A quantitative analysis of the interplay of environment, neighborhood, and cell state in 3D spheroids

Bridging systems biology and tissue engineering: Unleashing the full potential of complex 3D in vitro tissue models of disease

Rapid advances in tissue engineering have resulted in more complex and physiologically relevant 3D in vitro tissue models with applications in fundamental biology and therapeutic development. However, the complexity provided by these models is often not leveraged fully due to the reductionist methods used to analyze them. Computational and mathematical models developed in the field of systems biology can address this issue. Yet, traditional systems biology has been mostly applied to simpler in vitro models with little physiological relevance and limited cellular complexity. Therefore, integrating these two inherently interdisciplinary fields can result in new insights and move both disciplines forward. In this review, we provide a systematic overview of how systems biology has been integrated with 3D in vitro tissue models and discuss key application areas where the synergies between both fields have led to important advances with potential translational impact. We then outline key directions for future research and discuss a framework for further integration between fields.

Using random forests to uncover the predictive power of distance-varying cell interactions in tumor microenvironments

Tumor microenvironments (TMEs) contain vast amounts of information on patient's cancer through their cellular composition and the spatial distribution of tumor cells and immune cell populations. Exploring variations in TMEs between patient groups, as well as determining the extent to which this information can predict outcomes such as patient survival or treatment success with emerging immunotherapies, is of great interest. Moreover, in the face of a large number of cell interactions to consider, we often wish to identify specific interactions that are useful in making such predictions. We present an approach to achieve these goals based on summarizing spatial relationships in the TME using spatial K functions, and then applying functional data analysis and random forest models to both predict outcomes of interest and identify important spatial relationships. This approach is shown to be effective in simulation experiments at both identifying important spatial interactions while also controlling the false discovery rate. We further used the proposed approach to interrogate two real data sets of Multiplexed Ion Beam Images of TMEs in triple negative breast cancer and lung cancer patients. The methods proposed are publicly available in a companion R package funkycells.

Lactate receptor GPR81 drives breast cancer growth and invasiveness through regulation of ECM properties and Notch ligand DLL4

BACKGROUND

The lactate receptor GPR81 contributes to cancer development through unclear mechanisms. Here, we investigate the roles of GPR81 in three-dimensional (3D) and in vivo growth of breast cancer cells and study the molecular mechanisms involved.

METHODS

GPR81 was stably knocked down (KD) in MCF-7 human breast cancer cells which were subjected to RNA-seq analysis, 3D growth, in situ- and immunofluorescence analyses, and cell viability- and motility assays, combined with KD of key GPR81-regulated genes. Key findings were additionally studied in other breast cancer cell lines and in mammary epithelial cells.

RESULTS

GPR81 was upregulated in multiple human cancer types and further upregulated by extracellular lactate and 3D growth in breast cancer spheroids. GPR81 KD increased spheroid necrosis, reduced invasion and in vivo tumor growth, and altered expression of genes related to GO/KEGG terms extracellular matrix, cell adhesion, and Notch signaling. Single cell in situ analysis of MCF-7 cells revealed that several GPR81-regulated genes were upregulated in the same cell clusters. Notch signaling, particularly the Notch ligand Delta-like-4 (DLL4), was strikingly downregulated upon GPR81 KD, and DLL4 KD elicited spheroid necrosis and inhibited invasion in a manner similar to GPR81 KD.

CONCLUSIONS

GPR81 supports breast cancer aggressiveness, and in MCF-7 cells, this occurs at least in part via DLL4. Our findings reveal a new GPR81-driven mechanism in breast cancer and substantiate GPR81 as a promising treatment target.

PI3K signaling through a biochemical systems lens

Following 3 decades of extensive research into PI3K signaling, it is now evidently clear that the underlying network does not equate to a simple ON/OFF switch. This is best illustrated by the multifaceted nature of the many diseases associated with aberrant PI3K signaling, including common cancers, metabolic disease, and rare developmental disorders. However, we are still far from a complete understanding of the fundamental control principles that govern the numerous phenotypic outputs that are elicited by activation of this well-characterized biochemical signaling network, downstream of an equally diverse set of extrinsic inputs. At its core, this is a question on the role of PI3K signaling in cellular information processing and decision making. Here, we review the determinants of accurate encoding and decoding of growth factor signals and discuss outstanding questions in the PI3K signal relay network. We emphasize the importance of quantitative biochemistry, in close integration with advances in single-cell time-resolved signaling measurements and mathematical modeling.

Single-Cell and Spatial Analysis of Emergent Organoid Platforms

Organoids have emerged as a promising advancement of the two-dimensional (2D) culture systems to improve studies in organogenesis, drug discovery, precision medicine, and regenerative medicine applications. Organoids can self-organize as three-dimensional (3D) tissues derived from stem cells and patient tissues to resemble organs. This chapter presents growth strategies, molecular screening methods, and emerging issues of the organoid platforms. Single-cell and spatial analysis resolve organoid heterogeneity to obtain information about the structural and molecular cellular states. Culture media diversity and varying lab-to-lab practices have resulted in organoid-to-organoid variability in morphology and cell compositions. An essential resource is an organoid atlas that can catalog protocols and standardize data analysis for different organoid types. Molecular profiling of individual cells in organoids and data organization of the organoid landscape will impact biomedical applications from basic science to translational use.

Unsupervised discovery of tissue architecture in multiplexed imaging

Multiplexed imaging and spatial transcriptomics enable highly resolved spatial characterization of cellular phenotypes, but still largely depend on laborious manual annotation to understand higher-order patterns of tissue organization. As a result, higher-order patterns of tissue organization are poorly understood and not systematically connected to disease pathology or clinical outcomes. To address this gap, we developed an approach called UTAG to identify and quantify microanatomical tissue structures in multiplexed images without human intervention. Our method combines information on cellular phenotypes with the physical proximity of cells to accurately identify organ-specific microanatomical domains in healthy and diseased tissue. We apply our method to various types of images across healthy and disease states to show that it can consistently detect higher-level architectures in human tissues, quantify structural differences between healthy and diseased tissue, and reveal tissue organization patterns at the organ scale.

Assessment of Antitumor and Antiproliferative Efficacy and Detection of Protein-Protein Interactions in Cancer Cells from 3D Tumor Spheroids

When compared to two-dimensional (2D) cell cultures, 3D spheroids have been considered suitable in vitro models for drug discovery research and other studies of drug activity. Based on different 3D cell culture procedures, we describe procedures we have used to obtain 3D tumor spheroids by both the hanging-drop and ultra-low-attachment plate methods and to analyze the antiproliferative and antitumor efficacy of different chemotherapeutic agents, including a peptidomimetic. We have applied this method to breast and lung cancer cell lines such as BT-474, MCF-7, A549, and Calu-3. We also describe a proximity ligation assay of the cells from the spheroid model to detect protein-protein interactions of EGFR and HER2. © 2022 Wiley Periodicals LLC. Basic Protocol 1: Growth of 3D spheroids using the hanging-drop method Basic Protocol 2: Growth of spheroids using ultra-low-attachment plates Support Protocol 1: Cell viability assay of tumor spheroids Support Protocol 2: Antiproliferative and antitumor study in 3D tumor spheroids Support Protocol 3: Proximity ligation assay on cells derived from 3D spheroids.

Traject3d allows label-free identification of distinct co-occurring phenotypes within 3D culture by live imaging

Single cell profiling by genetic, proteomic and imaging methods has expanded the ability to identify programmes regulating distinct cell states. The 3-dimensional (3D) culture of cells or tissue fragments provides a system to study how such states contribute to multicellular morphogenesis. Whether cells plated into 3D cultures give rise to a singular phenotype or whether multiple biologically distinct phenotypes arise in parallel is largely unknown due to a lack of tools to detect such heterogeneity. Here we develop Traject3d (Trajectory identification in 3D), a method for identifying heterogeneous states in 3D culture and how these give rise to distinct phenotypes over time, from label-free multi-day time-lapse imaging. We use this to characterise the temporal landscape of morphological states of cancer cell lines, varying in metastatic potential and drug resistance, and use this information to identify drug combinations that inhibit such heterogeneity. Traject3d is therefore an important companion to other single-cell technologies by facilitating real-time identification via live imaging of how distinct states can lead to alternate phenotypes that occur in parallel in 3D culture.

Passiflora mollissima Seed Extract Induced Antiproliferative and Cytotoxic Effects on CAL 27 Spheroids

Multicellular tumor spheroids are used as models in drug development due to their characteristics simulating in vivo tumors. Likewise, antiproliferative properties of extracts derived from fruits have been widely described. Peels and seeds can be used as a matrix to obtain different compounds. Recently, a study demonstrated the antiproliferative activity from a P. mollissima extract (PME) on human colon cancer cells; however, its effect on oral spheroids is unknown. Objective. To evaluate the antiproliferative potential of an extract obtained from P. mollissima seeds on the spheroid-type-3D culture model of CAL 27. Methods. CAL 27-spheroids were treated with three concentrations of PME (10, 50, and 100 μg/ml). After 72 hr incubation, morphology and cellular changes, cytotoxic and proapoptotic effect, gene expression, and metastasis were determined. Additionally, changes in the cell cycle phases responded to the PME concentrations. Comparisons between groups were made through a U Mann-Whitney test. Results. It was shown that 100 μg/ml PE affects CAL 27 cells proliferation grown in spheroids through cell cycle arrest and gene regulation of p53, HIF 1α, and CDH1. However, none of the treatments employed induced MMP9 gene expression. Conclusion. Our study shows that PME inhibits the growth and proliferation of oral tumor cells cultured in spheroids through the positive regulation of cell death and metastasis genes.

Intrinsic Differences in Spatiotemporal Organization and Stromal Cell Interactions Between Isogenic Lung Cancer Cells of Epithelial and Mesenchymal Phenotypes Revealed by High-Dimensional Single-Cell Analysis of Heterotypic 3D Spheroid Models

The lack of inadequate preclinical models remains a limitation for cancer drug development and is a primary contributor to anti-cancer drug failures in clinical trials. Heterotypic multicellular spheroids are three-dimensional (3D) spherical structures generated by self-assembly from aggregates of two or more cell types. Compared to traditional monolayer cell culture models, the organization of cells into a 3D tissue-like structure favors relevant physiological conditions with chemical and physical gradients as well as cell-cell and cell-extracellular matrix (ECM) interactions that recapitulate many of the hallmarks of cancer in situ. Epidermal growth factor receptor (EGFR) mutations are prevalent in non-small cell lung cancer (NSCLC), yet various mechanisms of acquired resistance, including epithelial-to-mesenchymal transition (EMT), limit the clinical benefit of EGFR tyrosine kinase inhibitors (EGFRi). Improved preclinical models that incorporate the complexity induced by epithelial-to-mesenchymal plasticity (EMP) are urgently needed to advance new therapeutics for clinical NSCLC management. This study was designed to provide a thorough characterization of multicellular spheroids of isogenic cancer cells of various phenotypes and demonstrate proof-of-principle for the applicability of the presented spheroid model to evaluate the impact of cancer cell phenotype in drug screening experiments through high-dimensional and spatially resolved imaging mass cytometry (IMC) analyses. First, we developed and characterized 3D homotypic and heterotypic spheroid models comprising EGFRi-sensitive or EGFRi-resistant NSCLC cells. We observed that the degree of EMT correlated with the spheroid generation efficiency in monocultures. In-depth characterization of the multicellular heterotypic spheroids using immunohistochemistry and high-dimensional single-cell analyses by IMC revealed intrinsic differences between epithelial and mesenchymal-like cancer cells with respect to self-sorting, spatiotemporal organization, and stromal cell interactions when co-cultured with fibroblasts. While the carcinoma cells harboring an epithelial phenotype self-organized into a barrier sheet surrounding the fibroblasts, mesenchymal-like carcinoma cells localized to the central hypoxic and collagen-rich areas of the compact heterotypic spheroids. Further, deep-learning-based single-cell segmentation of IMC images and application of dimensionality reduction algorithms allowed a detailed visualization and multiparametric analysis of marker expression across the different cell subsets. We observed a high level of heterogeneity in the expression of EMT markers in both the carcinoma cell populations and the fibroblasts. Our study supports further application of these models in pre-clinical drug testing combined with complementary high-dimensional single-cell analyses, which in turn can advance our understanding of the impact of cancer-stroma interactions and epithelial phenotypic plasticity on innate and acquired therapy resistance in NSCLC.

3D-bioprinted cancer-on-a-chip: level-up organotypic in vitro models

Combinatorial conjugation of organ-on-a-chip platforms with additive manufacturing technologies is rapidly emerging as a disruptive approach for upgrading cancer-on-a-chip systems towards anatomic-sized dynamic in vitro models. This valuable technological synergy has potential for giving rise to truly physiomimetic 3D models that better emulate tumor microenvironment elements, bioarchitecture, and response to multidimensional flow dynamics. Herein, we showcase the most recent advances in bioengineering 3D-bioprinted cancer-on-a-chip platforms and provide a comprehensive discussion on design guidelines and possibilities for high-throughput analysis. Such hybrid platforms represent a new generation of highly sophisticated 3D tumor models with improved biomimicry and predictability of therapeutics performance.

In situ functional cell phenotyping reveals microdomain networks in colorectal cancer recurrence

Tumors are dynamic ecosystems comprising localized niches (microdomains), possessing distinct compositions and spatial configurations of cancer and non-cancer cell populations. Microdomain-specific network signaling coevolves with a continuum of cell states and functional plasticity associated with disease progression and therapeutic responses. We present LEAPH, an unsupervised machine learning algorithm for identifying cell phenotypes, which applies recursive steps of probabilistic clustering and spatial regularization to derive functional phenotypes (FPs) along a continuum. Combining LEAPH with pointwise mutual information and network biology analyses enables the discovery of outcome-associated microdomains visualized as distinct spatial configurations of heterogeneous FPs. Utilization of an immunofluorescence-based (51 biomarkers) image dataset of colorectal carcinoma primary tumors (n = 213) revealed microdomain-specific network dysregulation supporting cancer stem cell maintenance and immunosuppression that associated selectively with the recurrence phenotype. LEAPH enables an explainable artificial intelligence platform providing insights into pathophysiological mechanisms and novel drug targets to inform personalized therapeutic strategies.

Investigation of Different Free Image Analysis Software for High-Throughput Droplet Detection

Droplet microfluidics has revealed innovative strategies in biology and chemistry. This advancement has delivered novel quantification methods, such as droplet digital polymerase chain reaction (ddPCR) and an antibiotic heteroresistance analysis tool. For droplet analysis, researchers often use image-based detection techniques. Unfortunately, the analysis of images may require specific tools or programming skills to produce the expected results. In order to address the issue, we explore the potential use of standalone freely available software to perform image-based droplet detection. We select the four most popular software and classify them into rule-based and machine learning-based types after assessing the software's modules. We test and evaluate the software's (i) ability to detect droplets, (ii) accuracy and precision, and (iii) overall components and supporting material. In our experimental setting, we find that the rule-based type of software is better suited for image-based droplet detection. The rule-based type of software also has a simpler workflow or pipeline, especially aimed for non-experienced users. In our case, CellProfiler (CP) offers the most user-friendly experience for both single image and batch processing analyses.

24th "Nantes Actualités en Transplantation" and 4th "LabEx Immunotherapy-Graft-Oncology" NAT and IGO Joint Meeting "New Horizons in Immunotherapy"

The 24th edition of the annual NAT conference (Nantes Actualités Transplantation) and the 4th edition of the biennial LabEx IGO meeting (Immunotherapy Graft Oncology) were held jointly around a common theme: "New horizons in immunotherapy", on May 31st and June 1st 2021 to highlight new findings in the fields of transplantation, autoimmunity and cancer.

A deep convolutional neural network for segmentation of whole-slide pathology images identifies novel tumour cell-perivascular niche interactions that are associated with poor survival in glioblastoma

BACKGROUND

Glioblastoma is the most aggressive type of brain cancer with high-levels of intra- and inter-tumour heterogeneity that contribute to its rapid growth and invasion within the brain. However, a spatial characterisation of gene signatures and the cell types expressing these in different tumour locations is still lacking.

METHODS

We have used a deep convolutional neural network (DCNN) as a semantic segmentation model to segment seven different tumour regions including leading edge (LE), infiltrating tumour (IT), cellular tumour (CT), cellular tumour microvascular proliferation (CTmvp), cellular tumour pseudopalisading region around necrosis (CTpan), cellular tumour perinecrotic zones (CTpnz) and cellular tumour necrosis (CTne) in digitised glioblastoma histopathological slides from The Cancer Genome Atlas (TCGA). Correlation analysis between segmentation results from tumour images together with matched RNA expression data was performed to identify genetic signatures that are specific to different tumour regions.

RESULTS

We found that spatially resolved gene signatures were strongly correlated with survival in patients with defined genetic mutations. Further in silico cell ontology analysis along with single-cell RNA sequencing data from resected glioblastoma tissue samples showed that these tumour regions had different gene signatures, whose expression was driven by different cell types in the regional tumour microenvironment. Our results further pointed to a key role for interactions between microglia/pericytes/monocytes and tumour cells that occur in the IT and CTmvp regions, which may contribute to poor patient survival.

CONCLUSIONS

This work identified key histopathological features that correlate with patient survival and detected spatially associated genetic signatures that contribute to tumour-stroma interactions and which should be investigated as new targets in glioblastoma. The source codes and datasets used are available in GitHub: https://github.com/amin20/GBM_WSSM .