Organoid diversity

Artificial intelligence for chimeric antigen receptor-based therapies: a comprehensive review of current applications and future perspectives

Using artificial intelligence (AI) to enhance chimeric antigen receptor (CAR)-based therapies' design, production, and delivery is a novel and promising approach. This review provides an overview of the current applications and challenges of AI for CAR-based therapies and suggests some directions for future research and development. This paper examines some of the recent advances of AI for CAR-based therapies, for example, using deep learning (DL) to design CARs that target multiple antigens and avoid antigen escape; using natural language processing to extract relevant information from clinical reports and literature; using computer vision to analyze the morphology and phenotype of CAR cells; using reinforcement learning to optimize the dose and schedule of CAR infusion; and using AI to predict the efficacy and toxicity of CAR-based therapies. These applications demonstrate the potential of AI to improve the quality and efficiency of CAR-based therapies and to provide personalized and precise treatments for cancer patients. However, there are also some challenges and limitations of using AI for CAR-based therapies, for example, the lack of high-quality and standardized data; the need for validation and verification of AI models; the risk of bias and error in AI outputs; the ethical, legal, and social issues of using AI for health care; and the possible impact of AI on the human role and responsibility in cancer immunotherapy. It is important to establish a multidisciplinary collaboration among researchers, clinicians, regulators, and patients to address these challenges and to ensure the safe and responsible use of AI for CAR-based therapies.

Personalized cancer vaccine design using AI-powered technologies

Immunotherapy has ushered in a new era of cancer treatment, yet cancer remains a leading cause of global mortality. Among various therapeutic strategies, cancer vaccines have shown promise by activating the immune system to specifically target cancer cells. While current cancer vaccines are primarily prophylactic, advancements in targeting tumor-associated antigens (TAAs) and neoantigens have paved the way for therapeutic vaccines. The integration of artificial intelligence (AI) into cancer vaccine development is revolutionizing the field by enhancing various aspect of design and delivery. This review explores how AI facilitates precise epitope design, optimizes mRNA and DNA vaccine instructions, and enables personalized vaccine strategies by predicting patient responses. By utilizing AI technologies, researchers can navigate complex biological datasets and uncover novel therapeutic targets, thereby improving the precision and efficacy of cancer vaccines. Despite the promise of AI-powered cancer vaccines, significant challenges remain, such as tumor heterogeneity and genetic variability, which can limit the effectiveness of neoantigen prediction. Moreover, ethical and regulatory concerns surrounding data privacy and algorithmic bias must be addressed to ensure responsible AI deployment. The future of cancer vaccine development lies in the seamless integration of AI to create personalized immunotherapies that offer targeted and effective cancer treatments. This review underscores the importance of interdisciplinary collaboration and innovation in overcoming these challenges and advancing cancer vaccine development.

Unveiling the Human Brain on a Chip: An Odyssey to Reconstitute Neuronal Ensembles and Explore Plausible Applications in Neuroscience

The brain is an incredibly complex structure that consists of millions of neural networks. In developmental and cellular neuroscience, probing the highly complex dynamics of the brain remains a challenge. Furthermore, deciphering how several cues can influence neuronal growth and its interactions with different brain cell types (such as astrocytes and microglia) is also a formidable task. Traditional in vitro macroscopic cell culture techniques offer simple and straightforward methods. However, they often fall short of providing insights into the complex phenomena of neuronal network formation and the relevant microenvironments. To circumvent the drawbacks of conventional cell culture methods, recent advancements in the development of microfluidic device-based microplatforms have emerged as promising alternatives. Microfluidic devices enable precise spatiotemporal control over compartmentalized cell cultures. This feature facilitates researchers in reconstituting the intricacies of the neuronal cytoarchitecture within a regulated environment. Therefore, in this review, we focus primarily on modeling neuronal development in a microfluidic device and the various strategies that researchers have adopted to mimic neurogenesis on a chip. Additionally, we have presented an overview of the application of brain-on-chip models for the recapitulation of the blood-brain barrier and neurodegenerative diseases, followed by subsequent high-throughput drug screening. These lab-on-a-chip technologies have tremendous potential to mimic the brain on a chip, providing valuable insights into fundamental brain processes. The brain-on-chip models will also serve as innovative platforms for developing novel neurotherapeutics to address several neurological disorders.

A hybrid physics-based and data-driven framework for cellular biological systems: Application to the morphogenesis of organoids

How cells orchestrate their cellular functions remains a crucial question to unravel how they organize in different patterns. We present a framework based on artificial intelligence to advance the understanding of how cell functions are coordinated spatially and temporally in biological systems. It consists of a hybrid physics-based model that integrates both mechanical interactions and cell functions with a data-driven model that regulates the cellular decision-making process through a deep learning algorithm trained on image data metrics. To illustrate our approach, we used data from 3D cultures of murine pancreatic ductal adenocarcinoma cells (PDAC) grown in Matrigel as tumor organoids. Our approach allowed us to find the underlying principles through which cells activate different cell processes to self-organize in different patterns according to the specific microenvironmental conditions. The framework proposed here expands the tools for simulating biological systems at the cellular level, providing a novel perspective to unravel morphogenetic patterns.

3D Cell Cultures: Evolution of an Ancient Tool for New Applications

Recently, research is undergoing a drastic change in the application of the animal model as a unique investigation strategy, considering an alternative approach for the development of science for the future. Although conventional monolayer cell cultures represent an established and widely used in vitro method, the lack of tissue architecture and the complexity of such a model fails to inform true biological processes in vivo. Recent advances in cell culture techniques have revolutionized in vitro culture tools for biomedical research by creating powerful three-dimensional (3D) models to recapitulate cell heterogeneity, structure and functions of primary tissues. These models also bridge the gap between traditional two-dimensional (2D) single-layer cultures and animal models. 3D culture systems allow researchers to recreate human organs and diseases in one dish and thus holds great promise for many applications such as regenerative medicine, drug discovery, precision medicine, and cancer research, and gene expression studies. Bioengineering has made an important contribution in the context of 3D systems using scaffolds that help mimic the microenvironments in which cells naturally reside, supporting the mechanical, physical and biochemical requirements for cellular growth and function. We therefore speak of models based on organoids, bioreactors, organ-on-a-chip up to bioprinting and each of these systems provides its own advantages and applications. All of these techniques prove to be excellent candidates for the development of alternative methods for animal testing, as well as revolutionizing cell culture technology. 3D systems will therefore be able to provide new ideas for the study of cellular interactions both in basic and more specialized research, in compliance with the 3R principle. In this review, we provide a comparison of 2D cell culture with 3D cell culture, provide details of some of the different 3D culture techniques currently available by discussing their strengths as well as their potential applications.

The potential application of organoids in breast cancer research and treatment

Tumor heterogeneity is a major challenge for breast cancer researchers who have struggled to find effective treatments despite recent advances in oncology. Although the use of 2D cell culture methods in breast cancer research has been effective, it cannot model the heterogeneity of breast cancer as found within the body. The development of 3D culture of tumor cells and breast cancer organoids has provided a new approach in breast cancer research, allowing the identification of biomarkers, study of the interaction of tumor cells with the microenvironment, and for drug screening and discovery. In addition, the possibility of gene editing in organoids, especially using the CRISPR/Cas9 system, is convenient, and has allowed a more detailed study of tumor behavior in models closer to the physiological condition. The present review covers the application of organoids in breast cancer research. The recent use of gene-editing systems to provide insights into therapeutic approaches for breast cancer, is highlighted. The study of organoids and the possibility of gene manipulation may be a step towards the personalized treatment of breast cancer, which has so far remained unattainable due to the high heterogeneity of breast cancer.

Circulating Tumor Cells from Enumeration to Analysis: Current Challenges and Future Opportunities

Simple Summary

With estimated numbers of 1–10 per mL of blood, circulating tumor cells (CTCs) are extremely rare compared to white (a few million) or red (billions) blood cells. Given their critical role in metastasis, CTCs have enormous potential as a biomarker for cancer diagnosis, prognosis, and monitoring of treatment response. There are now efforts to characterize CTCs more precisely through molecular and functional analysis, expanding the CTC effort from one of diagnosis and prognosis to now include the use of CTCs to specifically target cancers and discover therapeutic solutions, establishing CTCs as critical in precision medicine. This article summarizes current knowledge about CTC isolation technologies and discusses the translational benefits of different types of downstream analysis approaches, including single-CTC analysis, ex vivo expansion of CTCs, and characterization of CTC-associated cells.

Abstract

Circulating tumor cells (CTCs) have been recognized as a major contributor to distant metastasis. Their unique role as metastatic seeds renders them a potential marker in the circulation for early cancer diagnosis and prognosis as well as monitoring of therapeutic response. In the past decade, researchers mainly focused on the development of isolation techniques for improving the recovery rate and purity of CTCs. These developed techniques have significantly increased the detection sensitivity and enumeration accuracy of CTCs. Currently, significant efforts have been made toward comprehensive molecular characterization, ex vivo expansion of CTCs, and understanding the interactions between CTCs and their associated cells (e.g., immune cells and stromal cells) in the circulation. In this review, we briefly summarize existing CTC isolation technologies and specifically focus on advances in downstream analysis of CTCs and their potential applications in precision medicine. We also discuss the current challenges and future opportunities in their clinical utilization.