TITLE: MARS-seq2.0: an experimental and analytical pipeline for indexed sorting combined with single-cell RNA sequencing
AUTHORS: Hadas Keren Shaul, Hadas Keren-Shaul, Ephraim Kenigsberg, Diego Adhemar Jaitin, Eyal David, Franziska Paul, Amos Tanay, Ido Amit

[DESCRIPTION]
<div class = "text-blocks"><div class = "text-block"><div class = "justify" style = "text-align:left"><span style = "font-weight:bold;">Human tissues comprise trillions of cells that populate a complex space of molecular phenotypes and functions and that vary in abundance by 4–9 orders of magnitude. Relying solely on unbiased sampling to characterize cellular niches becomes infeasible, as the marginal utility of collecting more cells diminishes quickly. Furthermore, in many clinical samples, the relevant cell types are scarce and efficient processing is critical. We developed an integrated pipeline for index sorting and massively parallel single-cell RNA sequencing (MARS-seq2.0) that builds on our previously published MARS-seq approach. MARS-seq2.0 is based on >1 million cells sequenced with this pipeline and allows identification of unique cell types across different tissues and diseases, as well as unique model systems and organisms. Here, we present a detailed step-by-step procedure for applying the method. In the improved procedure, we combine sub-microliter reaction volumes, optimization of enzymatic mixtures and an enhanced analytical pipeline to substantially lower the cost, improve reproducibility and reduce well-to-well contamination. Data analysis combines multiple layers of quality assessment and error detection and correction, graphically presenting key statistics for library complexity, noise distribution and sequencing saturation. Importantly, our combined FACS and single-cell RNA sequencing (scRNA-seq) workflow enables intuitive approaches for depletion or enrichment of cell populations in a data-driven manner that is essential to efficient sampling of complex tissues. The experimental protocol, from cell sorting to a ready-to-sequence library, takes 2–3 d. Sequencing and processing the data through the analytical pipeline take another 1–2 d.</span><span></span></div></div><div class = "text-block"><div class = "justify" style = "text-align:left"><span style = ":UNDERLINE;">Introduction</span></div></div><div class = "text-block"><div class = "justify" style = "text-align:left"><span>The remarkably rich repertoire of transcriptional states of cells within tissues has been studied for many decades. However, only recently have experimental and computational advances in the field of single-cell genomics opened the way for unbiased dissection of tissues into single cells and the de novo characterization of cell types, subtypes, and transcriptional states</span><span style = "vertical-align:super;">1–13</span><span>. Single-cell RNA sequencing (scRNA-seq) is emerging as a key molecular tool for elucidating biological complexity, promising to contribute to a variety of fields in both basic research and medicine</span><span style = "vertical-align:super;">14–17</span><span>. Methods for single-cell genome-wide expression analysis are continuously being developed, offering increasing coverage, precision and throughput</span><span style = "vertical-align:super;">1,10,18–31</span><span>. Here, we describe a robust method for massively parallel scRNA-seq that combines indexed FACS sorting (recording of surface marker levels for each sorted single cell) and robotics with multiple layers of molecular barcoding. Although drop-based and microwell-based methods increased the throughput of scRNA-seq methods</span><span style = "vertical-align:super;">17,29,30</span><span>, the complexity of the cellular hierarchy remains a major challenge for unbiased sampling of mammalian tissues. Human tissues comprise trillions of cells that populate a complex space of molecular phenotypes and functions such that states within this space may vary in their abundance by 4, 5 and up to 9 orders of magnitude</span><span style = "vertical-align:super;">32,33</span><span>.   A classic example of this remarkable property of the human cell state space is the difference in abundance between highly abundant red blood cells (N ~ 10</span><span style = "vertical-align:super;">13</span><span>) and rare hematopoietic stem cells (N ~ 10</span><span style = "vertical-align:super;">3−4</span><span style = "vertical-align:super;vertical-align:super;">)</span><span style = "vertical-align:super;">34,35</span><span>. A well-designed single-cell technology must consider the orders-of-magnitude variation in abundance of human cell lineages and develop experimental, computational and statistical approaches to overcome it.    </span></div></div><div class = "text-block"><div class = "justify" style = "text-align:left"><span>Because cells, and especially combinations of stem cells, progenitors and various types of mature cells, span many orders of magnitude in abundance, relying solely on unbiased sampling to accurately characterize rare populations becomes infeasible, as the marginal utility of collecting more cells diminishes quickly (Fig. 1a). Combining unbiased transcriptional maps with protein markers enables iterative refinement of cell sampling using FACS-based enrichment and/or depletion strategies for more efficient and deeper sampling of key rare subpopulations (Fig. 1a). Combining index sorting and scRNA-seq has many other potential utilities, including refinement and generation of new sorting panels for both diagnostics and basic research and for mapping fluorescent reporter markers</span><span style = "vertical-align:super;">36,37</span><span> for lineage tracing and CRISPR pooled screens (CRISP-seq</span><span style = "vertical-align:super;">38</span><span>; Fig. 1b,c).             </span></div></div><div class = "text-block"><div class = "justify" style = "text-align:left"></div></div><div class = "text-block"><div class = "justify" style = "text-align:left"><a href="#" style = "text-decoration:underline;color:blue;cursor:pointer;"><span style = ":;"> </span></a><span style = ":;"> </span></div></div><div style = "text-align :; float : ;"><img style = "" src = "https://s3.amazonaws.com/protocols-files/public/a46f459508ecc97a272dd84ceca048997b7960cbda5add162664b220d1dea5d6/brez7vcw.jpg" /></div><div class = "text-block"><div class = "justify" style = "text-align:left"><span style = "font-weight:bold;">Overview of the experimental protocol</span></div></div><div class = "text-block"><div class = "justify" style = "text-align:left"><span>MARS-seq2.0 provides a complete experimental and computational framework for massively parallel scRNA-seq. It integrates index sorting into the scRNA-seq pipeline, minimizes doublets (two independent cells that are captured and processed together) and technical cell-to-cell contamination rates (background noise), and supports processing of tens of thousands of cells a day in a simple and costeffective manner. MARS-seq2.0 is based on our MARS-seq protocol, which we presented in February 2014 (ref. </span><span style = "vertical-align:super;">10</span><span>). So far, we have used MARS-seq2.0 to process > 1 million cells from mice, non-model organisms and human studies</span><span style = "vertical-align:super;">10,35,36,38–42</span><span>, generating between ~100 and ~10,000 unique molecular identifiers (UMIs; molecular tags that serve to reduce quantitative bias) for each, with an average of ~1,700 UMIs and ~700 genes per cell (Supplementary Fig. 1). Using MARS-seq2.0, we identified a unique microglia type associated with restricting development of Alzheimer’s disease</span><span style = "vertical-align:super;">40,43</span><span>, characterized hematopoietic progenitors</span><span style = "vertical-align:super;">9,35</span><span>, mapped the cellular composition of the lung during development</span><span style = "vertical-align:super;">42</span><span> and in response to influenza virus infection</span><span style = "vertical-align:super;">44</span><span>, and detailed the heterogeneity of epithelial cells in the thymus</span><span style = "vertical-align:super;">41</span><span>. We also used MARS-seq2.0 for whole-organism mapping of early metazoan cell type–specific transcription</span><span style = "vertical-align:super;">45,46</span><span>. Further, we applied MARS-seq2.0 to large clinical studies characterizing the heterogeneity of plasma cells in healthy donors and multiple myeloma patients</span><span style = "vertical-align:super;">47</span><span>, as well as infiltrating immune cells in lung and melanoma patients</span><span style = "vertical-align:super;">48,49</span><span>. MARS-seq2.0 was also combined with other modalities, including single-cell CRISPR-pooled screens to dissect immune circuits in vitro and in mice</span><span style = "vertical-align:super;">38</span><span>, and photoactivatable reporters (NICHE-seq), single-molecule fluorescence in situ hybridization and paired-cell sequencing (gene expression profiling of pairs of joined cells) to spatially reconstruct immune niches</span><span style = "vertical-align:super;">36,50,51</span><span>.</span></div></div><div class = "text-block"><div class = "justify" style = "text-align:left"><span>In the MARS-seq2.0 framework, single cells are index-sorted by FACS into 384-well plates, lysed and automatically barcoded (with a cellular and a molecular barcode) by reverse transcription (RT) using a poly-T primer in a low-volume (500 nl) reaction. Then the single cells are pooled together, using a liquid-handling robot for subsequent molecular reactions to be performed on the pooled and labeled material. Each group of single cells amplified together is referred to as an ‘amplification batch’. The pooled cDNA is converted into double-stranded DNA (dsDNA) and is linearly amplified by T7 RNA polymerase in vitro transcription (IVT). The amplified RNA (aRNA) is fragmented and a pool barcode is added by RNA–DNA ligation, allowing efficient sequencing of 8,000–10,000 cells in a single run. Illumina adaptors are then added by RT and PCR, which enables quantification of the mRNA (Fig. 2). During library preparation, we routinely evaluate the complexity of the libraries, yielding a quality control (QC) score that is used to monitor MARS-seq2.0 performance before sequencing. On the basis of these scores, amplified libraries suitable for sequencing of 20–40 384-well plates are pooled and sequenced together on a single flow cell. Sequenced reads are automatically processed in the MARS-seq2.0 computational pipeline (</span><a href="http://compgenomics.weizmann.ac.il/tanay/?" style = "text-decoration:underline;color:blue;cursor:pointer;"><span style = ":;">http://compgenomics.weizmann.ac.il/tanay/?</span></a><span> page_id=672), in which the molecular signal is separated from the background noise.</span></div></div><div class = "text-block"><div class = "justify" style = "text-align:left"></div></div><div class = "text-block"><div class = "justify" style = "text-align:left"></div></div><div class = "text-block"><div class = "justify" style = "text-align:left"><a href="#" style = "text-decoration:underline;color:blue;cursor:pointer;"><span style = ":;"> </span></a><span style = ":;"> </span></div></div><div style = "text-align :; float : ;"><img style = "" src = "https://s3.amazonaws.com/protocols-files/public/a46f459508ecc97a272dd84ceca048997b7960cbda5add162664b220d1dea5d6/bre27vcw.jpg" /></div><div class = "text-block"><span style = "font-weight:bold;">Optimization of MARS-seq</span></div><div class = "text-block"><span>Background noise is a major problem in all massively parallel single-cell protocols. Our prior study</span><span style = "vertical-align:super;">10 </span><span>suggested that this cell-to-cell contamination noise could partially be associated with a molecular process that randomly links a gene, a UMI and a cell barcode during library preparation. To enhance the experimental stability and detection rate while controlling for potential sources of systematic noise in MARS-seq2.0, we set out to optimize RNA retention and amplification. Starting from the MARSseq protocol, we optimized all steps of the single-cell processing, including lysis conditions, reaction volume, primer design and the composition of enzymatic reactions. We evaluated the possibility of reducing the RT reaction volume by analyzing 768 single mouse embryonic stem (ES) cells: after sorting, we performed complete evaporation of the 2 μl of lysis buffer containing the RT primer at 95 °C, followed by addition of 0.5 μl of RT mix, using a liquid-handling robot. For these conditions, RT primer quantities were considerably reduced. In low-volume reactions, we did not observe a substantial increase in the number of molecules measured per cell as compared with those of high-volume reactions, but rather saw an improved signal-to-noise ratio as evaluated by the number of molecules in the wells containing cells as compared with our negative-control empty wells (Supplementary Fig. 2a). This is also a consequence of the 200-fold reduction in the RT primer final concentration that prevents well-to-well contamination during the pooling and downstream enzymatic reactions. In addition, such low-volume reactions are cost effective (reducing enzyme consumption) and scalable. Because second-strand-synthesis enzymes are potentially a major sensitivity-limiting factor but can also generate unwanted background noise post pooling, we further evaluated several different second-strand-synthesis mixtures. We observed a small but considerable inverse correlation between the number of molecules obtained in the different conditions and the signal-to-noise ratio, suggesting that although some mixtures may be slightly more efficient in converting the hybrid RNA–cDNA template into a double-stranded DNA, this is accompanied by unwanted hybrid molecular products reaching a noise level of up to 40 % of the true signal in several of the mixtures (Supplementary Fig. 2b). These results demonstrate that it is critical to optimize and control for noise level in scRNA-seq analysis and that this factor can be affected by various steps during single-cell library production.</span></div><div class = "text-block"><span style = "font-weight:bold;">Key procedural advancements in MARS-seq2.0</span></div><div class = "text-block">MARS-seq2.0 introduces important modifications of the MARS-seq method in almost every part of the protocol. These improvements are related to throughput, robustness, noise reduction and costs. Specifically, we have performed the optimizations discussed in the following sections.</div><div class = "text-block"><span style = "font-style:italic;">Experimental improvements</span></div><div class = "text-block"><ol style = "list-style-type: decimal;"><li style = "counter-reset:ol0;">Lowering of RT volume: we reduced the volume of the RT reaction by eightfold from 4 μl to 500 nl. Lowering of the RT volume was possible due to evaporation of the sample in the 384-well plate before addition of the RT mix. This is a direct sixfold reduction in the cost of the single-cell library preparation because the RT step is the most costly part of the protocol.</li><li style = "counter-reset:ol0;">Optimization of lysis buffer: we optimized the lysis buffer to make it compatible with the aforementioned volume reduction.</li><li style = "counter-reset:ol0;">Reduction of RT primer concentration: we reduced the concentration of the RT primer from 200 to 1 nM (enabled by the lower volume), which further reduced the contamination and background noise.</li><li style = "counter-reset:ol0;">Optimization of RT primer composition: we modified the cell barcode (7 bp) and the UMI (8 bp) to optimize yield with longer oligos that enable efficient error correction.</li><li style = "counter-reset:ol0;">Primer removal by exonuclease I is performed in each well before pooling in order to maximize the exclusion of any RT primer leftovers that were not used in the RT and could be a source of potential noise.</li><li style = "counter-reset:ol0;">Optimization of second-strand-synthesis enzymes: we tested all major commercially available second-strand-synthesis mixtures, in different dilutions, and identified a specific mixture and concentration that maximize yield and reduce PCR contaminants (major problem in the original MARS-seq and all IVT-based protocols).</li><li style = "counter-reset:ol0;">Optimization of barcoded ligation adaptor: we modified the barcoded ligation adaptor sequence (barcode of 4 bp, 5 Ns) to improve barcoding of amplification batches and reduce the noise level between them.</li><li style = "counter-reset:ol0;"></li><li style = "counter-reset:ol0;">Optimization of the conditions indicated above resulted in a sixfold reduction in the cost of library</li><li style = "counter-reset:ol0;">production (from $0.65 to $0.10 per cell) and reduced the background level (from 10–15 % to 2 %) as</li><li style = "counter-reset:ol0;">evaluated by the number of molecules in the wells containing cells as compared with our negative-control</li><li style = "counter-reset:ol0;">empty wells.</li><li style = "counter-reset:ol0;"></li><li style = "counter-reset:ol0;"><span style = "font-style:italic;">Analytical and QC improvements</span></li><li style = "counter-reset:ol0;">QC scheme to monitor MARS-seq2.0 performance during library preparation: we generated a QC pipeline based on quantitative real-time polymerase chain reaction (qPCR) of housekeeping genes in order to evaluate the complexity of the libraries before sequencing.</li><li style = "counter-reset:ol0;">Generation of a complete analytical pipeline: sequenced reads are de-multiplexed and the molecularsignal is separated from the background noise.</li><li style = "counter-reset:ol0;">QC measurements following sequencing: the analytical pipeline automatically generates a set of > 20 QC measurements (e.g., number of reads, percentage mapping to exons and number of UMIs) that can assist the users in evaluating and optimizing many important features in their single-cell data.</li><li style = "counter-reset:ol0;"></li><li style = "counter-reset:ol0;"></li><li style = "counter-reset:ol0;"><span style = "font-weight:bold;">MARS-seq2.0 evaluation</span></li><li style = "counter-reset:ol0;">To evaluate the efficiency of the method and data quality, we applied MARS-seq2.0 to 2,256 mouse ES cells and mouse embryonic fibroblasts (MEFs). We detected 11.3 million mRNA molecules (medians of ~6,000 and ~4,000 molecules per ES and MEF cell, respectively, Supplementary Fig. 3a) while sequencing 80,000–100,000 reads per cell (Supplementary Fig. 3b). As expected, the number of detected transcripts per cell is correlated with cell size (Supplementary Fig. 3c). Because MARS-seq2.0 is designed to allow deep population sampling and characterization of known and novel subpopulations, we quantify our detection rate by estimating the distribution of mean gene expression on independent pools of 100 single cells, revealing a dynamic range spreading over three orders of magnitude (Supplementary Fig. 3d). By comparing a pool of 1,128 ES cells with a pool of 752 MEF cells, we detected 2,350 genes with a significant (false-discovery rate (FDR) < 10−5) fourfold difference in expression (Supplementary Fig. 3e). In many cases, these expression differences are also pronounced at the single-cell level (Supplementary Fig. 3f–i).</li><li style = "counter-reset:ol0;"></li><li style = "counter-reset:ol0;">Advances in microfluidics technologies allow processing of several thousands of cells in parallel.</li><li style = "counter-reset:ol0;"><span>However, current microfluidic approaches display a relatively high rate (1–15 %) of sequencing of two cells (or more) in a single droplet (doublets)</span><span style = "vertical-align:super;">30,31,52,53</span><span>, which may complicate downstream analysis.</span></li><li style = "counter-reset:ol0;">To evaluate the doublet rate in MARS-seq2.0, we mixed human and mouse ES cells and analyzed the percentage of doublets. Our analysis demonstrates that MARS-seq2.0 has a negligible amount of doublet cells (< 0.2 %; 2 out of 1,041 cells) and provides high confidence in cell identity (Supplementary Fig. 4).</li><li style = "counter-reset:ol0;"></li><li style = "counter-reset:ol0;"></li><li style = "counter-reset:ol0;"><span style = "font-weight:bold;">The computational framework</span></li><li style = "counter-reset:ol0;">To implement an effective error-correction algorithm and to ensure the quality of sequenced libraries, we designed a computational framework that closely follows the different experimental steps in MARS-seq2.0. Our framework models RNA-seq data generation as a sequential process of multiple samplings and amplifications steps (see Supplementary Methods, Supplementary Fig. 5), in which single mRNA molecules in a single cell are distributed and sampled in different pools. The computational pipeline quantifies the number of tagged mRNA molecules per gene that went through this complex sampling process, while eliminating several types of experimental artifacts. In addition, it generates a graphical and user-friendly library diagnostics report for each amplification batch, highlighting important technical parameters that can be variable between batches (see Supplementary Figs. 6–8 for representative diagnostic reports). The report provides a detailed analysis of the reads’ fraction spent on sequencing of primer and other sequences used during library preparation, which can highlight biases such as those caused by an excess of primers. Our pipeline automatically filters sequencing and other polymerization errors that occur in earlier experimental steps (i.e., IVT) and generate spurious UMIs or alter cellular barcodes.</li><li style = "counter-reset:ol0;">Different sources of barcode contamination may substantially bias biological interpretation of the data by mixing genes between different single-cell subpopulations in the same amplification batch.</li><li style = "counter-reset:ol0;">We therefore developed an experimental design that allows us to routinely estimate the total cell-to-cell contamination levels by measuring the number of molecules associated with four negative-control wells (empty wells that do not contain cells) in each amplification batch. To visually relate the number of molecules per cell (Supplementary Fig. 3a) to the original single-cell position on the 384-well plate matrix, a plate-view heat-map is generated that shows how the number of recovered genes and spike-in molecules are distributed across the plate position. This can highlight potential sorting, robotic or other problems. Although technical QC is critical, as demonstrated above, we stress that the ultimate way to evaluate the effectiveness of a specific scRNA-seq method is to examine the novelty and richness of the biological insights emerging from the data and that these may depend on multiple factors, including the quality of biological materials.</li><li style = "counter-reset:ol0;"></li><li style = "counter-reset:ol0;"></li><li style = "counter-reset:ol0;"><span style = "font-weight:bold;">Strengths and limitations of the protocol</span></li><li style = "counter-reset:ol0;">The key strengths of MARS-seq2.0 are as follows:</li><li style = "counter-reset:ol0;">The combination of indexed FACS sorting and scRNA-seq enables refinement and enrichment of the cells of interest, which is especially critical for analysis of rare subpopulations and processing of rare cells in human clinical samples.</li><li style = "counter-reset:ol0;"><span>The improvements made in MARS-seq2.0 provide a cost-effective library preparation method that allows analysis of any number of single cells without compromising the cost per cell value. This is in contrast to other high-throughput methods, such as droplets</span><span style = "vertical-align:super;">30,31</span><span>, that become cost effective only when processing a very large number of cells at once.</span></li><li style = "counter-reset:ol0;">The detailed experimental and analytical QC pipelines allow the user to carefully monitor the quality of the libraries and data and to troubleshoot specific problems in the scRNA-seq process.</li><li style = "counter-reset:ol0;">The amount of cell doublets in the MARS-seq2.0 data is extremely low and allows for identification of rare cell types and subpopulations.</li><li style = "counter-reset:ol0;">MARS-seq2.0 provides strand-specific information about the transcripts.</li><li style = "counter-reset:ol0;"></li><li style = "counter-reset:ol0;">However, the following limitations remain:</li><li style = "counter-reset:ol0;"><span>MARS-seq2.0 is a 3ʹ-based scRNA-seq method and will yield information only about the 3ʹ end of the transcript. Therefore, it is not suitable for identifying alternative splicing isoforms or specific sequences at the 5ʹ end of the gene (for these purposes, single-cell full-length RNA-sequencing methodologies, such as SMART-seq</span><span style = "vertical-align:super;">24</span><span>, should be used).</span></li><li style = "counter-reset:ol0;">The method is selective for polyadenylated RNA.</li><li style = "counter-reset:ol0;">The method requires access to FACS facilities, as well as liquid-handling robotics.</li></ol></div><div class = "text-block">Optimization of the conditions indicated above resulted in a sixfold reduction in the cost of library</div><div class = "text-block">production (from $0.65 to $0.10 per cell) and reduced the background level (from 10–15 % to 2 %) as</div><div class = "text-block">evaluated by the number of molecules in the wells containing cells as compared with our negative-control</div><div class = "text-block">empty wells.</div><div class = "text-block"><span style = "font-style:italic;">Analytical and QC improvements</span></div><div class = "text-block"><ol style = "list-style-type: decimal;"><li style = "counter-reset:ol0;">QC scheme to monitor MARS-seq2.0 performance during library preparation: we generated a QC pipeline based on quantitative real-time polymerase chain reaction (qPCR) of housekeeping genes in order to evaluate the complexity of the libraries before sequencing.</li><li style = "counter-reset:ol0;">Generation of a complete analytical pipeline: sequenced reads are de-multiplexed and the molecularsignal is separated from the background noise.</li><li style = "counter-reset:ol0;">QC measurements following sequencing: the analytical pipeline automatically generates a set of > 20 QC measurements (e.g., number of reads, percentage mapping to exons and number of UMIs) that can assist the users in evaluating and optimizing many important features in their single-cell data.</li><li style = "counter-reset:ol0;"></li><li style = "counter-reset:ol0;"></li><li style = "counter-reset:ol0;"><span style = "font-weight:bold;">MARS-seq2.0 evaluation</span></li><li style = "counter-reset:ol0;">To evaluate the efficiency of the method and data quality, we applied MARS-seq2.0 to 2,256 mouse ES cells and mouse embryonic fibroblasts (MEFs). We detected 11.3 million mRNA molecules (medians of ~6,000 and ~4,000 molecules per ES and MEF cell, respectively, Supplementary Fig. 3a) while sequencing 80,000–100,000 reads per cell (Supplementary Fig. 3b). As expected, the number of detected transcripts per cell is correlated with cell size (Supplementary Fig. 3c). Because MARS-seq2.0 is designed to allow deep population sampling and characterization of known and novel subpopulations, we quantify our detection rate by estimating the distribution of mean gene expression on independent pools of 100 single cells, revealing a dynamic range spreading over three orders of magnitude (Supplementary Fig. 3d). By comparing a pool of 1,128 ES cells with a pool of 752 MEF cells, we detected 2,350 genes with a significant (false-discovery rate (FDR) < 10−5) fourfold difference in expression (Supplementary Fig. 3e). In many cases, these expression differences are also pronounced at the single-cell level (Supplementary Fig. 3f–i).</li><li style = "counter-reset:ol0;"></li><li style = "counter-reset:ol0;">Advances in microfluidics technologies allow processing of several thousands of cells in parallel.</li><li style = "counter-reset:ol0;"><span>However, current microfluidic approaches display a relatively high rate (1–15 %) of sequencing of two cells (or more) in a single droplet (doublets)</span><span style = "vertical-align:super;">30,31,52,53</span><span>, which may complicate downstream analysis.</span></li><li style = "counter-reset:ol0;">To evaluate the doublet rate in MARS-seq2.0, we mixed human and mouse ES cells and analyzed the percentage of doublets. Our analysis demonstrates that MARS-seq2.0 has a negligible amount of doublet cells (< 0.2 %; 2 out of 1,041 cells) and provides high confidence in cell identity (Supplementary Fig. 4).</li><li style = "counter-reset:ol0;"></li><li style = "counter-reset:ol0;"></li><li style = "counter-reset:ol0;"><span style = "font-weight:bold;">The computational framework</span></li><li style = "counter-reset:ol0;">To implement an effective error-correction algorithm and to ensure the quality of sequenced libraries, we designed a computational framework that closely follows the different experimental steps in MARS-seq2.0. Our framework models RNA-seq data generation as a sequential process of multiple samplings and amplifications steps (see Supplementary Methods, Supplementary Fig. 5), in which single mRNA molecules in a single cell are distributed and sampled in different pools. The computational pipeline quantifies the number of tagged mRNA molecules per gene that went through this complex sampling process, while eliminating several types of experimental artifacts. In addition, it generates a graphical and user-friendly library diagnostics report for each amplification batch, highlighting important technical parameters that can be variable between batches (see Supplementary Figs. 6–8 for representative diagnostic reports). The report provides a detailed analysis of the reads’ fraction spent on sequencing of primer and other sequences used during library preparation, which can highlight biases such as those caused by an excess of primers. Our pipeline automatically filters sequencing and other polymerization errors that occur in earlier experimental steps (i.e., IVT) and generate spurious UMIs or alter cellular barcodes.</li><li style = "counter-reset:ol0;">Different sources of barcode contamination may substantially bias biological interpretation of the data by mixing genes between different single-cell subpopulations in the same amplification batch.</li><li style = "counter-reset:ol0;">We therefore developed an experimental design that allows us to routinely estimate the total cell-to-cell contamination levels by measuring the number of molecules associated with four negative-control wells (empty wells that do not contain cells) in each amplification batch. To visually relate the number of molecules per cell (Supplementary Fig. 3a) to the original single-cell position on the 384-well plate matrix, a plate-view heat-map is generated that shows how the number of recovered genes and spike-in molecules are distributed across the plate position. This can highlight potential sorting, robotic or other problems. Although technical QC is critical, as demonstrated above, we stress that the ultimate way to evaluate the effectiveness of a specific scRNA-seq method is to examine the novelty and richness of the biological insights emerging from the data and that these may depend on multiple factors, including the quality of biological materials.</li><li style = "counter-reset:ol0;"></li><li style = "counter-reset:ol0;"></li><li style = "counter-reset:ol0;"><span style = "font-weight:bold;">Strengths and limitations of the protocol</span></li><li style = "counter-reset:ol0;">The key strengths of MARS-seq2.0 are as follows:</li><li style = "counter-reset:ol0;">The combination of indexed FACS sorting and scRNA-seq enables refinement and enrichment of the cells of interest, which is especially critical for analysis of rare subpopulations and processing of rare cells in human clinical samples.</li><li style = "counter-reset:ol0;"><span>The improvements made in MARS-seq2.0 provide a cost-effective library preparation method that allows analysis of any number of single cells without compromising the cost per cell value. This is in contrast to other high-throughput methods, such as droplets</span><span style = "vertical-align:super;">30,31</span><span>, that become cost effective only when processing a very large number of cells at once.</span></li><li style = "counter-reset:ol0;">The detailed experimental and analytical QC pipelines allow the user to carefully monitor the quality of the libraries and data and to troubleshoot specific problems in the scRNA-seq process.</li><li style = "counter-reset:ol0;">The amount of cell doublets in the MARS-seq2.0 data is extremely low and allows for identification of rare cell types and subpopulations.</li><li style = "counter-reset:ol0;">MARS-seq2.0 provides strand-specific information about the transcripts.</li><li style = "counter-reset:ol0;"></li><li style = "counter-reset:ol0;">However, the following limitations remain:</li><li style = "counter-reset:ol0;"><span>MARS-seq2.0 is a 3ʹ-based scRNA-seq method and will yield information only about the 3ʹ end of the transcript. Therefore, it is not suitable for identifying alternative splicing isoforms or specific sequences at the 5ʹ end of the gene (for these purposes, single-cell full-length RNA-sequencing methodologies, such as SMART-seq</span><span style = "vertical-align:super;">24</span><span>, should be used).</span></li><li style = "counter-reset:ol0;">The method is selective for polyadenylated RNA.</li><li style = "counter-reset:ol0;">The method requires access to FACS facilities, as well as liquid-handling robotics.</li></ol></div><div class = "text-block"><span style = "font-weight:bold;">MARS-seq2.0 evaluation</span></div><div class = "text-block">To evaluate the efficiency of the method and data quality, we applied MARS-seq2.0 to 2,256 mouse ES cells and mouse embryonic fibroblasts (MEFs). We detected 11.3 million mRNA molecules (medians of ~6,000 and ~4,000 molecules per ES and MEF cell, respectively, Supplementary Fig. 3a) while sequencing 80,000–100,000 reads per cell (Supplementary Fig. 3b). As expected, the number of detected transcripts per cell is correlated with cell size (Supplementary Fig. 3c). Because MARS-seq2.0 is designed to allow deep population sampling and characterization of known and novel subpopulations, we quantify our detection rate by estimating the distribution of mean gene expression on independent pools of 100 single cells, revealing a dynamic range spreading over three orders of magnitude (Supplementary Fig. 3d). By comparing a pool of 1,128 ES cells with a pool of 752 MEF cells, we detected 2,350 genes with a significant (false-discovery rate (FDR) < 10−5) fourfold difference in expression (Supplementary Fig. 3e). In many cases, these expression differences are also pronounced at the single-cell level (Supplementary Fig. 3f–i).</div><div class = "text-block">Advances in microfluidics technologies allow processing of several thousands of cells in parallel.</div><div class = "text-block"><span>However, current microfluidic approaches display a relatively high rate (1–15 %) of sequencing of two cells (or more) in a single droplet (doublets)</span><span style = "vertical-align:super;">30,31,52,53</span><span>, which may complicate downstream analysis.</span></div><div class = "text-block">To evaluate the doublet rate in MARS-seq2.0, we mixed human and mouse ES cells and analyzed the percentage of doublets. Our analysis demonstrates that MARS-seq2.0 has a negligible amount of doublet cells (< 0.2 %; 2 out of 1,041 cells) and provides high confidence in cell identity (Supplementary Fig. 4).</div><div class = "text-block"><span style = "font-weight:bold;">The computational framework</span></div><div class = "text-block">To implement an effective error-correction algorithm and to ensure the quality of sequenced libraries, we designed a computational framework that closely follows the different experimental steps in MARS-seq2.0. Our framework models RNA-seq data generation as a sequential process of multiple samplings and amplifications steps (see Supplementary Methods, Supplementary Fig. 5), in which single mRNA molecules in a single cell are distributed and sampled in different pools. The computational pipeline quantifies the number of tagged mRNA molecules per gene that went through this complex sampling process, while eliminating several types of experimental artifacts. In addition, it generates a graphical and user-friendly library diagnostics report for each amplification batch, highlighting important technical parameters that can be variable between batches (see Supplementary Figs. 6–8 for representative diagnostic reports). The report provides a detailed analysis of the reads’ fraction spent on sequencing of primer and other sequences used during library preparation, which can highlight biases such as those caused by an excess of primers. Our pipeline automatically filters sequencing and other polymerization errors that occur in earlier experimental steps (i.e., IVT) and generate spurious UMIs or alter cellular barcodes.</div><div class = "text-block">Different sources of barcode contamination may substantially bias biological interpretation of the data by mixing genes between different single-cell subpopulations in the same amplification batch.</div><div class = "text-block">We therefore developed an experimental design that allows us to routinely estimate the total cell-to-cell contamination levels by measuring the number of molecules associated with four negative-control wells (empty wells that do not contain cells) in each amplification batch. To visually relate the number of molecules per cell (Supplementary Fig. 3a) to the original single-cell position on the 384-well plate matrix, a plate-view heat-map is generated that shows how the number of recovered genes and spike-in molecules are distributed across the plate position. This can highlight potential sorting, robotic or other problems. Although technical QC is critical, as demonstrated above, we stress that the ultimate way to evaluate the effectiveness of a specific scRNA-seq method is to examine the novelty and richness of the biological insights emerging from the data and that these may depend on multiple factors, including the quality of biological materials.</div><div class = "text-block"><span style = "font-weight:bold;">Strengths and limitations of the protocol</span></div><div class = "text-block">The key strengths of MARS-seq2.0 are as follows:</div><div class = "text-block"><ol style = "list-style-type: decimal;"><li style = "counter-reset:ol0;">The combination of indexed FACS sorting and scRNA-seq enables refinement and enrichment of the cells of interest, which is especially critical for analysis of rare subpopulations and processing of rare cells in human clinical samples.</li><li style = "counter-reset:ol0;"><span>The improvements made in MARS-seq2.0 provide a cost-effective library preparation method that allows analysis of any number of single cells without compromising the cost per cell value. This is in contrast to other high-throughput methods, such as droplets</span><span style = "vertical-align:super;">30,31</span><span>, that become cost effective only when processing a very large number of cells at once.</span></li><li style = "counter-reset:ol0;">The detailed experimental and analytical QC pipelines allow the user to carefully monitor the quality of the libraries and data and to troubleshoot specific problems in the scRNA-seq process.</li><li style = "counter-reset:ol0;">The amount of cell doublets in the MARS-seq2.0 data is extremely low and allows for identification of rare cell types and subpopulations.</li><li style = "counter-reset:ol0;">MARS-seq2.0 provides strand-specific information about the transcripts.</li><li style = "counter-reset:ol0;"></li><li style = "counter-reset:ol0;">However, the following limitations remain:</li><li style = "counter-reset:ol0;"><span>MARS-seq2.0 is a 3ʹ-based scRNA-seq method and will yield information only about the 3ʹ end of the transcript. Therefore, it is not suitable for identifying alternative splicing isoforms or specific sequences at the 5ʹ end of the gene (for these purposes, single-cell full-length RNA-sequencing methodologies, such as SMART-seq</span><span style = "vertical-align:super;">24</span><span>, should be used).</span></li><li style = "counter-reset:ol0;">The method is selective for polyadenylated RNA.</li><li style = "counter-reset:ol0;">The method requires access to FACS facilities, as well as liquid-handling robotics.</li></ol></div><div class = "text-block">However, the following limitations remain:</div><div class = "text-block"><ol style = "list-style-type: decimal;"><li style = "counter-reset:ol0;"><span>MARS-seq2.0 is a 3ʹ-based scRNA-seq method and will yield information only about the 3ʹ end of the transcript. Therefore, it is not suitable for identifying alternative splicing isoforms or specific sequences at the 5ʹ end of the gene (for these purposes, single-cell full-length RNA-sequencing methodologies, such as SMART-seq</span><span style = "vertical-align:super;">24</span><span>, should be used).</span></li><li style = "counter-reset:ol0;">The method is selective for polyadenylated RNA.</li><li style = "counter-reset:ol0;">The method requires access to FACS facilities, as well as liquid-handling robotics.</li></ol></div><div class = "text-block"><span style = "font-weight:bold;">Future applications</span></div><div class = "text-block"><span>The reduction in cost per sample in MARS-seq2.0, and especially the ability to refine the population of interest by enriching or depleting cell populations in a data-driven manner, allows the researcher to focus on specific populations, including very rare cell types or states in animal models and clinical samples</span><span style = "vertical-align:super;">47,49</span><span style = "vertical-align:super;">.</span></div><div class = "text-block"><span>Furthermore, techniques for simultaneous acquisition of RNA and other molecular signatures of single cells, for example, spatial location</span><span style = "vertical-align:super;">36,51</span><span>, genetics</span><span style = "vertical-align:super;">54</span><span>, lineage</span><span style = "vertical-align:super;">55 </span><span>and signaling</span><span style = "vertical-align:super;">37</span><span>, are critical for deep molecular understanding of physiological processes and diseases. MARS-seq2.0 presents a flexible platform for combination of unbiased transcriptional mapping with a large number of fluorescent markers that enables collection of multiple information tiers on the same single cell, such as time, spatial location, signaling, genetics, epigenetics and many more</span><span style = "vertical-align:super;">56</span><span style = "vertical-align:super;">.</span></div></div>

[STEPS]
?. [Making MARS-seq2.0 384-well cell capture plates (Timing ~50 min for 24 plates)]
Turn on the Bravo liquid-handling platform and wait for initialization to be completed. Load the ‘cell capture plate preparation’ protocol file for the 96ST head. The appropriate protocol file can be found in the Supplementary Software.
?. [Making MARS-seq2.0 384-well cell capture plates (Timing ~50 min for 24 plates)]
Prepare cell capture plate master mixes in four 96-well plates: lysis buffer ( Triton X-100 in nuclease-free water) supplemented with  RNase inhibitor (RNasin Plus) and  RT1 primer from group 1 (barcodes 1–96), group 2 (barcodes 97–192), group 3 (barcodes 193–288) or group 4 (barcodes 289–384). Barcode groups are listed in Supplementary Table 1. To prepare about twenty-four 384-well plates, use a multichannel pipette to mix  of lysis buffer with  of  RT1 primer stock per well of a 96-well plate.
57.5 µl
5 µl
?. [Making MARS-seq2.0 384-well cell capture plates (Timing ~50 min for 24 plates)]
Place the 96-well cell capture plate master mixes, a box containing filtered tips and the destination 384-well plates in the appropriate locations in the Bravo robot. The cell capture plate preparation script mixes the group 1 master mix plate, aspirates  from it and dispenses it into destination 384-well plate 1. Then  is again aspirated from group 1 master mix plate to be dispensed into the other destination 384-well plates. If more than three cell capture plates are prepared, the robotic script will pause and prompt the user to replace the filled destination plates with new plates. Once group 1 master mix has been added to all desired plates, the tips are replaced and the process is repeated for groups 2, 3 and 4. The entire process takes ~ for 24 plates. See Fig. 3a for a schematic of the plates’ locations in this protocol.
2 µl
2 µl
?. [Making MARS-seq2.0 384-well cell capture plates (Timing ~50 min for 24 plates)]
Seal the prepared cell capture plates and freeze at  until use.Cell capture plates can be kept at  for 1–2 years.
-20 °C
-20 °C
?. [Sorting of single cells into MARS-seq2.0 capture plates (Timing ~7 min per capture plate, if the cell population sorted is not a limiting factor, i.e., not a rare cell population)]
Thaw the cell capture plates from Step 4 and spin them down. Keep the plates on ice until sorting.
?. [Sorting of single cells into MARS-seq2.0 capture plates (Timing ~7 min per capture plate, if the cell population sorted is not a limiting factor, i.e., not a rare cell population)]
Prepare a single-cell suspension for FACS (see ‘Biological materials’ section). ? TROUBLESHOOTING
We recommend gating on side scatter area (SSC-A) versus forward scatter area (FSC-A) to collect live cells, and then on forward scatter width (FSC-W) versus FSC-A to sort only singlets.
?. [Sorting of single cells into MARS-seq2.0 capture plates (Timing ~7 min per capture plate, if the cell population sorted is not a limiting factor, i.e., not a rare cell population)]
Calibrate the FACS machine for single-cell sorting into plates.
See Supplementary Manual 1 for detailed information on single-cell sorting optimization.
?. [Sorting of single cells into MARS-seq2.0 capture plates (Timing ~7 min per capture plate, if the cell population sorted is not a limiting factor, i.e., not a rare cell population)]
Place a cell capture plate in the appropriate location in the FACS instrument and sort the desiredpopulation. CRITICAL STEP Keep the single-cell suspension on ice until FACS sorting.CRITICAL STEP The time from single-cell suspension to FACS sorting into the capture plates should be minimal. We recommend not exceeding 4–5 h.
We recommend leaving between two and four empty wells in each 384-well plate as a no-cell control during data analysis.
?. [Sorting of single cells into MARS-seq2.0 capture plates (Timing ~7 min per capture plate, if the cell population sorted is not a limiting factor, i.e., not a rare cell population)]
Immediately after sorting of each plate, seal the plate with an aluminum sealer, spin it down to ensure cell immersion into the lysis solution, snap-freeze on dry ice and store at  until further processing.CRITICAL STEP The sealer used must have the ability to sustain low temperatures; otherwise the seal can break off during freezing at −80 °C.CRITICAL STEP It is very important to spin down the cell capture plate immediately after singlecell sorting and keep it on dry ice until storage at −80 °C.
-80 °C
?. [Indexed single-cell sorting (Timing ~14 min per capture plate, if cell population sorted is not a limiting factor)]
Indexed FACS sorting is an optional element of the MARS-seq2.0 pipeline that allows correlating transcription and protein level by recording the level of surface markers for each sorted single cell.
?. [Indexed single-cell sorting (Timing ~14 min per capture plate, if cell population sorted is not a limiting factor)]
If you desire to record fluorescent marker levels for each single cell, activate the FACS Diva ‘index sorting’ function during single-cell sorting. See Supplementary Manual 3 for a detailed protocol on how to extract index sorting data.
?. [Testing of single-cell sorting efficiency (Timing ~2 h for 1 or 2 plates)]
We recommend testing the single-cell sorting efficiency in each new project or as a routine procedure following each sorting session. In Steps 13–19, one or two plates from the sorting session are used for testing of the single-cell sorting efficiency and not for library preparation.
?. [Testing of single-cell sorting efficiency (Timing ~2 h for 1 or 2 plates)]
Thaw one of the sorted 384-well plates from Step 9 for  at  and spin itdown.
80 Room temperature
?. [Testing of single-cell sorting efficiency (Timing ~2 h for 1 or 2 plates)]
Incubate the sorted 384-well plate for  at  to denature secondary structures andimmediately transfer the plate to an ice block.
72 °C
?. [Testing of single-cell sorting efficiency (Timing ~2 h for 1 or 2 plates)]
Prepare a RT mix to add to 2–4 columns per plate according to the table below, and add of RTmix per reaction well:  ABC1ComponentAmount (μl) per 1 reaction (1×)Amount (μl) per 2 columns (36×)2AffinityScript buffer (10×)0.414.43DTT 100 mM0.414.44dNTPs 100 mM0.165.765AffinityScript enzyme0.27.26Nuclease-free water0.8430.247Sorted capture plate (Step 14/15)28Total reaction volume4
2 µl
ABC1ComponentAmount (μl) per 1 reaction (1×)Amount (μl) per 2 columns (36×)2AffinityScript buffer (10×)0.414.43DTT 100 mM0.414.44dNTPs 100 mM0.165.765AffinityScript enzyme0.27.26Nuclease-free water0.8430.247Sorted capture plate (Step 14/15)28Total reaction volume4
?. [Testing of single-cell sorting efficiency (Timing ~2 h for 1 or 2 plates)]
Seal the plate, spin it down and incubate for  at , for  at , and for at .
42 °C
45 °C
85 °C
?. [Testing of single-cell sorting efficiency (Timing ~2 h for 1 or 2 plates)]
After RT, spin down the plate and dilute the cDNA 1:20 as follows: (i) bring the volume in the columns in which RT was performed to 10 μl by adding  of EB8 to each well; (ii) add  of this diluted cDNA to  of EB8 in a separate dilution plate.
6 µl
1 µl
19 µl
?. [Testing of single-cell sorting efficiency (Timing ~2 h for 1 or 2 plates)]
Perform quantitative qPCR for mouse ActB or human GAPDH. Prepare the qPCR master mixaccording to the table below (for duplicates):  ABC1ComponentAmount (μl) per 1 reaction (1×)Amount (μl) per 2
columns (75×)2Primer mix (12.5 μM each forward and reverse primers; Reagent setup)0.2153Fast SYBR Green master mix5.843541:20 diluted cDNA6
ABC1ComponentAmount (μl) per 1 reaction (1×)Amount (μl) per 2
columns (75×)2Primer mix (12.5 μM each forward and reverse primers; Reagent setup)0.2153Fast SYBR Green master mix5.843541:20 diluted cDNA6
?. [Testing of single-cell sorting efficiency (Timing ~2 h for 1 or 2 plates)]
Run qPCR for 45 cycles as shown in the table below:  ABC1Cycle numberDenatureAnneal2195 °C, 20 s32–4698 °C, 3 s60 °C, 30 s? TROUBLESHOOTING
ABC1Cycle numberDenatureAnneal2195 °C, 20 s32–4698 °C, 3 s60 °C, 30 s
?. [Barcoding and RT reaction (Timing ~1.5 h for 1 plate)]
Turn on the Bravo liquid handler, initialize and load the ‘RT mix addition’ script.
?. [Barcoding and RT reaction (Timing ~1.5 h for 1 plate)]
Thaw the 384-well plates (from Step 9) to be processed at  for  and spindown. Keep on ice until the next step. Pre-heat a 384-well thermal cycler to .
85 Room temperature
95 °C
?. [Barcoding and RT reaction (Timing ~1.5 h for 1 plate)]
Prepare an RT reaction mix sufficient for the number of plates to be processed:  ABC1ComponentAmount (μl) per 1 well (1x)Amount (μl) per 1 plate (520x)2Superscript III buffer (5×)0.1523DTT 100 mM0.05264dNTPs 100 mM0.0210.45ERCC spike-in mix (1:80,000)0.015.26RNasin (40 U/μl)0.01256.57Superscript III enzyme0.015.28Nuclease-free water0.2975154.79Single cell + RT1 oligo (384-well plates; Step 22, after evaporation)--10Total0.5260
ABC1ComponentAmount (μl) per 1 well (1x)Amount (μl) per 1 plate (520x)2Superscript III buffer (5×)0.1523DTT 100 mM0.05264dNTPs 100 mM0.0210.45ERCC spike-in mix (1:80,000)0.015.26RNasin (40 U/μl)0.01256.57Superscript III enzyme0.015.28Nuclease-free water0.2975154.79Single cell + RT1 oligo (384-well plates; Step 22, after evaporation)--10Total0.5260
?. [Barcoding and RT reaction (Timing ~1.5 h for 1 plate)]
Divide the RT mix among two 8-well strips,  per well, and place the strips on a , 96-wellCPAC heating/cooling unit in the Bravo robot.
16 µl
4 °C
?. [Barcoding and RT reaction (Timing ~1.5 h for 1 plate)]
Evaporation. Remove the aluminum seal of the plate (Step 23) and place it at  for  forcomplete evaporation of the lysis buffer. Immediately transfer the plate to a cooling 384-well blockfor RT reaction mix addition using the robot.CRITICAL STEP Evaporation time should be adjusted depending on the thermal cycler used(typically between 3 and 4 min) in order to ensure complete evaporation.
95 °C
?. [Barcoding and RT reaction (Timing ~1.5 h for 1 plate)]
Activate the RT reaction mix addition script. This automated protocol adds  from the RTreaction mix (Step 24) to each well in the 384-well plate (placed on a , 384-well CPAC heating/cooling unit). The tips are replaced and the process is repeated until all wells in the entire 384-wellplate are supplemented with RT reaction mix. The entire process takes 17 min per 384-well plate.See Fig. 3b for a schematic of the plates’ locations in this protocol.In the RT reaction, the final Triton X-100 concentration is  and the RT primerconcentration is .
0.5 µl
4 °C
?. [Barcoding and RT reaction (Timing ~1.5 h for 1 plate)]
Following RT mix addition, seal the plate, spin down and transfer it to a 384-well thermal cycler forthe RT program ( at ,  at , and  at ).
50 °C
85 °C
?. [Primer removal by exonuclease I (Timing ~1 h for 1 plate)]
Spin down the 384-well plate after RT, and keep it on ice until the next step. Load the ‘ExonucleaseI addition’ Bravo script.
?. [Primer removal by exonuclease I (Timing ~1 h for 1 plate)]
Prepare an exonuclease I reaction mix sufficient for the number of plates to be processed:  ABC1ComponentAmount (μl) per 1 well (1×)Amount (μl) per 1 plate (520×)2Exonuclease buffer (10×)0.15783Exonuclease I enzyme0.0157.84EB80.835434.25cDNA (Step 29/30)0.5-6Total reaction volume1.5-
ABC1ComponentAmount (μl) per 1 well (1×)Amount (μl) per 1 plate (520×)2Exonuclease buffer (10×)0.15783Exonuclease I enzyme0.0157.84EB80.835434.25cDNA (Step 29/30)0.5-6Total reaction volume1.5-
?. [Primer removal by exonuclease I (Timing ~1 h for 1 plate)]
Divide the exonuclease I mix among two 8-well strips,  per well, and place the strips in a ,96-well CPAC heating/cooling unit in the Bravo robot.
32 µl
4 °C
?. [Primer removal by exonuclease I (Timing ~1 h for 1 plate)]
Activate the ‘Exonuclease I addition’ script. This automated protocol adds  from the exonuclease reaction mix to each well in the 384-well plate (placed in a  384-well CPAC heating/cooling unit). The entire process takes 17 min per 384-well plate. See Fig. 3b for a schematic of the plates’ locations in this protocol.
1 µl
4 °C
?. [Primer removal by exonuclease I (Timing ~1 h for 1 plate)]
Following Exonuclease addition, seal the plate, spin down and transfer to a 384-well thermal cyclerfor the exonculease program ( at  and  at ).
37 °C
80 °C
?. [Pooling of barcoded 384 single-cell samples and cleanup (Timing ~1 h for 1 plate)]
Load the ‘Pooling’ Bravo script. The pooling protocol pools together 384 single cells that are to be amplified together as an amplification batch. Prepare a 96-well pooling plate: dispense  of  Triton X-100 into each well in row H of a clean 96-well plate for tip pre-wash and place it in the appropriate location in the Bravo robot (destination 96-well plate).
55 µl
?. [Pooling of barcoded 384 single-cell samples and cleanup (Timing ~1 h for 1 plate)]
Spin down the 384-well plate after the exonculease program and place it in the appropriate location in the Bravo robot. Activate the ‘pooling’ script: the process begins by pre-washing the tips and then aspirating the contents of the 384-well plate and dispensing them into row A of the 96-well destination plate. This is the first step of the pooling, which results in 12 wells in a 96-well plate (1 row), each well containing single cells from 2 columns of the 384-well plate. If more than one 384-well plate is pooled, tips will be replaced and the second 384-well plate will be pooled into row B ofthe 96-well destination plate, and so on. The process takes 15 min per plate. See Fig. 3c for a schematic of the plates’ locations in this protocol.
?. [Pooling of barcoded 384 single-cell samples and cleanup (Timing ~1 h for 1 plate)]
Spin down the 96-well plate containing the pooled cDNA.
?. [Pooling of barcoded 384 single-cell samples and cleanup (Timing ~1 h for 1 plate)]
Purify the product with 0.9 volumes of AMPure XP beads: add  to each well and pipette to mix. Incubate for  at  and transfer the contents of each row to a LoBind Eppendorf tube. Discard the 96-well plate used for pooling. Continue with AMPure XP cleanup in the LoBind Eppendorf tube and elute in  of EB8. Transfer  of the pooled sample to  of EB8 for QC1 (Box 1), and  to the first column in a new 96-well plate for the next step. At the end of this process, all single cells in each 384-well plate are transferred into one well in a 96-well plate. If processing several 384-well plates in parallel, each plate is represented in one well in a 96-well plate, one after the other in the same column.CRITICAL STEP All AMPure XP cleanups are performed according to the manufacturing protocol. The amount of beads added in each step and the volume used for elution are indicated in the specific step.CRITICAL STEP From this step onward, the protocol is done in a 96-well plate, moving from one column to the next in each step. Each 384-well plate is represented in one well (one pool).? TROUBLESHOOTING
86.4 µl
80 Room temperature
19 µl
0.9 µl
19.1 µl
17 µl
Box 1: Experimental QC by qPCR of a housekeeping geneSee instructions on how to perform qPCR in Steps 20 and 21. This is an important measurement for estimating library quality:QC1: an estimation of the amount of starting material (Step 40). QC1 is performed after cells in one plate (or amplification batch) are barcoded, have undergone RT and are pooled together before the second-strand-synthesis step. Cells with high RNA content are expected to yield a lower CT value than cells with a lower RNA content (Anticipated results).QC2: a measurement of library linear amplification (see Step 53). QC2 is performed after the pooled single cells undergo in vitro transcription and reverse transcription (see Anticipated results).QC3: a measurement for final library quantity (see Step 56). QC3 is performed on the final amplified library (see Anticipated results).CRITICAL Primers for qPCR should be designed for a housekeeping gene on the 3ʹ end of the transcript (no more than 200 bp upstream of the polyA site). Recommended primer sequences for mouse and human are provided (Reagent setup).CRITICAL Although we recommend performing the above QC steps in order to maintain a foolproof process in the long run, these QC measurements can be skipped or sampled sporadically.
?. [Second-strand synthesis and IVT amplification (Timing ~2.5 h + overnight incubation)]
Convert the pooled cDNA to double-stranded DNA using a second-strand-synthesis kit in whichthe enzyme is diluted 1:8 in nuclease-free water:  AB1ComponentVolume (μl) per well2Second-strand-synthesis buffer (10×)23Second-strand-synthesis enzyme (diluted 1:8 in nuclease-free water)14Purified cDNA pool (Step 40/41)175Total reaction volume20CRITICAL STEP Mix the second-strand-synthesis reaction well with a 20-μl pipette.
AB1ComponentVolume (μl) per well2Second-strand-synthesis buffer (10×)23Second-strand-synthesis enzyme (diluted 1:8 in nuclease-free water)14Purified cDNA pool (Step 40/41)175Total reaction volume20
?. [Second-strand synthesis and IVT amplification (Timing ~2.5 h + overnight incubation)]
Spin down the plate and incubate at  for .
16 °C
?. [Second-strand synthesis and IVT amplification (Timing ~2.5 h + overnight incubation)]
Purify the product with 1.4 volumes of AMPure XP beads: add  of EB8 and  of AMPure XP beads. Continue with AMPure XP cleanup and elute in  of EB8. Do not transfer the supernatant to a new well; IVT is performed with the beads.
10 µl
42 µl
8 µl
?. [Second-strand synthesis and IVT amplification (Timing ~2.5 h + overnight incubation)]
Linearly amplify the cDNA by IVT. Prepare the following reaction mix and add  of IVT mixper well:  AB1ComponentVolume (μl) per well2rNTPs (mixture of A, C, U, G)83T7 RNA polymerase buffer (10×)24T7 RNA polymerase enzyme25Purified dsDNA (Step 44)86Total reaction volume20
12 µl
AB1ComponentVolume (μl) per well2rNTPs (mixture of A, C, U, G)83T7 RNA polymerase buffer (10×)24T7 RNA polymerase enzyme25Purified dsDNA (Step 44)86Total reaction volume20
?. [Second-strand synthesis and IVT amplification (Timing ~2.5 h + overnight incubation)]
Incubate at  overnight.CRITICAL STEP Following IVT incubation overnight, the AMPure XP beads sink to the bottom. This is normal.
37 °C
?. [Second-strand synthesis and IVT amplification (Timing ~2.5 h + overnight incubation)]
Following IVT, add  of Turbo DNase I mix to digest the DNA template:  AB1ComponentVolume (μl) per well2Turbo DNase buffer (10×)33Turbo DNase I24Nuclease-free water55IVT reaction (Step 46)206Total reaction volume30
10 µl
AB1ComponentVolume (μl) per well2Turbo DNase buffer (10×)33Turbo DNase I24Nuclease-free water55IVT reaction (Step 46)206Total reaction volume30
?. [Second-strand synthesis and IVT amplification (Timing ~2.5 h + overnight incubation)]
Incubate at  for .
37 °C
?. [Second-strand synthesis and IVT amplification (Timing ~2.5 h + overnight incubation)]
Purify the aRNA product with 1.2 volumes of AMPure XP beads: add  of EB7.5 and  of AMPure XP beads. Continue with AMPure XP cleanup. Elute with  of EB7.5. Transfer  to a well in a new column in the same 96-well plate.CRITICAL STEP Make sure to avoid transferring the beads, as this will interfere with the next step.
20 µl
60 µl
19 µl
18 µl
?. [RNA fragmentation (Timing ~30 min)]
CRITICAL The aRNA (Step 49) is fragmented by chemical fragmentation into a median size of ~300 nucleotides by  of incubation at  in Zn2+ RNA fragmentation solution.CRITICAL If aRNA (Step 49) was frozen, thaw the 96-well plate on ice and spin down before the next step.
70 °C
?. [RNA fragmentation (Timing ~30 min)]
Add  of 10× Zn2+ fragmentation reagent to the aRNA from Step 49.
2 µl
?. [RNA fragmentation (Timing ~30 min)]
Purify the product with 2 volumes of AMPure XP beads: add  of AMPure XP beads. Continue with AMPure XP cleanup. Elute with  of EB7.5. Transfer  to a well in new column and continue to ligation.CRITICAL STEP Drying time of the beads is considerably shorter than that used in the AMPure XP cleanup in other steps.CRITICAL STEP Make sure not to transfer beads in the elution step, as this will interfere with the next step.
44 µl
5.5 µl
5 µl
?. [Ligation of plate barcode (Timing ~2.5 h)]
Ligate a plate adaptor to the fragmented RNA (RNA:ssDNA ligation): select a unique plate adaptor(Supplementary Table 2) for each library to be sequenced together and record it.
?. [Ligation of plate barcode (Timing ~2.5 h)]
Add  of ligation plate adaptor () to the purified fragmented RNA (from Step 55) and incubate for  in a thermal cycler pre-heated to . Transfer immediately to a cooling block.
1 µl
70 °C
?. [Ligation of plate barcode (Timing ~2.5 h)]
Prepare a ligation master mix in a LoBind Eppendorf tube by adding the ingredients belowaccording to the order specified. The master mix should contain all the reagents, except for thefragmented RNA + ligation adaptor from Step 57.  AB1ComponentVolume (μl) per well2T4 ligation buffer (10×)23100% (vol/vol) DMSO1.94100 mM fresh ATP0.2550% (wt/vol) PEG86T4 RNA ligase 127Fragmented RNA + ligation adaptor (from Step 57)68Total reaction volume20CRITICAL STEP When preparing a mix for ligation, follow these guidelines: (i) vortex and spin down after addition of each reagent, (ii) place DMSO and PEG at 37 °C until use, (iii) work very slowly with PEG to ensure the correct volume is added and (iv) store ATP in small aliquots at −80 °C.
AB1ComponentVolume (μl) per well2T4 ligation buffer (10×)23100% (vol/vol) DMSO1.94100 mM fresh ATP0.2550% (wt/vol) PEG86T4 RNA ligase 127Fragmented RNA + ligation adaptor (from Step 57)68Total reaction volume20
?. [Ligation of plate barcode (Timing ~2.5 h)]
Add  from the ligation mix to the aRNA and ligation adaptor (from Step 57), mix very slowlyuntil homogeneous and spin down the plate.
14.1 µl
?. [Ligation of plate barcode (Timing ~2.5 h)]
Incubate at  for .
22 °C
?. [Ligation of plate barcode (Timing ~2.5 h)]
Purify the ligation product with 1.5 volumes of AMPure XP beads: add  of EB7.5 +  ofAMPure XP beads. Continue with AMPure XP cleanup. Elute with  of EB7.5 and transfer to a well in a new column of the plate.CRITICAL STEP Allow a longer magnetization time for the AMPure XP beads (7–8 min).CRITICAL STEP Drying time of the AMPure XP beads may be longer (7–8 min).
80 µl
100 µl
13.7 µl
13.2 µl
?. [Second reverse transcription (Timing ~1.5 h)]
CRITICAL The ligated product is reverse-transcribed with a primer complementary to the ligated adaptor.
?. [Second reverse transcription (Timing ~1.5 h)]
Add  of  RT2 primer to the purified ligated aRNA (from Step 61), mix by pipetting, seal and spin down the plate. You should have  per well.
1 µl
14.2 µl
?. [Second reverse transcription (Timing ~1.5 h)]
Prepare the RT mix according to the table below (omitting the ligated product + RT primer) andadd  of the mix per reaction well:  AB1ComponentVolume (μl) per well2AffinityScript buffer (10×)23100 mM DTT24100 mM dNTP mix0.85AffinityScript RT enzyme16Ligated product + RT primer (from Step 50)14.27Total reaction volume20
5.8 µl
AB1ComponentVolume (μl) per well2AffinityScript buffer (10×)23100 mM DTT24100 mM dNTP mix0.85AffinityScript RT enzyme16Ligated product + RT primer (from Step 50)14.27Total reaction volume20
?. [Second reverse transcription (Timing ~1.5 h)]
Seal the plate, spin down and incubate it for  at ,  at , and  at .
42 °C
45 °C
85 °C
?. [Second reverse transcription (Timing ~1.5 h)]
Purify the cDNA with 1.5 volumes of AMPure XP beads: add  of EB8 and  of AMPure XPbeads. Continue with AMPure XP cleanup and elute in  of EB8. Transfer  to a newcolumn. Take  of the cDNA and add it to  of EB8 for QC2 (Box 1).? TROUBLESHOOTING
10 µl
45 µl
25 µl
24 µl
1.2 µl
18.8 µl
?. [Enrichment of final library (Timing ~1 h)]
The library is completed by amplifying the cDNA using PCR: transfer  of cDNA (Step 67) toa well in a new column and add  of KAPA HiFi HotStart ReadyMix +  of  enrichment primer mix (Reagent setup).
11.5 µl
12.5 µl
1 µl
?. [Enrichment of final library (Timing ~1 h)]
Run a PCR program as follows:  ABCDE1Cycle no.DenatureAnnealExtendFinal2198 °C, 2 min32–398 °C, 20 s57 °C, 30 s72 °C, 30 s44–1598 °C, 20 s65 °C, 30 s72 °C, 30 s51672 °C, 5 min61710 °C, ∞CRITICAL STEP The optimal number of PCR cycles is a tradeoff between insufficient amplification and too much amplification (which can cause PCR bias). The number of cycles is dependent on the cell type used and should be calibrated for each project. Typically, the number of amplification cycles required for enrichment is between 11 and 17. In the table above, we have used 14 cycles (2 cycles at 57 °C and 12 cycles at 65 °C annealing temperature) as an example. If needed, the PCR can be repeated with an adjusted cycle number (altering the number of cycles performed at 65 °C annealing temperature) using the cDNA backup.CRITICAL STEP Tracking QC values in several experiments from the same cell type can help in estimating, empirically, the necessary enrichment. As QC2 (Step 67) indicates the amount of material in the sample before the final amplification, it can link the number of PCR cycles done to the final library concentration obtained at the end of the process.
ABCDE1Cycle no.DenatureAnnealExtendFinal2198 °C, 2 min32–398 °C, 20 s57 °C, 30 s72 °C, 30 s44–1598 °C, 20 s65 °C, 30 s72 °C, 30 s51672 °C, 5 min61710 °C, ∞
?. [Enrichment of final library (Timing ~1 h)]
Purify the amplified library with 0.7 volumes of AMPure XP beads: add  of EB8 and  of AMPure XP beads. Continue with AMPure XP cleanup. Elute with  of EB8. Transfer  of the final library to a new well. Take  of the final library and add it to  of EB8 for QC3 (Box 1).
35 µl
42 µl
20 µl
19 µl
2 µl
38 µl
?. [QC of libraries before sequencing (Timing ~1 h)]
CRITICAL All libraries should be carefully examined before sequencing.
?. [QC of libraries before sequencing (Timing ~1 h)]
Library concentration. Measure the final library concentration (ng/μl) using the dsDNA HS Assay Kit and a Qubit fluorometer. A typical library should yield between 1 and 10 ng/μl of dsDNA.CRITICAL STEP For better accuracy, it is recommended to use  of the library for Qubit measurement.? TROUBLESHOOTING
2 µl
?. [QC of libraries before sequencing (Timing ~1 h)]
Library profile. Evaluate the library profile and the mean molecule size using a 2200 TapeStationinstrument or equivalent.? TROUBLESHOOTING
?. [Sequencing (Timing ~12 h)]
CRITICAL Successful libraries (passing QC checks) can be sequenced.
?. [Sequencing (Timing ~12 h)]
Calculate the molarity of each pool, using the following formula:where [ng/μl] is the concentration obtained with the Qubit assay (Step 74) and [bp] is the libraryprofile peak obtained with TapeStation (Step 75).
?. [Sequencing (Timing ~12 h)]
Dilute each library pool to  (or another equivalent molar concentration of the pools) with EB8and mix the different pools in equal ratios.
?. [Sequencing (Timing ~12 h)]
Libraries of 20–40 384-well plates can be mixed and sequenced together on a NextSeq 500 HighOutput v2 kit flow cell to yield ~0.5 × 109 reads.
?. [Sequencing (Timing ~12 h)]
Sequence libraries with the following parameters: rd1 = 75 bp, rd2 = 15 bp, no indices.
?. [Analytical pipeline (Timing ~1 d)]
CRITICAL Detailed instructions on how to use the MARS-seq2.0 analytical pipeline and create a single-cell database (SCDB) can be found in the MARS-seq2.0 pipeline user manual on our website http://compgenomics.weizmann.ac.il/tanay/?page_id=672. See Supplementary Manual 2.
?. [Analytical pipeline (Timing ~1 d)]
Make sure you have the correct SCDB directory structure in your Linux storage.
?. [Analytical pipeline (Timing ~1 d)]
Make sure that the parameters defined in scdb/config/config.txt are correct.
?. [Analytical pipeline (Timing ~1 d)]
Add the metadata on the sequenced library according to the SCDB as described in detail in the usermanual.
?. [Analytical pipeline (Timing ~1 d)]
Add the .fastq files (raw reads) obtained at Step 80 to the SCDB.
?. [Analytical pipeline (Timing ~1 d)]
Run the pipeline locally or on an SGE (sun grid engine) cluster.
?. [Sorting of single cells into MARS-seq2.0 capture plates (Timing ~7 min per capture plate, if the cell population sorted is not a limiting factor, i.e., not a rare cell population)]
Sorted cell capture plates can be kept at −80 °C for several years.
?. [Testing of single-cell sorting efficiency (Timing ~2 h for 1 or 2 plates)]
Pre-heat a 384-well thermal cycler to .
72 °C
?. [Barcoding and RT reaction (Timing ~1.5 h for 1 plate)]
Prepare an ERCC spike-in mix (1:80,000) in three steps to use in the RT reaction mix above. First, prepare a 1:1,000 dilution by adding  of EB7.5 to a 1:100 ERCC aliquot. Then, prepare a 1:10,000 dilution by mixing of 1:1,000 ERCC with  of EB7.5. Last, prepare a 1:80,000 dilution by mixing  of 1:10,000 ERCC with  of EB7.5.
9 µl
2 µl
18 µl
5 µl
35 µl
?. [Barcoding and RT reaction (Timing ~1.5 h for 1 plate)]
Samples can be frozen at  until further processing (we have stored samplesat this stage for up to 1 year).
-20 °C
?. [Pooling of barcoded 384 single-cell samples and cleanup (Timing ~1 h for 1 plate)]
Using a multi-channel pipette with six barrels, transfer the contents of the last six wells,  in each well, to the first six wells in each row of the 96-well plate. This is the second step of the pooling, which results in 96 μl in each of the six wells.
48 µl
?. [Pooling of barcoded 384 single-cell samples and cleanup (Timing ~1 h for 1 plate)]
Samples can be frozen at  until further processing (we have stored samples at this stage for up to 1 year).
-20 °C
?. [Second-strand synthesis and IVT amplification (Timing ~2.5 h + overnight incubation)]
Samples can be frozen at  until further processing (we have stored samples at this stage for up to 1 year).
-80 °C
?. [RNA fragmentation (Timing ~30 min)]
Incubate for exactly  in a thermal cycler pre-heated to . Transfer immediately to a cooling block and add  of stop solution.
70 °C
2 µl
?. [RNA fragmentation (Timing ~30 min)]
Cool for  –  and spin down.
?. [Second reverse transcription (Timing ~1.5 h)]
Incubate the plate for  in a thermal cycler pre-heated to . Transfer immediately to a cooling block. Spin down the plate.
72 °C
?. [Second reverse transcription (Timing ~1.5 h)]
PAUSE POINT Samples can be frozen at  until further processing (we have stored samplesat this stage for up to 1 year).
-20 °C
?. [Enrichment of final library (Timing ~1 h)]
Vortex and spin down. The remaining half of the cDNA can be kept as a backup at .
-20 °C