scholarly journals Inferring the structures of signaling motifs from paired dynamic traces of single cells

2019 ◽  
Author(s):  
R. A. Haggerty ◽  
Jeremy E. Purvis
Keyword(s):  
Time Series ◽  
Single Cell ◽  
Single Cells ◽  
Underlying Mechanism ◽  
Repeated Measurements ◽  
Signaling Networks ◽  
Molecular Signaling ◽  
Signaling Mechanism ◽  
Signaling Dynamics ◽  
Cell To Cell Variability

AbstractIndividual cells show variability in their signaling dynamics that often correlates with phenotypic responses, indicating that cell-to-cell variability is not merely noise but can have functional consequences. Based on this observation, we reasoned that cell-to-cell variability under the same treatment condition could be explained by a single signaling motif that deterministically maps different upstream signals into a corresponding set of downstream responses. If this assumption holds, then repeated measurements of upstream and downstream signaling dynamics in single cells could provide information about the underlying signaling motif for a given pathway, even when no prior knowledge of that motif exists. To test these two hypotheses, we developed a computer algorithm called MISC (Motif Inference from Single Cells) that infers the underlying signaling motif from paired time-series measurements from individual cells. When applied to measurements of transcription factor and reporter gene expression in the yeast stress response, MISC predicted signaling motifs that were consistent with previous mechanistic models of transcription. The ability to detect the underlying mechanism became less certain when a cell’s upstream signal was randomly paired with another cell’s downstream response, demonstrating how averaging time-series measurements across a population obscures information about the underlying signaling mechanism. In some cases, motif predictions improved as more cells were added to the analysis. These results provide evidence that mechanistic information about cellular signaling networks can be embedded within the dynamical patterns of single cells.Author SummaryCells use molecular signaling networks to translate dynamically changing stimuli into appropriate downstream responses. Specialized network structures, or motifs, allow cells to properly decode a variety of temporal input signals. In this paper, we present an algorithm that infers signaling motifs from multiple examples of an upstream signal paired with its downstream response in the same cell. We compare the predictive power of single-cell versus averaged time-series traces and the incremental benefit of adding more single-cell traces to the algorithm. We use this approach to understand how yeast respond to environmental stresses.

scholarly journals Inferring the structures of signaling motifs from paired dynamic traces of single cells

2021 ◽  
Vol 17 (2) ◽  
pp. e1008657
Author(s):  
Raymond A. Haggerty ◽  
Jeremy E. Purvis
Keyword(s):  
Time Series ◽  
Single Cells ◽  
Underlying Mechanism ◽  
Repeated Measurements ◽  
Signaling Mechanism ◽  
Downstream Signaling ◽  
Signaling Dynamics ◽  
Functional Consequences ◽  
Averaging Time ◽  
Cell To Cell Variability

Individual cells show variability in their signaling dynamics that often correlates with phenotypic responses, indicating that cell-to-cell variability is not merely noise but can have functional consequences. Based on this observation, we reasoned that cell-to-cell variability under the same treatment condition could be explained in part by a single signaling motif that maps different upstream signals into a corresponding set of downstream responses. If this assumption holds, then repeated measurements of upstream and downstream signaling dynamics in a population of cells could provide information about the underlying signaling motif for a given pathway, even when no prior knowledge of that motif exists. To test these two hypotheses, we developed a computer algorithm called MISC (Motif Inference from Single Cells) that infers the underlying signaling motif from paired time-series measurements from individual cells. When applied to measurements of transcription factor and reporter gene expression in the yeast stress response, MISC predicted signaling motifs that were consistent with previous mechanistic models of transcription. The ability to detect the underlying mechanism became less certain when a cell’s upstream signal was randomly paired with another cell’s downstream response, demonstrating how averaging time-series measurements across a population obscures information about the underlying signaling mechanism. In some cases, motif predictions improved as more cells were added to the analysis. These results provide evidence that mechanistic information about cellular signaling networks can be systematically extracted from the dynamical patterns of single cells.

scholarly journals Symposium on single cell analysis and genomic approaches, Experimental Biology 2017 Chicago, Illinois, April 23, 2017

2017 ◽  
Vol 49 (9) ◽  
pp. 491-495
Author(s):  
Hilary A. Coller
Keyword(s):  
Single Cell ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Experimental Biology ◽  
Intratumor Heterogeneity ◽  
Cell Analysis ◽  
Single Cell Genomics ◽  
Critical Issues ◽  
Cell To Cell Variability ◽  
External Stimuli

Emerging technologies for the analysis of genome-wide information in single cells have the potential to transform many fields of biology, including our understanding of cell states, the response of cells to external stimuli, mosaicism, and intratumor heterogeneity. At Experimental Biology 2017 in Chicago, Physiological Genomics hosted a symposium in which five leaders in the field of single cell genomics presented their recent research. The speakers discussed emerging methodologies in single cell analysis and critical issues for the analysis of single cell data. Also discussed were applications of single cell genomics to understanding the different types of cells within an organism or tissue and the basis for cell-to-cell variability in response to stimuli.

scholarly journals Exploiting variability of single cells to uncover the in vivo hierarchy of miRNA targets

2015 ◽  
Author(s):  
Andrzej Jerzy Rzepiela ◽  
Arnau Vina-Vilaseca ◽  
Jeremie Breda ◽  
Souvik Ghosh ◽  
Afzal P Syed ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Single Cells ◽  
Human Embryonic Kidney Cells ◽  
Mirna Targets ◽  
Wide Range ◽  
Gene Expression Dynamics ◽  
Cell To Cell Variability ◽  
First Time

MiRNAs are post-transcriptional repressors of gene expression that may additionally reduce the cell-to-cell variability in protein expression, induce correlations between target expression levels and provide a layer through which targets can influence each other's expression as 'competing RNAs' (ceRNAs). Here we combined single cell sequencing of human embryonic kidney cells in which the expression of two distinct miRNAs was induced over a wide range, with mathematical modeling, to estimate Michaelis-Menten (KM)-type constants for hundreds of evolutionarily conserved miRNA targets. These parameters, which we inferred here for the first time in the context of the entire network of endogenous miRNA targets, vary over ~2 orders of magnitude. They reveal an in vivo hierarchy of miRNA targets, defined by the concentration of miRNA-Argonaute complexes at which the targets are most sensitively down-regulated. The data further reveals miRNA-induced correlations in target expression at the single cell level, as well as the response of target noise to the miRNA concentration. The approach is generalizable to other miRNAs and post-transcriptional regulators and provides a deeper understanding of gene expression dynamics.

scholarly journals McSNAC: A software to approximate first-order signaling networks from mass cytometry data

2021 ◽  
Author(s):  
Darren Wethington ◽  
Sayak Mukherjee ◽  
Jayajit Das
Keyword(s):  
Single Cell ◽  
In Silico ◽  
Single Cells ◽  
Signaling Network ◽  
Signaling Networks ◽  
Enzymatic Reactions ◽  
Signaling Proteins ◽  
Protein Species ◽  
Mass Cytometry ◽  
First Order

AbstractMass cytometry (CyTOF) gives unprecedented opportunity to simultaneously measure up to 40 proteins in single cells, with a theoretical potential to reach 100 proteins. This high-dimensional single-cell information can be very useful to dissecting mechanisms of cellular activity. In particular, measuring abundances of signaling proteins like phospho-proteins can provide detailed information on the dynamics of single-cell signaling processes. With a proper computational analysis, timestamped CyTOF data of signaling proteins could help develop predictive and mechanistic models for signaling kinetics. These models would be useful for predicting the effects of perturbations in cells, or comparing signaling networks across cell groups. We propose our Mass cytometry Signaling Network Analysis Code, or McSNAC, a new software capable of reconstructing signaling networks and estimating their kinetic parameters from CyTOF data.McSNAC approximates signaling networks as a network of first-order reactions between proteins. This assumption breaks down often as signaling reactions can involve binding and unbinding, enzymatic reactions, and other nonlinear constructions. Furthermore, McSNAC may be limited to approximating indirect interactions between protein species, as cytometry experiments are only able to assay a small fraction of the protein species that are involved in signaling. We carry out a series of in silico experiments here to show that 1) McSNAC is capable of accurately estimating the ground-truth model in a scalable manner when given data originating from a first-order system; 2) McSNAC is capable of qualitatively predicting outcomes to perturbations of species abundances in simple second-order reaction models and in a complex in silico nonlinear signaling network in which some proteins are unmeasured. These findings demonstrate that McSNAC can be a valuable screening tool for generating models of signaling networks from timestamped CyTOF data.

All Roads Lead to Rome: Phenotypic Channeling to Uniform Differentiation Structures from Diverse Leukemia Genotypes by Mass Cytometry

Blood ◽  
2013 ◽  
Vol 122 (21) ◽  
pp. SCI-22-SCI-22
Author(s):  
Garry Nolan
Keyword(s):  
Stem Cells ◽  
B Cell ◽  
Single Cell ◽  
Conflicts Of Interest ◽  
Single Cells ◽  
Lymphoblastic Leukemia ◽  
Myelogenous Leukemia ◽  
Signaling Networks ◽  
Mass Cytometry ◽  
Human Cord Blood

Abstract Emerging single-cell technologies have been pivotal in uncovering an extensive degree of heterogeneity between and within tissues (1). Analysis of single-cell data has shed light on many different cellular processes (2-7) and recent technological advances have enabled the study of a large number of parameters in single cells at unparalleled resolution. One such technology, mass cytometry (8), can measure up to 45 parameters simultaneously in tens of thousands of individual cells. Using mass cytometry and genomic sequencing of conventionally sorted subpopulations show that acute myelogenous leukemia (AML) in a given patient can simultaneously occupy multiple stages of differentiation. Occupation of these stages was correlated with the presence, or absence, of unique exonic mutation fingerprints. In another cancer, B-cell acute lymphoblastic leukemia (ALL), outgrowth of tumor at pro and pre-B cell stages was nearly always uniquely at a single stage - contrary to the results in AML. This suggests that evolutionary “niche” searching is not only for physical space in cancers, but also involves utilization of differentiation machinery as an additional elaboration mechanism. Each differentiation stage in both AML and B-cell ALL was characterized by utilization of cognate signaling networks which showed differential susceptibility to drug action. Using such deep profiling and signaling delineation approaches at the single-cell level will allow for fine structured indexing of patient disease and further tailoring of disease management. In addition, it will allow “heterogeneous” tumors to be organized by a maturation index associated with a granular catalog of mutations that drive cells to occupy these pseudo-differentiation niches. 1. Bendall, S.C., et al., A deep profiler's guide to cytometry.Trends Immunol, 2012. 33(7): p. 323-32. 2. Petilla Interneuron Nomenclature Group, et al., Petilla terminology: nomenclature of features of GABAergic interneurons of the cerebral cortex.Nat Rev Neurosci, 2008. 9(7): p. 557-68. 3. Irish, J.M., et al., Single cell profiling of potentiated phospho-protein networks in cancer cells.Cell, 2004. 118(2): p. 217-28. 4. Sachs, K., et al., Causal protein-signaling networks derived from multiparameter single-cell data.Science, 2005. 308(5721): p. 523-9. 5. Majeti, R., C.Y. Park, and I.L. Weissman, Identification of a hierarchy of multipotent hematopoietic progenitors in human cord blood. Cell Stem Cell, 2007. 1(6): p. 635-45. 6. Tarnok, A., H. Ulrich, and J. Bocsi, Phenotypes of stem cells from diverse origin.Cytometry A, 2010. 77(1): p. 6-10. 7. O'Brien, C.A., A. Kreso, and J.E. Dick, Cancer stem cells in solid tumors: an overview.Semin Radiat Oncol, 2009. 19(2): p. 71-7. 8. Bandura, D.R., et al., Mass cytometry: technique for real time single cell multitarget immunoassay based on inductively coupled plasma time-of-flight mass spectrometry. Anal Chem, 2009. 81(16): p. 6813-22. Disclosures: No relevant conflicts of interest to declare.

scholarly journals Sci-Hi-C: a single-cell Hi-C method for mapping 3D genome organization in large number of single cells

2019 ◽  
Author(s):  
Vijay Ramani ◽  
Xinxian Deng ◽  
Ruolan Qiu ◽  
Choli Lee ◽  
Christine M Disteche ◽  
...  
Keyword(s):  
Single Cell ◽  
Genome Organization ◽  
Single Cells ◽  
Three Dimensional ◽  
Population Based ◽  
Order Structure ◽  
Chromosome Conformation ◽  
3D Genome ◽  
A Cell ◽  
Cell To Cell Variability

AbstractThe highly dynamic nature of chromosome conformation and three-dimensional (3D) genome organization leads to cell-to-cell variability in chromatin interactions within a cell population, even if the cells of the population appear to be functionally homogeneous. Hence, although Hi-C is a powerful tool for mapping 3D genome organization, this heterogeneity of chromosome higher order structure among individual cells limits the interpretive power of population based bulk Hi-C assays. Moreover, single-cell studies have the potential to enable the identification and characterization of rare cell populations or cell subtypes in a heterogeneous population. However, it may require surveying relatively large numbers of single cells to achieve statistically meaningful observations in single-cell studies. By applying combinatorial cellular indexing to chromosome conformation capture, we developed single-cell combinatorial indexed Hi-C (sci-Hi-C), a high throughput method that enables mapping chromatin interactomes in large number of single cells. We demonstrated the use of sci-Hi-C data to separate cells by karytoypic and cell-cycle state differences and to identify cellular variability in mammalian chromosomal conformation. Here, we provide a detailed description of method design and step-by-step working protocols for sci-Hi-C.

scholarly journals Cell-to-cell and type-to-type heterogeneity of signaling networks: Insights from the crowd

2021 ◽  
Author(s):  
Attila Gabor ◽  
Marco Tognetti ◽  
Alice Driessen ◽  
Jovan Tanevski ◽  
Baosen Guo ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cell Signaling ◽  
Single Cell ◽  
Cell Lines ◽  
Kinase Inhibitors ◽  
Single Cells ◽  
Set Covering ◽  
Signaling Networks ◽  
List Type ◽  
Data Set

AbstractRecent technological developments allow us to measure the status of dozens of proteins in individual cells. This opens the way to understand the heterogeneity of complex multi-signaling networks across cells and cell-types, with important implications to understand and treat diseases such as cancer. These technologies are however limited to proteins for which antibodies are available and are fairly costly, making predictions of new markers and of existing markers under new conditions a valuable alternative. To assess our capacity to make such predictions and boost further methodological development, we organised the Single Cell Signaling in Breast Cancer DREAM challenge. We used a mass cytometry data set, covering 36 markers in over 4,000 conditions totalling 80 million single cells across 67 breast cancer cell lines. Through four increasingly difficult subchallenges, the participants predicted missing markers, new conditions, and the time course response of single cells to stimuli in the presence and absence of kinase inhibitors. The challenge results show that despite the stochastic nature of signal transduction in single cells, the signaling events are tightly controlled and machine learning methods can accurately predict new experimental data.Graphical AbstractKey pointsOver 80 million single-cell multiplexed measurements across 67 cell lines, 54 conditions and 10 time points to benchmark predictive models of single cell signaling73 approaches from 27 teams for predicting response to kinase inhibitors on single cell level, and dynamic response from unperturbed basal omics dataPredictions of single marker models correlate with measurements with a correlation coefficient of 0.76Top models of whole signaling response models perform almost as well as a biological replicateCell-line specific variation in dynamics can be predicted from basal omics

scholarly journals Inferring kinetic parameters of oscillatory gene regulation from single cell time series data

2021 ◽  
Author(s):  
Joshua Burton ◽  
Cerys S Manning ◽  
Magnus Rattray ◽  
Nancy Papalopulu ◽  
Jochen Kursawe
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Single Cell ◽  
Kinetic Parameters ◽  
Cell Fate ◽  
Time Series Data ◽  
Single Cells ◽  
Population Heterogeneity ◽  
Series Data ◽  
Gene Expression Dynamics

Gene expression dynamics, such as stochastic oscillations and aperiodic fluctuations, have been associated with cell fate changes in multiple contexts, including development and cancer. Single cell live imaging of protein expression with endogenous reporters is widely used to observe such gene expression dynamics. However, the experimental investigation of regulatory mechanisms underlying the observed dynamics is challenging, since these mechanisms include complex interactions of multiple processes, including transcription, translation, and protein degradation. Here, we present a Bayesian method to infer kinetic parameters of oscillatory gene expression regulation using an auto-negative feedback motif with delay. Specifically, we use a delay-adapted nonlinear Kalman filter within a Metropolis-adjusted Langevin algorithm to identify posterior probability distributions. Our method can be applied to time series data on gene expression from single cells and is able to infer multiple parameters simultaneously. We apply it to published data on murine neural progenitor cells and show that it outperforms alternative methods. We further analyse how parameter uncertainty depends on the duration and time resolution of an imaging experiment, to make experimental design recommendations. This work demonstrates the utility of parameter inference on time course data from single cells and enables new studies on cell fate changes and population heterogeneity.

scholarly journals Multiscale and integrative single-cell Hi-C analysis with Higashi

2021 ◽  
Author(s):  
Ruochi Zhang ◽  
Tianming Zhou ◽  
Jian Ma
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Three Dimensional ◽  
Representation Learning ◽  
Specific Gene ◽  
Separate Analysis ◽  
3D Genome ◽  
Genome Features ◽  
Single Nucleus ◽  
Cell To Cell Variability

AbstractSingle-cell Hi-C (scHi-C) can identify cell-to-cell variability of three-dimensional (3D) chromatin organization, but the sparseness of measured interactions poses an analysis challenge. Here we report Higashi, an algorithm based on hypergraph representation learning that can incorporate the latent correlations among single cells to enhance overall imputation of contact maps. Higashi outperforms existing methods for embedding and imputation of scHi-C data and is able to identify multiscale 3D genome features in single cells, such as compartmentalization and TAD-like domain boundaries, allowing refined delineation of their cell-to-cell variability. Moreover, Higashi can incorporate epigenomic signals jointly profiled in the same cell into the hypergraph representation learning framework, as compared to separate analysis of two modalities, leading to improved embeddings for single-nucleus methyl-3C data. In an scHi-C dataset from human prefrontal cortex, Higashi identifies connections between 3D genome features and cell-type-specific gene regulation. Higashi can also potentially be extended to analyze single-cell multiway chromatin interactions and other multimodal single-cell omics data.

scholarly journals Inferring kinetic parameters of oscillatory gene regulation from single cell time-series data

2021 ◽  
Vol 18 (182) ◽  
Author(s):  
Joshua Burton ◽  
Cerys S. Manning ◽  
Magnus Rattray ◽  
Nancy Papalopulu ◽  
Jochen Kursawe
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Single Cell ◽  
Kinetic Parameters ◽  
Cell Fate ◽  
Time Series Data ◽  
Single Cells ◽  
Population Heterogeneity ◽  
Series Data ◽  
Gene Expression Dynamics

Gene expression dynamics, such as stochastic oscillations and aperiodic fluctuations, have been associated with cell fate changes in multiple contexts, including development and cancer. Single cell live imaging of protein expression with endogenous reporters is widely used to observe such gene expression dynamics. However, the experimental investigation of regulatory mechanisms underlying the observed dynamics is challenging, since these mechanisms include complex interactions of multiple processes, including transcription, translation and protein degradation. Here, we present a Bayesian method to infer kinetic parameters of oscillatory gene expression regulation using an auto-negative feedback motif with delay. Specifically, we use a delay-adapted nonlinear Kalman filter within a Metropolis-adjusted Langevin algorithm to identify posterior probability distributions. Our method can be applied to time-series data on gene expression from single cells and is able to infer multiple parameters simultaneously. We apply it to published data on murine neural progenitor cells and show that it outperforms alternative methods. We further analyse how parameter uncertainty depends on the duration and time resolution of an imaging experiment, to make experimental design recommendations. This work demonstrates the utility of parameter inference on time course data from single cells and enables new studies on cell fate changes and population heterogeneity.

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