TY - JOUR
T1 - Size scaling in collective cell growth
AU - Diegmiller, Rocky
AU - Doherty, Caroline A.
AU - Stern, Tomer
AU - Imran Alsous, Jasmin
AU - Shvartsman, Stanislav Y.
N1 - Publisher Copyright:
© 2021. Published by The Company of Biologists Ltd.
PY - 2021/9/15
Y1 - 2021/9/15
N2 - Size is a fundamental feature of living entities and is intimately tied to their function. Scaling laws, which can be traced to D'Arcy Thompson and Julian Huxley, have emerged as a powerful tool for studying regulation of the growth dynamics of organisms and their constituent parts. Yet, throughout the 20th century, as scaling laws were established for single cells, quantitative studies of the coordinated growth of multicellular structures have lagged, largely owing to technical challenges associated with imaging and image processing. Here, we present a supervised learning approach for quantifying the growth dynamics of germline cysts during oogenesis. Our analysis uncovers growth patterns induced by the groupwise developmental dynamics among connected cells, and differential growth rates of their organelles. We also identify inter-organelle volumetric scaling laws, finding that nurse cell growth is linear over several orders of magnitude. Our approach leverages the ever-increasing quantity and quality of imaging data, and is readily amenable for studies of collective cell growth in other developmental contexts, including early mammalian embryogenesis and germline development.
AB - Size is a fundamental feature of living entities and is intimately tied to their function. Scaling laws, which can be traced to D'Arcy Thompson and Julian Huxley, have emerged as a powerful tool for studying regulation of the growth dynamics of organisms and their constituent parts. Yet, throughout the 20th century, as scaling laws were established for single cells, quantitative studies of the coordinated growth of multicellular structures have lagged, largely owing to technical challenges associated with imaging and image processing. Here, we present a supervised learning approach for quantifying the growth dynamics of germline cysts during oogenesis. Our analysis uncovers growth patterns induced by the groupwise developmental dynamics among connected cells, and differential growth rates of their organelles. We also identify inter-organelle volumetric scaling laws, finding that nurse cell growth is linear over several orders of magnitude. Our approach leverages the ever-increasing quantity and quality of imaging data, and is readily amenable for studies of collective cell growth in other developmental contexts, including early mammalian embryogenesis and germline development.
KW - Allometry
KW - Developmental dynamics
KW - Multicellular clusters
KW - Oogenesis
KW - Supervised learning
UR - http://www.scopus.com/inward/record.url?scp=85116956943&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85116956943&partnerID=8YFLogxK
U2 - 10.1242/dev.199663
DO - 10.1242/dev.199663
M3 - Article
C2 - 34463760
AN - SCOPUS:85116956943
SN - 0950-1991
VL - 148
JO - Development (Cambridge, England)
JF - Development (Cambridge, England)
IS - 18
ER -