Biology subjects

Reimann, M.

Publications and source records attributed to Reimann, M..

7 recordsLinked to original sources

bioRxiv2026

Different Paradigms from Computer Vision Align with Human Assessment of the Mouse Grimace Scale

Animal welfare is a central aspect in animal-based research where mice are most commonly used. Their facial expression can be analyzed to assess their well-being status using the Mouse Grimace Scale. However, its manual application becomes increasingly impractical when used on a large number of animals. This lead to the ongoing integration of computer vision methods to automate the analysis. While such methods have proven effective qualitatively, a systematic assessment to verify their reliability largely remains an open research gap. In this work, we attempted to close this gap as we evaluated three dominant paradigms (i.e., classification from supervised learning features, self-supervised learning features, or landmark locations) for the binary (i.e., well-being un-/impaired) classification of facial mouse images. Our quantitative results showed that such methods can be employed successfully with as low as 16% type II error rates. For qualitative assessment, we visualized the decision-making process and demonstrated that mainly pixels associated with the mouse rather than its environment are used. We further discovered that visual characteristics of the mice beyond those described by the Mouse Grimace Scale contributed to the classification. Our work showed that the automated well-being status assessment in mice is trustworthy and urges towards widespread adoption.

animal behavior and cognition↗

bioRxiv2026

Uncovering the Basis of Human ConnectomeComplexity: The Role of Neuronal Morphology

Comparative studies have established differences between the electrophysiology and anatomy of human and rodent cortical circuits. A consistent finding is that human neuronal morphologies display more elaborate neurite shapes than those of rodents, a feature that cannot be accounted for merely by their larger size according to recent findings. Here, we study the impact of these neurite shapes on the structure of synaptic connectivity in their local microcircuitry. Our approach is based on the idea that axonal and dendritic geometries constrain the locations of afferent and efferent synaptic contacts (potential connectivity). Although the mechanisms by which potential connectivity translates into actual synaptic connectivity are manifold and complex, the potential connectivity is nevertheless highly informative for the final structure of a biological connectome. We found that connectomes predicted from human reconstructed morphologies have higher complexity according to several measures that have been demonstrated to be functionally relevant. Going beyond a simple comparison, we demonstrate mechanistically how the shapes of neuron morphologies give rise to non-random and clustered structures observed in experimentally measured connectomes, and how the specific shapes of human neurons strengthen the process. Finally, we conceptually examine how synapse formation processes may interact with potential connectivity, showing that a process compatible with Hebbian plasticity leads to the highest complexity and best match experimentally observed patterns.

neuroscience↗

bioRxiv2025

iCSD can produce spurious results in dense electrode arrays

Estimation of the current source density (CSD) is a commonly-used method to interpret local field potential (LFP) signals by estimating the location of the neural sinks and sources of current that give rise to the LFP. We show analytically that, when the inter-electrode spacing is small relative to the width of the current distribution, commonly used methods to estimate the CSD produces spurious results, calculating true sources as sinks and vice versa. By simulating a biologically-detailed subvolume of the rat somatosensory cortex with over 200,000 biophysically-detailed neurons, we show that, for high-density recording electrodes, the estimated CSD diverges from expected results, necessitating awareness and careful interpretation of CSD results.

neuroscience↗

bioRxiv2022

Reconstruction and simulation of thalamoreticular microcircuitry

Thalamoreticular circuitry is known to play a key role in attention, cognition and the generation of sleep spindles, and is implicated in numerous brain disorders, but the cellular and synaptic mechanisms remain intractable. Therefore, we developed the first detailed computational model of mouse thalamus and thalamic reticular nucleus microcircuitry that captures morphological and biophysical properties of [~]14,000 neurons connected via [~]6M synapses, and recreates biological synaptic and gap junction connectivity. Simulations recapitulate multiple independent network-level experimental findings across different brain states, providing a novel unifying cellular and synaptic account of spontaneous and evoked activity in both wakefulness and sleep. Furthermore, we found that: 1.) inhibitory rebound produces frequency-selective enhancement of thalamic responses during wakefulness, in addition to its role in spindle generation; 2.) thalamic interactions generate the characteristic waxing and waning of spindle oscillations; and 3.) changes in thalamic excitability (e.g. due to neuromodulation) control spindle frequency and occurrence. The model is openly available and provides a new tool to interpret spindle oscillations and test hypotheses of thalamoreticular circuit function and dysfunction across different network states in health and disease.

neuroscience↗

bioRxiv2021

Atlas of Inhibitory Neurons in the Mouse Brain

The mouse brain contains a rich diversity of inhibitory neuron types that have been characterized by their patterns of gene expression. However, it is still unclear how these cell types are distributed across the mouse brain. We developed a computational method to estimate the densities of different inhibitory neuron types across the mouse brain. Our method allows the unbiased integration of diverse and disparate datasets into one framework to predict inhibitory neuron densities for uncharted brain regions. We constrained our estimates based on previously computed brain-wide neuron densities, gene expression data from in situ hybridization image stacks together with a wide range of values reported in the literature. Using constrained optimization, we derived coherent estimates of cell densities for the different inhibitory neuron types. We estimate that 20.3% of all neurons in the mouse brain are inhibitory. Among all inhibitory neurons, 18% predominantly express parvalbumin (PV), 16% express somatostatin (SST), 3% express vasoactive intestinal peptide (VIP), and the remainder 63% belong to the residual GABAergic population. We find that our density estimations improve as more literature values are integrated. Our pipeline is extensible, allowing new cell types or data to be integrated as they become available. The data, algorithms, software, and results of our pipeline are publicly available and update the Blue Brain Cell Atlas. This work therefore leverages the research community to collectively converge on the numbers of each cell type in each brain region. Author summaryObtaining a global understanding of the cellular composition of the brain is a very complex task, not only because of the great variability that exists between reports of similar counts but also because of the numerous brain regions and cell types that make up the brain. Previously, we presented a model of a cell atlas, which provided an estimate of the densities of neurons, glia and their subtypes for each region in the mouse brain. Here, we describe an extension of this model to include more inhibitory neuron types. We collected estimates of inhibitory neuron counts from literature and built a framework to combine them into a consistent cell atlas. Using brain slice images, we also estimated inhibitory neuron density in regions where no literature data are available. We estimated that in the mouse brain 20.3% of all neurons are inhibitory. Among all inhibitory neurons, 18% predominantly express parvalbumin (PV), 16% express somatostatin (SST), 3% express vasoactive intestinal peptide (VIP), and the remainder 63% belong to the residual GABAergic population. Our approach can be further extended to any other cell type and provides a resource to build tissue-level models of the rodent brain.

neuroscience↗

bioRxiv2021

Tuberculostearic acid (TSA)-containing phosphatidylinositols as reliable marker to determine Mycobacterium tuberculosis bacterial burden

It is estimated that approximately one-fourth of the world's population is infected with strains of the Mycobacterium tuberculosis complex (MTBC), the causative agents of tuberculosis (TB). In this study, we present rationally developed molecular markers for bacterial burden, which are derived from mycobacterial phospholipids. Using lipidomic approaches, we show that tuberculostearic acid (TSA)-containing phosphatidylinositols (PI) are present in all clinically relevant MTBC lineages investigated. For the major abundant lipid PI 16:0_19:0 (TSA), a detection limit equivalent to 102 colony forming units (CFU) was determined for bacterial cultures and approximately 103 for cell culture systems. We further developed a mass spectrometry based targeted lipid assay, which - in contrast to bacterial quantification on solid medium - can be performed within several hours including sample preparation. Translation of this indirect and culture-free detection approach allowed the determination of pathogen loads in infected murine macrophages, human neutrophils and murine lung tissue. We show that marker lipids inferred from the mycobacterial PIs are increased in peripheral blood mononuclear cells (PBMCs) of TB patients beyond the lipid metabolic background in comparison to healthy controls. In a small cohort of drug-susceptible TB patients elevated levels of these marker molecules were detected at therapy start and declined following successful anti-tuberculosis treatment. The concentration of TSA-containing PIs can be used as correlate for reliable and rapid quantification of Mycobacterium tuberculosis (Mtb) burden in experimental in vitro model systems and may also provide a clinically relevant tool for monitoring TB therapy. One Sentence SummaryTuberculostearic acid containing phosphatidylinositols represent a novel, fast to measure, reliable correlate of Mycobacterium tuberculosis bacterial burden in experimental model systems, which makes a future clinical application conceivable.

biochemistry↗

bioRxiv2020

WNT6-ACC2-induced accumulation of triacylglycerol rich lipid droplets is exploited by Mycobacterium tuberculosis

In view of emerging drug-resistant tuberculosis, host directed therapies are urgently needed to improve treatment outcomes with currently available anti-tuberculosis therapies. One option is to interfere with the formation of lipid-laden “foamy” macrophages in the infected host. Here, we provide evidence that WNT6, a member of the evolutionary conserved WNT signaling pathway, promotes foam cell formation by regulating key lipid metabolic genes including acetyl-CoA carboxylase-2 (ACC2) during pulmonary TB. In addition, we demonstrate that Mycobacterium tuberculosis (Mtb) facilitates its intracellular growth and dissemination in the host by exploiting the WNT6-ACC2 pathway. Using genetic and pharmacological approaches, we show that lack of functional WNT6 or ACC2 significantly reduces intracellular TAG levels, Mtb growth and necrotic cell death of macrophages. In combination with the anti-TB drug isoniazid, pharmacological inhibition of ACC2 improved anti-mycobacterial treatment in vitro and in vivo. Therefore, we propose the WNT6-ACC2 signaling pathway as a promising target for a host-directed therapy to reduce intracellular replication of Mtb by modulating neutral lipid metabolism.Competing Interest StatementDrs. N. Reiling and J. Brandenburg (Research Center Borstel, Leibniz Lung Center, 23845 Borstel, Germany) have filed a patent application entitled ACC inhibitors as means and methods for treating mycobacterial diseases(WO2018007430A1, patent pending).View Full Text

immunology↗