GliaMorph: A modular image analysis toolkit to quantify Müller glial cell morphology

Cell morphology is critical for all cell functions. This is particularly true for glial cells as they rely on complex shape to contact and support neurons. However, methods to quantify complex glial cell shape accurately and reproducibly are lacking. To address this, we developed the image analysis pipeline "GliaMorph". GliaMorph is a modular analysis toolkit developed to perform (i) image pre-processing, (ii) semi-automatic region-of-interest (ROI) selection, (iii) apicobasal texture analysis, (iv) glia segmentation, and (v) cell feature quantification. Müller Glia (MG) have a stereotypic shape linked to their maturation and physiological status. We here characterized MG on three levels, including (a) global image-level, (b) apicobasal texture, and (c) regional apicobasal vertical-to-horizontal alignment. Using GliaMorph we quantified MG development on a global and single-cell level, showing increased feature elaboration and subcellular morphological rearrangement in the zebrafish retina. As proof-of-principle, we analysed expression changes in a mouse glaucoma model, identifying subcellular protein localization changes in MG. Together, GliaMorph enables an in-depth understanding of MG morphology in the developing and diseased retina

Extracellular matrix stiffness activates mechanosensitive signals but limits breast cancer cell spheroid proliferation and invasion

Breast cancer is characterized by physical changes that occur in the tumor microenvironment throughout growth and metastasis of tumors. Extracellular matrix stiffness increases as tumors develop and spread, with stiffer environments thought to correlate with poorer disease prognosis. Changes in extracellular stiffness and other physical characteristics are sensed by integrins which integrate these extracellular cues to intracellular signaling, resulting in modulation of proliferation and invasion. However, the co-ordination of mechano-sensitive signaling with functional changes to groups of tumor cells within 3-dimensional environments remains poorly understood. Here we provide evidence that increasing the stiffness of collagen scaffolds results in increased activation of ERK1/2 and YAP in human breast cancer cell spheroids. We also show that ERK1/2 acts upstream of YAP activation in this context. We further demonstrate that YAP, matrix metalloproteinases and actomyosin contractility are required for collagen remodeling, proliferation and invasion in lower stiffness scaffolds. However, the increased activation of these proteins in higher stiffness 3-dimensional collagen gels is correlated with reduced proliferation and reduced invasion of cancer cell spheroids. Our data collectively provide evidence that higher stiffness 3-dimensional environments induce mechano-signaling but contrary to evidence from 2-dimensional studies, this is not sufficient to promote pro-tumorigenic effects in breast cancer cell spheroids

An anatomy-based lumped parameter model of cerebrospinal venous circulation: can an extracranial anatomical change impact intracranial hemodynamics?

Background The relationship between extracranial venous system abnormalities and central nervous system disorders has been recently theorized. In this paper we delve into this hypothesis by modeling the venous drainage in brain and spinal column areas and simulating the intracranial flow changes due to extracranial morphological stenoses. Methods A lumped parameter model of the cerebro-spinal venous drainage was created based on anatomical knowledge and vessels diameters and lengths taken from literature. Each vein was modeled as a hydraulic resistance, calculated through Poiseuille’s law. The inputs of the model were arterial flow rates of the intracranial, vertebral and lumbar districts. The effects of the obstruction of the main venous outflows were simulated. A database comprising 112 Multiple Sclerosis patients (Male/Female = 42/70; median age ± standard deviation = 43.7 ± 10.5 years) was retrospectively analyzed. Results The flow rate of the main veins estimated with the model was similar to the measures of 21 healthy controls (Male/Female = 10/11; mean age ± standard deviation = 31 ± 11 years), obtained with a 1.5 T Magnetic Resonance scanner. The intracranial reflux topography predicted with the model in cases of internal jugular vein diameter reduction was similar to those observed in the patients with internal jugular vein obstacles. Conclusions The proposed model can predict physiological and pathological behaviors with good fidelity. Despite the simplifications introduced in cerebrospinal venous circulation modeling, the key anatomical feature of the lumped parameter model allowed for a detailed analysis of the consequences of extracranial venous impairments on intracranial pressure and hemodynamics

Persistent and polarised global actin flow is essential for directionality during cell migration

Cell migration is hypothesized to involve a cycle of behaviours beginning with leading edge extension. However, recent evidence suggests that the leading edge may be dispensable for migration, raising the question of what actually controls cell directionality. Here, we exploit the embryonic migration of Drosophila macrophages to bridge the different temporal scales of the behaviours controlling motility. This approach reveals that edge fluctuations during random motility are not persistent and are weakly correlated with motion. In contrast, flow of the actin network behind the leading edge is highly persistent. Quantification of actin flow structure during migration reveals a stable organization and asymmetry in the cell-wide flowfield that strongly correlates with cell directionality. This organization is regulated by a gradient of actin network compression and destruction, which is controlled by myosin contraction and cofilin-mediated disassembly. It is this stable actin-flow polarity, which integrates rapid fluctuations of the leading edge, that controls inherent cellular persistence

A method for reproducible high-resolution imaging of 3D cancer cell spheroids

Multicellular tumour cell spheroids embedded within three-dimensional (3D) hydrogels or extracellular matrices (ECM) are widely used as models to study cancer growth and invasion. Standard methods to embed spheroids in 3D matrices result in random placement in space which limits the use of inverted fluorescence microscopy techniques, and thus the resolution that can be achieved to image molecular detail within the intact spheroid. Here, we leverage UV photolithography to microfabricate PDMS (polydimethylsiloxane) stamps that allow for generation of high-content, reproducible well-like structures in multiple different imaging chambers. Addition of multicellular tumour spheroids into stamped collagen structures allows for precise positioning of spheroids in 3D space for reproducible high-/super-resolution imaging. Embedded spheroids can be imaged live or fixed and are amenable to immunostaining, allowing for greater flexibility of experimental approaches. We describe the use of these spheroid imaging chambers to analyse cell invasion, cell–ECM interaction, ECM alignment, force-dependent intracellular protein dynamics and extension of fine actin-based protrusions with a variety of commonly used inverted microscope platforms. This method enables reproducible, high-/super-resolution live imaging of multiple tumour spheroids, that can be potentially extended to visualise organoids and other more complex 3D in vitro systems

Bridging imaging users to imaging analysis - A community survey

The “Bridging Imaging Users to Imaging Analysis” survey was conducted in 2022 by the Center for Open Bioimage Analysis (COBA), Bioimaging North America (BINA), and the Royal Microscopical Society Data Analysis in Imaging Section (RMS DAIM) to understand the needs of the imaging community. Through multi-choice and open-ended questions, the survey inquired about demographics, image analysis experiences, future needs, and suggestions on the role of tool developers and users. Participants of the survey were from diverse roles and domains of the life and physical sciences. To our knowledge, this is the first attempt to survey cross-community to bridge knowledge gaps between physical and life sciences imaging. Survey results indicate that respondents' overarching needs are documentation, detailed tutorials on the usage of image analysis tools, user-friendly intuitive software, and better solutions for segmentation, ideally in a format tailored to their specific use cases. The tool creators suggested the users familiarize themselves with the fundamentals of image analysis, provide constant feedback, and report the issues faced during image analysis while the users would like more documentation and an emphasis on tool friendliness. Regardless of the computational experience, there is a strong preference for ‘written tutorials’ to acquire knowledge on image analysis. We also observed that the interest in having ‘office hours’ to get an expert opinion on their image analysis methods has increased over the years. The results also showed less-than-expected usage of online discussion forums in the imaging community for solving image analysis problems. Surprisingly, we also observed a decreased interest among the survey respondents in deep/machine learning despite the increasing adoption of artificial intelligence in biology. In addition, the community suggests the need for a common repository for the available image analysis tools and their applications. The opinions and suggestions of the community, released here in full, will help the image analysis tool creation and education communities to design and deliver the resources accordingly