Integrative single-cell analysis.
Journal
Nature reviews. Genetics
ISSN: 1471-0064
Titre abrégé: Nat Rev Genet
Pays: England
ID NLM: 100962779
Informations de publication
Date de publication:
05 2019
05 2019
Historique:
pubmed:
31
1
2019
medline:
25
7
2019
entrez:
31
1
2019
Statut:
ppublish
Résumé
The recent maturation of single-cell RNA sequencing (scRNA-seq) technologies has coincided with transformative new methods to profile genetic, epigenetic, spatial, proteomic and lineage information in individual cells. This provides unique opportunities, alongside computational challenges, for integrative methods that can jointly learn across multiple types of data. Integrated analysis can discover relationships across cellular modalities, learn a holistic representation of the cell state, and enable the pooling of data sets produced across individuals and technologies. In this Review, we discuss the recent advances in the collection and integration of different data types at single-cell resolution with a focus on the integration of gene expression data with other types of single-cell measurement.
Identifiants
pubmed: 30696980
doi: 10.1038/s41576-019-0093-7
pii: 10.1038/s41576-019-0093-7
doi:
Substances chimiques
Proteins
0
RNA
63231-63-0
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Review
Langues
eng
Pagination
257-272Commentaires et corrections
Type : CommentIn
Références
Hashimshony, T., Wagner, F., Sher, N. & Yanai, I. CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification. Cell Rep. 2, 666–673 (2012).
pubmed: 22939981
Ramsköld, D. et al. Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells. Nat. Biotechnol. 30, 777–782 (2012).
pubmed: 22820318
pmcid: 3467340
Jaitin, D. A. et al. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science 343, 776–779 (2014).
pubmed: 24531970
pmcid: 4412462
Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).
pubmed: 26000488
pmcid: 26000488
Klein, A. M. et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161, 1187–1201 (2015). References 4 and 5 are two of the first published high-cell-throughput droplet-based methods for scRNA-seq.
pubmed: 26000487
pmcid: 4441768
Zheng, G. X. Y. et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 14049 (2017).
pubmed: 28091601
pmcid: 28091601
Cao, J. et al. Comprehensive single-cell transcriptional profiling of a multicellular organism. Science 357, 661–667 (2017).
pubmed: 28818938
pmcid: 5894354
Rosenberg, A. B. et al. Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding. Science 360, eaam8999 (2018).
Navin, N. et al. Tumour evolution inferred by single-cell sequencing. Nature 472, 90–94 (2011).
pubmed: 21399628
pmcid: 4504184
Vitak, S. A. et al. Sequencing thousands of single-cell genomes with combinatorial indexing. Nat. Methods 14, 302–308 (2017).
pubmed: 28135258
pmcid: 5908213
Pott, S. Simultaneous measurement of chromatin accessibility, DNA methylation, and nucleosome phasing in single cells. eLife 6, 1127 (2017).
Corces, M. R. et al. Lineage-specific and single-cell chromatin accessibility charts human hematopoiesis and leukemia evolution. Nat. Genet. 48, 1193–1203 (2016).
pubmed: 27526324
pmcid: 5042844
Buenrostro, J. D. et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature 523, 486–490 (2015).
pubmed: 26083756
pmcid: 4685948
Cusanovich, D. A. et al. Multiplex single cell profiling of chromatin accessibility by combinatorial cellular indexing. Science 348, 910–914 (2015).
pubmed: 25953818
pmcid: 4836442
Lake, B. B. et al. Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain. Nat. Biotechnol. 36, 70–80 (2018).
pubmed: 29227469
Luo, C. et al. Single-cell methylomes identify neuronal subtypes and regulatory elements in mammalian cortex. Science 357, 600–604 (2017).
pubmed: 28798132
pmcid: 5570439
Smallwood, S. A. et al. Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity. Nat. Methods 11, 817–820 (2014).
pubmed: 25042786
pmcid: 4117646
Guo, H. et al. Single-cell methylome landscapes of mouse embryonic stem cells and early embryos analyzed using reduced representation bisulfite sequencing. Genome Res. 23, 2126–2135 (2013).
pubmed: 24179143
pmcid: 3847781
Mulqueen, R. M. et al. Highly scalable generation of DNA methylation profiles in single cells. Nat. Biotechnol. 36, 428–431 (2018).
pubmed: 29644997
pmcid: 5938134
Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 9, 2579 (2017). This study presents a method for simultaneously measuring gene expression and proteins in single cells through an innovative barcoding strategy.
Peterson, V. M. et al. Multiplexed quantification of proteins and transcripts in single cells. Nat. Biotechnol. 161, 1202 (2017).
Faridani, O. R. et al. Single-cell sequencing of the small-RNA transcriptome. Nat. Biotechnol. 34, 1264–1266 (2016).
pubmed: 27798564
Gomez, D., Shankman, L. S., Nguyen, A. T. & Owens, G. K. Detection of histone modifications at specific gene loci in single cells in histological sections. Nat. Methods 10, 171–177 (2013).
pubmed: 23314172
pmcid: 3560316
Rotem, A. et al. Single-cell ChIP-seq reveals cell subpopulations defined by chromatin state. Nat. Biotechnol. 33, 1165–1172 (2015).
pubmed: 26458175
pmcid: 4636926
Ramani, V. et al. Massively multiplex single-cell Hi-C. Nat. Methods 14, 1–6 (2017).
Nagano, T. et al. Single-cell Hi-C reveals cell-to-cell variability in chromosome structure. Nature 502, 59–64 (2013).
pubmed: 24067610
Frieda, K. L. et al. Synthetic recording and in situ readout of lineage information in single cells. Nature 541, 107–111 (2017).
pubmed: 27869821
pmcid: 27869821
McKenna, A. et al. Whole-organism lineage tracing by combinatorial and cumulative genome editing. Science 353, aaf7907 (2016).
pubmed: 27229144
pmcid: 4967023
Shah, S., Lubeck, E., Zhou, W. & Cai, L. In situ transcription profiling of single cells reveals spatial organization of cells in the mouse hippocampus. Neuron 92, 342–357 (2016).
pubmed: 27764670
pmcid: 27764670
Lee, J. H. et al. Highly multiplexed subcellular RNA sequencing in situ. Science 343, 1360–1363 (2014).
pubmed: 24578530
pmcid: 4140943
Wang, X. et al. Three-dimensional intact-tissue sequencing of single-cell transcriptional states. Science 361, eaat5691 (2018). This study greatly increases the number of genes able to be spatially profiled in a single experiment through the development of combinatorial smFISH indexing and tissue clearing methods.
pubmed: 29930089
pmcid: 6339868
Raj, B. et al. Simultaneous single-cell profiling of lineages and cell types in the vertebrate brain. Nat. Biotechnol. 36, 442–450 (2018). This is one of the first studies to simultaneously measure the transcriptome and cell lineage relationships.
pubmed: 29608178
pmcid: 5938111
Alemany, A., Florescu, M., Baron, C. S., Peterson-Maduro, J. & van Oudenaarden, A. Whole-organism clone tracing using single-cell sequencing. Nature 556, 108–112 (2018).
pubmed: 29590089
Spanjaard, B. et al. Simultaneous lineage tracing and cell-type identification using CRISPR–Cas9-induced genetic scars. Nat. Biotechnol. 36, 469–473 (2018).
pubmed: 29644996
pmcid: 5942543
Codeluppi, S. et al. Spatial organization of the somatosensory cortex revealed by osmFISH. Nat. Methods 15, 932–935 (2018).
pubmed: 30377364
Eberwine, J. et al. Analysis of gene expression in single live neurons. Proc. Natl Acad. Sci. USA 89, 3010–3014 (1992).
pubmed: 1557406
Tang, F. et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat. Methods 6, 377–382 (2009).
pubmed: 19349980
pmcid: 19349980
Picelli, S. et al. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat. Methods 10, 1096–1098 (2013).
pubmed: 24056875
Hayashi, T. et al. Single-cell gene profiling of planarian stem cells using fluorescent activated cell sorting and its ‘index sorting’ function for stem cell research. Dev. Growth Differ. 52, 131–144 (2010).
pubmed: 20078655
Wilson, N. K. et al. Combined single-cell functional and gene expression analysis resolves heterogeneity within stem cell populations. Cell Stem Cell 16, 712–724 (2015).
pubmed: 26004780
pmcid: 4460190
Paul, F. et al. Transcriptional heterogeneity and lineage commitment in myeloid progenitors. Cell 163, 1663–1677 (2015). This study performs index sorting coupled to scRNA-seq on myeloid progenitor cells and identifies transcriptional heterogeneity within sorted populations.
doi: 10.1016/j.cell.2015.11.013
Nestorowa, S. et al. A single-cell resolution map of mouse hematopoietic stem and progenitor cell differentiation. Blood 128, e20–e31 (2016).
pubmed: 27365425
pmcid: 5305050
Hochgerner, H. et al. STRT-seq-2i: dual-index 5' single cell and nucleus RNA-seq on an addressable microwell array. Sci. Rep. 7, 16327 (2017).
pubmed: 29180631
pmcid: 5703850
Macaulay, I. C. et al. G&T-seq: parallel sequencing of single-cell genomes and transcriptomes. Nat. Methods 12, 519–522 (2015).
pubmed: 25915121
Dey, S. S., Kester, L., Spanjaard, B., Bienko, M. & van Oudenaarden, A. Integrated genome and transcriptome sequencing of the same cell. Nat. Biotechnol. 33, 285–289 (2015).
pubmed: 25599178
pmcid: 4374170
Angermueller, C. et al. Parallel single-cell sequencing links transcriptional and epigenetic heterogeneity. Nat. Methods 13, 229–232 (2016). This study performs parallel DNA methylome and transcriptome sequencing in the same cell and examines the relationships between DNA methylation and gene expression.
pubmed: 26752769
pmcid: 4770512
ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).
Cao, J. et al. Joint profiling of chromatin accessibility and gene expression in thousands of single cells. Science 361, 1380–1385 (2018).
pubmed: 30166440
Darmanis, S. et al. Simultaneous multiplexed measurement of RNA and proteins in single cells. Cell Rep. 14, 380–389 (2016).
pubmed: 26748716
Genshaft, A. S. et al. Multiplexed, targeted profiling of single-cell proteomes and transcriptomes in a single reaction. Genome Biol. 17, 1–15 (2016).
Di Giusto, D. A., Wlassoff, W. A., Gooding, J. J., Messerle, B. A. & King, G. C. Proximity extension of circular DNA aptamers with real-time protein detection. Nucleic Acids Res. 33, e64 (2005).
Dixit, A. et al. Perturb-Seq: dissecting molecular circuits with scalable single-cell RNA profiling of pooled genetic screens. Cell 167, 1853–1866 (2016).
pubmed: 27984732
pmcid: 5181115
Adamson, B. et al. A multiplexed single-cell CRISPR screening platform enables systematic dissection of the unfolded protein response. Cell 167, 1867–1873 (2016).
pubmed: 27984733
pmcid: 5315571
Datlinger, P. et al. Pooled CRISPR screening with single-cell transcriptome readout. Nat. Methods 14, 297–301 (2017).
pubmed: 28099430
pmcid: 5334791
Jaitin, D. A. et al. Dissecting immune circuits by linking CRISPR- pooled screens with single-cell RNA-Seq. Cell 167, 1883–1888 (2016). References 52–55 are the first to perform pooled genetic screens using CRISPR–Cas9 coupled to scRNA-seq to infer causal relationships in gene regulatory networks.
pubmed: 27984734
Klann, T. S. et al. CRISPR–Cas9 epigenome editing enables high-throughput screening for functional regulatory elements in the human genome. Nat. Biotechnol. 35, 561 (2017).
pubmed: 28369033
pmcid: 5462860
Thakore, P. I., Black, J. B., Hilton, I. B. & Gersbach, C. A. Editing the epigenome: technologies for programmable transcription and epigenetic modulation. Nat. Methods 13, 127–137 (2016).
pubmed: 26820547
pmcid: 4922638
Liu, X. S. et al. Editing DNA methylation in the mammalian genome. Cell 167, 233–247 (2016).
pubmed: 27662091
pmcid: 5062609
Hilton, I. B. et al. Epigenome editing by a CRISPR-Cas9-based acetyltransferase activates genes from promoters and enhancers. Nat. Biotechnol. 33, 510–517 (2015).
pubmed: 25849900
pmcid: 25849900
Konermann, S. et al. Genome-scale transcriptional activation by an engineered CRISPR-Cas9 complex. Nature 517, 583–588 (2015).
Gilbert, L. A. et al. Genome-scale CRISPR-mediated control of gene repression and activation. Cell 159, 647–661 (2014).
pubmed: 4253859
pmcid: 4253859
Boettcher, M. et al. Dual gene activation and knockout screen reveals directional dependencies in genetic networks. Nat. Biotechnol. 36, 170–178 (2018).
pubmed: 29334369
pmcid: 6072461
Schmidt, S. T., Zimmerman, S. M., Wang, J., Kim, S. K. & Quake, S. R. Quantitative analysis of synthetic cell lineage tracing using nuclease barcoding. ACS Synth. Biol. 6, 936–942 (2017).
pubmed: 28264564
pmcid: 5724935
Lodato, M. A. et al. Somatic mutation in single human neurons tracks developmental and transcriptional history. Science 350, 94–98 (2015).
pubmed: 26430121
pmcid: 4664477
Tirosh, I. et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352, 189–196 (2016).
pubmed: 27124452
pmcid: 27124452
Fan, J. et al. Linking transcriptional and genetic tumor heterogeneity through allele analysis of single-cell RNA-seq data. Genome Res. 28, 1217–1227 (2018).
pubmed: 29898899
pmcid: 6071640
Kang, H. M. et al. Multiplexed droplet single-cell RNA-sequencing using natural genetic variation. Nat. Biotechnol. 36, 89–94 (2018).
pubmed: 29227470
van der Wijst, M. G. P. et al. Single-cell RNA sequencing identifies celltype-specific cis-eQTLs and co-expression QTLs. Nat. Genet. 50, 493–497 (2018).
pubmed: 29610479
pmcid: 5905669
Aguet, F. et al. Genetic effects on gene expression across human tissues. Nature 550, 204–213 (2017).
La Manno, G. et al. RNA velocity of single cells. Nature 560, 494–498 (2018). This study develops a method of deriving the rate of change in gene expression from scRNA-seq data through the measurement of intronic RNA read abundance in each cell.
pubmed: 30089906
pmcid: 6130801
Qiu, X. et al. Reversed graph embedding resolves complex single-cell trajectories. Nat. Methods 14, 979–982 (2017).
pubmed: 28825705
pmcid: 5764547
Haghverdi, L., Büttner, M., Wolf, F. A., Buettner, F. & Theis, F. J. Diffusion pseudotime robustly reconstructs lineage branching. Nat. Methods 13, 845–848 (2016).
Trapnell, C. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32, 381–386 (2014). This study introduces the first method to order individual cells along a pseudotime trajectory.
pubmed: 24658644
pmcid: 24658644
Setty, M. et al. Wishbone identifies bifurcating developmental trajectories from single-cell data. Nat. Biotechnol. 34, 637–645 (2016).
pubmed: 27136076
pmcid: 4900897
Weinreb, C., Wolock, S., Tusi, B. K., Socolovsky, M. & Klein, A. M. Fundamental limits on dynamic inference from single-cell snapshots. Proc. Natl Acad. Sci. USA 115, E2467–E2476 (2018).
pubmed: 29463712
Meng, C. et al. Dimension reduction techniques for the integrative analysis of multi-omics data. Brief. Bioinform. 17, 628–641 (2016).
pubmed: 26969681
pmcid: 4945831
Argelaguet, R. et al. Multi-omics factor analysis-a framework for unsupervised integration of multi-omics data sets. Mol. Syst. Biol. 14, e8124 (2018).
pubmed: 29925568
pmcid: 6010767
Colomé-Tatché, M. & Theis, F. J. Statistical single cell multi-omics integration. Curr. Opin. Syst. Biol. 7, 54–59 (2018).
Leek, J. T. svaseq: removing batch effects and other unwanted noise from sequencing data. Nucleic Acids Res. 42, e161 (2014).
pmcid: 4245966
Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018). This study pioneers the use of CCA to jointly reduce dimensionality for a pair of scRNA-seq data sets, allowing common cell states to be identified across data sets.
pubmed: 29608179
pmcid: 29608179
Haghverdi, L., Lun, A. T. L., Morgan, M. D. & Marioni, J. C. Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors. Nat. Biotechnol. 36, 421–427 (2018). This study introduces the concept of using MNNs as a method for identifying equivalent cell states across data sets.
pubmed: 29608177
pmcid: 29608177
Satija, R., Farrell, J. A., Gennert, D., Schier, A. F. & Regev, A. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33, 495–502 (2015).
pubmed: 25867923
pmcid: 4430369
Dekel, T., Oron, S., Rubinstein, M., Avidan, S. & Freeman, W. T. in Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition 2021–2029 (IEEE, 2015).
Hie, B. L., Bryson, B. & Berger, B. Panoramic stitching of heterogeneous single-cell transcriptomic data. Preprint at bioRxiv https://doi.org/10.1101/371179 (2018).
doi: 10.1101/371179
Barkas, N. et al. Wiring together large single-cell RNA-seq sample collections. Preprint at bioRxiv https://doi.org/10.1101/460246 (2018).
doi: 10.1101/460246
Park, J.-E., Polanski, K., Meyer, K. & Teichmann, S. A. Fast batch alignment of single cell transcriptomes unifies multiple mouse cell atlases into an integrated landscape. Preprint at bioRxiv https://doi.org/10.1101/397042 (2018).
doi: 10.1101/397042
Korsunsky, I. et al. Fast, sensitive, and flexible integration of single cell data with Harmony. Preprint at bioRxiv https://doi.org/10.1101/461954 (2018).
doi: 10.1101/461954
Stuart, T. et al. Comprehensive integration of single cell data. Preprint at bioRxiv https://doi.org/10.1101/460147 (2018).
doi: 10.1101/460147
Welch, J. et al. Integrative inference of brain cell similarities and differences from single-cell genomics. Preprint at bioRxiv https://doi.org/10.1101/459891 (2018).
doi: 10.1101/459891
Karaiskos, N. et al. The Drosophila embryo at single-cell transcriptome resolution. Science 358, 194–198 (2017). This study combines scRNA-seq and in situ hybridization data to predict spatial patterns of gene expression in the Drosophila embryo.
Tosches, M. A. et al. Evolution of pallium, hippocampus, and cortical cell types revealed by single-cell transcriptomics in reptiles. Science 360, 881–888 (2018).
pubmed: 29724907
Baron, M. et al. A single-cell transcriptomic map of the human and mouse pancreas reveals inter- and intra-cell population structure. Cell Syst. 3, 346–360 (2016).
pubmed: 27667365
pmcid: 27667365
Alpert, A., Moore, L. S., Dubovik, T. & Shen-Orr, S. S. Alignment of single-cell trajectories to compare cellular expression dynamics. Nat. Methods 15, 267–270 (2018).
pubmed: 29529018
Regev, A. et al. Science forum: the human cell atlas. eLife 6, e27041 (2017).
pubmed: 29206104
pmcid: 5762154
Kiselev, V. Y., Yiu, A. & Hemberg, M. scmap: projection of single-cell RNA-seq data across data sets. Nat. Methods 15, 359–362 (2018).
pubmed: 29608555
pmcid: 29608555
Alquicira-Hernandez, J., Nguyen, Q. & Powell, J. E. scPred: single cell prediction using singular value decomposition and machine learning classification. Preprint at bioRxiv https://doi.org/10.1101/369538 (2018).
doi: 10.1101/369538
Boufea, K., Seth, S. & Batada, N. N. Mapping transcriptionally equivalent populations across single cell RNA-seq datasets. Preprint at bioRxiv https://doi.org/10.1101/470203 (2018).
doi: 10.1101/470203
Wagner, F. & Yanai, I. Moana: a robust and scalable cell type classification framework for single-cell RNA-Seq data. Preprint at bioRxiv https://doi.org/10.1101/456129 (2018).
doi: 10.1101/456129
Welch, J. D., Hartemink, A. J. & Prins, J. F. MATCHER: manifold alignment reveals correspondence between single cell transcriptome and epigenome dynamics. Genome Biol. 18, 138 (2017). This study presents a method of aligning pseudotime trajectories developed from different data modalities as a way to compare pseudotemporal changes in each modality.
pubmed: 28738873
pmcid: 5525279
Saunders, A. et al. Molecular diversity and specializations among the cells of the adult mouse brain. Cell 174, 1015–1030 (2018).
pubmed: 30096299
Scott, M. P. & Carroll, S. B. The segmentation and homeotic gene network in early Drosophila development. Cell 51, 689–698 (1987).
pubmed: 2890437
Raj, A., van den Bogaard, P., Rifkin, S. A., van Oudenaarden, A. & Tyagi, S. Imaging individual mRNA molecules using multiple singly labeled probes. Nat. Methods 5, 877–879 (2008).
pubmed: 18806792
pmcid: 3126653
Battich, N., Stoeger, T. & Pelkmans, L. Image-based transcriptomics in thousands of single human cells at single-molecule resolution. Nat. Methods 10, 1127–1133 (2013).
pubmed: 24097269
Chen, K. H., Boettiger, A. N., Moffitt, J. R., Wang, S. & Zhuang, X. Spatially resolved, highly multiplexed RNA profiling in single cells. Science 348, aaa6090 (2015).
pubmed: 25858977
pmcid: 25858977
Shah, S., Lubeck, E., Zhou, W. & Cai, L. seqFISH accurately detects transcripts in single cells and reveals robust spatial organization in the hippocampus. Neuron 94, 752–758 (2017).
pubmed: 28521130
Moffitt, J. R. et al. High-throughput single-cell gene-expression profiling with multiplexed error-robust fluorescence in situ hybridization. Proc. Natl Acad. Sci. USA 113, 11046–11051 (2016).
pubmed: 27625426
Moffitt, J. R. et al. High-performance multiplexed fluorescence in situ hybridization in culture and tissue with matrix imprinting and clearing. Proc. Natl Acad. Sci. USA 113, 14456–14461 (2016).
pubmed: 27911841
Moffitt, J. R. et al. Molecular, spatial and functional single-cell profiling of the hypothalamic preoptic region. Science 362, eaau5324 (2018).
pubmed: 30385464
Lein, E., Borm, L. E. & Linnarsson, S. The promise of spatial transcriptomics for neuroscience in the era of molecular cell typing. Science 358, 64–69 (2017).
pubmed: 28983044
pmcid: 28983044
Stahl, P. L. et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353, 78–82 (2016).
pubmed: 27365449
Shalek, A. K. et al. Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells. Nature 498, 236–240 (2013).
pubmed: 23685454
pmcid: 3683364
Achim, K. et al. High-throughput spatial mapping of single-cell RNA-seq data to tissue of origin. Nat. Biotechnol. 33, 503–509 (2015).
pubmed: 25867922
Halpern, K. B. et al. Single-cell spatial reconstruction reveals global division of labour in the mammalian liver. Nature 542, 352–356 (2017).
pubmed: 28166538
pmcid: 5321580
Svensson, V., Teichmann, S. A. & Stegle, O. SpatialDE: identification of spatially variable genes. Nat. Methods 15, 343–346 (2018).
pubmed: 29553579
pmcid: 6350895
Edsgärd, D., Johnsson, P. & Sandberg, R. Identification of spatial expression trends in single-cell gene expression data. Nat. Methods 15, 339–342 (2018).
pubmed: 29553578
pmcid: 29553578
Puram, S. V. et al. Single-cell transcriptomic analysis of primary and metastatic tumor ecosystems in head and neck cancer. Cell 171, 1611–1624 (2017).
pubmed: 29198524
pmcid: 5878932
Pandey, S., Shekhar, K., Regev, A. & Schier, A. F. Comprehensive identification and spatial mapping of habenular neuronal types using single-cell RNA-Seq. Curr. Biol. 28, 1052–1065 (2018).
pubmed: 6042852
pmcid: 6042852
Garalde, D. R. et al. Highly parallel direct RNA sequencing on an array of nanopores. Nat. Methods 15, 201–206 (2018).
pubmed: 29334379
Rand, A. C. et al. Mapping DNA methylation with high-throughput nanopore sequencing. Nat. Methods 14, 411–413 (2017).
pubmed: 28218897
pmcid: 5704956
Workman, R. E. et al. Nanopore native RNA sequencing of a human poly(A) transcriptome. Preprint at bioRxiv. https://doi.org/10.1101/459529 (2018).
doi: 10.1101/459529