Practical AI-based cell extraction and spatial
statistics for large 3D bone marrow tissue images
statistics for large 3D bone marrow tissue images
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
Although the molecular regulation of hematopoiesis is well characterized, the spatial organization of hematopoietic cells within bone marrow (BM) remains unclear. Advances in microscopy have produced increasingly detailed images of murine BM, yet accurate and scalable methods to extract and analyze these complex datasets are limited. The high cellular density of the BM complicates image segmentation, and current spatial analyses are often restricted to pairwise comparisons, unsuitable for investigating interactions between more than two cell types simultaneously. To overcome these limitations, we developed PACESS, a readily applicable neural network-based framework that classifies hundreds of thousands of cells in 3D BM samples and applies spatial statistical methods to evaluate multicellular interactions. Using PACESS, we investigate the spatial organization of T cells, megakaryocytes and leukemic cells, revealing that distinct leukemic clusters generate diverse, previously unrecognized neighborhood within the same BM cavity. PACESS thus provides a powerful tool to dissect BM architecture.
Editor(s)
Zearfoss, Ruth
Date Issued
2026-03-23
Date Acceptance
2026-01-27
Citation
Cell Reports: Methods, 2026, 6 (3)
ISSN
2667-2375
Publisher
Elsevier
Journal / Book Title
Cell Reports: Methods
Volume
6
Issue
3
Copyright Statement
© 2026 The Authors. Published by Elsevier Inc.
License URL
Identifier
10.1016/j.crmeth.2026.101334
Publication Status
Published
Article Number
101334
Date Publish Online
2026-03-13