Quantitative Kinetic Models from Intravital Microscopy: A Case Study Using Hepatic Transport

From Average Transient Transporter Currents to Microscopic Mechanism─A Bayesian Analysis

Electrophysiology studies of secondary active transporters have revealed quantitative mechanistic insights over many decades of research. However, the emergence of new experimental and analytical approaches calls for investigation of the capabilities and limitations of the newer methods. We examine the ability of solid-supported membrane electrophysiology (SSME) to characterize discrete-state kinetic models with >10 rate constants. We use a Bayesian framework applied to synthetic data for three tasks: to quantify and check (i) the precision of parameter estimates under different assumptions, (ii) the ability of computation to guide the selection of experimental conditions, and (iii) the ability of our approach to distinguish among mechanisms based on SSME data. When the general mechanism, i.e., event order, is known in advance, we show that a subset of kinetic parameters can be "practically identified" within ∼1 order of magnitude, based on SSME current traces that visually appear to exhibit simple exponential behavior. This remains true even when accounting for systematic measurement bias and realistic uncertainties in experimental inputs (concentrations) are incorporated into the analysis. When experimental conditions are optimized or different experiments are combined, the number of practically identifiable parameters can be increased substantially. Some parameters remain intrinsically difficult to estimate through SSME data alone, suggesting that additional experiments are required to fully characterize parameters. We also demonstrate the ability to perform model selection and determine the order of events when that is not known in advance, comparing Bayesian and maximum-likelihood approaches. Finally, our studies elucidate good practices for the increasingly popular but subtly challenging Bayesian calculations for structural and systems biology.

Liver Zonation - Revisiting Old Questions With New Technologies

Despite the ever-increasing prevalence of non-alcoholic fatty liver disease (NAFLD), the etiology and pathogenesis remain poorly understood. This is due, in part, to the liver's complex physiology and architecture. The liver maintains glucose and lipid homeostasis by coordinating numerous metabolic processes with great efficiency. This is made possible by the spatial compartmentalization of metabolic pathways a phenomenon known as liver zonation. Despite the importance of zonation to normal liver function, it is unresolved if and how perturbations to liver zonation can drive hepatic pathophysiology and NAFLD development. While hepatocyte heterogeneity has been identified over a century ago, its examination had been severely hindered due to technological limitations. Recent advances in single cell analysis and imaging technologies now permit further characterization of cells across the liver lobule. This review summarizes the advances in examining liver zonation and elucidating its regulatory role in liver physiology and pathology. Understanding the spatial organization of metabolism is vital to further our knowledge of liver disease and to provide targeted therapeutic avenues.

Intravital Microscopy for the Study of Hepatic Glucose Uptake

The liver is central in maintaining glucose homeostasis. Indeed, impaired hepatic glucose uptake has been implicated in the development of hyperglycemia in type II diabetes (T2D) and non-alcoholic fatty liver disease (NAFLD). However, current approaches to evaluate glucose mobilization rely on indirect measurements that do not provide spatial and temporal information. Here, we describe confocal-based intravital microscopy (IVM) of the liver that allows the identification of hepatocyte spatial organization and glucose transport. Specifically, we describe a method to fluorescently label hepatic landmarks to identify different compartments within the liver. In addition, we outline an in vivo fluorescent glucose uptake assay to quantitatively measure glucose mobilization in space and time. These protocols allow direct investigation of hepatic glycemic control and can be further applied to murine models of liver disease. © Published 2021. This article is a U.S. Government work and is in the public domain in the USA. Basic Protocol 1: Mouse surgical procedure and positioning for liver intravital imaging Basic Protocol 2: Fluorescent labeling and intravital imaging of mouse hepatic compartments Basic Protocol 3: Mouse hepatic glucose uptake assay and intravital imaging analysis.

Actually Seeing What Is Going on - Intravital Microscopy in Tissue Engineering

Intravital microscopy (IVM) study approach offers several advantages over in vitro, ex vivo, and 3D models. IVM provides real-time imaging of cellular events, which provides us a comprehensive picture of dynamic processes. Rapid improvement in microscopy techniques has permitted deep tissue imaging at a higher resolution. Advances in fluorescence tagging methods enable tracking of specific cell types. Moreover, IVM can serve as an important tool to study different stages of tissue regeneration processes. Furthermore, the compatibility of different tissue engineered constructs can be analyzed. IVM is also a promising approach to investigate host reactions on implanted biomaterials. IVM can provide instant feedback for improvising tissue engineering strategies. In this review, we aim to provide an overview of the requirements and applications of different IVM approaches. First, we will discuss the history of IVM development, and then we will provide an overview of available optical modalities including the pros and cons. Later, we will summarize different fluorescence labeling methods. In the final section, we will discuss well-established chronic and acute IVM models for different organs.

Pitching single-focus confocal data analysis one photon at a time with Bayesian nonparametrics

Fluorescence time traces are used to report on dynamical properties of molecules. The basic unit of information in these traces is the arrival time of individual photons, which carry instantaneous information from the molecule, from which they are emitted, to the detector on timescales as fast as microseconds. Thus, it is theoretically possible to monitor molecular dynamics at such timescales from traces containing only a sufficient number of photon arrivals. In practice, however, traces are stochastic and in order to deduce dynamical information through traditional means-such as fluorescence correlation spectroscopy (FCS) and related techniques-they are collected and temporally autocorrelated over several minutes. So far, it has been impossible to analyze dynamical properties of molecules on timescales approaching data acquisition without collecting long traces under the strong assumption of stationarity of the process under observation or assumptions required for the analytic derivation of a correlation function. To avoid these assumptions, we would otherwise need to estimate the instantaneous number of molecules emitting photons and their positions within the confocal volume. As the number of molecules in a typical experiment is unknown, this problem demands that we abandon the conventional analysis paradigm. Here, we exploit Bayesian nonparametrics that allow us to obtain, in a principled fashion, estimates of the same quantities as FCS but from the direct analysis of traces of photon arrivals that are significantly smaller in size, or total duration, than those required by FCS.