Your browser doesn't support javascript.
loading
Show: 20 | 50 | 100
Results 1 - 20 de 45
Filter
1.
Mol Syst Biol ; 2024 Jun 13.
Article in English | MEDLINE | ID: mdl-38872050

ABSTRACT

Macrophages sense pathogens and orchestrate specific immune responses. Stimulus specificity is thought to be achieved through combinatorial and dynamical coding by signaling pathways. While NFκB dynamics are known to encode stimulus information, dynamical coding in other signaling pathways and their combinatorial coordination remain unclear. Here, we established live-cell microscopy to investigate how NFκB and p38 dynamics interface in stimulated macrophages. Information theory and machine learning revealed that p38 dynamics distinguish cytokine TNF from pathogen-associated molecular patterns and high doses from low, but contributed little to information-rich NFκB dynamics when both pathways are considered. This suggests that immune response genes benefit from decoding immune signaling dynamics or combinatorics, but not both. We found that the heterogeneity of the two pathways is surprisingly uncorrelated. Mathematical modeling revealed potential sources of uncorrelated heterogeneity in the branched pathway network topology and predicted it to drive gene expression variability. Indeed, genes dependent on both p38 and NFκB showed high scRNAseq variability and bimodality. These results identify combinatorial signaling as a mechanism to restrict NFκB-AND-p38-responsive inflammatory cytokine expression to few cells.

2.
J R Soc Interface ; 20(203): 20230172, 2023 06.
Article in English | MEDLINE | ID: mdl-37282589

ABSTRACT

Single-cell genomic technologies offer vast new resources with which to study cells, but their potential to inform parameter inference of cell dynamics has yet to be fully realized. Here we develop methods for Bayesian parameter inference with data that jointly measure gene expression and Ca2+ dynamics in single cells. We propose to share information between cells via transfer learning: for a sequence of cells, the posterior distribution of one cell is used to inform the prior distribution of the next. In application to intracellular Ca2+ signalling dynamics, we fit the parameters of a dynamical model for thousands of cells with variable single-cell responses. We show that transfer learning accelerates inference with sequences of cells regardless of how the cells are ordered. However, only by ordering cells based on their transcriptional similarity can we distinguish Ca2+ dynamic profiles and associated marker genes from the posterior distributions. Inference results reveal complex and competing sources of cell heterogeneity: parameter covariation can diverge between the intracellular and intercellular contexts. Overall, we discuss the extent to which single-cell parameter inference informed by transcriptional similarity can quantify relationships between gene expression states and signalling dynamics in single cells.


Subject(s)
Genomics , Signal Transduction , Bayes Theorem
3.
Horm Behav ; 151: 105340, 2023 05.
Article in English | MEDLINE | ID: mdl-36933440

ABSTRACT

Organismal behavior, with its tremendous complexity and diversity, is generated by numerous physiological systems acting in coordination. Understanding how these systems evolve to support differences in behavior within and among species is a longstanding goal in biology that has captured the imagination of researchers who work on a multitude of taxa, including humans. Of particular importance are the physiological determinants of behavioral evolution, which are sometimes overlooked because we lack a robust conceptual framework to study mechanisms underlying adaptation and diversification of behavior. Here, we discuss a framework for such an analysis that applies a "systems view" to our understanding of behavioral control. This approach involves linking separate models that consider behavior and physiology as their own networks into a singular vertically integrated behavioral control system. In doing so, hormones commonly stand out as the links, or edges, among nodes within this system. To ground our discussion, we focus on studies of manakins (Pipridae), a family of Neotropical birds. These species have numerous physiological and endocrine specializations that support their elaborate reproductive displays. As a result, manakins provide a useful example to help imagine and visualize the way systems concepts can inform our appreciation of behavioral evolution. In particular, manakins help clarify how connectedness among physiological systems-which is maintained through endocrine signaling-potentiate and/or constrain the evolution of complex behavior to yield behavioral differences across taxa. Ultimately, we hope this review will continue to stimulate thought, discussion, and the emergence of research focused on integrated phenotypes in behavioral ecology and endocrinology.


Subject(s)
Passeriformes , Systems Biology , Humans , Animals , Endocrine System , Passeriformes/physiology , Hormones , Adaptation, Physiological
4.
NPJ Regen Med ; 8(1): 16, 2023 Mar 15.
Article in English | MEDLINE | ID: mdl-36922514

ABSTRACT

We developed an on-slide decellularization approach to generate acellular extracellular matrix (ECM) myoscaffolds that can be repopulated with various cell types to interrogate cell-ECM interactions. Using this platform, we investigated whether fibrotic ECM scarring affected human skeletal muscle progenitor cell (SMPC) functions that are essential for myoregeneration. SMPCs exhibited robust adhesion, motility, and differentiation on healthy muscle-derived myoscaffolds. All SPMC interactions with fibrotic myoscaffolds from dystrophic muscle were severely blunted including reduced motility rate and migration. Furthermore, SMPCs were unable to remodel laminin dense fibrotic scars within diseased myoscaffolds. Proteomics and structural analysis revealed that excessive collagen deposition alone is not pathological, and can be compensatory, as revealed by overexpression of sarcospan and its associated ECM receptors in dystrophic muscle. Our in vivo data also supported that ECM remodeling is important for SMPC engraftment and that fibrotic scars may represent one barrier to efficient cell therapy.

5.
Mol Syst Biol ; 18(8): e11001, 2022 08.
Article in English | MEDLINE | ID: mdl-35965452

ABSTRACT

Quantifying the dependency between mRNA abundance and downstream cellular phenotypes is a fundamental open problem in biology. Advances in multimodal single-cell measurement technologies provide an opportunity to apply new computational frameworks to dissect the contribution of individual genes and gene combinations to a given phenotype. Using an information theory approach, we analyzed multimodal data of the expression of 83 genes in the Ca2+ signaling network and the dynamic Ca2+ response in the same cell. We found that the overall expression levels of these 83 genes explain approximately 60% of Ca2+ signal entropy. The average contribution of each single gene was 17%, revealing a large degree of redundancy between genes. Using different heuristics, we estimated the dependency between the size of a gene set and its information content, revealing that on average, a set of 53 genes contains 54% of the information about Ca2+ signaling. Our results provide the first direct quantification of information content about complex cellular phenotype that exists in mRNA abundance measurements.


Subject(s)
Signal Transduction , Phenotype , RNA, Messenger/genetics , RNA, Messenger/metabolism
6.
Physiol Genomics ; 54(6): 220-229, 2022 06 01.
Article in English | MEDLINE | ID: mdl-35476585

ABSTRACT

Isogenic populations of mammalian cells exhibit significant gene expression variability. This variability can be separated into two components. Variance arises from events specific to the transcribed gene (i.e., cis or allele-specific sources) and variance from events that impact many genes at once (i.e., trans and global processes). Furthermore, the activity of the different regulatory factors that influence gene expression fluctuates at different timescales. Fast timescales will result in rapid fluctuation of gene expression, whereas slow timescales will result in longer persistence of gene expression levels over time. Here, we investigated sources of gene expression that are intrinsic, i.e., coming from cis-regulatory factors and follow slow timescales. To do so, we developed a reporter system that isolates allele-specific variability and measures its persistence in imaging and long-term fluctuation analysis experiments. Our results identify a new source of gene expression variability that is allele-specific but that fluctuates on timescales of days. We hypothesized that allele-specific fluctuations of epigenetic regulatory factors are responsible for the newly discovered allele-specific and slow source of gene expression variability. Using mathematical modeling, we showed that adding this effect to the two-state model is sufficient to account for all empirical observations. Furthermore, using direct assays of chromatin markers, we find fluctuation in H3K4me3 levels that match the observed changes in gene expression levels providing direct experimental support of our model. Collectively, our work shows that slow fluctuations of regulatory chromatin modifications contribute to the variability in gene expression.


Subject(s)
Chromatin , Epigenomics , Alleles , Animals , Chromatin/genetics , Epigenesis, Genetic/genetics , Gene Expression , Mammals/genetics
7.
Bioinformatics ; 37(Suppl_1): i358-i366, 2021 07 12.
Article in English | MEDLINE | ID: mdl-34252925

ABSTRACT

MOTIVATION: Single-cell RNA sequencing (scRNA-seq) captures whole transcriptome information of individual cells. While scRNA-seq measures thousands of genes, researchers are often interested in only dozens to hundreds of genes for a closer study. Then, a question is how to select those informative genes from scRNA-seq data. Moreover, single-cell targeted gene profiling technologies are gaining popularity for their low costs, high sensitivity and extra (e.g. spatial) information; however, they typically can only measure up to a few hundred genes. Then another challenging question is how to select genes for targeted gene profiling based on existing scRNA-seq data. RESULTS: Here, we develop the single-cell Projective Non-negative Matrix Factorization (scPNMF) method to select informative genes from scRNA-seq data in an unsupervised way. Compared with existing gene selection methods, scPNMF has two advantages. First, its selected informative genes can better distinguish cell types. Second, it enables the alignment of new targeted gene profiling data with reference data in a low-dimensional space to facilitate the prediction of cell types in the new data. Technically, scPNMF modifies the PNMF algorithm for gene selection by changing the initialization and adding a basis selection step, which selects informative bases to distinguish cell types. We demonstrate that scPNMF outperforms the state-of-the-art gene selection methods on diverse scRNA-seq datasets. Moreover, we show that scPNMF can guide the design of targeted gene profiling experiments and the cell-type annotation on targeted gene profiling data. AVAILABILITY AND IMPLEMENTATION: The R package is open-access and available at https://github.com/JSB-UCLA/scPNMF. The data used in this work are available at Zenodo: https://doi.org/10.5281/zenodo.4797997. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.


Subject(s)
Gene Expression Profiling , Single-Cell Analysis , Algorithms , Sequence Analysis, RNA , Software
8.
Mol Syst Biol ; 17(6): e10108, 2021 06.
Article in English | MEDLINE | ID: mdl-34057817

ABSTRACT

RNA hybridization-based spatial transcriptomics provides unparalleled detection sensitivity. However, inaccuracies in segmentation of image volumes into cells cause misassignment of mRNAs which is a major source of errors. Here, we develop JSTA, a computational framework for joint cell segmentation and cell type annotation that utilizes prior knowledge of cell type-specific gene expression. Simulation results show that leveraging existing cell type taxonomy increases RNA assignment accuracy by more than 45%. Using JSTA, we were able to classify cells in the mouse hippocampus into 133 (sub)types revealing the spatial organization of CA1, CA3, and Sst neuron subtypes. Analysis of within cell subtype spatial differential gene expression of 80 candidate genes identified 63 with statistically significant spatial differential gene expression across 61 (sub)types. Overall, our work demonstrates that known cell type expression patterns can be leveraged to improve the accuracy of RNA hybridization-based spatial transcriptomics while providing highly granular cell (sub)type information. The large number of newly discovered spatial gene expression patterns substantiates the need for accurate spatial transcriptomic measurements that can provide information beyond cell (sub)type labels.


Subject(s)
Gene Expression Profiling , Transcriptome , Animals , Computer Simulation , Mice , Neurons , RNA, Messenger , Transcriptome/genetics
9.
Nat Commun ; 12(1): 2992, 2021 05 20.
Article in English | MEDLINE | ID: mdl-34016976

ABSTRACT

Rapid death of infected cells is an important antiviral strategy. However, fast decisions that are based on limited evidence can be erroneous and cause unnecessary cell death and subsequent tissue damage. How cells optimize their death decision making strategy to maximize both speed and accuracy is unclear. Here, we show that exposure to TNF, which is secreted by macrophages during viral infection, causes cells to change their decision strategy from "slow and accurate" to "fast and error-prone". Mathematical modeling combined with experiments in cell culture and whole organ culture show that the regulation of the cell death decision strategy is critical to prevent HSV-1 spread. These findings demonstrate that immune regulation of cellular cognitive processes dynamically changes a tissues' tolerance for self-damage, which is required to protect against viral spread.


Subject(s)
Apoptosis/immunology , Herpes Simplex/immunology , Herpesvirus 1, Human/immunology , Macrophages/immunology , Tumor Necrosis Factor-alpha/metabolism , Animals , Cornea/immunology , Cornea/virology , Disease Models, Animal , Female , Herpes Simplex/virology , Host-Pathogen Interactions/immunology , Humans , Intravital Microscopy , Macrophages/metabolism , Male , Mice , Mice, Knockout , Models, Immunological , NIH 3T3 Cells , Organ Culture Techniques , Primary Cell Culture , Time-Lapse Imaging , Tumor Necrosis Factor-alpha/genetics
10.
Cell Syst ; 12(5): 388-400, 2021 05 19.
Article in English | MEDLINE | ID: mdl-34015260

ABSTRACT

Biological organization crosses multiple spatial scales: from molecular, cellular, to tissues and organs. The proliferation of molecular profiling technologies enables increasingly detailed cataloging of the components at each scale. However, the scarcity of spatial profiling has made it challenging to bridge across these scales. Emerging technologies based on highly multiplexed in situ profiling are paving the way to study the spatial organization of cells and tissues in greater detail. These new technologies provide the data needed to cross the scale from cell biology to physiology and identify the fundamental principles that govern tissue organization. Here, we provide an overview of these key technologies and discuss the current and future insights these powerful techniques enable.


Subject(s)
Cell Biology , Cell Physiological Phenomena , Humans
11.
Nat Commun ; 12(1): 1272, 2021 02 24.
Article in English | MEDLINE | ID: mdl-33627672

ABSTRACT

Cellular responses to environmental changes are encoded in the complex temporal patterns of signaling proteins. However, quantifying the accumulation of information over time to direct cellular decision-making remains an unsolved challenge. This is, in part, due to the combinatorial explosion of possible configurations that need to be evaluated for information in time-course measurements. Here, we develop a quantitative framework, based on inferred trajectory probabilities, to calculate the mutual information encoded in signaling dynamics while accounting for cell-cell variability. We use it to understand NFκB transcriptional dynamics in response to different immune threats, and reveal that some threats are distinguished faster than others. Our analyses also suggest specific temporal phases during which information distinguishing threats becomes available to immune response genes; one specific phase could be mapped to the functionality of the IκBα negative feedback circuit. The framework is generally applicable to single-cell time series measurements, and enables understanding how temporal regulatory codes transmit information over time.


Subject(s)
Molecular Dynamics Simulation , Humans , NF-kappa B/metabolism , Signal Transduction/genetics , Signal Transduction/physiology
12.
Am Nat ; 197(3): 390-391, 2021 Mar.
Article in English | MEDLINE | ID: mdl-33625973
13.
Mol Syst Biol ; 16(12): e9677, 2020 12.
Article in English | MEDLINE | ID: mdl-33314666

ABSTRACT

Balancing cell death is essential to maintain healthy tissue homeostasis and prevent disease. Tumor necrosis factor (TNF) not only activates nuclear factor κB (NFκB), which coordinates the cellular response to inflammation, but may also trigger necroptosis, a pro-inflammatory form of cell death. Whether TNF-induced NFκB affects the fate decision to undergo TNF-induced necroptosis is unclear. Live-cell microscopy and model-aided analysis of death kinetics identified a molecular circuit that interprets TNF-induced NFκB/RelA dynamics to control necroptosis decisions. Inducible expression of TNFAIP3/A20 forms an incoherent feedforward loop to interfere with the RIPK3-containing necrosome complex and protect a fraction of cells from transient, but not long-term TNF exposure. Furthermore, dysregulated NFκB dynamics often associated with disease diminish TNF-induced necroptosis. Our results suggest that TNF's dual roles in either coordinating cellular responses to inflammation, or further amplifying inflammation are determined by a dynamic NFκB-A20-RIPK3 circuit, that could be targeted to treat inflammation and cancer.


Subject(s)
NF-kappa B/metabolism , Necroptosis , Transcription Factor RelA/metabolism , Tumor Necrosis Factor-alpha/pharmacology , Animals , Cell Line , Inflammation/pathology , Kinetics , Mice , Models, Biological , Necroptosis/drug effects , Receptor-Interacting Protein Serine-Threonine Kinases/metabolism , Tumor Necrosis Factor alpha-Induced Protein 3/metabolism
14.
Sci Rep ; 10(1): 20566, 2020 11 25.
Article in English | MEDLINE | ID: mdl-33239733

ABSTRACT

Gene expression variability, differences in the number of mRNA per cell across a population of cells, is ubiquitous across diverse organisms with broad impacts on cellular phenotypes. The role of chromatin in regulating average gene expression has been extensively studied. However, what aspects of the chromatin contribute to gene expression variability is still underexplored. Here we addressed this problem by leveraging chromatin diversity and using a systematic investigation of randomly integrated expression reporters to identify what aspects of chromatin microenvironment contribute to gene expression variability. Using DNA barcoding and split-pool decoding, we created a large library of isogenic reporter clones and identified reporter integration sites in a massive and parallel manner. By mapping our measurements of reporter expression at different genomic loci with multiple epigenetic profiles including the enrichment of transcription factors and the distance to different chromatin states, we identified new factors that impact the regulation of gene expression distributions.


Subject(s)
Chromatin/genetics , Gene Expression Regulation/genetics , Chromatin/metabolism , Epigenesis, Genetic/genetics , Epigenomics/methods , Gene Expression/genetics , Gene Expression Regulation/physiology , Genome/genetics , Genomics/methods , Humans , K562 Cells , Transcription Factors/genetics , Transcription Factors/metabolism
15.
PLoS Comput Biol ; 16(8): e1008011, 2020 08.
Article in English | MEDLINE | ID: mdl-32797040

ABSTRACT

The dynamic signal encoding paradigm suggests that information flows from the extracellular environment into specific signaling patterns (encoding) that are then read by downstream effectors to control cellular behavior. Previous work empirically quantified the information content of dynamic signaling patterns. However, whether this information can be faithfully transmitted to the gene expression level is unclear. Here we used NFkB signaling as a model to understand the accuracy of information transmission from signaling dynamics into gene expression. Using a detailed mathematical model, we simulated realistic NFkB signaling patterns with different degrees of variability. The NFkB patterns were used as an input to a simple gene expression model. Analysis of information transmission between ligand and NFkB and ligand and gene expression allows us to determine information loss in transmission between receptors to dynamic signaling patterns and between signaling dynamics to gene expression. Information loss could occur due to biochemical noise or due to a lack of specificity. We found that noise-free gene expression has very little information loss suggesting that gene expression can preserve specificity in NFkB patterns. As expected, the addition of noise to the gene expression model results in information loss. Interestingly, this effect can be mitigated by a specific choice of parameters that can substantially reduce information loss due to biochemical noise during gene expression. Overall our results show that the cellular capacity for information transmission from dynamic signaling patterns to gene expression can be high enough to preserve ligand specificity and thereby the accuracy of cellular response to environmental cues.


Subject(s)
NF-kappa B/metabolism , Signal Transduction , Biochemical Phenomena , Gene Expression
16.
Cell Stem Cell ; 26(5): 693-706.e9, 2020 05 07.
Article in English | MEDLINE | ID: mdl-32302522

ABSTRACT

During early development, extrinsic triggers prompt pluripotent cells to begin the process of differentiation. When and how human embryonic stem cells (hESCs) irreversibly commit to differentiation is a fundamental yet unanswered question. By combining single-cell imaging, genomic approaches, and mathematical modeling, we find that hESCs commit to exiting pluripotency unexpectedly early. We show that bone morphogenetic protein 4 (BMP4), an important differentiation trigger, induces a subset of early genes to mirror the sustained, bistable dynamics of upstream signaling. Induction of one of these genes, GATA3, drives differentiation in the absence of BMP4. Conversely, GATA3 knockout delays differentiation and prevents fast commitment to differentiation. We show that positive feedback at the level of the GATA3-BMP4 axis induces fast, irreversible commitment to differentiation. We propose that early commitment may be a feature of BMP-driven fate choices and that interlinked feedback is the molecular basis for an irreversible transition from pluripotency to differentiation.


Subject(s)
Human Embryonic Stem Cells , Bone Morphogenetic Protein 4 , Cell Differentiation , GATA3 Transcription Factor/genetics , Humans , Signal Transduction
17.
Nucleic Acids Res ; 48(9): 4797-4810, 2020 05 21.
Article in English | MEDLINE | ID: mdl-32246716

ABSTRACT

Therapeutic targeting of epigenetic modulators offers a novel approach to the treatment of multiple diseases. The cellular consequences of chemical compounds that target epigenetic regulators (epi-drugs) are complex. Epi-drugs affect global cellular phenotypes and cause local changes to gene expression due to alteration of a gene chromatin environment. Despite increasing use in the clinic, the mechanisms responsible for cellular changes are unclear. Specifically, to what degree the effects are a result of cell-wide changes or disease related locus specific effects is unknown. Here we developed a platform to systematically and simultaneously investigate the sensitivity of epi-drugs at hundreds of genomic locations by combining DNA barcoding, unique split-pool encoding, and single cell expression measurements. Internal controls are used to isolate locus specific effects separately from any global consequences these drugs have. Using this platform we discovered wide-spread loci specific sensitivities to epi-drugs for three distinct epi-drugs that target histone deacetylase, DNA methylation and bromodomain proteins. By leveraging ENCODE data on chromatin modification, we identified features of chromatin environments that are most likely to be affected by epi-drugs. The measurements of loci specific epi-drugs sensitivities will pave the way to the development of targeted therapy for personalized medicine.


Subject(s)
Epigenesis, Genetic/drug effects , Acetylation/drug effects , Azacitidine/pharmacology , Azepines/pharmacology , Chromosomes, Human , DNA Methylation/drug effects , Genes, Reporter , Genetic Loci , Genomics/methods , Histones/metabolism , Humans , K562 Cells , Sequence Analysis, DNA , Triazoles/pharmacology
18.
Mol Syst Biol ; 16(2): e9146, 2020 02.
Article in English | MEDLINE | ID: mdl-32043799

ABSTRACT

Gene expression variability in mammalian systems plays an important role in physiological and pathophysiological conditions. This variability can come from differential regulation related to cell state (extrinsic) and allele-specific transcriptional bursting (intrinsic). Yet, the relative contribution of these two distinct sources is unknown. Here, we exploit the qualitative difference in the patterns of covariance between these two sources to quantify their relative contributions to expression variance in mammalian cells. Using multiplexed error robust RNA fluorescent in situ hybridization (MERFISH), we measured the multivariate gene expression distribution of 150 genes related to Ca2+ signaling coupled with the dynamic Ca2+ response of live cells to ATP. We show that after controlling for cellular phenotypic states such as size, cell cycle stage, and Ca2+ response to ATP, the remaining variability is effectively at the Poisson limit for most genes. These findings demonstrate that the majority of expression variability results from cell state differences and that the contribution of transcriptional bursting is relatively minimal.


Subject(s)
Calcium Signaling , Gene Expression Profiling/methods , RNA, Messenger/genetics , Cell Cycle , Cell Line , Cell Size , Female , Gene Expression Regulation , Humans , In Situ Hybridization, Fluorescence , Single-Cell Analysis , Systems Biology/methods
19.
Nat Cell Biol ; 21(5): 614-626, 2019 05.
Article in English | MEDLINE | ID: mdl-31036939

ABSTRACT

Cell growth is controlled by a lysosomal signalling complex containing Rag small GTPases and mammalian target of rapamycin complex 1 (mTORC1) kinase. Here, we carried out a microscopy-based genome-wide human short interfering RNA screen and discovered a lysosome-localized G protein-coupled receptor (GPCR)-like protein, GPR137B, that interacts with Rag GTPases, increases Rag localization and activity, and thereby regulates mTORC1 translocation and activity. High GPR137B expression can recruit and activate mTORC1 in the absence of amino acids. Furthermore, GPR137B also regulates the dissociation of activated Rag from lysosomes, suggesting that GPR137B controls a cycle of Rag activation and dissociation from lysosomes. GPR137B-knockout cells exhibited defective autophagy and an expanded lysosome compartment, similar to Rag-knockout cells. Like zebrafish RagA mutants, GPR137B-mutant zebrafish had upregulated TFEB target gene expression and an expanded lysosome compartment in microglia. Thus, GPR137B is a GPCR-like lysosomal regulatory protein that controls dynamic Rag and mTORC1 localization and activity as well as lysosome morphology.


Subject(s)
Basic Helix-Loop-Helix Leucine Zipper Transcription Factors/genetics , Genome, Human/genetics , Monomeric GTP-Binding Proteins/genetics , Receptors, G-Protein-Coupled/genetics , Animals , Autophagy/genetics , Gene Expression Regulation/genetics , Humans , Lysosomes/genetics , Mechanistic Target of Rapamycin Complex 1/genetics , Microglia/metabolism , Multiprotein Complexes/chemistry , Multiprotein Complexes/genetics , RNA, Small Interfering/genetics , Receptors, G-Protein-Coupled/antagonists & inhibitors , Zebrafish/genetics , Zebrafish/growth & development
20.
Curr Opin Syst Biol ; 8: 46-50, 2018 Apr.
Article in English | MEDLINE | ID: mdl-29308439

ABSTRACT

The robustness of biological systems is often depicted as a key system-level emergent property that allows uniform phenotypes in fluctuating environments. Yet, analysis of single-cell signaling responses identified multiple examples of cellular responses with high degrees of heterogeneity. Here we discuss the implications of the observed lack of response accuracy in the context of new observations coming from single-cell approaches. Single-cell approaches provide a new way to measure the abundance of thousands of molecular species in a single-cell. Repeatedly, analysis of cell distributions identifies clusters within these distributions where cells can be grouped into specific cell states. If cells in a population occupy distinct cell states, the observed variable response could in fact be accurate for each cell conditioned on its own internal state. In this view, the observed lack of accuracy, i.e. response heterogeneity, could in fact be beneficial and a potentially regulated feature of cell state variability. Therefore, to truly determine whether the observed response heterogeneity is a desired property or a physical limitation, future analysis of signaling heterogeneity must take into account the internal states of cells in the population.

SELECTION OF CITATIONS
SEARCH DETAIL
...