scholarly journals Information Theory and Stem Cell Biology

2017 ◽  
Author(s):  
Rosanna C. G. Smith ◽  
Ben D. MacArthur
Keyword(s):  
Information Theory ◽  
Stem Cell ◽  
Single Cell ◽  
Cell Biology ◽  
Cell Dynamics ◽  
Cell Behaviour ◽  
Single Cell Profiling ◽  
Cell To Cell Variability ◽  
Near Future ◽  
Cell Data

AbstractPurpose of ReviewTo outline how ideas from Information Theory may be used to analyze single cell data and better understand stem cell behaviour.Recent findingsRecent technological breakthroughs in single cell profiling have made it possible to interrogate cell-to-cell variability in a multitude of contexts, including the role it plays in stem cell dynamics. Here we review how measures from information theory are being used to extract biological meaning from the complex, high-dimensional and noisy datasets that arise from single cell profiling experiments. We also discuss how concepts linking information theory and statistical mechanics are being used to provide insight into cellular identity, variability and dynamics.SummaryWe provide a brief introduction to some basic notions from information theory and how they may be used to understand stem cell identities at the single cell level. We also discuss how work in this area might develop in the near future.

scholarly journals Mapping single-cell atlases throughout Metazoa unravels cell type evolution

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Alexander J Tarashansky ◽  
Jacob M Musser ◽  
Margarita Khariton ◽  
Pengyang Li ◽  
Detlev Arendt ◽  
...  
Keyword(s):  
Stem Cell ◽  
Single Cell ◽  
Cell Types ◽  
The Self ◽  
Cell Type ◽  
Germ Layers ◽  
Animal Evolution ◽  
Self Assembling ◽  
Animal Phyla ◽  
Cell Data

Comparing single-cell transcriptomic atlases from diverse organisms can elucidate the origins of cellular diversity and assist the annotation of new cell atlases. Yet, comparison between distant relatives is hindered by complex gene histories and diversifications in expression programs. Previously, we introduced the self-assembling manifold (SAM) algorithm to robustly reconstruct manifolds from single-cell data (Tarashansky et al., 2019). Here, we build on SAM to map cell atlas manifolds across species. This new method, SAMap, identifies homologous cell types with shared expression programs across distant species within phyla, even in complex examples where homologous tissues emerge from distinct germ layers. SAMap also finds many genes with more similar expression to their paralogs than their orthologs, suggesting paralog substitution may be more common in evolution than previously appreciated. Lastly, comparing species across animal phyla, spanning mouse to sponge, reveals ancient contractile and stem cell families, which may have arisen early in animal evolution.

scholarly journals Stem cell dynamics in mouse hair follicles: A story from cell division counting and single cell lineage tracing

Cell Cycle ◽  
2010 ◽  
Vol 9 (8) ◽  
pp. 1504-1510 ◽  
Author(s):  
Ying V. Zhang ◽  
Brian S. White ◽  
David I. Shalloway ◽  
Tudorita Tumbar
Keyword(s):  
Stem Cell ◽  
Cell Division ◽  
Single Cell ◽  
Cell Lineage ◽  
Hair Follicles ◽  
Lineage Tracing ◽  
Cell Dynamics

Su1736 Live Imaging of Single Cell Reveals Single Stem Cell Dynamics in an Organoid Derived From Murine Small Intestine

2013 ◽  
Vol 144 (5) ◽  
pp. S-464
Author(s):  
Nobukatsu Horita ◽  
Kiichiro Tsuchiya ◽  
Shuji Hibiya ◽  
Keita Fukushima ◽  
Yoshihito Kano ◽  
...  
Keyword(s):  
Small Intestine ◽  
Stem Cell ◽  
Single Cell ◽  
Live Imaging ◽  
Cell Dynamics ◽  
Murine Small Intestine

scholarly journals Machine learning of stem cell identities from single-cell expression data via regulatory network archetypes

2017 ◽  
Author(s):  
Patrick S Stumpf ◽  
Ben D MacArthur
Keyword(s):  
Machine Learning ◽  
Stem Cells ◽  
Stem Cell ◽  
Single Cell ◽  
Regulatory Network ◽  
Cell Biology ◽  
Embryonic Stem ◽  
Network Activity ◽  
Expression Data ◽  
Cell Expression

AbstractThe molecular regulatory network underlying stem cell pluripotency has been intensively studied, and we now have a reliable ensemble model for the ‘average’ pluripotent cell. However, evidence of significant cell-to-cell variability suggests that the activity of this network varies within individual stem cells, leading to differential processing of environmental signals and variability in cell fates. Here, we adapt a method originally designed for face recognition to infer regulatory network patterns within individual cells from single-cell expression data. Using this method we identify three distinct network configurations in cultured mouse embryonic stem cells – corresponding to naïve and formative pluripotent states and an early primitive endoderm state – and associate these configurations with particular combinations of regulatory network activity archetypes that govern different aspects of the cell’s response to environmental stimuli, cell cycle status and core information processing circuitry. These results show how variability in cell identities arise naturally from alterations in underlying regulatory network dynamics and demonstrate how methods from machine learning may be used to better understand single cell biology, and the collective dynamics of cell communities.

scholarly journals Advancing haematopoietic stem and progenitor cell biology through single-cell profiling

FEBS Letters ◽  
2016 ◽  
Vol 590 (22) ◽  
pp. 4052-4067 ◽  
Author(s):  
Fiona K. Hamey ◽  
Sonia Nestorowa ◽  
Nicola K. Wilson ◽  
Berthold Göttgens
Keyword(s):  
Single Cell ◽  
Cell Biology ◽  
Progenitor Cell ◽  
Single Cell Profiling

scholarly journals Stem cell lineage survival as a noisy competition for niche access

2020 ◽  
Vol 117 (29) ◽  
pp. 16969-16975 ◽  
Author(s):  
Bernat Corominas-Murtra ◽  
Colinda L. G. J. Scheele ◽  
Kasumi Kishi ◽  
Saskia I. J. Ellenbroek ◽  
Benjamin D. Simons ◽  
...  
Keyword(s):  
Stem Cell ◽  
Cell Biology ◽  
Functional Dependence ◽  
Cell Lineage ◽  
Intrinsic Property ◽  
Conveyor Belt ◽  
Cell Number ◽  
Emergent Behavior ◽  
Cell Dynamics ◽  
Cell Potential

Understanding to what extent stem cell potential is a cell-intrinsic property or an emergent behavior coming from global tissue dynamics and geometry is a key outstanding question of systems and stem cell biology. Here, we propose a theory of stem cell dynamics as a stochastic competition for access to a spatially localized niche, giving rise to a stochastic conveyor-belt model. Cell divisions produce a steady cellular stream which advects cells away from the niche, while random rearrangements enable cells away from the niche to be favorably repositioned. Importantly, even when assuming that all cells in a tissue are molecularly equivalent, we predict a common (“universal”) functional dependence of the long-term clonal survival probability on distance from the niche, as well as the emergence of a well-defined number of functional stem cells, dependent only on the rate of random movements vs. mitosis-driven advection. We test the predictions of this theory on datasets of pubertal mammary gland tips and embryonic kidney tips, as well as homeostatic intestinal crypts. Importantly, we find good agreement for the predicted functional dependency of the competition as a function of position, and thus functional stem cell number in each organ. This argues for a key role of positional fluctuations in dictating stem cell number and dynamics, and we discuss the applicability of this theory to other settings.

scholarly journals Ageing affects DNA methylation drift and transcriptional cell-to-cell variability in mouse muscle stem cells

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Irene Hernando-Herraez ◽  
Brendan Evano ◽  
Thomas Stubbs ◽  
Pierre-Henri Commere ◽  
Marc Jan Bonder ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Stem Cells ◽  
Stem Cell ◽  
Single Cell ◽  
Cell Number ◽  
Transcriptional Networks ◽  
Mouse Muscle ◽  
Muscle Stem Cells ◽  
Age Related ◽  
Cell To Cell Variability

Abstract Age-related tissue alterations have been associated with a decline in stem cell number and function. Although increased cell-to-cell variability in transcription or epigenetic marks has been proposed to be a major hallmark of ageing, little is known about the molecular diversity of stem cells during ageing. Here we present a single cell multi-omics study of mouse muscle stem cells, combining single-cell transcriptome and DNA methylome profiling. Aged cells show a global increase of uncoordinated transcriptional heterogeneity biased towards genes regulating cell-niche interactions. We find context-dependent alterations of DNA methylation in aged stem cells. Importantly, promoters with increased methylation heterogeneity are associated with increased transcriptional heterogeneity of the genes they drive. These results indicate that epigenetic drift, by accumulation of stochastic DNA methylation changes in promoters, is associated with the degradation of coherent transcriptional networks during stem cell ageing. Furthermore, our observations also shed light on the mechanisms underlying the DNA methylation clock.

scholarly journals BABEL enables cross-modality translation between multi-omic profiles at single-cell resolution

2020 ◽  
Author(s):  
Kevin E. Wu ◽  
Kathryn E. Yost ◽  
Howard Y. Chang ◽  
James Zou
Keyword(s):  
Single Cell ◽  
Cell Biology ◽  
Data Exploration ◽  
Grand Challenge ◽  
Fine Grained ◽  
Powerful Approach ◽  
Technical Innovations ◽  
Cell Data ◽  
Human And Mouse

AbstractSimultaneous profiling of multi-omic modalities within a single cell is a grand challenge for single-cell biology. While there have been impressive technical innovations demonstrating feasibility – for example generating paired measurements of scRNA-seq and scATAC-seq – wide-spread application of joint profiling is challenging due to the experimental complexity, noise, and cost. Here we introduce BABEL, a deep learning method that translates between the transcriptome and chromatin profiles of a single cell. Leveraging a novel interoperable neural network model, BABEL can generate scRNA-seq directly from a cell’s scATAC-seq, and vice versa. This makes it possible to computationally synthesize paired multi-omic measurements when only one modality is experimentally available. Across several paired scRNA-seq and scATAC-seq datasets in human and mouse, we validate that BABEL accurately translates between these modalities for individual cells. BABEL also generalizes well to new biological contexts not seen during training. For example, starting from scATAC-seq of patient derived basal cell carcinoma (BCC), BABEL generated scRNA-seq that enabled fine-grained classification of complex cell states, despite having never seen BCC data. These predictions are comparable to analyses of the experimental BCC scRNA-seq data. We further show that BABEL can incorporate additional single-cell data modalities, such as CITE-seq, thus enabling translation across chromatin, RNA, and protein. BABEL offers a powerful approach for data exploration and hypothesis generation.

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