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Despite the blockbuster popularity of drugs that act on catecholamine receptors, catecholamine dynamics in human health and disease remain an incomplete picture. Recent advances in fluorescent sensors have enabled unprecedented access to catecholamine dynamics in preclinical animal models, but the requirements of these technologies limit translational value for clinical diagnostics. Here, we present a flexible and convenient tool for fluorescent catecholamine detection by functionalizing optical fibers with single-walled carbon nanotube (SWNT)-based near-infrared catecholamine sensors (nIRCats), a form factor that has potential for more convenient and less invasive clinical translation. We show that these near-infrared functionalized (nIRF) fibers respond to dopamine in a biologically-relevant concentration range (10nM through 1 µM) with a mean ΔF/F0 of 0.022 through 0.411, with no statistically significant effect on signal magnitude after 16-hour exposure to human blood plasma. We further demonstrate the utility of these fibers in as little as 10 µL volumes of clinically relevant biofluids up to 24 weeks after preparation, with a ΔF/F0 of up to 0.059 through 1.127 for 10 nM through 1 µM dopamine. We also introduce a compact, mobile dual-near-infrared fiber photometry rig and demonstrate its success detecting dopamine with 0.005 ΔF/F0 in acute brain slices with nIRF fibers. Together, this fiber-based tool and photometry rig expand the toolbox of catecholamine detection technologies to a broader range of applications.
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Oxytocin plays a critical role in regulating social behaviors, yet our understanding of its function in both neurological health and disease remains incomplete. Real-time oxytocin imaging probes with spatiotemporal resolution relevant to its endogenous signaling are required to fully elucidate oxytocin's role in the brain. Herein, we describe a near-infrared oxytocin nanosensor (nIROXT), a synthetic probe capable of imaging oxytocin in the brain without interference from its structural analogue, vasopressin. nIROXT leverages the inherent tissue-transparent fluorescence of single-walled carbon nanotubes (SWCNT) and the molecular recognition capacity of an oxytocin receptor peptide fragment to selectively and reversibly image oxytocin. We employ these nanosensors to monitor electrically stimulated oxytocin release in brain tissue, revealing oxytocin release sites with a median size of 3 µm in the paraventricular nucleus of C57BL/6 mice, which putatively represents the spatial diffusion of oxytocin from its point of release. These data demonstrate that covalent SWCNT constructs, such as nIROXT, are powerful optical tools that can be leveraged to measure neuropeptide release in brain tissue.
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Encéfalo , Camundongos Endogâmicos C57BL , Nanotubos de Carbono , Imagem Óptica , Ocitocina , Vasopressinas , Animais , Ocitocina/metabolismo , Camundongos , Imagem Óptica/métodos , Vasopressinas/metabolismo , Nanotubos de Carbono/química , Encéfalo/metabolismo , Encéfalo/diagnóstico por imagem , Masculino , Receptores de Ocitocina/metabolismo , Espectroscopia de Luz Próxima ao Infravermelho/métodosRESUMO
Oxytocin is a neuropeptide thought to play a central role in regulating social and emotional behavior. Current techniques for neuropeptide imaging are generally limited in spatial and temporal resolution, real-time imaging capacity, selectivity for oxytocin over vasopressin, and application in young and non-model organisms. To avoid the use of endogenous oxytocin receptors for oxytocin probe development, we employed a protocol to evolve purely synthetic molecular recognition on the surface of near-infrared fluorescent single-walled carbon nanotubes (SWCNT) using single-stranded DNA (ssDNA). This probe reversibly undergoes up to a 172% fluorescence increase in response to oxytocin with a K d of 4.93 µM. Furthermore, this probe responds selectively to oxytocin over oxytocin analogs, receptor agonists and antagonists, and most other neurochemicals. Lastly, we show our probe can image synaptic evoked oxytocin release in live mouse brain slices. Optical probes with the specificity and resolution requisite to image endogenous oxytocin signaling can advance the study of oxytocin neurotransmission for its role in both health and disease.
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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.
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DNA , Aprendizado de Máquina , Nanotubos de Carbono , Serotonina , Nanotubos de Carbono/química , DNA/química , Espectrometria de Fluorescência , Técnicas Biossensoriais/métodos , Sequência de BasesRESUMO
The protein corona, a dynamic biomolecular layer that forms on nanoparticle (NP) surfaces upon exposure to biological fluids is emerging as a valuable diagnostic tool for improving plasma proteome coverage analyzed by liquid chromatography-mass spectrometry (LC-MS/MS). Here, we show that spiking small molecules, including metabolites, lipids, vitamins, and nutrients, into plasma can induce diverse protein corona patterns on otherwise identical NPs, significantly enhancing the depth of plasma proteome profiling. The protein coronas on polystyrene NPs when exposed to plasma treated with an array of small molecules (n=10) allowed for detection of 1793 proteins marking an 8.25-fold increase in the number of quantified proteins compared to plasma alone (218 proteins) and a 2.63-fold increase relative to the untreated protein corona (681 proteins). Furthermore, we discovered that adding 1000 µg/ml phosphatidylcholine could singularly increase the number of unique proteins within the protein corona (897 proteins). This specific concentration of phosphatidylcholine selectively depleted the four most abundant plasma proteins, including albumin, thus reducing concentration dynamic range of plasma proteome and boosting LC-MS/MS sensitivity for detection of proteins with lower abundance. By employing an optimized data-independent acquisition (DIA) approach, the inclusion of phosphatidylcholine led to the detection of 1436 proteins in plasma. This significant achievement is made utilizing only a single NP type and one small molecule to analyze a single plasma sample, setting a new standard in proteomic depth of the plasma sample. Given the critical role of plasma proteomics in biomarker discovery and disease monitoring, we anticipate widespread adoption of this methodology for identification and clinical translation of proteomic biomarkers into FDA approved diagnostics.
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Protein corona, a layer of biomolecules primarily comprising proteins, forms dynamically on nanoparticles in biological fluids and is crucial for predicting nanomedicine safety and efficacy. The protein composition of the corona layer is typically analyzed using liquid chromatography-mass spectrometry (LC-MS/MS). Our recent study, involving identical samples analyzed by 17 proteomics facilities, highlighted significant data variability, with only 1.8% of proteins consistently identified across these centers. Here, we implement an aggregated database search unifying parameters such as variable modifications, enzyme specificity, number of allowed missed cleavages and a stringent 1% false discovery rate at the protein and peptide levels. Such uniform search dramatically harmonizes the proteomics data, increasing the reproducibility and the percentage of consistency-identified unique proteins across distinct cores. Specifically, out of the 717 quantified proteins, 253 (35.3%) are shared among the top 5 facilities (and 16.2% among top 11 facilities). Furthermore, we note that reduction and alkylation are important steps in protein corona sample processing and as expected, omitting these steps reduces the number of total quantified peptides by around 20%. These findings underscore the need for standardized procedures in protein corona analysis, which is vital for advancing clinical applications of nanoscale biotechnologies.
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Nanopartículas , Coroa de Proteína , Proteômica , Cromatografia Líquida , Reprodutibilidade dos Testes , Espectrometria de Massas em TandemRESUMO
Continuous and non-invasive glucose monitoring and imaging is important for disease diagnosis, treatment, and management. However, glucose monitoring remains a technical challenge owing to the dearth of tissue-transparent glucose sensors. In this study, we present the development of near-infrared fluorescent single-walled carbon nanotube (SWCNT) based nanosensors directly functionalized with glucose oxidase (GOx) capable of immediate and reversible glucose imaging in biological fluids and tissues. We prepared GOx-SWCNT nanosensors by facile sonication of SWCNT with GOx in a manner that-surprisingly-does not compromise the ability of GOx to detect glucose. Importantly, we find by using denatured GOx that the fluorescence modulation of GOx-SWCNT is not associated with the catalytic oxidation of glucose but rather triggered by glucose-GOx binding. Leveraging the unique response mechanism of GOx-SWCNT nanosensors, we developed catalytically inactive apo-GOx-SWCNT that enables both sensitive and reversible glucose imaging, exhibiting a ΔF/F0 of up to 40 % within 1â s of exposure to glucose without consuming the glucose analyte. We finally demonstrate the potential applicability of apo-GOx-SWCNT in biomedical applications by glucose quantification in human plasma and glucose imaging in mouse brain slices.
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Técnicas Biossensoriais , Nanotubos de Carbono , Animais , Camundongos , Humanos , Glucose , Glucose Oxidase/metabolismo , Glicemia , Automonitorização da Glicemia , Técnicas Biossensoriais/métodosRESUMO
Single-walled carbon nanotubes (SWCNTs) with adsorbed single-stranded DNA (ssDNA) are applied as sensors to investigate biological systems, with potential applications ranging from clinical diagnostics to agricultural biotechnology. Unique ssDNA sequences render SWCNTs selectively responsive to target analytes such as (GT)n-SWCNTs recognizing the neuromodulator, dopamine. It remains unclear how the ssDNA conformation on the SWCNT surface contributes to functionality, as observations have been limited to computational models or experiments under dehydrated conditions that differ substantially from the aqueous biological environments in which the nanosensors are applied. We demonstrate a direct mode of measuring in-solution ssDNA geometries on SWCNTs via X-ray scattering interferometry (XSI), which leverages the interference pattern produced by AuNP tags conjugated to ssDNA on the SWCNT surface. We employ XSI to quantify distinct surface-adsorbed morphologies for two (GT)n ssDNA oligomer lengths (n = 6, 15) that are used on SWCNTs in the context of dopamine sensing and measure the ssDNA conformational changes as a function of ionic strength and during dopamine interaction. We show that the shorter oligomer, (GT)6, adopts a more periodically ordered ring structure along the SWCNT axis (inter-ssDNA distance of 8.6 ± 0.3 nm), compared to the longer (GT)15 oligomer (most probable 5'-to-5' distance of 14.3 ± 1.1 nm). During molecular recognition, XSI reveals that dopamine elicits simultaneous axial elongation and radial constriction of adsorbed ssDNA on the SWCNT surface. Our approach using XSI to probe solution-phase morphologies of polymer-functionalized SWCNTs can be applied to yield insights into sensing mechanisms and inform future design strategies for nanoparticle-based sensors.
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Nanotubos de Carbono , Nanotubos de Carbono/química , Raios X , Dopamina , DNA , DNA de Cadeia SimplesRESUMO
Using a fluorescence complementation assay, Delivered Complementation in Planta (DCIP), we demonstrate cell-penetrating peptide-mediated cytosolic delivery of peptides and recombinant proteins in Nicotiana benthamiana. We show that DCIP enables quantitative measurement of protein delivery efficiency and enables functional screening of cell-penetrating peptides for in-planta protein delivery. Finally, we demonstrate that DCIP detects cell-penetrating peptide-mediated delivery of recombinantly expressed proteins such as mCherry and Lifeact into intact leaves. We also demonstrate delivery of a recombinant plant transcription factor, WUSCHEL (AtWUS), into N. benthamiana. RT-qPCR analysis of AtWUS delivery in Arabidopsis seedlings also suggests delivered WUS can recapitulate transcriptional changes induced by overexpression of AtWUS. Taken together, our findings demonstrate that DCIP offers a new and powerful tool for interrogating cytosolic delivery of proteins in plants and highlights future avenues for engineering plant physiology.
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Proteínas de Arabidopsis , Arabidopsis , Peptídeos Penetradores de Células , Peptídeos Penetradores de Células/metabolismo , Arabidopsis/genética , Arabidopsis/metabolismo , Proteínas de Arabidopsis/genética , Proteínas de Arabidopsis/metabolismo , Fluorescência , Plantas Geneticamente Modificadas/genética , Plantas Geneticamente Modificadas/metabolismoRESUMO
The protein corona spontaneously develops and evolves on the surface of nanoscale materials when they are exposed to biological environments, altering their physiochemical properties and affecting their subsequent interactions with biosystems. In this Review, we provide an overview of the current state of protein corona research in nanomedicine. We next discuss remaining challenges in the research methodology and characterization of the protein corona that slow the development of nanoparticle therapeutics and diagnostics, and we address how artificial intelligence can advance protein corona research as a complement to experimental research efforts. We then review emerging opportunities provided by the protein corona to address major issues in healthcare and environmental sciences. This Review details how mechanistic insights into nanoparticle protein corona formation can broadly address unmet clinical and environmental needs, as well as enhance the safety and efficacy of nanobiotechnology products.
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The emergence of new tools to image neurotransmitters, neuromodulators, and neuropeptides has transformed our understanding of the role of neurochemistry in brain development and cognition, yet analysis of this new dimension of neurobiological information remains challenging. Here, we image dopamine modulation in striatal brain tissue slices with near-infrared catecholamine nanosensors (nIRCat) and implement machine learning to determine which features of dopamine modulation are unique to changes in stimulation strength, and to different neuroanatomical regions. We trained a support vector machine and a random forest classifier to decide whether the recordings were made from the dorsolateral striatum (DLS) versus the dorsomedial striatum (DMS) and find that machine learning is able to accurately distinguish dopamine release that occurs in DLS from that occurring in DMS in a manner unachievable with canonical statistical analysis. Furthermore, our analysis determines that dopamine modulatory signals including the number of unique dopamine release sites and peak dopamine released per stimulation event are most predictive of neuroanatomy. This is in light of integrated neuromodulator amount being the conventional metric used to monitor neuromodulation in animal studies. Lastly, our study finds that machine learning discrimination of different stimulation strengths or neuroanatomical regions is only possible in adult animals, suggesting a high degree of variability in dopamine modulatory kinetics during animal development. Our study highlights that machine learning could become a broadly utilized tool to differentiate between neuroanatomical regions or between neurotypical and disease states, with features not detectable by conventional statistical analysis.
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Catecolaminas , Dopamina , Animais , Dopamina/fisiologia , Corpo Estriado/fisiologia , Neostriado , Transdução de Sinais/fisiologia , NeurotransmissoresRESUMO
The protein corona forms spontaneously on nanoparticle surfaces when nanomaterials are introduced into any biological system/fluid. Reliable characterization of the protein corona is, therefore, a vital step in the development of safe and efficient diagnostic and therapeutic nanomedicine products. 2134 published manuscripts on the protein corona are reviewed and a down-selection of 470 papers spanning 2000-2021, comprising 1702 nanoparticle (NP) systems is analyzed. This analysis reveals: i) most corona studies have been conducted on metal and metal oxide nanoparticles; ii) despite their overwhelming presence in clinical practice, lipid-based NPs are underrepresented in protein corona research, iii) studies use new methods to improve reliability and reproducibility in protein corona research; iv) studies use more specific protein sources toward personalized medicine; and v) careful characterization of nanoparticles after corona formation is imperative to minimize the role of aggregation and protein contamination on corona outcomes. As nanoparticles used in biomedicine become increasingly prevalent and biochemically complex, the field of protein corona research will need to focus on developing analytical approaches and characterization techniques appropriate for each unique nanoparticle formulation. Achieving such characterization of the nano-bio interface of nanobiotechnologies will enable more seamless development and safe implementation of nanoparticles in medicine.
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Nanopartículas Metálicas , Nanopartículas , Coroa de Proteína , Coroa de Proteína/química , Reprodutibilidade dos Testes , Proteínas/química , Nanomedicina , Nanopartículas/químicaRESUMO
The emergence of new tools to image neurotransmitters, neuromodulators, and neuropeptides has transformed our understanding of the role of neurochemistry in brain development and cognition, yet analysis of this new dimension of neurobiological information remains challenging. Here, we image dopamine modulation in striatal brain tissue slices with near infrared catecholamine nanosensors (nIRCat) and implement machine learning to determine which features of dopamine modulation are unique to changes in stimulation strength, and to different neuroanatomical regions. We trained a support vector machine and a random forest classifier to determine whether recordings were made from the dorsolateral striatum (DLS) versus the dorsomedial striatum (DMS) and find that machine learning is able to accurately distinguish dopamine release that occurs in DLS from that occurring in DMS in a manner unachievable with canonical statistical analysis. Furthermore, our analysis determines that dopamine modulatory signals including the number of unique dopamine release sites and peak dopamine released per stimulation event are most predictive of neuroanatomy yet note that integrated neuromodulator amount is the conventional metric currently used to monitor neuromodulation in animal studies. Lastly, our study finds that machine learning discrimination of different stimulation strengths or neuroanatomical regions is only possible in adult animals, suggesting a high degree of variability in dopamine modulatory kinetics during animal development. Our study highlights that machine learning could become a broadly-utilized tool to differentiate between neuroanatomical regions, or between neurotypical and disease states, with features not detectable by conventional statistical analysis.
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The issue of reliability and repeatability of data in the nanomedicine literature is a growing concern among stakeholders. This perspective discusses the key differences between academia and industry in the reproducibility of data acquisition and protocols in the field of nanomedicine. We also discuss what academic researchers can learn from systems implemented in industry to standardize data acquisition and in which ways these can be efficiently adopted by the academic community.
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Nanomedicina , Nanomedicina/métodos , Reprodutibilidade dos TestesRESUMO
Single-walled carbon nanotubes (SWCNTs) are desirable nanoparticles for sensing biological analytes due to their photostability and intrinsic near-infrared fluorescence. Previous strategies for generating SWCNT nanosensors have leveraged nonspecific adsorption of sensing modalities to the hydrophobic SWCNT surface that often require engineering new molecular recognition elements. An attractive alternate strategy is to leverage pre-existing molecular recognition of proteins for analyte specificity, yet attaching proteins to SWCNT for nanosensor generation remains challenging. Towards this end, we introduce a generalizable platform to generate protein-SWCNT-based optical sensors and use this strategy to synthesize a hydrogen peroxide (H 2 O 2 ) nanosensor by covalently attaching horseradish peroxidase (HRP) to the SWCNT surface. We demonstrate a concentration-dependent response to H 2 O 2 , confirm the nanosensor can image H 2 O 2 in real-time, and assess the nanosensor's selectivity for H 2 O 2 against a panel of biologically relevant analytes. Taken together, these results demonstrate successful covalent attachment of enzymes to SWCNTs while preserving both intrinsic SWCNT fluorescence and enzyme function. We anticipate this platform can be adapted to covalently attach other proteins of interest including other enzymes for sensing or antibodies for targeted imaging and cargo delivery.
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Nonlinear optical imaging modalities, such as stimulated Raman scattering (SRS) microscopy, use pulsed-laser excitation with high peak intensity that can perturb the native state of cells. In this study, we used bulk RNA sequencing, quantitative measurement of cell proliferation, and fluorescent measurement of the generation of reactive oxygen species to assess phototoxic effects of near-IR pulsed laser radiation, at different time scales, for laser excitation settings relevant to SRS imaging. We define a range of laser excitation settings for which there was no significant change in mouse Neuro2A cells after laser exposure. This study provides guidance for imaging parameters that minimize photo-induced perturbations in SRS microscopy to ensure accurate interpretation of experiments with time-lapse imaging or with paired measurements of imaging and sequencing on the same cells.
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Microscopia , Microscopia Óptica não Linear , Camundongos , Animais , Microscopia Óptica não Linear/métodos , Microscopia/métodos , Análise Espectral Raman/métodos , Fármacos Fotossensibilizantes , Ciclo Celular , Estresse Oxidativo , Expressão GênicaRESUMO
Robust characterization of the protein corona-the layer of proteins that spontaneously forms on the surface of nanoparticles immersed in biological fluids-is vital for prediction of the safety, biodistribution, and diagnostic/therapeutic efficacy of nanomedicines. Protein corona identity and abundance characterization is entirely dependent on liquid chromatography coupled to mass spectroscopy (LC-MS/MS), though the variability of this technique for the purpose of protein corona characterization remains poorly understood. Here we investigate the variability of LC-MS/MS workflows in analysis of identical aliquots of protein coronas by sending them to different proteomics core-facilities and analyzing the retrieved datasets. While the shared data between the cores correlate well, there is considerable heterogeneity in the data retrieved from different cores. Specifically, out of 4022 identified unique proteins, only 73 (1.8%) are shared across the core facilities providing semiquantitative analysis. These findings suggest that protein corona datasets cannot be easily compared across independent studies and more broadly compromise the interpretation of protein corona research, with implications in biomarker discovery as well as the safety and efficacy of our nanoscale biotechnologies.
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Nanopartículas , Coroa de Proteína , Coroa de Proteína/química , Proteômica , Cromatografia Líquida , Distribuição Tecidual , Espectrometria de Massas em Tandem , Nanopartículas/química , Proteínas/metabolismoRESUMO
Understanding the interaction between biological structures and nanoscale technologies, dubbed the nano-bio interface, is required for successful development of safe and efficient nanomedicine products. The lack of a universal reporting system and decentralized methodologies for nanomaterial characterization have resulted in a low degree of reliability and reproducibility in the nanomedicine literature. As such, there is a strong need to establish a characterization system to support the reproducibility of nanoscience data particularly for studies seeking clinical translation. Here, we discuss the existing key standards for addressing robust characterization of nanomaterials based on their intended use in medical devices or as pharmaceuticals. We also discuss the challenges surrounding implementation of such standard protocols and their implication for translation of nanotechnology into clinical practice. We, however, emphasize that practical implementation of standard protocols in experimental laboratories requires long-term planning through integration of stakeholders including institutions and funding agencies.