Abstract
The so-called “Supercell paradigm” is a method for phenotyping based on single-cell multidimensional data, which has been recently proposed by the authors of this Chapter and collaborators within the larger context of single-cell biology. Supercells are multidimensional objects that represent the collective behavior of groups of cells and carry a distinct phenotype, which is often obscured at the single-cell level due to high cell-to-cell variability. The Supercell framework provides a quantitative assessment of the critical sample size and the number of simultaneous single-cell measurements needed to build a phenotype, which is a key piece of information given the fact that, in many single-cell applications, the number of measured cells and the number of measurements per cell are severely limited due to a variety of constraints, such as experimental costs, technological capabilities, specimen collection procedures, the availability of specialized personnel, and others. In this Chapter, we review the Supercell method and explore the potential for its application to single-cell sequencing datasets.
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References
Altschuler S, Wu LF. Cellular heterogeneity: do differences make a difference? Cell. 2010;141:559.
Baillie JK, Barnett MW, Upton KR, Gerhardt DJ, Richmond TA, et al. Somatic retrotransposition alters the genetic landscape of the human brain. Nature. 2011;479:534–7.
Beckman RA, Schemmann GS, Yeang C-H. Impact of genetic dynamics and single-cell heterogeneity on development of nonstandard personalized medicine strategies for cancer. Proc Natl Acad Sci U S A. 2012;109(36):14586–91.
Bedard PL, Hansen AR, Ratain MJ, Siu LL. Tumour heterogeneity in the clinic. Nature. 2013;501(7467):355–64.
Breiman L. Random forests. Mach Learn. 2001;45(1):5–32.
Burrell RA, McGranahan N, Bartek J, Swanton C. The causes and consequences of genetic heterogeneity in cancer evolution. Nature. 2013;501(7467):338–45.
Candia J, Maunu R, Driscoll M, Biancotto A, Dagur P, McCoy Jr JP, Sen HN, Wei L, Maritan A, Cao K, Nussenblatt RB, Banavar JR, Losert W. From cellular characteristics to disease diagnosis: uncovering phenotypes with supercells. PLoS Comput Biol. 2013;9:e1003215.
Candia J, Banavar JR, Losert W. Understanding health and disease with multidimensional single-cell methods. J Phys Condens Matter. 2014;26:073102.
Chakrabarti L, Abou-Antoun T, Vukmanovic S, Sandler AD. Front Oncol. 2012;2:82.
Christensen JL, Weissman IL. Flk-2 is a marker in hematopoietic stem cell differentiation: a simple method to isolate long-term stem cells. Proc Natl Acad Sci U S A. 2001;98(25):14541–6.
Coupland P, Chandra T, Quail M, Reik W, Swerdlow H. Direct sequencing of small genomes on the Pacific Biosciences RS without library preparation. Biotechniques. 2012;53:365–72.
de Souza N. Taming stem cell heterogeneity. Nat Method. 2012;9(7):645.
Drukker M, Tang C, Ardehali R, Rinkevich Y, Seita J, Lee AS, Mosley AR, Weissman IL, Soen Y. Isolation of primitive endoderm, mesoderm, vascular endothelial and trophoblast progenitors from human pluripotent stem cells. Nat Biotechnol. 2012;30(6):531–42.
Evrony GD, Cai X, Lee E, Hills LB, Elhosary PC, et al. Single-neuron sequencing analysis of L1 retrotransposition and somatic mutation in the human brain. Cell. 2012;151:483–96.
Falconer E, Lansdorp PM. Strand-seq: a unifying tool for studies of chromosome segregation. Semin Cell Dev Biol. 2013;24:643–52.
Falconer E, Hills M, Naumann U, Poon SS, Chavez EA, et al. DNA template strand sequencing of single-cells maps genomic rearrangements at high resolution. Nat Methods. 2012;9:1107–12.
Fujita M, Onami S. Cell-to-cell heterogeneity in cortical tension specifies curvature of contact surfaces in Caenorhabditis elegans embryos. PLoS ONE. 2012;7:e30224.
Garteh J, Witten D, Hastie T, Tibshirani R. An introduction to statistical learning. New York: Springer; 2013.
Guyon I, Gunn S, Nikravesh M, Zadeh L, editors. Feature extraction: foundations and applications. New York: Springer; 2006.
Hastie T, Tibshirani R, Friedman J. The elements of statistical learning. 2nd ed. New York: Springer; 2009.
http://cran.r-project.org/web/packages/randomForest/index.html
Junttila MR, de Sauvage FJ. Influence of tumour micro-environment heterogeneity on therapeutic response. Nature. 2013;501(7467):346–54.
Kiel MJ, Yilmaz ÖH, Iwashita T, Yilmaz OH, Terhorst C, Morrison SJ. Cell. 2001;121(7):1109–21.
Lopez-Otin C, Blasco MA, Partridge L, Serrano M, Kroemer G. The hallmarks of aging. Cell. 2013;153:1194–217.
Lupski JR. Genetics. Genome mosaicism – one human, multiple genomes. Science. 2013;341:358–9.
Macaulay IC, Voet T. Single cell genomics: advances and future perspectives. PLoS Genet. 2014;10(1):e1004126.
Marte B (ed) Tumour heterogeneity. Nature. 2013;501(67):327–72.
Meacham CE, Morrison SJ. Tumour heterogeneity and cancer cell plasticity. Nature. 2013;501(7467):328–37.
Ozsolak F, Platt AR, Jones DR, Reifenberger JG, Sass LE, et al. Direct RNA sequencing. Nature. 2009;461:814–18.
Schmid J, Dussmann H, Boukes GJ, Flanagan L, Lindner AU, O’Connor CL, Rehm M, Prehn JH, Huber HJ. J Biol Chem. 2012;287(49):41546–59.
Shapiro E, Biezuner T, Linnarsson S. Single-cell sequencing-based technologies will revolutionize whole-organism science. Nat Rev Genet. 2013;14(9):618–30.
Speicher MR. Single-cell analysis: toward the clinic. Genome Med. 2013;5:74.
Strobl C, Boulesteix A-L, Zeileis A, Hothorn T. Bias in random forest variable importance measures: illustrations, sources and a solution. BMC Bioinformatics. 2007;8:25.
Tang DG. Understanding cancer stem cell heterogeneity and plasticity. Cell Res. 2012;22(3):457–72.
Tomasetti C, Vogelstein B, Parmigiani G. Half or more of the somatic mutations in cancers of self-renewing tissues originate prior to tumor initiation. Proc Natl Acad Sci U S A. 2013;110:1999–2004.
Treutlein B, Brownfield DG, Wu AR, Neff NF, et al. Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq. Nature. 2014;509(7500):371–5.
Acknowledgments
We acknowledge our coauthors A. Biancotto, K. Cao, P. Dagur, M. Driscoll, A. Maritan, R. Maunu, J. P. McCoy Jr., R. B. Nussenblatt, H. N. Sen, and L. Wei, whose contributions to the Supercell approach (Candia et al. 2013) are extensively described in this Chapter. J. C. was supported by NIH Award Number T32CA154274 from the National Cancer Institute. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Cancer Institute or the National Institutes of Health.
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Candia, J., Banavar, J.R., Losert, W. (2015). Uncovering Phenotypes with Supercells: Applications to Single-Cell Sequencing. In: Wang, X. (eds) Single Cell Sequencing and Systems Immunology. Translational Bioinformatics, vol 5. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-9753-5_2
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DOI: https://doi.org/10.1007/978-94-017-9753-5_2
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