Biology subjects

Paukner, D.

Publications and source records attributed to Paukner, D..

3 recordsLinked to original sources

bioRxiv2026

Dissecting the Dynamic Evolution of Tensional Homeostasis in Fibroblasts using an Integrated Biomechanical Bioreactor Platform

Mechanical homeostasis indicates the remarkable ability displayed by cells in tissues to maintain their mechanical properties near a stable homeostatic set-point. Experimental investigations and theoretical studies indicate that mechanical stress represents a key homeostatic target that stromal cells, such as fibroblasts, seek to maintain by tuning the intracellular structure and by remodeling the extracellular matrix. Much of what is known about mechanical homeostasis of tissues under tension, or tensional homeostasis, is based on experiments on tissue equivalents, that is fibroblast-populated collagen gels. However, existing platforms used to investigate tensional homeostasis cannot infer mechanical stress dynamically. Here we developed an integrated biomechanical bioreactor combining force sensing with confocal and multiphoton microscopy to dissect the mechanobiological mechanisms of tensional homeostasis. We used our platform to test the hypothesis that fibroblasts maintain a constant state of stress across varying collagen concentrations. Contrary to this assumption, synchronized force and imaging measurements revealed that stress is not constant but rather elevated at low collagen concentrations, where fibroblast contraction drives earlier collagen alignment and greater tissue compaction. Conversely, force generation and -SMA expression increase with increasing collagen concentration, accompanied by modest transcriptional changes. However, at the highest collagen concentration, this homeostatic balance is disrupted, with lower force generation and -SMA expression, as gene expression shifts toward VEGFC-mediated autocrine survival signaling. These findings demonstrate that tensional homeostasis emerges from a dynamic balance between cellular contractility and extracellular matrix densification rather than stress maintenance while revealing that excessive matrix density disrupts this balance by triggering a pro-survival response.

bioengineering↗

bioRxiv2023

Synchrotron-source micro-x-ray computed tomography for examining butterfly eyes

1. Comparative anatomy is an important tool for investigating evolutionary relationships amongst species, but the lack of scalable imaging tools and stains for rapidly mapping the microscale anatomies of related species poses a major impediment to using comparative anatomy approaches for identifying evolutionary adaptations. 2. We describe a method using synchrotron source micro-x-ray computed tomography (syn-{micro}XCT) combined with machine learning algorithms for high-throughput imaging of Lepidoptera (i.e., butterfly and moth) eyes. Our pipeline allows for imaging at rates of [~] 15 min/mm3 at 600 nm3 resolution. Image contrast is generated using standard electron microscopy labeling approaches (e.g., osmium tetroxide) that unbiasedly labels all cellular membranes in a species independent manner thus removing any barrier to imaging any species of interest. 3. To demonstrate the power of the method, we analyzed the 3D morphologies of butterfly crystalline cones, a part of the visual system associated with acuity and sensitivity and found significant variation within six butterfly individuals. Despite this variation, a classic measure of optimization, the ratio of interommatidial angle to resolving power of ommatidia, largely agrees with early work on eye geometry across species. 4. We show that this method can successfully be used to determine compound eye organization and crystalline cone morphology. Our novel pipeline provides for fast, scalable visualization and analysis of eye anatomies that can be applied to any arthropod species, enabling new questions about evolutionary adaptations of compound eyes and beyond.

ecology↗

bioRxiv2020

Primate neuronal connections are sparse as compared to mouse

The mouse and macaque primary visual cortices are foundational models of cortical functioning, particularly at the level of single neurons. Therefore, detailing differences in how individual neurons connect across these species would inform models of cortical functioning and of how brains evolve. However, existing comparisons are limited, measuring synapse density without regard to where synapses are made or on what types of neurons. We use large volume electron microscopy to address this gap, reconstructing a total of 7735 synapses across 160 total neurons (146 excitatory, 14 inhibitory) from adult Rhesus macaque and mouse Layer 2/3 of primary visual cortex (V1). We find that primate connections are broadly sparse: primate excitatory and inhibitory neurons received 3-5 times fewer spine and somatic synapses with lower ratios of excitatory to inhibitory synapses than mouse equivalents. However, despite reductions in absolute synapse number, patterns of axonal innervation were preserved: inhibitory axons sparsely innervated neighboring excitatory neurons in macaque and mouse at similar rates and proportions. On the output side, most excitatory axons in mice myelinated close to the soma (81%) while most primate axons (68%) did not. Interestingly, primate axons, but not mouse axons, that myelinated had 3.3 fold more axon initial segment synapses than axons that did not myelinate, suggesting differential inhibitory control of long distance output in primate brains. Finally, we discover that when artificial recurrent neural networks (RNNs) are constrained by the metabolic cost of creating and maintaining synapses, increasing the number of nodes (e.g. neurons) as networks optimize for a cognitive task, reduces the number of connections per node, similar to primate neurons as compared to mice. One Sentence SummaryUsing large volume serial electron microscopy, we show that primate cortical neural networks are sparser than mouse and using recursive neural nets, we show that energetic costs of synaptic maintenance could underlie this difference.

neuroscience↗