mutagenesis was performed on age - synchronized l4 animals using ethyl methanesulfonate ( ems ) according to standard protocols .
briefly , when the animals reached the l4 stage , a large number of synchronized wild - type animals carrying the wyis85 transgene were suspended in a buffer solution and 20mmems ( sigma - aldrich ) for a period of four hours . following the incubation ,
these p0 animals were allowed to grow to adults , and once the f1 progeny from these animals became adults , the f2 offspring were age - synchronized .
f2 embryos were obtained by bleaching f1 adults using a solution containing about 1% naocl and 0.1 mnaoh , washed in m9 buffer , and cultured for 24 hours .
animals were then transferred and cultured on nematode growth medium ( ngm ) plates seeded with e. coli op50 until l4 stage .
the strains used in this project included xa7810 : n2 , wyis85 ( pitr-1 pb::gfp::rab-3 ) ; xa7812 : lin-44(n1792)i , wyis85(pitr-1 pb::gfp::rab-3 ) . for imaging and screening ,
animals were washed and suspended in m9 solution containing 0.01 volume% triton x100 ( sigma - aldrich ) .
after washing animals off of the plates , animals were allowed to settle , the supernatant removed and additional m9 added .
animals were screened under a compound microscope using a 40 ( na=1.4 ) oil objective using the microfluidic chips .
sorting decisions were made based on differences in the reporter expression pattern or intensity ; potential animals of interest were sorted into the mutant outlet and were collected directly from tubing connected to the mutant outlet with m9 solution containing 0.01% triton x100 .
the equipment utilized during our experiments includes the following : peltier cooler ( pjt-5 30 mm square and pjt-6 40 mm square peltier coolers ) , copper heat exchanger ( custom designed and machined ) , peristaltic pump for coolant : 400 f / a ( watson marlow ) , digital i / o card ( pacdrive from ultimarc ) , solenoid valves and manifold ( 3-way 10 mm solenoid valve , 188 series , asco valve ) , microscope ( leica dm4500 ) with lens ( 63x oil , na=1.4 ) . and camera ( infinity 3 - 1 , lumenera ) .
although manipulation and imaging of multicellular model organisms are an important part of modern biology , most methods are still labor intensive and time - consuming .
recent work , largely through multiwell plate methods , and a modified flow cytometer ( copas ) , has made significant improvements in sample handling and low - resolution imaging of c. elegans , and d. rerio and d. melanogaster embryos .
recently , microfluidic approaches have improved the throughput of high - resolution imaging of caenorhabditis elegans , but sorting has been based on user input   or simplistic criteria , such as the local intensity . to date , all c. elegans screens , both automated and manual , have required human intervention to set the sorting boundaries , and the few automated screens have only been performed at very low resolution .
thin glass capillary tubes have recently been used to image and then ablate axons in d. rerio , but image processing and ablation was performed manually using user input .
similarly , microfluidic traps have been employed to align d. melanogaster embryos to enable highly controlled imaging conditions .
automation of information extraction has largely been focused on post - processing of data acquired during an experiment . within multicellular model organisms ,
this has focused on understanding animal behavior and interactions , and with lineage tracking of cell fates .
tracing of cell lineages from early to late embryogenesis has allowed tracking of c. elegans embryos through many divisions . a digital atlas of c. elegans cell locations , and methods to identify them ,
performing image processing to identify quantitative information in near real time during a screen , however , requires a framework that places a significantly higher value on speed and throughput .
the framework provided in this work can be extended to tackling these problems for real - time screening of other model organisms .
included on the website is software used during screening . because of differences in equipment this
is primarily intended as a guide for development of customized code for the end user .
the code contains functions used to operate the microfluidic device , as well as that to extract relevant information from the fluorescent images .
the optimized feature extraction and phenotypical parameters can be modified to focus on identification of other small , fluorescent objects .
the device is a standard two - layer pdms device , similarly to our previous work , fabricated using a rapid prototyping method and thermal bonding between layers ( sfig 4b ) . rather than using conventional fullclosure valves that rely on a curved cross - section of the flow - layer ,
although valves incapable of fully closing are typically considered drawbacks in microfluidic devices , in this case they could be used advantageously to simplify the design . because the valves always allow a small amount of flow ( supplementary fig .
2c ) , the device was designed using partial closure valves to position animals in the imaging channels , instead of suction channels .
in addition to the development of a microfluidic device for screening , external systemic components were required to allow for automated sorting .
this required a closed loop control system and the development of specific hardware to interface with and control the on - chip components . creating external components that would allow computerized control of on - chip components was necessary ( supplementary fig .
because we needed to run the system for a very long period during the screen , a comprehensive framework was developed for handling errors robustly ( supplementary fig .
this includes an external macroscale control components to be integrated with the microfluidic device to allow closed - loop control , an error handling routines to reduce the need for operator intervention for the closed loop control , and optimization of the operational sequence to minimize the amount of time spent per animal .
external calls were to the machine learning libraries and a dll for valve control operation . due to potential stability issues with matlab operation , however , it could be advisable for future efforts to focus on the open microscopy environment and code in a more robust language such as java .
the screening methodology was optimized to maximize the number of independent f2 animals screened , and to minimize the likelihood of screening clonal siblings .
this was done by pooling f2 animals from a number of f1 mothers , and then screening a subset of this group . due to the screening methodology of the number of haploid genomes
can not be directly calculated , but several assumptions have to be made regarding the sampling from f2 population .
this results in approximately 1,500 f1 animals per plate , or 3,000 haploid genomes . given that f1 animals are a / b
, we can assume that half of the animals are homozygous ( a / a or b / b ) , and the nave assumption is that for every 2 f2 animals screened , we have screened 1 haploid genome . on average , 500 f2 animals were screened from each of these populations , resulting in 250 haploid genomes .
given that we were sampling from a larger population of animals , there is the possibility that an f2 animal could be homozygous for the same genome present in an animal that was previously screened . the probability of this occurring was calculated as 1(29993000)250=0.08 .
given there were 3,000 haploid genomes present , we screened 0.08 * 3,000 = 240 .
this yields a ratio of 240500=0.48 , implying that because we have screened a total of ~40,000 animals , that corresponds to nearly 20,000 haploid genomes .
following screening , the animals identified were cloned and then verified by reimaging . once the identified mutants have been cloned and decontaminated ,
furthermore , because genetic validation by sequencing is such a slow and time consuming process , it is important to visually confirm that each of the animals to be mapped or sequenced indeed have an altered synaptic expression .
validation was done using standard worm protocols using a worm - slide and sodium azide for immobilization .
a small population ( ~1520 ) of animals of each genotype was imaged , and these images were used to determine whether the animals were actual mutants , and thus the accuracy of the screening .
some of the mutants were phenotyped with larger numbers , and were characterized in greater detail to predict the potential pathway that was affected ( supplementary table 2 ) .
each of the genotypes classified as mutants of interest were phenotyped . for the purpose of these results , animals that were sorted as mutants but failed to produce any offspring that could be used to verify whether the animals were correctly or incorrectly sorted , were removed from the results . to determine the accuracy of the screening protocol ,
images of all processed animals were collected and analyzed to determine whether animals were correctly or incorrectly sorted as wild - type animals .
the false negative rate was determined by comparing the manual scores to the computer scores to determine the accuracy of the algorithm .
the screen using the discriminative classifier resulted in a false positive rate of 69% , a false negative rate of 9% , and a theoretical enrichment of 6,000% .
the screen using the outlier detection approach resulted in a false positive rate of 74% , a false negative rate of 7% , and an enrichment of 6,000% .
the marginally higher false negative rate could be a result of the discriminative classifier not recognizing some mutant phenotypes , or merely the sample size .
the false positive rates of these classifiers are similar to or better than the manual methods due to animal - to - animal variations and the manual method s subjectivity in scoring the phenotypes .
these false positive animals also do not pose an issue in these rare - sort problems since it is a very small number of candidate mutants that need to be characterized in the following steps .
false positives were calculated using the following equation where animals sterile animals were removed from the denominator : fp = wildtype sorted as mutantsanimals sorted as mutants .
the false negative rate was calculated using the following equation : fn = mutants sorted as wildtypemutants sorted as mutants+mutants sorted as wildtype .
the upper bound of the enrichment was calculated using the equation : enrichment = mutants correctly sorted / number of animals sorted as mutantsmutants in starting population / animals in starting population .
 morphometric studies in multicellular organisms are mostly performed manually because of the complexity of multidimensional features and lack of appropriate tools for handling these organisms . here 
 we present an integrated system to autonomously ( i.e. without human supervision ) identify and sort mutants with altered subcellular traits in real - time . 
 we performed self - directed screens of synapse formation 100 faster and found both novel genes and phenotypic classes previously unidentified in extensive manual screens . 
None
Materials and Equipment
Automation, Handling of Multicellular Organisms, and System Design
Genetic Screening
Supplementary Material