The development of three-dimensional (3D) cell culture systems has greatly expanded over the past few decades. 3D cell culture models – which are used to better illustrate the cellular structure of human biological diseases in the laboratory–are now used in a variety of research and therapeutic studies. These include, but are not limited to: developmental biology, tissue morphogenesis and engineering, gene and protein expression, disease pathology, drug discovery and other therapeutic developments, and regenerative medicine. This technology is leading to biological and biochemical discoveries and will be the focus of many conversations around innovation at this year’s Society for Laboratory Automation and Screening (SLAS) Conference and Exhibition. It is helping scientists transform research breakthroughs into real-world outcomes.
The need for more physiologically relevant cell culture models grew out of the observed limitations of the two-dimensional (2D) cell culture techniques that have been used since the early
20th century. 2D cell culture studies have provided many insights into cell development, disease pathology, tissue morphogenesis and other areas. However, such studies have limitations in that the monolayer and microenvironment of 2D culture systems do not mimic in vivo conditions, which limits the predictive power of data obtained using 2D models.
In the body, tissue is organized into a complex 3D configuration surrounded by extracellular matrix (ECM) to form the tissue’s microenvironment. Cells interact with neighboring cells and ECM components via chemical and mechanical signals, forming a complex communication network. Key events in the cell life cycle—and ultimately tissue function—are regulated by this communication network, such as viability, differentiation, morphology, proliferation, gene expression, response to stimuli and overall cell function. Thus, a physiologically relevant cell culture model is one that emulates the in vivo tissue organization and microenvironment so that cellular interaction and function within the cultured microtissue mimics the in vivo tissue as closely as possible.
Many innovative techniques, instruments and software have been designed over the last several years to increase the development and usability of 3D cell culture systems. This article considers the needs and challenges of 3D cell culture systems and some of the innovations that are supporting the development and use of such models to help power smarter science.
The need for more physiologically relevant cell culture models grew out of the observed limitations of the two-dimensional (2D) cell culture techniques that have been used since the early
20th century. 2D cell culture studies have provided many insights into cell development, disease pathology, tissue morphogenesis and other areas. However, such studies have limitations in that the monolayer and microenvironment of 2D culture systems do not mimic in vivo conditions, which limits the predictive power of data obtained using 2D models.
In the body, tissue is organized into a complex 3D configuration surrounded by extracellular matrix (ECM) to form the tissue’s microenvironment. Cells interact with neighboring cells and ECM components via chemical and mechanical signals, forming a complex communication network. Key events in the cell life cycle—and ultimately tissue function—are regulated by this communication network, such as viability, differentiation, morphology, proliferation, gene expression, response to stimuli and overall cell function. Thus, a physiologically relevant cell culture model is one that emulates the in vivo tissue organization and microenvironment so that cellular interaction and function within the cultured microtissue mimics the in vivo tissue as closely as possible.
Many innovative techniques, instruments and software have been designed over the last several years to increase the development and usability of 3D cell culture systems. This article considers the needs and challenges of 3D cell culture systems and some of the innovations that are supporting the development and use of such models to help power smarter science.
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