, we aim to shed light on this intriguing area of research. Our exploration extends to the methodologies employed to decipher the composition and structural intricacies of RNA granules, offering a comprehensive overview of the techniques used to characterize them, including relevant computational approaches. Through two detailed examples highlighting the significance of noncoding RNAs, NEAT1 and XIST, in the formation of phase-separated assemblies and their influence on the cellular landscape, we emphasize their crucial role in cellular organization and function. By elucidating the chemical underpinnings of RNA-mediated molecular crowding, investigating the role of modifications, structures, and composition of RNA granules, and exploring both physiological and aberrant phase separation phenomena, this Review provides a multifaceted understanding of the intriguing world of RNA-mediated biological condensates.

Infrared nanoimaging of neuronal ultrastructure and nanoparticle interaction with cells

Here we introduce scattering-type scanning near-field optical microscopy (s-SNOM) as a novel tool for nanoscale chemical-imaging of sub-cellular organelles, nanomaterials and of the interactions between them. Our setup uses a tuneable mid-infrared laser and a sharp scanning probe to image at a resolution substantially surpassing the diffraction limit. The laser can be tuned to excite vibrational modes of functional groups in biomolecules, (e.g. amide moieties), in a way that enables direct chemical mapping without the need for labelling. We, for the first time, chemically image neuronal ultrastructure, identify neuronal organelles and sub-organelle structures as small as 10 nm and validate our findings using transmission electron microscopy (TEM). We produce chemical and morphological maps of neurons treated with gold nanospheres and characterize nanoparticle size and intracellular location, and their interaction with the plasma membrane. Our results show that the label-free nature of s-SNOM means it has a 'true' chemical resolution of up to 20 nm which can be further improved. We argue that it offers significant potential in nanomedicine for nanoscale chemical imaging of cell ultrastructure and the subcellular distribution of nanomaterials within tissues.

Multi-omics integration strategy in the post-mortem interval of forensic science

Estimates of post-mortem interval (PMI), which often serve as pivotal evidence in forensic contexts, are fundamentally based on assessments of variability among diverse molecular markers (including proteins and metabolites), their correlations, and their temporal changes in post-mortem organisms. Nevertheless, the present approach to estimating the PMI is not comprehensive and exhibits poor performance. We developed an innovative approach that integrates multi-omics and artificial intelligence, using multimolecular, multimarker, and multidimensional information to accurately describe the intricate biological processes that occur after death, ultimately enabling inference of the PMI. Called the multi-omics stacking model (MOSM), it combines metabolomics, protein microarray electrophoresis, and fourier transform-infrared spectroscopy data. It shows improved prediction accuracy of the PMI, which is urgently needed in the forensic field. It achieved an accuracy of 0.93, generalized area under the receiver operating characteristic curve of 0.98, and minimum mean absolute error of 0.07. The MOSM integration framework not only considers multiple markers but also incorporates machine-learning models with distinct algorithmic principles. The diversity of biological mechanisms and algorithmic models further ensures the generalizability and robustness of PMI estimation.

Complete characterization of RNA biomarker fingerprints using a multi-modal ATR-FTIR and SERS approach for label-free early breast cancer diagnosis

Breast cancer is a prevalent form of cancer worldwide, and the current standard screening method, mammography, often requires invasive biopsy procedures for further assessment. Recent research has explored microRNAs (miRNAs) in circulating blood as potential biomarkers for early breast cancer diagnosis. In this study, we employed a multi-modal spectroscopy approach, combining attenuated total reflection Fourier transform infrared (ATR-FTIR) and surface-enhanced Raman scattering (SERS) to comprehensively characterize the full-spectrum fingerprints of RNA biomarkers in the blood serum of breast cancer patients. The sensitivity of conventional FTIR and Raman spectroscopy was enhanced by ATR-FTIR and SERS through the utilization of a diamond ATR crystal and silver-coated silicon nanopillars, respectively. Moreover, a wider measurement wavelength range was achieved with the multi-modal approach than with a single spectroscopic method alone. We have shown the results on 91 clinical samples, which comprised 44 malignant and 47 benign cases. Principal component analysis (PCA) was performed on the ATR-FTIR, SERS, and multi-modal data. From the peak analysis, we gained insights into biomolecular absorption and scattering-related features, which aid in the differentiation of malignant and benign samples. Applying 32 machine learning algorithms to the PCA results, we identified key molecular fingerprints and demonstrated that the multi-modal approach outperforms individual techniques, achieving higher average validation accuracy (95.1%), blind test accuracy (91.6%), specificity (94.7%), sensitivity (95.5%), and F-score (94.8%). The support vector machine (SVM) model showed the best area under the curve (AUC) characterization value of 0.9979, indicating excellent performance. These findings highlight the potential of the multi-modal spectroscopy approach as an accurate, reliable, and rapid method for distinguishing between malignant and benign breast tumors in women. Such a label-free approach holds promise for improving early breast cancer diagnosis and patient outcomes.

Inquest for the interaction of canonical and non-canonical DNA/RNA bases with ternary based 2D Si2BN and doped Si2BN for biosensing applications

Density functional theory (DFT) is invoked to investigate the interaction between the canonical (CN) and non-canonical (NC) bases with pristine Si2BN (Si2BN) and Phosphorous-doped Si2BN (P-dop-Si2BN) sheets. Inquest for the better sensing substrate is decided through the adsorption energy calculation which reveals that doping of phosphorous atom enhances the adsorption strength of AT (-83.74 kcal/mol) AU (-82.77 kcal/mol) and GC (-96.36 kcal/mol) base pairs. The CN and NC bases have higher adsorption energy than the previous reported values which concludes that the P-dop-Si2BN sheet will be optimal substrate to sense the bases. Meanwhile, the selected CN and NC (except hypoxanthine) bases interact with sheet in parallel manner which infers the π-π interaction with Si2BN and P-dop-Si2BN sheets. The energy gap variation (ΔEg%) of the P-dop-Si2BN complexes has a noticeable change, ranging from -24.75 to -197.28% which thrust the sensitivity of the P-dop-Si2BN sheet over the detection of CN and NC bases. The natural population analysis (NPA) and electron density difference map (EDDM) confirms that charges are transferred from CN and NC bases to Si2BN and P-dop-Si2BN sheet. The optical property of the P-dop-Si2BN complexes reveals that the noticeable red and blue shift in the visible and near-infrared regions (778 nm to 1143 nm) has been observed. Therefore, the above results conclude that the P-dop-Si2BN sheet plays a potential candidate to detect the CN and NC bases which contribute to the development of biosensors and DNA/RNA sequencing devices.Communicated by Ramaswamy H. Sarma.

Optimization and characterization of miRNA-129-5p-encapsulated poly (lactic-co-glycolic acid) nanoparticles to reprogram activated microglia

Microglia have become a therapeutic target of many inflammation-mediated diseases in the central nervous system (CNS). Recently, microRNA (miRNA) has been proposed as an important regulator of immune responses. Specifically, miRNA-129-5p has been shown to play critical roles in the regulation of microglia activation. We have demonstrated that biodegradable poly (lactic-co-glycolic acid) (PLGA)-based nanoparticles (NPs) modulated innate immune cells and limited neuroinflammation after injury to the CNS. In this study, we optimized and characterized PLGA-based NPs for miRNA-129-5p delivery to utilize their synergistic immunomodulatory features for activated microglia modulation. A series of nanoformulations employing multiple excipients including epigallocatechin gallate (EGCG), spermidine (Sp), or polyethyleneimine (PEI) for miRNA-129-5p complexation and miRNA-129-5p conjugation to PLGA (PLGA-miR) were utilized. We characterized a total of six nanoformulations through physicochemical, biochemical, and molecular biological methods. In addition, we investigated the immunomodulatory effects of multiple nanoformulations. The data indicated that the immunomodulatory effects of nanoformulation, PLGA-miR with the excipient Sp (PLGA-miR+Sp) and PEI (PLGA-miR+PEI) were significant compared to other nanoformulations including naked PLGA-based NP. These nanoformulations promoted a sustained release of miRNA-129-5p and polarization of activated microglia into a more pro-regenerative phenotype. Moreover, they enhanced the expression of multiple regeneration-associated factors, while alleviating the expression of pro-inflammatory factors. Collectively, the proposed nanoformulations in this study highlight the promising therapeutic tools for synergistic immunomodulatory effects between PLGA-based NPs and miRNA-129-5p to modulate activated microglia which will have numerous applications for inflammation-derived diseases.

Structural studies of protein-nucleic acid complexes: A brief overview of the selected techniques

Protein-nucleic acid complexes are involved in all vital processes, including replication, transcription, translation, regulation of gene expression and cell metabolism. Knowledge of the biological functions and molecular mechanisms beyond the activity of the macromolecular complexes can be determined from their tertiary structures. Undoubtably, performing structural studies of protein-nucleic acid complexes is challenging, mainly because these types of complexes are often unstable. In addition, their individual components may display extremely different surface charges, causing the complexes to precipitate at higher concentrations used in many structural studies. Due to the variety of protein-nucleic acid complexes and their different biophysical properties, no simple and universal guideline exists that helps scientists chose a method to successfully determine the structure of a specific protein-nucleic acid complex. In this review, we provide a summary of the following experimental methods, which can be applied to study the structures of protein-nucleic acid complexes: X-ray and neutron crystallography, nuclear magnetic resonance (NMR) spectroscopy, cryogenic electron microscopy (cryo-EM), atomic force microscopy (AFM), small angle scattering (SAS) methods, circular dichroism (CD) and infrared (IR) spectroscopy. Each method is discussed regarding its historical context, advancements over the past decades and recent years, and weaknesses and strengths. When a single method does not provide satisfactory data on the selected protein-nucleic acid complex, a combination of several methods should be considered as a hybrid approach; thus, specific structural problems can be solved when studying protein-nucleic acid complexes.

SARS-CoV-2 and omicron variant detection with a high selectivity, sensitivity, and low-cost silicon bio-nanosensor

The recent SARS-CoV-2 pandemic has highlighted the urgent need for novel point-of-care devices to be promptly used for a rapid and reliable large screening analysis of several biomarkers like genetic sequences and antibodies. Currently, one of the main limitations of rapid tests is the high percentage of false negatives in the presence of variants and, in particular for the Omicron one. We demonstrate in this work the detection of SARS-CoV-2 and the Omicron variant with a cost-effective silicon nanosensor enabling high sensitivity, selectivity, and fast response. We have shown that a silicon (Si) nanowires (NW) platform detects both Sars-CoV-2 and its Omicron variant with a limit of detection (LoD) of four effective copies (cps), without any amplification of the genome, and with high selectivity. This ultrasensitive detection of 4 cps allows to obtain an extremely early diagnosis paving the way for efficient and widespread tracking. The sensor is made with industrially compatible techniques, which in perspective may allow easy and cost-effective industrialization.

Antenna-enhanced mid-infrared detection of extracellular vesicles derived from human cancer cell cultures

BACKGROUND

Extracellular Vesicles (EVs) are sub-micrometer lipid-bound particles released by most cell types. They are considered a promising source of cancer biomarkers for liquid biopsy and personalized medicine due to their specific molecular cargo, which provides biochemical information on the state of parent cells. Despite this potential, EVs translation process in the diagnostic practice is still at its birth, and the development of novel medical devices for their detection and characterization is highly required.

RESULTS

In this study, we demonstrate mid-infrared plasmonic nanoantenna arrays designed to detect, in the liquid and dry phase, the specific vibrational absorption signal of EVs simultaneously with the unspecific refractive index sensing signal. For this purpose, EVs are immobilized on the gold nanoantenna surface by immunocapture, allowing us to select specific EV sub-populations and get rid of contaminants. A wet sample-handling technique relying on hydrophobicity contrast enables effortless reflectance measurements with a Fourier-transform infrared (FTIR) spectro-microscope in the wavelength range between 10 and 3 µm. In a proof-of-principle experiment carried out on EVs released from human colorectal adenocarcinoma (CRC) cells, the protein absorption bands (amide-I and amide-II between 5.9 and 6.4 µm) increase sharply within minutes when the EV solution is introduced in the fluidic chamber, indicating sensitivity to the EV proteins. A refractive index sensing curve is simultaneously provided by our sensor in the form of the redshift of a sharp spectral edge at wavelengths around 5 µm, where no vibrational absorption of organic molecules takes place: this permits to extract of the dynamics of EV capture by antibodies from the overall molecular layer deposition dynamics, which is typically measured by commercial surface plasmon resonance sensors. Additionally, the described metasurface is exploited to compare the spectral response of EVs derived from cancer cells with increasing invasiveness and metastatic potential, suggesting that the average secondary structure content in EVs can be correlated with cell malignancy.

CONCLUSIONS

Thanks to the high protein sensitivity and the possibility to work with small sample volumes-two key features for ultrasensitive detection of extracellular vesicles- our lab-on-chip can positively impact the development of novel laboratory medicine methods for the molecular characterization of EVs.

Challenges with Simulating Modified RNA: Insights into Role and Reciprocity of Experimental and Computational Approaches

RNA is critical to a broad spectrum of biological and viral processes. This functional diversity is a result of their dynamic nature; the variety of three-dimensional structures that they can fold into; and a host of post-transcriptional chemical modifications. While there are many experimental techniques to study the structural dynamics of biomolecules, molecular dynamics simulations (MDS) play a significant role in complementing experimental data and providing mechanistic insights. The accuracy of the results obtained from MDS is determined by the underlying physical models i.e., the force-fields, that steer the simulations. Though RNA force-fields have received a lot of attention in the last decade, they still lag compared to their protein counterparts. The chemical diversity imparted by the RNA modifications adds another layer of complexity to an already challenging problem. Insight into the effect of RNA modifications upon RNA folding and dynamics is lacking due to the insufficiency or absence of relevant experimental data. This review provides an overview of the state of MDS of modified RNA, focusing on the challenges in parameterization of RNA modifications as well as insights into relevant reference experiments necessary for their calibration.

Multifunctional Nanomachinery for Enhancement of Bone Healing

The visionary idea that RNA adopts nonbiological roles in today's nanomaterial world has been nothing short of phenomenal. These RNA molecules have ample chemical functionality and self-assemble to form distinct nanostructures in response to external stimuli. They may be combined with inorganic materials to produce nanomachines that carry cargo to a target site in a controlled manner and respond dynamically to environmental changes. Comparable to biological cells, programmed RNA nanomachines have the potential to replicate bone healing in vitro. Here, an RNA-biomineral nanomachine is developed, which accomplishes intrafibrillar and extrafibrillar mineralization of collagen scaffolds to mimic bone formation in vitro. Molecular dynamics simulation indicates that noncovalent hydrogen bonding provides the energy source that initiates self-assembly of these nanomachines. Incorporation of the RNA-biomineral nanomachines into collagen scaffolds in vivo creates an osteoinductive microenvironment within a bone defect that is conducive to rapid biomineralization and osteogenesis. Addition of RNA-degrading enzymes into RNA-biomineral nanomachines further creates a stop signal that inhibits unwarranted bone formation in tissues. The potential of RNA in building functional nanostructures has been underestimated in the past. The concept of RNA-biomineral nanomachines participating in physiological processes may transform the nanoscopic world of life science.

Pathophysiological Response to SARS-CoV-2 Infection Detected by Infrared Spectroscopy Enables Rapid and Robust Saliva Screening for COVID-19

Fourier transform infrared (FTIR) spectroscopy provides a (bio)chemical snapshot of the sample, and was recently used in proof-of-concept cohort studies for COVID-19 saliva screening. However, the biological basis of the proposed technology has not been established. To investigate underlying pathophysiology, we conducted controlled infection experiments on Vero E6 cells in vitro and K18-hACE2 mice in vivo. Potentially infectious culture supernatant or mouse oral lavage samples were treated with ethanol or 75% (v/v) Trizol for attenuated total reflectance (ATR)-FTIR spectroscopy and proteomics, or RT-PCR, respectively. Controlled infection with UV-inactivated SARS-CoV-2 elicited strong biochemical changes in culture supernatant/oral lavage despite a lack of viral replication, determined by RT-PCR or a cell culture infectious dose 50% assay. Nevertheless, SARS-CoV-2 infection induced additional FTIR signals over UV-inactivated SARS-CoV-2 infection in both cell and mouse models, which correspond to aggregated proteins and RNA. Proteomics of mouse oral lavage revealed increased secretion of kallikreins and immune modulatory proteins. Next, we collected saliva from a cohort of human participants (n = 104) and developed a predictive model for COVID-19 using partial least squares discriminant analysis. While high sensitivity of 93.48% was achieved through leave-one-out cross-validation, COVID-19 patients testing negative on follow-up on the day of saliva sampling using RT-PCR was poorly predicted in this model. Importantly, COVID-19 vaccination did not lead to the misclassification of COVID-19 negatives. Finally, meta-analysis revealed that SARS-CoV-2 induced increases in the amide II band in all arms of this study and in recently published cohort studies, indicative of altered β-sheet structures in secreted proteins. In conclusion, this study reveals a consistent secretory pathophysiological response to SARS-CoV-2, as well as a simple, robust method for COVID-19 saliva screening using ATR-FTIR.

A simple and fast spectroscopy-based technique for Covid-19 diagnosis

The coronavirus pandemic, which appeared in Wuhan, China, in December 2019, rapidly spread all over the world in only a few weeks. Faster testing techniques requiring less resources are key in managing the pandemic, either to enable larger scale testing or even just provide developing countries with limited resources, particularly in Africa, means to perform tests to manage the crisis. Here, we report an unprecedented, rapid, reagent-free and easy-to-use screening spectroscopic method for the detection of SARS-CoV-2 on RNA extracts. This method, validated on clinical samples collected from 280 patients with quantitative predictive scores on both positive and negative samples, is based on a multivariate analysis of FTIR spectra of RNA extracts. This technique, in agreement with RT-PCR, achieves 97.8% accuracy, 97% sensitivity and 98.3% specificity while reducing the testing time post RNA extraction from hours to minutes. Furthermore, this technique can be used in several laboratories with limited resources.