20 min listen
FiNuTyper: an automated deep learning-based platform for simultaneous fiber and nucleus type analysis in human skeletal muscle
FiNuTyper: an automated deep learning-based platform for simultaneous fiber and nucleus type analysis in human skeletal muscle
ratings:
Length:
20 minutes
Released:
Dec 10, 2022
Format:
Podcast episode
Description
Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2022.12.08.519285v1?rss=1
Authors: Lundquist, A., Lazar, E., Han, N. S., Emanuelsson, E., Reitzner, S. M., Chapman, M. A., Alkass, K., Druid, H., Petri, S., Sundberg, C. J., Bergmann, O.
Abstract:
While manual quantification is still considered the gold standard for skeletal muscle histological analysis, it is time-consuming and prone to investigator bias. We assembled an automated image analysis pipeline, FiNuTyper (Fiber and Nucleus Typer), from recently developed deep learning-based image segmentation methods, optimized for unbiased evaluation of fresh and postmortem human skeletal muscle. We validated and utilized SERCA1 and SERCA2 as type-specific myonucleus and myofiber markers. Parameters including myonuclei per fiber, myonuclear domain, central myonuclei per fiber, and grouped myofiber ratio were determined in a fiber type-specific manner, revealing a large degree of gender- and muscle-related heterogeneity. Our platform was also tested on pathological muscle tissue (ALS) and adapted for the detection of other resident cell types (leukocytes, satellite cells, capillary endothelium). In summary, we present an automated image analysis tool for the simultaneous quantification of myofiber and myonuclear types, to characterize the composition of healthy and diseased human skeletal muscle.
Copy rights belong to original authors. Visit the link for more info
Podcast created by Paper Player, LLC
http://biorxiv.org/cgi/content/short/2022.12.08.519285v1?rss=1
Authors: Lundquist, A., Lazar, E., Han, N. S., Emanuelsson, E., Reitzner, S. M., Chapman, M. A., Alkass, K., Druid, H., Petri, S., Sundberg, C. J., Bergmann, O.
Abstract:
While manual quantification is still considered the gold standard for skeletal muscle histological analysis, it is time-consuming and prone to investigator bias. We assembled an automated image analysis pipeline, FiNuTyper (Fiber and Nucleus Typer), from recently developed deep learning-based image segmentation methods, optimized for unbiased evaluation of fresh and postmortem human skeletal muscle. We validated and utilized SERCA1 and SERCA2 as type-specific myonucleus and myofiber markers. Parameters including myonuclei per fiber, myonuclear domain, central myonuclei per fiber, and grouped myofiber ratio were determined in a fiber type-specific manner, revealing a large degree of gender- and muscle-related heterogeneity. Our platform was also tested on pathological muscle tissue (ALS) and adapted for the detection of other resident cell types (leukocytes, satellite cells, capillary endothelium). In summary, we present an automated image analysis tool for the simultaneous quantification of myofiber and myonuclear types, to characterize the composition of healthy and diseased human skeletal muscle.
Copy rights belong to original authors. Visit the link for more info
Podcast created by Paper Player, LLC
Released:
Dec 10, 2022
Format:
Podcast episode
Titles in the series (100)
Uterine histotroph and conceptus development. III. Adrenomedullin stimulates proliferation, migration and adhesion of porcine trophectoderm cells via AKT-TSC2-MTOR cell signaling pathway. by PaperPlayer biorxiv cell biology