Larsen, M. S. | bioRxiv Science

bioRxiv2026

Revisiting Mouse Cardiac Myocyte Isolation: A Simplified Langendorff-based Method

This protocol describes a Langendorff-based method for isolating intact adult mouse ventricular myocytes using syringe pump-driven perfusion. The approach retains the key physiological advantage of the conventional Langendorff technique, continuous retrograde coronary perfusion, while simplifying the overall setup. By combining retrograde aortic perfusion with widely available laboratory equipment, the method provides an accessible alternative to traditional Langendorff systems. A precision syringe pump connected to an in-line heater is used to deliver temperature-controlled, constant-flow perfusion during enzymatic digestion. In contrast to gravity-driven constant-pressure systems, constant-flow perfusion maintains stable enzyme delivery despite changes in coronary resistance that occur during tissue digestion. Use of an inline heater allows precise, rapid temperature-controlled delivery, avoiding the complexity, leak risk, thermal lag, and contamination susceptibility associated with traditional water-jacketed systems. Our setup reduces variability in perfusion rate and minimizes susceptibility to occlusion, flow interruption, or compliance-related artifacts, enhancing reproducibility. The method consistently yields adult ventricular myocytes with high viability (>70% rod-shaped, calcium-tolerant), enabling a broad range of functional analyses including electrophysiology, contractile performance and calcium handling. Step-by-step instructions, troubleshooting guidance, and anticipated outcomes are provided to facilitate adoption in laboratories without dedicated isolated-heart perfusion infrastructure. Key FeaturesO_LISimplified Langendorff-based mouse cardiomyocyte isolation method that eliminates the need for specialized perfusion rigs. C_LIO_LISyringe pump-driven constant-flow perfusion combined with inline temperature control improves reproducibility by ensuring stable enzyme delivery and precise temperature regulation. C_LIO_LIGenerates high-yield, calcium-tolerant adult mouse ventricular myocytes suitable for functional studies. C_LI Graphical Overview O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=190 SRC="FIGDIR/small/718810v1_ufig1.gif" ALT="Figure 1"> View larger version (63K): org.highwire.dtl.DTLVardef@15061cdorg.highwire.dtl.DTLVardef@44fd48org.highwire.dtl.DTLVardef@1509285org.highwire.dtl.DTLVardef@c362a4_HPS_FORMAT_FIGEXP M_FIG Graphical overview of the simplified Langendorff-based mouse cardiac myocyte isolation protocol. C_FIG

physiology↗

bioRxiv2019

SHARQnet - Sophisticated Harmonic Artifact Reduction in Quantitative Susceptibility Mapping using a Deep Convolutional Neural Network

Quantitative susceptibility mapping (QSM) reveals pathological changes in widespread diseases such as Parkinsons disease, Multiple Sclerosis, or hepatic iron overload. QSM requires multiple processing steps after the acquisition of magnetic resonance imaging (MRI) phase measurements such as unwrapping, background field removal and the solution of an ill-posed field-to-source-inversion. Current techniques utilize iterative optimization procedures to solve the inversion and background field correction, which are computationally expensive and lead to suboptimal or over-regularized solutions requiring a careful choice of parameters that make a clinical application of QSM challenging. We have previously demonstrated that a deep convolutional neural network can invert the magnetic dipole kernel with a very efficient feed forward multiplication not requiring iterative optimization or the choice of regularization parameters. In this work, we extended this approach to remove background fields in QSM. The prototype method, called SHARQnet, was trained on simulated background fields and tested on 3T and 7T brain datasets. We show that SHARQnet outperforms current background field removal procedures and generalizes to a wide range of input data without requiring any parameter adjustments. In summary, we demonstrate that the solution of ill-posed problems in QSM can be achieved by learning the underlying physics causing the artifacts and removing them in an efficient and reliable manner and thereby will help to bring QSM towards clinical applications.

neuroscience↗