Processing MALDI Mass Spectra to Improve Mass Spectral Direct Tissue Analysis.
ABSTRACT Profiling and imaging biological specimens using MALDI mass spectrometry has significant potential to contribute to our understanding and diagnosis of disease. The technique is efficient and high-throughput providing a wealth of data about the biological state of the sample from a very simple and direct experiment. However, in order for these techniques to be put to use for clinical purposes, the approaches used to process and analyze the data must improve. This study examines some of the existing tools to baseline subtract, normalize, align, and remove spectral noise for MALDI data, comparing the advantages of each. A preferred workflow is presented that can be easily implemented for data in ASCII format. The advantages of using such an approach are discussed for both molecular profiling and imaging mass spectrometry.
Article: Proteomics in diagnostic pathology: profiling and imaging proteins directly in tissue sections.[show abstract] [hide abstract]
ABSTRACT: Direct tissue profiling and imaging mass spectrometry (MS) provide a molecular assessment of numerous expressed proteins within a tissue sample. MALDI MS (matrix-assisted laser desorption ionization) analysis of thin tissue sections results in the visualization of 500 to 1000 individual protein signals in the molecular weight range from 2000 to over 200,000. These signals directly correlate with protein distribution within a specific region of the tissue sample. The systematic investigation of the section allows the construction of ion density maps, or specific molecular images, for virtually every signal detected in the analysis. Ultimately, hundreds of images, each at a specific molecular weight, may be obtained. To date, profiling and imaging MS has been applied to multiple diseased tissues, including human non-small cell lung tumors, gliomas, and breast tumors. Interrogation of the resulting complex MS data sets using modern biocomputational tools has resulted in identification of both disease-state and patient-prognosis specific protein patterns. These studies suggest that such proteomic information will become more and more important in assessing disease progression, prognosis, and drug efficacy. Molecular histology has been known for some time and its value clear in the field of pathology. Imaging mass spectrometry brings a new dimension of molecular data, one focusing on the disease phenotype. The present article reviews the state of the art of the technology and its complementarity with traditional histopathological analyses.American Journal Of Pathology 11/2004; 165(4):1057-68. · 4.89 Impact Factor
Article: Proteomic-based prognosis of brain tumor patients using direct-tissue matrix-assisted laser desorption ionization mass spectrometry.[show abstract] [hide abstract]
ABSTRACT: Clinical diagnosis and treatment decisions for a subset of primary human brain tumors, gliomas, are based almost exclusively on tissue histology. Approaches for glioma diagnosis can be highly subjective due to the heterogeneity and infiltrative nature of these tumors and depend on the skill of the neuropathologist. There is therefore a critical need to develop more precise, non-subjective, and systematic methods to classify human gliomas. To this end, mass spectrometric analysis has been applied to these tumors to determine glioma-specific protein patterns. Protein profiles have been obtained from human gliomas of various grades through direct analysis of tissue samples using matrix-assisted laser desorption ionization mass spectrometry (MS). Statistical algorithms applied to the MS profiles from tissue sections identified protein patterns that correlated with tumor histology and patient survival. Using a data set of 108 glioma patients, two patient populations, a short-term and a long-term survival group, were identified based on the tissue protein profiles. In addition, a subset of 57 patients diagnosed with high-grade, grade IV, malignant gliomas were analyzed and a novel classification scheme that segregated short-term and long-term survival patients based on the proteomic profiles was developed. The protein patterns described served as an independent indicator of patient survival. These results show that this new molecular approach to monitoring gliomas can provide clinically relevant information on tumor malignancy and is suitable for high-throughput clinical screening.Cancer Research 10/2005; 65(17):7674-81. · 7.86 Impact Factor