For the past four years, I have been a postdoc in Tobias Meyer's lab working on the proliferation-quiescence decision. I am hoping to start my own lab within the next year to continue my work on this topic. I will be looking for talented postdocs, as well as grad students and technicians, so if you're interested in joining my future lab, please contact me! spencer1[at]stanford[dot]edu
Posted Jan 1, 2014.

Honors & Awards

  • Robert and Mary Ann Forsland Postdoctoral Fellowship, American Cancer Society (4/2013-present)
  • Postdoctoral Fellowship, Damon Runyon Cancer Research Foundation (3/2010-3/2013)
  • Henzl-Gabor Young Women in Science Travel Fellowship, Stanford University (11/2010)
  • Steinmetz Fellowship, Santa Fe Institute (7/2004-8/2004)
  • Scholarship, The Queen Elizabeth Hospital Research Foundation, Adelaide, Australia (1/2004-3/2004)
  • Scholarship to Complex Systems Summer School, Santa Fe Institute (6/2003)
  • Graduated Summa Cum Laude, The George Washington University (5/2001)
  • Phi Beta Kappa, Phi Beta Kappa (5/2000)
  • Presidential Recognition Award, The George Washington University (2000-2001)
  • Presidential Academic Scholarship, The George Washington University (1996-2001)

Professional Education

  • Doctor of Philosophy, Massachusetts Institute of Technology (2009)
  • MS, University of Michigan, Human Genetics (2003)
  • BS, The George Washington University, Biology (2001)
  • BA, The George Washington University, French Language and Literature (2001)

Stanford Advisors

Research & Scholarship

Current Research and Scholarly Interests

I am interested in how multiple signaling pathways converge to create an irreversible trigger point for quiescence vs. proliferation, motivated by the belief that a clearer mechanistic understanding of this decision will better enable us to tune post-mitotic cells toward proliferation in regenerative medicine and cancer cells toward quiescence or apoptosis.


Journal Articles

  • The Proliferation-Quiescence Decision Is Controlled by a Bifurcation in CDK2 Activity at Mitotic Exit. Cell Spencer, S. L., Cappell, S. D., Tsai, F., Overton, K. W., Wang, C. L., Meyer, T. 2013; 155 (2): 369-383


    Tissue homeostasis in metazoans is regulated by transitions of cells between quiescence and proliferation. The hallmark of proliferating populations is progression through the cell cycle, which is driven by cyclin-dependent kinase (CDK) activity. Here, we introduce a live-cell sensor for CDK2 activity and unexpectedly found that proliferating cells bifurcate into two populations as they exit mitosis. Many cells immediately commit to the next cell cycle by building up CDK2 activity from an intermediate level, while other cells lack CDK2 activity and enter a transient state of quiescence. This bifurcation is directly controlled by the CDK inhibitor p21 and is regulated by mitogens during a restriction window at the end of the previous cell cycle. Thus, cells decide at the end of mitosis to either start the next cell cycle by immediately building up CDK2 activity or to enter a transient G0-like state by suppressing CDK2 activity.

    View details for DOI 10.1016/j.cell.2013.08.062

    View details for PubMedID 24075009

  • Measuring and Modeling Apoptosis in Single Cells CELL Spencer, S. L., Sorger, P. K. 2011; 144 (6): 926-939


    Cell death plays an essential role in the development of tissues and organisms, the etiology of disease, and the responses of cells to therapeutic drugs. Here we review progress made over the last decade in using mathematical models and quantitative, often single-cell, data to study apoptosis. We discuss the delay that follows exposure of cells to prodeath stimuli, control of mitochondrial outer membrane permeabilization, switch-like activation of effector caspases, and variability in the timing and probability of death from one cell to the next. Finally, we discuss challenges facing the fields of biochemical modeling and systems pharmacology.

    View details for DOI 10.1016/j.cell.2011.03.002

    View details for Web of Science ID 000288543500011

    View details for PubMedID 21414484

  • Non-genetic origins of cell-to-cell variability in TRAIL-induced apoptosis NATURE Spencer, S. L., Gaudet, S., Albeck, J. G., Burke, J. M., Sorger, P. K. 2009; 459 (7245): 428-U144


    In microorganisms, noise in gene expression gives rise to cell-to-cell variability in protein concentrations. In mammalian cells, protein levels also vary and individual cells differ widely in their responsiveness to uniform physiological stimuli. In the case of apoptosis mediated by TRAIL (tumour necrosis factor (TNF)-related apoptosis-inducing ligand) it is common for some cells in a clonal population to die while others survive-a striking divergence in cell fate. Among cells that die, the time between TRAIL exposure and caspase activation is highly variable. Here we image sister cells expressing reporters of caspase activation and mitochondrial outer membrane permeabilization after exposure to TRAIL. We show that naturally occurring differences in the levels or states of proteins regulating receptor-mediated apoptosis are the primary causes of cell-to-cell variability in the timing and probability of death in human cell lines. Protein state is transmitted from mother to daughter, giving rise to transient heritability in fate, but protein synthesis promotes rapid divergence so that sister cells soon become no more similar to each other than pairs of cells chosen at random. Our results have implications for understanding 'fractional killing' of tumour cells after exposure to chemotherapy, and for variability in mammalian signal transduction in general.

    View details for DOI 10.1038/nature08012

    View details for Web of Science ID 000266243700046

    View details for PubMedID 19363473

  • Cells surviving fractional killing by TRAIL exhibit transient but sustainable resistance and inflammatory phenotypes. Molecular biology of the cell Flusberg, D. A., Roux, J., Spencer, S. L., Sorger, P. K. 2013; 24 (14): 2186-2200


    When clonal populations of human cells are exposed to apoptosis-inducing agents, some cells die and others survive. This fractional killing arises not from mutation but from preexisting, stochastic differences in the levels and activities of proteins regulating apoptosis. Here we examine the properties of cells that survive treatment with agonists of two distinct death receptors, tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) and anti-FasR antibodies. We find that "survivor" cells are highly resistant to a second ligand dose applied 1 d later. Resistance is reversible, resetting after several days of culture in the absence of death ligand. "Reset" cells appear identical to drug-naive cells with respect to death ligand sensitivity and gene expression profiles. TRAIL survivors are cross-resistant to activators of FasR and vice versa and exhibit an NF-?B-dependent inflammatory phenotype. Remarkably, reversible resistance is induced in the absence of cell death when caspase inhibitors are present and can be sustained for 1 wk or more, also without cell death, by periodic ligand exposure. Thus stochastic differences in cell state can have sustained consequences for sen-sitivity to prodeath ligands and acquisition of proinflammatory phenotypes. The important role played by periodicity in TRAIL exposure for induction of opposing apoptosis and survival mechanisms has implications for the design of optimal therapeutic agents and protocols.

    View details for DOI 10.1091/mbc.E12-10-0737

    View details for PubMedID 23699397

  • Exploring the Contextual Sensitivity of Factors that Determine Cell-to-Cell Variability in Receptor-Mediated Apoptosis PLOS COMPUTATIONAL BIOLOGY Gaudet, S., Spencer, S. L., Chen, W. W., Sorger, P. K. 2012; 8 (4)


    Stochastic fluctuations in gene expression give rise to cell-to-cell variability in protein levels which can potentially cause variability in cellular phenotype. For TRAIL (TNF-related apoptosis-inducing ligand) variability manifests itself as dramatic differences in the time between ligand exposure and the sudden activation of the effector caspases that kill cells. However, the contribution of individual proteins to phenotypic variability has not been explored in detail. In this paper we use feature-based sensitivity analysis as a means to estimate the impact of variation in key apoptosis regulators on variability in the dynamics of cell death. We use Monte Carlo sampling from measured protein concentration distributions in combination with a previously validated ordinary differential equation model of apoptosis to simulate the dynamics of receptor-mediated apoptosis. We find that variation in the concentrations of some proteins matters much more than variation in others and that precisely which proteins matter depends both on the concentrations of other proteins and on whether correlations in protein levels are taken into account. A prediction from simulation that we confirm experimentally is that variability in fate is sensitive to even small increases in the levels of Bcl-2. We also show that sensitivity to Bcl-2 levels is itself sensitive to the levels of interacting proteins. The contextual dependency is implicit in the mathematical formulation of sensitivity, but our data show that it is also important for biologically relevant parameter values. Our work provides a conceptual and practical means to study and understand the impact of cell-to-cell variability in protein expression levels on cell fate using deterministic models and sampling from parameter distributions.

    View details for DOI 10.1371/journal.pcbi.1002482

    View details for Web of Science ID 000303440400034

    View details for PubMedID 22570596

  • Systematic calibration of a cell signaling network model BMC BIOINFORMATICS Kim, K. A., Spencer, S. L., Albeck, J. G., Burke, J. M., Sorger, P. K., Gaudet, S., Kim, D. H. 2010; 11


    Mathematical modeling is being applied to increasingly complex biological systems and datasets; however, the process of analyzing and calibrating against experimental data is often challenging and a rate limiting step in model development. To address this problem, we developed a systematic methodology for calibrating quantitative models of dynamic biological processes and illustrate its utility by validating a model of TRAIL (Tumor necrosis factor Related Apoptosis-Inducing Ligand)-induced cell death.We propose a serial framework integrating analysis and calibration modules and we compare various methods for global sensitivity analysis and global parameter estimation. First, adequacy of the network structure is checked by global sensitivity analysis to changes in concentrations of molecular species, validating that the model can reproduce qualitative features of the system behavior derived from experiments or literature surveys. Second, rate parameters are ranked by importance using gradient-based and variance-based sensitivity indices, and we systematically determine the optimal number of parameters to include in model calibration. Third, deterministic, stochastic and hybrid algorithms for global optimization are applied to estimate the values of the most important parameters by fitting to time series data. We compare the performance of these three optimization algorithms.Our proposed framework covers the entire process from validating a proto-model to establishing a realistic model for in silico experiments and thereby provides a generalized workflow for the construction of predictive models of complex network systems.

    View details for DOI 10.1186/1471-2105-11-202

    View details for Web of Science ID 000278360600001

    View details for PubMedID 20416044

  • Non-genetic cell-to-cell variability and the consequences for pharmacology CURRENT OPINION IN CHEMICAL BIOLOGY Niepel, M., Spencer, S. L., Sorger, P. K. 2009; 13 (5-6): 556-561


    Recent advances in single-cell assays have focused attention on the fact that even members of a genetically identical group of cells or organisms in identical environments can exhibit variability in drug sensitivity, cellular response, and phenotype. Underlying much of this variability is stochasticity in gene expression, which can produce unique proteomes even in genetically identical cells. Here we discuss the consequences of non-genetic cell-to-cell variability in the cellular response to drugs and its potential impact for the treatment of human disease.

    View details for DOI 10.1016/j.cbpa.2009.09.015

    View details for Web of Science ID 000272984600009

    View details for PubMedID 19833543

  • Modeling a Snap-Action, Variable-Delay Switch Controlling Extrinsic Cell Death PLOS BIOLOGY Albeck, J. G., Burke, J. M., Spencer, S. L., Lauffenburger, D. A., Sorger, P. K. 2008; 6 (12): 2831-2852


    When exposed to tumor necrosis factor (TNF) or TNF-related apoptosis-inducing ligand (TRAIL), a closely related death ligand and investigational therapeutic, cells enter a protracted period of variable duration in which only upstream initiator caspases are active. A subsequent and sudden transition marks activation of the downstream effector caspases that rapidly dismantle the cell. Thus, extrinsic apoptosis is controlled by an unusual variable-delay, snap-action switch that enforces an unambiguous choice between life and death. To understand how the extrinsic apoptosis switch functions in quantitative terms, we constructed a mathematical model based on a mass-action representation of known reaction pathways. The model was trained against experimental data obtained by live-cell imaging, flow cytometry, and immunoblotting of cells perturbed by protein depletion and overexpression. The trained model accurately reproduces the behavior of normal and perturbed cells exposed to TRAIL, making it possible to study switching mechanisms in detail. Model analysis shows, and experiments confirm, that the duration of the delay prior to effector caspase activation is determined by initiator caspase-8 activity and the rates of other reactions lying immediately downstream of the TRAIL receptor. Sudden activation of effector caspases is achieved downstream by reactions involved in permeabilization of the mitochondrial membrane and relocalization of proteins such as Smac. We find that the pattern of interactions among Bcl-2 family members, the partitioning of Smac from its binding partner XIAP, and the mechanics of pore assembly are all critical for snap-action control.

    View details for DOI 10.1371/journal.pbio.0060299

    View details for Web of Science ID 000261913700022

    View details for PubMedID 19053173

  • Modeling somatic evolution in tumorigenesis PLOS COMPUTATIONAL BIOLOGY Spencer, S. L., Gerety, R. A., Pienta, K. J., Forrest, S. 2006; 2 (8): 939-947


    Tumorigenesis in humans is thought to be a multistep process where certain mutations confer a selective advantage, allowing lineages derived from the mutated cell to outcompete other cells. Although molecular cell biology has substantially advanced cancer research, our understanding of the evolutionary dynamics that govern tumorigenesis is limited. This paper analyzes the computational implications of cancer progression presented by Hanahan and Weinberg in The Hallmarks of Cancer. We model the complexities of tumor progression as a small set of underlying rules that govern the transformation of normal cells to tumor cells. The rules are implemented in a stochastic multistep model. The model predicts that (i) early-onset cancers proceed through a different sequence of mutation acquisition than late-onset cancers; (ii) tumor heterogeneity varies with acquisition of genetic instability, mutation pathway, and selective pressures during tumorigenesis; (iii) there exists an optimal initial telomere length which lowers cancer incidence and raises time of cancer onset; and (iv) the ability to initiate angiogenesis is an important stage-setting mutation, which is often exploited by other cells. The model offers insight into how the sequence of acquired mutations affects the timing and cellular makeup of the resulting tumor and how the cellular-level population dynamics drive neoplastic evolution.

    View details for DOI 10.1371/journal.pcbi.0020108

    View details for Web of Science ID 000240007100010

    View details for PubMedID 16933983

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