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Authors: Kevin Nee, Quy Nguyen, Kai Kessenbrock
Group: Human Cell Atlas Method Development Community
Summary: Breast cancer originates in the mammary gland epithelium, however growing evidence demonstrates that the diverse array of stromal tissues influence the behavior of breast epithelium and has key roles in the pathogenesis of breast cancer. Despite increased recognition and research, the heterogeneity of the breast stroma (endothelium, fibroblasts, and adipocytes) poses challenges in elucidating which stromal populations are responsible for the complex interactions of the breast microenvironment. To this end, we have employed single cell RNA sequencing (scRNAseq) of the stromal populations within the breast. However adipose tissue, due to its delicate and lipid filled nature is not amenable to these methods of interrogation. To overcome this obstacle, we have developed a method for isolation of adipose nuclei for 10x sequencing. Together, we will use these approaches to investigate the heterogeneity of stroma and adipocytes, and determine the interactions of the breast microenvironment at single-cell resolution. 

Proper citation: Kevin Nee, Quy Nguyen, Kai Kessenbrock 2018. Single Nuclei RNA Sequencing of Breast Adipose Tissue (10x Nuclei-Seq) . protocols.io https://dx.doi.org/10.17504/protocols.io.tdwei7e Copy   

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Authors: Anindita Basu, Inbal Avraham-Davidi, Naomi Habib, Aviv Regev, Feng Zhang, Karthik Shekhar, Matan Hofree, David Weitz, Orit Rozenblatt-Rosen, Tyler Burks, Sourav Choudhury, François Aguet, Ellen Gelfand, Kristin Ardlie
Group: Human Cell Atlas Method Development Community
Summary: Currently, most single cell protocols require the preparation of a single cell suspension from fresh tissue, a major roadblock to clinical deployment, to archived materials and to certain tissues such as adult brain. In the adult brain the harsh enzymatic dissociation harms the integrity of the cells and their RNA, and biases toward easily dissociated cell types, and is restricted to young animals.We developed DroNc-seq, a droplet microfluidic and DNA barcoding technique for analysis of RNA profiles of single nuclei from fresh, frozen or lightly fixed tissues at high throughput and low cost. The utility of DroNc-Seq lies in working with hard-to-dissociate, frozen and/or archived tissues. To demonstrate the utility of this technique, we sequenced over 39 thousand nuclei from mouse and human archived brain samples, including post-mortem human brain tissue from GTEx project.

Proper citation: Anindita Basu, Inbal Avraham-Davidi, Naomi Habib, Aviv Regev, Feng Zhang, Karthik Shekhar, Matan Hofree, David Weitz, Orit Rozenblatt-Rosen, Tyler Burks, Sourav Choudhury, François Aguet, Ellen Gelfand, Kristin Ardlie 2018. DroNc-seq step-by-step. protocols.io https://dx.doi.org/10.17504/protocols.io.md2c28e Copy   

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Authors: Hyun Min Kang, Meena Subramaniam, Sasha Targ, Michelle Nguyen, Lenka Maliskova, Elizabeth McCarthy, Eunice Wan, Simon Wong, Lauren Byrnes, Cristina M Lanata, Rachel E Gate, Sara Mostafavi, Alexander Marson, Noah Zaitlen, Lindsey A Criswell, Chun Jimmie Ye
Group: Human Cell Atlas Method Development Community
Summary: Genetic multiplexing of barcoded single cell RNA-seqIntroductiondemuxlet is a software tool to deconvolute sample identity and identify multiplets when multiple samples are pooled by barcoded single cell sequencing.demuxlet takes (1) a SAM/BAM/CRAM file produced by the standard 10x sequencing platform, or any other barcoded single cell RNA-seq (with proper --tag-UMI and --tag-group) options (2) a VCF/BCF file containing the genotype (GT), posterior probability (GP), or genotype likelihood (GL) to assign each barcode to a specific sample (or a pair of samples) in the VCF file.Tutorial for demuxletThis tutorial provides streamlined instructions for using the tool demuxlet. For a more detailed description of all of the options available to use with demuxlet, please refer to the README (attached in .zip file). demuxlet has several dependencies and this tutorial provides steps to use a docker instance to minimize compatibility issues and an alternative of installing demuxlet from source. demuxlet is a software tool to deconvolute sample identity and identify multiplets when multiple samples are pooled by barcoded single cell sequencing. demuxlet requires the following input files:a SAM/BAM/CRAM file produced by the standard 10x sequencing platform, or any other barcoded single cell RNA-seq (with proper --tag-UMI and --tag-group) options.a VCF/BCF file containing the genotype (GT), posterior probability (GP), or genotype likelihood (GL) to assign each barcode to a specific sample (or a pair of samples) in the VCF file.

Proper citation: Hyun Min Kang, Meena Subramaniam, Sasha Targ, Michelle Nguyen, Lenka Maliskova, Elizabeth McCarthy, Eunice Wan, Simon Wong, Lauren Byrnes, Cristina M Lanata, Rachel E Gate, Sara Mostafavi, Alexander Marson, Noah Zaitlen, Lindsey A Criswell, Chun Jimmie Ye 2019. Instructional tutorial for using demuxlet. protocols.io https://dx.doi.org/10.17504/protocols.io.233ggqn Copy   

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Authors: Oxford Hca, Nicholas Provine, Michael Fitzpatrick, Melanie Dunstan
Group: Human Cell Atlas Method Development Community
Summary: This protocol is designed for the freezing, thawing, and processing of human intestinal pinch biopsies for isolation of live CD45+ leukocytes and downstream applications such as flow cytometry or single-cell RNA-sequencing.

Proper citation: Oxford Hca, Nicholas Provine, Michael Fitzpatrick, Melanie Dunstan 2019. Freezing and processing intestinal biopsies for the isolation of CD45+ leukocytes. protocols.io https://dx.doi.org/10.17504/protocols.io.6nvhde6 Copy   

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Authors: Fatma Ayhan, Genevieve Konopka
Group: Human Cell Atlas Method Development Community, Neurodegeneration Method Development Community
Summary: This protocol outlines our preparation of single-cell suspension from surgically acquired fresh human adult brain tissue. This method is adapted from Habib et al1.1          Habib, N. et al. Massively parallel single-nucleus RNA-seq with DroNc-seq. Nat Methods 14, 955-958, doi:10.1038/nmeth.4407 (2017).

Proper citation: Fatma Ayhan, Genevieve Konopka 2018. Rapid Nuclei Isolation from Human Brain. protocols.io https://dx.doi.org/10.17504/protocols.io.scpeavn Copy   

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Authors: James Lyon, Tatsuya Kin, Austin Bautista, Nancy Smith, Doug O'Gorman, Patrick Macdonald, Jocelyn Manning Fox
Group: Human Cell Atlas Method Development Community
Summary: This protocol, and the five linked protocols, describe the equipment setup, solutions, and methodology for the isolation of pancreatic islets of Langerhans from human donor pancreas for research purposes. These protocols are employed by the Alberta Diabetes Institute IsletCore program in their work isolating, banking, and distributing human islets for research.

Proper citation: James Lyon, Tatsuya Kin, Austin Bautista, Nancy Smith, Doug O'Gorman, Patrick Macdonald, Jocelyn Manning Fox 2019. Isolation of Human Pancreatic Islets of Langerhans for Research. protocols.io https://dx.doi.org/10.17504/protocols.io.xgsfjwe Copy   

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Authors: Andrew Potter
Group: Human Cell Atlas Method Development Community
Summary: Protocol used to dissociate adult (8-10 wk) mouse spleen into single cells. Attained >95% viability, a variety of cell sizes, and ~10 million cells from 12 mg tissue.

Proper citation: Andrew Potter 2018. Adult Mouse Spleen Dissociation (On ice). protocols.io https://dx.doi.org/10.17504/protocols.io.p2ddqa6 Copy   

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Authors: Hatice Kaya-Okur, Steven Henikoff
Group: Human Cell Atlas Method Development Community
Summary: Here we describe a single-tube bench top protocol for Cleavage Under Targets and Tagmentation (CUT&Tag), an enzyme-tethering strategy that provides efficient high-resolution sequencing libraries for profiling diverse chromatin components. In CUT&Tag, a chromatin protein is bound in situ by a specific antibody, which then tethers a proteinA-Tn5 transposase fusion protein. Activation of the transposase efficiently generates fragment libraries with high resolution and exceptionally low background. All steps from live cells to sequencing-ready libraries can be performed in a single tube on the benchtop or a microwell in a high-throughput pipeline, and the entire procedure can be performed in one day. We have demonstrated the utility of CUT&Tag by profiling histone modifications, RNA Polymerase II and transcription factors on low cell numbers and single cells.

Proper citation: Hatice Kaya-Okur, Steven Henikoff 2019. Bench top CUT&Tag. protocols.io https://dx.doi.org/10.17504/protocols.io.z6hf9b6 Copy   

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Authors: Fatma Ayhan, Genevieve Konopka
Group: Human Cell Atlas Method Development Community, Neurodegeneration Method Development Community
Summary: This protocol outlines our preparation of single-nuclei suspension from surgically acquired fresh human adult brain tissue.

Proper citation: Fatma Ayhan, Genevieve Konopka 2018. Nuclei Isolation from Human Brain Using Sucrose Gradient. protocols.io https://dx.doi.org/10.17504/protocols.io.scneave Copy   

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Authors: Gloria Pryhuber, Heidie Huyck
Group: Human Cell Atlas Method Development Community, LungMap2 Consortium, URMC Pryhuber Lab
Summary: Purpose and Scope of the Procedure .justify:after { content: ""; display:inline-block; width: 100%; } Standardize process for processing lung donations into components for storage and distributionScope: coordination of receipt and gross dissection of tissue

Proper citation: Gloria Pryhuber, Heidie Huyck 2020. 603.3 & 604.5_URMC_HTC_Whole Lung and Lobe Processing . protocols.io https://dx.doi.org/10.17504/protocols.io.biz7kf9n Copy   

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Authors: Hadas Keren Shaul, Hadas Keren-Shaul, Ephraim Kenigsberg, Diego Adhemar Jaitin, Eyal David, Franziska Paul, Amos Tanay, Ido Amit
Group: Human Cell Atlas Method Development Community
Summary: 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.IntroductionThe 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 states1–13. 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 medicine14–17. Methods for single-cell genome-wide expression analysis are continuously being developed, offering increasing coverage, precision and throughput1,10,18–31. 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 methods17,29,30, 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 magnitude32,33. 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 ~ 1013) and rare hematopoietic stem cells (N ~ 103−4)34,35. 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. 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 markers36,37 for lineage tracing and CRISPR pooled screens (CRISP-seq38; Fig. 1b,c). Overview of the experimental protocolMARS-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. 10). So far, we have used MARS-seq2.0 to process > 1 million cells from mice, non-model organisms and human studies10,35,36,38–42, 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 disease40,43, characterized hematopoietic progenitors9,35, mapped the cellular composition of the lung during development42 and in response to influenza virus infection44, and detailed the heterogeneity of epithelial cells in the thymus41. We also used MARS-seq2.0 for whole-organism mapping of early metazoan cell type–specific transcription45,46. Further, we applied MARS-seq2.0 to large clinical studies characterizing the heterogeneity of plasma cells in healthy donors and multiple myeloma patients47, as well as infiltrating immune cells in lung and melanoma patients48,49. MARS-seq2.0 was also combined with other modalities, including single-cell CRISPR-pooled screens to dissect immune circuits in vitro and in mice38, 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 niches36,50,51.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 (http://compgenomics.weizmann.ac.il/tanay/? page_id=672), in which the molecular signal is separated from the background noise. Optimization of MARS-seqBackground noise is a major problem in all massively parallel single-cell protocols. Our prior study10 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.Key procedural advancements in MARS-seq2.0MARS-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.Experimental improvementsLowering 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.Optimization of lysis buffer: we optimized the lysis buffer to make it compatible with the aforementioned volume reduction.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.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.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.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).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.Optimization of the conditions indicated above resulted in a sixfold reduction in the cost of libraryproduction (from $0.65 to $0.10 per cell) and reduced the background level (from 10–15 % to 2 %) asevaluated by the number of molecules in the wells containing cells as compared with our negative-controlempty wells.Analytical and QC improvementsQC 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.Generation of a complete analytical pipeline: sequenced reads are de-multiplexed and the molecularsignal is separated from the background noise.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.MARS-seq2.0 evaluationTo 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) Advances in microfluidics technologies allow processing of several thousands of cells in parallel.However, current microfluidic approaches display a relatively high rate (1–15 %) of sequencing of two cells (or more) in a single droplet (doublets)30,31,52,53, which may complicate downstream analysis.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 (The computational frameworkTo 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.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.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.Strengths and limitations of the protocolThe key strengths of MARS-seq2.0 are as follows: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.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 droplets30,31, that become cost effective only when processing a very large number of cells at once.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.The amount of cell doublets in the MARS-seq2.0 data is extremely low and allows for identification of rare cell types and subpopulations.MARS-seq2.0 provides strand-specific information about the transcripts.However, the following limitations remain: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-seq24, should be used).The method is selective for polyadenylated RNA.The method requires access to FACS facilities, as well as liquid-handling robotics.Optimization of the conditions indicated above resulted in a sixfold reduction in the cost of libraryproduction (from $0.65 to $0.10 per cell) and reduced the background level (from 10–15 % to 2 %) asevaluated by the number of molecules in the wells containing cells as compared with our negative-controlempty wells.Analytical and QC improvementsQC 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.Generation of a complete analytical pipeline: sequenced reads are de-multiplexed and the molecularsignal is separated from the background noise.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.MARS-seq2.0 evaluationTo 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) Advances in microfluidics technologies allow processing of several thousands of cells in parallel.However, current microfluidic approaches display a relatively high rate (1–15 %) of sequencing of two cells (or more) in a single droplet (doublets)30,31,52,53, which may complicate downstream analysis.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 (The computational frameworkTo 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.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.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.Strengths and limitations of the protocolThe key strengths of MARS-seq2.0 are as follows: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.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 droplets30,31, that become cost effective only when processing a very large number of cells at once.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.The amount of cell doublets in the MARS-seq2.0 data is extremely low and allows for identification of rare cell types and subpopulations.MARS-seq2.0 provides strand-specific information about the transcripts.However, the following limitations remain: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-seq24, should be used).The method is selective for polyadenylated RNA.The method requires access to FACS facilities, as well as liquid-handling robotics.MARS-seq2.0 evaluationTo 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) Advances in microfluidics technologies allow processing of several thousands of cells in parallel.However, current microfluidic approaches display a relatively high rate (1–15 %) of sequencing of two cells (or more) in a single droplet (doublets)30,31,52,53, which may complicate downstream analysis.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 (The computational frameworkTo 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.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.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.Strengths and limitations of the protocolThe key strengths of MARS-seq2.0 are as follows: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.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 droplets30,31, that become cost effective only when processing a very large number of cells at once.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.The amount of cell doublets in the MARS-seq2.0 data is extremely low and allows for identification of rare cell types and subpopulations.MARS-seq2.0 provides strand-specific information about the transcripts.However, the following limitations remain: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-seq24, should be used).The method is selective for polyadenylated RNA.The method requires access to FACS facilities, as well as liquid-handling robotics.However, the following limitations remain: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-seq24, should be used).The method is selective for polyadenylated RNA.The method requires access to FACS facilities, as well as liquid-handling robotics.Future applicationsThe 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 samples47,49.Furthermore, techniques for simultaneous acquisition of RNA and other molecular signatures of single cells, for example, spatial location36,51, genetics54, lineage55 and signaling37, 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 more56.

Proper citation: Hadas Keren Shaul, Hadas Keren-Shaul, Ephraim Kenigsberg, Diego Adhemar Jaitin, Eyal David, Franziska Paul, Amos Tanay, Ido Amit 2019. MARS-seq2.0: an experimental and analytical pipeline for indexed sorting combined with single-cell RNA sequencing. protocols.io https://dx.doi.org/10.17504/protocols.io.7hkhj4w Copy   

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Authors: Ankur Sharma, Ramanuj dasgupta
Group: Human Cell Atlas Method Development Community
Summary: Liver Tissue dissociation of single-cell RNA-seq (10x Single-cell RNA-seq)

Proper citation: Ankur Sharma, Ramanuj dasgupta 2018. Human Liver Tissue Dissociation for 10x Single Cell RNA-seq. protocols.io https://dx.doi.org/10.17504/protocols.io.vhue36w Copy   

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Authors: Yury Goltsev, Nikolay Samusik, Julia Kennedy-Darling, Salil Bhate, Matthew Hale, Gustavo Vazquez, Sarah Black, Garry Nolan
Group: Human Cell Atlas Method Development Community
Summary: CODEX is a technology that uses oligo labeled antibodies, specialized fluorescent probes, and a companion instrument along-side a standard fluorescence microscope to create single-cell resolution fluorescence data across a multitude of parameters within spatial context in a single tissue. CODEX was developed by Yury Goltsev and Nikolay Samusik in the laboratory of Garry Nolan at Stanford. The technology is being commercialized by Akoya Biosciences.

Proper citation: Yury Goltsev, Nikolay Samusik, Julia Kennedy-Darling, Salil Bhate, Matthew Hale, Gustavo Vazquez, Sarah Black, Garry Nolan 2019. CODEX Oligo-labeled Antibody Conjugation. protocols.io https://dx.doi.org/10.17504/protocols.io.3fugjnw Copy   

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Authors: Andrew Potter
Group: Human Cell Atlas Method Development Community
Summary: Protocol for adult (8-10 week) mouse kidney dissociation performed on ice to reduce artifact gene expression. The first layer, consisting of collagenase digestion, breaks down the tissue and releases some cells and glomeruli and tubules. The second layer consists of bacillus licheniformis digestion for 15 min. augmented with a thermomixer at 1400 RPM and passaging with a 27 gauge needle. The second layer is meant to thoroughly break up remaining tubules and glomeruli, releasing cells such as podocytes. The final yield is 250K cells from 18 mg tissue with 98% viability, approximately 14,000 cells/mg tissue. Approximately 1% of released cells are podocytes (visualized using kidneys from MAFB-GFP+ mice using a hemocytometer).

Proper citation: Andrew Potter 2018. Adult mouse kidney dissociation (on ice). protocols.io https://dx.doi.org/10.17504/protocols.io.rynd7ve Copy   

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Authors: Sten Linnarsson, Emelie Braun
Group: Human Cell Atlas Method Development Community
Summary: Protocol for human embryo/fetal tissue handling and dissociation, suitable for subsequent single-cell RNA sequencing and in situ hybridization.

Proper citation: Sten Linnarsson, Emelie Braun 2019. Preparing single-cell suspension from human embryo/fetus. protocols.io https://dx.doi.org/10.17504/protocols.io.xmbfk2n Copy   

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Authors: Woong-Yang Park, Jay Shin, Shyam Prabhakar
Group: Human Cell Atlas Method Development Community

Proper citation: Woong-Yang Park, Jay Shin, Shyam Prabhakar 2020. PBMCs isolation from CPT™ tube. protocols.io https:// Copy   

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Authors: Marc Halushka
Group: Human Cell Atlas Method Development Community
Summary: This method describes harvesting of human heart tissues for spatial transcriptomics.

Proper citation: Marc Halushka 2021. Human heart tissue harvesting. protocols.io https://dx.doi.org/10.17504/protocols.io.brsvm6e6 Copy   

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Authors: Alyssa Ward, Gracie Gordon, Jimmie Ye
Group: Human Cell Atlas Method Development Community
Summary: The Immune Cell Census aims to collect expression data from 1000 blood samples collected from healthy individuals. This protocol outlines our inclusion/exclusion criteria and enrollment process.

Proper citation: Alyssa Ward, Gracie Gordon, Jimmie Ye 2020. Participant Recruitment and Enrollment. protocols.io https://dx.doi.org/10.17504/protocols.io.bbimikc6 Copy   

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Authors: Steve McCarroll’s lab
Group: Human Cell Atlas Method Development Community
Summary: Drop-seq is a technology we developed to enable biologists to analyze RNA expression genome-wide in thousands of individual cells at once.  We first described this in a 2015 paper in Cell.  Though commercial implementations of droplet-based single-cell RNA-seq also now exist, we have made Drop-seq open-source and want to make sure that any lab can build their own system.  The materials for constructing a Drop-seq setup in one’s own lab can be obtained for about $6,000. The reagents for performing Drop-seq cost about 6 cents per cell.This is a supplemental protocol of our Drop-Seq Protocol for measuring droplet volume in home-made microfluidic devices. 

Proper citation: Steve McCarroll’s lab 2018. Measuring Droplet Volume in Home-Made Microfluidic Devices. protocols.io https://dx.doi.org/10.17504/protocols.io.mj5c4q6 Copy   

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Authors: Hatice Kaya-Okur, Steven Henikoff
Group: Human Cell Atlas Method Development Community
Summary: Here we describe a single-tube bench top protocol for Cleavage Under Targets and Tagmentation (CUT&Tag), an enzyme-tethering strategy that provides efficient high-resolution sequencing libraries for profiling diverse chromatin components. In CUT&Tag, a chromatin protein is bound in situ by a specific antibody, which then tethers a proteinA-Tn5 transposase fusion protein. Activation of the transposase efficiently generates fragment libraries with high resolution and exceptionally low background. All steps from live cells to sequencing-ready libraries can be performed in a single tube on the benchtop or a microwell in a high-throughput pipeline (see the Kaya-Okur high-throughput protocol), and the entire procedure can be performed in one day. We have demonstrated the utility of CUT&Tag by profiling histone modifications, RNA Polymerase II and transcription factors on low cell numbers and single cells.

Proper citation: Hatice Kaya-Okur, Steven Henikoff 2019. Bench top CUT&Tag. protocols.io https://dx.doi.org/10.17504/protocols.io.wnufdew Copy   

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