Software tools for 3D nuclei segmentation and quantitative analysis in multicellular aggregates

A spheroid whole mount drug testing pipeline with machine-learning based image analysis identifies cell-type specific differences in drug efficacy on a single-cell level

BACKGROUND

The growth and drug response of tumors are influenced by their stromal composition, both in vivo and 3D-cell culture models. Cell-type inherent features as well as mutual relationships between the different cell types in a tumor might affect drug susceptibility of the tumor as a whole and/or of its cell populations. However, a lack of single-cell procedures with sufficient detail has hampered the automated observation of cell-type-specific effects in three-dimensional stroma-tumor cell co-cultures.

METHODS

Here, we developed a high-content pipeline ranging from the setup of novel tumor-fibroblast spheroid co-cultures over optical tissue clearing, whole mount staining, and 3D confocal microscopy to optimized 3D-image segmentation and a 3D-deep-learning model to automate the analysis of a range of cell-type-specific processes, such as cell proliferation, apoptosis, necrosis, drug susceptibility, nuclear morphology, and cell density.

RESULTS

This demonstrated that co-cultures of KP-4 tumor cells with CCD-1137Sk fibroblasts exhibited a growth advantage compared to tumor cell mono-cultures, resulting in higher cell counts following cytostatic treatments with paclitaxel and doxorubicin. However, cell-type-specific single-cell analysis revealed that this apparent benefit of co-cultures was due to a higher resilience of fibroblasts against the drugs and did not indicate a higher drug resistance of the KP-4 cancer cells during co-culture. Conversely, cancer cells were partially even more susceptible in the presence of fibroblasts than in mono-cultures.

CONCLUSION

In summary, this underlines that a novel cell-type-specific single-cell analysis method can reveal critical insights regarding the mechanism of action of drug substances in three-dimensional cell culture models.

Unveiling nuclear chromatin distribution using IsoConcentraChromJ: A flourescence imaging plugin for IsoRegional and IsoVolumetric based ratios analysis

Chromatin exhibits non-random distribution within the nucleus being arranged into discrete domains that are spatially organized throughout the nuclear space. Both the spatial distribution and structural rearrangement of chromatin domains in the nucleus depend on epigenetic modifications of DNA and/or histones and structural elements such as the nuclear envelope. These components collectively contribute to the organization and rearrangement of chromatin domains, thereby influencing genome architecture and functional regulation. This study develops an innovative, user-friendly, ImageJ-based plugin, called IsoConcentraChromJ, aimed quantitatively delineating the spatial distribution of chromatin regions in concentric patterns. The IsoConcentraChromJ can be applied to quantitative chromatin analysis in both two- and three-dimensional spaces. After DNA and histone staining with fluorescent probes, high-resolution images of nuclei have been obtained using advanced fluorescence microscopy approaches, including confocal and stimulated emission depletion (STED) microscopy. IsoConcentraChromJ workflow comprises the following sequential steps: nucleus segmentation, thresholding, masking, normalization, and trisection with specified ratios for either 2D or 3D acquisitions. The effectiveness of the IsoConcentraChromJ has been validated and demonstrated using experimental datasets consisting in nuclei images of pre-adipocytes and mature adipocytes, encompassing both 2D and 3D imaging. The outcomes allow to characterize the nuclear architecture by calculating the ratios between specific concentric nuclear areas/volumes of acetylated chromatin with respect to total acetylated chromatin and/or total DNA. The novel IsoConcentrapChromJ plugin could represent a valuable resource for researchers investigating the rearrangement of chromatin architecture driven by epigenetic mechanisms using nuclear images obtained by different fluorescence microscopy methods.

Vimentin promotes collective cell migration through collagen networks via increased matrix remodeling and spheroid fluidity

The intermediate filament (IF) protein vimentin is associated with many diseases with phenotypes of enhanced cellular migration and aggressive invasion through the extracellular matrix (ECM) of tissues, but vimentin's role in in-vivo cell migration is still largely unclear. Vimentin is important for proper cellular adhesion and force generation, which are critical to cell migration; yet the vimentin cytoskeleton also hinders the ability of cells to squeeze through small pores in ECM, resisting migration. To identify the role of vimentin in collective cell migration, we generate spheroids of wide-type and vimentin-null mouse embryonic fibroblasts (mEFs) and embed them in a 3D collagen matrix. We find that loss of vimentin significantly impairs the ability of the spheroid to collectively expand through collagen networks and remodel the collagen network. Traction force analysis reveals that vimentin null spheroids exert less contractile force than their wild-type counterparts. In addition, spheroids made of mEFs with only vimentin unit length filaments (ULFs) exhibit similar behavior as vimentin-null spheroids, suggesting filamentous vimentin is required to promote 3D collective cell migration. We find the vimentin-mediated collective cell expansion is dependent on matrix metalloproteinase (MMP) degradation of the collagen matrix. Further, 3D vertex model simulation of spheroid and embedded ECM indicates that wild-type spheroids behave more fluid-like, enabling more active pulling and reconstructing the surrounding collagen network. Altogether, these results signify that VIF plays a critical role in enhancing migratory persistence in 3D matrix environments through MMP transportation and tissue fluidity.

Software Tools for 2D Cell Segmentation

Cell segmentation is an important task in the field of image processing, widely used in the life sciences and medical fields. Traditional methods are mainly based on pixel intensity and spatial relationships, but have limitations. In recent years, machine learning and deep learning methods have been widely used, providing more-accurate and efficient solutions for cell segmentation. The effort to develop efficient and accurate segmentation software tools has been one of the major focal points in the field of cell segmentation for years. However, each software tool has unique characteristics and adaptations, and no universal cell-segmentation software can achieve perfect results. In this review, we used three publicly available datasets containing multiple 2D cell-imaging modalities. Common segmentation metrics were used to evaluate the performance of eight segmentation tools to compare their generality and, thus, find the best-performing tool.

Two-dimensional segmentation fusion tool: an extensible, free-to-use, user-friendly tool for combining different bidimensional segmentations

Introduction: In several fields, the process of fusing multiple two-dimensional (2D) closed lines is an important step. For instance, this is fundamental in histology and oncology in general. The treatment of a tumor consists of numerous steps and activities. Among them, segmenting the cancer area, that is, the correct identification of its spatial location by the segmentation technique, is one of the most important and at the same time complex and delicate steps. The difficulty in deriving reliable segmentations stems from the lack of a standard for identifying the edges and surrounding tissues of the tumor area. For this reason, the entire process is affected by considerable subjectivity. Given a tumor image, different practitioners can associate different segmentations with it, and the diagnoses produced may differ. Moreover, experimental data show that the analysis of the same area by the same physician at two separate timepoints may result in different lines being produced. Accordingly, it is challenging to establish which contour line is the ground truth. Methods: Starting from multiple segmentations related to the same tumor, statistical metrics and computational procedures could be exploited to combine them for determining the most reliable contour line. In particular, numerous algorithms have been developed over time for this procedure, but none of them is validated yet. Accordingly, in this field, there is no ground truth, and research is still active. Results: In this work, we developed the Two-Dimensional Segmentation Fusion Tool (TDSFT), a user-friendly tool distributed as a free-to-use standalone application for MAC, Linux, and Windows, which offers a simple and extensible interface where numerous algorithms are proposed to "compute the mean" (i.e., the process to fuse, combine, and "average") multiple 2D lines. Conclusions: The TDSFT can support medical specialists, but it can also be used in other fields where it is required to combine 2D close lines. In addition, the TDSFT is designed to be easily extended with new algorithms thanks to a dedicated graphical interface for configuring new parameters. The TDSFT can be downloaded from the following link: https://sourceforge.net/p/tdsft.

Quantitative Analysis of Whole-Mount Fluorescence-Stained Tumor Spheroids in Phenotypic Drug Screens

Three-dimensional cell cultures, such as spheroids or organoids, serve as important models for drug screening purposes. Optical tissue clearing (OTC) enhances the visualization of fluorescence stainings and enables in toto microscopy of 3D cell culture models. Furthermore, subsequent automated image analysis tools convert qualitative confocal image sets into quantitative data. In this chapter, we describe a detailed protocol for preparation of HT29 cancer spheroids, 3D in toto immunostaining, glycerol-based OTC, whole-mount imaging, and semi-automated downstream image processing and segmentation for nuclear image analysis using open-source software.

Dopamine-mediated striatal activity and function is enhanced in GlyRα2 knockout animals

The glycine receptor alpha 2 (GlyRα2) is a ligand-gated ion channel which upon activation induces a chloride conductance. Here, we investigated the role of GlyRα2 in dopamine-stimulated striatal cell activity and behavior. We show that depletion of GlyRα2 enhances dopamine-induced increases in the activity of putative dopamine D1 receptor-expressing striatal projection neurons, but does not alter midbrain dopamine neuron activity. We next show that the locomotor response to d-amphetamine is enhanced in GlyRα2 knockout animals, and that this increase correlates with c-fos expression in the dorsal striatum. 3-D modeling revealed an increase in the neuronal ensemble size in the striatum in response to D-amphetamine in GlyRα2 KO mice. Finally, we show enhanced appetitive conditioning in GlyRα2 KO animals that is likely due to increased motivation, but not changes in associative learning or hedonic response. Taken together, we show that GlyRα2 is an important regulator of dopamine-stimulated striatal activity and function.

In-silico and in-vitro morphometric analysis of intestinal organoids

Organoids offer a powerful model to study cellular self-organisation, the growth of specific tissue morphologies in-vitro, and to assess potential medical therapies. However, the intrinsic mechanisms of these systems are not entirely understood yet, which can result in variability of organoids due to differences in culture conditions and basement membrane extracts used. Improving the standardisation of organoid cultures is essential for their implementation in clinical protocols. Developing tools to assess and predict the behaviour of these systems may produce a more robust and standardised biological model to perform accurate clinical studies. Here, we developed an algorithm to automate crypt-like structure counting on intestinal organoids in both in-vitro and in-silico images. In addition, we modified an existing two-dimensional agent-based mathematical model of intestinal organoids to better describe the system physiology, and evaluated its ability to replicate budding structures compared to new experimental data we generated. The crypt-counting algorithm proved useful in approximating the average number of budding structures found in our in-vitro intestinal organoid culture images on days 3 and 7 after seeding. Our changes to the in-silico model maintain the potential to produce simulations that replicate the number of budding structures found on days 5 and 7 of in-vitro data. The present study aims to aid in quantifying key morphological structures and provide a method to compare both in-vitro and in-silico experiments. Our results could be extended later to 3D in-silico models.

NISNet3D: three-dimensional nuclear synthesis and instance segmentation for fluorescence microscopy images

The primary step in tissue cytometry is the automated distinction of individual cells (segmentation). Since cell borders are seldom labeled, cells are generally segmented by their nuclei. While tools have been developed for segmenting nuclei in two dimensions, segmentation of nuclei in three-dimensional volumes remains a challenging task. The lack of effective methods for three-dimensional segmentation represents a bottleneck in the realization of the potential of tissue cytometry, particularly as methods of tissue clearing present the opportunity to characterize entire organs. Methods based on deep learning have shown enormous promise, but their implementation is hampered by the need for large amounts of manually annotated training data. In this paper, we describe 3D Nuclei Instance Segmentation Network (NISNet3D) that directly segments 3D volumes through the use of a modified 3D U-Net, 3D marker-controlled watershed transform, and a nuclei instance segmentation system for separating touching nuclei. NISNet3D is unique in that it provides accurate segmentation of even challenging image volumes using a network trained on large amounts of synthetic nuclei derived from relatively few annotated volumes, or on synthetic data obtained without annotated volumes. We present a quantitative comparison of results obtained from NISNet3D with results obtained from a variety of existing nuclei segmentation techniques. We also examine the performance of the methods when no ground truth is available and only synthetic volumes were used for training.

Active mesh and neural network pipeline for cell aggregate segmentation

Segmenting cells within cellular aggregates in 3D is a growing challenge in cell biology due to improvements in capacity and accuracy of microscopy techniques. Here, we describe a pipeline to segment images of cell aggregates in 3D. The pipeline combines neural network segmentations with active meshes. We apply our segmentation method to cultured mouse mammary gland organoids imaged over 24 h with oblique plane microscopy, a high-throughput light-sheet fluorescence microscopy technique. We show that our method can also be applied to images of mouse embryonic stem cells imaged with a spinning disc microscope. We segment individual cells based on nuclei and cell membrane fluorescent markers, and track cells over time. We describe metrics to quantify the quality of the automated segmentation. Our segmentation pipeline involves a Fiji plugin that implements active mesh deformation and allows a user to create training data, automatically obtain segmentation meshes from original image data or neural network prediction, and manually curate segmentation data to identify and correct mistakes. Our active meshes-based approach facilitates segmentation postprocessing, correction, and integration with neural network prediction.

Advantages and Potential Benefits of Using Organoids in Nanotoxicology

Organoids are microtissues that recapitulate the complex structural organization and functions of tissues and organs. Nanoparticles have several specific properties that must be considered when replacing animal models with in vitro studies, such as the formation of a protein corona, accumulation, ability to overcome tissue barriers, and different severities of toxic effects in different cell types. An increase in the number of articles on toxicology research using organoid models is related to an increase in publications on organoids in general but is not related to toxicology-based publications. We demonstrate how the quantitative assessment of toxic changes in the structure of organoids and the state of their cell collections provide more valuable results for toxicological research and provide examples of research methods. The impact of the tested materials on organoids and their differences are also discussed. In conclusion, we highlight the main challenges, the solution of which will allow researchers to approach the replacement of in vivo research with in vitro research: biobanking and standardization of the structural characterization of organoids, and the development of effective screening imaging techniques for 3D organoid cell organization.

A comprehensive review of computational and image analysis techniques for quantitative evaluation of striated muscle tissue architecture

Unbiased evaluation of morphology is crucial to understanding development, mechanics, and pathology of striated muscle tissues. Indeed, the ability of striated muscles to contract and the strength of their contraction is dependent on their tissue-, cellular-, and cytoskeletal-level organization. Accordingly, the study of striated muscles often requires imaging and assessing aspects of their architecture at multiple different spatial scales. While an expert may be able to qualitatively appraise tissues, it is imperative to have robust, repeatable tools to quantify striated myocyte morphology and behavior that can be used to compare across different labs and experiments. There has been a recent effort to define the criteria used by experts to evaluate striated myocyte architecture. In this review, we will describe metrics that have been developed to summarize distinct aspects of striated muscle architecture in multiple different tissues, imaged with various modalities. Additionally, we will provide an overview of metrics and image processing software that needs to be developed. Importantly to any lab working on striated muscle platforms, characterization of striated myocyte morphology using the image processing pipelines discussed in this review can be used to quantitatively evaluate striated muscle tissues and contribute to a robust understanding of the development and mechanics of striated muscles.

CometAnalyser: a user-friendly, open-source deep-learning microscopy tool for quantitative comet assay analysis

•

Comet assay provides an easy solution to estimate DNA damage in single cells.

•

Today, an impressive number of works are based on Comet Assay analyses, especially in the field of cancer research.

•

Comet assay was originally performed as a qualitative analysis.

•

None of the free tools today available work on both fluorescent- and silver-stained images.

•

We developed CometAnalyser, an open-source deep-learning tool designed for easy segmentation and classification of comets in fluorescent- and silver-stained images.

Comet assay provides an easy solution to estimate DNA damage in single cells through microscopy assessment. It is widely used in the analysis of genotoxic damages induced by radiotherapy or chemotherapeutic agents. DNA damage is quantified at the single-cell level by computing the displacement between the genetic material within the nucleus, typically called “comet head”, and the genetic material in the surrounding part of the cell, considered as the “comet tail”. Today, the number of works based on Comet Assay analyses is really impressive. In this work, besides revising the solutions available to obtain reproducible and reliable quantitative data, we developed an easy-to-use tool named CometAnalyser. It is designed for the analysis of both fluorescent and silver-stained wide-field microscopy images and allows to automatically segment and classify the comets, besides extracting Tail Moment and several other intensity/morphological features for performing statistical analysis. CometAnalyser is an open-source deep-learning tool. It works with Windows, Macintosh, and UNIX-based systems. Source code, standalone versions, user manual, sample images, video tutorial and further documentation are freely available at: https://sourceforge.net/p/cometanalyser.

Marker-controlled watershed with deep edge emphasis and optimized H-minima transform for automatic segmentation of densely cultivated 3D cell nuclei

BACKGROUND

The segmentation of 3D cell nuclei is essential in many tasks, such as targeted molecular radiotherapies (MRT) for metastatic tumours, toxicity screening, and the observation of proliferating cells. In recent years, one popular method for automatic segmentation of nuclei has been deep learning enhanced marker-controlled watershed transform. In this method, convolutional neural networks (CNNs) have been used to create nuclei masks and markers, and the watershed algorithm for the instance segmentation. We studied whether this method could be improved for the segmentation of densely cultivated 3D nuclei via developing multiple system configurations in which we studied the effect of edge emphasizing CNNs, and optimized H-minima transform for mask and marker generation, respectively.

RESULTS

The dataset used for training and evaluation consisted of twelve in vitro cultivated densely packed 3D human carcinoma cell spheroids imaged using a confocal microscope. With this dataset, the evaluation was performed using a cross-validation scheme. In addition, four independent datasets were used for evaluation. The datasets were resampled near isotropic for our experiments. The baseline deep learning enhanced marker-controlled watershed obtained an average of 0.69 Panoptic Quality (PQ) and 0.66 Aggregated Jaccard Index (AJI) over the twelve spheroids. Using a system configuration, which was otherwise the same but used 3D-based edge emphasizing CNNs and optimized H-minima transform, the scores increased to 0.76 and 0.77, respectively. When using the independent datasets for evaluation, the best performing system configuration was shown to outperform or equal the baseline and a set of well-known cell segmentation approaches.

CONCLUSIONS

The use of edge emphasizing U-Nets and optimized H-minima transform can improve the marker-controlled watershed transform for segmentation of densely cultivated 3D cell nuclei. A novel dataset of twelve spheroids was introduced to the public.

Systematically quantifying morphological features reveals constraints on organoid phenotypes

Organoids recapitulate complex 3D organ structures and represent a unique opportunity to probe the principles of self-organization. While we can alter an organoid's morphology by manipulating the culture conditions, the morphology of an organoid often resembles that of its original organ, suggesting that organoid morphologies are governed by a set of tissue-specific constraints. Here, we establish a framework to identify constraints on an organoid's morphological features by quantifying them from microscopy images of organoids exposed to a range of perturbations. We apply this framework to Madin-Darby canine kidney cysts and show that they obey a number of constraints taking the form of scaling relationships or caps on certain parameters. For example, we found that the number, but not size, of cells increases with increasing cyst size. We also find that these constraints vary with cyst age and can be altered by varying the culture conditions. We observed similar sets of constraints in intestinal organoids. This quantitative framework for identifying constraints on organoid morphologies may inform future efforts to engineer organoids.

Cell3: a new vision for study of the endomembrane system in mammalian cells

The endomembrane system of mammalian cells provides massive capacity for the segregation of biochemical reactions into discrete locations. The individual organelles of the endomembrane system also require the ability to precisely transport material between these compartments in order to maintain cell homeostasis; this process is termed membrane traffic. For several decades, researchers have been systematically identifying and dissecting the molecular machinery that governs membrane trafficking pathways, with the overwhelming majority of these studies being carried out in cultured cells growing as monolayers. In recent years, a number of methodological innovations have provided the opportunity for cultured cells to be grown as 3-dimensional (3D) assemblies, for example as spheroids and organoids. These structures have the potential to better replicate the cellular environment found in tissues and present an exciting new opportunity for the study of cell function. In this mini-review, we summarize the main methods used to generate 3D cell models and highlight emerging studies that have started to use these models to study basic cellular processes. We also describe a number of pieces of work that potentially provide the basis for adaptation for deeper study of how membrane traffic is coordinated in multicellular assemblies. Finally, we comment on some of the technological challenges that still need to be overcome if 3D cell biology is to become a mainstream tool toward deepening our understanding of the endomembrane system in mammalian cells.

Intravital and high-content multiplex imaging of the immune system

Highly motile and functionally diverse immune cells orchestrate effective immune responses through complex and dynamic cooperative behavior. Multiphoton intravital microscopy (MP-IVM) presents a unique and powerful tool to study the coordinated action of immune cell interactions in situ. Here, we review the current state of intravital microscopy in deepening our understanding of the immune system and discuss its fundamental limitations. In addition, we draw insights from recent technical advances in multiplex static tissue-imaging methods and propose an approach that could enable simultaneous visualization of cellular dynamics, deep phenotyping, and transcriptional states through a new type of correlative microscopy that combines these imaging technologies with advances in complex data analysis.

2D <em>vs</em> 3D morphological analysis of dorsal root ganglia in health and painful neuropathy

Dorsal root ganglia (DRGs) are clusters of sensory neurons that transmit the sensory information from the periphery to the central nervous system, and satellite glial cells (SGCs), their supporting trophic cells. Sensory neurons are pseudounipolar neurons with a heterogeneous neurochemistry reflecting their functional features. DRGs, not protected by the blood brain barrier, are vulnerable to stress and damage of different origin (i.e., toxic, mechanical, metabolic, genetic) that can involve sensory neurons, SGCs or, considering their intimate intercommunication, both cell populations. DRG damage, primary or secondary to nerve damage, produces a sensory peripheral neuropathy, characterized by neurophysiological abnormalities, numbness, paraesthesia and dysesthesia, tingling and burning sensations and neuropathic pain. DRG stress can be morphologically detected by light and electron microscope analysis with alterations in cell size (swelling/atrophy) and in different subcellular compartments (i.e., mitochondria, endoplasmic reticulum, and nucleus) of neurons and/or SGCs. In addition, neurochemical changes can be used to portray abnormalities of neurons and SGC. Conventional immunostaining, i.e., immunohistochemical detection of specific molecules in tissue slices, can be employed to detect, localize and quantify particular markers of damage in neurons (i.e., nuclear expression of ATF3) or SGCs (i.e., increased expression of GFAP), markers of apoptosis (i.e., caspases), markers of mitochondrial suffering and oxidative stress (i.e., 8-OHdG), markers of tissue inflammation (i.e., CD68 for macrophage infiltration) etc. However classical (2D) methods of immunostaining disrupt the overall organization of the DRG, thus resulting in the loss of some crucial information. Whole-mount (3D) methods have been recently developed to investigate DRG morphology and neurochemistry without tissue slicing, giving the opportunity to study the intimate relationship between SGCs and sensory neurons in health and disease. Here, we aim to compare classical (2D) vs whole-mount (3D) approaches to highlight “pros” and “cons” of the two methodologies when analysing neuropathy-induced alterations in DRGs.

Single cell organization and cell cycle characterization of DNA stained multicellular tumor spheroids

Multicellular tumor spheroids (MCTSs) can serve as in vitro models for solid tumors and have become widely used in basic cancer research and drug screening applications. The major challenges when studying MCTSs by optical microscopy are imaging and analysis due to light scattering within the 3-dimensional structure. Herein, we used an ultrasound-based MCTS culture platform, where A498 renal carcinoma MCTSs were cultured, DAPI stained, optically cleared and imaged, to connect nuclear segmentation to biological information at the single cell level. We show that DNA-content analysis can be used to classify the cell cycle state as a function of position within the MCTSs. We also used nuclear volumetric characterization to show that cells were more densely organized and perpendicularly aligned to the MCTS radius in MCTSs cultured for 96 h compared to 24 h. The method presented herein can in principle be used with any stochiometric DNA staining protocol and nuclear segmentation strategy. Since it is based on a single counter stain a large part of the fluorescence spectrum is free for other probes, allowing measurements that correlate cell cycle state and nuclear organization with e.g., protein expression or drug distribution within MCTSs.

Tissue clearing and 3D imaging - putting immune cells into context

A better understanding of cell-cell and cell-niche interactions is crucial to comprehend the complexity of inflammatory or pathophysiological scenarios such as tissue damage during viral infections, the tumour microenvironment and neuroinflammation. Optical clearing and 3D volumetric imaging of large tissue pieces or whole organs is a rapidly developing methodology that holds great promise for the in-depth study of cells in their natural surroundings. These methods have mostly been applied to image structural components such as endothelial cells and neuronal architecture. Recent work now highlights the possibility of studying immune cells in detail within their respective immune niches. This Review summarizes recent developments in tissue clearing methods and 3D imaging, with a focus on the localization and quantification of immune cells. We first provide background to the optical challenges involved and their solutions before discussing published protocols for tissue clearing, the limitations of 3D imaging of immune cells and image analysis. Furthermore, we highlight possible applications for tissue clearing and propose future developments for the analysis of immune cells within homeostatic or inflammatory immune niches.

Three-Dimensional Culture Models to Study Innate Anti-Tumor Immune Response: Advantages and Disadvantages

Several approaches have shown that the immune response against tumors strongly affects patients' clinical outcome. Thus, the study of anti-tumor immunity is critical to understand and potentiate the mechanisms underlying the elimination of tumor cells. Natural killer (NK) cells are members of innate immunity and represent powerful anti-tumor effectors, able to eliminate tumor cells without a previous sensitization. Thus, the study of their involvement in anti-tumor responses is critical for clinical translation. This analysis has been performed in vitro, co-incubating NK with tumor cells and quantifying the cytotoxic activity of NK cells. In vivo confirmation has been applied to overcome the limits of in vitro testing, however, the innate immunity of mice and humans is different, leading to discrepancies. Different activating receptors on NK cells and counter-ligands on tumor cells are involved in the antitumor response, and innate immunity is strictly dependent on the specific microenvironment where it takes place. Thus, three-dimensional (3D) culture systems, where NK and tumor cells can interact in a tissue-like architecture, have been created. For example, tumor cell spheroids and primary organoids derived from several tumor types, have been used so far to analyze innate immune response, replacing animal models. Herein, we briefly introduce NK cells and analyze and discuss in detail the properties of 3D tumor culture systems and their use for the study of tumor cell interactions with NK cells.

Recent Trends and Perspectives in Cerebral Organoids Imaging and Analysis

Purpose: Since their first generation in 2013, the use of cerebral organoids has spread exponentially. Today, the amount of generated data is becoming challenging to analyze manually. This review aims to overview the current image acquisition methods and to subsequently identify the needs in image analysis tools for cerebral organoids. Methods: To address this question, we went through all recent articles published on the subject and annotated the protocols, acquisition methods, and algorithms used. Results: Over the investigated period of time, confocal microscopy and bright-field microscopy were the most used acquisition techniques. Cell counting, the most common task, is performed in 20% of the articles and area; around 12% of articles calculate morphological parameters. Image analysis on cerebral organoids is performed in majority using ImageJ software (around 52%) and Matlab language (4%). Treatments remain mostly semi-automatic. We highlight the limitations encountered in image analysis in the cerebral organoid field and suggest possible solutions and implementations to develop. Conclusions: In addition to providing an overview of cerebral organoids cultures and imaging, this work highlights the need to improve the existing image analysis methods for such images and the need for specific analysis tools. These solutions could specifically help to monitor the growth of future standardized cerebral organoids.

Segmentor: a tool for manual refinement of 3D microscopy annotations

BACKGROUND

Recent advances in tissue clearing techniques, combined with high-speed image acquisition through light sheet microscopy, enable rapid three-dimensional (3D) imaging of biological specimens, such as whole mouse brains, in a matter of hours. Quantitative analysis of such 3D images can help us understand how changes in brain structure lead to differences in behavior or cognition, but distinguishing densely packed features of interest, such as nuclei, from background can be challenging. Recent deep learning-based nuclear segmentation algorithms show great promise for automated segmentation, but require large numbers of accurate manually labeled nuclei as training data.

RESULTS

We present Segmentor, an open-source tool for reliable, efficient, and user-friendly manual annotation and refinement of objects (e.g., nuclei) within 3D light sheet microscopy images. Segmentor employs a hybrid 2D-3D approach for visualizing and segmenting objects and contains features for automatic region splitting, designed specifically for streamlining the process of 3D segmentation of nuclei. We show that editing simultaneously in 2D and 3D using Segmentor significantly decreases time spent on manual annotations without affecting accuracy as compared to editing the same set of images with only 2D capabilities.

CONCLUSIONS

Segmentor is a tool for increased efficiency of manual annotation and refinement of 3D objects that can be used to train deep learning segmentation algorithms, and is available at https://www.nucleininja.org/ and https://github.com/RENCI/Segmentor .

Watching the embryo: Evolution of the microscope for the study of embryogenesis

Embryos and microscopes share a long, remarkable history and biologists have always been intrigued to watch how embryos develop under the microscope. Here we discuss the advances in microscopy which have greatly influenced our current understanding of embryogenesis. We highlight the evolution of microscopes and the optical technologies that have been instrumental in studying various developmental processes. These imaging modalities provide mechanistic insights into the dynamic cellular and molecular events which drive lineage commitment and morphogenetic changes in the developing embryo. We begin the journey with a brief history of microscopy to study embryos. First, we review the principles and optics of light, fluorescence, confocal, and electron microscopy which have been key techniques for imaging cellular and molecular events during embryonic development. Next, we discuss recent key imaging modalities such as light-sheet microscopy, which are suitable for whole embryo imaging. Further, we highlight imaging techniques like multiphoton and super resolution microscopy for beyond light diffraction limit, high resolution imaging. Lastly, we review some of the scattering-based imaging methods and techniques used for imaging human embryos.