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.

From in vitro to in silico: a pipeline for generating virtual tissue simulations from real image data.

3D cell culture models replicate tissue complexity and aim to study cellular interactions and responses in a more physiologically relevant environment compared to traditional 2D cultures. However, the spherical structure of these models makes it difficult to extract meaningful data, necessitating advanced techniques for proper analysis. In silico simulations enhance research by predicting cellular behaviors and therapeutic responses, providing a powerful tool to complement experimental approaches. Despite their potential, these simulations often require advanced computational skills and significant resources, which creates a barrier for many researchers. To address these challenges, we developed an accessible pipeline using open-source software to facilitate virtual tissue simulations. Our approach employs the Cellular Potts Model, a versatile framework for simulating cellular behaviors in tissues. The simulations are constructed from real world 3D image stacks of cancer spheroids, ensuring that the virtual models are rooted in experimental data. By introducing a new metric for parameter optimization, we enable the creation of realistic simulations without requiring extensive computational expertise. This pipeline benefits researchers wanting to incorporate computational biology into their methods, even if they do not possess extensive expertise in this area. By reducing the technical barriers associated with advanced computational modeling, our pipeline enables more researchers to utilize these powerful tools. Our approach aims to foster a broader use of in silico methods in disease research, contributing to a deeper understanding of disease biology and the refinement of therapeutic interventions.

A multiparametric analysis including single-cell and subcellular feature assessment reveals differential behavior of spheroid cultures on distinct ultra-low attachment plate types.

Spheroids have become principal three-dimensional models to study cancer, developmental processes, and drug efficacy. Single-cell analysis techniques have emerged as ideal tools to gauge the complexity of cellular responses in these models. However, the single-cell quantitative assessment based on 3D-microscopic data of the subcellular distribution of fluorescence markers, such as the nuclear/cytoplasm ratio of transcription factors, has largely remained elusive. For spheroid generation, ultra-low attachment plates are noteworthy due to their simplicity, compatibility with automation, and experimental and commercial accessibility. However, it is unknown whether and to what degree the plate type impacts spheroid formation and biology. This study developed a novel AI-based pipeline for the analysis of 3D-confocal data of optically cleared large spheroids at the wholemount, single-cell, and sub-cellular levels. To identify relevant samples for the pipeline, automated brightfield microscopy was employed to systematically compare the size and eccentricity of spheroids formed in six different plate types using four distinct human cell lines. This showed that all plate types exhibited similar spheroid-forming capabilities and the gross patterns of growth or shrinkage during 4 days after seeding were comparable. Yet, size and eccentricity varied systematically among specific cell lines and plate types. Based on this prescreen, spheroids of HaCaT keratinocytes and HT-29 cancer cells were further assessed. In HaCaT spheroids, the in-depth analysis revealed a correlation between spheroid size, cell proliferation, and the nuclear/cytoplasm ratio of the transcriptional coactivator, YAP1, as well as an inverse correlation with respect to cell differentiation. These findings, yielded with a spheroid model and at a single-cell level, corroborate earlier concepts of the role of YAP1 in cell proliferation and differentiation of keratinocytes in human skin. Further, the results show that the plate type may influence the outcome of experimental campaigns and that it is advisable to scan different plate types for the optimal configuration during a specific investigation.

Synthesis of large scale 3D microscopic images of 3D cell cultures for training and benchmarking.

The analysis of 3D microscopic cell culture images plays a vital role in the development of new therapeutics. While 3D cell cultures offer a greater similarity to the human organism than adherent cell cultures, they introduce new challenges for automatic evaluation, like increased heterogeneity. Deep learning algorithms are able to outperform conventional analysis methods in such conditions but require a large amount of training data. Due to data size and complexity, the manual annotation of 3D images to generate large datasets is a nearly impossible task. We therefore propose a pipeline that combines conventional simulation methods with deep-learning-based optimization to generate large 3D synthetic images of 3D cell cultures where the labels are known by design. The hybrid procedure helps to keep the generated image structures consistent with the underlying labels. A new approach and an additional measure are introduced to model and evaluate the reduced brightness and quality in deeper image regions. Our analyses show that the deep learning optimization step consistently improves the quality of the generated images. We could also demonstrate that a deep learning segmentation model trained with our synthetic data outperforms a classical segmentation method on real image data. The presented synthesis method allows selecting a segmentation model most suitable for the user's data, providing an ideal basis for further data analysis.

Extracellular Matrix Components Regulate Bone Sialoprotein Expression in MDA-MB-231 Breast Cancer Cells.

Bone sialoprotein (BSP) has become a target in breast cancer research as it is associated with tumor progression and metastasis. The mechanisms underlying the regulation of BSP expression have been largely elusive. Given that BSP is involved in the homing of cancer cells in bone metastatic niches, we addressed regulatory effects of proteolytic cleavage and extracellular matrix components on BSP expression and distribution in cell culture models. Therefore, MDA-MB-231 human breast cancer cells were kept in 2D and 3D spheroid cultures and exposed to basement membrane extract in the presence or absence of matrix metalloproteinase 9 or the non-polar protease, dispase. Confocal imaging of immunofluorescence samples stained with different antibodies against human BSP demonstrated a strong inducing effect of basement membrane extract on anti-BSP immunofluorescence. Similarly, protease incubation led to acute upregulation of anti-BSP immunofluorescence signals, which was blocked by cycloheximide, suggesting de novo formation of BSP. In summary, our data show that extracellular matrix components play an important function in regulating BSP expression and hint at mechanisms for the formation of bone-associated metastasis in breast cancer that might involve local control of BSP levels by extracellular matrix degradation and release of growth factors.

epiTracker: A Framework for Highly Reliable Particle Tracking for the Quantitative Analysis of Fish Movements in Tanks.

Behavioral analysis of moving animals relies on a faithful recording and track analysis to extract relevant parameters of movement. To study group behavior and social interactions, often simultaneous analyses of individuals are required. To detect social interactions, for example to identify the leader of a group as opposed to followers, one needs an error-free segmentation of individual tracks throughout time. While automated tracking algorithms exist that are quick and easy to use, inevitable errors will occur during tracking. To solve this problem, we introduce a robust algorithm called epiTracker for segmentation and tracking of multiple animals in two-dimensional (2D) videos along with an easy-to-use correction method that allows one to obtain error-free segmentation. We have implemented two graphical user interfaces to allow user-friendly control of the functions. Using six labeled 2D datasets, the effort to obtain accurate labels is quantified and compared to alternative available software solutions. Both the labeled datasets and the software are publicly available.

hiPSC-Derived Schwann Cells Influence Myogenic Differentiation in Neuromuscular Cocultures.

Motoneurons, skeletal muscle fibers, and Schwann cells form synapses, termed neuromuscular junctions (NMJs). These control voluntary body movement and are affected in numerous neuromuscular diseases. Therefore, a variety of NMJ in vitro models have been explored to enable mechanistic and pharmacological studies. So far, selective integration of Schwann cells in these models has been hampered, due to technical limitations. Here we present robust protocols for derivation of Schwann cells from human induced pluripotent stem cells (hiPSC) and their coculture with hiPSC-derived motoneurons and C2C12 muscle cells. Upon differentiation with tuned BMP signaling, Schwann cells expressed marker proteins, S100b, Gap43, vimentin, and myelin protein zero. Furthermore, they displayed typical spindle-shaped morphologies with long processes, which often aligned with motoneuron axons. Inclusion of Schwann cells in coculture experiments with hiPSC-derived motoneurons and C2C12 myoblasts enhanced myotube growth and affected size and number of acetylcholine receptor plaques on myotubes. Altogether, these data argue for the availability of a consistent differentiation protocol for Schwann cells and their amenability for functional integration into neuromuscular in vitro models, fostering future studies of neuromuscular mechanisms and disease.

A Scaffold-Free 3-D Co-Culture Mimics the Major Features of the Reverse Warburg Effect In Vitro.

Most tumors consume large amounts of glucose. Concepts to explain the mechanisms that mediate the achievement of this metabolic need have proposed a switch of the tumor mass to aerobic glycolysis. Depending on whether primarily tumor or stroma cells undergo such a commutation, the terms 'Warburg effect' or 'reverse Warburg effect' were coined to describe the underlying biological phenomena. However, current in vitro systems relying on 2-D culture, single cell-type spheroids, or basal-membrane extract (BME/Matrigel)-containing 3-D structures do not thoroughly reflect these processes. Here, we aimed to establish a BME/Matrigel-free 3-D microarray cancer model to recapitulate the metabolic interplay between cancer and stromal cells that allows mechanistic analyses and drug testing. Human HT-29 colon cancer and CCD-1137Sk fibroblast cells were used in mono- and co-cultures as 2-D monolayers, spheroids, and in a cell-chip format. Metabolic patterns were studied with immunofluorescence and confocal microscopy. In chip-based co-cultures, HT-29 cells showed facilitated 3-D growth and increased levels of hexokinase-2, TP53-induced glycolysis and apoptosis regulator (TIGAR), lactate dehydrogenase, and: translocase of outer mitochondrial membrane 20 (TOMM20), when compared with HT-29 mono-cultures. Fibroblasts co-cultured with HT-29 cells expressed higher levels of mono-carboxylate transporter 4, hexokinase-2, microtubule-associated proteins 1A/1B light chain 3, and ubiquitin-binding protein p62 than in fibroblast mono-cultures, in both 2-D cultures and chips. Tetramethylrhodamin-methylester (TMRM) live-cell imaging of chip co-cultures revealed a higher mitochondrial potential in cancer cells than in fibroblasts. The findings demonstrate a crosstalk between cancer cells and fibroblasts that affects cellular growth and metabolism. Chip-based 3-D co-cultures of cancer cells and fibroblasts mimicked features of the reverse Warburg effect.

Routine Optical Clearing of 3D-Cell Cultures: Simplicity Forward.

Three-dimensional cell cultures, such as spheroids and organoids, serve as increasingly important models in fundamental and applied research and start to be used for drug screening purposes. Optical tissue clearing procedures are employed to enhance visualization of fluorescence-stained organs, tissues, and three-dimensional cell cultures. To get a more systematic overview about the effects and applicability of optical tissue clearing on three-dimensional cell cultures, we compared six different clearing/embedding protocols on seven types of spheroid- and chip-based three-dimensional cell cultures of approximately 300 µm in size that were stained with nuclear dyes, immunofluorescence, cell trackers, and cyan fluorescent protein. Subsequent whole mount confocal microscopy and semi-automated image analysis were performed to quantify the effects. Quantitative analysis included fluorescence signal intensity and signal-to-noise ratio as a function of z-depth as well as segmentation and counting of nuclei and immunopositive cells. In general, these analyses revealed five key points, which largely confirmed current knowledge and were quantified in this study. First, there was a massive variability of effects of different clearing protocols on sample transparency and shrinkage as well as on dye quenching. Second, all tested clearing protocols worked more efficiently on samples prepared with one cell type than on co-cultures. Third, z-compensation was imperative to minimize variations in signal-to-noise ratio. Fourth, a combination of sample-inherent cell density, sample shrinkage, uniformity of signal-to-noise ratio, and image resolution had a strong impact on data segmentation, cell counts, and relative numbers of immunofluorescence-positive cells. Finally, considering all mentioned aspects and including a wish for simplicity and speed of protocols - in particular, for screening purposes - clearing with 88% Glycerol appeared to be the most promising option amongst the ones tested.