Segmentation of phase contrast microscopy images based on multi-scale local Basic Image Features histograms

Impact of Protein Coronas on Lipid Nanoparticle Uptake and Endocytic Pathways in Cells

Lipid nanoparticles (LNPs), widely used in disease diagnosis and drug delivery, face the challenge of being surrounded by biological macromolecules such as proteins upon entering the human body. These molecules compete for binding sites on the nanoparticle surfaces, forming a protein corona. The impact of different types of protein coronas on LNP delivery remains unclear. In this study, we employed a newly developed, highly sensitive LNP labeling platform and analyzed the endocytosis of HeLa cells under different nutritional conditions using proteomics to address this critical issue. Our research found that under conditions of complete medium and amino acid starvation, most DNA-FITC vesicles in HeLa cells were located in the perinuclear region 4 h after transfection. In contrast, under serum starvation conditions, only a small portion of DNA-FITC vesicles were in the perinuclear region. On the other hand, through proteomics, we discovered that cells that were enriched in amino acids and complete medium contained more proteins, whereas those under serum starvation had relatively fewer enriched proteins. Through KEGG pathway enrichment analysis, we identified the phagosome and endocytosis pathways as particularly important. Lastly, differential analysis of proteins in these pathways revealed that proteins such as F-actin, Coronin, vATPase, TUBA, TUBB, MHCII, and TSP may have significant impacts on cellular endocytosis. Our research findings indicate that it is necessary to regulate cellular endocytosis based on different protein coronas to achieve optimal cytoplasmic release.

Phase Contrast Image Restoration by Formulating Its Imaging Principle and Reversing the Formulation With Deep Neural Networks

Phase contrast microscopy, as a noninvasive imaging technique, has been widely used to monitor the behavior of transparent cells without staining or altering them. Due to the optical principle of the specifically-designed microscope, phase contrast microscopy images contain artifacts such as halo and shade-off which hinder the cell segmentation and detection tasks. Some previous works developed simplified computational imaging models for phase contrast microscopes by linear approximations and convolutions. The approximated models do not exactly reflect the imaging principle of the phase contrast microscope and accordingly the image restoration by solving the corresponding deconvolution process is not perfect. In this paper, we revisit the optical principle of the phase contrast microscope to precisely formulate its imaging model without any approximation. Based on this model, we propose an image restoration procedure by reversing this imaging model with a deep neural network, instead of mathematically deriving the inverse operator of the model which is technically impossible. Extensive experiments are conducted to demonstrate the superiority of the newly derived phase contrast microscopy imaging model and the power of the deep neural network on modeling the inverse imaging procedure. Moreover, the restored images enable that high quality cell segmentation task can be easily achieved by simply thresholding methods. Implementations of this work are publicly available at https://github.com/LiangHann/Phase-Contrast-Microscopy-Image-Restoration.

Machine-Learning-Aided Quantification of Area Coverage of Adherent Cells from Phase-Contrast Images

The advances in machine learning (ML) software availability, efficiency, and friendliness, combined with the increase in the computation power of personal computers, are harnessed to rapidly and (relatively) effortlessly analyze time-lapse image series of adherent cell cultures, taken with phase-contrast microscopy (PCM). Since PCM is arguably the most widely used technique to visualize adherent cells in a label-free, noninvasive, and nondisruptive manner, the ability to easily extract quantitative information on the area covered by cells, should provide a valuable tool for investigation. We demonstrate two cases, in one we monitor the shrinking of cells in response to a toxicant, and in the second we measure the proliferation curve of mesenchymal stem cells (MSCs).

Microfluidic Devices as Process Development Tools for Cellular Therapy Manufacturing

Cellular therapies are creating a paradigm shift in the biomanufacturing industry. Particularly for autologous therapies, small-scale processing methods are better suited than the large-scale approaches that are traditionally employed in the industry. Current small-scale methods for manufacturing personalized cell therapies, however, are labour-intensive and involve a number of 'open events'. To overcome these challenges, new cell manufacturing platforms following a GMP-in-a-box concept have recently come on the market (GMP: Good Manufacturing Practice). These are closed automated systems with built-in pumps for fluid handling and sensors for in-process monitoring. At a much smaller scale, microfluidic devices exhibit many of the same features as current GMP-in-a-box systems. They are closed systems, fluids can be processed and manipulated, and sensors integrated for real-time detection of process variables. Fabricated from polymers, they can be made disposable, i.e. single-use. Furthermore, microfluidics offers exquisite spatiotemporal control over the cellular microenvironment, promising both reproducibility and control of outcomes. In this chapter, we consider the challenges in cell manufacturing, highlight recent advances of microfluidic devices for each of the main process steps, and summarize our findings on the current state of the art. As microfluidic cell culture devices have been reported for both adherent and suspension cell cultures, we report on devices for the key process steps, or unit operations, of both stem cell therapies and cell-based immunotherapies.

A Novel Method for Effective Cell Segmentation and Tracking in Phase Contrast Microscopic Images

Cell migration plays an important role in the identification of various diseases and physiological phenomena in living organisms, such as cancer metastasis, nerve development, immune function, wound healing, and embryo formulation and development. The study of cell migration with a real-time microscope generally takes several hours and involves analysis of the movement characteristics by tracking the positions of cells at each time interval in the images of the observed cells. Morphological analysis considers the shapes of the cells, and a phase contrast microscope is used to observe the shape clearly. Therefore, we developed a segmentation and tracking method to perform a kinetic analysis by considering the morphological transformation of cells. The main features of the algorithm are noise reduction using a block-matching 3D filtering method, k-means clustering to mitigate the halo signal that interferes with cell segmentation, and the detection of cell boundaries via active contours, which is an excellent way to detect boundaries. The reliability of the algorithm developed in this study was verified using a comparison with the manual tracking results. In addition, the segmentation results were compared to our method with unsupervised state-of-the-art methods to verify the proposed segmentation process. As a result of the study, the proposed method had a lower error of less than 40% compared to the conventional active contour method.

SARM1 is required in human derived sensory neurons for injury-induced and neurotoxic axon degeneration

Axonal degeneration contributes to the pathogenesis of many neurodegenerative disorders, motivating efforts to dissect the mechanism of pathological axon loss in order to develop therapies for axonal preservation. SARM1 is a particularly attractive therapeutic target, as it is an inducible NAD⁺ cleaving enzyme that is required for axon loss in multiple mouse models of traumatic and degenerative neurological disease. However, it is essential to establish whether SARM1 triggers axon degeneration in human neurons before proceeding with the development of SARM1-directed therapeutics. Here we combine genome engineering with the production of human stem cell-derived neurons to test the role of human SARM1 in traumatic and neurotoxic axon degeneration. We have generated two independent SARM1 knockout human iPSC lines that do not express SARM1 protein upon differentiation into neurons. We have developed a modified sensory neuron differentiation protocol that generates human sensory neurons with high yield and purity. We find that SARM1 is required for axon degeneration in response to both physical trauma and in a cellular model of chemotherapy-induced peripheral neuropathy. Finally, we identify cADPR as a biomarker of SARM1 enzyme activity in both healthy and injured human sensory neurons. These findings are consistent with prior molecular and cellular studies in mouse neurons, and highlight the therapeutic potential of SARM1 inhibition for the prevention and treatment of human neurological disease.

The Atlas Structure of Images

Many operations of vision require image regions to be isolated and inter-related. This is challenging when they are different in detail and extent. Practical methods of Computer Vision approach this through the tools of downsampling, pyramids, cropping and patches. In this paper we develop an ideal geometric structure for this, compatible with the existing scale space model of image measurement. Its elements are apertures which view the image like fuzzy-edged portholes of frosted glass. We establish containment and cause/effect relations between apertures, and show that these link them into cross-scale atlases. Atlases formed of Gaussian apertures are shown to be a continuous version of the image pyramid used in Computer Vision, and allow various types of image description to naturally be expressed within their framework. We show that views through Gaussian apertures are approximately equivalent to the jets of derivative of Gaussian filter responses that form part of standard Scale Space theory. This supports a view of the simple cells of mammalian V1 as implementing a system of local views of the retinal image of varying extent and resolution. As a worked example we develop a keypoint descriptor scheme that outperforms previous schemes that do not make use of learning.

Development of an optical system for the non-invasive tracking of stem cell growth on microcarriers

The emergence of medicinal indications for stem cell therapies has seen a need to develop the manufacturing capacity for adherent cells such as mesenchymal stem cells (MSCs). One such development is in the use of microcarriers, which facilitate enhanced cell densities for adherent stem cell cultures when compared with 2D culture platforms. Given the variety of stem cell expansion systems commercially available, novel methods of non-invasive and automated monitoring of cell number, confluence, and aggregation, within disparate environments, will become imperative to process control, ensuring reliable and consistent performance. The in situ epi-illumination of mouse embryonic fibroblasts and human mesenchymal stem cells attached to Cytodex 1 and 3 microcarriers was achieved using a bespoke microscope. Robust image processing techniques were developed to provide quantitative measurements of confluence, aggregate recognition, and cell number, without the need for fluorescent labeling or cell detachment. Large datasets of cells counted on individual microcarriers were statistically analyzed and compared with NucleoCounter measurements, with an average difference of less than 7% observed from days 0 to 6 of a 12-day culture noted, prior to the onset of aggregation. The developed image acquisition system and post-processing methodologies were successfully applied to dynamically moving colonized microcarriers. The proposed system offers a novel method of cell identification at the individual level, to consistently and accurately assess viable cell number, confluence, and cell distribution, while also minimizing the variability inherent in the current invasive means by which cells adhered to microcarriers are analyzed. Biotechnol. Bioeng. 2017;114: 2032-2042. © 2017 The Authors. Biotechnology and Bioengineering Published by Wiley Periodicals, Inc.

Label-Free Automated Cell Tracking: Analysis of the Role of E-cadherin Expression in Collective Electrotaxis

Collective cell migration plays an important role in wound healing, organogenesis, and the progression of metastatic disease. Analysis of collective migration typically involves laborious and time-consuming manual tracking of individual cells within cell clusters over several dozen or hundreds of frames. Herein, we develop a label-free, automated algorithm to identify and track individual epithelial cells within a free-moving cluster. We use this algorithm to analyze the effects of partial E-cadherin knockdown on collective migration of MCF-10A breast epithelial cells directed by an electric field. Our data show that E-cadherin knockdown in free-moving cell clusters diminishes electrotactic potential, with empty vector MCF-10A cells showing 16% higher directedness than cells with E-cadherin knockdown. Decreased electrotaxis is also observed in isolated cells at intermediate electric fields, suggesting an adhesion-independent role of E-cadherin in regulating electrotaxis. In additional support of an adhesion-independent role of E-cadherin, isolated cells with reduced E-cadherin expression reoriented within an applied electric field 60% more quickly than control. These results have implications for the role of E-cadherin expression in electrotaxis and demonstrate proof-of-concept of an automated algorithm that is broadly applicable to the analysis of collective migration in a wide range of physiological and pathophysiological contexts.

Real-time monitoring of specific oxygen uptake rates of embryonic stem cells in a microfluidic cell culture device

Oxygen plays a key role in stem cell biology as a signaling molecule and as an indicator of cell energy metabolism. Quantification of cellular oxygen kinetics, i.e. the determination of specific oxygen uptake rates (sOURs), is routinely used to understand metabolic shifts. However current methods to determine sOUR in adherent cell cultures rely on cell sampling, which impacts on cellular phenotype. We present real‐time monitoring of cell growth from phase contrast microscopy images, and of respiration using optical sensors for dissolved oxygen. Time‐course data for bulk and peri‐cellular oxygen concentrations obtained for Chinese hamster ovary (CHO) and mouse embryonic stem cell (mESCs) cultures successfully demonstrated this non‐invasive and label‐free approach. Additionally, we confirmed non‐invasive detection of cellular responses to rapidly changing culture conditions by exposing the cells to mitochondrial inhibiting and uncoupling agents. For the CHO and mESCs, sOUR values between 8 and 60 amol cell⁻¹ s⁻¹, and 5 and 35 amol cell⁻¹ s⁻¹ were obtained, respectively. These values compare favorably with literature data. The capability to monitor oxygen tensions, cell growth, and sOUR, of adherent stem cell cultures, non‐invasively and in real time, will be of significant benefit for future studies in stem cell biology and stem cell‐based therapies.