Holographic immunoassays: direct detection of antibodies binding to colloidal spheres

Abstract

The size of a probe bead reported by holographic particle characterization depends on the proportion of the surface area covered by bound target molecules and so can be used as an assay for molecular binding.

Holographic characterization and tracking of colloidal dimers in the effective-sphere approximation

“…Each point in this scatter plot represents the effective diameter and refractive index of a single particle, either a monomeric sphere or a randomly oriented dimer. The spheres' properties are selected to mimic the monodisperse silica probe beads used for holographic immunoassays [7]. Values obtained for the spheres' properties are found to be independent of axial position over the range 20 µm < z p < 120 µm, with the lower bound being set by the spatial resolution of the imaging grid and the upper bound by the images' signal-to-noise ratio.…”

“…Holographic particle characterization [1] is a fast and robust technique for measuring the size and refractive index of colloidal spheres and has a wide variety of applications in basic research [2][3][4][5], industrial materials analysis [6] and medical diagnostics [5,7]. As illustrated in Fig.…”

“…1(b). The ability to identify and interpret holograms of such clusters within the effectivesphere framework therefore would create immediate applications in areas as diverse as semiconductor processing [16] and medical testing [7].…”

“…These studies demonstrate that small clusters can be clearly identified in dispersions of monodisperse colloidal spheres. The ability to identify and count colloidal dimers provides a basis for measuring their relative abundance, which is useful in such applications as detecting agglutination in bead-based medical assays [5,7].…”

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

“…4,9,10 Using holography to count, track and characterize colloidal particles provides unprecedented insights into the composition and microscopic dynamics of colloidal dispersions, [11][12][13] with applications ranging from fundamental research in statistical physics 14,15 to formulation and manufacture of biopharmaceuticals [16][17][18][19] and medical testing. 20,21 Hologram analysis is a challenging inverse problem 7,22 both because recorded intensity patterns necessarily omit half of the information about the light's amplitude and phase profiles and also because the underlying Lorenz-Mie theory of light scattering is notoriously complicated. [23][24][25] Extracting quantitative information from holograms is an unusual application for machine learning in two respects: (1) it involves regression of continuously varying properties from experimental data and (2) the machine-learning system can be trained with synthetic data generated from an exact theory.…”