ReviewBiotechnology journal2013
The role of single-cell analyses in understanding cell lineage commitment.
Review in Biotechnology journal, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
1 citing paper in PubMed.
- Single-molecule analysis of myocyte differentiation reveals bimodal lineage commitment.Integrative biology : quantitative biosciences from nano to macro · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
The study of cell lineage commitment is critical for improving our understanding of tissue development and regeneration, and for realizing stem cell-based therapies and engineered tissue replacements. Recently, the discovery of an unanticipated degree of variability in fundamental biological processes, including divergent responses of genetically identical cells to various stimuli, has provided mechanistic insight into cellular decision making and the collective behavior of cell populations. Therefore, the study of lineage commitment with single-cell resolution could provide greater knowledge of cellular differentiation mechanisms and the influence of noise on cellular processes. This will require the adoption of new technologies for single-cell analysis as traditional methods typically measure average values of bulk population behavior. This review discusses the recent developments in methods for analyzing the behavior of individual cells, and how these approaches are leading to a deeper understanding and better control of cellular decision making.
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What OpenQuestion holds
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.