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

AI for biofabrication

Biofabrication is an advanced technology that holds great promise for constructing highly biomimeticin vitrothree-dimensional human organs. Such technology would help address the issues of immune rejection and organ donor shortage in organ transplantation, aiding doctors in formulating personalized treatments for clinical patients and replacing animal experiments. Biofabrication typically involves the interdisciplinary application of biology, materials science, mechanical engineering, and medicine to generate large amounts of data and correlations that require processing and analysis. Artificial intelligence (AI), with its excellent capabilities in big data processing and analysis, can play a crucial role in handling and processing interdisciplinary data and relationships and in better integrating and applying them in biofabrication. In recent years, the development of the semiconductor and integrated circuit industries has propelled the rapid advancement of computer processing power. An AI program can learn and iterate multiple times within a short period, thereby gaining strong automation capabilities for a specific research content or issue. To date, numerous AI programs have been applied to various processes around biofabrication, such as extracting biological information, designing and optimizing structures, intelligent cell sorting, optimizing biomaterials and processes, real-time monitoring and evaluation of models, accelerating the transformation and development of these technologies, and even changing traditional research patterns. This article reviews and summarizes the significant changes and advancements brought about by AI in biofabrication, and discusses its future application value and direction.

[A review on depth perception techniques in organoid images]

Organoids are an in vitro model that can simulate the complex structure and function of tissues in vivo. Functions such as classification, screening and trajectory recognition have been realized through organoid image analysis, but there are still problems such as low accuracy in recognition classification and cell tracking. Deep learning algorithm and organoid image fusion analysis are the most advanced organoid image analysis methods. In this paper, the organoid image depth perception technology is investigated and sorted out, the organoid culture mechanism and its application concept in depth perception are introduced, and the key progress of four depth perception algorithms such as organoid image and classification recognition, pattern detection, image segmentation and dynamic tracking are reviewed respectively, and the performance advantages of different depth models are compared and analyzed. In addition, this paper also summarizes the depth perception technology of various organ images from the aspects of depth perception feature learning, model generalization and multiple evaluation parameters, and prospects the development trend of organoids based on deep learning methods in the future, so as to promote the application of depth perception technology in organoid images. It provides an important reference for the academic research and practical application in this field.

Paired organoids from primary gastric cancer and lymphatic metastasis are useful for personalized medicine

BACKGROUND

Organoids are approved by the US FDA as an alternative to animal experiments to guide drug development and for sensitivity screening. Stable organoids models of gastric cancer are desirable for personalized medicine and drug screening.

METHODS

Tumor tissues from a primary cancer of the stomach and metastatic cancer of the lymph node were collected for 3D culture. By long-term culture for over 50 generations in vitro, we obtained stably growing organoid lines. We analyzed short tandem repeats (STRs) and karyotypes of cancer cells, and tumorigenesis of the organoids in nude mice, as well as multi-omics profiles of the organoids. A CCK8 method was used to determine the drugs sensitivity to fluorouracil (5-Fu), platinum and paclitaxel.

RESULTS

Paired organoid lines from primary cancer (SPDO1P) and metastatic lymph node (SPDO1LM) were established with unique STRs and karyotypes. The organoid lines resulted in tumorigenesis in vivo and had clear genetic profiles. Compared to SPDO1P from primary cancer, upregulated genes of SPDO1LM from the metastatic lymph node were enriched in pathways of epithelial-mesenchymal transition and angiogenesis with stronger abilities of cell migration, invasion, and pro-angiogenesis. Based on drug sensitivity analysis, the SOX regimen (5-Fu plus oxaliplatin) was used for chemotherapy with an optimal clinical outcome.

CONCLUSIONS

The organoid lines recapitulate the drug sensitivity of the parental tissues. The paired organoid lines present a step-change toward living biobanks for further translational usage.

Landscape of human organoids: Ideal model in clinics and research

In the last decade, organoid research has entered a golden era, signifying a pivotal shift in the biomedical landscape. The year 2023 marked a milestone with the publication of thousands of papers in this arena, reflecting exponential growth. However, amid this burgeoning expansion, a comprehensive and accurate overview of the field has been conspicuously absent. Our review is intended to bridge this gap, providing a panoramic view of the rapidly evolving organoid landscape. We meticulously analyze the organoid field from eight distinctive vantage points, harnessing our rich experience in academic research, industrial application, and clinical practice. We present a deep exploration of the advances in organoid technology, underpinned by our long-standing involvement in this arena. Our narrative traverses the historical genesis of organoids and their transformative impact across various biomedical sectors, including oncology, toxicology, and drug development. We delve into the synergy between organoids and avant-garde technologies such as synthetic biology and single-cell omics and discuss their pivotal role in tailoring personalized medicine, enhancing high-throughput drug screening, and constructing physiologically pertinent disease models. Our comprehensive analysis and reflective discourse provide a deep dive into the existing landscape and emerging trends in organoid technology. We spotlight technological innovations, methodological evolution, and the broadening spectrum of applications, emphasizing the revolutionary influence of organoids in personalized medicine, oncology, drug discovery, and other fields. Looking ahead, we cautiously anticipate future developments in the field of organoid research, especially its potential implications for personalized patient care, new avenues of drug discovery, and clinical research. We trust that our comprehensive review will be an asset for researchers, clinicians, and patients with keen interest in personalized medical strategies. We offer a broad view of the present and prospective capabilities of organoid technology, encompassing a wide range of current and future applications. In summary, in this review we attempt a comprehensive exploration of the organoid field. We offer reflections, summaries, and projections that might be useful for current researchers and clinicians, and we hope to contribute to shaping the evolving trajectory of this dynamic and rapidly advancing field.

Exploring the immune escape mechanisms in gastric cancer patients based on the deep AI algorithms and single-cell sequencing analysis

Gastric cancer is a prevalent and deadly malignancy, and the response to immunotherapy varies among patients. This study aimed to develop a prognostic model for gastric cancer patients and investigate immune escape mechanisms using deep machine learning and single-cell sequencing analysis. Data from public databases were analysed, and a prediction model was constructed using 101 algorithms. The high-AIDPS group, characterized by increased AIDPS expression, exhibited worse survival, genomic variations and immune cell infiltration. These patients also showed immunotherapy tolerance. Treatment strategies targeting the high-AIDPS group identified three potential drugs. Additionally, distinct cluster groups and upregulated AIDPS-associated genes were observed in gastric adenocarcinoma cell lines. Inhibition of GHRL expression suppressed cancer cell activity, inhibited M2 polarization in macrophages and reduced invasiveness. Overall, AIDPS plays a critical role in gastric cancer prognosis, genomic variations, immune cell infiltration and immunotherapy response, and targeting GHRL expression holds promise for personalized treatment. These findings contribute to improved clinical management in gastric cancer.

Establishment and Characterization of Patient-derived Gastric Organoids from Biopsies of Benign Gastric Body and Antral Epithelium

Gastric patient-derived organoids (PDOs) offer a unique tool for studying gastric biology and pathology. Consequently, these PDOs find increasing use in a wide array of research applications. However, a shortage of published approaches exists for producing gastric PDOs from single-cell digests while maintaining a standardized initial cell seeding density. In this protocol, the emphasis is on the initiation of gastric organoids from isolated single cells and the provision of a method for passaging organoids through fragmentation. Importantly, the protocol demonstrates that a standardized approach to the initial cell seeding density consistently yields gastric organoids from benign biopsy tissue and allows for standardized quantification of organoid growth. Finally, evidence supports the novel observation that gastric PDOs display varying rates of formation and growth based on whether the organoids originate from biopsies of the body or antral regions of the stomach. Specifically, it is revealed that the use of antral biopsy tissue for organoid initiation results in a greater number of organoids formed and more rapid organoid growth over a 20-day period when compared to organoids generated from biopsies of the gastric body. The protocol described herein offers investigators a timely and reproducible method for successfully generating and working with gastric PDOs.

Benchmark for establishment of organoids from gastrointestinal epithelium and cancer based on available consumables and reagents

Gastrointestinal cancers are a public health problem that threatens the lives of human being. A good experimental model is a powerful tool to promote the uncovering pathogenesis and establish novel treatment methods. High-quality biomedical research requires experimental models to recapitulate the physiological and pathological states of their parental tissues as much as possible. Organoids are such experimental models. Organoids refer to small organ-like cellular clusters formed by the expansion and passaging of living tissues in 3D culture medium in vitro. Organoids are highly similar to the original tissues in terms of cellular composition, cell functions, and genomic profiling. Organoids have many advantages, such as short preparation cycles, long-term storage based on cryopreservation, and reusability. In recent years, researchers carried out the establishment of organoids from gastrointestinal mucosa and cancer tissues, and accumulated valuable experiences. In order to promote effective usage and further development of organoid-related technologies in the research of gastrointestinal diseases, this study proposes a benchmark based on utilization of available experimental consumables and reagents, which are involved in the key steps such as collection and pretreatment of biospecimen, organoid construction, organoid cryopreservation and recovery, growth status evaluation, and organoid quality control. We believe that the standard for the construction and preservation of organoids derived from human gastrointestinal epithelium and cancer tissues can provide an important reference for the majority of scientific researchers.