Reconstructing the in vivo dynamics of hematopoietic stem cells from telomere length distributions
- Max Planck Institute for Evolutionary Biology, Germany
- Rheinisch-Westfälische Technische Hochschule Aachen University Hospital, Germany
- University Hospital of Zürich, Switzerland
- Mayo Clinic, United States
Abstract
We investigate the in vivo patterns of stem cell divisions in the human hematopoietic system throughout life. In particular, we analyze the shape of telomere length distributions underlying stem cell behavior within individuals. Our mathematical model shows that these distributions contain a fingerprint of the progressive telomere loss and the fraction of symmetric cell proliferations. Our predictions are tested against measured telomere length distributions in humans across all ages, collected from lymphocyte and granulocyte sorted telomere length data of 356 healthy individuals, including 47 cord blood and 28 bone marrow samples. We find an increasing stem cell pool during childhood and adolescence and an approximately maintained stem cell population in adults. Furthermore, our method is able to detect individual differences from a single tissue sample, i.e. a single snapshot. Prospectively, this allows us to compare cell proliferation between individuals and identify abnormal stem cell dynamics, which affects the risk of stem cell related diseases.
Article and author information
Author details
Reviewing Editor
- Frank Jülicher, Max Planck Institute for the Physics of Complex Systems, Germany
Ethics
Human subjects: All samples and the approval for publication were taken with informed consent of all patients at the University Hospital Aachen according to the guidelines and the approval of the ethics committees at the University Hospital Aachen.
Version history
- Received: May 13, 2015
- Accepted: October 14, 2015
- Accepted Manuscript published: October 15, 2015 (version 1)
- Version of Record published: January 27, 2016 (version 2)
Copyright
© 2015, Werner et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 2,823
- views
-
- 709
- downloads
-
- 72
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Reconstructing the in vivo dynamics of hematopoietic stem cells from telomere length distributions
eLife 4:e08687.
https://doi.org/10.7554/eLife.08687
Further reading
-
- Computational and Systems Biology
- Developmental Biology
The gain-of-function mutation in the TALK-1 K+ channel (p.L114P) is associated with maturity-onset diabetes of the young (MODY). TALK-1 is a key regulator of β-cell electrical activity and glucose-stimulated insulin secretion. The KCNK16 gene encoding TALK-1 is the most abundant and β-cell-restricted K+ channel transcript. To investigate the impact of KCNK16 L114P on glucose homeostasis and confirm its association with MODY, a mouse model containing the Kcnk16 L114P mutation was generated. Heterozygous and homozygous Kcnk16 L114P mice exhibit increased neonatal lethality in the C57BL/6J and the CD-1 (ICR) genetic background, respectively. Lethality is likely a result of severe hyperglycemia observed in the homozygous Kcnk16 L114P neonates due to lack of glucose-stimulated insulin secretion and can be reduced with insulin treatment. Kcnk16 L114P increased whole-cell β-cell K+ currents resulting in blunted glucose-stimulated Ca2+ entry and loss of glucose-induced Ca2+ oscillations. Thus, adult Kcnk16 L114P mice have reduced glucose-stimulated insulin secretion and plasma insulin levels, which significantly impairs glucose homeostasis. Taken together, this study shows that the MODY-associated Kcnk16 L114P mutation disrupts glucose homeostasis in adult mice resembling a MODY phenotype and causes neonatal lethality by inhibiting islet insulin secretion during development. These data suggest that TALK-1 is an islet-restricted target for the treatment for diabetes.
-
- Computational and Systems Biology
The reconstruction of complete microbial metabolic pathways using ‘omics data from environmental samples remains challenging. Computational pipelines for pathway reconstruction that utilize machine learning methods to predict the presence or absence of KEGG modules in incomplete genomes are lacking. Here, we present MetaPathPredict, a software tool that incorporates machine learning models to predict the presence of complete KEGG modules within bacterial genomic datasets. Using gene annotation data and information from the KEGG module database, MetaPathPredict employs deep learning models to predict the presence of KEGG modules in a genome. MetaPathPredict can be used as a command line tool or as a Python module, and both options are designed to be run locally or on a compute cluster. Benchmarks show that MetaPathPredict makes robust predictions of KEGG module presence within highly incomplete genomes.
