Machine Learning of Hematopoietic Stem Cell Divisions from Paired Daughter Cell Expression Profiles Reveals Effects of Aging on Self-Renewal
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Changes in stem cell activity may underpin aging. However, these changes are not completely understood. Here, we combined single-cell profiling with machine learning and in vivo functional studies to explore how hematopoietic stem cell (HSC) divisions patterns evolve with age. We first trained an artificial neural network (ANN) to accurately identify cell types in the hematopoietic hierarchy and predict their age from single-cell gene-expression patterns. We then used this ANN to compare identities of daughter cells immediately after HSC divisions and found that the self-renewal ability of individual HSCs declines with age. Furthermore, while HSC cell divisions are deterministic and intrinsically regulated in young and old age, they are variable and niche sensitive in mid-life. These results indicate that the balance between intrinsic and extrinsic regulation of stem cell activity alters substantially with age and help explain why stem cell numbers increase through life, yet regenerative potency declines.
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Machine learning models of HSC division as a function of increasing age suggested that HSC division is intrinsically regulated in young adults and old adults, but is more extrinsically regulated in middle age.
“Consistent with this possibility, machine learning models of HSC division as a function of increasing age suggested that HSC division is intrinsically regulated in young adults and old adults, but is more extrinsically regulated in middle age47.”
From Ageing and rejuvenation of tissue stem cells and their niches (2023) · cites this paper as [47]
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Sorted by citations
Ageing and rejuvenation of tissue stem cells and their niches
Brunet, A., Goodell, M. A., & Rando, T. A. (2023) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-022-00510-w · Cited by 407 · see citing works
Application of Machine Learning in 3D Bioprinting: Focus on Development of Big Data and Digital Twin
An, J., Chua, C. K., & Mironov, V. (2021) · International Journal of Bioprinting · doi:10.18063/ijb.v7i1.342 · Cited by 122 · see citing works
“ML models have also been used in in vitro study for identification of cell signature genes out of complex gene expression profiles among different cell groups[13,14].”
MethodSu, T., Hauenstein, J., Somuncular, E., Dumral, Ö., Leonard, E., Gustafsson, C., Tzortzis, E., Forlani, A., Johansson, A.-S., Qian, H., et al. (2023) · bioRxiv · doi:10.1038/s41467-024-52318-1 · Cited by 56 · see citing works
“In juvenile mice, this period involves active tissue growth and is characterized by high self-renewal activity 56 , which could contribute to the higher likelihood of lymphoid malignancies in children 5 .”
A primer on machine learning techniques for genomic applications
Monaco, A., Pantaleo, E., Amoroso, N., Lacalamita, A., Giudice, C. L., Fonzino, A., Fosso, B., Picardi, E., Tangaro, S., Pesole, G., et al. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.07.021 · Cited by 41 · see citing works
“An interesting example is offered by Arai et al. [50] in which the authors combined single cell gene expression profiles with machine learning analysis and in vivo functional studies to explore how hematopoietic stem cell (HSC) divisions rate change with ageing.”
BackgroundDecoding Aging Hallmarks at the Single-Cell Level
Ma, S., Chi, X., Cai, Y., Ji, Z., Wang, S., Ren, J., & Liu, G. (2023) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-020722-120642 · Cited by 37 · see citing works
Mitochondria pleiotropism in stem cell senescence: Mechanisms and therapeutic approaches
Mas‐Bargues, C. (2023) · Free Radical Biology and Medicine · doi:10.1016/j.freeradbiomed.2023.09.019 · Cited by 33 · see citing works
Symmetric and asymmetric activation of hematopoietic stem cells
Loeffler, D., & Schroeder, T. (2021) · Current Opinion in Hematology · doi:10.1097/moh.0000000000000644 · Cited by 21 · see citing works
A culture platform to study quiescent hematopoietic stem cells following genome editing
Shiroshita, K., Kobayashi, H., Watanuki, S., Karigane, D., Sorimachi, Y., Fujita, S., Tamaki, S., Haraguchi, M., Itokawa, N., Aoyama, K., et al. (2022) · Cell Reports Methods · doi:10.1016/j.crmeth.2022.100354 · Cited by 11 · see citing works
“The Evi1 expression level predicts HSC capacity; therefore, we monitored Evi1 expression following genome editing of HSCs from Evi1-GFP mice.(70,71) The frequency of Evi1-GFP cells was inversely correlated with the cytokine concentration in culture without electroporation and was highest upon culture in the presence o…”
Yang, X.-Y., Vladimirovich, R. I., Beeraka, N., Satyavathi, A., Kamble, D., Nikolenko, V., Lakshmi, A. N., Basappa, B., Padmanabha Reddy, Y., Fan, R.-T., et al. (2024) · Stem cell research & therapeutics · doi:10.1186/s13287-024-04008-4 · Cited by 9 · see citing works
“It’s important to note that HSCs transition from a fetal to an adult phenotype approximately one month after birth, during a phase characterized by rapid tissue growth and high self-renewal activity [224, 225].”
BackgroundHematopoietic Stem Cell Aging: Mechanisms, Microenvironment Influences, and Rejuvenation Strategies
Cui, J., Li, X., Liu, B., Dong, C., & Chang, Y. (2025) · Bioengineering · doi:10.3390/bioengineering12111166 · Cited by 6 · see citing works
“…have identified key hallmarks of aged HSCs, including dysregulated metabolic pathways, altered division patterns marked by reduced self-renewal and myeloid bias, and dynamic changes in gene expression profiles, such as upregulated inflammatory signaling and impaired DNA repair capacity [31,54,55].”
Mechanistic insights into stem cell aging: Pathways and processes
Fauzi, A., Wong, C. Z. E., Tee, P. Y. E., Yau, A. W. N., Rashid, E. A., Tang, Y. Q., & Chia, A. Y. Y. (2026) · Mechanisms of Ageing and Development · doi:10.1016/j.mad.2026.112160 · Cited by 5 · see citing works
Transcriptomic advances in studies of muscle stem cell aging: From bulk to single-cell and beyond
Kim, S., Pack, S. P., & Rando, T. A. (2026) · Cell Research · doi:10.1038/s41422-026-01240-w · Cited by 1 · see citing works
Dynamical Modeling of Stem Cell Regeneration
Lei, J. (2021) · Lecture notes on mathematical modelling in the life sciences · doi:10.1007/978-3-030-73033-8_6 · Cited by 1 · see citing works
Studying Epigenetics of Cardiovascular Diseases on Chip Guide
Alghamdi, B. A., Al-Johani, I., Alotaibi, B., Ahmed, M. Z., Almazmomi, K., Aloufi, S., & Alshamrani, J. (2022) · Cardiogenetics · doi:10.3390/cardiogenetics12030021 · Cited by 1 · see citing works
Kawahigashi, T., Iwanami, S., Takahashi, M., Bhadury, J., Iwami, S., & Yamazaki, S. (2024) · PLoS ONE · doi:10.1371/journal.pone.0292575 · Cited by 2 · see citing works
Saeidi, H., Hassani, H., Dahaghin, M. S., & Mehrabi, S. (2024) · The European Physical Journal Plus · doi:10.1140/epjp/s13360-024-05308-7 · Cited by 1 · see citing works
Hormoz, S. (2020) · Cell Systems · doi:10.1016/j.cels.2020.11.009
Kawahigashi, T., Iwanami, S., Takahashi, M., Bhadury, J., Iwami, S., & Yamazaki, S. (2022) · Research Square · doi:10.21203/rs.3.rs-2300063/v1
Machine Learning in Stem Cell Research: From Biological Data to Clinical Translation
Nicolaou, A., Hong, Y., Zhou, D., Geukgeuzian, G., Yegneshwaran, V., Ali, N. K., Fraidenraich, D., & Huang, X. (2026) · Computational and Structural Biotechnology Journal · doi:10.34133/csbj.0212
“…epigenomic, and single-cell molecular profiling studies characterize differentiation states, identify lineage-associated molecular signatures, annotate hematopoietic stem and progenitor cell populations, and model lineage commitment and differentiation trajectories [ 37 , 64 , 66 – 71 ].”
- Authors
- Arai, F., Stumpf, P. S., Ikushima, Y. M., Hosokawa, K., Roch, A., Lutolf, M. P., Suda, T., & MacArthur, B. D.
- Affiliations
- Kyushu University (JP)
- University of Southampton (GB)
- National Center for Global Health and Medicine (JP)
- École Polytechnique Fédérale de Lausanne (CH)
- National University of Singapore (SG)
- Corresponding author
- Arai, Fumio
- MacArthur, Ben D.
- ORCID
- Venue
- Cell Systems
- Publisher
- Elsevier BV
- Published
- 2020-12
- Volume
- 11
- Issue
- 6
- Pages
- 640-652.e5
- ISSN
- 2405-4712 (ISSN-L)
- OpenAlex
- W3111173134
- Cited by
- 19
- References
- 89
- Citations by year
- 2026: 3 · 2025: 1 · 2024: 4 · 2023: 2 · 2022: 4 · 2021: 4 · 2020: 1
- FWCI
- 1.74
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Cell Systems (publishedVersion)
- PubMed (publishedVersion)
- ePrints Soton (University of Southampton) (acceptedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- Funders
- University of Tokyo
- Korea Advanced Institute of Science and Technology (17H04208, LS108)
- Takeda Science Foundation
- Ministry of Education, Culture, Sports, Science and Technology
- Topics
- Health Sciences › Medicine › Hematology › Hematopoietic Stem Cell Transplantation
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Health Sciences › Medicine › Genetics › Mesenchymal stem cell research
- Keywords
- hematopoietic stem cells, stem cell aging, self-renewal, cell division, single-cell gene expression, artificial neural networks, stem cell niche, hematopoietic hierarchy
- MeSH terms
- Machine Learning, Aging, Cell Differentiation, Cell Division, Hematopoietic Stem Cells, Humans
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