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 m

SUBMITTER: Wang S 

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

REPOSITORIES: biostudies-literature