Analysis of the Diffusivity Change from Single-Molecule Trajectories on Living Cells

Single-Molecule Imaging Reveals Differential AT1R Stoichiometry Change in Biased Signaling

G protein-coupled receptors (GPCRs) represent promising therapeutic targets due to their involvement in numerous physiological processes mediated by downstream G protein- and β-arrestin-mediated signal transduction cascades. Although the precise control of GPCR signaling pathways is therapeutically valuable, the molecular details for governing biased GPCR signaling remain elusive. The Angiotensin II type 1 receptor (AT1R), a prototypical class A GPCR with profound implications for cardiovascular functions, has become a focal point for biased ligand-based clinical interventions. Herein, we used single-molecule live-cell imaging techniques to evaluate the changes in stoichiometry and dynamics of AT1R with distinct biased ligand stimulations in real time. It was revealed that AT1R existed predominantly in monomers and dimers and underwent oligomerization upon ligand stimulation. Notably, β-arrestin-biased ligands induced the formation of higher-order aggregates, resulting in a slower diffusion profile for AT1R compared to G protein-biased ligands. Furthermore, we demonstrated that the augmented aggregation of AT1R, triggered by activation from each biased ligand, was completely abrogated in β-arrestin knockout cells. These findings furnish novel insights into the intricate relationship between GPCR aggregation states and biased signaling, underscoring the pivotal role of molecular behaviors in guiding the development of selective therapeutic agents.

Uncovering diffusive states of the yeast membrane protein, Pma1, and how labeling method can change diffusive behavior

We present and analyze video-microscopy-based single-particle-tracking measurements of the budding yeast (Saccharomyces cerevisiae) membrane protein, Pma1, fluorescently labeled either by direct fusion to the switchable fluorescent protein, mEos3.2, or by a novel, light-touch, labeling scheme, in which a 5 amino acid tag is directly fused to the C-terminus of Pma1, which then binds mEos3.2. The track diffusivity distributions of these two populations of single-particle tracks differ significantly, demonstrating that labeling method can be an important determinant of diffusive behavior. We also applied perturbation expectation maximization (pEMv2) (Koo and Mochrie in Phys Rev E 94(5):052412, 2016), which sorts trajectories into the statistically optimum number of diffusive states. For both TRAP-labeled Pma1 and Pma1-mEos3.2, pEMv2 sorts the tracks into two diffusive states: an essentially immobile state and a more mobile state. However, the mobile fraction of Pma1-mEos3.2 tracks is much smaller ([Formula: see text]) than the mobile fraction of TRAP-labeled Pma1 tracks ([Formula: see text]). In addition, the diffusivity of Pma1-mEos3.2's mobile state is several times smaller than the diffusivity of TRAP-labeled Pma1's mobile state. Thus, the two different labeling methods give rise to very different overall diffusive behaviors. To critically assess pEMv2's performance, we compare the diffusivity and covariance distributions of the experimental pEMv2-sorted populations to corresponding theoretical distributions, assuming that Pma1 displacements realize a Gaussian random process. The experiment-theory comparisons for both the TRAP-labeled Pma1 and Pma1-mEos3.2 reveal good agreement, bolstering the pEMv2 approach.

Diffusion Analysis of NAnoscopic Ensembles: A Tracking-Free Diffusivity Analysis for NAnoscopic Ensembles in Biological Samples and Nanotechnology

The rapid development of microscopic techniques over the past decades enables the establishment of single molecule fluorescence imaging as a powerful tool in biological and biomedical sciences. Single molecule fluorescence imaging allows to study the chemical, physicochemical, and biological properties of target molecules or particles by tracking their molecular position in the biological environment and determining their dynamic behavior. However, the precise determination of particle distribution and diffusivities is often challenging due to high molecule/particle densities, fast diffusion, and photobleaching/blinking of the fluorophore. A novel, accurate, and fast statistical analysis tool, Diffusion Analysis of NAnoscopic Ensembles (DANAE), that solves all these obstacles is introduced. DANAE requires no approximations or any a priori input regarding unknown system-inherent parameters, such as background distributions; a requirement that is vitally important when studying the behavior of molecules/particles in living cells. The superiority of DANAE with various data from simulations is demonstrated. As experimental applications of DANAE, membrane receptor diffusion in its natural membrane environment, and cargo mobility/distribution within nanostructured lipid nanoparticles are presented. Finally, the method is extended to two-color channel fluorescence microscopy.

Multiscale microscopy to decipher plant cell structure and dynamics

New imaging methodologies with high contrast and molecular specificity allow researchers to analyze dynamic processes in plant cells at multiple scales, from single protein and RNA molecules to organelles and cells, to whole organs and tissues. These techniques produce informative images and quantitative data on molecular dynamics to address questions that cannot be answered by conventional biochemical assays. Here, we review selected microscopy techniques, focusing on their basic principles and applications in plant science, discussing the pros and cons of each technique, and introducing methods for quantitative analysis. This review thus provides guidance for plant scientists in selecting the most appropriate techniques to decipher structures and dynamic processes at different levels, from protein dynamics to morphogenesis.

Deep learning in single-molecule imaging and analysis: recent advances and prospects

Single-molecule microscopy is advantageous in characterizing heterogeneous dynamics at the molecular level. However, there are several challenges that currently hinder the wide application of single molecule imaging in bio-chemical studies, including how to perform single-molecule measurements efficiently with minimal run-to-run variations, how to analyze weak single-molecule signals efficiently and accurately without the influence of human bias, and how to extract complete information about dynamics of interest from single-molecule data. As a new class of computer algorithms that simulate the human brain to extract data features, deep learning networks excel in task parallelism and model generalization, and are well-suited for handling nonlinear functions and extracting weak features, which provide a promising approach for single-molecule experiment automation and data processing. In this perspective, we will highlight recent advances in the application of deep learning to single-molecule studies, discuss how deep learning has been used to address the challenges in the field as well as the pitfalls of existing applications, and outline the directions for future development.

Receptor tyrosine kinase MET ligand-interaction classified via machine learning from single-particle tracking data

Internalin B-mediated activation of the membrane-bound receptor tyrosine kinase MET is accompanied by a change in receptor mobility. Conversely, it should be possible to infer from receptor mobility whether a cell has been treated with internalin B. Here, we propose a method based on hidden Markov modeling and explainable artificial intelligence that machine-learns the key differences in MET mobility between internalin B-treated and -untreated cells from single-particle tracking data. Our method assigns receptor mobility to three diffusion modes (immobile, slow, and fast). It discriminates between internalin B-treated and -untreated cells with a balanced accuracy of >99% and identifies three parameters that are most affected by internalin B treatment: a decrease in the mobility of slow molecules (1) and a depopulation of the fast mode (2) caused by an increased transition of fast molecules to the slow mode (3). Our approach is based entirely on free software and is readily applicable to the analysis of other membrane receptors.

Deep learning-based classification of breast cancer cells using transmembrane receptor dynamics

MOTIVATION

Motions of transmembrane receptors on cancer cell surfaces can reveal biophysical features of the cancer cells, thus providing a method for characterizing cancer cell phenotypes. While conventional analysis of receptor motions in the cell membrane mostly relies on the mean-squared displacement plots, much information is lost when producing these plots from the trajectories. Here we employ deep learning to classify breast cancer cell types based on the trajectories of epidermal growth factor receptor (EGFR). Our model is an artificial neural network trained on the EGFR motions acquired from six breast cancer cell lines of varying invasiveness and receptor status: MCF7 (hormone receptor positive), BT474 (HER2-positive), SKBR3 (HER2-positive), MDA-MB-468 (triple negative, TN), MDA-MB-231 (TN) and BT549 (TN).

RESULTS

The model successfully classified the trajectories within individual cell lines with 83% accuracy and predicted receptor status with 85% accuracy. To further validate the method, epithelial-mesenchymal transition (EMT) was induced in benign MCF10A cells, noninvasive MCF7 cancer cells and highly invasive MDA-MB-231 cancer cells, and EGFR trajectories from these cells were tested. As expected, after EMT induction, both MCF10A and MCF7 cells showed higher rates of classification as TN cells, but not the MDA-MB-231 cells. Whereas deep learning-based cancer cell classifications are primarily based on the optical transmission images of cell morphology and the fluorescence images of cell organelles or cytoskeletal structures, here we demonstrated an alternative way to classify cancer cells using a dynamic, biophysical feature that is readily accessible.

AVAILABILITY AND IMPLEMENTATION

A python implementation of deep learning-based classification can be found at https://github.com/soonwoohong/Deep-learning-for-EGFR-trajectory-classification.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Photo-affinity pulling down of low-affinity binding proteins mediated by post-translational modifications

Weak and transient protein-protein interactions (PPIs) mediated by the post-translational modifications (PTMs) play key roles in biological systems. However, technical challenges to investigate the PTM-mediated PPIs have impeded many research advances. In this work, we develop a photo-affinity pull-down assay method to pull-down low-affinity binding proteins, thus for the screen of PTM-mediated PPIs. In this method, the PTM-mediated non-covalent interactions can be converted to the covalent interactions by the photo-activated linkage, so as to freeze frame the low-affinity binding interactions. The fabricated photo-affinity magnetic beads (PAMBs) ensure high specificity and resolution to capture the interacted proteins. Besides, the introduction of PEG passivation layer on PAMB has significantly reduced the non-specific interaction as compared to the traditional pull-down assay. For proof-of-concept, by using this newly developed assay method, we have identified a set of proteins that can interact with a specific methylation site on Flap Endonuclease 1 (FEN1) protein. Less interfering proteins (decreased over 80%) and more proteins sub-classes are profiled as compared to the traditional biotin-avidin pull-down system. Therefore, this new pull-down method may provide a useful tool for the study of low-affinity PPIs, and contribute to the discovery of potential targets for renewed PTM-mediated interactions that is fundamentally needed in biomedical research.