Daniel Midtvedt

Nanofluidic scattering microscopy characterizes single molecules in subwavelength nanofluidic channels label-free, using the interference of visible light scattered by the molecule and nanochannel. It determines a molecule’s hydrodynamic radius by tracking its diffusion trajectory and its molecular weight by analyzing its scattering intensity along that trajectory. However, using standard analysis algorithms, it is limited to characterization of proteins larger than ≈ 60 kDa. Here, we push this limit by one order of magnitude to below ≈ 6 kDa molecular weight and ≈ 1.5 nm hydrodynamic radius — as we exemplify on the peptide hormone insulin — by using ultrasmall nanofluidic channels and by analyzing the data with a hierarchical vision transformer. When we benchmark this approach against the theoretical limit set by the Cramér–Rao Lower Bound, we find that it can be approached with sufficiently long molecular trajectories. This enables quantitative label-free single-molecule microscopy for biologically relevant families of sub-10-kDa molecules, such as cytokines, chemokines and peptide hormones.

The detection of biological nanoparticles (NPs), such as viruses and extracellular vesicles (EVs), plays a critical role in medical diagnostics. However, these particles are optically faint, making microscopic detection in complex solutions challenging. Recent advancements have demonstrated that distinguishing between metallic and dielectric signals with twilight off-axis holographic microscopy makes it possible to differentiate between metal and biological NPs and to quantify complexes formed from metal and biological NPs binding together. Here, this method is employed to investigate complex formation through specific interactions between streptavidin (StrAv)-modified gold NPs (StrAv-AuNPs) and large biotin-containing unilamellar lipid vesicles (biotin-LUVs), serving as virus and EV mimics. To minimize AuNP self-aggregation during functionalization of PEGylated 25 nm radius AuNPs with tetrameric StrAv, 0.06% biotin-PEG (∼5 biotin per AuNP) was used, which also serves to ensure that aggregation involving multiple LUVs is effectively prevented. While the StrAv-biotin ratio did not significantly affect AuNP self-aggregation upon fabrication of StrAv-AuNPs, a 1000-fold StrAv excess with respect to biotin-PEG on the AuNPs was required to fabricate StrAv-AuNPs with the anticipated reactivity with biotin-LUVs. Through a combination of waveguide scattering microscopy, surface plasmon resonance, and twilight off-axis holographic microscopy, we demonstrate that this likely stems from a dramatic reduction in the association rate constant between StrAv and biotin within the PEG layer. Furthermore, by using a mixture of 3 kDa nonbiotinylated PEG and 5 kDa biotin-PEG, functional StrAv-AuNPs were successfully fabricated at an orders of magnitude lower StrAv-to-biotin ratio, enabling a sub-pM limit of detection of biotin-LUVs using off-axis holography.

Nanofluidic scattering microscopy characterizes single molecules in subwavelength nanofluidic channels label-free, using the interference of visible light scattered by the molecule and nanochannel. It determines a molecule’s hydrodynamic radius by tracking its diffusion trajectory and its molecular weight by analyzing its scattering intensity along that trajectory. However, using standard analysis algorithms, it is limited to characterization of proteins larger than ≈ 60 kDa. Here, we push this limit by one order of magnitude to below ≈ 6 kDa molecular weight and ≈ 1.5 nm hydrodynamic radius — as we exemplify on the peptide hormone insulin — by using ultrasmall nanofluidic channels and by analyzing the data with a hierarchical vision transformer. When we benchmark this approach against the theoretical limit set by the Cramér-Rao Lower Bound, we find that it can be approached with sufficiently long molecular trajectories. This opens interesting prospects for quantitative label-free single-molecule microscopy to characterize biologically relevant families of sub-10-kDa molecules, such as cytokines, chemokines and peptide hormones.

Different optical microscopy methods provide different but related single particle information. This tutorial reviews how optical microscopy images can be related to particle properties, including optical theory and strategies for image analysis.

Environmental humidity variations are ubiquitous and high humidity characterizes fuel cell and electrolyzer operation conditions. Since hydrogen-air mixtures are highly flammable, humidity tolerant H₂ sensors are important from safety and process monitoring perspectives. Here, we report an optical nanoplasmonic hydrogen sensor operated at elevated temperature that combined with Deep Dense Neural Network or Transformer data treatment involving the entire spectral response of the sensor enables a 100 ppm H₂ limit of detection in synthetic air at 80% relative humidity. This significantly exceeds the 2 and show no signs of performance loss after 140 h continuous operation. Our results thus demonstrate the potential of plasmonic hydrogen sensors for use in high humidity and how neural-network-based data treatment can significantly boost their performance.

Traditional single-nanoparticle sizing using optical microscopy techniques assesses size via the diffusion constant, which requires suspended particles to be in a medium of known viscosity. However, these assumptions are typically not fulfilled in complex natural sample environments. Here, we introduce dual-angle interferometric scattering microscopy (DAISY), enabling optical quantification of both size and polarizability of individual nanoparticles (radius a priori information regarding the surrounding media or super-resolution imaging. DAISY achieves this by combining the information contained in concurrently measured forward and backward scattering images through twilight off-axis holography and interferometric scattering (iSCAT). Going beyond particle size and polarizability, single-particle morphology can be deduced from the fact that the hydrodynamic radius relates to the outer particle radius, while the scattering-based size estimate depends on the internal mass distribution of the particles. We demonstrate this by differentiating biomolecular fractal aggregates from spherical particles in fetal bovine serum at the single-particle level.

During diffusion of nanoparticles bound to a cellular membrane by ligand–receptor pairs, the distance to the laterally mobile interface is sufficiently short for their motion to depend not only on the membrane-mediated diffusivity of the tethers but also in a not yet fully understood manner on nanoparticle size and interfacial hydrodynamics. By quantifying diffusivity, velocity, and size of individual membrane-bound liposomes subjected to a hydrodynamic shear flow, we have successfully separated the diffusivity contributions from particle size and number of tethers. The obtained diffusion-size relations for synthetic and extracellular lipid vesicles are not well-described by the conventional no-slip boundary condition, suggesting partial slip as well as a significant diffusivity dependence on the distance to the lipid bilayer. These insights, extending the understanding of diffusion of biological nanoparticles at lipid bilayers, are of relevance for processes such as cellular uptake of viruses and lipid nanoparticles or labeling of cell-membrane-residing molecules.

Characterization of nanoparticles in their native environment plays a central role in a wide range of fields, from medical diagnostics and nanoparticle-enhanced drug delivery to nanosafety and environmental nanopollution assessment. I will present a label-free method to quantify size and refractive index of individual nanoparticles using two orders of magnitude shorter trajectories than required by standard methods, and without prior knowledge about the physicochemical properties of the medium. This is achieved through a weighted average convolutional neural network which analyzes holographic scattering images of single particles. I will demonstrate how deep learning enhanced holography opens up completely new possibilities to temporally characterize particle interactions and particle properties in complex environments.

Characterization of suspended nanoparticles in their native environment plays a central role in a wide range of fields, from medical diagnostics and nanoparticle-enhanced drug delivery to nanosafety and environmental nanopollution assessment. Standard optical approaches for nanoparticle sizing assess the size via the diffusion constant and, as a consequence, require long trajectories and that the medium has a known and uniform viscosity. However, in most biological applications, only short trajectories are available, while simultaneously, the medium viscosity is unknown and tends to display spatiotemporal variations. In this work, we demonstrate a label-free method to quantify not only size but also refractive index of individual subwavelength particles using 2 orders of magnitude shorter trajectories than required by standard methods and without prior knowledge about the physicochemical properties of the medium. We achieved this by developing a weighted average convolutional neural network to analyze holographic images of single particles, which was successfully applied to distinguish and quantify both size and refractive index of subwavelength silica and polystyrene particles without prior knowledge of solute viscosity or refractive index. We further demonstrate how these features make it possible to temporally resolve aggregation dynamics of 31 nm polystyrene nanoparticles, revealing previously unobserved time-resolved dynamics of the monomer number and fractal dimension of individual subwavelength aggregates.

Quantitative analysis of cell structures is essential for biomedical and pharmaceutical research. The standard imaging approach relies on fluorescence microscopy, where cell structures of interest are labeled by chemical staining techniques. However, these techniques are often invasive and sometimes even toxic to the cells, in addition to being time-consuming, labor-intensive, and expensive. Here, we introduce an alternative deep-learning-powered approach based on the analysis of brightfield images by a conditional generative adversarial neural network (cGAN). We show that this approach can extract information from the brightfield images to generate virtually-stained images, which can be used in subsequent downstream quantitative analyses of cell structures. Specifically, we train a cGAN to virtually stain lipid droplets, cytoplasm, and nuclei using brightfield images of human stem-cell-derived fat cells (adipocytes), which are of particular interest for nanomedicine and vaccine development. Subsequently, we use these virtually-stained images to extract quantitative measures about these cell structures. Generating virtually-stained fluorescence images is less invasive, less expensive, and more reproducible than standard chemical staining; furthermore, it frees up the fluorescence microscopy channels for other analytical probes, thus increasing the amount of information that can be extracted from each cell.