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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 dx.doi.org/10.17504/protocols.io.rynd7ve Copy
Authors: Ran Zhou
Group: Human Cell Atlas Method Development Community, Helmsley project_Basu lab
Summary: The protocol is adapted from Fujii's and Smillies's reports for single cell transcriptome analysis from human intestines. It provides details on acquirement of single cell suspension from epithelium and lamina propria. This methods is modified to generate appropriate meterials from patient's intestinal biopsies for sinlge-cell transcriptome and genomic applications.
Proper citation: Ran Zhou 2020. Preparation of single cell suspensions from human intestinal biopsies for single cell genomics applications. protocols.io dx.doi.org/10.17504/protocols.io.bde3i3gn Copy
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 dx.doi.org/10.17504/protocols.io.brsvm6e6 Copy
Authors: Jeffrey R. Moffitt, Xiaowei Zhuang
Group: Human Cell Atlas Method Development Community, Neurodegeneration Method Development Community
Summary: The first step in any MERFISH experiment is the design of the oligonucleotide probes that will be used to label individual RNA species. In our current implementation of MERFISH, each oligonucleotide encoding probe consists of three basic components as illustrated in Figure 2. The first region is a 30-nt targeting region that is complementary to a portion of the sequence of the RNA to which it is designed to bind. The second region is a set of sequences that are called readout sequences, which were designed to be complementary and hence only bind to MERFISH readout probes and not other nucleic acid in the cell. Finally, the third region is a set of priming regions used in the construction of these probes, which will be discussed in detail in Probe Construction. In addition to the nucleotide sequences for each of these components, a codebook —the specific set of binary barcodes that will be used and their association with different RNA species of interest—must also be designed. In this section, we provide protocols to design these sequences and to build a codebook. Example code to perform these steps can be found at http://zhuang.harvard.edu/merfish/ .
Proper citation: Jeffrey R. Moffitt, Xiaowei Zhuang 2018. RNA Imaging with MERFISH - Design of Oligonucleotide Probes. protocols.io dx.doi.org/10.17504/protocols.io.menc3de Copy
Authors: Mandy Xu, Troy Ketela
Group: Human Cell Atlas Method Development Community, CZI START Project
Summary: This protocol has been developed by the Princess Margaret Genomics Centre specifically for Nuc-Seq, both at the bulk and single-cell levels.
Proper citation: Mandy Xu, Troy Ketela 2019. Nuclei-seq. protocols.io dx.doi.org/10.17504/protocols.io.8b8hsrw Copy
Authors: Marlon Stoeckius, Peter Smibert
Group: Human Cell Atlas Method Development Community
Summary: This protocol is for performing CITE-seq and Cell Hashing in parallel. CITE-seq: Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) is a multimodal single cell phenotyping method developed in the Technology Innovation lab at the New York Genome Center in collaboration with the Satija lab.CITE-seq uses DNA-barcoded antibodies to convert detection of proteins into a quantitative, sequenceable readout. Antibody-bound oligos act as synthetic transcripts that are captured during most large-scale oligodT-based scRNA-seq library preparation protocols (e.g. 10x Genomics, Drop-seq, ddSeq).This allows for immunophenotyping of cells with a potentially limitless number of markers and unbiased transcriptome analysis using existing single-cell sequencing approaches.Cell Hashing:Sample multiplexing and super-loading on single cell RNA-sequencing platforms.Cell Hashing uses a series of oligo-tagged antibodies against ubiquitously expressed surface proteins with different barcodes to uniquely label cells from distinct samples, which can be subsequently pooled in one scRNA-seq run. By sequencing these tags alongside the cellular transcriptome, we can assign each cell to its sample of origin, and robustly identify doublets originating from multiple samples.
Proper citation: Marlon Stoeckius, Peter Smibert 2018. CITE-seq and Cell Hashing. protocols.io dx.doi.org/10.17504/protocols.io.nhqdb5w Copy
Authors: Andrew Potter
Group: Human Cell Atlas Method Development Community
Summary: Protocol for human small intestine cell dissociation, performed on ice to reduce artifact gene expression.
Proper citation: Andrew Potter 2018. Adult human small intestine cell dissociation (on ice). protocols.io dx.doi.org/10.17504/protocols.io.rnnd5de Copy
Authors: James Lyon, Patrick Macdonald, Jocelyn Manning Fox
Group: Human Cell Atlas Method Development Community
Proper citation: James Lyon, Patrick Macdonald, Jocelyn Manning Fox 2019. Human Islet Isolation Media Preparation. protocols.io dx.doi.org/10.17504/protocols.io.sfyebpw Copy
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 dx.doi.org/10.17504/protocols.io.tdwei7e Copy
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 dx.doi.org/10.17504/protocols.io.md2c28e Copy
Authors: Adam Hunter
Group: Human Cell Atlas Method Development Community
Proper citation: Adam Hunter 2018. CGAP MACS Live Dead Separation. protocols.io dx.doi.org/10.17504/protocols.io.qz5dx86 Copy
Authors: Anna Wilbrey-Clark, Adam Hunter
Group: Human Cell Atlas Method Development Community
Proper citation: Anna Wilbrey-Clark, Adam Hunter 2019. CGAP Human Lung Dissociation - Tissue Stability Study. protocols.io dx.doi.org/10.17504/protocols.io.34kgquw Copy
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 dx.doi.org/10.17504/protocols.io.scneave Copy
Authors: Brian Hie, Bryan Bryson, Bonnie Berger
Group: Human Cell Atlas Method Development Community
Summary: Scanorama enables efficient integration of heterogeneous scRNA-seq data sets.
Proper citation: Brian Hie, Bryan Bryson, Bonnie Berger 2019. Scanorama. protocols.io dx.doi.org/10.17504/protocols.io.9gch3sw Copy
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 dx.doi.org/10.17504/protocols.io.7hkhj4w Copy
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 dx.doi.org/10.17504/protocols.io.3fugjnw Copy
Authors: Benjamin Emert
Group: Human Cell Atlas Method Development Community, RajLab
Summary: Protocol for making invertedClampFISH probes.
Proper citation: Benjamin Emert 2018. invertedClampFISH ligation. protocols.io dx.doi.org/10.17504/protocols.io.qnkdvcw Copy
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 dx.doi.org/10.17504/protocols.io.rnmd5c6 Copy
Authors: Peter J. Skene, Steven Henikoff
Group: Human Cell Atlas Method Development Community
Summary: Cleavage Under Targets and Release Using Nuclease (CUT&RUN) is an epigenomic profiling strategy in which antibody-targeted controlled cleavage by micrococcal nuclease releases specific protein-DNA complexes into the supernatant for paired-end DNA sequencing. As only the targeted fragments enter into solution, and the vast majority of DNA is left behind, CUT&RUN has exceptionally low background levels. CUT&RUN outperforms the most widely used Chromatin Immunoprecipitation (ChIP) protocols in resolution, signal-to-noise, and depth of sequencing required. In contrast to ChIP, CUT&RUN is free of solubility and DNA accessibility artifacts and can be used to profile insoluble chromatin and to detect long-range 3D contacts without cross-linking. Here we present an improved CUT&RUN protocol that does not require isolation of nuclei and provides high-quality data starting with only 100 cells for a histone modification and 1000 cells for a transcription factor. From cells to purified DNA CUT&RUN requires less than a day at the lab bench.In summary, CUT&RUN has several advantages over ChIP-seq: (1) The method is performed in situ in non-crosslinked cells and does not require chromatin fragmentation or solubilization; (2) The intrinsically low background allows low sequence depth and identification of low signal genomic features invisible to ChIP; (3) The simple procedure can be completed within a day and is suitable for robotic automation; (4) The method can be used with low cell numbers compared to existing methodologies; (5) A simple spike-in strategy can be used for accurate quantitation of protein-DNA interactions. As such, CUT&RUN represents an attractive replacement for ChIPseq, which is one of the most popular methods in biological research.
Proper citation: Peter J. Skene, Steven Henikoff 2018. CUT&RUN: Targeted in situ genome-wide profiling with high efficiency for low cell numbers. protocols.io dx.doi.org/10.17504/protocols.io.mgjc3un Copy
Authors: Elizabeth Butterworth, Wesley Dickerson, Vindhya Vijay, Kristina Weitzel, Julia Cooper, Eric W. Atkinson, Jason E. Coleman, Kevin Otto, Martha Campbell Thompson
Group: Human Cell Atlas Method Development Community, Optical Clearing of Tissue, SPARC
Summary: Using traditional histological methods, researchers are hampered in their ability to image whole tissues or organs in large-scale 3D. Histological sections are generally limited to 500 μm using traditional methods. In addition, light scatters from macromolecules within tissues, particularly lipids, prevents imaging to a depth >150 μm with most confocal microscopes. To reduce light scatter and to allow for deep tissue imaging using simple confocal microscopy, various optical clearing methods have been developed that are relevant for rodent and human tissue samples fixed by immersion. Several methods are related and use protein crosslinking with acrylamide and tissue clearing with sodium dodecyl sulfate (SDS). Other optical clearing techniques used various solvents though each modification had various advantages and disadvantages. Here, an optimized passive optical clearing method is described for studies of the human pancreas innervation and specifically for interrogation of the innervation of human islets.
Proper citation: Elizabeth Butterworth, Wesley Dickerson, Vindhya Vijay, Kristina Weitzel, Julia Cooper, Eric W. Atkinson, Jason E. Coleman, Kevin Otto, Martha Campbell Thompson 2020. Human Pancreas PACT Optical Clearing and High Resolution 3D Microscopy. protocols.io dx.doi.org/10.17504/protocols.io.9gbh3sn Copy
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