Showing papers by "Fredrik Höök published in 2022"

Versailles project on advanced materials and standards (VAMAS) interlaboratory study on measuring the number concentration of colloidal gold nanoparticles.

Abstract: We describe the outcome of a large international interlaboratory study of the measurement of particle number concentration of colloidal nanoparticles, project 10 of the technical working area 34, "Nanoparticle Populations" of the Versailles Project on Advanced Materials and Standards (VAMAS). A total of 50 laboratories delivered results for the number concentration of 30 nm gold colloidal nanoparticles measured using particle tracking analysis (PTA), single particle inductively coupled plasma mass spectrometry (spICP-MS), ultraviolet-visible (UV-Vis) light spectroscopy, centrifugal liquid sedimentation (CLS) and small angle X-ray scattering (SAXS). The study provides quantitative data to evaluate the repeatability of these methods and their reproducibility in the measurement of number concentration of model nanoparticle systems following a common measurement protocol. We find that the population-averaging methods of SAXS, CLS and UV-Vis have high measurement repeatability and reproducibility, with between-labs variability of 2.6%, 11% and 1.4% respectively. However, results may be significantly biased for reasons including inaccurate material properties whose values are used to compute the number concentration. Particle-counting method results are less reproducibile than population-averaging methods, with measured between-labs variability of 68% and 46% for PTA and spICP-MS respectively. This study provides the stakeholder community with important comparative data to underpin measurement reproducibility and method validation for number concentration of nanoparticles.

Dissimilar Deformation of Fluid- and Gel-Phase Liposomes upon Multivalent Interaction with Cell Membrane Mimics Revealed Using Dual-Wavelength Surface Plasmon Resonance

Abstract: The mechanical properties of biological nanoparticles play a crucial role in their interaction with the cellular membrane, in particular for cellular uptake. This has significant implications for the design of pharmaceutical carrier particles. In this context, liposomes have become increasingly popular, among other reasons due to their customizability and easily varied physicochemical properties. With currently available methods, it is, however, not trivial to characterize the mechanical properties of nanoscopic liposomes especially with respect to the level of deformation induced upon their ligand–receptor-mediated interaction with laterally fluid cellular membranes. Here, we utilize the sensitivity of dual-wavelength surface plasmon resonance to probe the size and shape of bound liposomes (∼100 nm in diameter) as a means to quantify receptor-induced deformation during their interaction with a supported cell membrane mimic. By comparing biotinylated liposomes in gel and fluid phases, we demonstrate that fluid-phase liposomes are more prone to deformation than their gel-phase counterparts upon binding to the cell membrane mimic and that, as expected, the degree of deformation depends on the number of ligand–receptor pairs that are engaged in the multivalent binding.

Interaction Kinetics of Individual mRNA-Containing Lipid Nanoparticles with an Endosomal Membrane Mimic: Dependence on pH, Protein Corona Formation, and Lipoprotein Depletion

Abstract: Lipid nanoparticles (LNPs) have emerged as potent carriers for mRNA delivery, but several challenges remain before this approach can offer broad clinical translation of mRNA therapeutics. To improve their efficacy, a better understanding is required regarding how LNPs are trapped and processed at the anionic endosomal membrane prior to mRNA release. We used surface-sensitive fluorescence microscopy with single LNP resolution to investigate the pH dependency of the binding kinetics of ionizable lipid-containing LNPs to a supported endosomal model membrane. A sharp increase of LNP binding was observed when the pH was lowered from 6 to 5, accompanied by stepwise large-scale LNP disintegration. For LNPs preincubated in serum, protein corona formation shifted the onset of LNP binding and subsequent disintegration to lower pH, an effect that was less pronounced for lipoprotein-depleted serum. The LNP binding to the endosomal membrane mimic was observed to eventually become severely limited by suppression of the driving force for the formation of multivalent bonds during LNP attachment or, more specifically, by charge neutralization of anionic lipids in the model membrane due to their association with cationic lipids from earlier attached LNPs upon their disintegration. Cell uptake experiments demonstrated marginal differences in LNP uptake in untreated and lipoprotein-depleted serum, whereas lipoprotein-depleted serum increased mRNA-controlled protein (eGFP) production substantially. This complies with model membrane data and suggests that protein corona formation on the surface of the LNPs influences the nature of the interaction between LNPs and endosomal membranes.

Single-shot self-supervised object detection in microscopy

Abstract: Abstract Object detection is a fundamental task in digital microscopy, where machine learning has made great strides in overcoming the limitations of classical approaches. The training of state-of-the-art machine-learning methods almost universally relies on vast amounts of labeled experimental data or the ability to numerically simulate realistic datasets. However, experimental data are often challenging to label and cannot be easily reproduced numerically. Here, we propose a deep-learning method, named LodeSTAR (Localization and detection from Symmetries, Translations And Rotations), that learns to detect microscopic objects with sub-pixel accuracy from a single unlabeled experimental image by exploiting the inherent roto-translational symmetries of this task. We demonstrate that LodeSTAR outperforms traditional methods in terms of accuracy, also when analyzing challenging experimental data containing densely packed cells or noisy backgrounds. Furthermore, by exploiting additional symmetries we show that LodeSTAR can measure other properties, e.g., vertical position and polarizability in holographic microscopy.

Time-Resolved Thickness and Shape-Change Quantification using a Dual-Band Nanoplasmonic Ruler with Sub-Nanometer Resolution

Abstract: Time-resolved measurements of changes in the size and shape of nanobiological objects and layers are crucial to understand their properties and optimize their performance. Optical sensing is particularly attractive with high throughput and sensitivity, and label-free operation. However, most state-of-the-art solutions require intricate modeling or multiparameter measurements to disentangle conformational or thickness changes of biomolecular layers from complex interfacial refractive index variations. Here, we present a dual-band nanoplasmonic ruler comprising mixed arrays of plasmonic nanoparticles with spectrally separated resonance peaks. As electrodynamic simulations and model experiments show, the ruler enables real-time simultaneous measurements of thickness and refractive index variations in uniform and heterogeneous layers with sub-nanometer resolution. Additionally, nanostructure shape changes can be tracked, as demonstrated by quantifying the degree of lipid vesicle deformation at the critical coverage prior to rupture and supported lipid bilayer formation. In a broader context, the presented nanofabrication approach constitutes a generic route for multimodal nanoplasmonic optical sensing.

Probing the Separation Distance between Biological Nanoparticles and Cell Membrane Mimics Using Neutron Reflectometry with Sub-Nanometer Accuracy

Abstract: Nanoparticle interactions with cellular membranes are controlled by molecular recognition reactions and regulate a multitude of biological processes, including virus infections, biological nanoparticle-mediated cellular communication, and drug delivery applications. Aided by the design of various supported cell membrane mimics, multiple methods have been employed to investigate these types of interactions, revealing information on nanoparticle coverage, interaction kinetics, as well as binding strength; however, precise quantification of the separation distance across which these delicate interactions occur remains elusive. Here, we demonstrate that carefully designed neutron reflectometry (NR) experiments followed by an attentive selection and application of suitable theoretical models offer a means to quantify the distance separating biological nanoparticles from a supported lipid bilayer (SLB) with sub-nanometer precision. The distance between the nanoparticles and SLBs was tuned by exploiting either direct adsorption or specific binding using DNA tethers with different conformations, revealing separation distances of around 1, 3, and 7 nm with nanometric accuracy. We also show that NR provides precise information on nanoparticle coverage, size distribution, material composition, and potential structural changes in the underlying planar SLB induced upon nanoparticle binding. The precision with which these parameters could be quantified should pave an attractive path for investigations of the interactions between nanoparticles and interfaces at length scales and resolutions that were previously inaccessible. This thus makes it possible to, for example, gain an in-depth understanding of the molecular recognition reactions of inorganic and biological nanoparticles with cellular membranes.

Enhanced Optical Biosensing by Aerotaxy Ga(As)P Nanowire Platforms Suitable for Scalable Production

Abstract: Sensitive detection of low-abundance biomolecules is central for diagnostic applications. Semiconductor nanowires can be designed to enhance the fluorescence signal from surface-bound molecules, prospectively improving the limit of optical detection. However, to achieve the desired control of physical dimensions and material properties, one currently uses relatively expensive substrates and slow epitaxy techniques. An alternative approach is aerotaxy, a high-throughput and substrate-free production technique for high-quality semiconductor nanowires. Here, we compare the optical sensing performance of custom-grown aerotaxy-produced Ga(As)P nanowires vertically aligned on a polymer substrate to GaP nanowires batch-produced by epitaxy on GaP substrates. We find that signal enhancement by individual aerotaxy nanowires is comparable to that from epitaxy nanowires and present evidence of single-molecule detection. Platforms based on both types of nanowires show substantially higher normalized-to-blank signal intensity than planar glass surfaces, with the epitaxy platforms performing somewhat better, owing to a higher density of nanowires. With further optimization, aerotaxy nanowires thus offer a pathway to scalable, low-cost production of highly sensitive nanowire-based platforms for optical biosensing applications.

Single-shot self-supervised particle tracking

Abstract: Particle tracking is a fundamental task in digital microscopy. Recently, machine-learning approaches have made great strides in overcoming the limitations of more classical approaches. The training of state-of-the-art machine-learning methods almost universally relies on either vast amounts of labeled experimental data or the ability to numerically simulate realistic datasets. However, the data produced by experiments are often challenging to label and cannot be easily reproduced numerically. Here, we propose a novel deep-learning method, named LodeSTAR (Low-shot deep Symmetric Tracking And Re-gression), that learns to tracks objects with sub-pixel accuracy from a single unlabeled experimental image. This is made possible by exploiting the inherent roto-translational symmetries of the data. We demonstrate that LodeSTAR outperforms traditional methods in terms of accuracy. Furthermore, we analyze challenging experimental data containing densely packed cells or noisy backgrounds. We also exploit additional symmetries to extend the measurable particle properties to the particle’s vertical position by propagating the signal in Fourier space and its polarizability by scaling the signal strength. Thanks to the ability to train deep-learning models with a single unlabeled image, LodeSTAR can accelerate the development of high-quality microscopic analysis pipelines for engineering, biology, and medicine.

Single-shot self-supervised particle tracking (Conference Presentation)

Abstract: We present LodeSTAR, a label-free, single-shot particle tracker. We design a method for exploiting the symmetries of problem statements to train neural networks using extremely small datasets and without ground truth. We demonstrate that LodeSTAR outperforms traditional methods in terms of accuracy and that it reliably tracks experimental data of packed cells. Finally, we show that LodeSTAR can exploit additional symmetries to extend the measurable particle properties to the axial position of objects and particle polarizability.