Automated tracking of tumor-stroma morphology in microtissues identifies functional targets within the tumor microenvironment for therapeutic intervention

Depth-enhanced high-throughput microscopy by compact PSF engineering

High-throughput microscopy is vital for screening applications, where three-dimensional (3D) cellular models play a key role. However, due to defocus susceptibility, current 3D high-throughput microscopes require axial scanning, which lowers throughput and increases photobleaching and photodamage. Point spread function (PSF) engineering is an optical method that enables various 3D imaging capabilities, yet it has not been implemented in high-throughput microscopy due to the cumbersome optical extension it typically requires. Here we demonstrate compact PSF engineering in the objective lens, which allows us to enhance the imaging depth of field and, combined with deep learning, recover 3D information using single snapshots. Beyond the applications shown here, this work showcases the usefulness of high-throughput microscopy in obtaining training data for deep learning-based algorithms, applicable to a variety of microscopy modalities.

Ratiometric Inclusion of Fibroblasts Promotes Both Castration-Resistant and Androgen-Dependent Tumorigenic Progression in Engineered Prostate Cancer Tissues

To investigate the ratiometric role of fibroblasts in prostate cancer (PCa) progression, this work establishes a matrix-inclusive, 3D engineered prostate cancer tissue (EPCaT) model that enables direct coculture of neuroendocrine-variant castration-resistant (CPRC-ne) or androgen-dependent (ADPC) PCa cells with tumor-supporting stromal cell types. Results show that the inclusion of fibroblasts within CRPC-ne and ADPC EPCaTs drives PCa aggression through significant matrix remodeling and increased proliferative cell populations. Interestingly, this is observed to a much greater degree in EPCaTs formed with a small number of fibroblasts relative to the number of PCa cells. Fibroblast coculture also results in ADPC behavior more similar to the aggressive CRPC-ne condition, suggesting fibroblasts play a role in elevating PCa disease state and may contribute to the ADPC to CRPC-ne switch. Bulk transcriptomic analyses additionally elucidate fibroblast-driven enrichment of hallmark gene sets associated with tumorigenic progression. Finally, the EPCaT model clinical relevancy is probed through a comparison to the Cancer Genome Atlas (TCGA) PCa patient cohort; notably, similar gene set enrichment is observed between EPCaT models and the patient primary tumor transcriptome. Taken together, study results demonstrate the potential of the EPCaT model to serve as a PCa-mimetic tool in future therapeutic development efforts.

Optimization of a Three-Dimensional Culturing Method for Assessing the Impact of Cisplatin on Notch Signaling in Head and Neck Squamous Cell Carcinoma (HNSCC)

Head and neck squamous cell carcinoma (HNSCC) is a prevalent cancer type, with cisplatin being a primary treatment approach. However, drug resistance and therapy failure pose a significant challenge, affecting nearly 50% of patients over time. This research had two aims: (1) to optimize a 3D cell-culture method for assessing the interplay between tumor cells and cancer-associated fibroblasts (CAFs) in vitro; and (2) to study how cisplatin impacts the Notch pathway, particularly considering the role of CAFs. Using our optimized "3D sheet model" approach, we tested two HNSCC cell lines with different cisplatin sensitivities and moderate, non-mutated NOTCH1 and -3 expressions. Combining cisplatin with a γ-secretase inhibitor (crenigacestat) increased sensitivity and induced cell death in the less sensitive cell line, while cisplatin alone was more effective in the moderately sensitive line and sensitivity decreased with the Notch inhibitor. Cisplatin boosted the expression of core Notch signaling proteins in 3D monocultures of both lines, which was counteracted by crenigacestat. In contrast, the presence of patient-derived CAFs mitigated effects and protected both cell lines from cisplatin toxicity. Elevated NOTCH1 and NOTCH3 protein levels were consistently correlated with reduced cisplatin sensitivity and increased cell survival. Additionally, the Notch ligand JAG2 had additional, protective effects reducing cell death from cisplatin exposure. In summary, we observed an inverse relationship between NOTCH1 and NOTCH3 levels and cisplatin responsiveness, overall protective effects by CAFs, and a potential link between JAG2 expression with tumor cell survival.

Evaluation of the anticancer activity of RIN-1, a Notch signaling modulator, in head and neck squamous cell carcinoma

Notch signalling is one of the key molecular pathways involved in cell-to-cell signal transduction. Although the mechanisms of action of the NOTCH receptors are already relatively well known, their biological implications remain unclear, especially during the initiation and progression of head and neck squamous cell carcinoma (HNSCC). Here, we present the growth- and differentiation-modulating effects of various "next generation" small molecule Notch modulators represented by RIN-1, and CB-103, on HNSCC, compared to gamma secretase inhibitors as "conventional" NOTCH interfering compounds, like DAPT. These molecules were tested in different cell- and tissue culture conditions represented by 2D monolayer, non-adherent or spheroid culture, 3D organoid cultures, and zebrafish in vivo model. The most pronounced, pleiotropic effects were observed for the NOTCH modulator RIN-1. At the molecular level, RIN-1-dependent activation of Notch signalling led to characteristic changes in the expression of NOTCH-regulated targets, i.e., the transcriptional suppressors HES1 and HEY1, p21 (CDKN1A) cell cycle inhibitor, and pro-apoptotic BAX markers. These changes led to restriction of proliferation, growth, and reduced motility of HNSCC cells in 2D cultures. Consequently, cell cycle arrest in the G2-M phase and induction of apoptosis were observed. Similar anticancer effects were observed in 3D cultures and in the zebrafish model. In contrast, RIN-1 treatment resulted in inhibition of Notch signalling and the growth of HNSCC spheroids under non-adherent cell culture conditions. Our results suggest that modulation of Notch signalling could be used as a chemotherapeutic agent in selected patients with intact NOTCH signaling.

Urological cancer organoids, patients' avatars for precision medicine: past, present and future

Urological cancers are common malignant cancers worldwide, with annually increasing morbidity and mortality rates. For decades, two-dimensional cell cultures and animal models have been widely used to study the development and underlying molecular mechanisms of urological cancers. However, they either fail to reflect cancer heterogeneity or are time-consuming and labour-intensive. The recent emergence of a three-dimensional culture model called organoid has the potential to overcome the shortcomings of traditional models. For example, organoids can recapitulate the histopathological and molecular diversity of original cancer and reflect the interaction between cancer and surrounding cells or stroma by simulating tumour microenvironments. Emerging evidence suggests that urine-derived organoids can be generated, which could be a novel non-invasive liquid biopsy method that provides new ideas for clinical precision therapy. However, the current research on organoids has encountered some bottlenecks, such as the lack of a standard culture process, the need to optimize the culture medium and the inability to completely simulate the immune system in vivo. Nonetheless, cell co-culture and organoid-on-a-chip have significant potential to solve these problems. In this review, the latest applications of organoids in drug screening, cancer origin investigation and combined single-cell sequencing are illustrated. Furthermore, the development and application of organoids in urological cancers and their challenges are summarised.

Spatiotemporal Tracking of Different Cell Populations in Cancer Organoid Models for Investigations on Photodynamic Therapy

Three-dimensional (3D) in vitro models of tumors are gaining interest as versatile platforms for treatment screening. In this context, heterocellular cultures in which various cell types are co-cultured are being explored to investigate whether partner cells can influence the treatment efficacies. However, when the cells are co-cultured, it is challenging to distinguish them and it becomes impossible to identify whether the treatment affects each cell line in a similar way or if there is a certain selectivity. Here, we propose a protocol in which different cell types are pre-labeled with fluorescent reporters prior to 3D culture initiation. Subsequently, the internal architecture of the 3D cancer models can be longitudinally monitored for model characterization, and to potentially detect architectural and treatment selectivity in response to therapy. This protocol employs quantum dots as non-photobleaching dyes and two-photon excited microscopy as a widely accessible imaging modality. In combination with an appropriate image analysis workflow, this protocol will help to investigate the architectural development of heterotypic microtumor/spheroid/organoid models and possibly identify treatment efficacies on individual cell populations represented within the models.

Current status of organoid culture in urological malignancy

Urological cancers are common malignancies worldwide. Several conventional models, for example, two-dimensional cell culture and animal models have been used for decades to study tumor genetics. Nonetheless, these methods have limitations in reflecting the real tumor microenvironment in vivo, thereby hindering the development of anti-cancer therapeutic agents. Recently, three-dimensional culture models have gained attention because they can overcome the drawbacks of traditional methods. Above all, three-dimensional organoid models are able to mimic the tumor microenvironment in human bodies more closely as they are able to demonstrate the interactions between cells and extracellular matrix. This type of model has therefore extended our understanding of urological cancers. Tumor cells in organoid models can also be co-cultured with other cellular components, such as peripheral blood lymphocytes, and allow further understanding of the effect of tumor microenvironments on tumor growth. Furthermore, organoid models allow a prolonged culturing period, therefore, tumor evolution, progression and maintenance can also be assessed. Organoid models can be derived from each specific patient, and this facilitates investigation of individual cancer-specific mutations and their subtypes. As a result, the development of personalized medication targeting the signaling pathways or biomolecules of interest will be possible. In the present review, we summarize the development and applications of three-dimensional organoid cultures in urological cancers, mainly focusing on prostate, urinary bladder and kidney cancers, and assess the future prospects of this model.

Phenotypic screening system using three-dimensional (3D) culture models for natural product screening

Recent progress in three-dimensional (3D) cell culture systems has attracted much attention in the fields of basic life science and drug development. Newly established methods include 3D co-culture, spheroid culture, and organoid culture; these methods enable more human tissue-like culture and have largely replaced traditional two-dimensional (2D) monolayer culture. By combining 3D culture methods with high-content imaging analysis, it is possible to obtain diverse and convincing data even during initial screening (which requires rapid and easy operating procedures). Until recently, 3D culture methods were considered expensive, time-consuming, complex, and unstable. However, by exploiting the self-assembling nature of cells and adding several technical improvements, we have developed several phenotypic screenings aimed at discovering anticancer compounds.

3D Modeling of Epithelial Tumors-The Synergy between Materials Engineering, 3D Bioprinting, High-Content Imaging, and Nanotechnology

The current statistics on cancer show that 90% of all human cancers originate from epithelial cells. Breast and prostate cancer are examples of common tumors of epithelial origin that would benefit from improved drug treatment strategies. About 90% of preclinically approved drugs fail in clinical trials, partially due to the use of too simplified in vitro models and a lack of mimicking the tumor microenvironment in drug efficacy testing. This review focuses on the origin and mechanism of epithelial cancers, followed by experimental models designed to recapitulate the epithelial cancer structure and microenvironment, such as 2D and 3D cell culture models and animal models. A specific focus is put on novel technologies for cell culture of spheroids, organoids, and 3D-printed tissue-like models utilizing biomaterials of natural or synthetic origins. Further emphasis is laid on high-content imaging technologies that are used in the field to visualize in vitro models and their morphology. The associated technological advancements and challenges are also discussed. Finally, the review gives an insight into the potential of exploiting nanotechnological approaches in epithelial cancer research both as tools in tumor modeling and how they can be utilized for the development of nanotherapeutics.

OrgDyn: feature- and model-based characterization of spatial and temporal organoid dynamics

SUMMARY

Organoid model systems recapitulate key features of mammalian tissues and enable high throughput experiments. However, the impact of these experiments may be limited by manual, non-standardized, static or qualitative phenotypic analysis. OrgDyn is an open-source and modular pipeline to quantify organoid shape dynamics using a combination of feature- and model-based approaches on time series of 2D organoid contour images. Our pipeline consists of (i) geometrical and signal processing feature extraction, (ii) dimensionality reduction to differentiate dynamical paths, (iii) time series clustering to identify coherent groups of organoids and (iv) dynamical modeling using point distribution models to explain temporal shape variation. OrgDyn can characterize, cluster and model differences among unique dynamical paths that define diverse final shapes, thus enabling quantitative analysis of the molecular basis of tissue development and disease.

AVAILABILITY AND IMPLEMENTATION

https://github.com/zakih/organoidDynamics (BSD 3-Clause License).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Radiation-induced bystander and abscopal effects: important lessons from preclinical models

Radiotherapy is a pivotal component in the curative treatment of patients with localised cancer and isolated metastasis, as well as being used as a palliative strategy for patients with disseminated disease. The clinical efficacy of radiotherapy has traditionally been attributed to the local effects of ionising radiation, which induces cell death by directly and indirectly inducing DNA damage, but substantial work has uncovered an unexpected and dual relationship between tumour irradiation and the host immune system. In clinical practice, it is, therefore, tempting to tailor immunotherapies with radiotherapy in order to synergise innate and adaptive immunity against cancer cells, as well as to bypass immune tolerance and exhaustion, with the aim of facilitating tumour regression. However, our understanding of how radiation impacts on immune system activation is still in its early stages, and concerns and challenges regarding therapeutic applications still need to be overcome. With the increasing use of immunotherapy and its common combination with ionising radiation, this review briefly delineates current knowledge about the non-targeted effects of radiotherapy, and aims to provide insights, at the preclinical level, into the mechanisms that are involved with the potential to yield clinically relevant combinatorial approaches of radiotherapy and immunotherapy.

The Multifaceted Roles of MSCs in the Tumor Microenvironment: Interactions With Immune Cells and Exploitation for Therapy

The tumor microenvironment (TME) plays a critical role in tumorigenesis and is composed of different cellular components, including immune cells and mesenchymal stromal cells (MSCs). In this review, we will discuss MSCs in the TME setting and more specifically their interactions with immune cells and how they can both inhibit (immunosurveillance) and favor (immunoediting) tumor growth. We will also discuss how MSCs are used as a therapeutic strategy in cancer. Due to their unique immunomodulatory properties, MSCs isolated from perinatal tissues are intensely explored as therapeutic interventions in various inflammatory-based disorders with promising results. However, their therapeutic applications in cancer remain for the most part controversial and, importantly, the interactions between administered perinatal MSC and immune cells in the TME remain to be clearly defined.

In Vitro Modeling of the Tumor Microenvironment in Tumor Organoids

BACKGROUND

The tumor microenvironment (TME) represents the many components occupying the space within and surrounding a tumor, including cells, signaling factors, extracellular matrix, and vasculature. Each component has the potential to assume many forms and functions which in turn contribute to the overall state of the TME, and further contribute to the progression and disposition of the tumor itself. The sum of these components can drive a tumor towards progression, keep a migratory tumor at bay, or even control chemotherapeutic response. The wide potential for interaction that the TME is an integral part of a tumor's ecosystem, and it is imperative to include it when studying and modeling cancer in vitro. Fortunately, the development of tissue engineering and biofabrication technologies and methodologies have allowed widespread inclusion of TME-based factors into in vitro tissue-equivalent models.

METHODS

In this review, we compiled contemporary literature sources to provide an overview of the field of TME models, ranging from simple to complex.

RESULTS

We have identified important components of the TME, how they can be included in in vitro study, and cover examples across a range of cancer types.

CONCLUSION

Our goal with this text is to provide a foundation for prospective research into the TME.

Alterations in the methylome of the stromal tumour microenvironment signal the presence and severity of prostate cancer

Background

Prostate cancer changes the phenotype of cells within the stromal microenvironment, including fibroblasts, which in turn promote tumour progression. Functional changes in prostate cancer-associated fibroblasts (CAFs) coincide with alterations in DNA methylation levels at loci-specific regulatory regions. Yet, it is not clear how these methylation changes compare across CAFs from different patients. Therefore, we examined the consistency and prognostic significance of genome-wide DNA methylation profiles between CAFs from patients with different grades of primary prostate cancer.

Results

We used Infinium MethylationEPIC BeadChips to evaluate genome-wide DNA methylation profiles from 18 matched CAFs and non-malignant prostate tissue fibroblasts (NPFs) from men with moderate to high grade prostate cancer, as well as five unmatched benign prostate tissue fibroblasts (BPFs) from men with benign prostatic hyperplasia. We identified two sets of differentially methylated regions (DMRs) in patient CAFs. One set of DMRs reproducibly differed between CAFs and fibroblasts from non-malignant tissue (NPFs and BPFs). Indeed, more than 1200 DMRs consistently changed in CAFs from every patient, regardless of tumour grade. The second set of DMRs varied between CAFs according to the severity of the tumour. Notably, hypomethylation of the EDARADD promoter occurred specifically in CAFs from high-grade tumours and correlated with increased transcript abundance and increased EDARADD staining in patient tissue. Across multiple cohorts, tumours with low EDARADD DNA methylation and high EDARADD mRNA expression were consistently associated with adverse clinical features and shorter recurrence free survival.

Conclusions

We identified a large set of DMRs that are commonly shared across CAFs regardless of tumour grade and outcome, demonstrating highly consistent epigenome changes in the prostate tumour microenvironment. Additionally, we found that CAFs from aggressive prostate cancers have discrete methylation differences compared to CAFs from moderate risk prostate cancer. Together, our data demonstrates that the methylome of the tumour microenvironment reflects both the presence and the severity of the prostate cancer and, therefore, may provide diagnostic and prognostic potential.

Prostate Stroma Increases the Viability and Maintains the Branching Phenotype of Human Prostate Organoids

The fibromuscular stroma of the prostate regulates normal epithelial differentiation and contributes to carcinogenesis in vivo. We developed and characterized a human 3D prostate organoid co-culture model that incorporates prostate stroma. Primary prostate stromal cells increased organoid formation and directed organoid morphology into a branched acini structure similar to what is observed in vivo. Organoid branching occurred distal to physical contact with stromal cells, demonstrating non-random branching. Stroma-induced phenotypes were similar in all patients examined, yet they maintained inter-patient heterogeneity in the degree of response. Stromal cells expressed growth factors involved in epithelial differentiation, which was not observed in non-prostatic fibroblasts. Organoids derived from areas of prostate cancer maintained differential expression of alpha-methylacyl-CoA racemase and showed increased viability and passaging when co-cultured with stroma. The addition of stroma to epithelial cells in vitro improves the ability of organoids to recapitulate features of the tissue and enhances the viability of organoids.

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Co-culture with human primary prostate stroma improves epithelial organoid viability

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Stromal cell contact in co-culture directs epithelial organoid branching

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Prostate stromal cells express morphogenic factors unique from non-prostate fibroblasts

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Co-culture with stroma maintains AMACR and increases survival of cancer derived-organoids

Desmoglein 3 - Influence on oral carcinoma cell migration and invasion

Desmoglein 3 (Dsg3) is an adhesion receptor in desmosomes, but its role in carcinoma cell migration and invasion is mostly unknown. Our aim was to quantitatively analyse the motion of Dsg3-modified carcinoma cells in 2D settings and in 3D within tumour microenvironment mimicking (TMEM) matrices. We tested mutant constructs of C-terminally truncated Dsg3 (∆238 and ∆560), overexpressed full-length (FL) Dsg3, and empty vector control (Ct) of buccal mucosa squamous cell carcinoma (SqCC/Y1) cells. We captured live cell images and analysed migration velocities and accumulated and Euclidean distances. We compared rodent collagen and Matrigel^® with human Myogel TMEM matrices for these parameters in 3D sandwich, in which we also tested the effects of monoclonal antibody AK23, which targets the EC1 domain of Dsg3. In monolayer culture, FL and both truncated constructs migrated faster and had higher accumulated distances than Ct cells. However, in the 3D assays, only the mutants invaded faster relative to Ct cells. Of the mutants, the shorter form (Δ238) exhibited faster migration and invasion than Δ560 cells. In the Transwell, all of the cells invaded faster through Myogel than Matrigel^® coated wells. In 3D sandwich, AK23 antibody inhibited only the invasion of FL cells. We conclude that different experimental 2D and 3D settings can markedly influence the movement of oral carcinoma cells with various Dsg3 modifications.

Patient-derived Models Reveal Impact of the Tumor Microenvironment on Therapeutic Response

Background

Androgen deprivation therapy is a first-line treatment for disseminated prostate cancer (PCa). However, virtually all tumors become resistant and recur as castration-resistant PCa, which has no durable cure. One major hurdle in the development of more effective therapies is the lack of preclinical models that adequately recapitulate the heterogeneity of PCa, significantly hindering the ability to accurately predict therapeutic response.

Objective

To leverage the ex vivo culture method termed patient-derived explant (PDE) to examine the impact of PCa therapeutics on a patient-by-patient basis.

Design setting and participants

Fresh PCa tissue from patients who underwent radical prostatectomy was cultured as PDEs to examine therapeutic response.

Outcome measurements and statistical analysis

The impact of genomic and chemical perturbations in PDEs was assessed using various parameters (eg, AR levels, Ki67 staining, and desmoplastic indices).

Results and limitations

PDE maintained the integrity of the native tumor microenvironment (TME), tumor tissue morphology, viability, and endogenous hormone signaling. Tumor cells in this model system exhibited de novo proliferative capacity. Examination of the native TME in the PDE revealed a first-in-field insight into patient-specific desmoplastic stromal indices and predicted responsiveness to AR-directed therapeutics.

Conclusions

The PDE model allows for a comprehensive evaluation of individual tumors in their native TME to ultimately develop more effective therapeutic regimens tailored to individuals. Discernment of novel stromal markers may provide a basis for applying precision medicine in treating advanced PCa, which would have a transformative effect on patient outcomes.

Patient summary

In this study, an innovative model system was used to more effectively mimic human disease. The patient-derived explant (PDE) system can be used to predict therapeutic response and identify novel targets in advanced disease. Thus, the PDE will be an asset for the development of novel metrics for the implementation of precision medicine in prostate cancer.The patient-derived explant (PDE) model allows for a comprehensive evaluation of individual human tumors in their native tumor microenvironment (TME). TME analysis revealed first-in-field insight into predicted tumor responsiveness to AR-directed therapeutics through evaluation of patient-specific desmoplastic stromal indices.

Quantitative Phenotypic Image Analysis of Three-Dimensional Organotypic Cultures

Glandular epithelial cells differentiate into three-dimensional (3D) multicellular or acinar structures, particularly when embedded in laminin-rich extracellular matrix (ECM). The spectrum of different multicellular morphologies formed in 3D is a reliable indicator for the differentiation potential of normal, non-transformed cells compared to different stages of malignant progression. Motile cancer cells may actively invade the matrix, utilizing epithelial, mesenchymal, or mixed modes of motility. Dynamic phenotypic changes involved in 3D tumor cell invasion are also very sensitive to small-molecule inhibitors that, e.g., target the actin cytoskeleton. Our strategy is to recapitulate the formation and the histology of complex solid cancer tissues in vitro, based on cell culture technologies that promote the intrinsic differentiation potential of normal and transformed epithelial cells, and also including stromal fibroblasts and other key components of the tumor microenvironment. We have developed a streamlined stand-alone software solution that supports the detailed quantitative phenotypic analysis of organotypic 3D cultures. This approach utilizes the power of automated image analysis as a phenotypic readout in cell-based assays. AMIDA (Automated Morphometric Image Data Analysis) allows quantitative measurements of a large number of multicellular structures, which can form a multitude of different organoid shapes, sizes, and textures according to their capacity to engage in epithelial differentiation programs or not. At the far end of this spectrum of tumor-relevant differentiation properties, there are highly invasive tumor cells or multicellular structures that may rapidly invade the surrounding ECM, but fail to form higher-order epithelial tissue structures. Furthermore, this system allows us to monitor dynamic changes that can result from the extraordinary plasticity of tumor cells, e.g., epithelial-to-mesenchymal transition in live cell settings. Furthermore, AMIDA supports an automated workflow, and can be combined with quality control and statistical tools for data interpretation and visualization. Our approach supports the growing needs for user-friendly, straightforward solutions that facilitate cell-based organotypic 3D assays in basic research, drug discovery, and target validation.

Comprehensive high-throughput image analysis for therapeutic efficacy of architecturally complex heterotypic organoids

Bioengineered three-dimensional (3D) tumor models that incorporate heterotypic cellular communication are gaining interest as they can recapitulate key features regarding the intrinsic heterogeneity of cancer tissues. However, the architectural complexity and heterogeneous contents associated with these models pose a challenge for toxicological assays to accurately report treatment outcomes. To address this issue, we describe a comprehensive image analysis procedure for structurally complex organotypic cultures (CALYPSO) applied to fluorescence-based assays to extract multiparametric readouts of treatment effects for heterotypic tumor cultures that enables advanced analyses. The capacity of this approach is exemplified on various 3D models including adherent/suspension, mono-/heterocellular cultures and several disease types. The subsequent analysis revealed specific morphological effects of oxaliplatin chemotherapy, radiotherapy, and photodynamic therapy. The procedure can be readily implemented in most laboratories to facilitate high-throughput toxicological screening of pharmaceutical agents and treatment regimens on organotypic cultures of human disease to expedite drug and therapy development.

Impaired Expression of Focal Adhesion Kinase in Mesenchymal Stromal Cells from Low-Risk Myelodysplastic Syndrome Patients

The pathogenic role of mesenchymal stromal cells (MSCs) in myelodysplastic syndromes (MDS) development and progression has been investigated by numerous studies, yet, it remains controversial in some aspects (1, 2). In the present study, we found distinct features of MSCs from low-risk (LR)-MDS stromal microenvironment as compared to those from healthy subjects. At the molecular level, focal adhesion kinase, a key tyrosine kinase in control of cell proliferation, survival, and adhesion process, was found profoundly suppressed in expression and activation in LR-MDS MSC. At a functional level, LR-MDS MSCs showed impaired growth and clonogenic capacity, which were independent of cellular senescence and apoptosis. The pro-adipogenic differentiation and attenuated osteogenic capacity along with reduced SDF-1 expression could be involved in creating an unfavorable microenvironment for hematopoiesis. In conclusion, our experiments support the theory that the stromal microenvironment is fundamentally altered in LR-MDS, and these preliminary data offer a new perspective on LR-MDS pathophysiology.

A high-content image analysis approach for quantitative measurements of chemosensitivity in patient-derived tumor microtissues

Organotypic, three-dimensional (3D) cancer models have enabled investigations of complex microtissues in increasingly realistic conditions. However, a drawback of these advanced models remains the poor biological relevance of cancer cell lines, while higher clinical significance would be obtainable with patient-derived cell cultures. Here, we describe the generation and data analysis of 3D microtissue models from patient-derived xenografts (PDX) of non-small cell lung carcinoma (NSCLC). Standard of care anti-cancer drugs were applied and the altered multicellular morphologies were captured by confocal microscopy, followed by automated image analyses to quantitatively measure phenotypic features for high-content chemosensitivity tests. The obtained image data were thresholded using a local entropy filter after which the image foreground was split into local regions, for a supervised classification into tumor or fibroblast cell types. Robust statistical methods were applied to evaluate treatment effects on growth and morphology. Both novel and existing computational approaches were compared at each step, while prioritizing high experimental throughput. Docetaxel was found to be the most effective drug that blocked both tumor growth and invasion. These effects were also validated in PDX tumors in vivo. Our research opens new avenues for high-content drug screening based on patient-derived cell cultures, and for personalized chemosensitivity testing.

The potential of organoids in urological cancer research

Technical advances in the development of organoid systems enable cell lines, primary adult cells, or stem or progenitor cells to develop into diverse, multicellular entities, which can self-renew, self-organize, and differentiate. These 3D organoid cultures have proven to be of value in increasing our understanding of the biology of disease and offer the potential of regenerative and genetic therapies. The successful application of 3D organoids derived from adult tissue into urological cancer research can further our understanding of these diseases and could also provide preclinical cancer models to realize the precision medicine paradigm by therapeutic screening of individual patient samples ex vivo. Kidney organoids derived from induced pluripotent stem cells provide personalized biomarkers, which can be correlated with genetic and clinical information. Organoid models can also improve our comprehension of aspects of particular diseases; for example, in prostate cancer, 3D organoids can aid in the identification of tumour-initiating cells from an epithelial cell lineage. Furthermore, kidney organoid differentiation from human pluripotent stem cells enables gene editing to model disease in kidney tubular epithelial cells. State-of-the-art human organoid cultures have potential as tools in basic and clinical research in renal, bladder, and prostatic diseases.

Contributions of 3D Cell Cultures for Cancer Research

Cancer cell lines have contributed immensely in understanding the complex physiology of cancers. They are excellent material for studies as they offer homogenous samples without individual variations and can be utilised with ease and flexibility. Also, the number of assays and end-points one can study is almost limitless; with the advantage of improvising, modifying or altering several variables and methods. Literally, a new dimension to cancer research has been achieved by the advent of 3Dimensional (3D) cell culture techniques. This approach increased many folds the ways in which cancer cell lines can be utilised for understanding complex cancer biology. 3D cell culture techniques are now the preferred way of using cancer cell lines to bridge the gap between the 'absolute in vitro' and 'true in vivo'. The aspects of cancer biology that 3D cell culture systems have contributed include morphology, microenvironment, gene and protein expression, invasion/migration/metastasis, angiogenesis, tumour metabolism and drug discovery, testing chemotherapeutic agents, adaptive responses and cancer stem cells. We present here, a comprehensive review on the applications of 3D cell culture systems for these aspects of cancers. J. Cell. Physiol. 232: 2679-2697, 2017. © 2016 Wiley Periodicals, Inc.

Drug screening in 3D in vitro tumor models: overcoming current pitfalls of efficacy read-outs

There is cumulating evidence that in vitro 3D tumor models with increased physiological relevance can improve the predictive value of pre-clinical research and ultimately contribute to achieve decisions earlier during the development of cancer-targeted therapies. Due to the role of tumor microenvironment in the response of tumor cells to therapeutics, the incorporation of different elements of the tumor niche on cell model design is expected to contribute to the establishment of more predictive in vitro tumor models. This review is focused on the several challenges and adjustments that the field of oncology research is facing to translate these advanced tumor cells models to drug discovery, taking advantage of the progress on culture technologies, imaging platforms, high throughput and automated systems. The choice of 3D cell model, the experimental design, choice of read-outs and interpretation of data obtained from 3D cell models are critical aspects when considering their implementation in drug discovery. In this review, we foresee some of these aspects and depict the potential directions of pre-clinical oncology drug discovery towards improved prediction of drug efficacy.

A high-content image-based method for quantitatively studying context-dependent cell population dynamics

Tumor progression results from a complex interplay between cellular heterogeneity, treatment response, microenvironment and heterocellular interactions. Existing approaches to characterize this interplay suffer from an inability to distinguish between multiple cell types, often lack environmental context, and are unable to perform multiplex phenotypic profiling of cell populations. Here we present a high-throughput platform for characterizing, with single-cell resolution, the dynamic phenotypic responses (i.e. morphology changes, proliferation, apoptosis) of heterogeneous cell populations both during standard growth and in response to multiple, co-occurring selective pressures. The speed of this platform enables a thorough investigation of the impacts of diverse selective pressures including genetic alterations, therapeutic interventions, heterocellular components and microenvironmental factors. The platform has been applied to both 2D and 3D culture systems and readily distinguishes between (1) cytotoxic versus cytostatic cellular responses; and (2) changes in morphological features over time and in response to perturbation. These important features can directly influence tumor evolution and clinical outcome. Our image-based approach provides a deeper insight into the cellular dynamics and heterogeneity of tumors (or other complex systems), with reduced reagents and time, offering advantages over traditional biological assays.

Image Quality Ranking Method for Microscopy

Automated analysis of microscope images is necessitated by the increased need for high-resolution follow up of events in time. Manually finding the right images to be analyzed, or eliminated from data analysis are common day-to-day problems in microscopy research today, and the constantly growing size of image datasets does not help the matter. We propose a simple method and a software tool for sorting images within a dataset, according to their relative quality. We demonstrate the applicability of our method in finding good quality images in a STED microscope sample preparation optimization image dataset. The results are validated by comparisons to subjective opinion scores, as well as five state-of-the-art blind image quality assessment methods. We also show how our method can be applied to eliminate useless out-of-focus images in a High-Content-Screening experiment. We further evaluate the ability of our image quality ranking method to detect out-of-focus images, by extensive simulations, and by comparing its performance against previously published, well-established microscopy autofocus metrics.

Segmentation of Image Data from Complex Organotypic 3D Models of Cancer Tissues with Markov Random Fields

Organotypic, three dimensional (3D) cell culture models of epithelial tumour types such as prostate cancer recapitulate key aspects of the architecture and histology of solid cancers. Morphometric analysis of multicellular 3D organoids is particularly important when additional components such as the extracellular matrix and tumour microenvironment are included in the model. The complexity of such models has so far limited their successful implementation. There is a great need for automatic, accurate and robust image segmentation tools to facilitate the analysis of such biologically relevant 3D cell culture models. We present a segmentation method based on Markov random fields (MRFs) and illustrate our method using 3D stack image data from an organotypic 3D model of prostate cancer cells co-cultured with cancer-associated fibroblasts (CAFs). The 3D segmentation output suggests that these cell types are in physical contact with each other within the model, which has important implications for tumour biology. Segmentation performance is quantified using ground truth labels and we show how each step of our method increases segmentation accuracy. We provide the ground truth labels along with the image data and code. Using independent image data we show that our segmentation method is also more generally applicable to other types of cellular microscopy and not only limited to fluorescence microscopy.