Predictive power of cell-to-cell variability

Abstract

Much of our current knowledge of biology has been constructed based on population-average measurements. However, advances in single-cell analysis have demonstrated the omnipresent nature of cell-to-cell variability in any population. On one hand, tremendous efforts have been made to examine how such variability arises, how it is regulated by cellular networks, and how it can affect cell-fate decisions by single cells. On the other hand, recent studies suggest that the variability may carry valuable information that can facilitate the elucidation of underlying regulatory networks or the classification of cell states. To this end, a major challenge is determining what aspects of variability bear significant biological meaning. Addressing this challenge requires the development of new computational tools, in conjunction with appropriately chosen experimental platforms, to more effectively describe and interpret data on cell-cell variability. Here, we discuss examples of when population heterogeneity plays critical roles in determining biologically and clinically significant phenotypes, how it serves as a rich information source of regulatory mechanisms, and how we can extract such information to gain a deeper understanding of biological systems.

References

[1] Balázsi, G., van Oudenaarden, A. and Collins, J. J. (2011) Cellular decision making and biological noise: From microbes to mammals. Cell, 144, 910–925

[2] Nachman, I., Regev, A. and Ramanathan, S. (2007) Dissecting timing variability in yeast meiosis. Cell, 131, 544–556

[3] Raj, A. and van Oudenaarden, A. (2008) Nature, nurture, or chance: Stochastic gene expression and its consequences. Cell, 135, 216–226

[4] Spencer, S. L. and Sorger, P. K. (2011) Measuring and modeling apoptosis in single cells. Cell, 144, 926–939

[5] Chang, H. H., Hemberg, M., Barahona, M., Ingber, D. E. and Huang, S. (2008) Transcriptome-wide noise controls lineage choice in mammalian progenitor cells. Nature, 453, 544–547

[6] Colman-Lerner, A., Gordon, A., Serra, E., Chin, T., Resnekov, O., Endy, D., Pesce, C. G. and Brent, R. (2005) Regulated cell-to-cell variation in a cell-fate decision system. Nature, 437, 699–706

[7] Raj, A., Rifkin, S. A., Andersen, E. and van Oudenaarden, A. (2010) Variability in gene expression underlies incomplete penetrance. Nature, 463, 913–918

[8] Spencer, S. L., Gaudet, S., Albeck, J. G., Burke, J. M. and Sorger, P. K. (2009) Non-genetic origins of cell-to-cell variability in TRAIL-induced apoptosis. Nature, 459, 428–432

[9] Tay, S., Hughey, J. J., Lee, T. K., Lipniacki, T., Quake, S. R. and Covert, M. W. (2010) Single-cell NF-kappaB dynamics reveal digital activation and analogue information processing. Nature, 466, 267–271

[10] Khan, M., Vaes, E. and Mombaerts, P. (2011) Regulation of the probability of mouse odorant receptor gene choice. Cell, 147, 907–921

[11] Zeng, L., Skinner, S. O., Zong, C., Sippy, J., Feiss, M. and Golding, I. (2010) Decision making at a subcellular level determines the outcome of bacteriophage infection. Cell, 141, 682–691

[12] Vlamakis, H., Aguilar, C., Losick, R. and Kolter, R. (2008) Control of cell fate by the formation of an architecturally complex bacterial community. Genes. Dev., 22, 945–953

[13] St-Pierre, F. and Endy, D. (2008) Determination of cell fate selection during phage lambda infection. Proc. Natl. Acad. Sci. USA, 105, 20705–20710

[14] Zong, C., So, L. H., Sepúlveda, L. A., Skinner, S. O. and Golding, I. (2010) Lysogen stability is determined by the frequency of activity bursts from the fate-determining gene. Mol. Syst. Biol., 6, 440

[15] Snijder, B., Sacher, R., Rämö P., Damm, E. M., Liberali, P. and Pelkmans, L. (2009) Population context determines cell-to-cell variability in endocytosis and virus infection. Nature, 461, 520–523

[16] Feinerman, O., Jentsch, G., Tkach, K. E., Coward, J. W., Hathorn, M. M., Sneddon, M. W., Emonet, T., Smith, K. A. and Altan-Bonnet, G. (2010) Single-cell quantification of IL-2 response by effector and regulatory T cells reveals critical plasticity in immune response. Mol. Syst. Biol., 6, 437

[17] Cohen, A. A., Geva-Zatorsky, N., Eden, E., Frenkel-Morgenstern, M., Issaeva, I., Sigal, A., Milo, R., Cohen-Saidon, C., Liron, Y., Kam, Z., (2008) Dynamic proteomics of individual cancer cells in response to a drug. Science, 322, 1511–1516

[18] Snijder, B. and Pelkmans, L. (2011) Origins of regulated cell-to-cell variability. Nat. Rev. Mol. Cell Biol., 12, 119–125

[19] Mettetal, J. T., Muzzey, D., Pedraza, J. M., Ozbudak, E. M. and van Oudenaarden, A. (2006) Predicting stochastic gene expression dynamics in single cells. Proc. Natl. Acad. Sci. USA, 103, 7304–7309

[20] Swain, P. S., Elowitz, M. B. and Siggia, E. D. (2002) Intrinsic and extrinsic contributions to stochasticity in gene expression. Proc. Natl. Acad. Sci. USA, 99, 12795–12800

[21] Elowitz, M. B., Levine, A. J., Siggia, E. D. and Swain, P. S. (2002) Stochastic gene expression in a single cell. Science, 297, 1183–1186

[22] Losick, R. and Desplan, C. (2008) Stochasticity and cell fate. Science, 320, 65–68

[23] Acar, M., Mettetal, J. T. and van Oudenaarden, A. (2008) Stochastic switching as a survival strategy in fluctuating environments. Nat. Genet., 40, 471–475

[24] Wernet, M. F., Mazzoni, E. O., Celik, A., Duncan, D. M., Duncan, I. and Desplan, C. (2006) Stochastic spineless expression creates the retinal mosaic for colour vision. Nature, 440, 174–180

[25] Johnston, R. J. Jr and Desplan, C. (2010) Stochastic mechanisms of cell fate specification that yield random or robust outcomes. Annu. Rev. Cell Dev. Biol., 26, 689–719

[26] Chabot, J. R., Pedraza, J. M., Luitel, P. and van Oudenaarden, A. (2007) Stochastic gene expression out-of-steady-state in the cyanobacterial circadian clock. Nature, 450, 1249–1252

[27] Weinberger, L. S., Burnett, J. C., Toettcher, J. E., Arkin, A. P. and Schaffer, D. V. (2005) Stochastic gene expression in a lentiviral positive-feedback loop: HIV-1 Tat fluctuations drive phenotypic diversity. Cell, 122, 169–182

[28] Maamar, H., Raj, A. and Dubnau, D. (2007) Noise in gene expression determines cell fate in Bacillus subtilis. Science, 317, 526–529

[29] Eldar, A. and Elowitz, M. B. (2010) Functional roles for noise in genetic circuits. Nature, 467, 167–173

[30] Yao, G., Lee, T. J., Mori, S., Nevins, J. R. and You, L. C. (2008) A bistable Rb-E2F switch underlies the restriction point. Nat. Cell Biol. 10, 476–482

[31] Batchelor, E., Loewer, A., Mock, C. and Lahav, G. (2011) Stimulus-dependent dynamics of p53 in single cells. Mol. Syst. Biol., 7, 488

[32] Pelkmans, L. (2012) Cell Biology. Using cell-to-cell variability—a new era in molecular biology. Science, 336, 425–426

[33] Wang, C., Tian, Y. W., Wu, X. W. and Zhao, X. Z. (1990) Genetic polymorphisms of HLA class III and GLO1 in Chinese Yao nationality. Gene Geography: A Computerized Bulletin on Human Gene Frequencies 4, 29–34.

[34] Wong, J. V., Yao, G. A., Nevins, J. R. and You, L. C. (2011) Viral-mediated noisy gene expression reveals biphasic E2f1 response to MYC. Mol. Cell, 41, 275–285

[35] Austin, D. W., Allen, M. S., McCollum, J. M., Dar, R. D., Wilgus, J. R., Sayler, G. S., Samatova, N. F., Cox, C. D. and Simpson, M. L. (2006) Gene network shaping of inherent noise spectra. Nature, 439, 608–611

[36] Lestas, I., Vinnicombe, G. and Paulsson, J. (2010) Fundamental limits on the suppression of molecular fluctuations. Nature, 467, 174–178

[37] Volfson, D., Marciniak, J., Blake, W. J., Ostroff, N., Tsimring, L. S. and Hasty, J. (2006) Origins of extrinsic variability in eukaryotic gene expression. Nature, 439, 861–864

[38] Bialek, W. and Setayeshgar, S. (2008) Cooperativity, sensitivity, and noise in biochemical signaling. Phys. Rev. Lett., 100, 258101

[39] Friedman, N., Cai, L. and Xie, X. S. (2006) Linking stochastic dynamics to population distribution: an analytical framework of gene expression. Phys. Rev. Lett., 97, 168302

[40] Elf, J., Li, G. W. and Xie, X. S. (2007) Probing transcription factor dynamics at the single-molecule level in a living cell. Science, 316, 1191–1194

[41] Pedraza, J. M. and Paulsson, J. (2008) Effects of molecular memory and bursting on fluctuations in gene expression. Science, 319, 339–343

[42] Pedraza, J. M. and van Oudenaarden, A. (2005) Noise propagation in gene networks. Science, 307, 1965–1969

[43] Suter, D. M., Molina, N., Gatfield, D., Schneider, K., Schibler, U. and Naef, F. (2011) Mammalian genes are transcribed with widely different bursting kinetics. Science, 332, 472–474

[44] To, T.L. and Maheshri, N. (2010) Noise can induce bimodality in positive transcriptional feedback loops without bistability. Science, 327, 1142–1145

[45] Hallen, M., Li, B. C., Tanouchi, Y., Tan, C. E. M., West, M. and You, L. C. (2011) Computation of steady-state probability distributions in stochastic models of cellular networks. PLoS Comput. Biol., 7, e1002209

[46] Ma, R., Wang, J. C., Hou, Z. H. and Liu, H. Y. (2012) Small-number effects: a third stable state in a genetic bistable toggle switch. Phys. Rev. Lett., 109, 248107

[47] Cağatay, T., Turcotte, M., Elowitz, M. B., Garcia-Ojalvo, J. and Süel, G. M. (2009) Architecture-dependent noise discriminates functionally analogous differentiation circuits. Cell, 139, 512–522

[48] Johnston, R. J. Jr, Otake, Y., Sood, P., Vogt, N., Behnia, R., Vasiliauskas, D., McDonald, E., Xie, B., Koenig, S., Wolf, R., (2011) Interlocked feedforward loops control cell-type-specific Rhodopsin expression in the Drosophila eye. Cell, 145, 956–968

[49] Dobrzynski, M. and Bruggeman, F. J. (2009) Elongation dynamics shape bursty transcription and translation. Proc. Natl. Acad. Sci. USA, 106, 2583–2588

[50] Tan, C., Marguet, P. and You, L. C. (2009) Emergent bistability by a growth-modulating positive feedback circuit. Nat. Chem. Biol., 5, 842–848

[51] Munsky, B. and Khammash, M. (2010) Identification from stochastic cell-to-cell variation: a genetic switch case study. IET Syst. Biol., 4, 356–366

[52] Warmflash, A. and Dinner, A. R. (2008) Signatures of combinatorial regulation in intrinsic biological noise. Proc. Natl. Acad. Sci. USA, 105, 17262–17267

[53] Maienschein-Cline, M., Warmflash, A. and Dinner, A. R. (2010) Defining cooperativity in gene regulation locally through intrinsic noise. IET Syst. Biol., 4, 379–392

[54] Singh, A., Razooky, B. S., Dar, R. D. and Weinberger, L. S. (2012) Dynamics of protein noise can distinguish between alternate sources of gene-expression variability. Mol. Syst. Biol., 8, 607

[55] Bar-Even, A., Paulsson, J., Maheshri, N., Carmi, M., O’Shea, E., Pilpel, Y. and Barkai, N. (2006) Noise in protein expression scales with natural protein abundance. Nat. Genet., 38, 636–643

[56] Lopes, F. M., de Oliveira, E. A. and Cesar, R. M. Jr. (2011) Inference of gene regulatory networks from time series by Tsallis entropy. BMC Syst. Biol., 5, 61

[57] Bendall, S. C. and Nolan, G. P. (2012) From single cells to deep phenotypes in cancer. Nat. Biotechnol., 30, 639–647

[58] Munsky, B., Trinh, B. and Khammash, M. (2009) Listening to the noise: random fluctuations reveal gene network parameters. Mol. Syst. Biol., 5, 318

[59] Hasenauer, J., Waldherr, S., Doszczak, M., Radde, N., Scheurich, P. and Allgöwer, F. (2011) Identification of models of heterogeneous cell populations from population snapshot data. BMC Bioinformatics, 12, 125

[60] Bonassi, F. V., You, L. C. and West, M. (2011) Bayesian learning from marginal data in bionetwork models. Stat. Appl. Genet. Mol., 10.

[61] Zechner, C., Ruess, J., Krenn, P., Pelet, S., Peter, M., Lygeros, J. and Koeppl, H. (2012) Moment-based inference predicts bimodality in transient gene expression. Proc. Natl. Acad. Sci. USA, 109, 8340–8345

[62] Lim, C. A., Yao, F., Wong, J. J. Y., George, J., Xu, H., Chiu, K. P., Sung, W. K., Lipovich, L., Vega, V. B., Chen, J., (2007) Genome-wide mapping of RELA(p65) binding identifies E2F1 as a transcriptional activator recruited by NF-kappaB upon TLR4 activation. Mol. Cell, 27, 622–635

[63] Lillacci, G. and Khammash, M. (2012) A distribution-matching method for parameter estimation and model selection in computational biology. International Journal of Robust and Nonlinear Control, 22, 1065–1081.

[64] Kügler, P. (2012) Moment fitting for parameter inference in repeatedly and partially observed stochastic biological models. PLoS One, 7, e43001

[65] August, E. (2012) Using noise for model-testing. Journal of Computational Biology: A Journal of Computational Molecular Cell Biology 19, 968–977.

[66] Cox, C. D., McCollum, J. M., Allen, M. S., Dar, R. D. and Simpson, M. L. (2008) Using noise to probe and characterize gene circuits. Proc. Natl. Acad. Sci. USA, 105, 10809–10814

[67] Kim, D., Debusschere, B. J. and Najm, H. N. (2007) Spectral methods for parametric sensitivity in stochastic dynamical systems. Biophys. J., 92, 379–393

[68] Ren, J., Wang, W. X., Li, B. and Lai, Y. C. (2010) Noise bridges dynamical correlation and topology in coupled oscillator networks. Phys. Rev. Lett., 104, 058701

[69] Feinerman, O., Veiga, J., Dorfman, J. R., Germain, R. N. and Altan-Bonnet, G. (2008) Variability and robustness in T cell activation from regulated heterogeneity in protein levels. Science, 321, 1081–1084

[70] Brock, A., Chang, H. and Huang, S. (2009) Non-genetic heterogeneity—a mutation-independent driving force for the somatic evolution of tumours. Nat. Rev. Genet., 10, 336–342

[71] Creixell, P., Schoof, E. M., Erler, J. T. and Linding, R. (2012) Navigating cancer network attractors for tumor-specific therapy. Nat. Biotechnol., 30, 842–848

[72] Gerlinger, M., Rowan, A. J., Horswell, S., Larkin, J., Endesfelder, D., Gronroos, E., Martinez, P., Matthews, N., Stewart, A., Tarpey, P., (2012) Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N. Engl. J. Med., 366, 883–892

[73] Irish, J. M., Hovland, R., Krutzik, P. O., Perez, O. D., Bruserud, O., Gjertsen, B. T. and Nolan, G.P. (2004) Single cell profiling of potentiated phospho-protein networks in cancer cells. Cell, 118, 217–228

[74] Kotecha, N., Flores, N. J., Irish, J. M., Simonds, E. F., Sakai, D. S., Archambeault, S., Diaz-Flores, E., Coram, M., Shannon, K. M., Nolan, G. P., (2008) Single-cell profiling identifies aberrant STAT5 activation in myeloid malignancies with specific clinical and biologic correlates. Cancer Cell, 14, 335–343

[75] Hayashi, M., Okabe-Kado, J. and Hozumi, M. (1994) Flow-cytometric analysis of in vivo induction of differentiation of WEHI-3B myelomonocytic leukemia cells by recombinant granulocyte colony-stimulating factor. Exp. Hematol., 22, 393–398

[76] Marusyk, A., Almendro, V. and Polyak, K. (2012) Intra-tumour heterogeneity: a looking glass for cancer? Nat. Rev. Cancer, 12, 323–334

[77] Li, W., Cui, L. B. and Ng, M. K. (2012) On computation of the steady-state probability distribution of probabilistic Boolean networks with gene perturbation. J. Comput. Appl. Math., 236, 4067–4081.

[78] Gupta, P. B., Fillmore, C. M., Jiang, G., Shapira, S. D., Tao, K., Kuperwasser, C. and Lander, E. S. (2011) Stochastic state transitions give rise to phenotypic equilibrium in populations of cancer cells. Cell, 146, 633–644

[79] Krutzik, P. O., Crane, J. M., Clutter, M. R. and Nolan, G. P. (2008) High-content single-cell drug screening with phosphospecific flow cytometry. Nat. Chem. Biol., 4, 132–142

[80] Ghimire, P., Wu, G. Y. and Zhu, L. (2010) Primary esophageal lymphoma in immunocompetent patients: Two case reports and literature review. World Journal of Radiology, 2, 334–338.

[81] Song, Y., Yang, Z., Ke, Z., Yao, Y., Hu, X., Sun, Y., Li, H., Yin, J. and Zeng, C. (2012) Expression of 14-3-3gamma in patients with breast cancer: correlation with clinicopathological features and prognosis. Cancer Epidemiology, 36, 533–536.

[82] Bosch, J., Gerstein, H. C., Dagenais, G. R., Díaz, R., Dyal, L., Jung, H., Maggiono, A. P., Probstfield, J., Ramachandran, A., Riddle, M. C., and the ORIGIN Trial Investigators. (2012) n-3 fatty acids and cardiovascular outcomes in patients with dysglycemia. N. Engl. J. Med., 367, 309–318

[83] Gerstein, H. C., Bosch, J., Dagenais, G. R., Díaz, R., Jung, H., Maggioni, A. P., Pogue, J., Probstfield, J., Ramachandran, A., Riddle, M. C., and the ORIGIN Trial Investigators. (2012) Basal insulin and cardiovascular and other outcomes in dysglycemia. N. Engl. J. Med., 367, 319–328

[84] Canham, M. A., Sharov, A. A., Ko, M. S. and Brickman, J. M. (2010) Functional heterogeneity of embryonic stem cells revealed through translational amplification of an early endodermal transcript. PLoS Biol., 8, e1000379

[85] Novershtern, N., Subramanian, A., Lawton, L. N., Mak, R. H., Haining, W. N., McConkey, M. E., Habib, N., Yosef, N., Chang, C. Y., Shay, T., (2011) Densely interconnected transcriptional circuits control cell states in human hematopoiesis. Cell, 144, 296–309

[86] Caie, P. D., Walls, R. E., Ingleston-Orme, A., Daya, S., Houslay, T., Eagle, R., Roberts, M. E., and Carragher, N. O. (2010) High-content phenotypic profiling of drug response signatures across distinct cancer cells. Mol. Cancer Ther., 9, 1913–1926

[87] Sutherland, J. J., Low, J., Blosser, W., Dowless, M., Engler, T. A. and Stancato, L. F. (2011) A robust high-content imaging approach for probing the mechanism of action and phenotypic outcomes of cell-cycle modulators. Mol. Cancer Ther., 10, 242–254

[88] Elghetany, M. T. (2002) Diagnostic utility of flow cytometric immunophenotyping in myelodysplastic syndrome. Blood, 99, 391–392

[89] Stetler-Stevenson, M., Arthur, D. C., Jabbour, N., Xie, X. Y., Molldrem, J., Barrett, A. J., Venzon, D. and Rick, M. E. (2001) Diagnostic utility of flow cytometric immunophenotyping in myelodysplastic syndrome. Blood, 98, 979–987

[90] Finn, W. G., Harrington, A. M., Carter, K. M., Raich, R., Kroft, S. H. and Hero, A. O. 3rd. (2011) Immunophenotypic signatures of benign and dysplastic granulopoiesis by cytomic profiling. Cytometry B Clin. Cytom., 80, 282–290

[91] Stachurski, D., Smith, B. R., Pozdnyakova, O., Andersen, M., Xiao, Z., Raza, A., Woda, B. A., and Wang, S. A. (2008) Flow cytometric analysis of myelomonocytic cells by a pattern recognition approach is sensitive and specific in diagnosing myelodysplastic syndrome and related marrow diseases: emphasis on a global evaluation and recognition of diagnostic pitfalls. Leuk. Res., 32, 215–224

[92] Pyne, S., Hu, X., Wang, K., Rossin, E., Lin, T. I., Maier, L. M., Baecher-Allan, C., McLachlan, G. J., Tamayo, P., Hafler, D. A., (2009) Automated high-dimensional flow cytometric data analysis. Proc. Natl. Acad. Sci. USA, 106, 8519–8524

[93] Finn, W. G., Carter, K. M., Raich, R., Stoolman, L. M. and Hero, A. O. (2009) Analysis of clinical flow cytometric immunophenotyping data by clustering on statistical manifolds: treating flow cytometry data as high-dimensional objects. Cytometry B Clin. Cytom., 76, 1–7

[94] Rogers, W. T. and Holyst, H. A. (2009) FlowFP: A bioconductor package for fingerprinting flow cytometric data. Adv. Bioinformatics,193947

[95] Rogers, W. T., Moser, A. R., Holyst, H. A., Bantly, A., Mohler, E. R., 3rd, Scangas, G. and Moore, J. S. (2008) Cytometric fingerprinting: quantitative characterization of multivariate distributions. Cytometry. Part A: the Journal of the International Society for Analytical Cytology, 73, 430–441.

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