Rare Cell Detection by Single-Cell RNA Sequencing as Guided by Single-Molecule RNA FISH

Cell Systems

Volumn 6, Issue 2, 2018, Pages 171-179.e5

  Torre, Eduardo ^a   Dueck, Hannah ^a   Shaffer, Sydney ^a,b   Gospocic, Janko ^a   Gupte, Rohit ^b   Bonasio, Roberto ^a   Kim, Junhyong ^c   Murray, John ^a   Raj, Arjun ^a,b  

^a UNIVERSITY OF PENNSYLVANIA   (United States)

^b UNIVERSITY OF PENNSYLVANIA   (United States)

^c UNIVERSITY OF PENNSYLVANIA   (United States)

Author keywords

single molecule RNA FISH; single cell analysis; single cell RNA sequencing

Indexed keywords

AXL PROTEIN; CELL PROTEIN; EPIDERMAL GROWTH FACTOR RECEPTOR; MESSENGER RNA; NGFR PROTEIN; RNA; TRANSCRIPTION FACTOR SOX10; TRANSCRIPTOME; UNCLASSIFIED DRUG; WNT5A PROTEIN;

ANIMAL CELL; ARTICLE; CELL CYCLE G1 PHASE; CELL CYCLE G2 PHASE; CELL CYCLE M PHASE; CELL POPULATION; DNA PROBE; FEMALE; FLUORESCENCE IN SITU HYBRIDIZATION; GENE EXPRESSION; GENE NUMBER; HUMAN; HUMAN CELL; MALE; MARKER GENE; MELANOMA CELL LINE; MOUSE; NONHUMAN; NUCLEOTIDE SEQUENCE; PRIORITY JOURNAL; RNA SEQUENCE; SINGLE CELL ANALYSIS; SINGLE MOLECULE IMAGING; TRANSCRIPTOMICS; GENE EXPRESSION PROFILING; GENETICS; HIGH THROUGHPUT SEQUENCING; MELANOMA; PROCEDURES; SEQUENCE ANALYSIS; TUMOR CELL LINE; WHOLE EXOME SEQUENCING;

BASE SEQUENCE; CELL LINE, TUMOR; GENE EXPRESSION PROFILING; HIGH-THROUGHPUT NUCLEOTIDE SEQUENCING; HUMANS; IN SITU HYBRIDIZATION, FLUORESCENCE; MELANOMA; RNA; RNA, MESSENGER; SEQUENCE ANALYSIS, RNA; SINGLE-CELL ANALYSIS; TRANSCRIPTOME; WHOLE EXOME SEQUENCING;

EID: 85041907515     PISSN: 24054712     EISSN: 24054720     Source Type: Journal    
DOI: 10.1016/j.cels.2018.01.014     Document Type: Article

References (27)

1. Battich, N., Stoeger, T., Pelkmans, L., Control of transcript variability in single mammalian cells. Cell 163 (2015), 1596–1610.
2. Brennecke, P., Anders, S., Kim, J.K., Kołodziejczyk, A.A., Zhang, X., Proserpio, V., Baying, B., Benes, V., Teichmann, S.A., Marioni, J.C., et al. Accounting for technical noise in single-cell RNA-Seq experiments. Nat. Methods 10 (2013), 1093–1095.
3. Cabili, M.N., Dunagin, M.C., McClanahan, P.D., Biaesch, A., Padovan-Merhar, O., Regev, A., Rinn, J.L., Raj, A., Localization and abundance analysis of human lncRNAs at single-cell and single-molecule resolution. Genome Biol., 16, 2015, 20.
4. Cote, A.J., McLeod, C.M., Farrell, M.J., McClanahan, P.D., Dunagin, M.C., Raj, A., Mauck, R.L., Single-cell differences in matrix gene expression do not predict matrix deposition. Nat. Commun., 7, 2016, 10865.
5. van Dijk, D., Nainys, J., Sharma, R., Kathail, P., Carr, A.J., Moon, K.R., Mazutis, L., Wolf, G., Krishnaswamy, S., Pe'er, D., MAGIC: a diffusion-based imputation method reveals gene-gene interactions in single-cell RNA-sequencing data. bioRxiv, 2017, 10.1101/111591.
6. Dueck, H., Khaladkar, M., Kim, T.K., Spaethling, J.M., Francis, C., Suresh, S., Fisher, S.A., Seale, P., Beck, S.G., Bartfai, T., et al. Deep sequencing reveals cell-type-specific patterns of single-cell transcriptome variation. Genome Biol., 16, 2015, 122.
7. Dueck, H., Eberwine, J., Kim, J., Variation is function: are single cell differences functionally important?: testing the hypothesis that single cell variation is required for aggregate function. BioEssays 38 (2016), 172–180.
8. Dueck, H.R., Ai, R., Camarena, A., Ding, B., Dominguez, R., Evgrafov, O.V., Fan, J.B., Fisher, S.A., Herstein, J.S., Kim, T.K., et al. Assessing characteristics of RNA amplification methods for single cell RNA sequencing. BMC Genomics, 17, 2016, 966.
9. Grün, D., Kester, L., van Oudenaarden, A., Validation of noise models for single-cell transcriptomics. Nat. Methods 11 (2014), 637–640.
10. Grün, D., Lyubimova, A., Kester, L., Wiebrands, K., Basak, O., Sasaki, N., Clevers, H., van Oudenaarden, A., Single-cell messenger RNA sequencing reveals rare intestinal cell types. Nature 525 (2015), 251–255.
11. Heimberg, G., Bhatnagar, R., El-Samad, H., Thomson, M., Low dimensionality in gene expression data enables the accurate extraction of transcriptional programs from shallow sequencing. Cell Syst. 2 (2016), 239–250.
12. Huang, M., Wang, J., Torre, E., Dueck, H., Shaffer, S., Bonasio, R., Murray, J., Raj, A., Li, M., Zhang, N.R., Gene expression recovery for single cell RNA sequencing. bioRxiv, 2017, 10.1101/138677.
13. Jiang, L., Chen, H., Pinello, L., Yuan, G.C., GiniClust: detecting rare cell types from single-cell gene expression data with Gini index. Genome Biol., 17, 2016, 144.
14. Kempe, H., Schwabe, A., Crémazy, F., Verschure, P.J., Bruggeman, F.J., The volumes and transcript counts of single cells reveal concentration homeostasis and capture biological noise. Mol. Biol. Cell 26 (2015), 797–804.
15. Kolodziejczyk, A.A., Kim, J.K., Svensson, V., Marioni, J.C., Teichmann, S.A., The technology and biology of single-cell RNA sequencing. Mol. Cell 58 (2015), 610–620.
16. Macosko, E.Z., Basu, A., Satija, R., Nemesh, J., Shekhar, K., Goldman, M., Tirosh, I., Bialas, A.R., Kamitaki, N., Martersteck, E.M., et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161 (2015), 1202–1214.
17. Marinov, G.K., Williams, B.A., McCue, K., Schroth, G.P., Gertz, J., Myers, R.M., Wold, B.J., From single-cell to cell-pool transcriptomes: stochasticity in gene expression and RNA splicing. Genome Res. 24 (2014), 496–510.
18. Padovan-Merhar, O., Nair, G.P., Biaesch, A.G., Mayer, A., Scarfone, S., Foley, S.W., Wu, A.R., Churchman, L.S., Singh, A., Raj, A., Single mammalian cells compensate for differences in cellular volume and DNA copy number through independent global transcriptional mechanisms. Mol. Cell 58 (2015), 339–352.
19. Pierson, E., Yau, C., ZIFA: dimensionality reduction for zero-inflated single-cell gene expression analysis. Genome Biol., 2015, 10.1186/s13059-015-0805-z.
20. 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 (2008), 877–879.
21. Raj, A., van Oudenaarden, A., Nature, nurture, or chance: stochastic gene expression and its consequences. Cell 135 (2008), 216–226.
22. Shaffer, S.M., Dunagin, M.C., Torborg, S.R., Torre, E.A., Emert, B., Krepler, C., Beqiri, M., Sproesser, K., Brafford, P.A., Xiao, M., et al. Rare cell variability and drug-induced reprogramming as a mode of cancer drug resistance. Nature 546 (2017), 431–435.
23. Shapiro, E., Biezuner, T., Linnarsson, S., Single-cell sequencing-based technologies will revolutionize whole-organism science. Nat. Rev. Genet. 14 (2013), 618–630.
24. Symmons, O., Raj, A., What's luck got to do with it: single cells, multiple fates, and biological nondeterminism. Mol. Cell 62 (2016), 788–802.
25. Whitfield, M.L., Sherlock, G., Saldanha, A.J., Murray, J.I., Ball, C.A., Alexander, K.E., Matese, J.C., Perou, C.M., Hurt, M.M., Brown, P.O., et al. Identification of genes periodically expressed in the human cell cycle and their expression in tumors. Mol. Biol. Cell 13 (2002), 1977–2000.
26. Wu, A.R., Neff, N.F., Kalisky, T., Dalerba, P., Treutlein, B., Rothenberg, M.E., Mburu, F.M., Mantalas, G.L., Sim, S., Clarke, M.F., et al. Quantitative assessment of single-cell RNA-sequencing methods. Nat. Methods 11 (2014), 41–46.
27. Ziegenhain, C., Vieth, B., Parekh, S., Reinius, B., Guillaumet-Adkins, A., Smets, M., Leonhardt, H., Heyn, H., Hellmann, I., Enard, W., Comparative analysis of single-cell RNA sequencing methods. Mol. Cell 65 (2017), 631–643.e4.