摘要
Caenorhabditis elegans(C.elegans)has been a popular model organism for several decades since its first discovery of the huge research potential for modeling human diseases and genetics.Sorting is an important means of providing stage-or age-synchronized worm populations for many worm-based bioassays.However,conventional manual techniques for C.elegans sorting are tedious and inefficient,and commercial complex object parametric analyzer and sorter is too expensive and bulky for most laboratories.Recently,the development of lab-on-a-chip(microfluidics)technology has greatly facilitated C.elegans studies where large numbers of synchronized worm populations are required and advances of new designs,mechanisms,and automation algorithms.Most previous reviews have focused on the development of microfluidic devices but lacked the summaries and discussion of the biological research demands of C.elegans,and are hard to read for worm researchers.We aim to comprehensively review the up-to-date microfluidic-assisted C.elegans sorting developments from several angles to suit different background researchers,i.e.,biologists and engineers.First,we highlighted the microfluidic C.elegans sorting devices'advantages and limitations compared to the conventional commercialized worm sorting tools.Second,to benefit the engineers,we reviewed the current devices from the perspectives of active or passive sorting,sorting strategies,target populations,and sorting criteria.Third,to benefit the biologists,we reviewed the contributions of sorting to biological research.We expect,by providing this comprehensive review,that each researcher from this multidisciplinary community can effectively find the needed information and,in turn,facilitate future research.
基金
support from the programs of the Natural Science Foundation of the Jiangsu Higher Education(20KJB460024 and 22KJB460033)
Jiangsu Science and Technology Programme-Young Scholar(BK20200251)
Jiangsu Province High-level Innovation and Entrepreneurship Talent Plan(2020-30803)
XJTLU Key Programme Special Fund-Exploratory Research Programme(KSF-E-39)
XJTLU Research Development Fund(RDF-18-02-20)
support from Xi'an Jiaotong-Liverpool University to W.Y.(PGRS1906040)and S.D.(PGRS1912019)
supported by the XJTLU AI University Research Centre and Jiangsu Province Engineering Research Centre of Data Science and Cognitive Computation at XJTLU.