The goal of this research is to quantify the effect of aging on the dynamics and lineage of adult neural stem cells (NSCs). While it is known that there is reduced NSC activity with aging and that this decline may play a role in aging and age-related diseases, a number of fundamental questions remain. Which aspects of stem cell activity change with aging? Are there changes in the progenitor cell population, their movements within the niche and their proliferation kinetics? Do progenitor cells switch fate with aging, making more glial cells rather than neurons or do they undergo senescence? Another important unresolved issue is whether the declines in NSC activity are the result of cell autonomous changes or due to aging of the stem cell niche. The proposed research is designed to address these important unanswered questions. We will determine the lineage and dynamic properties of NSCs in young adult and aged mouse brains. This collaborative effort involving stem cell biologists and computer engineers will focus on direct observation - capturing time-lapse image sequences showing the dynamics of NSCs within the niche and their production of identified progeny. The live cell imaging results will be quantified and analyzed with state-of-the-art automated software tools for tracking stem cells and generating lineage trees, enabling us to accurately identify differences in NSC behavior at different ages. Applying techniques developed for NSC lineage analysis in embryos to the adult system will allow a much more complete understanding of stem cell properties and progenitor relationships, of the construction of adult lineage trees and how these elements change with age. Specifically, the proposed research will determine and compare the lineage and dynamic properties of NSCs in the subventricular zone (SVZ) in young and aged mice by long-term time lapse microscopy. It will provide insight into the important question of whether decreased neurogenesis with aging is cell autonomous or is due to changes in the niche by transplanting stem cells from young to aged and from aged to young SVZ, and measuring changes in the integration, lineage and dynamic properties of the transplanted cells. Furthermore, it will investigate molecular events underlying age-related declines in NSC activity by exploring the role that the chemokine SDF1 and the corresponding CXCR4 receptor play in the aging process.SDF/CXCR4 signaling declines with age in the brain; reduced CXCR4 expression has been suggested to contribute to a decline in stem cell activity in other tissues. Here we will measure SDF1 andCXCR4 levels in young and aged brains. SDF1 /CXCR4 signaling will be blocked using a specific antagonist or conditional knockout of CXCR4 in the NSC lineage in young and aged mice and changes in lineage progression will be analyzed. This work will provide an understanding of how changes in progenitor behavior might contribute to diseases of aging such as cancer and memory loss and begin to identify molecular targets to alleviate aging- related neurodegenerative changes in the adult stem cell niche.

Public Health Relevance

With aging and age-related diseases, stem cells in the nervous system decline, which can contribute to deficits in learning and memory. We will study age-related changes in neural stem cells and their environment using state of the art live imaging and automated cell tracking software to measure critical features of stem cell proliferation, movement and differentiation into nervous system cells. This work will provide an understanding of how changes in stem cell behavior might contribute to diseases of aging such as cancer and memory loss and has the potential to identify molecular targets to alleviate aging-related neurodegenerative changes.

Agency
National Institute of Health (NIH)
Institute
National Institute on Aging (NIA)
Type
Research Project (R01)
Project #
3R01AG041861-04S1
Application #
8987796
Study Section
Cellular Mechanisms in Aging and Development Study Section (CMAD)
Program Officer
Wise, Bradley C
Project Start
2012-06-01
Project End
2017-05-31
Budget Start
2015-09-01
Budget End
2016-05-31
Support Year
4
Fiscal Year
2015
Total Cost
$100,000
Indirect Cost
$41,176
Name
Regenerative Research Foundation
Department
Type
DUNS #
786575667
City
Rensselaer
State
NY
Country
United States
Zip Code
12144
Winter, Mark; Mankowski, Walter; Wait, Eric et al. (2018) Separating Touching Cells using Pixel Replicated Elliptical Shape Models. IEEE Trans Med Imaging :
Apostolopoulou, Maria; Kiehl, Thomas R; Winter, Mark et al. (2017) Non-monotonic Changes in Progenitor Cell Behavior and Gene Expression during Aging of the Adult V-SVZ Neural Stem Cell Niche. Stem Cell Reports 9:1931-1947
Dumont, Courtney M; Piselli, Jennifer M; Kazi, Nadeem et al. (2017) Factors Released from Endothelial Cells Exposed to Flow Impact Adhesion, Proliferation, and Fate Choice in the Adult Neural Stem Cell Lineage. Stem Cells Dev 26:1199-1213
Valm, Alex M; Cohen, Sarah; Legant, Wesley R et al. (2017) Applying systems-level spectral imaging and analysis to reveal the organelle interactome. Nature 546:162-167
Winter, Mark; Mankowski, Walter; Wait, Eric et al. (2016) LEVER: software tools for segmentation, tracking and lineaging of proliferating cells. Bioinformatics 32:3530-3531
De La Hoz, Edgar Cardenas; Winter, Mark R; Apostolopoulou, Maria et al. (2016) Measuring Process Dynamics and Nuclear Migration for Clones of Neural Progenitor Cells. Comput Vis ECCV 9913:291-305
Bjornsson, Christopher S; Apostolopoulou, Maria; Tian, Yangzi et al. (2015) It takes a village: constructing the neurogenic niche. Dev Cell 32:435-46
Cohen, Andrew R; Vitányi, Paul M B (2015) Normalized Compression Distance of Multisets with Applications. IEEE Trans Pattern Anal Mach Intell 37:1602-14
Winter, Mark R; Liu, Mo; Monteleone, David et al. (2015) Computational Image Analysis Reveals Intrinsic Multigenerational Differences between Anterior and Posterior Cerebral Cortex Neural Progenitor Cells. Stem Cell Reports 5:609-20
Wait, Eric; Winter, Mark; Bjornsson, Chris et al. (2014) Visualization and correction of automated segmentation, tracking and lineaging from 5-D stem cell image sequences. BMC Bioinformatics 15:328

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