Non-invasive characterization of hBM-MSCs using autofluorescence multispectral imaging: future growth and pluripotency

This study developed a non-invasive assay using autofluorescence multispectral imaging (AFMI) for detailed characterization of human bone marrow-derived mesenchymal stromal cells (hBM-MSCs). Cells were cultured, stained for various markers, and imaged to analyze features like intensity, texture, and morphology. Canonical discriminant analysis distinguished different cell groups. AFMI predicted STRO-1 positivity with an AUC of 0.67, and differentiation, cell cycle entry, and senescence with AUCs of 0.66, 0.77, and 0.77, respectively. Population doublings were predicted with Mean Absolute Error of ±1.86 doublings remaining in lifespan. This label-free AFMI method offers valuable insights into cell status and behavior, benefiting clinical applications and research.

Authors

Publication Details

Published
2025-03-19
DOI
https://doi.org/10.1117/12.3041463
Primary Topic
Medical Imaging Techniques and Applications
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Non-invasive characterization of hBM-MSCs using autofluorescence multispectral imaging: future growth and pluripotency

Robert B. Gilchrist, Ewa M. Goldys, Ayad G. Anwer, Jared M. Campbell et al.
Medical Imaging Techniques and Applications
article

Non-invasive characterization of hBM-MSCs using autofluorescence multispectral imaging: future growth and pluripotency

Robert B. Gilchrist, Ewa M. Goldys, Ayad G. Anwer, Jared M. Campbell, Saabah B. Mahbub, Abbas Habibalahi, Shannon Handley, Shane T. Grey, Akanksha Bhargava, Stan Gronthos, Adnan Agha, Lindsay E. Wu, Aline Knab, Xiaohu Xu, Max Mackevicius, Yuan Tian, Zhilin Lei
article en

Abstract

This study developed a non-invasive assay using autofluorescence multispectral imaging (AFMI) for detailed characterization of human bone marrow-derived mesenchymal stromal cells (hBM-MSCs). Cells were cultured, stained for various markers, and imaged to analyze features like intensity, texture, and morphology. Canonical discriminant analysis distinguished different cell groups. AFMI predicted STRO-1 positivity with an AUC of 0.67, and differentiation, cell cycle entry, and senescence with AUCs of 0.66, 0.77, and 0.77, respectively. Population doublings were predicted with Mean Absolute Error of ±1.86 doublings remaining in lifespan. This label-free AFMI method offers valuable insights into cell status and behavior, benefiting clinical applications and research.

Zero hunger
Openalex Percentile: Top 92%
Medical Imaging Techniques and Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.