This project was supported by National Science Foundation Career Award DBI-1253293, National Institutes of Health New Innovator Award DP2AR068129 and grant R01HG008978, the National Science Foundation Technology Center grant DBI-1548297, and the UCSF Center for Cellular Construction. ARA and ZJG are Chan-Zuckerberg Biohub Investigators.

1. Microfluidic device fabrication
<ol>
	<li>SU-8 master fabrication
	<ol>
		<li>Design the negative mask for the microfluidic channels for Device A and B (Supplemental File 1 and 2) using computer-assisted design software and have them printed on circuit board film with at least 10 &#181;m resolution.</li>
		<li>Place a clean 75 mm silicon wafer on a spin coater and pour about 1 mL of SU-8 onto its center. Turn on the vacuum to secure the wafer to the chuck.</li>
		<li>For Device A, spin coat SU-8 2150 at 500 rpm for 30 s, followed by 30 s at 2,750 rpm. For Device B, spin-coat SU-8 2100 at 500 rpm for 30 s, followed by 30 s at 2,500 rpm. This will yield SU-8 layers of thickness 200 &#181;m and 120 &#181;m, respectively.</li>
		<li>Remove the wafer from the spin-coater and place on a hotplate at 95 &#176;C for 60 min to soft bake.</li>
		<li>Remove the wafer from the hotplate and let it cool down to room temperature. Place the mask on top of the wafer, and expose under a collimator 190 mW, 365 nm UV LED for 2 min.</li>
		<li>Place the wafer on a hotplate set at 95 &#176;C for 5 min for postexposure baking.</li>
		<li>Remove the wafer and let it cool down to room temperature. Place the wafer in a bath of propylene glycol monomethyl ether acetate (PGMEA) for 20 min.</li>
		<li>Rinse the wafer with PGMEA followed by isopropanol. If any opaque residue is visible during this process, repeat the rinsing with PGMEA and isopropanol. Air dry the wafer.</li>
		<li>Place the wafer on a hotplate at 95 &#176;C for 3 min.</li>
		<li>Remove and place the wafer in a 90 mm diameter Petri dish.</li>
	</ol>
	</li>
	<li>Polydimethylsiloxane (PDMS) casting on SU-8 master
	<ol>
		<li>Mix together a 10:1 mass ratio of silicone base to curing agent. Degas the PDMS after mixing for about 30 min.</li>
		<li>Pour degassed PDMS on top of the SU-8 master until at least a 5 mm thick layer is formed on top of the wafer.</li>
		<li>Degas the PDMS on top of the wafer for about 30 min.</li>
		<li>Place the wafer in a 65 &#176;C oven for at least 80 min to cure the PDMS.</li>
		<li>Cut out the cured PDMS slab from the wafer.</li>
		<li>Place the PDMS slab with the microfluidic features facing upwards, and punch inlet and outlet holes with a 0.75 mm biopsy punch.</li>
		<li>Clean a 50 mm x 75 mm glass slide with isopropanol and remove all dust from the microfluidic features side of the PDMS slab with tape.</li>
		<li>Expose the cleaned glass slide and the PDMS slab with the microfluidic features face up to 100 Pa (1 mbar O2) plasma for 1 min.</li>
		<li>Place the PDMS slab with the features face down onto the glass slide to allow for bonding. Place the slide in a 65 &#176;C oven for at least 30 min to complete bonding.</li>
		<li>Treat all microfluidic channels by flushing with a fluorinated surface treatment fluid. Bake the device in a 65 &#176;C oven for at least 10 min to evaporate the fluid.</li>
	</ol>
	</li>
</ol>
2. Yeast encapsulation in hydrogels using Device A
<ol>
	<li>Take yeast growing in a suspension culture and count on a hemocytometer.</li>
	<li>Resuspend the cells in phosphate buffered saline (PBS) to a concentration of about 750 k/mL. This ensures that 30% of hydrogels will have one yeast cell in them. Only about half of yeast cells grow into colonies, leading to ~15% of hydrogels containing yeast colonies.</li>
	<li>Mix ultralow melting point agarose at 2% w/v in PBS and heat at 90 &#176;C until melted. This takes ~10 min.</li>
	<li>Load the agarose mix into a syringe with an attached 0.22 &#181;m filter into a syringe pump in front of the space heater set to 80 &#176;C.</li>
	<li>Load a syringe filled with the yeast suspension and a syringe containing fluorinated oil with 2% w/v ionic fluorosurfactant18 into syringe pumps.</li>
	<li>Take the coflow drop splitter device made in section 1 and connect the tubing from the syringes into the device. Guide the tubing from the outlet into a 15 mL conical tube in an ice bucket for drop collection.</li>
	<li>Flow in the three solutions into the device with the following flow rates:
	<ol>
		<li>Flow the yeast suspension with the flow rate of 3 mL/h.</li>
		<li>Flow the agarose mixture at the flow rate of 3 mL/h.</li>
		<li>Flow the fluorinated oil at the flow rate of 15 mL/h.</li>
	</ol>
	</li>
	<li>Collect about 1 mL of emulsion. Wait an additional 5 min to allow the agarose to fully set.</li>
</ol>
3. Breaking and washing gel for culture
<ol>
	<li>Add an equal volume of 20% perfluorooctanol (PFO) in fluorinated oil to the emulsion. Invert the conical tube a few times to allow for the mixing.</li>
	<li>Spin down the broken emulsion at 2,000 x g for 2 min. The gels will pellet above the oil and PFO phases.</li>
	<li>Remove the oil phase and add 2 mL of TE-TW buffer (10 mM Tris pH = 8.0, 1 mM EDTA, 0.01% Tween-20) to resuspend the gels. Transfer the suspension into a new 15 mL conical tube.</li>
	<li>Pellet down the gels as in step 3.2 and wash one more time in TE-TW for a total of two washes.</li>
	<li>Remove supernatant and resuspend gels in 2 mL of media. Transfer to a 5 mL culture tube.</li>
	<li>Incubate at 30 &#176;C overnight under shaking. 
	NOTE: After the overnight incubation, yeast hydrogels can be kept at 4 &#176;C for several days.</li>
</ol>
4. Yeast colony lysis
<ol>
	<li>Transfer gels to a 15 mL conical tube and pellet hydrogels at 2,000 x g for 2 min.</li>
	<li>Wash hydrogels in PBS 2x.</li>
	<li>Wash in 1x spheroplasting buffer 1x.</li>
	<li>Perform a 2&#8211;50x dilution of spheroplasting enzyme in spheroplasting buffer and add 1 mL to the hydrogels.</li>
	<li>Incubate at 37 &#176;C for 1 h. The treated yeast will look more transparent (Figure 3A).</li>
	<li>Take the bottom 0.8 mL of hydrogel suspension and transfer into a 1 mL uncapped syringe.</li>
	<li>Place the syringe in the 3D printed syringe holder (Supplemental file 3) and spin at 2,000 x g for 2 min. This will cause the hydrogels to close pack in the syringe head.</li>
</ol>
5. mRNA capture from lysed yeast colonies using Device B
<ol>
	<li>Take 240,000 Drop-Seq beads and transfer into a 15 mL conical tube.</li>
	<li>Pellet Drop-Seq beads by spinning down at 1,000 x g for 1 min.</li>
	<li>Remove the supernatant and resuspend beads in 2 mL of 0.9x yeast lysis buffer with 500 mM sodium chloride for a bead suspension concentration of 120,000 beads/mL.</li>
	<li>Transfer the bead suspension to a 3 mL syringe with a stir bar inserted.</li>
	<li>Prepare a syringe containing several milliliters of 2% w/v perfluoropolyether-polyethylene glycol (PFPE-PEG) surfactant in fluorinated oil.</li>
	<li>Evacuate all the aqueous head of the syringe containing close-packed hydrogels and cap the syringe.</li>
	<li>Insert the hydrogel, bead suspension, and oil syringes into syringe pumps and connect via tubing into the encapsulation device made in section 1.</li>
	<li>Connect from the outlet tubing into a 50 mL conical tube on ice.</li>
	<li>Flow in the three solutions into the device with the following flow rates:
	<ol>
		<li>Flow the hydrogels at 0.4 mL/h.</li>
		<li>Flow the bead suspension at 0.4 mL/h.</li>
		<li>Flow the fluorinated oil at 1.6 mL/h.</li>
	</ol>
	</li>
	<li>Collect ~1&#160;mL of emulsion or run the device until there are no more hydrogels left.</li>
</ol>
6. cDNA generation, sequencing library preparation, and sequencing
<ol>
	<li>Add 30 mL of 6x SSC buffer and 1 mL of PFO to the collected emulsion as stated in the Drop-Seq protocol14.</li>
	<li>Continue to follow the Drop-Seq protocol to generate cDNA from mRNA captured on beads, sequencing library preparation, and sequencing data analysis.</li>
</ol>

<ol>
	<li>Peralta-Yahya, P. P., Zhang, F., Del Cardayre, S. B., Keasling, J. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Microbial+engineering+for+the+production+of+advanced+biofuels.">Microbial engineering for the production of advanced biofuels.</a> Nature. 488 (7411), 320-328 (2012).</li><li>Curran, K. A., Alper, H. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Expanding+the+chemical+palate+of+cells+by+combining+systems+biology+and+metabolic+engineering.">Expanding the chemical palate of cells by combining systems biology and metabolic engineering.</a> Metabolic Engineering. 14 (4), 289-297 (2012).</li><li>Botstein, D., Chervitz, S. A., Cherry, J. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Yeast+as+a+model+organism.">Yeast as a model organism.</a> Science. 277 (5330), 1259-1260 (1997).</li><li>Mager, W. H., Winderickx, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Yeast+as+a+model+for+medical+and+medicinal+research.">Yeast as a model for medical and medicinal research.</a> Trends in Pharmacological Sciences. 26 (5), 265-273 (2005).</li><li>Krivoruchko, A., Siewers, V., Nielsen, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Opportunities+for+yeast+metabolic+engineering:+Lessons+from+synthetic+biology.">Opportunities for yeast metabolic engineering: Lessons from synthetic biology.</a> Biotechnology Journal. 6 (3), 262-276 (2011).</li><li>Vanella, R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Yeast-based+assays+for+screening+11&#946;-HSD1+inhibitors.">Yeast-based assays for screening 11&#946;-HSD1 inhibitors.</a> Microbial Cell Factories. 15 (1), (2016).</li><li>Zhuang, X., Chappell, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Building+terpene+production+platforms+in+yeast.">Building terpene production platforms in yeast.</a> Biotechnology and Bioengineering. 112 (9), 1854-1864 (2015).</li><li>Agresti, J. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Ultrahigh-throughput+screening+in+drop-based+microfluidics+for+directed+evolution.">Ultrahigh-throughput screening in drop-based microfluidics for directed evolution.</a> Proceedings of the National Academy of Sciences. 107 (9), 4004-4009 (2010).</li><li>Wang, G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=RNAi+expression+tuning,+microfluidic+screening,+and+genome+recombineering+for+improved+protein+production+in+Saccharomyces+cerevisiae.">RNAi expression tuning, microfluidic screening, and genome recombineering for improved protein production in Saccharomyces cerevisiae.</a> Proceedings of the National Academy of Sciences. 116 (19), 9324-9332 (2019).</li><li>Beneyton, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Droplet-based+microfluidic+high-throughput+screening+of+heterologous+enzymes+secreted+by+the+yeast+Yarrowia+lipolytica.">Droplet-based microfluidic high-throughput screening of heterologous enzymes secreted by the yeast Yarrowia lipolytica.</a> Microbial Cell Factories. 16 (1), 18 (2017).</li><li>Sjostrom, S. L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High-throughput+screening+for+industrial+enzyme+production+hosts+by+droplet+microfluidics.">High-throughput screening for industrial enzyme production hosts by droplet microfluidics.</a> Lab on a Chip. 14 (4), 806-813 (2014).</li><li>Nadal-Ribelles, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Sensitive+high-throughput+single-cell+RNA-seq+reveals+within-clonal+transcript+correlations+in+yeast+populations.">Sensitive high-throughput single-cell RNA-seq reveals within-clonal transcript correlations in yeast populations.</a> Nature Microbiology. 4 (4), 683-692 (2019).</li><li>Gasch, A. P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Single-cell+RNA+sequencing+reveals+intrinsic+and+extrinsic+regulatory+heterogeneity+in+yeast+responding+to+stress.">Single-cell RNA sequencing reveals intrinsic and extrinsic regulatory heterogeneity in yeast responding to stress.</a> PLoS Biology. 15 (12), 2004050 (2017).</li><li>Macosko, E. Z., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Highly+parallel+genome-wide+expression+profiling+of+individual+cells+using+nanoliter+droplets.">Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.</a> Cell. 161 (5), 1202-1214 (2015).</li><li>Klein, A. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Droplet+barcoding+for+single-cell+transcriptomics+applied+to+embryonic+stem+cells.">Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells.</a> Cell. 161 (5), 1187-1201 (2015).</li><li>von der Haar, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+quantitative+estimation+of+the+global+translational+activity+in+logarithmically+growing+yeast+cells.">A quantitative estimation of the global translational activity in logarithmically growing yeast cells.</a> BMC Systems Biology. 2, 87 (2008).</li><li>Liu, L., Dalal, C. K., Heineike, B. M., Abate, A. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High+throughput+gene+expression+profiling+of+yeast+colonies+with+microgel-culture+Drop-seq.">High throughput gene expression profiling of yeast colonies with microgel-culture Drop-seq.</a> Lab on a Chip. 19 (10), 1838-1849 (2019).</li><li>Fallah-Araghi, A., Baret, J. C., Ryckelynck, M., Griffiths, A. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+completely+in+vitro+ultrahigh-throughput+droplet-based+microfluidic+screening+system+for+protein+engineering+and+directed+evolution.">A completely in vitro ultrahigh-throughput droplet-based microfluidic screening system for protein engineering and directed evolution.</a> Lab on a Chip. 12 (5), 882-891 (2012).</li><li>Berman, J., Sudbery, P. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Candida+albicans:+A+molecular+revolution+built+on+lessons+from+budding+yeast.">Candida albicans: A molecular revolution built on lessons from budding yeast.</a> Nature Reviews Genetics. 3 (12), 918-930 (2002).</li><li>Srikantha, T., Soll, D. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+white-specific+gene+in+the+white-opaque+switching+system+of+Candida+albicans.">A white-specific gene in the white-opaque switching system of Candida albicans.</a> Gene. 131 (1), 53-60 (1993).</li><li>van der Maaten, L., Hinton, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Visualizing+data+using+t-SNE.">Visualizing data using t-SNE.</a> Journal of Machine Learning Research. 9, 2579-2605 (2008).</li><li>Sun, Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Deletion+of+a+yci1+domain+protein+of+Candida+albicans+allows+homothallic+mating+in+MTL+heterozygous+cells.">Deletion of a yci1 domain protein of Candida albicans allows homothallic mating in MTL heterozygous cells.</a> mBio. 7 (2), 00465-00516 (2016).</li><li>McGinnis, C. S., Murrow, L. M., Gartner, Z. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=DoubletFinder:+Doublet+Detection+in+Single-Cell+RNA+Sequencing+Data+Using+Artificial+Nearest+Neighbors.">DoubletFinder: Doublet Detection in Single-Cell RNA Sequencing Data Using Artificial Nearest Neighbors.</a> Cell Systems. 8 (4), 329-337 (2019).</li><li>Baran-Gale, J., Chandra, T., Kirschner, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Experimental+design+for+single-cell+RNA+sequencing.">Experimental design for single-cell RNA sequencing.</a> Briefings in Functional Genomics. 17 (4), 233-239 (2018).</li><li>Silander, O. K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+Genome-Wide+Analysis+of+Promoter-Mediated+Phenotypic+Noise+in+Escherichia+coli.">A Genome-Wide Analysis of Promoter-Mediated Phenotypic Noise in Escherichia coli.</a> PLoS Genetics. 8 (1), 1002443 (2012).</li><li>Newman, J. R. S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Single-cell+proteomic+analysis+of+S.+cerevisiae+reveals+the+architecture+of+biological+noise.">Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise.</a> Nature. 441 (7095), 840-846 (2006).</li><li>Tuch, B. B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Transcriptomes+of+Two+Heritable+Cell+Types+Illuminate+the+Circuit+Governing+Their+Differentiation.">The Transcriptomes of Two Heritable Cell Types Illuminate the Circuit Governing Their Differentiation.</a> PLoS Genetics. 6 (8), 1001070 (2010).</li><li>Romo, J. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Global+Transcriptomic+Analysis+of+the+Candida+albicans+Response+to+Treatment+with+a+Novel+Inhibitor+of+Filamentation.">Global Transcriptomic Analysis of the Candida albicans Response to Treatment with a Novel Inhibitor of Filamentation.</a> mSphere. 4 (5), 00620 (2019).</li><li>Huang, H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Generation+and+manipulation+of+hydrogel+microcapsules+by+droplet-based+microfluidics+for+mammalian+cell+culture.">Generation and manipulation of hydrogel microcapsules by droplet-based microfluidics for mammalian cell culture.</a> Lab on a Chip. 17 (11), 1913-1932 (2017).</li><li>Lin, X., Nishio, K., Konno, T., Ishihara, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+effect+of+the+encapsulation+of+bacteria+in+redox+phospholipid+polymer+hydrogels+on+electron+transfer+efficiency+in+living+cell-based+devices.">The effect of the encapsulation of bacteria in redox phospholipid polymer hydrogels on electron transfer efficiency in living cell-based devices.</a> Biomaterials. 33 (33), 8221-8227 (2012).</li></ol>

The authors declare no conflicts of interest.

Our method for isogenic yeast colony RNA sequencing (ICO-seq) adapts a published single cell RNA sequencing platform, Drop-Seq, for high-throughput screening of engineered yeast strains. Yeast cells contain less than 10% of the copies of mRNA of a typical mammalian cell and have a cell wall that needs to be degraded prior to mRNA capture16. These two factors preclude the direct application of yeast to Drop-Seq or other droplet-based scRNA-seq platforms. To address these issues, we encapsulate single cells within hydrogels and grow them into colonies to provide enough input material for RNA sequencing and we digest the yeast cell wall to generate spheroplasts prior to lysis and mRNA capture. These changes add additional complexity in the ICO-seq workflow when compared with the original Drop-Seq workflow and are critical steps that users must ensure proceed smoothly.
Proper operation of Device A is necessary for encapsulating single yeast cells within agarose hydrogels. Proper counting of the input yeast suspension must be followed to minimize the number of hydrogels with more than one yeast cell, while ensuring that enough hydrogels contain a single cell to ensure a reasonable cell capture efficiency during mRNA capture. During microfluidic device operation, the agarose gel mixture must be well dissolved and passed through a syringe filter to minimize the chance of device clogging. The agarose gel mixture is viscous and the region in which a single channel splits into eight is especially prone to clogging. By centering a high-speed camera to visualize device operation in that region of the device, users can monitor the uniformity of droplets emerging from each of the eight channels and quickly react if the uniformity changes due to clogs in any of the channels. Inspection of a small quantity of collected emulsion under the microscope provides a secondary method for confirming a high-quality emulsion.
Following growth of yeast colonies within hydrogels, several precautions are necessary for ensuring quality mRNA extraction at the single colony level. It is important to optimize the time yeast are in hydrogel culture, because if the yeast are left in culture for too long, many will escape the confines of the hydrogels, leading to a higher background signal during RNA sequencing and lower sensitivity when discriminating between cell types. Proper generation of spheroplasts using Zymolyase ensures that mRNA will be released following cell exposure to lysis buffer. Visual inspection of yeast colonies following Zymolyase should yield shinier yeast cells. Improper cell wall digestion will lead to lower RNA capture efficiency. Lastly, the hydrogels should be close-packed as they are injected into Device B. Monitoring the hydrogel input with a high-speed camera will allow for the termination of emulsion collection once the hydrogels are no longer close-packed upon input into the device, otherwise capture efficiency will be impacted.
A potential concern with our method is that microgel culture of yeast may significantly alter gene expression. Previous work investigating yeast gene expression in microgels and on agar demonstrate differences in gene expression averages but overall a positive correlation17, though further investigation of this claim on a variety of yeast strains is prudent. The method also has limited cell capture efficiency due to stochastic loading of mRNA capture beads following Poisson statistics14. Currently about 10% of drops contain a bead and a colony, and the rate of double encapsulations is expected to be under 1%. Double encapsulations lead to confounding elements during RNA-seq data analysis and their filtering remains challenging23; a capture rate of 25% would lead to a corresponding increase of double encapsulations to 5% (Supplemental Figure 2). Although we demonstrate ICO-seq using the Drop-Seq platform, there are other droplet RNA-seq platforms that introduce mRNA capture beads deterministically rather than statistically, such as the commercially available 10x Genomics Chromium platform15,24. The integration of those platforms with ICO-seq could boost capture efficiencies beyond what Poisson statistics allow. Lastly, a fundamental limitation of droplet RNA-seq is the inability to recover cells of interest after sequencing. This limitation should be taken into account when considering the types of yeast libraries to analyze using this method.
Cell-to-cell heterogeneity has been demonstrated at the clonal level for microbes such as E. coli25 and S. cerevisiae26 revealing new cell states that a bulk-level analysis would otherwise mask. Bulk RNA-seq analyses performed on C. albicans tend to either look at population-wide transcriptome changes, or white and opaque cells as two separate populations27,28. The application of ICO-seq could lead to the discovery of additional substates and provide an analytical framework for discovering new cell states within other yeast species. However, the growth of cells within hydrogels is not limited to yeast: other cell types, such as mammalian, bacterial, and other fungal cells may also be cultivated within hydrogels29,30. The sequencing of isogenic colonies versus single cells leads to the averaging out of biological noise due to cell-to-cell variation, improving discrimination between cell types. This may help when analyzing cells where genetic diversity centers on specific synthesis pathways. The expanded possibilities of cell type input to ICO-seq and its potential integration with commercially available droplet RNA-seq platforms positions ICO-seq as a promising platform for dissecting cellular heterogeneity at the genetic level.

A primary goal of microbial engineering is to modify microbes to induce them to produce valuable compounds1,2. S. cerevisiae has been the primary organism for microbial engineering due to its ease of culture and the breadth of tools available for engineering its genome3,4,5. However, a hurdle remains in performing functional screens on the modified yeast: screening throughput lags behind genome engineering by orders of magnitude. Screening typically involves isolating strains in microwell plates and phenotyping them by measuring production of a specific compound6,7. The throughput of this process is limited by the large amounts of reagent needed for assaying individual strains in hundred microliter reactions. Droplet microfluidics provides an attractive solution to increase the throughput of yeast screening by orders of magnitude by downscaling reactions normally performed in well plates8. However, as with well plate screens, droplet screens typically detect single product compounds, which provides limited information into the global function of the engineered pathway9,10,11.
RNA sequencing (RNA-seq) may enable more comprehensive characterization of pathway operation by allowing expression levels of all relevant genes to be assessed simultaneously12,13. Moreover, droplet methods allow thousands of cells to be profiled per experiment, providing the throughput necessary to screen libraries of engineered variants14,15. However, RNA-seq methods are optimized for mammalian cells; yeast, by comparison, have less mRNA per cell and a cell wall that is difficult to remove16, precluding their sequencing by existing methods. If a high-throughput droplet method could be devised to enable yeast RNA-seq, it would provide a scalable, cost-effective, and information-rich phenotyping platform for yeast engineering.
We present a detailed protocol of our recently developed method for sequencing yeast cells using high-throughput droplet microfluidics17. To overcome the challenge of limited RNA, we encapsulate and culture single yeast cells in picoliter hydrogel spheres. Culture duplicates the cells, yielding hundreds of copies sharing the same engineered pathway; this reduces variation due to single cell gene expression while significantly increasing the amount of RNA available for sequencing. After culture-based amplification, we spheroplast the cells, removing the cell wall via bulk enzymatic digestion. Cell membranes remain intact, so that each isogenic colony and its associated mRNA remain encapsulated in their hydrogel spheres. This allows us to pair the individual colonies with mRNA capture reagents and lysis buffer, and the mRNA to be captured, barcoded, and sequenced following the Drop-Seq workflow14. Our method allows transcriptome-wide screening of thousands of isogenic yeast colonies per experiment.

<table>
	<tbody>
		<tr>
			<td>0.22 um syringe filter</td>
			<td>Milipore Sigma</td>
			<td>SLGP033RS</td>
			<td></td>
		</tr>
		<tr>
			<td>0.5M EDTA, pH 8.0</td>
			<td>Thermo-Fisher</td>
			<td>15575020</td>
			<td>Used to make TE-TW buffer</td>
		</tr>
		<tr>
			<td>0.75 mm biopsy punch</td>
			<td>World Precision Instruments</td>
			<td>504529</td>
			<td></td>
		</tr>
		<tr>
			<td>1 mL syringes</td>
			<td>BD</td>
			<td>309628</td>
			<td></td>
		</tr>
		<tr>
			<td>1H,1H,2H-Perfluoro-1-Octanol (PFO)</td>
			<td>Sigma-Aldrich</td>
			<td>370533</td>
			<td></td>
		</tr>
		<tr>
			<td>1M Tris-HCI, pH 8.0</td>
			<td>Thermo-Fisher</td>
			<td>15568025</td>
			<td>Used to make TE-TW buffer</td>
		</tr>
		<tr>
			<td>27 gauge needles</td>
			<td>BD</td>
			<td>305109</td>
			<td></td>
		</tr>
		<tr>
			<td>3 mL syringes</td>
			<td>BD</td>
			<td>309657</td>
			<td></td>
		</tr>
		<tr>
			<td>3&#34; silicon wafers, P type, virgin test grade</td>
			<td>University Wafers</td>
			<td>447</td>
			<td></td>
		</tr>
		<tr>
			<td>3D-printed centrifuge syringe holder</td>
			<td>(custom)</td>
			<td>(custom)</td>
			<td>See Supplemental Files for 3D print file</td>
		</tr>
		<tr>
			<td>Agarose, low gelling temperature</td>
			<td>Sigma-Aldrich</td>
			<td>a9414</td>
			<td></td>
		</tr>
		<tr>
			<td>Aquapel (hydrophobic glass treatment)</td>
			<td>Pittsburgh Glass Works</td>
			<td>47100</td>
			<td></td>
		</tr>
		<tr>
			<td>Drop-Seq Beads</td>
			<td>ChemGenes</td>
			<td>MACOSKO-2011-10</td>
			<td></td>
		</tr>
		<tr>
			<td>Glass microscope slides (75 mm x 50 mm)</td>
			<td>Corning</td>
			<td>294775X50</td>
			<td></td>
		</tr>
		<tr>
			<td>Ionic Krytox Surfactant</td>
			<td></td>
			<td></td>
			<td>Synthesis instructions in ref 14. Can substitute with PEG-PFPE surfactant.</td>
		</tr>
		<tr>
			<td>Isopropanol</td>
			<td>Sigma-Aldrich</td>
			<td>109827</td>
			<td></td>
		</tr>
		<tr>
			<td>NaCl</td>
			<td>Sigma-Aldrich</td>
			<td>S9888</td>
			<td></td>
		</tr>
		<tr>
			<td>Novec 7500</td>
			<td>3M</td>
			<td>98-0212-2928-5</td>
			<td>Commonly knowns as HFE 7500</td>
		</tr>
		<tr>
			<td>PBS</td>
			<td>Fisher Scientific</td>
			<td>BP243820</td>
			<td></td>
		</tr>
		<tr>
			<td>PE-2 polyethylene tubing</td>
			<td>Scientific Commodities</td>
			<td>B31695-PE/2</td>
			<td></td>
		</tr>
		<tr>
			<td>PEG-PFPE surfactant</td>
			<td>Ran Biotechnologies</td>
			<td>008-FluoroSurfactant</td>
			<td></td>
		</tr>
		<tr>
			<td>PGMEA developer</td>
			<td>Sigma-Aldrich</td>
			<td>484431</td>
			<td></td>
		</tr>
		<tr>
			<td>Photomasks</td>
			<td>CadArt Servcies</td>
			<td>(custom)</td>
			<td>See Supplemental Files for mask designs</td>
		</tr>
		<tr>
			<td>Spin coater</td>
			<td>Specialty Coating Systems</td>
			<td>G3P-8</td>
			<td></td>
		</tr>
		<tr>
			<td>SSC Buffer</td>
			<td>Sigma-Aldrich</td>
			<td>S6639</td>
			<td></td>
		</tr>
		<tr>
			<td>SU-8 2100</td>
			<td>MicroChem</td>
			<td>Y111075</td>
			<td></td>
		</tr>
		<tr>
			<td>SU-8 2150</td>
			<td>MicroChem</td>
			<td>Y111077</td>
			<td></td>
		</tr>
		<tr>
			<td>Sylgard 184 silicone elastomer kit</td>
			<td>Krayden</td>
			<td>4019862</td>
			<td></td>
		</tr>
		<tr>
			<td>Tween-20</td>
			<td>Sigma-Aldrich</td>
			<td>P1379</td>
			<td>Used to make TE-TW buffer</td>
		</tr>
		<tr>
			<td>YR Digestion buffer</td>
			<td>Zymo Research</td>
			<td>R1001-1</td>
			<td>Spheroplasting buffer</td>
		</tr>
		<tr>
			<td>YR Lysis Buffer</td>
			<td>Zymo Research</td>
			<td>R1001-2</td>
			<td></td>
		</tr>
		<tr>
			<td>Zymolyase</td>
			<td>Zymo Research</td>
			<td>E1005</td>
			<td>Spheroplasting enzyme mixture</td>
		</tr>
	</tbody>
</table>

high throughput yeast strain phenotyping with droplet-based rna sequencing

The powerful tools available to edit yeast genomes have made this microbe a valuable platform for engineering. While it is now possible to construct libraries of millions of genetically distinct strains, screening for a desired phenotype remains a significant obstacle. With existing screening techniques, there is a tradeoff between information output and throughput, with high-throughput screening typically being performed on one product of interest. Therefore, we present an approach to accelerate strain screening by adapting single cell RNA sequencing to isogenic picoliter colonies of genetically engineered yeast strains. To address the unique challenges of performing RNA sequencing on yeast cells, we culture isogenic yeast colonies within hydrogels and spheroplast prior to performing RNA sequencing. The RNA sequencing data can be used to infer yeast phenotypes and sort out engineered pathways. The scalability of our method addresses a critical obstruction in microbial engineering.

We adapted the previously published Drop-Seq workflow14 for isogenic colony sequencing (ICO-seq) to perform gene expression profiling of isogenic yeast colonies. We isolated single yeast cells and encapsulated them into agarose microgels (Figure 1A). Following overnight incubation of microgels, these encapsulated yeast cells grew into isogenic colonies. Before loading gels into a second microfluidic device for mRNA capture, we digested the yeast cell wall to make the mRNA more accessible (Figure 1B, left). We close-packed these microgels and merged the mRNA capture beads and lysis buffer. Some droplets contained exactly one bead paired with a lysed yeast colony. All beads in the emulsion were collected and the cDNA synthesized and sequenced following the Drop-Seq protocol.
We generated isogenic yeast colonies through single yeast cell encapsulation within agarose microgels using a coencapsulation microfluidic device with an eight drop splitter attached (Figure 2A). We diluted the input yeast suspension to a concentration of ~750,000/mL so that ~30% of microgels have exactly one yeast in them. Prior to inserting the ultralow melting temperature agarose into the device, we dissolved it at an elevated temperature and maintained the syringe at this temperature to prevent premature gelation. At the drop-generation junction (Figure 2B), yeast cells were initially encapsulated into 160 &#181;m droplets. Following the drop-generation junction an eight fold splitter divided these droplets into eight 80 &#181;m droplets (Figure 2C). A syringe filter was attached to the molten agarose to prevent clogs from forming within the channels, which can be as narrow as 37 &#181;m during the drop-splitting. We collected the emulsion on ice, which immediately began the agarose gelation process. We calculated the polydispersity of a typical emulsion to be ~6% (Supplemental Figure 1), though polydispersity values up to 10% are acceptable. Once the agarose gels set, we broke the emulsion and removed the oil phase. The gels were washed in aqueous buffer before immersion in growth media. Overnight incubation of the microgels resulted in isogenic colonies growing within some of the microgels (Figure 2D). The percentage of hydrogels containing colonies of at least 20 cells depended on the culture conditions, including incubation time and media composition. In our demonstration using C. albicans, we determined that about 15% of hydrogels contained a colony after 20 h of suspension culture.
A second coencapsulation device extracted the mRNA from isogenic colonies (Figure 3A). Prior to loading the yeast microgels into the microfluidic device, we washed and immersed the gels in a solution to digest the yeast cell walls. Proper digestion of the yeast cells was verified by microscopy, with treated yeast having a more reflective morphology (Figure 3B). We close-packed the microgels in a syringe and tuned the gel input flow rate such that one gel was in each drop. A stream of mRNA capture beads in lysis buffer mixed with the close-packed gel stream prior to the drop-making junction (Figure 3C). We collected a resulting emulsion of 160 &#181;m droplets, and colonies began to lyse and release their cellular contents. We loaded beads at a limiting dilution to minimize the number of drops containing multiple beads, but close-packing of the gels during drop-making resulted in about 10% of collected drops containing one bead with a lysed colony (Figure 3D).
We analyzed gene expression of C. albicans, a species of yeast present in the human gut microbiome, using the ICO-seq workflow. C. albicans is noted for its ability to switch between two different cell states, termed white and opaque19. We use an engineered C albicans strain, strain RZY122, which replaces one copy of the WH11 gene, only active in white cells with YFP20. We obtained a set of gene expression profiles using the workflow and used them for analysis of colonies expressing at least 300 unique genes. As a reference dataset, we used C. Albicans expression data obtained from a previously published study17 and filtered out colonies expressing fewer than 600 unique genes. After performing principal component (PC) analysis and a t-stochastic neighbor embedding (tSNE) dimensionality reduction21, we found general concordance between our sample dataset and the reference (Figure 4A). PC analysis revealed that YFP and WH11 significantly contributed to the first two PC&#8217;s. Furthermore, tSNE analysis revealed three clusters (Figure 4B). While cluster 2 was predominantly comprised of cells from the sample dataset, clusters 0 and 1 were comprised of cells from both samples. By overlaying WH11 expression on the tSNE (Figure 4C, upper panel), we determined that cluster 1 likely contained white colonies. We also found that STF2 expression increased in cluster 1 (Figure 4C, lower panel), consistent with previously obtained data17. In clusters 0 and 2, WH11 and STF2 were significantly downregulated compared with cluster 1 (Figure 4D). Genes involved in fermentation, such as ADH1, were upregulated in cluster 0, consistent with previous studies of opaque cells22. We found that colonies in cluster 2 had decreased ribosomal RNA compared with colonies in clusters 0 and 1. Though the sample and reference datasets were obtained using the same stock of cells, this result suggests that even subtle differences in experimental handling can affect gene expression.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/61014/61014fig1v2.jpg" /> 
Figure 1: Overview of ICO-seq workflow.&#160;(A) Yeast growing in a suspension culture were diluted in buffer and coencapsulated with molten agarose in a flow-focusing droplet generator device to enable the Poisson loading of agarose microgels with single yeast cells. The gels set when the agarose cooled, the oil/water suspension was broken, and the oil was removed, yielding a suspension of gel beads in water. Following overnight culture, yeast cells grew into isogenic colonies within the microgels. (B) Colonies were subjected to a cell wall degradation buffer, after which they were close-packed and coencapsulated with mRNA capture beads in a second microfluidic device. Close packing of the microgels ensured each drop had one gel, while Poisson loading of the beads reduced the chance of multiple beads within one drop. Collected drops were processed for cDNA synthesis and generation of a sequencing library. <a href="https://www.jove.com/files/ftp_upload/61014/61014fig1v2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/61014/61014fig2v2.jpg" /> 
Figure 2: Generation of isogenic yeast colonies within agarose microgels using Device A.&#160;(A) Schematic of microfluidic device, showing locations of the three inputs and output ports. The drop-making junction is highlighted in red. (B) Close-up of the drop-making junction during normal device operation. (C) Micrograph of collected droplets, with a close-up of a droplet containing an encapsulated cell (inset). (D) Micrograph of isogenic yeast colonies in agarose microgels following a 24-hour incubation, with a close-up of two colonies (inset). All scale bars = 100 &#181;m. <a href="https://www.jove.com/files/ftp_upload/61014/61014fig2v2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/61014/61014fig3v2.jpg" /> 
Figure 3: Lysis and mRNA capture from isogenic colonies using Device B.&#160;(A) Schematic of microfluidic device, showing locations of the three inputs and output ports. The drop-making junction is highlighted in red. (B) Micrograph of yeast colonies following cell wall digestion, with a close-up of one colony (inset). (C) Close-up of the drop-making junction during normal device operation. (D) Micrograph of collected emulsions following microgel and bead pairing, with a close-up showing a drop with a bead and a lysed colony (inset). All scale bars = 100 &#181;m. <a href="https://www.jove.com/files/ftp_upload/61014/61014fig3v2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/61014/61014fig4v2.jpg" /> 
Figure 4: Analysis of white-opaque switching response in C. albicans.&#160;(A) tSNE plot of a sample dataset combined with a reference dataset from Liu17. (B) Clustering of transcriptomes reveals three&#160;clusters visualized on a tSNE plot. (C) Key genes involved in the white-opaque switching response contributed to variation as determined through principal component analysis. (D) Violin plots of normalized expression levels of YFP and WH11 by clusters marked on tSNE plot. **indicates p &#60;&#60;&#60; 0.05 and * indicates p &#60;&#60; 0.05. <a href="https://www.jove.com/files/ftp_upload/61014/61014fig4v2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Supplemental Figures 1 and 2. <a href="https://www.jove.com/files/ftp_upload/61014/jove_SI.docx" target="_blank">Please click here to download these filgures.</a>
Supplemental Files 1-3. <a href="https://www.jove.com/files/ftp_upload/61014/Supplemental_Files.zip" target="_blank">Please click here to download these files.</a>

Watch this Scientific Journal Video about High Throughput Yeast Strain Phenotyping with Droplet-Based RNA Sequencing at JoVE.com

High Throughput Yeast Strain Phenotyping with Droplet-Based RNA Sequencing

A bottleneck in the &#8216;design-build-test&#8217; cycle of microbial engineering is the speed at which we can perform functional screens of strains. We describe a high-throughput method for strain screening applied to hundreds to thousands of yeast cells per experiment that utilizes droplet-based RNA sequencing.

The powerful tools available to edit yeast genomes have made this microbe a valuable platform for engineering. While it is now possible to construct ...

high-throughput-yeast-strain-phenotyping-with-droplet-based-rna

Research

JoVE Journal

Bioengineering

6.8K Views.  University of California San Francisco. A bottleneck in the ‘design-build-test’ cycle of microbial engineering is the speed at which we can perform functional screens of strains. We describe a high-throughput method for strain screening applied to hundreds to thousands of yeast cells per experiment that utilizes droplet-based RNA sequencing.

Video: High Throughput Yeast Strain Phenotyping with Droplet-Based RNA Sequencing

Polymer Microarrays for High Throughput Discovery of Biomaterials

The discovery of novel biomaterials that are optimized for a specific biological application is readily achieved using polymer microarrays, which allows a combinatorial library of materials to be screened in a parallel, high throughput format1. Herein is described the formation and characterization of a polymer microarray using an on-chip photopolymerization technique 2. This involves mixing monomers at varied ratios to produce a library of monomer solutions, transferring the solution to a glass slide format using a robotic printing device and curing with UV irradiation. This format is readily amenable to many biological assays, including stem cell attachment and proliferation, cell sorting and low bacterial adhesion, allowing the ready identification of 'hit' materials that fulfill a specific biological criterion3-5. Furthermore, the use of high throughput surface characterization (HTSC) allows the biological performance to be correlated with physio-chemical properties, hence elucidating the biological-material interaction6. HTSC makes use of water contact angle (WCA) measurements, atomic force microscopy (AFM), X-ray photoelectron spectroscopy (XPS) and time-of-flight secondary ion mass spectrometry (ToF-SIMS). In particular, ToF-SIMS provides a chemically rich analysis of the sample that can be used to correlate the cell response with a molecular moiety. In some cases, the biological performance can be predicted from the ToF-SIMS spectra, demonstrating the chemical dependence of a biological-material interaction, and informing the development of hit materials5,3.

A description of the formation of a polymer microarray using an on-chip photopolymerization technique. The high throughput surface characterization using atomic force microscopy, water contact angle measurements, X-ray photoelectron spectroscopy and time of flight secondary ion mass spectrometry and a cell attachment assay is also described.

The discovery of novel biomaterials that are optimized for a specific biological application is readily achieved using polymer microarrays, which allows a ...

High-throughput Protein Expression Generator Using a Microfluidic Platform

Rapidly increasing fields, such as systems biology, require the development and implementation of new technologies, enabling high-throughput and high-fidelity measurements of large systems. Microfluidics promises to fulfill many of these requirements, such as performing high-throughput screening experiments on-chip, encompassing biochemical, biophysical, and cell-based assays1. Since the early days of microfluidics devices, this field has drastically evolved, leading to the development of microfluidic large-scale integration2,3. This technology allows for the integration of thousands of micromechanical valves on a single device with a postage-sized footprint (Figure 1). We have developed a high-throughput microfluidic platform for generating in vitro expression of protein arrays (Figure 2) named PING (Protein Interaction Network Generator). These arrays can serve as a template for many experiments such as protein-protein 4, protein-RNA5 or protein-DNA6 interactions.
The device consist of thousands of reaction chambers, which are individually programmed using a microarrayer. Aligning of these printed microarrays to microfluidics devices programs each chamber with a single spot eliminating potential contamination or cross-reactivity Moreover, generating microarrays using standard microarray spotting techniques is also very modular, allowing for the arraying of proteins7, DNA8, small molecules, and even colloidal suspensions. The potential impact of microfluidics on biological sciences is significant. A number of microfluidics based assays have already provided novel insights into the structure and function of biological systems, and the field of microfluidics will continue to impact biology.

We present a microfluidic approach for the expression of protein arrays. The device consists of thousands of reaction chambers controlled by micro-mechanical valves. The microfluidic device is mated to a microarray-printed gene library. These genes are then transcribed and translated on-chip, resulting in a protein array ready for experimental use.

Rapidly increasing fields, such as systems biology, require the development and implementation of new technologies, enabling high-throughput and ...

High Throughput Single-cell and Multiple-cell Micro-encapsulation

Microfluidic encapsulation methods have been previously utilized to capture cells in picoliter-scale aqueous, monodisperse drops, providing confinement from a bulk fluid environment with applications in high throughput screening, cytometry, and mass spectrometry. We describe a method to not only encapsulate single cells, but to repeatedly capture a set number of cells (here we demonstrate one- and two-cell encapsulation) to study both isolation and the interactions between cells in groups of controlled sizes. By combining drop generation techniques with cell and particle ordering, we demonstrate controlled encapsulation of cell-sized particles for efficient, continuous encapsulation. Using an aqueous particle suspension and immiscible fluorocarbon oil, we generate aqueous drops in oil with a flow focusing nozzle. The aqueous flow rate is sufficiently high to create ordering of particles which reach the nozzle at integer multiple frequencies of the drop generation frequency, encapsulating a controlled number of cells in each drop. For representative results, 9.9 &mu;m polystyrene particles are used as cell surrogates. This study shows a single-particle encapsulation efficiency Pk=1 of 83.7% and a double-particle encapsulation efficiency Pk=2 of 79.5% as compared to their respective Poisson efficiencies of 39.3% and 33.3%, respectively. The effect of consistent cell and particle concentration is demonstrated to be of major importance for efficient encapsulation, and dripping to jetting transitions are also addressed. 
Introduction 
Continuous media aqueous cell suspensions share a common fluid environment which allows cells to interact in parallel and also homogenizes the effects of specific cells in measurements from the media. High-throughput encapsulation of cells into picoliter-scale drops confines the samples to protect drops from cross-contamination, enable a measure of cellular diversity within samples, prevent dilution of reagents and expressed biomarkers, and amplify signals from bioreactor products. Drops also provide the ability to re-merge drops into larger aqueous samples or with other drops for intercellular signaling studies.1,2 The reduction in dilution implies stronger detection signals for higher accuracy measurements as well as the ability to reduce potentially costly sample and reagent volumes.3 Encapsulation of cells in drops has been utilized to improve detection of protein expression,4 antibodies,5,6 enzymes,7 and metabolic activity8 for high throughput screening, and could be used to improve high throughput cytometry.9 Additional studies present applications in bio-electrospraying of cell containing drops for mass spectrometry10 and targeted surface cell coatings.11 Some applications, however, have been limited by the lack of ability to control the number of cells encapsulated in drops. Here we present a method of ordered encapsulation12 which increases the demonstrated encapsulation efficiencies for one and two cells and may be extrapolated for encapsulation of a larger number of cells.
To achieve monodisperse drop generation, microfluidic "flow focusing" enables the creation of controllable-size drops of one fluid (an aqueous cell mixture) within another (a continuous oil phase) by using a nozzle at which the streams converge.13 For a given nozzle geometry, the drop generation frequency f and drop size can be altered by adjusting oil and aqueous flow rates Qoil and Qaq. As the flow rates increase, the flows may transition from drop generation to unstable jetting of aqueous fluid from the nozzle.14 
When the aqueous solution contains suspended particles, particles become encapsulated and isolated from one another at the nozzle. For drop generation using a randomly distributed aqueous cell suspension, the average fraction of drops Dk containing k cells is dictated by Poisson statistics, where Dk = &lambda;k exp(-&lambda;)/(k!) and &lambda; is the average number of cells per drop. The fraction of cells which end up in the "correctly" encapsulated drops is calculated using Pk = (k x Dk)/&Sigma;(k' x Dk'). The subtle difference between the two metrics is that Dk relates to the utilization of aqueous fluid and the amount of drop sorting that must be completed following encapsulation, and Pk relates to the utilization of the cell sample. As an example, one could use a dilute cell suspension (low &lambda;) to encapsulate drops where most drops containing cells would contain just one cell. While the efficiency metric Pk would be high, the majority of drops would be empty (low Dk), thus requiring a sorting mechanism to remove empty drops, also reducing throughput.15
Combining drop generation with inertial ordering provides the ability to encapsulate drops with more predictable numbers of cells per drop and higher throughputs than random encapsulation. Inertial focusing was first discovered by Segre and Silberberg16 and refers to the tendency of finite-sized particles to migrate to lateral equilibrium positions in channel flow. Inertial ordering refers to the tendency of the particles and cells to passively organize into equally spaced, staggered, constant velocity trains. Both focusing and ordering require sufficiently high flow rates (high Reynolds number) and particle sizes (high Particle Reynolds number).17,18 Here, the Reynolds number Re =uDh/&nu; and particle Reynolds number Rep =Re(a/Dh)2, where u is a characteristic flow velocity, Dh [=2wh/(w+h)] is the hydraulic diameter, &nu; is the kinematic viscosity, a is the particle diameter, w is the channel width, and h is the channel height. Empirically, the length required to achieve fully ordered trains decreases as Re and Rep increase. Note that the high Re and Rep requirements (for this study on the order of 5 and 0.5, respectively) may conflict with the need to keep aqueous flow rates low to avoid jetting at the drop generation nozzle. Additionally, high flow rates lead to higher shear stresses on cells, which are not addressed in this protocol. The previous ordered encapsulation study demonstrated that over 90% of singly encapsulated HL60 cells under similar flow conditions to those in this study maintained cell membrane integrity.12 However, the effect of the magnitude and time scales of shear stresses will need to be carefully considered when extrapolating to different cell types and flow parameters. The overlapping of the cell ordering, drop generation, and cell viability aqueous flow rate constraints provides an ideal operational regime for controlled encapsulation of single and multiple cells.
Because very few studies address inter-particle train spacing,19,20 determining the spacing is most easily done empirically and will depend on channel geometry, flow rate, particle size, and particle concentration. Nonetheless, the equal lateral spacing between trains implies that cells arrive at predictable, consistent time intervals. When drop generation occurs at the same rate at which ordered cells arrive at the nozzle, the cells become encapsulated within the drop in a controlled manner. This technique has been utilized to encapsulate single cells with throughputs on the order of 15 kHz,12 a significant improvement over previous studies reporting encapsulation rates on the order of 60-160 Hz.4,15 In the controlled encapsulation work, over 80% of drops contained one and only one cell, a significant efficiency improvement over Poisson (random) statistics, which predicts less than 40% efficiency on average.12
In previous controlled encapsulation work,12 the average number of particles per drop &lambda; was tuned to provide single-cell encapsulation. We hypothesize that through tuning of flow rates, we can efficiently encapsulate any number of cells per drop when &lambda; is equal or close to the number of desired cells per drop. While single-cell encapsulation is valuable in determining individual cell responses from stimuli, multiple-cell encapsulation provides information relating to the interaction of controlled numbers and types of cells. Here we present a protocol, representative results using polystyrene microspheres, and discussion for controlled encapsulation of multiple cells using a passive inertial ordering channel and drop generation nozzle.

Combining monodisperse drop generation with inertial ordering of cells and particles, we describe a method to encapsulate a desired number of cells or particles in a single drop at kHz rates. We demonstrate efficiencies twice exceeding those of unordered encapsulation for single- and double-particle drops.

Microfluidic encapsulation methods have been previously utilized to capture cells in picoliter-scale aqueous, monodisperse drops, providing confinement ...

On-Chip Endothelial Inflammatory Phenotyping

Atherogenesis is potentiated by metabolic abnormalities that contribute to a heightened state of systemic inflammation resulting in endothelial dysfunction. However, early functional changes in endothelium that signify an individual's level of risk are not directly assessed clinically to help guide therapeutic strategy. Moreover, the regulation of inflammation by local hemodynamics contributes to the non-random spatial distribution of atherosclerosis, but the mechanisms are difficult to delineate in vivo. We describe a lab-on-a-chip based approach to quantitatively assay metabolic perturbation of inflammatory events in human endothelial cells (EC) and monocytes under precise flow conditions. Standard methods of soft lithography are used to microfabricate vascular mimetic microfluidic chambers (VMMC), which are bound directly to cultured EC monolayers.1 These devices have the advantage of using small volumes of reagents while providing a platform for directly imaging the inflammatory events at the membrane of EC exposed to a well-defined shear field. We have successfully applied these devices to investigate cytokine-,2 lipid-3, 4 and RAGE-induced5 inflammation in human aortic EC (HAEC). Here we document the use of the VMMC to assay monocytic cell (THP-1) rolling and arrest on HAEC monolayers that are conditioned under differential shear characteristics and activated by the inflammatory cytokine TNF-&alpha;. Studies such as these are providing mechanistic insight into atherosusceptibility under metabolic risk factors.

Microfluidic flow chambers etched by photolithography and fabricated from PDMS are applied to probe functional outcomes associated with EC dysfunction and inflammation. In a representative experiment, the ability of differential shear stress to modulate monocytic cell adhesion to cytokine activated EC monolayers is demonstrated.

Atherogenesis is potentiated by metabolic abnormalities that contribute to a heightened state of systemic inflammation resulting in endothelial ...

Continuous High-resolution Microscopic Observation of Replicative Aging in Budding Yeast

We demonstrate the use of a simple microfluidic setup, in which single budding yeast cells can be tracked throughout their entire lifespan. The microfluidic chip exploits the size difference between mother and daughter cells using an array of micropads. Upon loading, cells are trapped underneath these micropads, because the distance between the micropad and cover glass is similar to the diameter of a yeast cell (3-4 &mu;m). After the loading procedure, culture medium is continuously flushed through the chip, which not only creates a constant and defined environment throughout the entire experiment, but also flushes out the emerging daughter cells, which are not retained underneath the pads due to their smaller size. The setup retains mother cells so efficiently that in a single experiment up to 50 individual cells can be monitored in a fully automated manner for 5 days or, if necessary, longer. In addition, the excellent optical properties of the chip allow high-resolution imaging of cells during the entire aging process.

We describe here the operation of a microfluidic device that allows continuous and high-resolution microscopic imaging of single budding yeast cells during their complete replicative and/or chronological lifespan.

We demonstrate the use of a simple microfluidic setup, in which single budding yeast cells can be tracked throughout their entire lifespan. The ...

High-throughput, Automated Extraction of DNA and RNA from Clinical Samples using TruTip Technology on Common Liquid Handling Robots

TruTip is a simple nucleic acid extraction technology whereby a porous, monolithic binding matrix is inserted into a pipette tip. The geometry of the monolith can be adapted for specific pipette tips ranging in volume from 1.0 to 5.0 ml. The large porosity of the monolith enables viscous or complex samples to readily pass through it with minimal fluidic backpressure. Bi-directional flow maximizes residence time between the monolith and sample, and enables large sample volumes to be processed within a single TruTip. The fundamental steps, irrespective of sample volume or TruTip geometry, include cell lysis, nucleic acid binding to the inner pores of the TruTip monolith, washing away unbound sample components and lysis buffers, and eluting purified and concentrated nucleic acids into an appropriate buffer. The attributes and adaptability of TruTip are demonstrated in three automated clinical sample processing protocols using an Eppendorf epMotion 5070, Hamilton STAR and STARplus liquid handling robots, including RNA isolation from nasopharyngeal aspirate, genomic DNA isolation from whole blood, and fetal DNA extraction and enrichment from large volumes of maternal plasma (respectively).

TruTip is a simple nucleic acid extraction technology whereby a porous, monolithic binding matrix is inserted into a pipette tip. Consequently, the sample preparation format is compatible with most liquid handling instruments, and can be used for many medium to high-throughput clinical applications and sample types.

TruTip is a simple nucleic acid extraction  technology whereby a porous, monolithic binding matrix is inserted into a  pipette tip. The geometry of the ...

High Throughput Microfluidic Rapid and Low Cost Prototyping Packaging Methods

In this work, 3 different packaging and assembly techniques are presented. They can be classified into two categories: one-time use and reusable packaging techniques.
The one-time use packaging technique employs UV-based and temperature curing epoxies to connect microtubes to access holes, wire-bonding for integrated circuit connections, and silver epoxy for electrical connections. This method is based on a robust assembly technique that can support relatively high pressure close to 1 psi and does not need any support to strengthen the microfluidic architecture.
Reusable packaging techniques consist of PDMS-based microtube interconnectors and anisotropic adhesive films for electrical connections. These devices are more sensitive and fragile. Consequently, Plexiglas support is added to the microfluidic structure to improve the electrical contact when anisotropic adhesive films are used, and also to strengthen the microfluidic architecture. In addition, a micromanipulator is needed to maintain tubes while using a thin PDMS layer to connect them to the access holes. Different PDMS layer thicknesses, ranging from 0.45-3 mm, are tested to compare the best adherence versus injection rates. Applied injection rates are varied from 50-300 &#956;l/hr for 0.45-3 mm PDMS layers, respectively. These techniques are mainly applicable for low-pressure applications. However, they can be extended for high-pressure ones through plasma-oxygen process to permanently seal the PDMS to glass substrates. The main advantage of this technique, besides the fact that it is reusable, consists of keeping the device observable when the microchannel length is very short (in the range of 3 mm or lower).

In this article we describe different techniques for microfluidic rapid prototyping platforms. The proposed techniques are based on ultraviolet (UV) sensitive and temperature curing epoxies, polydimethylsiloxane (PDMS) based tubing, wire-bonding, and anisotropic adhesive films. The assembling procedures presented are developed for both one-time use devices as well as reusable microfluidic systems.

In this work, 3 different packaging and assembly techniques are presented. They can be classified into two categories: one-time use and reusable packaging ...

High-throughput Identification of Bacteria Repellent Polymers for Medical Devices

Medical devices are often associated with hospital-acquired infections, which place enormous strain on patients and the healthcare system as well as contributing to antimicrobial resistance. One possible avenue for the reduction of device-associated infections is the identification of bacteria-repellent polymer coatings for these devices, which would prevent bacterial binding at the initial attachment step. A method for the identification of such repellent polymers, based on the parallel screening of hundreds of polymers using a microarray, is described here. This high-throughput method resulted in the identification of a range of promising polymers that resisted binding of various clinically relevant bacterial species individually and also as multi-species communities. One polymer, PA13 (poly(methylmethacrylate-co-dimethylacrylamide)), demonstrated significant reduction in attachment of a number of hospital isolates when coated onto two commercially available central venous catheters. The method described could be applied to identify polymers for a wide range of applications in which modification of bacterial attachment is important.

A high-throughput microarray method for the identification of polymers which reduce bacterial surface binding on medical devices is described.

Medical devices are often associated with hospital-acquired infections, which place enormous strain on patients and the healthcare system as well as ...

Gel-seq: A Method for Simultaneous Sequencing Library Preparation of DNA and RNA Using Hydrogel Matrices

The ability to amplify and sequence either DNA or RNA from small starting samples has only been achieved in the last five years. Unfortunately, the standard protocols for generating genomic or transcriptomic libraries are incompatible and researchers must choose whether to sequence DNA or RNA for a particular sample. Gel-seq solves this problem by enabling researchers to simultaneously prepare libraries for both DNA and RNA starting with 100 - 1000 cells using a simple hydrogel device. This paper presents a detailed approach for the fabrication of the device as well as the biological protocol to generate paired libraries. We designed Gel-seq so that it could be easily implemented by other researchers; many genetics labs already have the necessary equipment to reproduce the Gel-seq device fabrication. Our protocol employs commonly-used kits for both whole-transcript amplification (WTA) and library preparation, which are also likely to be familiar to researchers already versed in generating genomic and transcriptomic libraries. Our approach allows researchers to bring to bear the power of both DNA and RNA sequencing on a single sample without splitting and with negligible added cost.

Gel-seq enables researchers to simultaneously prepare libraries for both DNA- and RNA-seq at negligible added cost starting from 100 - 1000 cells using a simple hydrogel device. This paper presents a detailed approach for the fabrication of the device as well as the biological protocol to generate paired libraries.

The ability to amplify and sequence either DNA or RNA from small starting samples has only been achieved in the last five years. Unfortunately, the ...

An Ultrahigh-throughput Microfluidic Platform for Single-cell Genome Sequencing

Sequencing technologies have undergone a paradigm shift from bulk to single-cell resolution in response to an evolving understanding of the role of cellular heterogeneity in biological systems. However, single-cell sequencing of large populations has been hampered by limitations in processing genomes for sequencing. In this paper, we describe a method for single-cell genome sequencing (SiC-seq) which uses droplet microfluidics to isolate, amplify, and barcode the genomes of single cells. Cell encapsulation in microgels allows the compartmentalized purification and tagmentation of DNA, while a microfluidic merger efficiently pairs each genome with a unique single-cell oligonucleotide barcode, allowing &gt;50,000 single cells to be sequenced per run. The sequencing data is demultiplexed by barcode, generating groups of reads originating from single cells. As a high-throughput and low-bias method of single-cell sequencing, SiC-seq will enable a broader range of genomic studies targeted at diverse cell populations.

Single-cell sequencing reveals genotypic heterogeneity in biological systems, but current technologies lack the throughput necessary for the deep profiling of community composition and function. Here, we describe a microfluidic workflow for sequencing &gt;50,000 single-cell genomes from diverse cell populations.

Sequencing technologies have undergone a paradigm shift from bulk to single-cell resolution in response to an evolving understanding of the role of ...