ReviewCurrent opinion in biotechnology2016
Single-cell gene expression profiling and cell state dynamics: collecting data, correlating data points and connecting the dots.
Review in Current opinion in biotechnology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed.
- From static definitions to dynamic landscapes: physiological states in yeast.Frontiers in microbiology · 2026Article
- Serial Imaging of Tumour and microEnvironment (SITE) platform for live-cellbioRxiv : the preprint server for biology · 2025Article
- Is Cancer Reversible? Rethinking Carcinogenesis Models-A New Epistemological Tool.Biomolecules · 2023Review
- Spatially and Temporally Distributed Complexity-A Refreshed Framework for the Study of GRN Evolution.Cells · 2022Article
- Self-organization of whole-gene expression through coordinated chromatin structural transition.Biophysics reviews · 2021Review
- Controlling cardiac fibrosis through fibroblast state space modulation.Cellular signalling · 2021Review
- Potentiality of multiple modalities for single-cell analyses to evaluate the tumor microenvironment in clinical specimens.Scientific reports · 2021Article
- Behavior-related gene regulatory networks: A new level of organization in the brain.Proceedings of the National Academy of Sciences of the United States of America · 2020Review
- Attractor Concepts to Evaluate the Transcriptome-wide Dynamics Guiding Anaerobic to Aerobic State Transition in Escherichia coli.Scientific reports · 2020Article
- Regulatory Dynamics of Cell Differentiation Revealed by True Time Series From Multinucleate Single Cells.Frontiers in genetics · 2020Article
- Morphological State Transition Dynamics in EGF-Induced Epithelial to Mesenchymal Transition.Journal of clinical medicine · 2019Article
- Expansion microscopy: enabling single cell analysis in intact biological systems.The FEBS journal · 2019Review
- Structural robustness of mammalian transcription factor networks reveals plasticity across development.Scientific reports · 2018Article
- Reverse-engineering of gene networks for regulating early blood development from single-cell measurements.BMC medical genomics · 2017Article
- Using single-cell multiple omics approaches to resolve tumor heterogeneity.Clinical and translational medicine · 2017Review
- Quantifying enzyme activity in living cells.The Journal of biological chemistry · 2017Article
- Single-Cell RNA Sequencing Reveals Expanded Clones of Islet Antigen-Reactive CD4Journal of immunology (Baltimore, Md. : 1950) · 2017Article
- Cell population structure prior to bifurcation predicts efficiency of directed differentiation in human induced pluripotent cells.Proceedings of the National Academy of Sciences of the United States of America · 2017Article
- Single cell proteomics in biomedicine: High-dimensional data acquisition, visualization, and analysis.Proteomics · 2017Review
- Cell Fate Decision as High-Dimensional Critical State Transition.PLoS biology · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Single-cell analyses of transcript and protein expression profiles-more precisely, single-cell resolution analysis of molecular profiles of cell populations-have now entered the center stage with widespread applications of single-cell qPCR, single-cell RNA-Seq and CyTOF. These high-dimensional population snapshot techniques are complemented by low-dimensional time-resolved, microscopy-based monitoring methods. Both fronts of advance have exposed a rich heterogeneity of cell states within uniform cell populations in many biological contexts, producing a new kind of data that has triggered computational analysis methods for data visualization, dimensionality reduction, and cluster (subpopulation) identification. The next step is now to go beyond collecting data and correlating data points: to connect the dots, that is, to understand what actually underlies the identified data patterns. This entails interpreting the 'clouds of points' in state space as a manifestation of the underlying molecular regulatory network. In that way control of cell state dynamics can be formalized as a quasi-potential landscape, as first proposed by Waddington. We summarize key methods of data acquisition and computational analysis and explain the principles that link the single-cell resolution measurements to dynamical systems theory.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.