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>

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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&quot; 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.bdigitaluss.remotexs.co/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.bdigitaluss.remotexs.co/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.bdigitaluss.remotexs.co/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.bdigitaluss.remotexs.co/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.bdigitaluss.remotexs.co/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.bdigitaluss.remotexs.co/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

Universidad San Sebastian

Research

JoVE Journal

Bioengineering

6.9K 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

Measuring mRNA Levels Over Time During the Yeast S. cerevisiae Hypoxic Response

Complex changes in gene expression typically mediate a large portion of a cellular response. Each gene may change expression with unique kinetics as the gene is regulated by the particular timing of one of many stimuli, signaling pathways or secondary effects. In order to capture the entire gene expression response to hypoxia in the yeast S. cerevisiae, RNA-seq analysis was used to monitor the mRNA levels of all genes at specific times after exposure to hypoxia. Hypoxia was established by growing cells in ~100% N2 gas. Importantly, unlike other hypoxic studies, ergosterol and unsaturated fatty acids were not added to the media because these metabolites affect gene expression. Time points were chosen in the range of 0 - 4 h after hypoxia because that period captures the major changes in gene expression. At each time point, mid-log hypoxic cells were quickly filtered and frozen, limiting exposure to O2 and concomitant changes in gene expression. Total RNA was extracted from cells and used to enrich for mRNA, which was then converted to cDNA. From this cDNA, multiplex libraries were created and eight or more samples were sequenced in one lane of a next-generation sequencer. A post-sequencing pipeline is described, which includes quality base trimming, read mapping and determining the number of reads per gene. DESeq2 within the R statistical environment was used to identify genes that change significantly at any one of the hypoxic time points. Analysis of three biological replicates revealed high reproducibility, genes of differing kinetics and a large number of expected O2-regulated genes. These methods can be used to study how the cells of various organisms respond to hypoxia over time and adapted to study gene expression during other cellular responses.

Measuring mRNA Levels Over Time During the Yeast S. cerevisiae Hypoxic Response

Here, we present a protocol using RNA-seq to monitor mRNA levels over time during the hypoxic response of S. cerevisiae cells. This method can be adapted to analyze gene expression during any cellular response.

Complex changes in gene expression typically mediate a large portion of a cellular response. Each gene may change expression with unique kinetics as the ...

Genetics

G2-seq: A High Throughput Sequencing-based Technique for Identifying Late Replicating Regions of the Genome

Numerous techniques have been developed to follow the progress of DNA replication through the S phase of the cell cycle. Most of these techniques have been directed toward elucidation of the location and timing of initiation of genome duplication rather than its completion. However, it is critical that we understand regions of the genome that are last to complete replication, because these regions suffer elevated levels of chromosomal breakage and mutation, and they have been associated with both disease and aging. Here we describe how we have extended a technique that has been used to monitor replication initiation to instead identify those regions of the genome last to complete replication. This approach is based on a combination of flow cytometry and high throughput sequencing. Although this report focuses on the application of this technique to yeast, the approach can be used with any cells that can be sorted in a flow cytometer according to DNA content.

We describe a technique for combining flow cytometry and high throughput sequencing to identify late replicating regions of the genome.

Numerous techniques have been developed to follow the progress of DNA replication through the S phase of the cell cycle. Most of these techniques have ...

Genome-wide Quantification of Translation in Budding Yeast by Ribosome Profiling

Translation of mRNA into proteins is a complex process involving several layers of regulation. It is often assumed that changes in mRNA transcription reflect changes in protein synthesis, but many exceptions have been observed. Recently, a technique called ribosome profiling (or Ribo-Seq) has emerged as a powerful method that allows identification, with high accuracy, which regions of mRNA are translated into proteins and quantification of translation at the genome-wide level. Here, we present a generalized protocol for genome-wide quantification of translation using Ribo-Seq in budding yeast. In addition, combining Ribo-Seq data with mRNA abundance measurements allows us to simultaneously quantify translation efficiency of thousands of mRNA transcripts in the same sample and compare changes in these parameters in response to experimental manipulations or in different physiological states. We describe a detailed protocol for generation of ribosome footprints using nuclease digestion, isolation of intact ribosome-footprint complexes via sucrose gradient fractionation, and preparation of DNA libraries for deep sequencing along with appropriate quality controls necessary to ensure accurate analysis of in vivo translation.

Translational regulation plays an important role in the control of protein abundance. Here, we describe a high-throughput method for quantitative analysis of translation in the budding yeast Saccharomyces cerevisiae.

Translation of mRNA into proteins is a complex process involving several layers of regulation. It is often assumed that changes in mRNA transcription ...

Biochemistry

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

We have adapted the yeast 2-hybrid assay to simultaneously uncover dozens of transient and static protein interactions within a single screen utilizing high-throughput short-read DNA sequencing. The resulting sequence datasets can not only track what genes in a population that are enriched during selection for positive yeast 2-hybrid interactions, but also give detailed information about the relevant subdomains of proteins sufficient for interaction. Here, we describe a full suite of stand-alone software programs that allow non-experts to perform all the bioinformatics and statistical steps to process and analyze DNA sequence fastq files from a batch yeast 2-hybrid assay. The processing steps covered by these software include: 1) mapping and counting sequence reads corresponding to each candidate protein encoded within a yeast 2-hybrid prey library; 2) a statistical analysis program that evaluates the enrichment profiles; and 3) tools to examine the translational frame and position within the coding region of each enriched plasmid that encodes the interacting proteins of interest.

Deep sequencing of yeast populations selected for positive yeast 2-hybrid interactions potentially yields a wealth of information about interacting partner proteins. Here, we describe the operation of specific bioinformatics tools and customized updated software to analyze sequence data from such screens.

We have adapted the yeast 2-hybrid assay to simultaneously uncover dozens of transient and static protein interactions within a single screen utilizing ...

A Deep-sequencing-assisted, Spontaneous Suppressor Screen in the Fission Yeast Schizosaccharomyces pombe

A genetic screen for mutant alleles that suppress phenotypic defects caused by a mutation is a powerful approach to identify genes that belong to closely related biochemical pathways. Previous methods such as the Synthetic Genetic Array (SGA) analysis, and random mutagenesis techniques using ultraviolet (UV) or chemicals like ethyl methanesulfonate (EMS) or N-ethyl-N- nitrosourea (ENU), have been widely used but are often costly and laborious. Also, these mutagen-based screening methods are frequently associated with severe side effects on the organism, inducing multiple mutations that add to the complexity of isolating the suppressors. Here, we present a simple and effective protocol to identify suppressor mutations in mutants which confer a growth defect in Schizosaccharomyces pombe. The fitness of cells with a growth deficiency in standard rich liquid media or synthetic liquid media can be monitored for recovery using an automated 96-well plate reader over an extended period. Once a cell acquires a suppressor mutation in the culture, its descendants outcompete those of the parental cells. The recovered cells that have a competitive growth advantage over the parental cells can then be isolated and backcrossed with the parental cells. The suppressor mutations are then identified using whole-genome sequencing. Using this approach, we have successfully isolated multiple suppressors that alleviate the severe growth defects caused by loss of Elf1, an AAA+ family ATPase that is important in nuclear mRNA transport and maintenance of genomic stability. There are currently over 400 genes in S. pombe with mutants conferring a growth defect. As many of these genes are uncharacterized, we propose that our method will hasten the identification of novel functional interactions with this user-friendly, high-throughput approach.

A Deep-sequencing-assisted, Spontaneous Suppressor Screen in the Fission Yeast Schizosaccharomyces pombe

We present a simple suppressor screen protocol in fission yeast. This method is efficient, mutagen-free, and&#160; selective&#160; for&#160; mutations that&#160; often&#160; occur at&#160; &#160;a&#160; single&#160; genomic&#160; locus.&#160; The protocol is suitable for isolating suppressors that alleviate growth defects in liquid culture that are caused by a mutation or a drug.

A genetic screen for mutant alleles that suppress phenotypic defects caused by a mutation is a powerful approach to identify genes that belong to closely ...

A Comparative Approach to Characterize the Landscape of Host-Pathogen Protein-Protein Interactions

Significant efforts were gathered to generate large-scale comprehensive protein-protein interaction network maps. This is instrumental to understand the pathogen-host relationships and was essentially performed by genetic screenings in yeast two-hybrid systems. The recent improvement of protein-protein interaction detection by a Gaussia luciferase-based fragment complementation assay now offers the opportunity to develop integrative comparative interactomic approaches necessary to rigorously compare interaction profiles of proteins from different pathogen strain variants against a common set of cellular factors.
This paper specifically focuses on the utility of combining two orthogonal methods to generate protein-protein interaction datasets: yeast two-hybrid (Y2H) and a new assay, high-throughput Gaussia princeps protein complementation assay (HT-GPCA) performed in mammalian cells.
A large-scale identification of cellular partners of a pathogen protein is performed by mating-based yeast two-hybrid screenings of cDNA libraries using multiple pathogen strain variants. A subset of interacting partners selected on a high-confidence statistical scoring is further validated in mammalian cells for pair-wise interactions with the whole set of pathogen variants proteins using HT-GPCA. This combination of two complementary methods improves the robustness of the interaction dataset, and allows the performance of a stringent comparative interaction analysis. Such comparative interactomics constitute a reliable and powerful strategy to decipher any pathogen-host interplays.

This article focuses on the identification of high-confident interaction datasets between host and pathogen proteins using a combination of two orthogonal methods: yeast two-hybrid followed by a high-throughput interaction assay in mammalian cells called HT-GPCA.

Significant efforts were gathered to generate  large-scale comprehensive protein-protein interaction network maps. This is  instrumental to understand the ...

Immunology and Infection

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 ...

High-throughput Yeast Plasmid Overexpression Screen

The budding yeast, Saccharomyces cerevisiae, is a powerful model system for defining fundamental mechanisms of many important cellular processes, including those with direct relevance to human disease. Because of its short generation time and well-characterized genome, a major experimental advantage of the yeast model system is the ability to perform genetic screens to identify genes and pathways that are involved in a given process. Over the last thirty years such genetic screens have been used to elucidate the cell cycle, secretory pathway, and many more highly conserved aspects of eukaryotic cell biology 1-5. In the last few years, several genomewide libraries of yeast strains and plasmids have been generated 6-10. These collections now allow for the systematic interrogation of gene function using gain- and loss-of-function approaches 11-16. Here we provide a detailed protocol for the use of a high-throughput yeast transformation protocol with a liquid handling robot to perform a plasmid overexpression screen, using an arrayed library of 5,500 yeast plasmids. We have been using these screens to identify genetic modifiers of toxicity associated with the accumulation of aggregation-prone human neurodegenerative disease proteins. The methods presented here are readily adaptable to the study of other cellular phenotypes of interest.

Here we describe a plasmid overexpression screen in Saccharomyces cerevisiae, using an arrayed plasmid library and a high-throughput yeast transformation protocol with a liquid handling robot.

The budding yeast, Saccharomyces cerevisiae, is a powerful model system for defining fundamental mechanisms of many important cellular processes, ...

Biology

Competitive Genomic Screens of Barcoded Yeast Libraries

By virtue of advances in next generation sequencing technologies, we have access to new genome sequences almost daily. The tempo of these advances is accelerating, promising greater depth and breadth. In light of these extraordinary advances, the need for fast, parallel methods to define gene function becomes ever more important. Collections of genome-wide deletion mutants in yeasts and E. coli have served as workhorses for functional characterization of gene function, but this approach is not scalable, current gene-deletion approaches require each of the thousands of genes that comprise a genome to be deleted and verified. Only after this work is complete can we pursue high-throughput phenotyping. Over the past decade, our laboratory has refined a portfolio of competitive, miniaturized, high-throughput genome-wide assays that can be performed in parallel. This parallelization is possible because of the inclusion of DNA 'tags', or 'barcodes,' into each mutant, with the barcode serving as a proxy for the mutation and one can measure the barcode abundance to assess mutant fitness. In this study, we seek to fill the gap between DNA sequence and barcoded mutant collections. To accomplish this we introduce a combined transposon disruption-barcoding approach that opens up parallel barcode assays to newly sequenced, but poorly characterized microbes. To illustrate this approach we present a new Candida albicans barcoded disruption collection and describe how both microarray-based and next generation sequencing-based platforms can be used to collect 10,000 - 1,000,000 gene-gene and drug-gene interactions in a single experiment.

We have developed comprehensive, unbiased genome-wide screens to understand gene-drug and gene-environment interactions. Methods for screening these mutant collections are presented.

By virtue of advances in next generation sequencing technologies, we have access to new genome sequences almost daily.  The tempo of these advances is ...

qPCRTag Analysis - A High Throughput, Real Time PCR Assay for Sc2.0 Genotyping

The Synthetic Yeast Genome Project (Sc2.0) aims to build 16 designer yeast chromosomes and combine them into a single yeast cell. To date one synthetic chromosome, synIII1, and one synthetic chromosome arm, synIXR2, have been constructed and their in vivo function validated in the absence of the corresponding wild type chromosomes. An important design feature of Sc2.0 chromosomes is the introduction of PCRTags, which are short, re-coded sequences within open reading frames (ORFs) that enable differentiation of synthetic chromosomes from their wild type counterparts. PCRTag primers anneal selectively to either synthetic or wild type chromosomes and the presence/absence of each type of DNA can be tested using a simple PCR assay. The standard readout of the PCRTag assay is to assess presence/absence of amplicons by agarose gel electrophoresis. However, with an average PCRTag amplicon density of one per 1.5 kb and a genome size of ~12 Mb, the completed Sc2.0 genome will encode roughly 8,000 PCRTags. To improve throughput, we have developed a real time PCR-based detection assay for PCRTag genotyping that we call qPCRTag analysis. The workflow specifies 500 nl reactions in a 1,536 multiwell plate, allowing us to test up to 768 PCRTags with both synthetic and wild type primer pairs in a single experiment.

Designer chromosomes of the Synthetic Yeast Genome project, Sc2.0, can be distinguished from their native counterparts using a PCR-based genotyping assay called PCRTagging, which has a presence/absence endpoint. Here we describe a high-throughput real time PCR detection method for PCRTag genotyping.

The Synthetic Yeast Genome Project (Sc2.0) aims to build 16 designer yeast chromosomes and combine them into a single yeast cell. To date one synthetic ...

High Throughput Yeast Strain Phenotyping with Droplet-Based RNA Sequencing

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Measuring mRNA Levels Over Time During the Yeast S. cerevisiae Hypoxic Response

G2-seq: A High Throughput Sequencing-based Technique for Identifying Late Replicating Regions of the Genome

Genome-wide Quantification of Translation in Budding Yeast by Ribosome Profiling

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

A Deep-sequencing-assisted, Spontaneous Suppressor Screen in the Fission Yeast Schizosaccharomyces pombe

A Comparative Approach to Characterize the Landscape of Host-Pathogen Protein-Protein Interactions

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

High-throughput Yeast Plasmid Overexpression Screen

Competitive Genomic Screens of Barcoded Yeast Libraries

qPCRTag Analysis - A High Throughput, Real Time PCR Assay for Sc2.0 Genotyping