Related Experiment Video
Updated: May 12, 2026

07:58
Capturing Small Molecule Communication Between Tissues and Cells Using Imaging Mass Spectrometry
Published on: April 3, 2019
Imaging mass spectrometry: from tissue sections to cell cultures
Eric M Weaver1, Amanda B Hummon
1Department of Chemistry & Biochemistry, University of Notre Dame, 251 Nieuwland Science Hall, Notre Dame, IN 46556, USA.
Advanced Drug Delivery Reviews
|April 11, 2013
Summary
Imaging mass spectrometry (IMS) analyzes molecular distributions in tissues. New advances are enabling its application to 3D cell and tissue cultures for better in vivo models.
Area of Science:
- Biomedical Imaging
- Analytical Chemistry
- Molecular Biology
Background:
- Imaging mass spectrometry (IMS) has been utilized for over 15 years to study molecular distributions in biological tissues.
- Key advantages of IMS include a wide mass range, label-free multiplexed detection, and preservation of spatial information.
- Current IMS applications primarily focus on animal tissue sections and tumor biopsies.
Purpose of the Study:
- This review highlights recent advancements in IMS technology.
- It explores the emerging application of IMS to three-dimensional (3D) cell and tissue culture systems.
- The review discusses the future potential of IMS in 3D culture models.
Main Methods:
- Review of recent literature on IMS technology and its applications.
- Focus on the adaptation of IMS for 3D cell and tissue culture.
- Discussion of new tissue engineering and culture techniques.
Main Results:
- IMS offers label-free, multiplexed analysis with spatial resolution.
- 3D cell and tissue cultures provide increasingly accurate, high-throughput, and cost-effective in vivo models.
- Advancements in IMS technology are enabling its use with these complex 3D models.
Conclusions:
- The integration of IMS with 3D cell and tissue cultures represents a significant advancement in biological research.
- This approach enhances the recapitulation of in vivo cellular and tissue characteristics.
- Future applications promise more sophisticated and predictive models for drug discovery and disease understanding.
