Basis of Image Analysis for Evaluating Cell Biomaterial Interaction Using Brightfield Microscopy

Abstract

Medical imaging is a growing field that has stemmed from the need to conduct noninvasive diagnosis, monitoring, and analysis of biological systems. With the developments and advances in the medical field and the new techniques that are used in the intervention of diseases, very soon the prevalence of implanted biomedical devices will be even more significant. The implanted materials in a biological system are used in diverse fields, which require lengthy evaluation and validation processes. However, currently the evaluation of the toxicity of biomaterials has not been fully automated yet. Moreover, image analysis is an integral part of biomaterial research, but it is not within the core capacities of a significant portion of biomaterial scientists, which results in the use of predominantly ready-made tools. The detailed image analysis can be conducted once all the relevant parameters including the inherent characteristics of image acquisition techniques are considered. Herein, we cover the currently used image analysis-based techniques for assessment of biomaterial/cell interaction with a specific focus on unstained brightfield microscopy acquired mostly in but not limited to microfluidic systems, which serve as multiparametric sensing platforms for noninvasive experimental measurements. We present the major imaging acquisition techniques that enable point-of-care testing when incorporated with microfluidic cells, discuss the constraints enforced by the geometry of the system and the material that is analyzed, and the challenges that rise in the image analysis when unstained cell imaging is employed. Emerging techniques such as utilization of machine learning and cell-specific pattern recognition algorithms and potential future directions are discussed. Automation and optimization of biomaterial assessment can facilitate the discovery of novel biomaterials together with making the validation of biomedical innovations cheaper and faster.

Characterization of the Impact of Classical Cell‐culture Media on the Response of Electrochemical Sensors

“…For the end‐point of the cytotoxicity tests, MTT tests were used instead. For validation of the metabolic activity results, bright field images of the cells were taken regularly and analysed as described elsewhere [17].…”

Preclinical in vitro evaluation of implantable materials: conventional approaches, new models and future directions

“…Complex systems for standardized biomaterials evaluation are being developed too. For instance, PANBioRA biomaterials risk assessment system has been developing a modular apparatus for preclinical material's evaluation using cutting edge technologies such as cytotoxicity monitoring using sensors (Chmayssem et al, 2021;Chmayssem et al, 2022), organ-on-a-chip models (Huang et al, 2021), and mathematical modelling (Uka et al, 2021;Šušteršič et al, 2021).…”

A miniaturized genotoxicity evaluation system for fast biomaterial-related risk assessment

“…Among them, the PANBioRA project has developed a modular system for preclinical materials evaluation using cutting edge technologies, including cytotoxicity monitoring by means of sensors, 11,12 organ-on-a-chip models, 13 and mathematical modelling. 14,15…”

“…Among them, the PANBioRA project has developed a modular system for preclinical materials evaluation using cutting edge technologies, including cytotoxicity monitoring by means of sensors, 11,12 organ-on-a-chip models, 13 and mathematical modelling. 14,15 In the current work, we have described a new miniaturized and simplied genotoxicity testing system that saves space and can be observed using uorescence microscopy aer only 24 hours of incubation, thereby reducing the test time by two, as compared to the classic Ames test, and allowing for an easier readout that can be automatized by image analysis. In this test, S. typhimurium strain is pre-incubated with test chemicals and then deposited into agar-coated interconnected wells in a microuidic chip, allowing us to perform each test in triplicate using a single injection (Fig.…”