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The process of blood cell formation is called hematopoiesis. Hematopoiesis starts early during development, on the seventh day of embryogenesis. This phase of hematopoiesis is called the primitive wave, wherein the extraembryonic yolk sac allows the production of erythroid cells and endothelial cells from a common precursor called hemangioblast. The erythroid cells provide oxygen to support the growth of the rapidly dividing embryo. Hemangioblasts later develop into hematopoietic stem cells or...
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Bone marrow transplant is a potential cure for several diseases, including cancer and specific genetic disorders. Notably, this procedure is applicable for patients suffering from aplastic anemia, certain types of leukemia, severe combined immunodeficiency disease (SCID), Hodgkin's disease, non-Hodgkin's lymphoma, multiple myeloma, thalassemia, sickle-cell disease, and certain cancers.
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Updated: Jan 11, 2026

Author Spotlight: Analyzing Bone Marrow Microenvironment in Murine Hematological Malignancies
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Normal Hematopoietic Stem Cells in Leukemic Bone Marrow Environment Undergo Morphological Changes Identifiable by

Dongguang Li1, Athena Li2, Ngoc DeSouza1

  • 1Division of Hematology/Oncology, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA.

International Journal of Molecular Sciences
|November 13, 2025
PubMed
Summary

Artificial intelligence can now identify distinct morphological changes in non-leukemic hematopoietic stem cells (HSCs) within leukemia bone marrow. This AI-driven approach offers a new way to monitor disease severity and predict therapy responses in patients.

Keywords:
artificial intelligencehematopoietic stem cellsleukemia stem cellspolycythemia vera

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Area of Science:

  • Hematology
  • Computational Biology
  • Oncology

Background:

  • Leukemia stem cells (LSCs) drive hematologic malignancies, impacting non-leukemic hematopoietic stem cells (HSCs) in the bone marrow.
  • Morphological changes in non-leukemic HSCs are hypothesized to correlate with disease severity and treatment outcomes.
  • Current methods cannot detect these subtle morphological stem cell changes.

Purpose of the Study:

  • To determine if Artificial Intelligence (AI) deep learning can identify and quantify non-leukemic HSCs in a leukemia environment.
  • To investigate if non-leukemic HSCs exhibit AI-recognizable morphological differences compared to normal HSCs.
  • To establish a proof-of-concept for using AI to assess disease prognosis and therapy response.

Main Methods:

  • Utilized a polycythemia vera (PV) mouse model with JAK2V617F oncogene.
  • Applied AI deep learning to analyze single-cell images of bone marrow cells.
  • Quantified and distinguished between non-JAK2V617F HSCs, LSCs, and normal HSCs.

Main Results:

  • AI accurately distinguished non-JAK2V617F HSCs from LSCs (>96% accuracy).
  • AI identified significant morphological differences between non-JAK2V617F HSCs from PV mice and normal HSCs (>98% accuracy).
  • Demonstrated that non-leukemic HSCs undergo AI-detectable morphological alterations in a leukemia microenvironment.

Conclusions:

  • Non-leukemic HSCs display unique, AI-recognizable morphological features in the leukemia bone marrow.
  • AI analysis of HSC morphology holds potential for assessing therapy response and prognosis in hematologic malignancies.
  • This approach could lead to improved patient management strategies for diseases like PV.