In this section
@article{Adams:2026,
author = {Adams, G and Tissot, F and Liu, C and Brunsdon, C and Duffy, K and Lo, Celso C},
journal = {Cell Reports: Methods},
title = {Practical AI-based cell extraction and spatialstatistics for large 3D bone marrow tissue images},
volume = {6},
year = {2026}
}
TY - JOUR
AB - 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.
AU - Adams,G
AU - Tissot,F
AU - Liu,C
AU - Brunsdon,C
AU - Duffy,K
AU - Lo,Celso C
PY - 2026///
SN - 2667-2375
TI - Practical AI-based cell extraction and spatialstatistics for large 3D bone marrow tissue images
T2 - Cell Reports: Methods
VL - 6
ER -
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