Literature DB >> 33247337

Potential impact of tissue molecular heterogeneity on ambient mass spectrometry profiles: a note of caution in choosing the right disease model.

Lauren Katz1,2, Michael Woolman1,2, Alessandra Tata3, Arash Zarrine-Afsar4,5,6,7.   

Abstract

This review provides a summary of known molecular alterations in commonly used cancer models and strives to stipulate how they may affect ambient mass spectrometry profiles. Immortalized cell lines are known to accumulate mutations, and xenografts derived from cell lines are known to contain tumour microenvironment elements from the host animal. While the use of human specimens for mass spectrometry profiling studies is highly encouraged, patient-derived xenografts with low passage numbers could provide an alternative means of amplifying material for ambient MS research when needed. Similarly, genetic preservation of patient tissue seen in some organoid models, further verified by qualitative proteomic and transcriptomic analyses, may argue in favor of organoid suitability for certain ambient profiling studies. However, to choose the appropriate model, pre-evaluation of the model's molecular characteristics in the context of the research question(s) being asked will likely provide the most appropriate strategy to move research forward. This can be achieved by performing comparative ambient MS analysis of the disease model of choice against a small amount of patient tissue to verify concordance. Disease models, however, will continue to be useful tools to orthogonally validate metabolic states of patient tissues through controlled genetic alterations that are not possible with patient specimens.

Entities:  

Keywords:  Ambient mass spectrometry; Animal models of cancer; Cell line models of disease; Lipid imaging and analysis; Mass spectrometry imaging; Metabolomics

Mesh:

Substances:

Year:  2020        PMID: 33247337     DOI: 10.1007/s00216-020-03054-0

Source DB:  PubMed          Journal:  Anal Bioanal Chem        ISSN: 1618-2642            Impact factor:   4.142


  53 in total

Review 1.  Genomic evolution of cancer models: perils and opportunities.

Authors:  Uri Ben-David; Rameen Beroukhim; Todd R Golub
Journal:  Nat Rev Cancer       Date:  2019-02       Impact factor: 60.716

Review 2.  Lipid metabolism in cancer.

Authors:  Claudio R Santos; Almut Schulze
Journal:  FEBS J       Date:  2012-07-03       Impact factor: 5.542

3.  Three-dimensional cell culture: A powerful tool in tumor research and drug discovery.

Authors:  Donglai Lv; Zongtao Hu; Lin Lu; Husheng Lu; Xiuli Xu
Journal:  Oncol Lett       Date:  2017-10-03       Impact factor: 2.967

4.  Predicting Breast Cancer by Paper Spray Ion Mobility Spectrometry Mass Spectrometry and Machine Learning.

Authors:  Ying-Chen Huang; Hsin-Hsiang Chung; Ewelina P Dutkiewicz; Chih-Lin Chen; Hua-Yi Hsieh; Bo-Rong Chen; Ming-Yang Wang; Cheng-Chih Hsu
Journal:  Anal Chem       Date:  2019-12-10       Impact factor: 6.986

5.  Ambient Ionization Mass Spectrometry: Recent Developments and Applications.

Authors:  Clara L Feider; Anna Krieger; Rachel J DeHoog; Livia S Eberlin
Journal:  Anal Chem       Date:  2019-03-14       Impact factor: 6.986

Review 6.  Ambient Mass Spectrometry in Cancer Research.

Authors:  Z Takats; N Strittmatter; J S McKenzie
Journal:  Adv Cancer Res       Date:  2016-12-29       Impact factor: 6.242

Review 7.  Modelling breast cancer: one size does not fit all.

Authors:  Tracy Vargo-Gogola; Jeffrey M Rosen
Journal:  Nat Rev Cancer       Date:  2007-09       Impact factor: 60.716

Review 8.  Ambient Ionization Mass Spectrometry for Cancer Diagnosis and Surgical Margin Evaluation.

Authors:  Demian R Ifa; Livia S Eberlin
Journal:  Clin Chem       Date:  2015-11-10       Impact factor: 8.327

Review 9.  Choosing the right cell line for breast cancer research.

Authors:  Deborah L Holliday; Valerie Speirs
Journal:  Breast Cancer Res       Date:  2011-08-12       Impact factor: 6.466

10.  Comparison of Machine Learning Algorithms for Predictive Modeling of Beef Attributes Using Rapid Evaporative Ionization Mass Spectrometry (REIMS) Data.

Authors:  Devin A Gredell; Amelia R Schroeder; Keith E Belk; Corey D Broeckling; Adam L Heuberger; Soo-Young Kim; D Andy King; Steven D Shackelford; Julia L Sharp; Tommy L Wheeler; Dale R Woerner; Jessica E Prenni
Journal:  Sci Rep       Date:  2019-04-05       Impact factor: 4.379

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