Efficient spheroid morphology assessment with a ChatGPT data analyst: implications for cell therapy

Takuya Sakamoto; Hiroto Koma; Ayane Kuwano; Tetsuhiro Horie; Atsushi Fuku; Hironori Kitajima; Yuka Nakamura; Ikuhiro Tanida; Yujiro Nakade; Hiroaki Hirata; Yoshiyuki Tachi; Hiroshi Sunami; Daisuke Sakamoto; Sohsuke Yamada; Naoki Yamamoto; Yusuke Shimizu; Yasuhito Ishigaki; Toru Ichiseki; Ayumi Kaneuji; Satoshi Osawa; Norio Kawahara

doi:10.1080/07366205.2025.2493489

Efficient spheroid morphology assessment with a ChatGPT data analyst: implications for cell therapy

Takuya Sakamoto
, Hiroto Koma
, Ayane Kuwano
, Tetsuhiro Horie
, Atsushi Fuku
, Hironori Kitajima
, Yuka Nakamura
, Ikuhiro Tanida
, Yujiro Nakade
, Hiroaki Hirata
, Yoshiyuki Tachi
, Hiroshi Sunami
, Daisuke Sakamoto
, Sohsuke Yamada
, Naoki Yamamoto
, Yusuke Shimizu
, Yasuhito Ishigaki
, Toru Ichiseki
, Ayumi Kaneuji
, Satoshi Osawa

Norio Kawahara

Research output: Contribution to journal › Article › peer-review

Abstract

Background: Adipose-derived stem cells (ADSCs) exhibit promising potential for the treatment of various diseases, including osteoarthritis. Spheroids derived from ADSCs are a viable treatment option with enhanced anti-inflammatory effects and tissue repair capabilities. Objective: SphereRing^® is a rotating donut-shaped tube that efficiently produces large quantities of spheroids. However, accurately measuring spheroid size for spheroid quality assessment is challenging. This study aimed to develop an automated method for measuring spheroid size using deep learning through the ChatGPT Data Analyst for image recognition and processing. Method: The area, perimeter, and circularity of spheroids generated with the SphereRing system were analyzed using ChatGPT Data Analyst and ImageJ. Measurement accuracy was validated using Bland–Altman analysis and scatter plot correlation coefficients. Results: ChatGPT Data Analyst was consistent with ImageJ for all parameters. Bland–Altman plots demonstrated strong agreement; most data points were within the 95% limits. Conclusion: The ChatGPT Data Analyst provides a reliable and efficient alternative for assessing spheroid quality. This method reduces human error and improves reproducibility to enhance spheroid quality control. Thus, this method has potential applications in regenerative medicine.

Original language	English
Pages (from-to)	137-149
Number of pages	13
Journal	BioTechniques
Volume	77
Issue number	3
DOIs	https://doi.org/10.1080/07366205.2025.2493489
Publication status	Published - 2025
Externally published	Yes

All Science Journal Classification (ASJC) codes

Biotechnology
General Biochemistry,Genetics and Molecular Biology

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1080/07366205.2025.2493489

Cite this

Sakamoto, T., Koma, H., Kuwano, A., Horie, T., Fuku, A., Kitajima, H., Nakamura, Y., Tanida, I., Nakade, Y., Hirata, H., Tachi, Y., Sunami, H., Sakamoto, D., Yamada, S., Yamamoto, N., Shimizu, Y., Ishigaki, Y., Ichiseki, T., Kaneuji, A., ... Kawahara, N. (2025). Efficient spheroid morphology assessment with a ChatGPT data analyst: implications for cell therapy. BioTechniques, 77(3), 137-149. https://doi.org/10.1080/07366205.2025.2493489

@article{082a622dcba640108457b60057e3f3e7,

title = "Efficient spheroid morphology assessment with a ChatGPT data analyst: implications for cell therapy",

abstract = "Background: Adipose-derived stem cells (ADSCs) exhibit promising potential for the treatment of various diseases, including osteoarthritis. Spheroids derived from ADSCs are a viable treatment option with enhanced anti-inflammatory effects and tissue repair capabilities. Objective: SphereRing{\textregistered} is a rotating donut-shaped tube that efficiently produces large quantities of spheroids. However, accurately measuring spheroid size for spheroid quality assessment is challenging. This study aimed to develop an automated method for measuring spheroid size using deep learning through the ChatGPT Data Analyst for image recognition and processing. Method: The area, perimeter, and circularity of spheroids generated with the SphereRing system were analyzed using ChatGPT Data Analyst and ImageJ. Measurement accuracy was validated using Bland–Altman analysis and scatter plot correlation coefficients. Results: ChatGPT Data Analyst was consistent with ImageJ for all parameters. Bland–Altman plots demonstrated strong agreement; most data points were within the 95\% limits. Conclusion: The ChatGPT Data Analyst provides a reliable and efficient alternative for assessing spheroid quality. This method reduces human error and improves reproducibility to enhance spheroid quality control. Thus, this method has potential applications in regenerative medicine.",

keywords = "Adipose-derived stem cells, cell therapy, ChatGPT data analyst, SphereRing, spheroids",

author = "Takuya Sakamoto and Hiroto Koma and Ayane Kuwano and Tetsuhiro Horie and Atsushi Fuku and Hironori Kitajima and Yuka Nakamura and Ikuhiro Tanida and Yujiro Nakade and Hiroaki Hirata and Yoshiyuki Tachi and Hiroshi Sunami and Daisuke Sakamoto and Sohsuke Yamada and Naoki Yamamoto and Yusuke Shimizu and Yasuhito Ishigaki and Toru Ichiseki and Ayumi Kaneuji and Satoshi Osawa and Norio Kawahara",

note = "Publisher Copyright: {\textcopyright} 2025 The Author(s). Published by Informa UK Limited, trading as Taylor \& Francis Group.",

year = "2025",

doi = "10.1080/07366205.2025.2493489",

language = "English",

volume = "77",

pages = "137--149",

journal = "BioTechniques",

issn = "0736-6205",

publisher = "Eaton Publishing Company",

number = "3",

}

Sakamoto, T, Koma, H, Kuwano, A, Horie, T, Fuku, A, Kitajima, H, Nakamura, Y, Tanida, I, Nakade, Y, Hirata, H, Tachi, Y, Sunami, H, Sakamoto, D, Yamada, S, Yamamoto, N, Shimizu, Y, Ishigaki, Y, Ichiseki, T, Kaneuji, A, Osawa, S & Kawahara, N 2025, 'Efficient spheroid morphology assessment with a ChatGPT data analyst: implications for cell therapy', BioTechniques, vol. 77, no. 3, pp. 137-149. https://doi.org/10.1080/07366205.2025.2493489

TY - JOUR

T1 - Efficient spheroid morphology assessment with a ChatGPT data analyst

T2 - implications for cell therapy

AU - Sakamoto, Takuya

AU - Koma, Hiroto

AU - Kuwano, Ayane

AU - Horie, Tetsuhiro

AU - Fuku, Atsushi

AU - Kitajima, Hironori

AU - Nakamura, Yuka

AU - Tanida, Ikuhiro

AU - Nakade, Yujiro

AU - Hirata, Hiroaki

AU - Tachi, Yoshiyuki

AU - Sunami, Hiroshi

AU - Sakamoto, Daisuke

AU - Yamada, Sohsuke

AU - Yamamoto, Naoki

AU - Shimizu, Yusuke

AU - Ishigaki, Yasuhito

AU - Ichiseki, Toru

AU - Kaneuji, Ayumi

AU - Osawa, Satoshi

AU - Kawahara, Norio

PY - 2025

Y1 - 2025

N2 - Background: Adipose-derived stem cells (ADSCs) exhibit promising potential for the treatment of various diseases, including osteoarthritis. Spheroids derived from ADSCs are a viable treatment option with enhanced anti-inflammatory effects and tissue repair capabilities. Objective: SphereRing® is a rotating donut-shaped tube that efficiently produces large quantities of spheroids. However, accurately measuring spheroid size for spheroid quality assessment is challenging. This study aimed to develop an automated method for measuring spheroid size using deep learning through the ChatGPT Data Analyst for image recognition and processing. Method: The area, perimeter, and circularity of spheroids generated with the SphereRing system were analyzed using ChatGPT Data Analyst and ImageJ. Measurement accuracy was validated using Bland–Altman analysis and scatter plot correlation coefficients. Results: ChatGPT Data Analyst was consistent with ImageJ for all parameters. Bland–Altman plots demonstrated strong agreement; most data points were within the 95% limits. Conclusion: The ChatGPT Data Analyst provides a reliable and efficient alternative for assessing spheroid quality. This method reduces human error and improves reproducibility to enhance spheroid quality control. Thus, this method has potential applications in regenerative medicine.

AB - Background: Adipose-derived stem cells (ADSCs) exhibit promising potential for the treatment of various diseases, including osteoarthritis. Spheroids derived from ADSCs are a viable treatment option with enhanced anti-inflammatory effects and tissue repair capabilities. Objective: SphereRing® is a rotating donut-shaped tube that efficiently produces large quantities of spheroids. However, accurately measuring spheroid size for spheroid quality assessment is challenging. This study aimed to develop an automated method for measuring spheroid size using deep learning through the ChatGPT Data Analyst for image recognition and processing. Method: The area, perimeter, and circularity of spheroids generated with the SphereRing system were analyzed using ChatGPT Data Analyst and ImageJ. Measurement accuracy was validated using Bland–Altman analysis and scatter plot correlation coefficients. Results: ChatGPT Data Analyst was consistent with ImageJ for all parameters. Bland–Altman plots demonstrated strong agreement; most data points were within the 95% limits. Conclusion: The ChatGPT Data Analyst provides a reliable and efficient alternative for assessing spheroid quality. This method reduces human error and improves reproducibility to enhance spheroid quality control. Thus, this method has potential applications in regenerative medicine.

KW - Adipose-derived stem cells

KW - cell therapy

KW - ChatGPT data analyst

KW - SphereRing

KW - spheroids

UR - https://www.scopus.com/pages/publications/105005947535

UR - https://www.scopus.com/pages/publications/105005947535#tab=citedBy

U2 - 10.1080/07366205.2025.2493489

DO - 10.1080/07366205.2025.2493489

M3 - Article

C2 - 40264428

AN - SCOPUS:105005947535

SN - 0736-6205

VL - 77

SP - 137

EP - 149

JO - BioTechniques

JF - BioTechniques

IS - 3

ER -

Efficient spheroid morphology assessment with a ChatGPT data analyst: implications for cell therapy

Abstract

All Science Journal Classification (ASJC) codes

UN SDGs

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