TITLE: No Title
AUTHORS: 

[DESCRIPTION]
<p>For the past several decades, due to technical limitations, the field of transcriptomics has focused on population‐level measurements that can mask significant differences between individual cells. With the advent of single‐cell RNA‐Seq, it is now possible to profile the responses of individual cells at unprecedented depth and thereby uncover, transcriptome‐wide, the heterogeneity that exists within these populations. This unit describes a method that merges several important technologies to produce, in high‐throughput, single‐cell RNA‐Seq libraries. Complementary DNA (cDNA) is made from full‐length mRNA transcripts using a reverse transcriptase that has terminal transferase activity. This, when combined with a second “template‐switch” primer, allows for cDNAs to be constructed that have two universal priming sequences. Following preamplification from these common sequences, Nextera XT is used to prepare a pool of 96 uniquely indexed samples ready for Illumina sequencing. <a href="https://currentprotocols.onlinelibrary.wiley.com/doi/abs/10.1002/0471142727.mb0422s107" target="_blank" rel="noopener noreferrer"><em>Curr. Protoc. Mol. Biol</em>. 107:4.22.1‐4.22.17.</a> © 2014 by John Wiley &amp; Sons, Inc.</p>
<p> </p>
<p style="text-align: justify;">In single-cell RNA-Seq, small amounts of sample loss during a number of steps can lead to significant decreases in transcript detection sensitivity. A decrease in assay sensitivity results in data that is only accurate and reproducible for highly expressed genes, limiting the scope and confidence of gene expression analyses. Further complications in assay sensitivity arise from an uneven distribution of sequencing reads along a transcript; usually, in SMARTer, there is a bias towards more reads at the 3′ end of the transcript. Even coverage along a transcript improves the accuracy of analytical tools used to quantify gene expression and transcript isoform abundance. A method published by Picelli et al (Nature Methods, 2013) modified the traditional SMARTer protocol to address this by improving transcript detection, coverage, accuracy, yield, and cost. Following the same strategy as SMARTer library construction, Smart-seq2 uses several alternative reagents to generate whole-transcriptome full-length cDNA libraries.</p>
<p style="text-align: justify;"> </p>
<p style="text-align: justify;">Avoiding small-volume, bead-based SPRI cleanups of each sample is an effective way of reducing loss and increasing assay sensitivity. Lysing single cells in a guanidine thiocyanate buffer necessitates SPRI cleanup due to the protein denaturing effects of the compound, which will affect downstream reactions, like reverse transcription. Multiple alternative lysis buffers exist that address this. The Ambion Single Cell Lysis buffer (Life technologies, #4458235), often used for single-cell RT-PCR, only requires the addition of a stop solution to inactivate its lytic activity before subsequent reactions. A hypotonic lysis buffer with small amounts of RNase-inhibitor and surfactant, as described in Smart-seq2, is the preferred buffer due to the lack of a need for a post-lysis cleanup or the addition of a stop solution prior to reverse transcription. However, the optimal lysis strategy will depend on the experimental system being analyzed.</p>
<p style="text-align: justify;"> </p>
<p style="text-align: justify;">Smart-seq2 takes additional steps to minimize sample loss during library construction. The reverse transcription is improved by the addition of betaine and additional magnesium chloride to the reaction mix and by the use of a template-switch oligonucleotide with one locked nucleic acid (LNA) riboguanosine base. These improvements assist in the hybridization between the template-switch oligonucleotide and the cDNA product, thereby increasing the probability of successfully introducing a second PCR adapter onto the cDNA product (see Figure 1). A second key improvement was made in the preamplification PCR step, which can be heavily biased against either long transcripts or those containing regions with high G/C content. Picelli et al found that the preamplification PCR is improved by using the KAPA HiFi HotStart ReadyMix, which dramatically improved coverage and sensitivity, particularly for GC-rich transcripts.</p>

[BEFORE_START]
<p><strong>TE Buffer, 1x </strong></p>
<ul>
<li>10 mM Tris-HCl, pH 8.0</li>
<li>1 mM EDTA</li>
</ul>
<p> </p>

[GUIDELINES]
<p style="text-align: justify;"><strong>Introduction </strong></p>
<p> </p>
<p style="text-align: justify;">The development of single-cell RNA-Seq affords new opportunities to study complex cellular systems at unprecedented resolution. While previous RNA-Seq methods were only capable of capturing the average behavior of a population, single-cell methods now enable studies of cellular heterogeneity, as well as the discovery of novel cell types and states. These methods employ a variety of techniques to address the technical challenges posed by single cells – most notably, the ultra-low input quantity of RNA.</p>
<p style="text-align: justify;"> </p>
<p style="text-align: justify;">Here, we exploit the terminal transferase activity of the SMARTScribe reverse transcriptase in conjunction with a “template-switch” primer to make cDNAs that have PCR priming sites on both ends directly from full length mRNA (Figure 1). First, single cells are lysed in a guanidine thiocyanate solution. Second, a bead cleanup is used to isolate RNA. Third, cDNA is synthesized from the full-length transcripts. Fourth, these cDNA products are amplified by PCR and normalized. Fifth, the amplified cDNA is incubated with a Tn5 transposase to fragment full-length transcripts and append adapters on each molecule. Sixth, each single-cell library is individually barcoded by PCR with index primers, and the sample set is pooled. And finally, the resulting pool is purified by a bead cleanup and is then sent for Illumina sequencing.</p>
<p style="text-align: justify;"> </p>
<p> </p>
<p><img id="s-mce-img" class="s-mce-img" style="display: block; margin-left: auto; margin-right: auto;" src="https://s3.amazonaws.com/pr-journal/vekfw26.jpg" width="529" height="436" data-src="https://s3.amazonaws.com/pr-journal/vekfw26.jpg" data-ofn="nihms611453f1.jpg" /></p>
<p><strong>Figure 1.</strong> Single-cell RNA-Seq library construction. Illustrated are the steps required to convert mRNA to cDNA with sequencing adapters.</p>
<p> </p>
<p> </p>
<p><strong>Oligonucleotide Primer Sequences</strong></p>
<p> </p>
<p>3′ SMART CDS Primer IIA: 5′ AAGCAGTGGTATCAACGCAGAGTACT(30)VN</p>
<p> </p>
<p>SMARTer II A Oligonucleotide: 5′AAGCAGTGGTATCAACGCAGAGTACATrGrGrG</p>
<p> </p>
<p>IS PCR Primer: 5′ AAGCAGTGGTATCAACGCAGAGT</p>
<p> </p>
<p>TSO: 5′ AAGCAGTGGTATCAACGCAGAGTACATrGrG+G</p>
<p> </p>
<p> </p>
<p><strong>The protocol workflow is as follows:</strong> </p>
<p> </p>
<p><em><strong>Stage I: Preparation of single-cell lysates</strong></em></p>
<p>Note: In this protocol, we use Microseal F to seal for long-term storage and Microseal B when thermal cycling.</p>
<p> </p>
<p><em><strong>Stage II: RT of mRNA species</strong></em></p>
<p>Note: These steps can be performed inside a biosafety cabinet or an RNA workstation (if available); otherwise, they can be carefully performed on a standard benchtop.</p>
<p> </p>
<p><em><strong>Stage III: Performing WTA and Post-PCR Cleanup</strong></em></p>
<p> </p>
<p><em><strong>Stage IV: Nextera XT sequencing-library construction</strong></em></p>
<p> </p>
<p><em><strong>Stage V: Pooling and DNA SPRI bead cleanup</strong></em></p>
<p> </p>
<p> </p>
<p><em><strong> </strong></em></p>
<p><strong>Background Information</strong></p>
<p> </p>
<p style="text-align: justify;">It has long been known that population-level observations of cellular transcription do not always accurately represent transcriptional patterns across the single cells that comprise them (Eberwine et al. 1992; Sandberg 2014; Shapiro et al. 2013). Earlier methods developed to explore this transcriptional heterogeneity in cellular populations first interrogated individual genes of interest in single cells (Eberwine et al. 1992), and later allowed for highly multiplexed qRT-PCR of a up to 96 genes simultaneously across 96 single cells (see, for example, Sanchez-Freire et al. 2012).</p>
<p style="text-align: justify;"> </p>
<p style="text-align: justify;">Recent technical advances that make high-throughput construction of next-generation sequencing libraries possible from picogram quantities of RNA facilitate studies of whole-transcriptome expression profiles in single cells. Relative to population measurements, single-cell RNA-Seq is able to deconvolute transcriptional profiles of cellular subpopulations and reveal transcriptional profiles of rare cellular subtypes that would be hidden below the detection threshold in population-level analysis.</p>
<p style="text-align: justify;"> </p>
<p style="text-align: justify;">A key tool that makes the construction of single-cell RNA-Seq cDNA libraries possible is the introduction of uniform adapter sequences to the transcripts of interest, allowing for the amplification and sequencing of all mRNA transcripts. A number of strategies have been developed to efficiently add these adapters. Pioneering work on mRNA-Seq utilized poly(A) tailing of reverse transcribed products followed by poly(T)-primed PCR amplification (Tang et al. 2009). CEL-Seq, a more recently published method, relies on <em>in vitro</em> transcription (IVT) to linearly amplify reverse transcribed products, followed by ligation of adapter sequences to the 3′ end of amplified RNA (Hashimshony et al. 2012). Presented here, the SMARTer protocol leverages the terminal transferase activity of a M-MLV-derived reverse transcriptase to reverse transcribe mRNA and then, with a template-switch primer, add an adapter sequence in a single reaction (Zhu et al. 2001). Each method has its own distinct advantages, disadvantages, and biases specific to the biochemical reactions underlying each protocol. For example, CEL-Seq avoids biases introduced by PCR amplification of reverse transcription products by linearly amplifying its reverse transcription products with IVT; this, however, necessitates a cleanup of both reverse transcription products and IVT amplification products prior to subsequent reactions (Hashimshony et al. 2012). With varying levels and tolerances for biases arising in specific experimental contexts, selecting the appropriate method for a given study depends on the system being assayed.</p>
<p style="text-align: justify;"> </p>
<p style="text-align: justify;">As the throughput and power of single-cell transcriptome profiling increases, these methodologies can be applied to increasingly complex biological questions. Whole-transcriptome sequencing across cell types can reveal transcriptional profiles specific to each type, as well as the heterogeneity within them. Identification of cell types from a population of cells of unknown composition based solely on transcriptional profiles is now possible and is increasingly easy as more single-cell RNA-Seq profiling data is added to the knowledge base (Shapiro et al. 2013). Meanwhile, primary cells, or rare subtypes of cells, desirable for study, may not appear in extracted populations in sufficient frequency to conduct RNA-Seq at a population level, necessitating single-cell methods. The transcriptional profiles of a patient’s tumor cells may elucidate the origin, development, and function of various cell lineages within a tumor. Other questions about cellular response to stimuli (Shalek et al. 2013), transcriptional fluctuation across time (Shapiro et al. 2013), and differential gene expression in disease or pathological tissue (Sandberg 2014) are now being explored; we envision that many more topics will be fruitfully investigated with single-cell RNA-Seq as it becomes a more widely used tool.</p>
<p style="text-align: justify;"> </p>
<p> </p>
<p><strong>Critical Parameters and Troubleshooting </strong></p>
<p> </p>
<p style="text-align: justify;">Proper sample preparation is key when preparing viable single-cell RNA-Seq libraries, as degradation and contamination of input material are the most common sources of error in downstream steps. Prior to sorting, sample integrity can be improved by minimizing the time spent preparing cells for sorting, and then freezing lysate plates no more than 10 minutes after sorting has concluded. When preparing for cDNA synthesis, it is vitally important to ensure that all workspaces, equipment, and reagents are RNase free by using products such as RNase-ZAP and RNase inhibitors. Cross contamination is also of great concern throughout this protocol, and care should be taken to avoid sharing tips between samples. Additionally, samples should be covered whenever possible to prevent airborne contamination.</p>
<p style="text-align: justify;"> </p>
<p style="text-align: justify;">Because sample integrity is a common source of error, and because small quantities of starting material are frequently used in this protocol, several parameters must be assayed following cDNA synthesis, amplification and cleanup to ensure library quality. Average fragment size, cDNA concentration, and the presence of transcripts for housekeeping genes are effective measures of library quality. Measuring, via qPCR, at least two well-established housekeeping genes is particularly important at this stage. In virtually any set of singe cell libraries, there will be samples of low complexity that lack expression of key housekeeping genes, and the results of qPCR measurements can be used to eliminate these samples.</p>
<p style="text-align: justify;"> </p>
<p style="text-align: justify;">The Tn5 transposase used in Nextera XT library construction fragments dsDNA at a rate proportional to [dsDNA] in your library. Thus, it is crucial to normalize the concentration of your SMARTer single-cell amplified cDNAs prior to Nextera XT library construction. This ensures that dsDNA from each single cell sample will yield tagmentation products with similar distributions of fragment length, and further that after pooling, cleanup, and sequencing the distribution of reads across each sample will be unbiased.</p>
<p> </p>
<p><strong>Table 1.</strong> Troubleshooting for Single-Cell RNA-Seq Library Preparation </p>
<table style="height: 228px; width: 793px;">
<tbody>
<tr>
<td style="width: 190px;"><strong>Problem </strong></td>
<td style="width: 187px;"><strong>Likely Cause</strong></td>
<td style="width: 398px;"><strong>Solution </strong></td>
</tr>
<tr>
<td style="width: 190px;" rowspan="6">Little or no WTA yield</td>
<td style="width: 187px;">RNA degradation</td>
<td style="width: 398px;">Start with high-quality RNA; use RNA with RNA integrity number &gt;8; begin with a single RNA sample and work quickly; work in an RNase-free environment</td>
</tr>
<tr>
<td style="width: 187px;">RNA-SPRI failure</td>
<td style="width: 398px;">Make sure beads have been fully resuspended prior to use; make sure beads are never exposed to RNases. Use freshly prepared ethanol. Don’t overdry beads.</td>
</tr>
<tr>
<td style="width: 187px;">Reverse transcription failure</td>
<td style="width: 398px;">Make sure all primers are added to the correct reaction. </td>
</tr>
<tr>
<td style="width: 187px;">Too few PCR cycles</td>
<td style="width: 398px;">Increase nul/exZc mber of PCR cycles</td>
</tr>
<tr>
<td style="width: 187px;">PCR failure</td>
<td style="width: 398px;">Poor library generation, perhaps due to inactive enzymes/reagents</td>
</tr>
<tr>
<td style="width: 187px;">Cell sorting failure</td>
<td style="width: 398px;">Make sure cell sorter dispenses cells accurately to center of each well</td>
</tr>
<tr>
<td style="width: 190px;">Presence of WTA products that are longer than expected (&gt;4 kb)</td>
<td style="width: 187px;">Too many PCR cycles</td>
<td style="width: 398px;">Reduce number of PCR cycles</td>
</tr>
<tr>
<td style="width: 190px;" rowspan="2">Presence of WTA products that are shorter than expected (&lt;500 bp) </td>
<td style="width: 187px;">RNA degradation</td>
<td style="width: 398px;">Start with high-quality RNA; use RNA with RNA integrity number &gt;8; begin with a single RNA sample and work quickly; work in an RNase-free environment</td>
</tr>
<tr>
<td style="width: 187px;">PCR-SPRI failure or ineffective cleanup</td>
<td style="width: 398px;">Make sure beads have been fully resuspended prior to use; repeat 0.8X SPRI cleanup</td>
</tr>
<tr>
<td style="width: 190px;">Regularly-spaced spiky peaks on WTA Bioanalyzer trace</td>
<td style="width: 187px;">Contamination</td>
<td style="width: 398px;">Make sure to prepare samples and reactions in a clean and sterile environment</td>
</tr>
<tr>
<td style="width: 190px;" rowspan="2">Small fragment size after Nextera </td>
<td style="width: 187px;">Tagmentation reaction ran too long</td>
<td style="width: 398px;">Reduce reaction time of Nextera tagmentation</td>
</tr>
<tr>
<td style="width: 187px;">Low tagmentation input cDNA concentration</td>
<td style="width: 398px;">Make sure tagmentation input cDNA is at the correct concentration; make sure the cDNA Bioanalyzer traces show little primer contamination (&lt;500 bp peaks) after Nextera</td>
</tr>
<tr>
<td style="width: 190px;">Low alignment rate of reads to genome</td>
<td style="width: 187px;">Contamination</td>
<td style="width: 398px;">Make sure to prepare samples and reactions in a clean and sterile environment</td>
</tr>
<tr>
<td style="width: 190px;" rowspan="2">Low library complexity/ number of genes detected </td>
<td style="width: 187px;">Too many PCR cycles</td>
<td style="width: 398px;">Reduce number of PCR cycles</td>
</tr>
<tr>
<td style="width: 187px;">Loss of material prior to PCR</td>
<td style="width: 398px;">Make sure loss of material is limited during the initial RNA-SPRI cleanup</td>
</tr>
</tbody>
</table>
<p><strong> </strong></p>
<p> </p>
<p><strong>Time Considerations </strong></p>
<p> </p>
<p>The SMARTer library construction and normalization take 2–3 days, and Nextera XT library construction and pooling takes 1 day.</p>
<p> </p>
<p> </p>
<p><strong>Acknowledgments </strong></p>
<p> </p>
<p style="text-align: justify;">Work was supported by an NIH Postdoctoral Fellowship (1F32HD075541-01, RS), an NIH CEGS Award (1P50HG006193-01, AR), NIH Pioneer Awards (DP1OD003958-01 to AR), the Broad Institute (AR), HHMI (AR), and the Klarman Cell Observatory at the Broad Institute (AR).</p>
<p> </p>
<p> </p>
<p><strong>Literature Cited </strong></p>
<ul>
<li>Eberwine J, Yeh H, Miyashiro K, Cao Y, Nair S, Finnell R, Zettel M, Coleman P. Analysis of gene expression in single live neurons. Proc Natl Acad Sci USA. 1992;89(7):3010–4. [<a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC48793/" target="_blank" rel="noopener noreferrer">PMC free article</a>][<a href="https://www.ncbi.nlm.nih.gov/pubmed/1557406" target="pmc_ext">PubMed</a>]</li>
<li>Langmead B, Trapnell C, Pop M, Salzberg S. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 2009;10(3):R25. [<a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2690996/" target="_blank" rel="noopener noreferrer">PMC free article</a>] [<a href="https://www.ncbi.nlm.nih.gov/pubmed/19261174" target="pmc_ext">PubMed</a>]</li>
<li>Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R. Genome Project Data Processing Subgroup. 2009. The Sequence alignment/map (SAM) format and SAMtools. Bioinformatics. 1000;25(16):2078–2079. [<a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2723002/" target="_blank" rel="noopener noreferrer">PMC free article</a>] [<a href="https://www.ncbi.nlm.nih.gov/pubmed/19505943" target="pmc_ext">PubMed</a>]</li>
<li>Hashimshony T, Wagner F, Sher N, Yanai I. CEL-Seq: Single-Cell RNA-Seq by Multiplexed Linear Amplification. Cell-Reports. 2012;2(3):666–673. [<a href="https://www.ncbi.nlm.nih.gov/pubmed/22939981" target="pmc_ext">PubMed</a>]</li>
<li>Picelli S, Björklund Å, Faridani O, Sagasser S, Winberg G, Sandberg R. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nature Methods. 2013;10(11):1096–1098.[<a href="https://www.ncbi.nlm.nih.gov/pubmed/24056875" target="pmc_ext">PubMed</a>]</li>
<li>Sanchez-Freire V, Ebert A, Kalisky T, Quake S, Wu J. Microfluidic single-cell real-time PCR for comparative analysis of gene expression patterns. Nature Protocols. 2012;7(5):829–838.[<a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3657501/" target="_blank" rel="noopener noreferrer">PMC free article</a>] [<a href="https://www.ncbi.nlm.nih.gov/pubmed/22481529" target="pmc_ext">PubMed</a>]</li>
<li>Sandberg R. Entering the era of single-cell transcriptomics in biology and medicine. Nat Methods. 2014;11:22–24. [<a href="https://www.ncbi.nlm.nih.gov/pubmed/24524133" target="pmc_ext">PubMed</a>]</li>
<li>Shalek A, Satija R, Adiconis X, Gertner R, Gaublomme J, Raychowdhury R, Schwartz S, Yosef N, Malboeuf C, Lu D, Trombetta J, Gennert D, Gnirke A, Goren A, Hacohen N, Levin J, Park H, Regev A. Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells. Nature. 2013;498(7453):236–240. [<a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3683364/" target="_blank" rel="noopener noreferrer">PMC free article</a>] [<a href="https://www.ncbi.nlm.nih.gov/pubmed/23685454" target="pmc_ext">PubMed</a>]</li>
<li>Shapiro E, Biezuner T, Linnarsson S. Single-cell sequencing-based technologies will revolutionize whole-organism science. Nature Reviews Genetics. 2013;14:618–630. [<a href="https://www.ncbi.nlm.nih.gov/pubmed/23897237" target="pmc_ext">PubMed</a>]</li>
<li>Tang F, Barbacioru C, Wang Y, Nordman E, Lee C, Xu N, Wang X, Bodeau J, Tuch B, Siddiqui A, Lao K, Surani M. mRNA-Seq whole transcriptome analysis of a single cell. Nature Methods. 2009;6:377–382. [<a href="https://www.ncbi.nlm.nih.gov/pubmed/19349980" target="pmc_ext">PubMed</a>]</li>
<li>Trapnell C, Pachter L, Salzberg L. TopHat: discovering splice junctions with RNA-Seq. Bioninformatics. 2009;25(9):1105–1111. [<a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2672628/" target="_blank" rel="noopener noreferrer">PMC free article</a>] [<a href="https://www.ncbi.nlm.nih.gov/pubmed/19289445" target="pmc_ext">PubMed</a>]</li>
<li>Trapnell C, Roberts A, Goff L, Pertea G, Kim D, Kelley D, Pimentel H, Salzberg S, Rinn, Pachter L. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat Protocols. 2012;7:562–578. [<a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3334321/" target="_blank" rel="noopener noreferrer">PMC free article</a>] [<a href="https://www.ncbi.nlm.nih.gov/pubmed/22383036" target="pmc_ext">PubMed</a>]</li>
<li>Zhu Y, Machleder E, Chenchik A, Li R, Siebert P. Reverse Transcriptase Template Switching: a SMART Approach for Full-Length cDNA Library Construction. Biotechniques. 2001;30(4):892–897. [<a href="https://www.ncbi.nlm.nih.gov/pubmed/11314272" target="pmc_ext">PubMed</a>]</li>
</ul>

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