Integration of human inspection and AI-based morphological typing of PDOs reveals inter-patient heterogeneity of colorectal cancer

Tumour organoids and assembloids: Patient-derived cancer avatars for immunotherapy

BACKGROUND

Organoid technology is an emerging and rapidly growing field that shows promise in studying organ development and screening therapeutic regimens. Although organoids have been proposed for a decade, concerns exist, including batch-to-batch variations, lack of the native microenvironment and clinical applicability.

MAIN BODY

The concept of organoids has derived patient-derived tumour organoids (PDTOs) for personalized drug screening and new drug discovery, mitigating the risks of medication misuse. The greater the similarity between the PDTOs and the primary tumours, the more influential the model will be. Recently, 'tumour assembloids' inspired by cell-coculture technology have attracted attention to complement the current PDTO technology. High-quality PDTOs must reassemble critical components, including multiple cell types, tumour matrix, paracrine factors, angiogenesis and microorganisms. This review begins with a brief overview of the history of organoids and PDTOs, followed by the current approaches for generating PDTOs and tumour assembloids. Personalized drug screening has been practised; however, it remains unclear whether PDTOs can predict immunotherapies, including immune drugs (e.g. immune checkpoint inhibitors) and immune cells (e.g. tumour-infiltrating lymphocyte, T cell receptor-engineered T cell and chimeric antigen receptor-T cell). PDTOs, as cancer avatars of the patients, can be expanded and stored to form a biobank.

CONCLUSION

Fundamental research and clinical trials are ongoing, and the intention is to use these models to replace animals. Pre-clinical immunotherapy screening using PDTOs will be beneficial to cancer patients.

KEY POINTS

The current PDTO models have not yet constructed key cellular and non-cellular components. PDTOs should be expandable and editable. PDTOs are promising preclinical models for immunotherapy unless mature PDTOs can be established. PDTO biobanks with consensual standards are urgently needed.

The ncRNA-TGF-β axis: Unveiling new frontiers in colorectal cancer research

Colorectal cancer (CRC) poses a substantial global challenge, necessitating a deeper understanding of the molecular underpinnings governing its onset and progression. The transforming growth factor beta (TGF-β) network has been a well-recognized cornerstone in advancing CRC. Nevertheless, a recent study has highlighted the growing importance of non-coding RNAs (ncRNAs) in this context. This comprehensive review aims to present an extensive examination of the interaction between ncRNAs and TGF-signaling. Noncoding RNAs (ncRNAs), encompassing circular RNAs (circRNAs), long-ncRNAs (lncRNAs), and microRNAs (miRNAs), have surfaced as pivotal modulators governing various aspects of TGF-β signaling. MiRNAs have been discovered to target elements within the TGF-β signaling, either enhancing or inhibiting signaling, depending on the context. LncRNAs have been associated with CRC progression, functioning as miRNA sponges or directly influencing TGF-β pathway elements. Even circRNAs, a relatively recent addition to the ncRNA family, have impacted CRC, affecting TGF-β signaling through diverse mechanisms. This review encompasses recent progress in comprehending specific ncRNAs involved in TGF-β signaling, their functional roles, and their clinical relevance in CRC. We investigate the possibility of ncRNAs as targets for detection, prognosis, and therapy. Additionally, we explore the interaction of TGF-β and other pathways in CRC and the role of ncRNAs within this intricate network. As we unveil the intricate regulatory function of ncRNAs in the TGF-β signaling in CRC, we gain valuable insights into the disease's pathogenesis. Incorporating these discoveries into clinical settings holds promise for more precise diagnosis, prognosis, and targeted therapeutic approaches, ultimately enhancing the care of CRC patients. This comprehensive review underscores the ever-evolving landscape of ncRNA research in CRC and the potential for novel interventions in the battle against this formidable disease.

Deep-Orga: An improved deep learning-based lightweight model for intestinal organoid detection

PROBLEM

Organoids are 3D cultures that are commonly used for biological and medical research in vitro due to their functional and structural similarity to source organs. The development of organoids can be assessed by morphological tests. However, manual analysis of organoid morphology requires intensive labor from professionals and is prone to observer discrepancies.

AIM

Computer-assisted methods alleviate the pressure of manual labor, especially with the development of deep learning, the performance of morphological detection has been further improved. The aim of this paper is to automate the assessment of organoid morphology using deep learning techniques to reduce the labor pressure of professionals.

METHODS

Based on the lightweight model YOLOX, a lightweight intestinal organoid detection model named Deep-Orga is proposed. First, the performance of the Deep-Orga model is compared with other classical models on the intestinal organoids dataset. Then, ablation experiments are used to validate the improvement of the model detection performance by the improved module. Finally, Deep-Orga is compared with other methods.

RESULTS

Deep-Orga achieves optimal organoid detection with a partial increase in computational effort. Using Deep-Orga to replace the manual analysis process provides a new automated method for organoid morphology evaluation.

CONCLUSION

Deep-Orga proposed in this paper is able to accurately assess organoid development, effectively relieving the labor pressure of professionals and avoiding the subjectivity of assessment. This paper demonstrates the potential application of deep learning in the field of organoid morphology analysis.

Paneth-like cells produced from OLFM4+ stem cells support OLFM4+ stem cell growth in advanced colorectal cancer

Tumor tissues consist of heterogeneous cells that originate from stem cells; however, their cell fate determination program remains incompletely understood. Using patient-derived organoids established from patients with advanced colorectal cancer (CRC), we evaluated the potential of olfactomedin 4 (OLFM4)+ stem cells to produce a bifurcated lineage of progenies with absorptive and secretory properties. In the early phases of organoid reconstruction, OLFM4+ cells preferentially gave rise to secretory cells. Additionally, we found that Paneth-like cells, which do not exist in the normal colon, were induced in response to Notch signaling inhibition. Video recordings of single OLFM4+ cells revealed that organoids containing Paneth-like cells were effectively propagated and that their selective ablation led to organoid collapse. In tumor tissues, Paneth-like cells were identified only in the region where tumor cells lost cell adhesion. These findings indicate that Paneth-like cells are directly produced by OLFM4+ stem cells and that their interaction contributes to tumor formation by providing niche factors. This study reveals the importance of the cell fate specification program for building a complete tumor cellular ecosystem, which might be targeted with novel therapeutics.

A quick and reliable image-based AI algorithm for evaluating cellular senescence of gastric organoids

OBJECTIVE

Organoids are a powerful tool with broad application prospects in biomedicine. Notably, they provide alternatives to animal models for testing potential drugs before clinical trials. However, the number of passages for which organoids maintain cellular vitality ex vivo remains unclear.

METHODS

Herein, we constructed 55 gastric organoids from 35 individuals, serially passaged the organoids, and captured microscopic images for phenotypic evaluation. Senescence-associated β-galactosidase (SA-β-Gal), cell diameter in suspension, and gene expression reflecting cell cycle regulation were examined. The YOLOv3 object detection algorithm integrated with a convolutional block attention module (CBAM) was used to evaluate organoid vitality.

RESULTS

SA-β-Gal staining intensity; single-cell diameter; and expression of p15, p16, p21, CCNA2, CCNE2, and LMNB1 reflected the progression of aging in organoids during passaging. The CBAM-YOLOv3 algorithm precisely evaluated aging organoids on the basis of organoid average diameter, organoid number, and number × diameter, and the findings positively correlated with SA-β-Gal staining and single-cell diameter. Organoids derived from normal gastric mucosa had limited passaging ability (passages 1-5), before aging, whereas tumor organoids showed unlimited passaging potential for more than 45 passages (511 days) without showing clear senescence.

CONCLUSIONS

Given the lack of indicators for evaluating organoid growth status, we established a reliable approach for integrated analysis of phenotypic parameters that uses an artificial intelligence algorithm to indicate organoid vitality. This method enables precise evaluation of organoid status in biomedical studies and monitoring of living biobanks.

Patient-derived organoids in translational oncology and drug screening

Patient-derived organoids (PDO) are a new biomedical research model that can reconstruct phenotypic and genetic characteristics of the original tissue and are useful for research on pathogenesis and drug screening. To introduce the progression in this field, we review the key factors of constructing organoids derived from epithelial tissues and cancers, covering culture medium and matrix, morphological characteristics, genetic profiles, high-throughput drug screening, and application potential. We also discuss the co-culture system of cancer organoids with tumor microenvironment (TME) associated cells. The co-culture system is widely used in evaluating crosstalk of cancer cells with TME components, such as fibroblasts, endothelial cells, immune cells, and microorganisms. The article provides a prospective for standardized cultivation mode, automatic morphological evaluation, and drug sensitivity screening using high-throughput methods.