Large depth-of-field tracking of colloidal spheres in holographic microscopy by modeling the objective lens

Measuring Vesicle Loading with Holographic Microscopy and Bulk Light Scattering

We report efforts to quantify the loading of cell-sized lipid vesicles using in-line digital holographic microscopy. This method does not require fluorescent reporters, fluorescent tracers, or radioactive tracers. A single-color LED light source takes the place of conventional illumination to generate holograms rather than bright field images. By modeling the vesicle's scattering in a microscope with a Lorenz-Mie light scattering model and comparing the results to data holograms, we are able to measure the vesicle's refractive index and thus loading. Performing the same comparison for bulk light scattering measurements enables the retrieval of vesicle loading for nanoscale vesicles.

Characterization of Optical, Thermal, and Viscoelastic Properties of Pollenkitt in Angiosperm Pollen Using In-Line Digital Holographic Microscopy

Pollen grains are remarkable material composites, with various organelles in their fragile interior protected by a strong shell made of sporopollenin. The outermost layer of angiosperm pollen grains contains a lipid-rich substance called pollenkitt, which is a natural bioadhesive that helps preserve structural integrity when the pollen grain is exposed to external environmental stresses. In addition, its viscous nature enables it to adhere to various floral and insect surfaces, facilitating the pollination process. To examine the physicochemical properties of aqueous pollenkitt droplets, we used in-line digital holographic microscopy to capture light scattering from individual pollenkitt particles. Comparison of pollenkitt holograms to those modeled using the Lorenz-Mie theory enables investigations into the minute variations in the refractive index and size resulting from changes in local temperature and pollen aging.

Precise characterization of nanometer-scale systems using interferometric scattering microscopy and Bayesian analysis

Interferometric scattering microscopy can image the dynamics of nanometer-scale systems. The typical approach to analyzing interferometric images involves intensive processing, which discards data and limits the precision of measurements. We demonstrate an alternative approach: modeling the interferometric point spread function and fitting this model to data within a Bayesian framework. This approach yields best-fit parameters, including the particle's three-dimensional position and polarizability, as well as uncertainties and correlations between these parameters. Building on recent work, we develop a model that is parameterized for rapid fitting. The model is designed to work with Hamiltonian Monte Carlo techniques that leverage automatic differentiation. We validate this approach by fitting the model to interferometric images of colloidal nanoparticles. We apply the method to track a diffusing particle in three dimensions, to directly infer the diffusion coefficient of a nanoparticle without calculating a mean-square displacement, and to quantify the ejection of DNA from an individual lambda phage virus, demonstrating that the approach can be used to infer both static and dynamic properties of nanoscale systems.

Machine learning enables precise holographic characterization of colloidal materials in real time

Holographic particle characterization uses in-line holographic video microscopy to track and characterize individual colloidal particles dispersed in their native fluid media. Applications range from fundamental research in statistical physics to product development in biopharmaceuticals and medical diagnostic testing. The information encoded in a hologram can be extracted by fitting to a generative model based on the Lorenz-Mie theory of light scattering. Treating hologram analysis as a high-dimensional inverse problem has been exceptionally successful, with conventional optimization algorithms yielding nanometer precision for a typical particle's position and part-per-thousand precision for its size and index of refraction. Machine learning previously has been used to automate holographic particle characterization by detecting features of interest in multi-particle holograms and estimating the particles' positions and properties for subsequent refinement. This study presents an updated end-to-end neural-network solution called CATCH (Characterizing and Tracking Colloids Holographically) whose predictions are fast, precise, and accurate enough for many real-world high-throughput applications and can reliably bootstrap conventional optimization algorithms for the most demanding applications. The ability of CATCH to learn a representation of Lorenz-Mie theory that fits within a diminutive 200 kB hints at the possibility of developing a greatly simplified formulation of light scattering by small objects.

Improving holographic particle characterization by modeling spherical aberration

Holographic microscopy combined with forward modeling and inference allows colloidal particles to be characterized and tracked in three dimensions with high precision. However, current models ignore the effects of optical aberrations on hologram formation. We investigate the effects of spherical aberration on the structure of single-particle holograms and on the accuracy of particle characterization. We find that in a typical experimental setup, spherical aberration can result in systematic shifts of about 2% in the inferred refractive index and radius. We show that fitting with a model that accounts for spherical aberration decreases this aberration-dependent error by a factor of two or more, even when the level of spherical aberration in the optical train is unknown. With the new generative model, the inferred parameters are consistent across different levels of aberration, making particle characterization more robust.

Precision single-particle localization using radial variance transform

We introduce an image transform designed to highlight features with high degree of radial symmetry for identification and subpixel localization of particles in microscopy images. The transform is based on analyzing pixel value variations in radial and angular directions. We compare the subpixel localization performance of this algorithm to other common methods based on radial or mirror symmetry (such as fast radial symmetry transform, orientation alignment transform, XCorr, and quadrant interpolation), using both synthetic and experimentally obtained data. We find that in all cases it achieves the same or lower localization error, frequently reaching the theoretical limit.

Point spread function in interferometric scattering microscopy (iSCAT). Part I: aberrations in defocusing and axial localization

Interferometric scattering (iSCAT) microscopy is an emerging label-free technique optimized for the sensitive detection of nano-matter. Previous iSCAT studies have approximated the point spread function in iSCAT by a Gaussian intensity distribution. However, recent efforts to track the mobility of nanoparticles in challenging speckle environments and over extended axial ranges has necessitated a quantitative description of the interferometric point spread function (iPSF). We present a robust vectorial diffraction model for the iPSF in tandem with experimental measurements and rigorous FDTD simulations. We examine the iPSF under various imaging scenarios to understand how aberrations due to the experimental configuration encode information about the nanoparticle. We show that the lateral shape of the iPSF can be used to achieve nanometric three-dimensional localization over an extended axial range on the order of 10 µm either by means of a fit to an analytical model or calibration-free unsupervised machine learning. Our results have immediate implications for three-dimensional single particle tracking in complex scattering media.

CATCH: Characterizing and Tracking Colloids Holographically Using Deep Neural Networks

In-line holographic microscopy provides an unparalleled wealth of information about the properties of colloidal dispersions. Analyzing one colloidal particle's hologram with the Lorenz-Mie theory of light scattering yields the particle's three-dimensional position with nanometer precision while simultaneously reporting its size and refractive index with part-per-thousand resolution. Analyzing a few thousand holograms in this way provides a comprehensive picture of the particles that make up a dispersion, even for complex multicomponent systems. All of this valuable information comes at the cost of three computationally expensive steps: (1) identifying and localizing features of interest within recorded holograms, (2) estimating each particle's properties based on characteristics of the associated features, and finally (3) optimizing those estimates through pixel-by-pixel fits to a generative model. Here, we demonstrate an end-to-end implementation that is based entirely on machine-learning techniques. Characterizing and Tracking Colloids Holographically (CATCH) with deep convolutional neural networks is fast enough for real-time applications and otherwise outperforms conventional analytical algorithms, particularly for heterogeneous and crowded samples. We demonstrate this system's capabilities with experiments on free-flowing and holographically trapped colloidal spheres.

Holographic molecular binding assays

We demonstrate that holographic particle characterization can directly detect binding of proteins to functionalized colloidal probe particles by monitoring the associated change in the particles' size. This label-free molecular binding assay uses in-line holographic video microscopy to measure the diameter and refractive index of individual probe spheres as they flow down a microfluidic channel. Pooling measurements on 104 particles yields the population-average diameter with an uncertainty smaller than 0.5 nm, which is sufficient to detect sub-monolayer coverage by bound proteins. We demonstrate this method by monitoring binding of NeutrAvidin to biotinylated spheres and binding of immunoglobulin G to spheres functionalized with protein A.