In this webinar, expert Joanna Hawryluk explores how the OLYMPUS Provi™ CM20 incubation monitoring system can help improve the health and stability of cell cultures through machine learning. With the aid of AI, the CM20 monitor automatically measures cell conditions using constant analysis parameters to provide quantitative data—all while your cultures remain safely in the incubator.

Turning organoids into impactful translational models includes being able to culture them and assess those that develop robustly with physiologically relevant architecture. However, quantitative comparisons and statistical analysis at high content, which are mandatory to describe the complexity of such multicellular 3D objects are not possible owing to the lack of high-throughput 3D imaging methods. We have thus engineered a versatile High Content Screening (HCS) device to streamline all the steps of organoid culture to exploit its potential in morphogenesis understanding. Our approach comprises a new generation of versatile scaffolding cell culture multiwell chips with embedded optical components (= lighting JeWells) that enables rapid 3D imaging.

Organoid and three-dimensional (3D) cell culture are emerging as pivotal systems for understanding human organ development, modeling disease, screening for drug efficacy or toxicity, and investigating personalized medicine. Usually they are derived from primary tissue, embryonic stem cells (ESCs), or induced pluripotent stem cells (iPSCs), which are capable of self-renewal and differentiation.

Globally colorectal cancer is a significant public health burden. It is the third most commonly diagnosed cancer and fourth leading cause of cancer related deaths in the world. A subset of patients diagnosed with rectal cancer require neoadjuvant chemoradiotherapy (NACRT) prior to surgery. However, there is a spectrum of response to this therapy with only 10-20% of patients achieving a complete pathological response. In addition, 20-40% of patients will demonstrate no response to this treatment. There is currently no method that predicts how a patient will respond to NACRT accurately. In order to investigate the mechanisms underpinning how patients respond to therapy, patient derived tumouroids have been utilised. These personalised in vitro three-dimensional tumour models recapitulate the in vivo tumour of origin genotypically. The Ramsay laboratory (Peter MacCallum, Melbourne) has successfully co-cultured patient-matched rectal cancer tumouroids with tumour infiltrating lymphocytes (TILS) in a novel in vitro assay, preliminary data generated suggests this assay has the ability to predict the response of a patient to treatment with NACRT prior to instigation of neo-adjuvant therapy. This assay provides a pre-clinical platform that encapsulates the hosts immune response toward their tumour. However, manual analysis of the data generated from this assay is time consuming and limits the clinical utility of this platform. Machine-based learning to develop artificial neural networks capable of analysing data produced from the killing assay has been developed to automate analysis. Automated analysis utilising artificial neural networks is a feasible approach to expedite the processing of data generated from the cytotoxic killing assays and will improve the clinical utility of this platform to direct personalised patient therapy.

Biomedical optical Imaging, as a powerful tool has been applied for observing biomedical tissue structural and functional information with high resolution and contrast unattainable by any other method. However, the high scattering of turbid biological tissues limits the penetration of light, leading to strongly decreased imaging resolution and contrast as light propagates deeper into the tissue. Fortunately, novel tissue optical clearing technique provide a way for reducing the scattering of tissue and improving the optical imaging quality. This presentation will introduce our progress from in vitro and in vivo of tissue optical clearing imaging, including developing in vitro optical clearing methods, such as FDISCO and MACS. Meanwhile, we will also demonstrate in vivo skull/skin optical clearing window for imaging structural and functional of cutaneous / cortical vascular and cells, also manipulating cortical vasculature.

Biologists have a significant toolbox at their disposal when it comes to microscopically imaging 3D samples, such as organoids. From widefield microscopy to confocal, superresolution, multiphoton and lightsheet, each have their own set of pros and cons that must be carefully considered before making an informed choice on the most suitable to address your biological question. Often a correlative approach is required, applying several techniques to address the question from different perspectives. It is also crucial to consider the method of sample preparation and optimise each of the potential steps which can include fixation, permeabilisation, labelling and mounting. Further, the images generated by all techniques can be enhanced with post-processing techniques, such as deconvolution, which can enable or help to improve subsequent image analysis and interpretation.

With rapid development in fluorescent proteins, synthetic fluorochromes, and digital imaging, advanced 3D imaging technologies are now available to investigators to provide critical insights into the fundamental nature of cellular and tissue functions. 3D and 4D imaging systems have become very common tools among biologists. However, there are several technical challenges and limitations in performing successful 3D and 4D imaging. Olympus has developed a wide range of 3D imaging microscopes to overcome these challenges and to satisfy the requirements of researchers across different disciplines.

Phenotypic tumour heterogeneity arising due to differentially cycling cell populations has been implicated in increased therapy resistance. This phenomenon cannot be assessed in adherent cell culture, where microenvironmental conditions are homogeneous. Thus, we utilise melanoma spheroids to model the 3D tumour microenvironment including the extracellular matrix (ECM) and study spheroid structure, necrotic region, individual cell arrangement within and gene expression patterns. We achieve this by exploiting the fluorescence ubiquitination cell cycle indicator (FUCCI) system to monitor cell cycle stages as a surrogate marker for phenotypic tumour heterogeneity, tissue clearing and confocal microscopy using FV3000.

Prostate cancer is one of the most common cancers worldwide and also the second leading cause of cancer-related death in males in Western countries. Although the majority of human primary prostate cancers have a luminal phenotype, both basal cells and luminal cells can serve as cellular origins of prostate cancer in model systems. However, the stem cell-like plasticity of defined prostate epithelial cells and the cellular origin of prostate cancer under physiological conditions have not been identified. Recently, prostate basal and luminal cell populations were both shown to be self-sustaining, and both cell types could initiate prostate cancer. However, the oncogenic transformation of basal cells requires basal to luminal cell transition. In addition, luminal cells were shown to have greater tendency to be the cells of origin for prostate cancer in some contexts.

Cells are 3D functional elements in biology science and they are actively moving to perform their functions. Collective cell migration is appreciated as an important model for the understanding of the mechanism governing the cell movement in Vivo and in Vitro. It is a highly kinetic process involved in immune response, wound healing, tissue development and cancer metastasis. Recent decades have seen the fast development of various optical imaging techniques with excellent spatial-temporal resolution, dimensionality and scale. The generation of novel probes have also allowed us to acquire the movies of migrating cells with specific proteins/molecules. However, we lack of advanced solution to analyse such high-content and highly dynamic images/videos.

Patient-derived tumor organoids (PDOs) represent a promising preclinical cancer model that better replicates disease, compared with traditional cell culture models. We have established a novel series of patient-derived tumor organoids (PDOs) from various types of tumor tissues from the Fukushima Translational Research Project, which are designated as Fukushima (F)-PDOs. F-PDOs could be cultured for >6 months and formed cell clusters with similar morphologies to their source tumors. Comparative histological and comprehensive gene-expression analyses also demonstrated that the characteristics of PDOs were similar to those of their source tumors, even following long-term expansion in culture. In addition, suitable high-throughput assay systems were constructed for each F-PDO in 96- and 384-well plate formats.

For a long period of time, intestinal mesenchymal stromal cells have been considered as a relatively simple and homogeneous group of cells. With the help of single cell transcriptomics studies, it has now been clear that these cells are quite complex and heterogeneous. However, the detailed cellular and molecular mechanisms that regulate the function of these cells remains poorly understood. Therefore, the ability to perturb and evaluate the function of these stromal cells is critical to the understanding of intestinal stem cell niche and the etiology of the inflammatory bowel diseases and colitis associated colorectal cancer.

Three-dimensional cell culture models such as patient-derived organoids (PDO) and spheroids have increased in popularity because they can provide a 3D microenvironment that more closely reproduces in vivo conditions compared to 2D monolayer culture. Phenotypic and functional heterogeneity arise among cancer cells within the same tumor because of genetic change, environmental differences and reversible changes in cell properties. Therefore, evaluation of cell-specific responses is important for accurate prediction of drug efficacy and kinetics in vivo.

During the Olympus Organoid Conference, cell biologists, microscopists, and image analysis experts shared their insights and answered questions on the latest developments in organoid technologies.

Improvements to in vitro three-dimensional (3D) models are making them increasingly better at mimicking in vivo-like cellular behavior. Every tissue presents a distinct microenvironment with a unique blend of biochemical and biophysical components that dictate cellular behavior. Recreation of critical features of tissues that nurtures recapitulation of in vivo-like cellular behavior is the essence of an effective 3D model. In this webinar, through specific examples of 3D models for tissue development and cancer, we will revisit the fundamental principles of designing 3D models that can effectively recapitulate critical features of the tissues in vitro and applications of such models in mechanistic studies and drug testing. Our work also highlights the importance of 3D imaging systems, such as laser scanning confocal microscopes, which are necessary for such work.

Organoids and spheroids can more faithfully reproduce in vivo conditions, and imaging-based analysis can monitor cell-specific responses with high spatial resolution. Therefore, we have been developing imaging-based three-dimensional analysis and drug evaluation methods using patient-derived cancer organoids and spheroids.

In our first webinar on this topic, we learnt that images taken with a light microscope are never quite a perfect reflection of our samples. There are always sources of error, and as we can only minimize such errors, the raw data of an imaging experiment will often need some digital image processing before final image analysis.

In this webinar, Olympus imaging experts Stefan, Lauren, and Chunsong will discuss how to more easily create super resolution images.

Images captured with a light microscope are never true representations of the specimen; there are always sources of error that must be controlled. In this webinar, we will discuss how these sources of error can be managed.