Fundamental limits on dynamic inference from single-cell snapshots

Weinreb, Caleb; Wolock, Samuel L.; Tusi, Betsabeh Khoramian; Socolovsky, Merav; Klein, Allon M.

doi:10.1073/pnas.1714723115

Cited by 274 publications

(317 citation statements)

References 45 publications

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“…Along with technological developments, single-cell biology brings new conceptual challenges. Foremost among the latter is the definition of cell identity itself, and in particular, our interpretations of a cell type and its associated dynamical states [5][6][7] . Technical and conceptual progress in such matters largely depends on the availability of flexible computational frameworks for efficient, meaningful, and interpretable large-scale data analysis.…”

Section: Introductionmentioning

confidence: 99%

A multiresolution framework to characterize single-cell state landscapes

Mohammadi

Dávila-Velderrain

Kellis

2019

Preprint

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Dissecting the cellular heterogeneity embedded in single-cell transcriptomic data is challenging. Although a large number of methods and approaches exist, robustly identifying underlying cell states and their associations is still a major challenge; given the nonexclusive and dynamic influence of multiple unknown sources of variability, the existence of state continuum at the time-scale of observation, and the inevitable snapshot nature of experiments. As a way to address some of these challenges, here we introduce ACTIONet, a comprehensive framework that combines archetypal analysis and network theory to provide a ready-to-use analytical approach for multiresolution single-cell state characterization. ACTIONet uses multilevel matrix decomposition and network reconstruction to simultaneously learn cell state patterns, quantify single-cell states, and reconstruct a reproducible structural representation of the transcriptional state space that is geometrically mapped to a color space. A color-enhanced quantitative view of cell states enables novel visualization, prediction, and annotation approaches. Using data from multiple tissues, organisms, and developmental conditions, we illustrate how ACTIONet facilitates the reconstruction and exploration of single-cell state landscapes.

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Section: Introductionmentioning

confidence: 99%

A multiresolution framework to characterize single-cell state landscapes

Mohammadi

Dávila-Velderrain

Kellis

2019

Preprint

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“…It is commonly stated in the field that single-cell measurements contain information that is obscured by population-averaged methods such as western blotting and quantitative PCR [31][32][33][34]. Indeed, single-cell approaches have revealed a staggering degree of heterogeneity among individual cells in terms of gene expression and protein dynamics.…”

Section: Discussionmentioning

confidence: 99%

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

Haggerty¹,

Purvis²

2019

Preprint

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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 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.

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“…The method was applied on synthetic CyTOF data generated for in silico signaling networks and produced excellent to reasonable reconstructions for a range of conditions. Fundamental constraints on reconstructing trajectories and inferring underlying models using snapshot data were studied by Weinreb et al 101 using a physical flux balance law.…”

Section: Lower Dimensional Visualizationmentioning

confidence: 99%

Data analysis to modeling to building theory in NK cell biology and beyond: How can computational modeling contribute?

Das

Lanier

2019

Journal of Leukocyte Biology

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The use of mathematical and computational tools in investigating Natural Killer (NK) cell biology and in general the immune system has increased steadily in the last few decades. However, unlike the physical sciences, there is a persistent ambivalence, which however is increasingly diminishing, in the biology community toward appreciating the utility of quantitative tools in addressing questions of biological importance. We survey some of the recent developments in the application of quantitative approaches for investigating different problems in NK cell biology and evaluate opportunities and challenges of using quantitative methods in providing biological insights in NK cell biology.

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Fundamental limits on dynamic inference from single-cell snapshots

Cited by 274 publications

References 45 publications

A multiresolution framework to characterize single-cell state landscapes

A multiresolution framework to characterize single-cell state landscapes

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

Data analysis to modeling to building theory in NK cell biology and beyond: How can computational modeling contribute?

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