Biology subjects

Büttner, M.

Publications and source records attributed to Büttner, M..

6 recordsLinked to original sources

bioRxiv2024

Disease-associated microglia and activation of CD8+ T cells precede neuronal cell loss in a model of hereditary spastic paraplegia

In central nervous system (CNS) diseases characterized by late-onset neurodegeneration, the interplay between innate and adaptive immune responses remains poorly understood. This knowledge gap is amplified by the prolonged nature of these diseases, complicating the delineation of brain-resident and infiltrating cells. Here, we conducted a comprehensive profiling of innate and adaptive immune cells across various CNS regions in a murine model of spastic paraplegia 15 (SPG15), a complicated form of hereditary spastic paraplegia (HSP). Using fate-mapping of bone marrow-derived cells via genetic labeling, we identified microgliosis and microglial MHC-II upregulation accompanied by infiltration and local expansion of T cells in the CNS of Spg15-/- mice. Single-cell analysis revealed an increase of disease-associated microglia (DAM) and clonal expansion of effector CD8+ T cells across CNS regions occurring prior to neuronal loss. Analysis of potential cell-cell communication pathways suggested bidirectional interactions between DAM and effector CD8+ T cells potentially contributing to disease progression in Spg15-/- mice. In summary, we identified a shift in microglial phenotypes associated with recruitment and clonal expansion of T cells as a new characteristic of Spg15-driven neuropathology. Targeting activated microglia, CD8+ T cells and their communication represent promising avenues to prevent the loss of neuronal function in HSP.

immunology↗

bioRxiv2024

Action potential propagation speed compensates for traveling distance in the human retina

Neural information processing requires accurately timed action potentials arriving from presynaptic neurons at the postsynaptic neuron. However, axons of ganglion cells in the human retina feature low axonal conduction speeds and vastly different lengths, which poses a challenge to the brain for constructing a temporally coherent image over the visual field. Combining results from microelectrode array recordings, human behavioral measurements, transmission electron microscopy, and mathematical modelling of the retinal nerve fiber layer, we demonstrate that axonal propagation speeds compensate for variations in axonal length across the human retina including the fovea. The human brain synchronizes the arrival times of action potentials at the optic disc by increasing the diameters of longer axons, which increases their propagation speeds.

neuroscience↗

bioRxiv2023

Longitudinal single-cell data informs deterministic modelling of inflammatory bowel disease

Single-cell mRNA sequencing (scRNA-seq) allows deep molecular and cellular profiling of immunological processes. Longitudinal scRNA-seq datasets can be used for deterministic ordinary differential equation (ODE)-based modelling to mechanistically describe immune dynamics. Here, we derived longitudinal changes in the abundance of six colonic cell types during inflammatory bowel disease (IBD) from scRNA-seq data of a mouse model of colitis using ODE-based models. We then predicted the immune dynamics of a different mouse colitis protocol and confirmed these scRNA-seq-based predictions with our previously published single-cell-based flow cytometry data. We further hypothesised that the estimated model parameters reflect biological processes. We validated this prediction of cellular turnover rates with KI-67 staining and with gene expression information from the scRNA-seq data not used for model fitting. Finally, we tested the translational relevance of the model simulations by predicting genes indicative of treatment response in human IBD patients. The predictive power of IBD deterministic modelling from scRNA-seq data highlights its potential to advance our understanding of immune dynamics in health and disease.

systems biology↗

bioRxiv2021

ENIGMA HALFpipe: Interactive, reproducible, and efficient analysis for resting-state and task-based fMRI data

The reproducibility crisis in neuroimaging has led to an increased demand for standardized data processing workflows. Within the ENIGMA consortium, we developed HALFpipe (Harmonized AnaLysis of Functional MRI pipeline), an open-source, containerized, user-friendly tool that facilitates reproducible analysis of task-based and resting-state fMRI data through uniform application of preprocessing, quality assessment, single-subject feature extraction, and group-level statistics. It provides state-of-the-art preprocessing using fMRIPrep without the requirement for input data in Brain Imaging Data Structure (BIDS) format. HALFpipe extends the functionality of fMRIPrep with additional preprocessing steps, which include spatial smoothing, grand mean scaling, temporal filtering, and confound regression. HALFpipe generates an interactive quality assessment (QA) webpage to assess the quality of key preprocessing outputs and raw data in general. HALFpipe features myriad post-processing functions at the individual subject level, including calculation of task-based activation, seed-based connectivity, network-template (or dual) regression, atlas-based functional connectivity matrices, regional homogeneity (ReHo), and fractional amplitude of low frequency fluctuations (fALFF), offering support to evaluate a combinatorial number of features or preprocessing settings in one run. Finally, flexible factorial models can be defined for mixed-effects regression analysis at the group level, including multiple comparison correction. Here, we introduce the theoretical framework in which HALFpipe was developed, and present an overview of the main functions of the pipeline. HALFpipe offers the scientific community a major advance toward addressing the reproducibility crisis in neuroimaging, providing a workflow that encompasses preprocessing, post-processing, and QA of fMRI data, while broadening core principles of data analysis for producing reproducible results. Instructions and code can be found at https://github.com/HALFpipe/HALFpipe.

neuroscience↗

bioRxiv2019

Deep learning does not outperform classical machine learning for cell-type annotation

Deep learning has revolutionized image analysis and natural language processing with remarkable accuracies in prediction tasks, such as image labeling and semantic segmentation or named-entity recognition and semantic role labeling. Specifically, the combination of algorithmic and hardware advances with the appearance of large and well-labeled datasets has led up to seminal contributions in these fields. The emergence of large amounts of data from single-cell RNA-seq and the recent global effort to chart all cell types in the Human Cell Atlas has attracted an interest in deep-learning applications. However, all current approaches are unsupervised, i.e., learning of latent spaces without using any cell labels, even though supervised learning approaches are often more powerful in feature learning and the most popular approach in the current AI revolution by far. Here, we ask why this is the case. In particular we ask whether supervised deep learning can be used for cell annotation, i.e. to predict cell-type labels from single-cell gene expression profiles. After evaluating 10 classification methods across 14 datasets, we notably find that deep learning does not outperform classical machine-learning methods in the task. Thus, cell-type prediction based on gene-signature derived cell-type labels is potentially too simplistic a task for complex non-linear methods, which demands better labels of functional single-cell readouts.

bioinformatics↗

bioRxiv2018

Pyrophosphate modulates stress responses via SUMOylation

Pyrophosphate (PPi), a byproduct of macromolecule biosynthesis is maintained at low levels by soluble inorganic pyrophosphatases (sPPase) found in all eukaryotes. In plants, H+-pumping pyrophosphatases (H+-PPase) convert the substantial energy present in PPi into an electrochemical gradient. We show here, that both cold- and heat stress sensitivity of fugu5 mutants lacking the major H+-PPase isoform AVP1 is caused by reduced SUMOylation. In addition, we show that increased PPi concentrations interfere with SUMOylation in yeast and we provide evidence that SUMO activating E1-enzymes are inhibited by micromolar concentrations of PPi in a non-competitive manner. Taken together, our results do not only provide a mechanistic explanation for the beneficial effects of AVP1 overexpression in plants but they also highlight PPi as an important integrator of metabolism and stress tolerance.

biochemistry↗