Publications

Aim: To unveil the influence of cell-matrix adhesions in the establishment of gap junction intercellular communication (GJIC) during cell condensation in chondrogenesis. Materials & methods: Previously developed nanopatterns of the cell adhesive ligand arginine-glycine-aspartic acid were used as cell culture substrates to control cell adhesion at the nanoscale. In vitro chondrogenesis of mesenchymal stem cells was conducted on the nanopatterns. Cohesion and GJIC were evaluated in cell condensates. Results: Mechanical stability and GJIC are enhanced by a nanopattern configuration in which 90% of the surface area presents adhesion sites separated less than 70 nm, thus providing an onset for cell signaling. Conclusion: Cell-matrix adhesions regulate GJIC of mesenchymal cell condensates during in vitro chondrogenesis from a threshold configuration at the nanoscale.

JTD Keywords: arginine-glycine-aspartic acid, arginine–glycine–aspartic acid, cell adhesion, condensation, dendrimer-based nanopatterning, gap junction intercellular communication, Actin, Adhesion, Arginine-glycine-aspartic acid, Cell adhesion, Collagen, Condensation, Connexin-43, Dendrimer-based nanopatterning, Dynamics, Extracellular-matrix, Fibronectin, Gap junction intercellular communication, Mesenchymal stem cells, Permeability, Phenotype, Vinculin

Remarkable progress in bioengineering over the past two decades has enabled the formulation of fundamental design principles for a variety of medical and non-medical applications. These advancements have laid the foundation for building multicellular engineered living systems (M-CELS) from biological parts, forming functional modules integrated into living machines. These cognizant design principles for living systems encompass novel genetic circuit manipulation, self-assembly, cell–cell/matrix communication, and artificial tissues/organs enabled through systems biology, bioinformatics, computational biology, genetic engineering, and microfluidics. Here, we introduce design principles and a blueprint for forward production of robust and standardized M-CELS, which may undergo variable reiterations through the classic design-build-test-debug cycle. This Review provides practical and theoretical frameworks to forward-design, control, and optimize novel M-CELS. Potential applications include biopharmaceuticals, bioreactor factories, biofuels, environmental bioremediation, cellular computing, biohybrid digital technology, and experimental investigations into mechanisms of multicellular organisms normally hidden inside the “black box” of living cells.

JTD Keywords: cell-fate specification, endothelial-cells, escherichia-coli, extracellular-matrix, gene-expression noise, nuclear hormone-receptors, pluripotent stem-cells, primitive endoderm, transcription factors, Artificial tissues, Assembly cells, Biological parts, Biological systems, Bioremediation, Blood-brain-barrier, Cell engineering, Cell/matrix communication, Design principles, Environmental technology, Functional modules, Fundamental design, Genetic circuits, Genetic engineering, Living machines, Living systems, Medical applications, Molecular biology, Synthetic biology

This chapter introduces the “Capabilities” section of the Handbook of Living Machines. Where the previous section considered building blocks, we recognize that components or modules do not automatically make systems. Hence, in the remainder of this handbook, the emphasis is toward the capabilities of living systems and their emulation in artifacts. Capabilities often arise from the integration of multiple components and thus sensitize us to the need to develop a system-level perspective on living machines. Here we summarize and consider the 14 contributions in this section which cover perception, action, cognition, communication, and emotion, and the integration of these through cognitive architectures into systems that can emulate the full gamut of integrated behaviors seen in animals including, potentially, our own capacity for consciousness.

JTD Keywords: Action, Cognition, Cognitive architecture, Communication, Consciousness, Emotion, Perception

Eph receptor signaling plays key roles in vertebrate tissue boundary formation, axonal pathfinding, and stem cell regeneration by steering cells to positions defined by its ligand ephrin. Some of the key events in Eph-ephrin signaling are understood: ephrin binding triggers the clustering of the Eph receptor, fostering transphosphorylation and signal transduction into the cell. However, a quantitative and mechanistic understanding of how the signal is processed by the recipient cell into precise and proportional responses is largely lacking. Studying Eph activation kinetics requires spatiotemporal data on the number and distribution of receptor oligomers, which is beyond the quantitative power offered by prevalent imaging methods. Here we describe an enhanced fluorescence fluctuation imaging analysis, which employs statistical resampling to measure the Eph receptor aggregation distribution within each pixel of an image. By performing this analysis over time courses extending tens of minutes, the information-rich 4D space (x, y, oligomerization, time) results were coupled to straightforward biophysical models of protein aggregation. This analysis reveals that Eph clustering can be explained by the combined contribution of polymerization of receptors into clusters, followed by their condensation into far larger aggregates. The modeling reveals that these two competing oligomerization mechanisms play distinct roles: polymerization mediates the activation of the receptor by assembling monomers into 6- to 8-mer oligomers; condensation of the preassembled oligomers into large clusters containing hundreds of monomers dampens the signaling. We propose that the polymerization–condensation dynamics creates mechanistic explanation for how cells properly respond to variable ligand concentrations and gradients.

JTD Keywords: Eph, Ephrin, Receptor tyrosine kinase, Gradients, Cell communication

Wireless sensors are an emerging technology that allows to assist physicians in the monitoring of patients health status. This approach can be used for the non-invasive recording of the electrical respiratory muscle activity of the diaphragm (EMGdi). In this work, we acquired the EMGdi signal of a healthy subject performing an inspiratory load test. To this end, the EMGdi activity was captured from a single channel of electromyography using a wireless platform which was compared with the EMGdi and the inspiratory mouth pressure (Pmouth) recorded with a conventional lab equipment. From the EMGdi signal we were able to evaluate the neural respiratory drive, a biomarker used for assessing the respiratory muscle function. In addition, we evaluated the breathing movement and the cardiac activity, estimating two cardio-respiratory parameters: the respiratory rate and the heart rate. The correlation between the two EMGdi signals and the Pmouth improved with increasing the respiratory load (Pearson's correlation coefficient ranges from 0.33 to 0.85). The neural respiratory drive estimated from both EMGdi signals showed a positive trend with an increase of the inspiratory load and being higher in the conventional EMGdi recording. The respiratory rate comparison between measurements revealed similar values of around 16 breaths per minute. The heart rate comparison showed a root mean error of less than 0.2 beats per minute which increased when incrementing the inspiratory load. In summary, this preliminary work explores the use of wireless devices to record the muscle respiratory activity to derive several physiological parameters. Its use can be an alternative to conventional measuring systems with the advantage of being portable, lightweight, flexible and operating at low energy. This technology can be attractive for medical staff and may have a positive impact in the way healthcare is being delivered.

JTD Keywords: Biomedical monitoring, Electrodes, Medical services, Monitoring, Muscles, Wireless communication, Wireless sensor networks

Ischemic and dilated cardiomyopathy are associated with disorders of myocardium. Using the blood pressure (BP) signal and the values of the ventricular ejection fraction, we obtained parameters for stratifying cardiomyopathy patients as low- and high-risk. We studied 48 cardiomyopathy patients characterized by NYHA ≥2: 19 patients with dilated cardiomyopathy (DCM) and 29 patients with ischemic cardiomyopathy (ICM). The left ventricular ejection fraction (LVEF) percentage was used to classify patients in low risk (LR: LVEF > 35%, 17 patients) and high risk (HR: LVEF ≤ 35%, 31 patients) groups. From the BP signal, we extracted the upward systolic slope (BP_sl), the difference between systolic and diastolic BP (BPA), and systolic time intervals (STI). When we compared the LR and HR groups in the time domain analysis, the best parameters were standard deviation (SD) of 1=STI, kurtosis (K) of BP_sl, and K of BPA. In the frequency domain analysis, very low frequency (VLF) and high frequency (HF) bands showed statistically significant differences in comaprisons of LR and HR groups. The area under the curve of power spectral density was the best parameter in all classifications, and particularly in the very-low-and high- frequency bands (p <; 0.001). These parameters could help to improve the risk stratification of cardiomyopathy patients.

JTD Keywords: blood pressure measurement, cardiovascular system, diseases, medical disorders, medical signal processing, statistical analysis, time-domain analysis, BP signal, HR groups, LR groups, blood pressure signal analysis, cardiomyopathy patients, diastolic BP, dilated cardiomyopathy, frequency domain analysis, high-frequency bands, ischemic cardiomyopathy, left ventricular ejection fraction, low-frequency bands, myocardium disorders, patient characterization, power spectral density curve, standard deviation, statistical significant differences, systolic BP, systolic slope, systolic time intervals, time domain analysis, ventricular ejection fraction, Abstracts, Databases, Parameter extraction, Telecommunication standards, Time-frequency analysis