Practical AI-based cell extraction and spatial statistics for large 3D bone marrow tissue images.

George Adams, Floriane S Tissot, Chang Liu, Cera Mai, Chris Brunsdon, Ken R Duffy, Cristina Lo Celso

Abstract read

In one paragraph

Article in Cell reports methods, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

7 authors.

George AdamsDepartment of Life Sciences, Sir Alexander Fleming Building, Imperial College London, London SW7 2AZ, UK; Centre for Haematology, Department of Immunology and Inflammation, Imperial College London, London W12 0NN, UK.

Floriane S TissotDepartment of Life Sciences, Sir Alexander Fleming Building, Imperial College London, London SW7 2AZ, UK; Centre for Haematology, Department of Immunology and Inflammation, Imperial College London, London W12 0NN, UK; The Francis Crick Institute, London WC2A 3LY, UK.

Chang LiuHamilton Institute, Maynooth University, Maynooth, Co. Kildare, Ireland.

Cera MaiDepartment of Life Sciences, Sir Alexander Fleming Building, Imperial College London, London SW7 2AZ, UK; Centre for Haematology, Department of Immunology and Inflammation, Imperial College London, London W12 0NN, UK; The Francis Crick Institute, London WC2A 3LY, UK.

Chris BrunsdonNational Centre for Geocomputation, Maynooth University, Maynooth, Co. Kildare, Ireland.

Ken R DuffyDepartment of Electrical and Computer Engineering, Northeastern University, Boston, MA 02115, USA; Department of Mathematics, Northeastern University, Boston, MA 02115, USA. Electronic address: k.duffy@northeastern.edu.

Cristina Lo CelsoDepartment of Life Sciences, Sir Alexander Fleming Building, Imperial College London, London SW7 2AZ, UK; Centre for Haematology, Department of Immunology and Inflammation, Imperial College London, London W12 0NN, UK; The Francis Crick Institute, London WC2A 3LY, UK. Electronic address: c.lo-celso@imperial.ac.uk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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. We present PACESS, a computational workflow for BM analysis that combines convolutional neural networks for 2D cell detection and classification with an automated method to extrapolate into 3D, spatial statistical analyses to define tissue regions based on local cell-type densities, and logistic regression to assess whether the relative abundances of cell types reflect reciprocal dependencies. Using PACESS, we investigate the spatial organization of T cells, megakaryocytes, and leukemic cells, revealing that distinct leukemic clusters generate diverse, previously unrecognized neighborhoods within the same BM cavity. PACESS, thus, provides a powerful tool to dissect BM architecture.

Indexed as

Artificial IntelligenceBone MarrowBone Marrow CellsImaging, Three-DimensionalAnimalsConvolutional Neural NetworksHumansMegakaryocytesMice3Dbone marrow tissue architecturecell clustersconvolutional neural networkCP: computational biologyCP: systems biologyleukemialymphocytesmegakaryocytesspatial statisticsthick histological preparationstissue clearing

Identifiers

PMID41831446

DOI10.1016/j.crmeth.2026.101334

PMCPMC13030987

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.