Dual topologies of myotomal collagen XV and Tenascin C act in concert to guide and shape developing motor axons.

During development, motor axons are guided toward muscle target by various extrinsic cues including extracellular matrix (ECM) proteins whose identities and cellular source remain poorly characterized. Here, using single-cell RNAseq of sorted GFP+ cells from smyhc1:gfp-injected zebrafish embryos, we unravel the slow muscle progenitors (SMP) pseudotemporal trajectory at the single-cell level and show that differentiating SMPs are a major source of ECM proteins. The SMP core-matrisome was characterized and computationally predicted to form a basement membrane-like structure tailored for motor axon guidance, including basement membrane-associated ECM proteins, as collagen XV-B, one of the earliest core-matrisome gene transcribed in differentiating SMPs and the glycoprotein Tenascin C. To investigate how contact-mediated guidance cues are organized along the motor path to exert their function in vivo, we used microscopy-based methods to analyze and quantify motor axon navigation in tnc and col15a1b knock-out fish. We show that motor axon shape and growth rely on the timely expression of the attractive cue Collagen XV-B that locally provides axons with a permissive soft microenvironment and separately organizes the repulsive cue Tenascin C into a unique functional dual topology. Importantly, bioprinted micropatterns that mimic this in vivo ECM topology were sufficient to drive directional motor axon growth. Our study offers evidence that not only the composition of ECM cues but their topology critically influences motor axon navigation in vertebrates with potential applications in regenerative medicine for peripheral nerve injury as regenerating nerves follow their original path.

Mapping uncertainties involved in sound source reconstruction with a cross-spectral-matrix-based Gibbs sampler.

The reconstruction of sound sources by using inverse methods is known to be prone to estimation errors due to measurement noise, model mismatch, and poor conditioning of the inverse problem. This paper introduces a solution to map the estimation errors together with the reconstructed sound sources. From a Bayesian perspective, it initializes a Gibbs sampler with the Bayesian focusing method. The proposed Gibbs sampler is shown to converge within a few iterations, which makes it realistic for practical purposes. It also turns out to be very flexible in various scenarios. One peculiarity is the capability to directly operate on the cross-spectral matrix. Another one is to easily accommodate sparse priors. Eventually, it can also account for uncertainties in the microphone positions, which reinforces the regularization of the inverse problem.