Biology subjects

Madany, M.

Publications and source records attributed to Madany, M..

5 recordsLinked to original sources

bioRxiv2026

Resilience to neuronal hyperactivity and restoration of the neuroimmune interactome by blocking fibrin-induced microglia activation in Alzheimers disease

Cerebrovascular pathology and neuronal network dysfunction are early features of Alzheimers disease (AD) associated with neuroinflammation and cognitive decline, but the vascular and immune triggers of neuronal hyperactivity remain largely unknown. Here, we show that the blood coagulation protein fibrin disrupts microglia-neuron interactions, promoting neuronal hyperactivity in an AD mouse model. Genetic elimination of the fibrin inflammatory domain reduced neuronal hyperactivity, restored dynamic microglial interactions with active neurons and protected from high-risk decision making in 5XFAD mice. Leveraging the transcriptional signatures of microglia and inhibitory and excitatory neurons, a ligand-receptor atlas revealed fibrin-dependent disruption of innate immune and glutamatergic signaling between microglia and neurons in AD mice. Patients with AD also showed a correlation of cerebrospinal fluid (CSF) fibrinogen levels with biomarkers of inflammation, vascular and synaptic dysfunction. Thus, resilience to neuronal hyperactivity and restoration of the neuroimmune interactome by targeting fibrin may have therapeutic implications for Alzheimers disease and related conditions. There is a companion manuscript submitted to bioRxiv (Lauderdale et al., 2026) HighlightsO_LIVascular-microglia axis drives neuronal hyperactivity C_LIO_LIFibrin inflammatory activity disrupts the microglia-neuron interactome C_LIO_LIMicroglia activation by fibrin impairs decision-making in AD mice C_LIO_LISynaptic dysfunction and immune biomarkers correlate with CSF fibrinogen in AD patients C_LI

neuroscience↗

bioRxiv2021

Systematic morphological and morphometric analysis of identified olfactory receptor neurons in Drosophila melanogaster

The biophysical properties of sensory neurons are influenced by their morphometric and morphological features, whose precise measurements require high-quality volume electron microscopy (EM). However, systematic surveys of these nanoscale characteristics for identified neurons are scarce. Here, we characterize the morphology of Drosophila olfactory receptor neurons (ORNs) across the majority of genetically identified sensory hairs. By analyzing serial block-face electron microscopy (SBEM) images of cryofixed antennal tissues, we compile an extensive morphometric dataset based on 122 reconstructed 3D models of 33 identified ORN types. In addition, we observe multiple novel features--including extracellular vacuoles within sensillum lumen, intricate dendritic branching, mitochondria enrichment in select ORNs, novel sensillum types, and empty sensilla containing no neurons--which raise new questions pertinent to cell biology and sensory neurobiology. Our systematic survey is critical for future investigations into how the size and shape of sensory neurons influence their responses, sensitivity and circuit function.

neuroscience↗

bioRxiv2020

A genetic probe for visualizing glutamatergic synapses and vesicles by 3D electron microscopy

Communication between neurons relies on the release of diverse neurotransmitters, which represent a key-defining feature of a neurons chemical and functional identity. Neurotransmitters are packaged into vesicles by specific vesicular transporters. However, tools for labeling and imaging synapses and synaptic vesicles based on their neurochemical identity remain limited. We developed a genetically encoded probe to identify glutamatergic synaptic vesicles at the levels of both light and electron microscopy (EM) by fusing the mini singlet oxygen generator (miniSOG) probe to an intra-lumenal loop of the vesicular glutamate transporter-2. We then used a 3D imaging method, serial block face scanning EM, combined with a deep learning approach for automatic segmentation of labeled synaptic vesicles to assess the subcellular distribution of transporter-defined vesicles at nanometer scale. These tools represent a new resource for accessing the subcellular structure and molecular machinery of neurotransmission and for transmitter-defined tracing of neuronal connectivity.

neuroscience↗

bioRxiv2020

CDeep3M-Preview: Online segmentation using the deep neural network model zoo

Sharing deep neural networks and testing the performance of trained networks typically involves a major initial commitment towards one algorithm, before knowing how the network will perform on a different dataset. Here we release a free online tool, CDeep3M-Preview, that allows end-users to rapidly test the performance of any of the pre-trained neural network models hosted on the CIL-CDeep3M modelzoo. This feature makes part of a set of complementary strategies we employ to facilitate sharing, increase reproducibility and enable quicker insights into biology. Namely we: (1) provide CDeep3M deep learning image segmentation software through cloud applications (Colab and AWS) and containerized installations (Docker and Singularity) (2) co-hosting trained deep neural networks with the relevant microscopy images and (3) providing a CDeep3M-Preview feature, enabling quick tests of trained networks on user provided test data or any of the publicly hosted large datasets. The CDeep3M-modelzoo and the cellimagelibrary.org are open for contributions of both, trained models as well as image datasets by the community and all services are free of charge.

neuroscience↗

bioRxiv2018

CDeep3M - Plug-and-Play cloud based deep learning for image segmentation of light, electron and X-ray microscopy

As biological imaging datasets increase in size, deep neural networks are considered vital tools for efficient image segmentation. While a number of different network architectures have been developed for segmenting even the most challenging biological images, community access is still limited by the difficulty of setting up complex computational environments and processing pipelines, and the availability of compute resources. Here, we address these bottlenecks, providing a ready-to-use image segmentation solution for any lab, with a pre-configured, publicly available, cloud-based deep convolutional neural network on Amazon Web Services (AWS). We provide simple instructions for training and applying CDeep3M for segmentation of large and complex 2D and 3D microscopy datasets of diverse biomedical imaging modalities.

cell biology↗