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Deep learning-based predictive classification of functional subpopulations of hematopoietic stem cells and multipotent progenitors.


ABSTRACT:

Background

Hematopoietic stem cells (HSCs) and multipotent progenitors (MPPs) play a pivotal role in maintaining lifelong hematopoiesis. The distinction between stem cells and other progenitors, as well as the assessment of their functions, has long been a central focus in stem cell research. In recent years, deep learning has emerged as a powerful tool for cell image analysis and classification/prediction.

Methods

In this study, we explored the feasibility of employing deep learning techniques to differentiate murine HSCs and MPPs based solely on their morphology, as observed through light microscopy (DIC) images.

Results

After rigorous training and validation using extensive image datasets, we successfully developed a three-class classifier, referred to as the LSM model, capable of reliably distinguishing long-term HSCs, short-term HSCs, and MPPs. The LSM model extracts intrinsic morphological features unique to different cell types, irrespective of the methods used for cell identification and isolation, such as surface markers or intracellular GFP markers. Furthermore, employing the same deep learning framework, we created a two-class classifier that effectively discriminates between aged HSCs and young HSCs. This discovery is particularly significant as both cell types share identical surface markers yet serve distinct functions. This classifier holds the potential to offer a novel, rapid, and efficient means of assessing the functional states of HSCs, thus obviating the need for time-consuming transplantation experiments.

Conclusion

Our study represents the pioneering use of deep learning to differentiate HSCs and MPPs under steady-state conditions. This novel and robust deep learning-based platform will provide a basis for the future development of a new generation stem cell identification and separation system. It may also provide new insight into the molecular mechanisms underlying stem cell self-renewal.

SUBMITTER: Wang S 

PROVIDER: S-EPMC10935795 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature

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Publications

Deep learning-based predictive classification of functional subpopulations of hematopoietic stem cells and multipotent progenitors.

Wang Shen S   Han Jianzhong J   Huang Jingru J   Islam Khayrul K   Shi Yuheng Y   Zhou Yuyuan Y   Kim Dongwook D   Zhou Jane J   Lian Zhaorui Z   Liu Yaling Y   Huang Jian J  

Stem cell research & therapy 20240313 1


<h4>Background</h4>Hematopoietic stem cells (HSCs) and multipotent progenitors (MPPs) play a pivotal role in maintaining lifelong hematopoiesis. The distinction between stem cells and other progenitors, as well as the assessment of their functions, has long been a central focus in stem cell research. In recent years, deep learning has emerged as a powerful tool for cell image analysis and classification/prediction.<h4>Methods</h4>In this study, we explored the feasibility of employing deep learn  ...[more]

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