Hyperspectral unmixing for Raman spectroscopy via physics-constrained autoencoders.

Raman spectroscopy is widely used across scientific domains to characterize the chemical composition of samples in a nondestructive, label-free manner. Many applications entail the unmixing of signals from mixtures of molecular species to identify the individual components present and their proportions, yet conventional methods for chemometrics often struggle with complex mixture scenarios encountered in practice. Here, we develop hyperspectral unmixing algorithms based on autoencoder neural networks, and we systematically validate them using both synthetic and experimental benchmark datasets created in-house. Our results demonstrate that unmixing autoencoders provide improved accuracy, robustness, and efficiency compared to standard unmixing methods. We also showcase the applicability of autoencoders to complex biological settings by showing improved biochemical characterization of volumetric Raman imaging data from a monocytic cell.

In vitro modeling of the human dopaminergic system using spatially arranged ventral midbrain-striatum-cortex assembloids.

Ventral midbrain dopaminergic neurons project to the striatum as well as the cortex and are involved in movement control and reward-related cognition. In Parkinson's disease, nigrostriatal midbrain dopaminergic neurons degenerate and cause typical Parkinson's disease motor-related impairments, while the dysfunction of mesocorticolimbic midbrain dopaminergic neurons is implicated in addiction and neuropsychiatric disorders. Study of the development and selective neurodegeneration of the human dopaminergic system, however, has been limited due to the lack of an appropriate model and access to human material. Here, we have developed a human in vitro model that recapitulates key aspects of dopaminergic innervation of the striatum and cortex. These spatially arranged ventral midbrain-striatum-cortical organoids (MISCOs) can be used to study dopaminergic neuron maturation, innervation and function with implications for cell therapy and addiction research. We detail protocols for growing ventral midbrain, striatal and cortical organoids and describe how they fuse in a linear manner when placed in custom embedding molds. We report the formation of functional long-range dopaminergic connections to striatal and cortical tissues in MISCOs, and show that injected, ventral midbrain-patterned progenitors can mature and innervate the tissue. Using these assembloids, we examine dopaminergic circuit perturbations and show that chronic cocaine treatment causes long-lasting morphological, functional and transcriptional changes that persist upon drug withdrawal. Thus, our method opens new avenues to investigate human dopaminergic cell transplantation and circuitry reconstruction as well as the effect of drugs on the human dopaminergic system.

Taking connected mobile-health diagnostics of infectious diseases to the field.

Mobile health, or 'mHealth', is the application of mobile devices, their components and related technologies to healthcare. It is already improving patients' access to treatment and advice. Now, in combination with internet-connected diagnostic devices, it offers novel ways to diagnose, track and control infectious diseases and to improve the efficiency of the health system. Here we examine the promise of these technologies and discuss the challenges in realizing their potential to increase patients' access to testing, aid in their treatment and improve the capability of public health authorities to monitor outbreaks, implement response strategies and assess the impact of interventions across the world.

RamanSPy: An Open-Source Python Package for Integrative Raman Spectroscopy Data Analysis.

Raman spectroscopy is a nondestructive and label-free chemical analysis technique, which plays a key role in the analysis and discovery cycle of various branches of science. Nonetheless, progress in Raman spectroscopic analysis is still impeded by the lack of software, methodological and data standardization, and the ensuing fragmentation and lack of reproducibility of analysis workflows thereof. To address these issues, we introduce RamanSPy, an open-source Python package for Raman spectroscopic research and analysis. RamanSPy provides a comprehensive library of tools for spectroscopic analysis that supports day-to-day tasks, integrative analyses, the development of methods and protocols, and the integration of advanced data analytics. RamanSPy is modular and open source, not tied to a particular technology or data format, and can be readily interfaced with the burgeoning ecosystem for data science, statistical analysis, and machine learning in Python. RamanSPy is hosted at https://github.com/barahona-research-group/RamanSPy, supplemented with extended online documentation, available at https://ramanspy.readthedocs.io, that includes tutorials, example applications, and details about the real-world research applications presented in this paper.

Transfer learning Bayesian optimization for competitor DNA molecule design for use in diagnostic assays.

With the rise in engineered biomolecular devices, there is an increased need for tailor-made biological sequences. Often, many similar biological sequences need to be made for a specific application meaning numerous, sometimes prohibitively expensive, lab experiments are necessary for their optimization. This paper presents a transfer learning design of experiments workflow to make this development feasible. By combining a transfer learning surrogate model with Bayesian optimization, we show how the total number of experiments can be reduced by sharing information between optimization tasks. We demonstrate the reduction in the number of experiments using data from the development of DNA competitors for use in an amplification-based diagnostic assay. We use cross-validation to compare the predictive accuracy of different transfer learning models, and then compare the performance of the models for both single objective and penalized optimization tasks.

Thermally Robust Solvent-Free Liquid Polyplexes for Heat-Shock Protection and Long-Term Room Temperature Storage of Therapeutic Nucleic Acids.

Nucleic acid therapeutics have attracted recent attention as promising preventative solutions for a broad range of diseases. Nonviral delivery vectors, such as cationic polymers, improve the cellular uptake of nucleic acids without suffering the drawbacks of viral delivery vectors. However, these delivery systems are faced with a major challenge for worldwide deployment, as their poor thermal stability elicits the need for cold chain transportation. Here, we demonstrate a biomaterial strategy to drastically improve the thermal stability of DNA polyplexes. Importantly, we demonstrate long-term room temperature storage with a transfection efficiency maintained for at least 9 months. Additionally, extreme heat shock studies show retained luciferase expression after heat treatment at 70 °C. We therefore provide a proof of concept for a platform biotechnology that could provide long-term room temperature storage for temperature-sensitive nucleic acid therapeutics, eliminating the need for the cold chain, which in turn would reduce the cost of distributing life-saving therapeutics worldwide.

Strain-Promoted Cycloadditions in Lipid Bilayers Triggered by Liposome Fusion.

Due to the variety of roles served by the cell membrane, its composition and structure are complex, making it difficult to study. Bioorthogonal reactions, such as the strain promoted azide-alkyne cycloaddition (SPAAC), are powerful tools for exploring the function of biomolecules in their native environment but have been largely unexplored within the context of lipid bilayers. Here, we developed a new approach to study the SPAAC reaction in liposomal membranes using azide- and strained alkyne-functionalized Förster resonance energy transfer (FRET) dye pairs. This study represents the first characterization of the SPAAC reaction between diffusing molecules inside liposomal membranes. Potential applications of this work include in situ bioorthogonal labeling of membrane proteins, improved understanding of membrane dynamics and fluidity, and the generation of new probes for biosensing assays.

RegiSTORM: channel registration for multi-color stochastic optical reconstruction microscopy.

BACKGROUND: Stochastic optical reconstruction microscopy (STORM), a super-resolution microscopy technique based on single-molecule localizations, has become popular to characterize sub-diffraction limit targets. However, due to lengthy image acquisition, STORM recordings are prone to sample drift. Existing cross-correlation or fiducial marker-based algorithms allow correcting the drift within each channel, but misalignment between channels remains due to interchannel drift accumulating during sequential channel acquisition. This is a major drawback in multi-color STORM, a technique of utmost importance for the characterization of various biological interactions. RESULTS: We developed RegiSTORM, a software for reducing channel misalignment by accurately registering STORM channels utilizing fiducial markers in the sample. RegiSTORM identifies fiducials from the STORM localization data based on their non-blinking nature and uses them as landmarks for channel registration. We first demonstrated accurate registration on recordings of fiducials only, as evidenced by significantly reduced target registration error with all the tested channel combinations. Next, we validated the performance in a more practically relevant setup on cells multi-stained for tubulin. Finally, we showed that RegiSTORM successfully registers two-color STORM recordings of cargo-loaded lipid nanoparticles without fiducials, demonstrating the broader applicability of this software. CONCLUSIONS: The developed RegiSTORM software was demonstrated to be able to accurately register multiple STORM channels and is freely available as open-source (MIT license) at https://github.com/oystein676/RegiSTORM.git and https://doi.org/10.5281/zenodo.5509861 (archived), and runs as a standalone executable (Windows) or via Python (Mac OS, Linux).

STING agonist delivery by tumour-penetrating PEG-lipid nanodiscs primes robust anticancer immunity.

Activation of the innate immune STimulator of INterferon Genes (STING) pathway potentiates antitumour immunity, but systemic delivery of STING agonists to tumours is challenging. We conjugated STING-activating cyclic dinucleotides (CDNs) to PEGylated lipids (CDN-PEG-lipids; PEG, polyethylene glycol) via a cleavable linker and incorporated them into lipid nanodiscs (LNDs), which are discoid nanoparticles formed by self-assembly. Compared to state-of-the-art liposomes, intravenously administered LNDs carrying CDN-PEG-lipid (LND-CDNs) exhibited more efficient penetration of tumours, exposing the majority of tumour cells to STING agonist. A single dose of LND-CDNs induced rejection of established tumours, coincident with immune memory against tumour rechallenge. Although CDNs were not directly tumoricidal, LND-CDN uptake by cancer cells correlated with robust T-cell activation by promoting CDN and tumour antigen co-localization in dendritic cells. LNDs thus appear promising as a vehicle for robust delivery of compounds throughout solid tumours, which can be exploited for enhanced immunotherapy.

Noble Metal Nanoparticle Biosensors: From Fundamental Studies toward Point-of-Care Diagnostics.

Noble metal nanoparticles (NMNPs) have become firmly established as effective agents to detect various biomolecules with extremely high sensitivity. This ability stems from the collective oscillation of free electrons and extremely large electric field enhancement under exposure to light, leading to various light-matter interactions such as localized surface plasmon resonance (LSPR) and surface-enhanced Raman scattering. A remarkable feature of NMNPs is their customizability by mechanisms such as particle etching, growth, and aggregation/dispersion, yielding distinct color changes and excellent opportunities for colorimetric biosensing in user-friendly assays and devices. They are readily functionalized with a large variety of capping agents and biomolecules, with resultant bioconjugates often possessing excellent biocompatibility, which can be used to quantitatively detect analytes from physiological fluids. Furthermore, they can possess excellent catalytic properties that can achieve significant signal amplification through mechanisms such as the catalytic transformation of colorless substrates to colored reporters. The various excellent attributes of NMNP biosensors have put them in the spotlight for developing high-performance in vitro diagnostic (IVD) devices that are particularly well-suited to mitigate the societal threat that infectious diseases pose. This threat continues to dominate the global health care landscape, claiming millions of lives annually. NMNP IVDs possess the potential to sensitively detect infections even at very early stages with affordable and field-deployable devices, which will be key to strengthening infectious disease management. This has been the major focal point of current research, with a view to new avenues for early multiplexed detection of infectious diseases with portable devices such as smartphones, especially in resource-limited settings.In this Account, we provide an overview of our original inspiration and efforts in NMNP-based assay development, together with some more sophisticated IVD assays by ourselves and many others. Our work in the area has led to our recent efforts in developing IVDs for high-profile infectious diseases, including Ebola and HIV. We emphasize that integration with digital platforms represents an opportunity to establish and efficiently manage widespread testing, tracking, epidemiological intelligence, and data sharing backed by community participation. We highlight how digital technologies can address the limitations of conventional diagnostic technologies at the point of care (POC) and how they may be used to abate and contain the spread of infectious diseases. Finally, we focus on more recent integrations of noble metal nanoparticles with Raman spectroscopy for accurate, noninvasive POC diagnostics with improved sensitivity and specificity.

Modeling the transport of nuclear proteins along single skeletal muscle cells.

Skeletal muscle cells contain hundreds of myonuclei within a shared cytoplasm, presenting unique challenges for regulating gene expression. Certain transcriptional programs (e.g., postsynaptic machinery) are segregated to specialized domains, while others (e.g., contractile proteins) do not show spatial confinement. Furthermore, local stimuli, such as denervation, can induce transcriptional responses that are propagated along the muscle cells. Regulated transport of nuclear proteins (e.g., transcription factors) between myonuclei represents a potential mechanism for coordinating gene expression. However, the principles underlying the transport of nuclear proteins within multinucleated cells remain poorly defined. Here we used a mosaic transfection model to create myotubes that contained exactly one myonucleus expressing a fluorescent nuclear reporter and monitored its distribution among all myonuclei. We found that the transport properties of these model nuclear proteins in myotubes depended on molecular weight and nuclear import rate, as well as on myotube width. Interestingly, muscle hypertrophy increased the transport of high molecular weight nuclear proteins, while atrophy restricted the transport of smaller nuclear proteins. We have developed a mathematical model of nuclear protein transport within a myotube that recapitulates the results of our in vitro experiments. To test the relevance to nuclear proteins expressed in skeletal muscle, we studied the transport of two transcription factors-aryl hydrocarbon receptor nuclear translocator and sine oculis homeobox 1-and found that their distributions were similar to the reporter proteins with corresponding molecular weights. Together, these results define a set of variables that can be used to predict the spatial distributions of nuclear proteins within a myotube.

DNA origami protection and molecular interfacing through engineered sequence-defined peptoids.

DNA nanotechnology has established approaches for designing programmable and precisely controlled nanoscale architectures through specific Watson-Crick base-pairing, molecular plasticity, and intermolecular connectivity. In particular, superior control over DNA origami structures could be beneficial for biomedical applications, including biosensing, in vivo imaging, and drug and gene delivery. However, protecting DNA origami structures in complex biological fluids while preserving their structural characteristics remains a major challenge for enabling these applications. Here, we developed a class of structurally well-defined peptoids to protect DNA origamis in ionic and bioactive conditions and systematically explored the effects of peptoid architecture and sequence dependency on DNA origami stability. The applicability of this approach for drug delivery, bioimaging, and cell targeting was also demonstrated. A series of peptoids (PE1-9) with two types of architectures, termed as "brush" and "block," were built from positively charged monomers and neutral oligo-ethyleneoxy monomers, where certain designs were found to greatly enhance the stability of DNA origami. Through experimental and molecular dynamics studies, we demonstrated the role of sequence-dependent electrostatic interactions of peptoids with the DNA backbone. We showed that octahedral DNA origamis coated with peptoid (PE2) can be used as carriers for anticancer drug and protein, where the peptoid modulated the rate of drug release and prolonged protein stability against proteolytic hydrolysis. Finally, we synthesized two alkyne-modified peptoids (PE8 and PE9), conjugated with fluorophore and antibody, to make stable DNA origamis with imaging and cell-targeting capabilities. Our results demonstrate an approach toward functional and physiologically stable DNA origami for biomedical applications.

Artificial Antigen Presenting Cells for Detection and Desensitization of Autoreactive T cells Associated with Type 1 Diabetes.

Autoimmune diseases and in particular type 1 diabetes rely heavily on treatments that target the symptoms rather than prevent the underlying disease. One of the barriers to better therapeutic strategies is the inability to detect and efficiently target rare autoreactive T-cell populations that are major drivers of these conditions. Here, we develop a unique artificial antigen-presenting cell (aAPC) system from biocompatible polymer particles that allows specific encapsulation of bioactive ingredients. Using our aAPC, we demonstrate that we are able to detect rare autoreactive CD4 populations in human patients, and using mouse models, we demonstrate that our particles are able to induce desensitization in the autoreactive population. This system provides a promising tool that can be used in the prevention of autoimmunity before disease onset.

Polysaccharide-Polyplex Nanofilm Coatings Enhance Nanoneedle-Based Gene Delivery and Transfection Efficiency.

Non-viral vectors represent versatile and immunologically safer alternatives for nucleic acid delivery. Nanoneedles and high-aspect ratio nanostructures are unconventional but interesting delivery systems, in which delivery is mediated by surface interactions. Herein, nanoneedles are synergistically combined with polysaccharide-polyplex nanofilms and enhanced transfection efficiency is observed, compared to polyplexes in suspension. Different polyplex-polyelectrolyte nanofilm combinations are assessed and it is found that transfection efficiency is enhanced when using polysaccharide-based polyanions, rather than being only specific for hyaluronic acid, as suggested in earlier studies. Moreover, results show that enhanced transfection is not mediated by interactions with the CD44 receptor, previously hypothesized as a major mechanism mediating enhancement via hyaluronate. In cardiac tissue, nanoneedles are shown to increase the transfection efficiency of nanofilms compared to flat substrates; while in vitro, high transfection efficiencies are observed in nanostructures where cells present large interfacing areas with the substrate. The results of this study demonstrate that surface-mediated transfection using this system is efficient and safe, requiring amounts of nucleic acid with an order of magnitude lower than standard culture transfection. These findings expand the spectrum of possible polyelectrolyte combinations that can be used for the development of suitable non-viral vectors for exploration in further clinical trials.

Block Length-Dependent Protein Fouling on Poly(2-oxazoline)-Based Polymersomes: Influence on Macrophage Association and Circulation Behavior.

Polymersomes are vesicular structures self-assembled from amphiphilic block copolymers and are considered an alternative to liposomes for applications in drug delivery, immunotherapy, biosensing, and as nanoreactors and artificial organelles. However, the limited availability of systematic stability, protein fouling (protein corona formation), and blood circulation studies hampers their clinical translation. Poly(2-oxazoline)s (POx) are valuable antifouling hydrophilic polymers that can replace the current gold-standard, poly(ethylene glycol) (PEG), yet investigations of POx functionality on nanoparticles are relatively sparse. Herein, a systematic study is reported of the structural, dynamic and antifouling properties of polymersomes made of poly(2-methyl-2-oxazoline)-block-poly(dimethylsiloxane)-block-poly(2-methyl-2-oxazoline) (PMOXA-b-PDMS-b-PMOXA). The study relates in vitro antifouling performance of the polymersomes to atomistic molecular dynamics simulations of polymersome membrane hydration behavior. These observations support the experimentally demonstrated benefit of maximizing the length of PMOXA (degree of polymerization (DP) > 6) while keeping PDMS at a minimal length that still provides sufficient membrane stability (DP > 19). In vitro macrophage association and in vivo blood circulation evaluation of polymersomes in zebrafish embryos corroborate these findings. They further suggest that single copolymer presentation on polymersomes is outperformed by blends of varied copolymer lengths. This study helps to rationalize design rules for stable and low-fouling polymersomes for future medical applications.

Peptide-Folding Triggered Phase Separation and Lipid Membrane Destabilization in Cholesterol-Rich Lipid Vesicles.

Liposome-based drug delivery systems are widely used to improve drug pharmacokinetics but can suffer from slow and unspecific release of encapsulated drugs. Membrane-active peptides, based on sequences derived or inspired from antimicrobial peptides (AMPs), could offer means to trigger and control the release. Cholesterol is used in most liposomal drug delivery systems (DDS) to improve the stability of the formulation, but the activity of AMPs on cholesterol-rich membranes tends to be very low, complicating peptide-triggered release strategies. Here, we show a de novo designed AMP-mimetic peptide that efficiently triggers content release from cholesterol-containing lipid vesicles when covalently conjugated to headgroup-functionalized lipids. Binding to vesicles induces peptide folding and triggers a lipid phase separation, which in the presence of cholesterol results in high local peptide concentrations at the lipid bilayer surface and rapid content release. We anticipate that these results will facilitate the development of peptide-based strategies for controlling and triggering drug release from liposomal drug delivery systems.

High-Throughput Molecular Imaging via Deep-Learning-Enabled Raman Spectroscopy.

Raman spectroscopy enables nondestructive, label-free imaging with unprecedented molecular contrast, but is limited by slow data acquisition, largely preventing high-throughput imaging applications. Here, we present a comprehensive framework for higher-throughput molecular imaging via deep-learning-enabled Raman spectroscopy, termed DeepeR, trained on a large data set of hyperspectral Raman images, with over 1.5 million spectra (400 h of acquisition) in total. We first perform denoising and reconstruction of low signal-to-noise ratio Raman molecular signatures via deep learning, with a 10× improvement in the mean-squared error over common Raman filtering methods. Next, we develop a neural network for robust 2-4× spatial super-resolution of hyperspectral Raman images that preserve molecular cellular information. Combining these approaches, we achieve Raman imaging speed-ups of up to 40-90×, enabling good-quality cellular imaging with a high-resolution, high signal-to-noise ratio in under 1 min. We further demonstrate Raman imaging speed-up of 160×, useful for lower resolution imaging applications such as the rapid screening of large areas or for spectral pathology. Finally, transfer learning is applied to extend DeepeR from cell to tissue-scale imaging. DeepeR provides a foundation that will enable a host of higher-throughput Raman spectroscopy and molecular imaging applications across biomedicine.

Fiber-Based Electrochemical Biosensors for Monitoring pH and Transient Neurometabolic Lactate.

Developing tools that are able to monitor transient neurochemical dynamics is important to decipher brain chemistry and function. Multifunctional polymer-based fibers have been recently applied to monitor and modulate neural activity. Here, we explore the potential of polymer fibers comprising six graphite-doped electrodes and two microfluidic channels within a flexible polycarbonate body as a platform for sensing pH and neurometabolic lactate. Electrodes were made into potentiometric sensors (responsive to pH) or amperometric sensors (lactate biosensors). The growth of an iridium oxide layer made the fiber electrodes responsive to pH in a physiologically relevant range. Lactate biosensors were fabricated via platinum black growth on the fiber electrode, followed by an enzyme layer, making them responsive to lactate concentration. Lactate fiber biosensors detected transient neurometabolic lactate changes in an in vivo mouse model. Lactate concentration changes were associated with spreading depolarizations, known to be detrimental to the injured brain. Induced waves were identified by a signature lactate concentration change profile and measured as having a speed of ∼2.7 mm/min (n = 4 waves). Our work highlights the potential applications of fiber-based biosensors for direct monitoring of brain metabolites in the context of injury.

Design of Lipid-Based Nanocarriers via Cation Modulation of Ethanol-Interdigitated Lipid Membranes.

Short-chain alcohols (i.e., ethanol) can induce membrane interdigitation in saturated-chain phosphatidylcholines (PCs). In this process, alcohol molecules intercalate between phosphate heads, increasing lateral separation and favoring hydrophobic interactions between opposing acyl chains, which interpenetrate forming an interdigitated phase. Unraveling mechanisms underlying the interactions between ethanol and model lipid membranes has implications for cell biology, biochemistry, and for the formulation of lipid-based nanocarriers. However, investigations of ethanol-lipid membrane systems have been carried out in deionized water, which limits their applicability. Here, using a combination of small- and wide-angle X-ray scattering, small-angle neutron scattering, and all-atom molecular dynamics simulations, we analyzed the effect of varying CaCl2 and NaCl concentrations on ethanol-induced interdigitation. We observed that while ethanol addition leads to the interdigitation of bulk phase 1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC) bilayers in the presence of CaCl2 and NaCl regardless of the salt concentration, the ethanol-induced interdigitation of vesicular DPPC depends on the choice of cation and its concentration. These findings unravel a key role for cations in the ethanol-induced interdigitation of lipid membranes in either bulk phase or vesicular form.