Prospective on Imaging Mass Spectrometry in Clinical Diagnostics.
Prospective on Imaging Mass Spectrometry in Clinical Diagnostics.
复制标题
成像质谱在临床诊断中的前景。
DOI:
10.1016/j.mcpro.2023.100576
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发表时间:
2023-09
影响因子:
7
通讯作者:
Caprioli, Richard M.
中科院分区:
文献类型:
--
作者:
Moore, Jessica L.;Patterson, Nathan Heath;Norris, Jeremy L.;Caprioli, Richard M.
Imaging mass spectrometry (IMS) is a molecular technology utilized for spatially driven research, providing molecular maps from tissue sections. This article reviews matrix-assisted laser desorption ionization (MALDI) IMS and its progress as a primary tool in the clinical laboratory. MALDI mass spectrometry has been used to classify bacteria and perform other bulk analyses for plate-based assays for many years. However, the clinical application of spatial data within a tissue biopsy for diagnoses and prognoses is still an emerging opportunity in molecular diagnostics. This work considers spatially driven mass spectrometry approaches for clinical diagnostics and addresses aspects of new imaging-based assays that include analyte selection, quality control/assurance metrics, data reproducibility, data classification, and data scoring. It is necessary to implement these tasks for the rigorous translation of IMS to the clinical laboratory; however, this requires detailed standardized protocols for introducing IMS into the clinical laboratory to deliver reliable and reproducible results that inform and guide patient care. We review the use of IMS for the development of new molecular assays We discuss quality control and quality assurance metrics for IMS assays We explore IMS for diagnostic and prognostic clinical applications We discuss regulatory requirements necessary for practical implementation Imaging mass spectrometry is an emerging technology for clinical applications in that it provides spatial molecular data within a tissue biopsy for patient diagnoses and prognoses. This article considers various aspects of the use of this technology for new imaging-based assays that include analyte selection, quality control/assurance metrics, data reproducibility, data classification, and data scoring.
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影响因子:
4.6
作者:
Bankhead P;Loughrey MB;Fernández JA;Dombrowski Y;McArt DG;Dunne PD;McQuaid S;Gray RT;Murray LJ;Coleman HG;James JA;Salto-Tellez M;Hamilton PW
通讯作者:
Hamilton PW
影响因子:
2.5
作者:
Borren, Nienke Z.;Ananthakrishnan, Ashwin N.
通讯作者:
Ananthakrishnan, Ashwin N.
DOI:
10.1097/jto.0b013e31826c1155
发表时间:
2012-11
期刊:
Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer
影响因子:
--
作者:
Carbone DP;Ding K;Roder H;Grigorieva J;Roder J;Tsao MS;Seymour L;Shepherd FA
通讯作者:
Shepherd FA
影响因子:
7.4
作者:
Boskamp, Tobias;Lachmund, Delf;Maass, Peter
通讯作者:
Maass, Peter
影响因子:
9.3
作者:
Darebna, Petra;Spicka, Jan;Pompach, Petr
通讯作者:
Pompach, Petr