Sensor technologies for quality control in engineered tissue manufacturing

Callus organoids reveal distinct cartilage to bone transition mechanisms across donors and a role for biological sex

Clinical translation of tissue-engineered advanced therapeutic medicinal products is hindered by a lack of patient-dependent and independent in-process biological quality controls that are reflective of in vivo outcomes. Recent insights into the mechanism of native bone repair highlight a robust path dependence. Organoid-based bottom-up developmental engineering mimics this path-dependence to design personalized living implants scaffold-free, with in-build outcome predictability. Yet, adequate (noninvasive) quality metrics of engineered tissues are lacking. Moreover, insufficient insight into the role of donor variability and biological sex as influencing factors for the mechanism toward bone repair hinders the implementation of such protocols for personalized bone implants. Here, male and female bone-forming organoids were compared to non-bone-forming organoids regarding their extracellular matrix composition, transcriptome, and secreted proteome signatures to directly link in vivo outcomes to quality metrics. As a result, donor variability in bone-forming callus organoids pointed towards two distinct pathways to bone, through either a hypertrophic cartilage or a fibrocartilaginous template. The followed pathway was determined early, as a biological sex-dependent activation of distinct progenitor populations. Independent of donor or biological sex, a cartilage-to-bone transition was driven by a common panel of secreted factors that played a role in extracellular matrix remodeling, mineralization, and attraction of vasculature. Hence, the secreted proteome is a source of noninvasive biomarkers that report on biological potency and could be the missing link toward data-driven decision-making in organoid-based bone tissue engineering.

Advancements in Applications of Manufacturing and Measurement Sensors

Manufacturing and measurement sensors are an integral part of advanced manufacturing technology, which requires sensors that can precisely capture and analyze various physical parameters during the manufacturing process [...].

Challenges and opportunities in engineering next-generation 3D microelectronic devices: improved performance and higher integration density

In recent years, nanotechnology and materials science have evolved and matured, making it increasingly easier to design and fabricate next-generation 3D microelectronics. The process has changed drastically from traditional 2D microelectronics, resulting in improved performance, higher integration density, and new functionalities. As applications become more complex and power-intensive, this technology can address the demands of high-performance computing, advanced sensors, and cutting-edge communication systems via wearable, flexible devices, etc. To manufacture higher-density microelectronics, recent advances in the fabrication of such 3D devices are discussed. Furthermore, the paper stresses the importance of novel materials and architectures, such as monolithic 3D integration and heterogeneous integration, in overcoming these challenges. We emphasize the importance of addressing complex issues to achieve better performance and higher integration density, which will play an important role in shaping the next generation of microelectronic devices. The multifaceted challenges involved in developing next-generation 3D microelectronic devices are also highlighted.

An Orthogonal Workflow of Electrochemical, Computational, and Thermodynamic Methods Reveals Limitations of Using a Literature-Reported Insulin Binding Peptide in Biosensors

Developing a continuous insulin-monitoring biosensor is of great importance for both the cellular biomanufacturing industry and for treating diabetes mellitus. Such a sensor needs to be able to effectively monitor insulin across a range of temperatures and pHs and with varying concentrations of competing analytes. One of the two main components of any biosensor is the recognition element, which is responsible for interacting with the molecule of interest. Prior literature describes an insulin-binding peptide (IBP) that was reported to bind to insulin with a 3 nM affinity. Here, we used orthogonal and complementary electrochemical, computational, and thermodynamic characterization methods to evaluate IBP's appropriateness for use in a biosensor. Unfortunately, all three methods failed to produce evidence of IBP-insulin binding either on surfaces or in solution. This indicates that the binding exhibited in previous reports is likely restricted to a limited set of conditions and that IBP is not a suitable recognition element for a continuous insulin biosensor.

Non-neotissue constituents as underestimated confounders in the assessment of tissue engineered constructs by near-infrared spectroscopy

Non-destructive assessments are required for the quality control of tissue-engineered constructs and the optimization of the tissue culture process. Near-infrared (NIR) spectroscopy coupled with machine learning (ML) provides a promising approach for such assessment. However, due to its nonspecific nature, each spectrum incorporates information on both neotissue and non-neotissue constituents of the construct; the effect of these constituents on the NIR-based assessments of tissue-engineered constructs has been overlooked in previous studies. This study investigates the effect of scaffolds, growth factors, and buffers on NIR-based assessments of tissue-engineered constructs. To determine if these non-neotissue constituents have a measurable effect on the NIR spectra of the constructs that can introduce bias in their assessment, nine ML algorithms were evaluated in classifying the NIR spectra of engineered cartilage according to the scaffold used to prepare the constructs, the growth factors added to the culture media, and the buffers used for storing the constructs. The effect of controlling for these constituents was also evaluated using controlled and uncontrolled NIR-based ML models for predicting tissue maturity as an example of neotissue-related properties of interest. Samples used in this study were prepared using norbornene-modified hyaluronic acid scaffolds with or without the conjugation of an N-cadherin mimetic peptide. Selected samples were supplemented with transforming growth factor-beta1 or bone morphogenetic protein-9 growth factor. Some samples were frozen in cell lysis buffer, while the remaining samples were frozen in PBS until required for NIR analysis. The ML models for classifying the spectra of the constructs according to the four constituents exhibited high to fair performances, with F1 scores ranging from 0.9 to 0.52. Moreover, controlling for the four constituents significantly improved the performance of the models for predicting tissue maturity, with improvement in F1 scores ranging from 0.09 to 0.77. In conclusion, non-neotissue constituents have measurable effects on the NIR spectra of tissue-engineered constructs that can be detected by ML algorithms and introduce bias in the assessment of the constructs by NIR spectroscopy. Therefore, controlling for these constituents is necessary for reliable NIR-based assessments of tissue-engineered constructs.