Deciphering the internal complexity of living cells with quantitative phase microscopy: a multiscale approach

DisGUVery: A Versatile Open-Source Software for High-Throughput Image Analysis of Giant Unilamellar Vesicles

Giant unilamellar vesicles (GUVs) are cell-sized aqueous compartments enclosed by a phospholipid bilayer. Due to their cell-mimicking properties, GUVs have become a widespread experimental tool in synthetic biology to study membrane properties and cellular processes. In stark contrast to the experimental progress, quantitative analysis of GUV microscopy images has received much less attention. Currently, most analysis is performed either manually or with custom-made scripts, which makes analysis time-consuming and results difficult to compare across studies. To make quantitative GUV analysis accessible and fast, we present DisGUVery, an open-source, versatile software that encapsulates multiple algorithms for automated detection and analysis of GUVs in microscopy images. With a performance analysis, we demonstrate that DisGUVery's three vesicle detection modules successfully identify GUVs in images obtained with a wide range of imaging sources, in various typical GUV experiments. Multiple predefined analysis modules allow the user to extract properties such as membrane fluorescence, vesicle shape, and internal fluorescence from large populations. A new membrane segmentation algorithm facilitates spatial fluorescence analysis of nonspherical vesicles. Altogether, DisGUVery provides an accessible tool to enable high-throughput automated analysis of GUVs, and thereby to promote quantitative data analysis in synthetic cell research.

Light-Triggered Drug Release from Red Blood Cells Suppresses Arthritic Inflammation

Arthritis is a leading cause of disability in adults, which can be intensely incapacitating. The location and intensity of the pain is both subjective and challenging to manage. Consequently, patient-directed delivery of anti-inflammatories is an essential component of future therapeutic strategies for the management of this disorder. We describe the design and application of a light responsive red blood cell (RBC) conveyed dexamethasone (Dex) construct that enables targeted drug delivery upon illumination of the inflamed site. The red wavelength (650 nm) responsive nature of the phototherapeutic was validated using tissue phantoms mimicking the light absorbing properties of various skin types. Furthermore, photoreleased Dex has the same impact on cellular responses as conventional Dex. Murine RBCs containing the photoactivatable therapeutic display comparable circulation properties as fluorescently labelled RBCs. In addition, a single dose of light-targeted Dex delivery is 5-fold more effective in suppressing inflammation than the parent drug, delivered serially over multiple days. These results are consistent with the notion that the circulatory system be used as an on-command drug depot, providing the means to therapeutically target diseased sites both efficiently and effectively.

PhUn-Net: ready-to-use neural network for unwrapping quantitative phase images of biological cells

We present a deep-learning approach for solving the problem of 2π phase ambiguities in two-dimensional quantitative phase maps of biological cells, using a multi-layer encoder-decoder residual convolutional neural network. We test the trained network, PhUn-Net, on various types of biological cells, captured with various interferometric setups, as well as on simulated phantoms. These tests demonstrate the robustness and generality of the network, even for cells of different morphologies or different illumination conditions than PhUn-Net has been trained on. In this paper, for the first time, we make the trained network publicly available in a global format, such that it can be easily deployed on every platform, to yield fast and robust phase unwrapping, not requiring prior knowledge or complex implementation. By this, we expect our phase unwrapping approach to be widely used, substituting conventional and more time-consuming phase unwrapping algorithms.

Revealing the assembly of filamentous proteins with scanning transmission electron microscopy

Filamentous proteins are responsible for the superior mechanical strength of our cells and tissues. The remarkable mechanical properties of protein filaments are tied to their complex molecular packing structure. However, since these filaments have widths of several to tens of nanometers, it has remained challenging to quantitatively probe their molecular mass density and three-dimensional packing order. Scanning transmission electron microscopy (STEM) is a powerful tool to perform simultaneous mass and morphology measurements on filamentous proteins at high resolution, but its applicability has been greatly limited by the lack of automated image processing methods. Here, we demonstrate a semi-automated tracking algorithm that is capable of analyzing the molecular packing density of intra- and extracellular protein filaments over a broad mass range from STEM images. We prove the wide applicability of the technique by analyzing the mass densities of two cytoskeletal proteins (actin and microtubules) and of the main protein in the extracellular matrix, collagen. The high-throughput and spatial resolution of our approach allow us to quantify the internal packing of these filaments and their polymorphism by correlating mass and morphology information. Moreover, we are able to identify periodic mass variations in collagen fibrils that reveal details of their axially ordered longitudinal self-assembly. STEM-based mass mapping coupled with our tracking algorithm is therefore a powerful technique in the characterization of a wide range of biological and synthetic filaments.

Multifractal Desynchronization of the Cardiac Excitable Cell Network During Atrial Fibrillation. I. Multifractal Analysis of Clinical Data

Atrial fibrillation (AF) is a cardiac arrhythmia characterized by rapid and irregular atrial electrical activity with a high clinical impact on stroke incidence. Best available therapeutic strategies combine pharmacological and surgical means. But when successful, they do not always prevent long-term relapses. Initial success becomes all the more tricky to achieve as the arrhythmia maintains itself and the pathology evolves into sustained or chronic AF. This raises the open crucial issue of deciphering the mechanisms that govern the onset of AF as well as its perpetuation. In this study, we develop a wavelet-based multi-scale strategy to analyze the electrical activity of human hearts recorded by catheter electrodes, positioned in the coronary sinus (CS), during episodes of AF. We compute the so-called multifractal spectra using two variants of the wavelet transform modulus maxima method, the moment (partition function) method and the magnitude cumulant method. Application of these methods to long time series recorded in a patient with chronic AF provides quantitative evidence of the multifractal intermittent nature of the electric energy of passing cardiac impulses at low frequencies, i.e., for times (≳0.5 s) longer than the mean interbeat (≃ 10-1 s). We also report the results of a two-point magnitude correlation analysis which infers the absence of a multiplicative time-scale structure underlying multifractal scaling. The electric energy dynamics looks like a "multifractal white noise" with quadratic (log-normal) multifractal spectra. These observations challenge concepts of functional reentrant circuits in mechanistic theories of AF, still leaving open the role of the autonomic nervous system (ANS). A transition is indeed observed in the computed multifractal spectra which group according to two distinct areas, consistently with the anatomical substrate binding to the CS, namely the left atrial posterior wall, and the ligament of Marshall which is innervated by the ANS. In a companion paper (II. Modeling), we propose a mathematical model of a denervated heart where the kinetics of gap junction conductance alone induces a desynchronization of the myocardial excitable cells, accounting for the multifractal spectra found experimentally in the left atrial posterior wall area.

Refractive Index Imaging of Cells with Variable-Angle Near-Total Internal Reflection (TIR) Microscopy

The refractive index in the interior of single cells affects the evanescent field depth in quantitative studies using total internal reflection (TIR) fluorescence, but often that index is not well known. We here present method to measure and spatially map the absolute index of refraction in a microscopic sample, by imaging a collimated light beam reflected from the substrate/buffer/cell interference at variable angles of incidence. Above the TIR critical angle (which is a strong function of refractive index), the reflection is 100%, but in the immediate sub-critical angle zone, the reflection intensity is a very strong ascending function of incidence angle. By analyzing the angular position of that edge at each location in the field of view, the local refractive index can be estimated. In addition, by analyzing the steepness of the edge, the distance-to-substrate can be determined. We apply the technique to liquid calibration samples, silica beads, cultured Chinese hamster ovary cells, and primary culture chromaffin cells. The optical technique suffers from decremented lateral resolution, scattering, and interference artifacts. However, it still provides reasonable results for both refractive index (~1.38) and for distance-to-substrate (~150 nm) for the cells, as well as a lateral resolution to about 1 µm.

Revealing stiffening and brittling of chronic myelogenous leukemia hematopoietic primary cells through their temporal response to shear stress

Cancer cell transformation is often accompanied by a modification of their viscoelastic properties. When capturing the stress-to-strain response of primary chronic myelogenous leukemia (CML) cells, from two data sets of CD34+ hematopoietic cells isolated from healthy and leukemic bone marrows, we show that the mean shear relaxation modulus increases upon cancer transformation. This stiffening of the cells comes along with local rupture events, detected as reinforced sharp local maxima of this modulus, suggesting that these cancer cells respond to a local mechanical stress by a cascade of local brittle failure events.