TY - JOUR
T1 - ERnet
T2 - a tool for the semantic segmentation and quantitative analysis of endoplasmic reticulum topology
AU - Lu, Meng
AU - Christensen, Charles N.
AU - Weber, Jana M.
AU - Konno, Tasuku
AU - Läubli, Nino F.
AU - Scherer, Katharina M.
AU - Avezov, Edward
AU - Lio, Pietro
AU - Lapkin, Alexei A.
AU - Kaminski Schierle, Gabriele S.
AU - Kaminski, Clemens F.
N1 - Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
PY - 2023
Y1 - 2023
N2 - The ability to quantify structural changes of the endoplasmic reticulum (ER) is crucial for understanding the structure and function of this organelle. However, the rapid movement and complex topology of ER networks make this challenging. Here, we construct a state-of-the-art semantic segmentation method that we call ERnet for the automatic classification of sheet and tubular ER domains inside individual cells. Data are skeletonized and represented by connectivity graphs, enabling precise and efficient quantification of network connectivity. ERnet generates metrics on topology and integrity of ER structures and quantifies structural change in response to genetic or metabolic manipulation. We validate ERnet using data obtained by various ER-imaging methods from different cell types as well as ground truth images of synthetic ER structures. ERnet can be deployed in an automatic high-throughput and unbiased fashion and identifies subtle changes in ER phenotypes that may inform on disease progression and response to therapy.
AB - The ability to quantify structural changes of the endoplasmic reticulum (ER) is crucial for understanding the structure and function of this organelle. However, the rapid movement and complex topology of ER networks make this challenging. Here, we construct a state-of-the-art semantic segmentation method that we call ERnet for the automatic classification of sheet and tubular ER domains inside individual cells. Data are skeletonized and represented by connectivity graphs, enabling precise and efficient quantification of network connectivity. ERnet generates metrics on topology and integrity of ER structures and quantifies structural change in response to genetic or metabolic manipulation. We validate ERnet using data obtained by various ER-imaging methods from different cell types as well as ground truth images of synthetic ER structures. ERnet can be deployed in an automatic high-throughput and unbiased fashion and identifies subtle changes in ER phenotypes that may inform on disease progression and response to therapy.
UR - http://www.scopus.com/inward/record.url?scp=85151353386&partnerID=8YFLogxK
U2 - 10.1038/s41592-023-01815-0
DO - 10.1038/s41592-023-01815-0
M3 - Article
C2 - 36997816
AN - SCOPUS:85151353386
SN - 1548-7091
VL - 20
SP - 569
EP - 579
JO - Nature Methods
JF - Nature Methods
IS - 4
ER -