3D cell culture has emerged as a promising approach to replicate the complex behaviors of cells within living organisms. This study aims to analyze spatiotemporal behavior of the morphological characteristics of cell structure at multiscale in 3D scaffold-free spheroids using chondrogenic progenitor ATDC5 cells. Over a 14-day culture period, it exhibited cell hypertrophy in the spheroids regarding cellular and nuclear size as well as changes in morphology. Moreover, biological analysis indicated a signification up-regulation of normal chondrocyte as well as hypertrophic chondrocyte markers, suggesting early hypertrophic chondrocyte differentiation. Cell nuclei underwent changes in volume, sphericity, and distribution in spheroid over time, indicating alterations in chromatin organization. The ratio of chromatin condensation volume to cell nuclear volume decreased as the cell nuclei enlarged, potentially signifying changes in chromatin state during hypertrophic chondrocyte differentiation. Our image analysis techniques in this present study enabled detailed morphological measurement of cell structure at multi-scale, which can be applied to various 3D culture models for in-depth investigation.

Structural colors arise from selective light interaction with (nano)structures, which give them advantages over pigmented colors such as resistance to fading and possibility to be fabricated out of traditional low-cost and non-toxic materials. Since the color arises from the photonic (nano)structures, different structural features can impact their photonic response and thus, their color. Therefore, the detailed characterization of their structural features is crucial for further improvement of structural colors. In this work, we present a detailed multi-scale structural characterization of ceramic-based photonic glasses by using a combination of high-resolution ptychographic X-ray computed tomography and small angle X-ray scattering. Our results uncover the structure-processing-properties\' relationships of such nanoparticles-based photonic glasses and point out to the need of a review of the structural features used in simulation models concomitantly with the need for further investigations by experimentalists, where we point out exactly which structural features need to be improved.

Measurement of auricle parameters for planning and post-operative evaluation presents substantial challenges due to the complex 3D structure of the human auricle. Traditional measurement methods rely on manual techniques, resulting in limited precision. This study introduces a novel automated surface-based three-dimensional measurement method for quantifying human auricle parameters. The method was applied to virtual auricles reconstructed from Computed Tomography (CT) scans of a cadaver head and subsequent measurement of important clinically relevant aesthetical auricular parameters (length, width, protrusion, position, auriculocephalic angle, and inclination angle). Reference measurements were done manually (using a caliper and using a 3D landmarking method) and measurement precision was compared to the automated method. The CT scans were performed using both a contemporary high-end and a low-end CT scanner. Scans were conducted at a standard scanning dose, and at half the dose. The automatic method demonstrated significantly higher precision in measuring auricle parameters compared to manual methods. Compared to traditional manual measurements, precision improved for auricle length (9×), width (5×), protrusion (5×), Auriculocephalic Angle (5-54×) and posteroanterior position (23×). Concerning parameters without comparison with a manual method, the precision level of supero-inferior position was 0.489 mm; and the precisions of the inclination angle measurements were 1.365 mm and 0.237 mm for the two automated methods investigated. Improved precision of measuring auricle parameters was associated with using the high-end scanner. A higher dose was only associated with a higher precision for the left auricle length. The findings of this study emphasize the advantage of automated surface-based auricle measurements, showcasing improved precision compared to traditional methods. This novel algorithm has the potential to enhance auricle reconstruction and other applications in plastic surgery, offering a promising avenue for future research and clinical application.

Recent developments in technology and research have offered a wide variety of new techniques for image and data analysis within the medical field. Medical research helps doctors and researchers acquire not only knowledge about health and new diseases, but also techniques of prevention and treatment. In particular, radiomic analysis is mainly used to extract quantitative data from medical images and to build a model strong enough to diagnose focal diseases. However, finding a model capable to fit all patient situations is not an easy task. In this paper frame prediction models and classification models are reported in order to predict the evolution of a given data series and determine whether an anomaly exists or not. This article also shows how to build and make use of a convolutional neural network-based architecture aiming to accomplish prediction task for medical images, not only using common computer tomography scans, but also 3D volumes.

Adult muscle stem cells (MuSCs) show remarkable capability in repairing injured tissues. Studying MuSCs in suitable model organisms, which show strong homology with vertebrate counterparts, helps in dissecting the mechanisms regulating their behavior. Additionally, ease of handling and access to technological tools make model organisms well suited for studying biological processes that are conserved across species. MuSCs quiescence, proliferation, and migration are regulated by various input of signals from the surrounding tissues that constitute the MuSCs niche. Observing MuSCs along with their niche in vivo through live imaging provides key information on how MuSCs behave in quiescent and activated states. Drosophila melanogaster is well known for its genetic tool arsenal and the similarity of its different biological processes with vertebrates. Hence, it is widely used to study different types of stem cells. Gained knowledge could then be extrapolated to the vertebrate/mammalian homologous systems to enhance our knowledge in stem cell fields. In this protocol, we discuss how to perform live cell imaging of Drosophila MuSCs, called adult muscle precursors (AMPs) at embryonic stages, using dual-color labelling to visualize both AMPs and the surrounding tissues. This dual-color fluorescent labelling enables the observation of in vivo behavior of two types of cells simultaneously and provides key information on their interactions. The originality of this protocol resides in its biological application to MuSCs and their niche.

The efficient extraction of image data from curved tissue sheets embedded in volumetric imaging data remains a serious and unsolved problem in quantitative studies of embryogenesis. Here, we present DeepProjection (DP), a trainable projection algorithm based on deep learning. This algorithm is trained on user-generated training data to locally classify 3D stack content, and to rapidly and robustly predict binary masks containing the target content, e.g. tissue boundaries, while masking highly fluorescent out-of-plane artifacts. A projection of the masked 3D stack then yields background-free 2D images with undistorted fluorescence intensity values. The binary masks can further be applied to other fluorescent channels or to extract local tissue curvature. DP is designed as a first processing step than can be followed, for example, by segmentation to track cell fate. We apply DP to follow the dynamic movements of 2D-tissue sheets during dorsal closure in Drosophila embryos and of the periderm layer in the elongating Danio embryo. DeepProjection is available as a fully documented Python package.

BACKGROUND: The three-dimensional nuclear arrangement of chromatin impacts many cellular processes operating at the DNA level in animal and plant systems. Chromatin organization is a dynamic process that can be affected by biotic and abiotic stresses. Three-dimensional imaging technology allows to follow these dynamic changes, but only a few semi-automated processing methods currently exist for quantitative analysis of the 3D chromatin organization.
RESULTS: We present an automated method, Nuclear Object DetectionJ (NODeJ), developed as an imageJ plugin. This program segments and analyzes high intensity domains in nuclei from 3D images. NODeJ performs a Laplacian convolution on the mask of a nucleus to enhance the contrast of intra-nuclear objects and allow their detection. We reanalyzed public datasets and determined that NODeJ is able to accurately identify heterochromatin domains from a diverse set of Arabidopsis thaliana nuclei stained with DAPI or Hoechst. NODeJ is also able to detect signals in nuclei from DNA FISH experiments, allowing for the analysis of specific targets of interest.
UNASSIGNED: NODeJ allows for efficient automated analysis of subnuclear structures by avoiding the semi-automated steps, resulting in reduced processing time and analytical bias. NODeJ is written in Java and provided as an ImageJ plugin with a command line option to perform more high-throughput analyses. NODeJ can be downloaded from https://gitlab.com/axpoulet/image2danalysis/-/releases with source code, documentation and further information avaliable at https://gitlab.com/axpoulet/image2danalysis . The images used in this study are publicly available at https://www.brookes.ac.uk/indepth/images/ and https://doi.org/10.15454/1HSOIE .

Digital imaging using high-resolution X-ray micro-computed tomography has become a methodology with increasing importance for assessing wood tissues. This chapter aims to describe the use of this nondestructive, noninvasive, and reproducible technique to new researchers interested in analyzing the three-dimensional properties of the resin duct systems in pine stem wood samples.

The cells which make up plant tissues remain fixed together through shared cell walls. Cell-to-cell communication principally takes place through these shared interfaces through a combination of plasmodesmata, transporters, and the apoplastic space. To better understand the capacity for intercellular communication in plant tissues, this chapter outlines a method which can be used to quantify the surface area of shared intercellular interfaces using whole mount imaging and quantitative 3D image analysis. This method allows the potential for intercellular communication as prescribed by cellular architecture to be measured at single cell resolution.

BACKGROUND: The technical development of imaging techniques in life sciences has enabled the three-dimensional recording of living samples at increasing temporal resolutions. Dynamic 3D data sets of developing organisms allow for time-resolved quantitative analyses of morphogenetic changes in three dimensions, but require efficient and automatable analysis pipelines to tackle the resulting Terabytes of image data. Particle image velocimetry (PIV) is a robust and segmentation-free technique that is suitable for quantifying collective cellular migration on data sets with different labeling schemes. This paper presents the implementation of an efficient 3D PIV package using the Julia programming language-quickPIV. Our software is focused on optimizing CPU performance and ensuring the robustness of the PIV analyses on biological data.
RESULTS: QuickPIV is three times faster than the Python implementation hosted in openPIV, both in 2D and 3D. Our software is also faster than the fastest 2D PIV package in openPIV, written in C++. The accuracy evaluation of our software on synthetic data agrees with the expected accuracies described in the literature. Additionally, by applying quickPIV to three data sets of the embryogenesis of Tribolium castaneum, we obtained vector fields that recapitulate the migration movements of gastrulation, both in nuclear and actin-labeled embryos. We show normalized squared error cross-correlation to be especially accurate in detecting translations in non-segmentable biological image data.
CONCLUSIONS: The presented software addresses the need for a fast and open-source 3D PIV package in biological research. Currently, quickPIV offers efficient 2D and 3D PIV analyses featuring zero-normalized and normalized squared error cross-correlations, sub-pixel/voxel approximation, and multi-pass. Post-processing options include filtering and averaging of the resulting vector fields, extraction of velocity, divergence and collectiveness maps, simulation of pseudo-trajectories, and unit conversion. In addition, our software includes functions to visualize the 3D vector fields in Paraview.