Discovery of DNA-Carbon Nanotube Sensors for Serotonin with Machine Learning and Near-infrared Fluorescence Spectroscopy

Recent advances on applications of single-walled carbon nanotubes as cutting-edge optical nanosensors for biosensing technologies

Single-walled carbon nanotubes (SWCNTs) possess outstanding photophysical properties which has garnered interest towards utilizing these materials for biosensing and imaging applications. The near-infrared (NIR) fluorescence within the tissue transparent region along with their photostability and sizes in the nanoscale make SWCNTs valued candidates for the development of optical sensors. In this review, we discuss recent advances in the development and the applications of SWCNT-based nano-biosensors. An overview of SWCNT's structural and photophysical properties, sensor development, and sensing mechanisms are described. Examples of SWCNT-based optical nanosensors for detection of disease biomarkers, pathogens (bacteria and viruses), plant stressors, and environmental contaminants including heavy metals and disinfectants are provided. Molecular detection in biofluids, in vitro, and in vivo (small animal models and plants) are highlighted, and sensor integration into portable substrates for implantable and wearable sensing devices has been discussed. Recent advancements, which include high throughput assays and the use of machine learning models to predict more sensitive and robust sensing outcomes are discussed. Current limitations and future perspectives on translation of SWCNT optical probes into clinical practices have been provided.

Recent advances of electrochemical and optical point-of-care biosensors for detecting neurotransmitter serotonin biomarkers

Since its discovery in 1984, the monoamine serotonin (5-HT) has been recognized for its critical role as a neuromodulator in both the central and peripheral nervous systems. Recent research reveals that serotonin also significantly influences various neuronal activities. Historically, it was believed that peripheral serotonin, produced by tryptophan hydroxylase in intestinal cells, functioned primarily as a hormone. However, new insights have expanded its known roles, necessitating advanced detection methods. Biosensors have emerged as indispensable tools in biomedical diagnostics, enabling the rapid and minimally invasive detection of target analytes with high spatial and temporal resolution. This review summarizes the progress made in the past decade in developing optical and electrochemical biosensors for serotonin detection. We evaluate various sensing strategies that optimize performance in terms of detection limits, sensitivity, and specificity. The study also explores recent innovations in biosensing technologies utilizing surface-modified electrodes with nanomaterials, including gold, graphite, carbon nanotubes, and metal oxide particles. Applications range from in vivo studies to chemical imaging and diagnostics, highlighting future prospects in the field.

Systematic Selection of High-Affinity ssDNA Sequences to Carbon Nanotubes

Single-walled carbon nanotubes (SWCNTs) have gained significant interest for their potential in biomedicine and nanoelectronics. The functionalization of SWCNTs with single-stranded DNA (ssDNA) enables the precise control of SWCNT alignment and the development of optical and electronic biosensors. This study addresses the current gaps in the field by employing high-throughput systematic selection, enriching high-affinity ssDNA sequences from a vast random library. Specific base compositions and patterns are identified that govern the binding affinity between ssDNA and SWCNTs. Molecular dynamics simulations validate the stability of ssDNA conformations on SWCNTs and reveal the pivotal role of hydrogen bonds in this interaction. Additionally, it is demonstrated that machine learning could accurately distinguish high-affinity ssDNA sequences, providing an accessible model on a dedicated webpage (http://service.k-medai.com/ssdna4cnt). These findings open new avenues for high-affinity ssDNA-SWCNT constructs for stable and sensitive molecular detection across diverse scientific disciplines.

Enhanced cellular internalization of near-infrared fluorescent single-walled carbon nanotubes facilitated by a transfection reagent

Functionalized single-walled carbon nanotubes (SWCNTs) hold immense potential for diverse biomedical applications due to their biocompatibility and optical properties, including near-infrared fluorescence. Specifically, SWCNTs have been utilized to target cells as a vehicle for drug delivery and gene therapy, and as sensors for various intracellular biomarkers. While the main internalization route of SWCNTs into cells is endocytosis, methods for enhancing the cellular uptake of SWCNTs are of great importance. In this research, we demonstrate the use of a transfecting reagent for promoting cell internalization of functionalized SWCNTs. We explore different types of SWCNT functionalization, namely single-stranded DNA (ssDNA) or polyethylene glycol (PEG)-lipids, and two different cell types, embryonic kidney cells and adenocarcinoma cells. We show that internalizing PEGylated functionalized SWCNTs is enhanced in the presence of the transfecting reagent, where the effect is more pronounced for negatively charged PEG-lipid. However, ssDNA-SWCNTs tend to form aggregates in the presence of the transfecting reagent, rendering it unsuitable for promoting internalization. For all cases, cellular uptake is visualized by near-infrared fluorescence microscopy, showing that the SWCNTs are typically localized within the lysosome. Generally, cellular internalization was higher in the adenocarcinoma cells, thereby paving new avenues for drug delivery and sensing in malignant cells.

Predicting Serotonin Detection with DNA-Carbon Nanotube Sensors across Multiple Spectral Wavelengths

Owing to the value of DNA-wrapped single-walled carbon nanotube (SWNT)-based sensors for chemically specific imaging in biology, we explore machine learning (ML) predictions DNA-SWNT serotonin sensor responsivity as a function of DNA sequence based on the whole SWNT fluorescence spectra. Our analysis reveals the crucial role of DNA sequence in the binding modes of DNA-SWNTs to serotonin, with a smaller influence of SWNT chirality. Regression ML models trained on existing data sets predict the change in the fluorescence emission in response to serotonin, ΔF/F, at over a hundred wavelengths for new DNA-SWNT conjugates, successfully identifying some high- and low-response DNA sequences. Despite successful predictions, we also show that the finite size of the training data set leads to limitations on prediction accuracy. Nevertheless, incorporating entire spectra into ML models enhances prediction robustness and facilitates the discovery of novel DNA-SWNT sensors. Our approaches show promise for identifying new chemical systems with specific sensing response characteristics, marking a valuable advancement in DNA-based system discovery.

Turn-on fluorescent aptasensing for the determination of serotonin via target-induced knot displacement at corona

A turn-on fluorescence aptasensing approach for the highly sensitive and selective determination of 5-HT was proposed via target-induced knot displacement. 5-HT can be determined in a range from 0.5 nM to 100 nM with a limit of detection as low as 0.1 nM and a low dissociation constant of 2.3 nM.

Directed Evolution of Near-Infrared Serotonin Nanosensors with Machine Learning-Based Screening

In this study, we employed a novel approach to improve the serotonin-responsive ssDNA-wrapped single-walled carbon nanotube (ssDNA-SWCNT) nanosensors, combining directed evolution and machine learning-based prediction. Our iterative optimization process is aimed at the sensitivity and selectivity of ssDNA-SWCNT nanosensors. In the three rounds for higher serotonin sensitivity, we substantially improved sensitivity, achieving a remarkable 2.5-fold enhancement in fluorescence response compared to the original sequence. Following this, we directed our efforts towards selectivity for serotonin over dopamine in the two rounds. Despite the structural similarity between these neurotransmitters, we achieved a 1.6-fold increase in selectivity. This innovative methodology, offering high-throughput screening of mutated sequences, marks a significant advancement in biosensor development. The top-performing nanosensors, N2-1 (sensitivity) and L1-14 (selectivity) present promising reference sequences for future studies involving serotonin detection.

Unraveling the Possibilities: Recent Progress in DNA Biosensing

Due to the advantages of its numerous modification sites, predictable structure, high thermal stability, and excellent biocompatibility, DNA is the ideal choice as a key component of biosensors. DNA biosensors offer significant advantages over existing bioanalytical techniques, addressing limitations in sensitivity, selectivity, and limit of detection. Consequently, they have attracted significant attention from researchers worldwide. Here, we exemplify four foundational categories of functional nucleic acids: aptamers, DNAzymes, i-motifs, and G-quadruplexes, from the perspective of the structure-driven functionality in constructing DNA biosensors. Furthermore, we provide a concise overview of the design and detection mechanisms employed in these DNA biosensors. Noteworthy advantages of DNA as a sensor component, including its programmable structure, reaction predictility, exceptional specificity, excellent sensitivity, and thermal stability, are highlighted. These characteristics contribute to the efficacy and reliability of DNA biosensors. Despite their great potential, challenges remain for the successful application of DNA biosensors, spanning storage and detection conditions, as well as associated costs. To overcome these limitations, we propose potential strategies that can be implemented to solve these issues. By offering these insights, we aim to inspire subsequent researchers in related fields.

Enhancing Intracellular Optical Performance and Stability of Engineered Nanomaterials via Aqueous Two-Phase Purification

Supramolecular hybrids of DNA and single-walled carbon nanotubes (SWCNTs) have been introduced in numerous biosensing applications due to their unique optical properties. Recent aqueous two-phase (ATP) purification methods for SWCNTs have gained popularity by introducing specificity and homogeneity into the sensor design process. Using murine macrophages probed by near-infrared and Raman microscopies, we show that ATP purification increases the retention time of DNA-SWCNTs within cells while simultaneously enhancing the optical performance and stability of the engineered nanomaterial. Over a period of 6 h, we observe 45% brighter fluorescence intensity and no significant change in emission wavelength of ATP-purified DNA-SWCNTs relative to as-dispersed SWCNTs. These findings provide strong evidence of how cells differentially process engineered nanomaterials depending on their state of purification, lending to the future development of more robust and sensitive biosensors with desirable in vivo optical parameters using surfactant-based ATP systems with a subsequent exchange to biocompatible functionalization.

Converting Nanotoxicity Data to Information Using Artificial Intelligence and Simulation

Decades of nanotoxicology research have generated extensive and diverse data sets. However, data is not equal to information. The question is how to extract critical information buried in vast data streams. Here we show that artificial intelligence (AI) and molecular simulation play key roles in transforming nanotoxicity data into critical information, i.e., constructing the quantitative nanostructure (physicochemical properties)-toxicity relationships, and elucidating the toxicity-related molecular mechanisms. For AI and molecular simulation to realize their full impacts in this mission, several obstacles must be overcome. These include the paucity of high-quality nanomaterials (NMs) and standardized nanotoxicity data, the lack of model-friendly databases, the scarcity of specific and universal nanodescriptors, and the inability to simulate NMs at realistic spatial and temporal scales. This review provides a comprehensive and representative, but not exhaustive, summary of the current capability gaps and tools required to fill these formidable gaps. Specifically, we discuss the applications of AI and molecular simulation, which can address the large-scale data challenge for nanotoxicology research. The need for model-friendly nanotoxicity databases, powerful nanodescriptors, new modeling approaches, molecular mechanism analysis, and design of the next-generation NMs are also critically discussed. Finally, we provide a perspective on future trends and challenges.

Characterizing the Interactions of Cell-Membrane-Disrupting Peptides with Lipid-Functionalized Single-Walled Carbon Nanotubes

Lipid-functionalized single-walled carbon nanotubes (SWNTs) have garnered significant interest for their potential use in a wide range of biomedical applications. In this work, we used molecular dynamics simulations to study the equilibrium properties of SWNTs surrounded by the phosphatidylcholine (POPC) corona phase and their interactions with three cell membrane disruptor peptides: colistin, TAT peptide, and crotamine-derived peptide. Our results show that SWNTs favor asymmetrical positioning within the POPC corona, so that one side of the SWNT, covered by the thinnest part of the corona, comes in contact with charged and polar functional groups of POPC and water. We also observed that colistin and TAT insert deeply into the POPC corona, while crotamine-derived peptide only adsorbs to the corona surface. In separate simulations, we show that three examined peptides exhibit similar insertion and adsorption behaviors when interacting with POPC bilayers, confirming that peptide-induced perturbations to POPC in conjugates and bilayers are similar in nature and magnitude. Furthermore, we observed correlations between the peptide-induced structural perturbations and the near-infrared emission of the lipid-functionalized SWNTs, which suggest that the optical signal of the conjugates transduces the morphological changes in the lipid corona. Overall, our findings indicate that lipid-functionalized SWNTs could serve as simplified cell membrane model systems for prescreening of new antimicrobial compounds that disrupt cell membranes.

BinderSpace: A package for sequence space analyses for datasets of affinity-selected oligonucleotides and peptide-based molecules

Discovery of target-binding molecules, such as aptamers and peptides, is usually performed with the use of high-throughput experimental screening methods. These methods typically generate large datasets of sequences of target-binding molecules, which can be enriched with high affinity binders. However, the identification of the highest affinity binders from these large datasets often requires additional low-throughput experiments or other approaches. Bioinformatics-based analyses could be helpful to better understand these large datasets and identify the parts of the sequence space enriched with high affinity binders. BinderSpace is an open-source Python package that performs motif analysis, sequence space visualization, clustering analyses, and sequence extraction from clusters of interest. The motif analysis, resulting in text-based and visual output of motifs, can also provide heat maps of previously measured user-defined functional properties for all the motif-containing molecules. Users can also run principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) analyses on whole datasets and on motif-related subsets of the data. Functionally important sequences can also be highlighted in the resulting PCA and t-SNE maps. If points (sequences) in two-dimensional maps in PCA or t-SNE space form clusters, users can perform clustering analyses on their data, and extract sequences from clusters of interest. We demonstrate the use of BinderSpace on a dataset of oligonucleotides binding to single-wall carbon nanotubes in the presence and absence of a bioanalyte, and on a dataset of cyclic peptidomimetics binding to bovine carbonic anhydrase protein. BinderSpace is openly accessible to the public via the GitHub website: https://github.com/vukoviclab/BinderSpace.

Second Near-Infrared (NIR-II) Window for Imaging-Navigated Modulation of Brain Structure and Function

For a long time, optical imaging of the deep brain with high resolution has been a challenge. Recently, with the advance in second near-infrared (NIR-II) bioimaging techniques and imaging contrast agents, NIR-II window bioimaging has attracted great attention to monitoring deeper biological or pathophysiological processes with high signal-to-noise ratio (SNR) and spatiotemporal resolution. Assisted with NIR-II bioimaging, the modulation of structure and function of brain is promising to be noninvasive and more precise. Herein, in this review, first the advantage of NIR-II light in brain imaging from the interaction between NIR-II and tissue is elaborated. Then, several specific NIR-II bioimaging technologies are introduced, including NIR-II fluorescence imaging, multiphoton fluorescence imaging, and photoacoustic imaging. Furthermore, the corresponding contrast agents are summarized. Next, the application of various NIR-II bioimaging technologies in visualizing the characteristics of cerebrovascular network and monitoring the changes of the pathology signals will be presented. After that, the modulation of brain structure and function based on NIR-II bioimaging will be discussed, including treatment of glioblastoma, guidance of cell transplantation, and neuromodulation. In the end, future perspectives that would help improve the clinical translation of NIR-II light are proposed.

Characterizing the Interactions of Cell Membrane-Disrupting Peptides with Lipid-Functionalized Single-Walled Carbon Nanotube Systems for Antimicrobial Screening

Lipid-functionalized single-walled carbon nanotubes (SWNTs) have garnered significant interest for their potential use in a wide range of biomedical applications. In this work, we used molecular dynamics simulations to study the equilibrium properties of SWNTs surrounded by the phosphatidylcholine (POPC) corona phase, and their interactions with three cell membrane disruptor peptides: colistin, TAT peptide, and crotamine-derived peptide. Our results show that SWNTs favor asymmetrical positioning within the POPC corona, so that one side of the SWNT, covered by the thinnest part of the corona, comes in contact with charged and polar functional groups of POPC and water. We also observed that colistin and TAT insert deeply into POPC corona, while crotamine-derived peptide only adsorbs to the corona surface. Compared to crotamine-derived peptide, colistin and TAT also induce larger perturbations in the thinnest region of the corona, by allowing more water molecules to directly contact the SWNT surface. In separate simulations, we show that three examined peptides exhibit similar insertion and adsorption behaviors when interacting with POPC bilayers, confirming that peptide-induced perturbations to POPC in conjugates and bilayers are similar in nature and magnitude. Furthermore, we observed correlations between the peptide-induced structural perturbations and the near-infrared emission of the lipid-functionalized SWNTs, which suggest that the optical signal of the conjugates transduces the morphological changes in the lipid corona. Overall, our findings indicate that lipid-functionalized SWNTs could serve as simplified cell membrane model systems for pre-screening of new antimicrobial compounds that disrupt cell membranes.

The Promising Nanovectors for Gene Delivery in Plant Genome Engineering

Highly efficient gene delivery systems are essential for genetic engineering in plants. Traditional delivery methods have been widely used, such as Agrobacterium-mediated transformation, polyethylene glycol (PEG)-mediated delivery, biolistic particle bombardment, and viral transfection. However, genotype dependence and other drawbacks of these techniques limit the application of genetic engineering, particularly genome editing in many crop plants. There is a great need to develop newer gene delivery vectors or methods. Recently, nanomaterials such as mesoporous silica particles (MSNs), AuNPs, carbon nanotubes (CNTs), and layer double hydroxides (LDHs), have emerged as promising vectors for the delivery of genome engineering tools (DNA, RNA, proteins, and RNPs) to plants in a species-independent manner with high efficiency. Some exciting results have been reported, such as the successful delivery of cargo genes into plants and the generation of genome stable transgenic cotton and maize plants, which have provided some new routines for genome engineering in plants. Thus, in this review, we summarized recent progress in the utilization of nanomaterials for plant genetic transformation and discussed the advantages and limitations of different methods. Furthermore, we emphasized the advantages and potential broad applications of nanomaterials in plant genome editing, which provides guidance for future applications of nanomaterials in plant genetic engineering and crop breeding.