The promise of single-cell RNA sequencing for kidney disease investigation
2017; Elsevier BV; Volume: 92; Issue: 6 Linguagem: Inglês
10.1016/j.kint.2017.06.033
ISSN1523-1755
AutoresHao Wu, Benjamin D. Humphreys,
Tópico(s)Renal and related cancers
ResumoRecent techniques for single-cell RNA sequencing (scRNA-seq) at high throughput are leading to profound new discoveries in biology. The ability to generate vast amounts of transcriptomic data at cellular resolution represents a transformative advance, allowing the identification of novel cell types, states, and dynamics. In this review, we summarize the development of scRNA-seq methodologies and highlight their advantages and drawbacks. We discuss available software tools for analyzing scRNA-Seq data and summarize current computational challenges. Finally, we outline ways in which this powerful technology might be applied to discovery research in kidney development and disease. Recent techniques for single-cell RNA sequencing (scRNA-seq) at high throughput are leading to profound new discoveries in biology. The ability to generate vast amounts of transcriptomic data at cellular resolution represents a transformative advance, allowing the identification of novel cell types, states, and dynamics. In this review, we summarize the development of scRNA-seq methodologies and highlight their advantages and drawbacks. We discuss available software tools for analyzing scRNA-Seq data and summarize current computational challenges. Finally, we outline ways in which this powerful technology might be applied to discovery research in kidney development and disease. Understanding kidney cell function and defining the gene regulatory mechanisms that underlie cell behavior represent questions of fundamental importance in nephrology. The kidney is a highly complex tissue with a broad range of specialized cell types organized into functionally distinct compartments. Traditional approaches to characterization of kidney cell types have relied on microscopy or fluorescence-activated cell sorting. These offer high spatial resolution but rely on a limited number of markers, precluding comprehensive characterization of kidney cell types and states. A single genome gives rise to the remarkable diversity of cell types through differences in gene expression. For this reason, transcriptional profiling is a powerful approach to categorize heterogeneous cell types and states. Over the past 2 decades, knowledge regarding the transcriptional landscape in the kidney has come largely from whole-organ profiling using either microarray1Supavekin S. Zhang W. Kucherlapati R. et al.Differential gene expression following early renal ischemia/reperfusion.Kidney Int. 2003; 63: 1714-1724Abstract Full Text Full Text PDF PubMed Scopus (394) Google Scholar or next-generation RNA sequencing (bulk RNA-seq).2Zhou Q. Xiong Y. Huang X.R. et al.Identification of genes associated with Smad3-dependent renal injury by RNA-seq-based transcriptome Analysis.Sci Rep. 2015; 5: 17901Crossref PubMed Scopus (17) Google Scholar, 3Nakagawa S. Nishihara K. Miyata H. et al.Molecular markers of tubulointerstitial fibrosis and tubular cell damage in patients with chronic kidney disease.PLoS One. 2015; 10: e0136994Google Scholar These studies have been highly informative but are fundamentally limited to describing a transcriptional average across a cell population, which may hide or skew signals of interest. Alternative approaches have attempted to achieve a finer separation of kidney compartments to address the issue of mixed cell-type signatures. For example, Lee et al.4Lee J.W. Chou C.L. Knepper M.A. Deep sequencing in microdissected renal tubules identifies nephron segment-specific transcriptomes.J Am Soc Nephrol. 2015; 26: 2669-2677Crossref PubMed Scopus (346) Google Scholar microdissected 14 tubule segments and analyzed the transcriptome of each individual segment. However, this approach does not reveal individual cell states and cannot distinguish separate cell types within a particular segment such as principal and intercalated cells. Laser-capture microdissection can achieve compartment-specific transcriptional profiles, but, like microdissection, it cannot resolve interstitial or glomerular cell types.5McMahon A.P. Aronow B.J. Davidson D.R. et al.GUDMAP: the genitourinary developmental molecular anatomy project.J Am Soc Nephrol. 2008; 19: 667-671Crossref PubMed Scopus (193) Google Scholar Other more recent advances have improved researchers’ ability to perform cell type–specific mRNA profiling. RNA-seq of fluorescence-activated cell sorted cells6Boerries M. Grahammer F. Eiselein S. et al.Molecular fingerprinting of the podocyte reveals novel gene and protein regulatory networks.Kidney Int. 2013; 83: 1052-1064Abstract Full Text Full Text PDF PubMed Scopus (118) Google Scholar and translating ribosome affinity purification7Grgic I. Krautzberger A.M. Hofmeister A. et al.Translational profiles of medullary myofibroblasts during kidney fibrosis.J Am Soc Nephrol. 2014; 25: 1979-1990Crossref PubMed Scopus (59) Google Scholar, 8Liu J. Krautzberger A.M. Sui S.H. et al.Cell-specific translational profiling in acute kidney injury.J Clin Invest. 2014; 124: 1242-1254Crossref PubMed Scopus (128) Google Scholar, 9Grgic I. Hofmeister A.F. Genovese G. et al.Discovery of new glomerular disease-relevant genes by translational profiling of podocytes in vivo.Kidney Int. 2014; 86: 1116-1129Abstract Full Text Full Text PDF PubMed Scopus (31) Google Scholar have provided great insight into the molecular signatures and gene regulatory networks for specific cell types in kidney development, homeostasis, and disease. However, these techniques require advance knowledge of cell markers to define cell types. In addition, the profiling data obtained from those techniques still represent the averaged expression of a group of cells. Important features such as intercell heterogeneity and cell subtypes may be masked in these population-averaged measurements. Single-cell RNA-seq (scRNA-seq)10Wang Y. Navin N.E. Advances and applications of single-cell sequencing technologies.Mol Cell. 2015; 58: 598-609Abstract Full Text Full Text PDF PubMed Scopus (366) Google Scholar, 11Gawad C. Koh W. Quake S.R. Single-cell genome sequencing: current state of the science.Nat Rev Genet. 2016; 17: 175-188Crossref PubMed Scopus (813) Google Scholar combines comprehensive genomics with single-cell resolution and represents a fundamentally new method for the comprehensive measurement of a cell state. It allows the characterization of cell identity independent of predefined markers or assumptions regarding cell hierarchies. scRNA-seq also enables the bioinformatic reconstruction of a dynamic cellular process such as development, differentiation, and disease progression, something not possible with bulk profiling techniques. In this review, we discuss the development of scRNA-seq techniques and summarize potential applications in kidney disease investigation. All scRNA-seq techniques share several common steps: single-cell isolation, cell lysis and RNA capture, reverse transcription, amplification, library generation, and next-generation sequencing (Figure 1). Since the first scRNA-seq paper published in 2009 by Tang et al,12Tang F. Barbacioru C. Wang Y. et al.mRNA-Seq whole-transcriptome analysis of a single cell.Nat Methods. 2009; 6: 377-382Crossref PubMed Scopus (1901) Google Scholar numerous improvements have been made. One challenge concerns how to prepare cDNA libraries from the minute amount of RNA in a single cell. A mammalian cell contains ∼10 picograms of total RNA. Only 10% to 20% of this is reverse-transcribed regardless of the scRNA-seq protocol, and, as a consequence, all protocols use an RNA amplification step. The original protocol developed by Tang et al.12Tang F. Barbacioru C. Wang Y. et al.mRNA-Seq whole-transcriptome analysis of a single cell.Nat Methods. 2009; 6: 377-382Crossref PubMed Scopus (1901) Google Scholar used polymerase chain reaction (PCR) to amplify libraries from single cells, but it required multiple PCR tubes and a gel purification step, leading to substantial loss of genetic material. Later amplification methods took advantage of PCR amplification but omitted gel purification, including STRT-seq,13Islam S. Kjallquist U. Moliner A. et al.Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq.Genome Res. 2011; 21: 1160-1167Crossref PubMed Scopus (601) Google Scholar Smart-seq,14Ramskold D. Luo S. Wang Y.C. et al.Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells.Nat Biotechnol. 2012; 30: 777-782Crossref PubMed Scopus (1040) Google Scholar Smart-seq2,15Picelli S. Bjorklund A.K. Faridani O.R. et al.Smart-seq2 for sensitive full-length transcriptome profiling in single cells.Nat Methods. 2013; 10: 1096-1098Crossref PubMed Scopus (1279) Google Scholar SC3-seq,16Nakamura T. Yabuta Y. Okamoto I. et al.SC3-seq: a method for highly parallel and quantitative measurement of single-cell gene expression.Nucleic Acids Res. 2015; 43: e60Crossref PubMed Scopus (83) Google Scholar DropSeq,17Macosko E.Z. Basu A. Satija R. et al.Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.Cell. 2015; 161: 1202-1214Abstract Full Text Full Text PDF PubMed Scopus (3700) Google Scholar and SeqWell (Table 1).18Gierahn T.M. Wadsworth 2nd, M.H. Hughes T.K. et al.Seq-Well: portable, low-cost RNA sequencing of single cells at high throughput.Nat Methods. 2017; 14: 395-398Crossref PubMed Scopus (464) Google Scholar An alternative approach to amplify libraries, in vitro transcription, was developed and incorporated into CELL-Seq,19Hashimshony T. Wagner F. Sher N. et al.CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification.Cell Rep. 2012; 2: 666-673Abstract Full Text Full Text PDF PubMed Scopus (758) Google Scholar CELL-Seq2,20Hashimshony T. Senderovich N. Avital G. et al.CEL-Seq2: sensitive highly-multiplexed single-cell RNA-Seq.Genome Biol. 2016; 17: 77Crossref PubMed Scopus (572) Google Scholar MARS-Seq,21Jaitin D.A. Kenigsberg E. Keren-Shaul H. et al.Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types.Science. 2014; 343: 776-779Crossref PubMed Scopus (1104) Google Scholar and InDrops22Klein A.M. Mazutis L. Akartuna I. et al.Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells.Cell. 2015; 161: 1187-1201Abstract Full Text Full Text PDF PubMed Scopus (1885) Google Scholar (Table 1). The strengths and drawbacks of these protocols were reviewed in detail by Kolodziejczyk et al.23Kolodziejczyk A.A. Kim J.K. Svensson V. et al.The technology and biology of single-cell RNA sequencing.Mol Cell. 2015; 58: 610-620Abstract Full Text Full Text PDF PubMed Scopus (642) Google ScholarTable 1Comparison of plate and microfluidic-based scRNA-seqMethodPlate-based scRNA-seqMicrofluidic scRNA-seqSTRT-seq (V1/V2)SMART-seq (V1/V2)CELL-seq (V1/V2)MARS-seqDropSeqInDrops10× ChromiumYear2012/20142012/20132012/20162014201520152016UMINo/YesNo/NoNo/YesNoYesYesYesRNA spike-inNo/YesNo/NoYes/YesYesNoNoNoCommercialFluidigmFluidigmFluidigmNANA1CellBio10× GenomicsFull-length coverageNo/NoYes/YesNo/NoNoNoNoNo2nd strand synthesisTemplate switchingTemplate switchingRNAseH +DNA PolRNAseH +DNA PolTemplate switchingRNAseH +DNA PolTemplate switchingLibrary AmplificationPCRPCRIVTIVTPCRIVTPCRCell barcodeYes/YesNo/YesYes/YesYesYesYes∼0.3Library cost ($ per cell)∼2∼3 (in-house)∼9∼1.3∼0.1∼0.1Dropout rateaBased on Ziegenhain C, Vieth B, Parekh S, et al. Comparative Analysis of Single-Cell RNA Sequencing Methods. Mol Cell. 2017;65:631–643.e634.29NA0.45/0.26NA/0.450.740.72NANAProfiling capacity (#cells) 10,000>10,000∼80,000IVT, in vitro transcription; NA, not available; PCR, polymerase chain reaction; scRNA-seq, single-cell RNA-seq; UMI, unique molecular identifier.a Based on Ziegenhain C, Vieth B, Parekh S, et al. Comparative Analysis of Single-Cell RNA Sequencing Methods. Mol Cell. 2017;65:631–643.e634.29Ziegenhain C. Vieth B. Parekh S. et al.Comparative Analysis of Single-Cell RNA Sequencing Methods.Mol Cell. 2017; 65: 631-643.e634Abstract Full Text Full Text PDF PubMed Scopus (738) Google Scholar Open table in a new tab IVT, in vitro transcription; NA, not available; PCR, polymerase chain reaction; scRNA-seq, single-cell RNA-seq; UMI, unique molecular identifier. The introduction of unique molecular identifiers (UMIs)24Islam S. Zeisel A. Joost S. et al.Quantitative single-cell RNA-seq with unique molecular identifiers.Nat Methods. 2014; 11: 163-166Crossref PubMed Scopus (738) Google Scholar represents a critical advance because they correct for amplification artifacts that arise during library preparation. UMIs are random sequences of bases (or barcodes) that label each transcript before library amplification. After sequencing, reads that have different barcodes represent different original molecules, but reads that have the same barcode result from PCR duplication of 1 original molecule. Artifactual PCR duplicates can therefore be tracked and eliminated during downstream analysis. This technology was first integrated in STRT-seq24Islam S. Zeisel A. Joost S. et al.Quantitative single-cell RNA-seq with unique molecular identifiers.Nat Methods. 2014; 11: 163-166Crossref PubMed Scopus (738) Google Scholar and later inherited by CELL-Seq2,20Hashimshony T. Senderovich N. Avital G. et al.CEL-Seq2: sensitive highly-multiplexed single-cell RNA-Seq.Genome Biol. 2016; 17: 77Crossref PubMed Scopus (572) Google Scholar DropSeq,17Macosko E.Z. Basu A. Satija R. et al.Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.Cell. 2015; 161: 1202-1214Abstract Full Text Full Text PDF PubMed Scopus (3700) Google Scholar InDrops,22Klein A.M. Mazutis L. Akartuna I. et al.Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells.Cell. 2015; 161: 1187-1201Abstract Full Text Full Text PDF PubMed Scopus (1885) Google Scholar and SeqWell (Table 1).18Gierahn T.M. Wadsworth 2nd, M.H. Hughes T.K. et al.Seq-Well: portable, low-cost RNA sequencing of single cells at high throughput.Nat Methods. 2017; 14: 395-398Crossref PubMed Scopus (464) Google Scholar UMIs correct for amplification bias, but they do not compensate for biological variation resulting from disparate RNA amounts across cells. This requires a “spike-in” of a known amount of synthetic RNA into the libraries (e.g., External RNA Controls Consortium or ERCC spike-ins),25Brennecke P. Anders S. Kim J.K. et al.Accounting for technical noise in single-cell RNA-seq experiments.Nat Methods. 2013; 10: 1093-1095Crossref PubMed Scopus (594) Google Scholar and this technique has been widely used by many newer single-cell techniques. However, droplet-based scRNA-seq techniques cannot easily accommodate spiked-in RNA due to the nature of the microfluidic design. Alternative data quality-control measures must be performed on scRNA-seq techniques without spike-ins. For example, cell-specific biases can be normalized by a cell-based size factor26Lun A.T. Bach K. Marioni J.C. Pooling across cells to normalize single-cell RNA sequencing data with many zero counts.Genome Biol. 2016; 17: 75Crossref PubMed Scopus (506) Google Scholar and cell-cell variations including the cell mapping rate, number of detected transcripts, and mitochondrial gene fraction can be controlled for during downstream analysis. Recent technological advances in robotics, microfluidics, and reverse emulsion droplets have dramatically increased assay throughput from hundreds of cells to tens of thousands of cells per experiment. This increased throughput has increased the power of the approach but also increased the complexity of experimental design and interpretation. We illustrate some of these issues in the next section by comparing plate-based and microfluidic-based scRNASeq. Constructing a cDNA library from a single cell is fundamentally similar to creating a cDNA library from a large group of cells except for the scale. The main difference is cell compartmentalization. The original method developed by Tang et al.12Tang F. Barbacioru C. Wang Y. et al.mRNA-Seq whole-transcriptome analysis of a single cell.Nat Methods. 2009; 6: 377-382Crossref PubMed Scopus (1901) Google Scholar, 27Tang F. Barbacioru C. Bao S. et al.Tracing the derivation of embryonic stem cells from the inner cell mass by single-cell RNA-Seq analysis.Cell Stem Cell. 2010; 6: 468-478Abstract Full Text Full Text PDF PubMed Scopus (403) Google Scholar used manual cell transfer into an individual PCR tube, making it difficult to scale up to hundreds of cells. Plate-based scRNA-seq such as STRT-seq significantly improved efficiency by transferring cells into a 96-well plate through a custom-built semiautomated cell picker.13Islam S. Kjallquist U. Moliner A. et al.Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq.Genome Res. 2011; 21: 1160-1167Crossref PubMed Scopus (601) Google Scholar STRT-seq also introduced barcoding to cDNA libraries, allowing pooling and multiplexed analysis of all 96 samples. This important advance was achieved using a “template switching” technique. Reverse transcriptases derived from Moloney murine leukemia virus possess intrinsic terminal transferase activity resulting in the addition of several cytosines at the 3′ terminus of the RNA molecule. When an oligonucleotide with complementary guanosine bases is present, transient annealing may occur to the protruding cytosines, and the reverse transcriptase can switch templates and incorporate new sequences (encoded by the oligonucleotide) at both ends of the cDNA molecule. By encoding different barcodes on the template, switching the oligonucleotide in each well of the 96-well plate, libraries can be combined, pooled, and separated bioinformatically after sequencing. Other improvements followed. SMART-seq uses a similar template-switching strategy as STRT-seq but has an enhanced ability to capture full-length transcripts.14Ramskold D. Luo S. Wang Y.C. et al.Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells.Nat Biotechnol. 2012; 30: 777-782Crossref PubMed Scopus (1040) Google Scholar SMART-seq2 further improves cDNA yield and optimized reverse transcription, template switching, and PCR amplification.15Picelli S. Bjorklund A.K. Faridani O.R. et al.Smart-seq2 for sensitive full-length transcriptome profiling in single cells.Nat Methods. 2013; 10: 1096-1098Crossref PubMed Scopus (1279) Google Scholar To reduce bias introduced by nonlinear PCR amplification, CELL-Seq was developed that features the use of in vitro transcription and multiplexing to increase efficiency and accuracy.19Hashimshony T. Wagner F. Sher N. et al.CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification.Cell Rep. 2012; 2: 666-673Abstract Full Text Full Text PDF PubMed Scopus (758) Google Scholar CELL-Seq2 further enhanced the sensitivity and improved the quality of the data with the introduction of UMIs.20Hashimshony T. Senderovich N. Avital G. et al.CEL-Seq2: sensitive highly-multiplexed single-cell RNA-Seq.Genome Biol. 2016; 17: 77Crossref PubMed Scopus (572) Google Scholar All of these approaches require multiple steps and remain relatively laborious. Plate- and tube-based scRNA-seq is costly due to the relatively large volumes required in a microliter well/tube, ∼$5 to $10/cell for library creation. Modifications have been made to reduce the cost while preserving the capacity of scRNA-seq. A good example is to adapt STRT-seq, SMART-seq, and CELL-Seq to integrated fluidic circuits in the Fluidigm C1 system.20Hashimshony T. Senderovich N. Avital G. et al.CEL-Seq2: sensitive highly-multiplexed single-cell RNA-Seq.Genome Biol. 2016; 17: 77Crossref PubMed Scopus (572) Google Scholar, 24Islam S. Zeisel A. Joost S. et al.Quantitative single-cell RNA-seq with unique molecular identifiers.Nat Methods. 2014; 11: 163-166Crossref PubMed Scopus (738) Google Scholar, 28Wu A.R. Neff N.F. Kalisky T. et al.Quantitative assessment of single-cell RNA-sequencing methods.Nat Methods. 2014; 11: 41-46Crossref PubMed Scopus (513) Google Scholar The Fluidigm C1 system can capture up to 800 single cells in nanoliter chambers, reducing cost. The autoprep system in Fluidigm C1 also allows the user to simply load the cell suspension onto the chip without relying on fluorescence-activated cell sorting, reducing labor. However, the cell capture chamber is a fixed size, which may bias against capture of cells of different sizes. Moreover, the cost of the Fluidigm C1 system remains high—$3.50/cell. On the other hand, plate-based scRNA-seq provides unparalleled transcript-detection sensitivity. One recent study concluded that plate-based scRNA-seq can detect 2-fold more genes per cell than microfluid-based scRNA-seq, even though the total number of detected genes across many cells is comparable across techniques.29Ziegenhain C. Vieth B. Parekh S. et al.Comparative Analysis of Single-Cell RNA Sequencing Methods.Mol Cell. 2017; 65: 631-643.e634Abstract Full Text Full Text PDF PubMed Scopus (738) Google Scholar Moreover, this study also reported that full-length scRNA-seq methods (e.g., SMART-seq, SMART-seq2) had higher sensitivity (e.g., more genes being detected and a higher fraction of transcripts being turned into sequencible molecules) than other plate-based methods.29Ziegenhain C. Vieth B. Parekh S. et al.Comparative Analysis of Single-Cell RNA Sequencing Methods.Mol Cell. 2017; 65: 631-643.e634Abstract Full Text Full Text PDF PubMed Scopus (738) Google Scholar Very recently, microfluidic droplet technologies were reported that allow co-encapsulation of a cell, barcoded DNA oligonucleotides, and cell lysis buffer within a tiny droplet of ∼2 nanoliters,17Macosko E.Z. Basu A. Satija R. et al.Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.Cell. 2015; 161: 1202-1214Abstract Full Text Full Text PDF PubMed Scopus (3700) Google Scholar, 22Klein A.M. Mazutis L. Akartuna I. et al.Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells.Cell. 2015; 161: 1187-1201Abstract Full Text Full Text PDF PubMed Scopus (1885) Google Scholar which dramatically reduces the amount of reverse transcriptase reaction buffer used while increasing throughput. Droplets are 1000 times smaller than volumes in a traditional plate-/tube-based scRNA-seq assay, and so these approaches are highly scalable (the number of “chambers” is unlimited), allowing for parallel processing of thousands of cells within an hour. DropSeq was developed by the McCarroll lab and reported in 2015.17Macosko E.Z. Basu A. Satija R. et al.Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.Cell. 2015; 161: 1202-1214Abstract Full Text Full Text PDF PubMed Scopus (3700) Google Scholar The technique relies on 2 design concepts: chip microfluidics and barcoded beads. The microfluidic chip allows coflow of oil, cells, and barcoded beads to generate millions of nanoliter-size droplets within an hour. Cells and barcoded beads are randomly co-encapsulated in the droplets, resulting in thousands of droplets containing 1 cell and 1 bead.17Macosko E.Z. Basu A. Satija R. et al.Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.Cell. 2015; 161: 1202-1214Abstract Full Text Full Text PDF PubMed Scopus (3700) Google Scholar The oligonucleotide sequences carried on DropSeq beads have 4 functions: primer handle for PCR amplification, cell barcodes to tag millions of individual cells, a UMI for accurate transcript counts, and oligo-dT to capture mRNA released from each cell.17Macosko E.Z. Basu A. Satija R. et al.Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.Cell. 2015; 161: 1202-1214Abstract Full Text Full Text PDF PubMed Scopus (3700) Google Scholar Library construction in DropSeq consists of template-switching reverse transcription followed by PCR amplification. Due to the inexpensive and high-throughput nature of DropSeq, it has rapidly spread to hundreds of labs worldwide as a core scRNA-seq technique. There are publicly available resources, videos, and discussion groups (Table 2). To date, DropSeq has been successfully applied to study the cell diversity in retina17Macosko E.Z. Basu A. Satija R. et al.Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.Cell. 2015; 161: 1202-1214Abstract Full Text Full Text PDF PubMed Scopus (3700) Google Scholar and retina bipolar cells30Shekhar K. Lapan S.W. Whitney I.E. et al.Comprehensive classification of retinal bipolar neurons by single-cell transcriptomics.Cell. 2016; 166: 1308-1323.e1330Abstract Full Text Full Text PDF PubMed Scopus (605) Google Scholar and brain organoids, to list just a few examples.31Quadrato G. Nguyen T. Macosko E.Z. et al.Cell diversity and network dynamics in photosensitive human brain organoids.Nature. 2017; 545: 48-53Crossref PubMed Scopus (652) Google ScholarTable 2Online resources for scRNA-seqToolsFunctionsResourcesRaw sequencing data processing toolsFastQCRaw data QChttps://www.bioinformatics.babraham.ac.uk/projects/fastqc/RSeQCRNA-seq QC packagehttp://dldcc-web.brc.bcm.edu/lilab/liguow/CGI/rseqc/_build/html/TrimmomaticRead trimminghttp://www.usadellab.org/cms/index.php?page=trimmomaticPicardCommand line tools for RNA-seq fileshttps://broadinstitute.github.io/picard/STARUltrafast read mapperTools: https://github.com/alexdobin/STARForum: https://groups.google.com/forum/#!forum/rna-starBowtieMemory-efficient mapperBowtie: http://bowtie-bio.sourceforge.net/index.shtmlBowtie2: http://bowtie-bio.sourceforge.net/bowtie2/index.shtmlHISATFast and sensitive mapperHISAT: http://www.ccb.jhu.edu/software/hisat/index.shtmlHISAT2: http://ccb.jhu.edu/software/hisat2/index.shtmlscRNA data normalization and dimensionality reduction toolsMASTModel-based analysis of scRNA-seq datahttps://github.com/RGLab/MASTSCDEBayesian approach to model scRNA-seq datahttps://github.com/hms-dbmi/scdeZIFADimensionality reduction for scRNA-seq datahttps://github.com/epierson9/ZIFAtSNENonlinear dimensionality reduction approachhttp://lvdmaaten.github.io/tsne/viSNEDimensionality reduction toolhttp://www.c2b2.columbia.edu/danapeerlab/html/cyt.htmlscRNA-seq clustering and pseudotemporal ordering toolsPhenoGraphClustering method designed scRNA-seqhttps://github.com/jacoblevine/PhenoGraphSNN-cliqGraph-based clustering approacheshttp://bioinfo.uncc.edu/SNNCliq/MonocleTool kit for pseudotemporal ordering of single cellshttp://cole-trapnell-lab.github.io/monocle-release/WanderlustGraph-based trajectory detection algorithmhttp://www.c2b2.columbia.edu/danapeerlab/html/wanderlust.htmlWishboneTool for analyzing bifurcating brancheshttp://www.c2b2.columbia.edu/danapeerlab/html/wishbone.htmlMicrofluidic-based scRNA-seq resourcesDropSeqDropSeq protocol and computational toolshttp://mccarrolllab.com/dropseq/DropSeq groupScientific community for DropSeq troubleshootinghttps://groups.google.com/forum/#!forum/dropseqDropSeq videoYouTube channel providing Drop-seq tutorialhttps://www.youtube.com/channel/UCRptwggZzFyM51R5iAI-07QInDrops resourcesInDrops computational pipelinehttps://github.com/indrops/indropsSeuratQC and clustering for DropSeq/inDrops datahttp://satijalab.org/seurat/QC, quality control. Open table in a new tab QC, quality control. Although DropSeq has exploded onto the scientific scene, there are drawbacks to this approach for single-cell studies. It has low cell-capture efficiency, capturing only ∼5% of input cells, making it unsuitable for analysis of precious clinical samples where the cell number is limited (e.g., kidney biopsy). Another drawback of DropSeq concerns the low read mapping rate. Only 60% to 80% of high-quality raw reads will map to the genome, and of these, 30% are removed during analysis because they are associated with low quality beads. Thus, sequencing costs are considerable. Finally, like other microfluidic techniques, DropSeq can only detect the top 20% most abundant transcripts. Because many signaling molecules and transcription factors are expressed at low levels, DropSeq will not be able to detect many of them. InDrops is another microfluidic-based scRNA-seq established by Klein et al.22Klein A.M. Mazutis L. Akartuna I. et al.Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells.Cell. 2015; 161: 1187-1201Abstract Full Text Full Text PDF PubMed Scopus (1885) Google Scholar and reported at the same time as DropSeq. Its unique advantage is that it can barcode 60% to 90% of the total input cells. That is achieved by use of a deformable hydrogel that contains the barcodes, allowing very dense packing and synchronization of hydrogel release with droplet formation. InDrops is therefore a good choice for high throughput scRNA-seq analysis of small samples. Another feature of InDrops is that it adapts the in vitro transcription amplification method from CELL-Seq for library construction, minimizing amplification artifact.22Klein A.M. Mazutis L. Akartuna I. et al.Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells.Cell. 2015; 161: 1187-1201Abstract Full Text Full Text PDF PubMed Scopus (1885) Google Scholar InDrops has also been applied across species and conditions. For example, Baron et al.32Baron M. Veres A. Wolock S.L. et al.A single-cell transcriptomic map of the human and mouse pancreas reveals inter- and intra-cell population structure.Cell Syst. 2016; 3: 346-360.e344Abstract Full Text Full Text PDF PubMed Scopus (603) Google Scholar successfully implemented InDrops to compare the single-cell transcriptome in the mouse and human pancreas. Briggs et al.33Briggs J.A. Lee S. Woolf C.J. et al.Mouse embryonic stem cells can differentiate via multiple paths to the s
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