Measuring LTGF-beta activation in live tissues in situ is a major challenge due to the short half-life of activated TGF-beta in cartilage (due to rapid receptor internalization/degradation). As such, activation assessments typically require analysis of downstream events. However, assessments of intracellular TGF-beta signaling molecules (Smad2/3 phosphorylation) yield mostly qualitative measures and reporter cell assays are not compatible with intact cartilage tissues. Alternatively, in the current project, we proposed quantifying LTGF-beta activation in situ through a novel assay that capitalizes on TGF-beta’s robust autoinduction behavior; active TGF-beta activity induces a predictable increase in synthesis of soluble LTGF-beta. The dominant fraction of newly synthesized LTGF-beta is secreted from the tissue (not retained in ECM) and stable. Accordingly, measurements of LTGF-beta secretion into culture medium allows for quantifications of TGF-beta activity in cartilage. In order to confirm that LTGF-beta secretion enhancements result from TGF-beta activity (and not other load-initiated signaling cascades), a control group can readily be utilized, consisting of TGF-beta activity inhibition from a TGF-beta-receptor specific kinase inhibitor. Using this platform, we performed the first-ever measurement of the activity of TGF-beta in cartilage explants from load-induced activation. Results demonstrate that LTGF-beta secretion rates do indeed increase with cartilage mechanical loading. Upon exposuremore »
Deep learning-enabled analysis reveals distinct neuronal phenotypes induced by aging and cold-shock
Abstract Background Access to quantitative information is crucial to obtain a deeper understanding of biological systems. In addition to being low-throughput, traditional image-based analysis is mostly limited to error-prone qualitative or semi-quantitative assessment of phenotypes, particularly for complex subcellular morphologies. The PVD neuron in Caenorhabditis elegans , which is responsible for harsh touch and thermosensation, undergoes structural degeneration as nematodes age characterized by the appearance of dendritic protrusions. Analysis of these neurodegenerative patterns is labor-intensive and limited to qualitative assessment. Results In this work, we apply deep learning to perform quantitative image-based analysis of complex neurodegeneration patterns exhibited by the PVD neuron in C. elegans . We apply a convolutional neural network algorithm (Mask R-CNN) to identify neurodegenerative subcellular protrusions that appear after cold-shock or as a result of aging. A multiparametric phenotypic profile captures the unique morphological changes induced by each perturbation. We identify that acute cold-shock-induced neurodegeneration is reversible and depends on rearing temperature and, importantly, that aging and cold-shock induce distinct neuronal beading patterns. Conclusion The results of this work indicate that implementing deep learning for challenging image segmentation of PVD neurodegeneration enables quantitatively tracking subtle morphological changes in an unbiased manner. This analysis revealed that distinct more »
- Award ID(s):
- 1838314
- Publication Date:
- NSF-PAR ID:
- 10302689
- Journal Name:
- BMC Biology
- Volume:
- 18
- Issue:
- 1
- ISSN:
- 1741-7007
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Publisher's Version of Record
- https://doi.org/10.1186/s12915-020-00861-w
