Motta, G. | Search bioRxiv

bioRxiv2025

Biplanar nulling coil system for OPM-MEG using printed circuit boards

Optically pumped magnetometers (OPMs) are a promising sensor technology for non-invasive measurement of human electrophysiological signals, in particular the magnetoencephalogram (MEG). OPMs do not need cryogenic cooling and can be placed conformal to the subjects scalp, thus greatly reducing the sensor-to-source distance and improving signal sensitivity. OPMs, however, require near-zero background magnetic field to achieve linearity and minimize signal distortion. Prior work has proposed the use of biplanar field nulling coils to remove the uniform and gradient components of the background magnetic field. Biplanar coils have been expensive to construct, involving tedious error-prone manual winding of over 1000 m of copper wire. In this work, we designed and fabricated background field nulling coils (three uniform and three gradient components) on two-layer Printed Circuit Boards (PCBs). We used an open-source software (bfieldtools) to determine the current loops needed to produce the target magnetic field in a 50-cm-diameter spherical volume. We developed a software-based approach to connect the discrete current loops into a continuous conducting path traversing the two layers of the PCB. For ease of manufacture, the designed (1.5 x 1.5 m2) coils were cut along the symmetry axis and printed as pairs of 1.5 x 0.75 m2 PCBs (2 oz Cu), soldered together and mounted on a sliding aluminum frame. The efficiency of the coils (1.3 - 7.1 nT/mA) was similar or higher than previously reported in the literature. We mapped the field inside the target region after field nulling inside our single-layer shielded room and were able to reduce the largest component of the background field from 21 to 2 nT. Using our nulling coil system, we were able to operate OPMs in a lightly shielded room (background field varying from 6.5 to 108 nT in the floor-to-ceiling direction) to record somatosensory evoked fields (SEFs) comparable to those measured using SQUID-based MEG in a 3-layer shielded room. We disseminate the software and hardware as an open-source package opmcoils. This work will facilitate access to more affordable field nulling coils for OPM-MEG and help to realize the potential of OPM-MEG as an accessible sensor technology for use in human neuroscience.

neuroscience↗

bioRxiv2024

AI-assisted imaging screening reveals mechano-molecular tissue organizers and network of signaling hubs.

Cadherin-mediated adhesions are crucial mechanical and signaling hubs that connect cells within a tissue and probe the mechanics of the surrounding environment. They constitute a physical link between the actin cytoskeleton of neighboring cells, providing the mechanical coordination needed for morphogenetic processes, tissue homeostasis, collective migration, and regeneration. Disruptions in adhesion mechanisms are closely linked to the breakdown of epithelial structure and the emergence of disease-related traits characteristic of cancer progression. The cadhesome network comprises over 170 structural and regulatory proteins involved in cadherin-mediated adhesion. While this network is essential for coordinating tissue responses to mechanical stress, its complexity has historically limited our understanding of how individual components contribute to force transmission and tissue homeostasis. Recent technological advances offer tools to investigate large molecular networks in cellular function and pathology (functional omics). Leveraging these advances, we developed an experimental and analytical platform combining high-throughput gene silencing, imaging, and artificial intelligence (AI) to systematically profile each role of each protein in tissue formation, mechanical stability, and response to induced tension. Using EpH4 cells as an epithelial tissue model, we performed systematic silencing in triplicate, capturing a range of tissue phenotypes under baseline and tension-inducing conditions. Machine learning methods were used to analyze complex imaging data, quantify tissue ruptures, characterize junctional organization, and measure tension states of the tissue. By incorporating machine learning algorithms, we automated image feature extraction, clustering, and classification, enabling an unprecedented quantitative evaluation of tissue mechanics at scale. Our machine learning models allowed us to identify significant patterns, including protein-specific responses to tension and their roles in tissue-level mechanical integrity. Finally, we constructed a protein interaction network detailing the roles of each protein, their physical interactions, and known links to cancer. The network analysis revealed three prominent mechanotransductive and signaling subnetworks centered around E-cadherin, EGFR, and RAC1. Our study provides a foundational framework for investigating mechanosensing proteins and it offers a scalable blueprint for discovering potential therapeutic targets in diseases like cancer, where tissue mechanics play a crucial role. TeaserAI-aided screening identifies key regulators of epithelial tissue mechanics, uncovering potential therapeutic targets in cancer.

cell biology↗