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Biology subjects

Burckhardt, S.

Publications and source records attributed to Burckhardt, S..

4 recordsLinked to original sources

3D-MAESTRO: A scalable, modular, portable pipeline for automated processing of large-scale volumetric brain microscopy data

Light microscopy is routinely used to explore the cellular and molecular structure of tissues, but the scale and complexity of data remains a bottleneck for discovery. We introduce 3D-MAESTRO (3D-Microscopy Automation and Execution with Scalable Tools, Rendering, and Orchestration): an automated image processing workflow for large-scale microscopy data, built for scalable execution across cloud and local computing environments. 3D-MAESTRO orchestrates denoising, stitching of image tiles, atlas registration, and segmentation. Its modular architecture permits the integration and benchmarking of new packages, ensuring that performance evolves as more efficient or accurate algorithms emerge. We introduce a new 3D image template for automated registration of mouse brains cleared with aqueous reagents and an efficient method for detection of fluorescent cells. We apply 3D-MAESTRO to lightsheet images of whole mouse brains in the context of diverse anatomical and functional experiments, illustrating high-throughput and reproducible mapping of microscopic structures across the brain.

neuroscience↗

Compartmental Profiling of PDE4B in Systemic Sclerosis

ObjectivesThe preferential phosphodiesterase 4B (PDE4B) inhibitor nerandomilast was recently approved for treatment of idiopathic pulmonary fibrosis (IPF) and progressive pulmonary fibrosis. Its proposed immunomodulatory, anti-fibrotic, and endothelial-stabilising actions target all three cardinal features of SSc, yet PDE4B expression has not been systematically characterised in SSc tissue. We aimed to define PDE4B expression across fibrotic organs and cellular compartments in SSc. MethodsPDE4B expression was profiled in SSc lung, peripheral blood mononuclear cells (PBMCs) and skin on the transcript level using single-cell RNA sequencing data and on the protein level using immunohistochemistry, immunofluorescence and multiplexed immunofluorescent stainings. ResultsPDE4B was consistently dysregulated in immune cells across SSc tissue and PBMCs, with compartment-specific direction and distribution. In SSc-ILD lung, expression was increased in CD8 and CD4 memory T-cells. In PBMCs, expression was increased in B cells, monocytes, and CD8 T-cells, and stratified patients into three endotypes (PDE4B//hi) not distinguishable by clinical variables. In skin, bulk RNA-seq showed a significant global increase, which localized to myeloid cells in scRNA-seq data. Approximately 90% of FAP activated fibroblasts co-expressed PDE4B at the protein level in SSc skin, identifying the activated fibroblast compartment as a candidate target for PDE4B inhibition. No PDE4B dysregulation was detected in vascular cell types. ConclusionsThis first cell-type-resolved characterisation of PDE4B in SSc demonstrates consistent immune-cell dysregulation across tissues and protein-level enrichment in activated fibroblasts. This provides a human-tissue rationale for the immunomodulatory and anti-fibrotic effects of PDE4B inhibition and supporting PDE4B as a disease-relevant therapeutic target in SSc. Key messagesO_ST_ABSWhat is already known on this topicC_ST_ABSO_LINerandomilast (BI 1015550), a PDE4B-preferential inhibitor, was approved for idiopathic pulmonary fibrosis and progressive pulmonary fibrosis. C_LIO_LIPre-clinical studies indicate that PDE4B inhibition may act on all cardinal features of SSc. C_LI What this study addsO_LIFirst cell-type-resolved characterization of PDE4B expression across SSc-affected lung, PBMCs, and skin. C_LIO_LIPBMC PDE4B expression is heterogeneous, stratifying patients into PDE4B// endotypes independent of standard clinical variables. C_LIO_LIscRNA-seq shows increased myeloid PDE4B expression in SSc skin, while [~]90% of FAP activated fibroblasts in SSc skin express PDE4B protein. C_LI How this study might affect research, practice or policyO_LIThe study strengthens the human-level evidence underpinning the target rationale for PDE4B inhibition in SSc. C_LI

molecular biology↗

lncRNA 3222401L13Rik/ENSG00000272070 modulates microglial inflammatory programs in association with PU.1

Long non-coding RNAs (lncRNAs) are emerging as key regulators of brain function, but their contribution to microglial aging and neurodegenerative disease remains largely unknown. Because only 1.5% of the human genome encodes proteins, whereas the vast majority of transcripts belong to the largely unexplored non-coding RNAome, elucidating the functions of non-coding RNAs provides an unprecedented opportunity to expand the space for therapeutic discovery. We recently identified the glia-enriched lncRNA 3222401L13Rik as upregulated in the aging mouse hippocampus. Here, we investigated its function in microglia and its human homolog ENSG00000272070. We found that 3222401L13Rik is expressed in both astrocytes and microglia and increases with age. Knockdown of 3222401L13Rik in primary microglia led to enhanced expression of pro-inflammatory cytokines, including TNF, and increased phagocytic activity. RNA-sequencing revealed widespread transcriptional changes enriched for TNF and complement signaling pathways. The human homolog ENSG00000272070 showed conserved functions in iPSC-derived microglia, where its loss similarly promoted inflammatory gene expression and phagocytosis. Mechanistically, 3222401L13Rik interacts with the microglial transcription factor PU.1, and its depletion overlapped with PU.1-driven transcriptional programs. Consistent with these findings, ENSG00000272070 expression was significantly reduced in postmortem Alzheimers disease (AD) brains, and AD-associated genes were enriched among 3222401L13Rik-regulated targets. Together, our results identify 3222401L13Rik/ENSG00000272070 as a conserved, aging-associated lncRNA that modulates microglial inflammatory states through interaction with PU.1. This work links glial lncRNA regulation to AD-related neuroinflammation and suggests 3222401L13Rik as a potential molecular target to fine-tune microglial activity in neurodegenerative diseases. HighlightsO_LI3222401L13Rik is a glia-enriched long non-coding RNA regulating microglial state C_LIO_LIKnockdown of 3222401L13Rik increases TNF signaling C_LIO_LIThe human homolog ENSG00000272070 shows reduced expression in AD brains C_LIO_LI3222401L13Rik interacts with the AD-associated transcription factor PU.1 C_LIO_LIA conserved 3222401L13Rik-PU.1 axis modulates microglial inflammation in aging and disease C_LI

neuroscience↗

Predicting and Shaping Human-Machine Interactions in Closed-loop, Co-adaptive Neural Interfaces

Neural interfaces can restore or augment human sensorimotor capabilities by converting high-bandwidth biological signals into control signals for an external device via a decoder algorithm. Leveraging user and decoder adaptation to create co-adaptive interfaces presents opportunities to improve usability and personalize devices. However, we lack principled methods to model and optimize the complex two-learner dynamics that arise in co-adaptive interfaces. Here, we present computational methods based on control theory and game theory to analyze and generate predictions for user-decoder co-adaptive outcomes in continuous interactions. We tested these computational methods using an experimental platform where human participants (N=14) learn to control a cursor using an adaptive myoelectric interface to track a target on a computer display. Our framework allowed us to characterize user and decoder changes within co-adaptive myoelectric interfaces. Our framework further allowed us to predict how decoder algorithm changes impacted co-adaptive interface performance and revealed how interface properties can shape user behavior. Our findings demonstrate an experimentally-validated computational framework that can be used to design user-decoder interactions in closed-loop, co-adaptive neural interfaces. This framework opens future opportunities to optimize co-adaptive neural interfaces to expand the performance and application domains for neural interfaces.

neuroscience↗