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Machine learning of hematopoietic stem cell divisions from paired daughter cell expression profiles reveals effects of aging on self-renewal

Machine learning of hematopoietic stem cell divisions from paired daughter cell expression profiles reveals effects of aging on self-renewal
Machine learning of hematopoietic stem cell divisions from paired daughter cell expression profiles reveals effects of aging on self-renewal

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. Changes in stem cell activity may underpin aging. We trained an artificial neural network to interpret gene-expression patterns of paired daughter cells from individual stem cell divisions. Our results show that the self-renewal ability of individual stem cells alters substantially with age and help explain why stem cell numbers increase through life, yet regenerative potency declines.

aging, artificial neural network, hematopoietic stem cell, machine learning, self-renewal
2405-4712
640-652.e5
Arai, Fumio
55b7859b-98f0-450a-b355-74e1f1e1945d
Stumpf, Patrick S
dfdb286c-b321-46d3-8ba2-85b3b4a7f092
Matsumoto Ikushima, Yoshiko
85ccf5ba-9e7d-4ab6-9906-06ec94d15a0d
Hosokawa, Kentaro
8868fb61-102f-4776-9270-77bf23b52f65
Roch, Aline
433e5bce-bcfb-450c-91a1-20980d07af95
Lutolf, Matthias
1a620234-1263-4225-97f6-43db49bd960d
Suda, Toshio
897b6c6e-ada7-4803-bc44-19041b03180b
Macarthur, Benjamin
2c0476e7-5d3e-4064-81bb-104e8e88bb6b
Arai, Fumio
55b7859b-98f0-450a-b355-74e1f1e1945d
Stumpf, Patrick S
dfdb286c-b321-46d3-8ba2-85b3b4a7f092
Matsumoto Ikushima, Yoshiko
85ccf5ba-9e7d-4ab6-9906-06ec94d15a0d
Hosokawa, Kentaro
8868fb61-102f-4776-9270-77bf23b52f65
Roch, Aline
433e5bce-bcfb-450c-91a1-20980d07af95
Lutolf, Matthias
1a620234-1263-4225-97f6-43db49bd960d
Suda, Toshio
897b6c6e-ada7-4803-bc44-19041b03180b
Macarthur, Benjamin
2c0476e7-5d3e-4064-81bb-104e8e88bb6b

Arai, Fumio, Stumpf, Patrick S, Matsumoto Ikushima, Yoshiko, Hosokawa, Kentaro, Roch, Aline, Lutolf, Matthias, Suda, Toshio and Macarthur, Benjamin (2020) Machine learning of hematopoietic stem cell divisions from paired daughter cell expression profiles reveals effects of aging on self-renewal. Cell Systems, 11 (6), 640-652.e5. (doi:10.1016/j.cels.2020.11.004).

Record type: Article

Abstract

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. Changes in stem cell activity may underpin aging. We trained an artificial neural network to interpret gene-expression patterns of paired daughter cells from individual stem cell divisions. Our results show that the self-renewal ability of individual stem cells alters substantially with age and help explain why stem cell numbers increase through life, yet regenerative potency declines.

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Accepted/In Press date: 10 November 2020
e-pub ahead of print date: 8 December 2020
Published date: 16 December 2020
Keywords: aging, artificial neural network, hematopoietic stem cell, machine learning, self-renewal

Identifiers

Local EPrints ID: 445274
URI: http://eprints.soton.ac.uk/id/eprint/445274
ISSN: 2405-4712
PURE UUID: eab40636-695c-4e29-862b-7606431268f9
ORCID for Patrick S Stumpf: ORCID iD orcid.org/0000-0003-0862-0290

Catalogue record

Date deposited: 30 Nov 2020 17:31
Last modified: 13 Nov 2021 05:30

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Contributors

Author: Fumio Arai
Author: Patrick S Stumpf ORCID iD
Author: Yoshiko Matsumoto Ikushima
Author: Kentaro Hosokawa
Author: Aline Roch
Author: Matthias Lutolf
Author: Toshio Suda

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