MultiSero: An Open-Source Multiplex-ELISA Platform for Measuring Antibody Responses to Infection.

A multiplexed enzyme-linked immunosorbent assay (ELISA) that simultaneously measures antibody binding to multiple antigens can extend the impact of serosurveillance studies, particularly if the assay approaches the simplicity, robustness, and accuracy of a conventional single-antigen ELISA. Here, we report on the development of multiSero, an open-source multiplex ELISA platform for measuring antibody responses to viral infection. Our assay consists of three parts: (1) an ELISA against an array of proteins in a 96-well format; (2) automated imaging of each well of the ELISA array using an open-source plate reader; and (3) automated measurement of optical densities for each protein within the array using an open-source analysis pipeline.

DynaMorph: self-supervised learning of morphodynamic states of live cells.

A cell's shape and motion represent fundamental aspects of cell identity and can be highly predictive of function and pathology. However, automated analysis of the morphodynamic states remains challenging for most cell types, especially primary human cells where genetic labeling may not be feasible. To enable automated and quantitative analysis of morphodynamic states, we developed DynaMorph-a computational framework that combines quantitative live cell imaging with self-supervised learning.

multiSero: open multiplex-ELISA platform for analyzing antibody responses to SARS-CoV-2 infection.

Serology has provided valuable diagnostic and epidemiological data on antibody responses to SARS-CoV-2 in diverse patient cohorts. Deployment of high content, multiplex serology platforms across the world, including in low and medium income countries, can accelerate longitudinal epidemiological surveys. Here we report multiSero, an open platform to enable multiplex serology with up to 48 antigens in a 96-well format.

Revealing architectural order with quantitative label-free imaging and deep learning.

We report quantitative label-free imaging with phase and polarization (QLIPP) for simultaneous measurement of density, anisotropy, and orientation of structures in unlabeled live cells and tissue slices. We combine QLIPP with deep neural networks to predict fluorescence images of diverse cell and tissue structures. QLIPP images reveal anatomical regions and axon tract orientation in prenatal human brain tissue sections that are not visible using brightfield imaging.

Multiplexed and high-throughput neuronal fluorescence imaging with diffusible probes.

Synapses contain hundreds of distinct proteins whose heterogeneous expression levels are determinants of synaptic plasticity and signal transmission relevant to a range of diseases. Here, we use diffusible nucleic acid imaging probes to profile neuronal synapses using multiplexed confocal and super-resolution microscopy. Confocal imaging is performed using high-affinity locked nucleic acid imaging probes that stably yet reversibly bind to oligonucleotides conjugated to antibodies and peptides.

DoGNet: A deep architecture for synapse detection in multiplexed fluorescence images.

Neuronal synapses transmit electrochemical signals between cells through the coordinated action of presynaptic vesicles, ion channels, scaffolding and adapter proteins, and membrane receptors. In situ structural characterization of numerous synaptic proteins simultaneously through multiplexed imaging facilitates a bottom-up approach to synapse classification and phenotypic description. Objective automation of efficient and reliable synapse detection within these datasets is essential for the high-throughput investigation of synaptic features.

Bayesian model selection applied to the analysis of fluorescence correlation spectroscopy data of fluorescent proteins in vitro and in vivo.

Fluorescence correlation spectroscopy (FCS) is a powerful technique to investigate molecular dynamics with single molecule sensitivity. In particular, in the life sciences it has found widespread application using fluorescent proteins as molecularly specific labels. However, FCS data analysis and interpretation using fluorescent proteins remains challenging due to typically low signal-to-noise ratio of FCS data and correlated noise in autocorrelated data sets.

Bayesian total internal reflection fluorescence correlation spectroscopy reveals hIAPP-induced plasma membrane domain organization in live cells.

Amyloid fibril deposition of human islet amyloid polypeptide (hIAPP) in pancreatic islet cells is implicated in the pathogenesis of type II diabetes. A growing number of studies suggest that small peptide aggregates are cytotoxic via their interaction with the plasma membrane, which leads to membrane permeabilization or disruption. A recent study using imaging total internal reflection-fluorescence correlation spectroscopy (ITIR-FCS) showed that monomeric hIAPP induced the formation of cellular plasma membrane microdomains containing dense lipids, in addition to the modulation of membrane fluidity.

Bayesian approach to MSD-based analysis of particle motion in live cells.

Quantitative tracking of particle motion using live-cell imaging is a powerful approach to understanding the mechanism of transport of biological molecules, organelles, and cells. However, inferring complex stochastic motion models from single-particle trajectories in an objective manner is nontrivial due to noise from sampling limitations and biological heterogeneity. Here, we present a systematic Bayesian approach to multiple-hypothesis testing of a general set of competing motion models based on particle mean-square displacements that automatically classifies particle motion, properly accounting for sampling limitations and correlated noise while appropriately penalizing model complexity according to Occam's Razor to avoid over-fitting.

Bayesian approach to the analysis of fluorescence correlation spectroscopy data II: application to simulated and in vitro data.

Fluorescence correlation spectroscopy (FCS) is a powerful approach to characterizing the binding and transport dynamics of macromolecules. The unbiased interpretation of FCS data relies on the evaluation of multiple competing hypotheses to describe an underlying physical process under study, which is typically unknown a priori. Bayesian inference provides a convenient framework for this evaluation based on the temporal autocorrelation function (TACF), as previously shown theoretically using model TACF curves (He, J.

Bayesian approach to the analysis of fluorescence correlation spectroscopy data I: theory.

Fluorescence correlation spectroscopy (FCS) is a powerful tool to infer the physical process of macromolecules including local concentration, binding, and transport from fluorescence intensity measurements. Interpretation of FCS data relies critically on objective multiple hypothesis testing of competing models for complex physical processes that are typically unknown a priori. Here, we propose an objective Bayesian inference procedure for testing multiple competing models to describe FCS data based on temporal autocorrelation functions.

Adsorption of a statherin peptide fragment on the surface of nanocrystallites of hydroxyapatite.

Statherin is an active inhibitor of calcium phosphate precipitation in the oral cavity. For many studies of the interaction between statherin and hydroxyapatite (HAp), the samples are prepared by a direct mixing of statherin or its fragment with well-crystalline HAp crystals. In this work, the HAp sample is precipitated in the presence of peptide fragment derived from the N-terminal 15 amino acids of statherin (SN-15).