Approaching Solid Tumor Heterogeneity on a Cellular Basis by Tissue Proteomics Using Laser Capture Microdissection and Biological Mass Spectrometry

The purpose of this study was to examine solid tumor heterogeneity on a cellular basis using tissue proteomics that relies on a functional relationship between Laser Capture Microdissection (LCM) and biological mass spectrometry (MS). With the use of LCM, homogeneous regions of cells exhibiting uniform histology were isolated and captured from fresh frozen tissue specimens, which were obtained from a human lymph node containing breast carcinoma metastasis. Six specimens ∼50 000 cell each (three from tumor proper and three from tumor stroma) were collected by LCM. Specimens were processed directly on LCM caps, using sonication in buffered methanol to lyse captured cells, solubilize, and digest extracted proteins. Prepared samples were analyzed by LC/MS/MS resulting in more than 500 unique protein identifications. Decoy database searching revealed a false-positive rate between 5 and 10%. Subcellular localization analysis for stromal cells revealed plasma membrane 14%, cytoplasm 39%, nucleus 11%, extracellular space 27%, and unknown 9%; and tumor cell results were 5%, 58%, 26%, 4%, and 7%, respectively. Western blot analysis confirmed specific linkage of validated proteins to underlying pathology and their potential role in solid tumor heterogeneity. With continued research and optimization of this method including analysis of additional clinical specimens, this approach may lead to an improved understanding of tumor heterogeneity, and serve as a platform for solid tumor biomarker discovery

Approaching Solid Tumor Heterogeneity on a Cellular Basis by Tissue Proteomics Using Laser Capture Microdissection and Biological Mass Spectrometry

The purpose of this study was to examine solid tumor heterogeneity on a cellular basis using tissue proteomics that relies on a functional relationship between Laser Capture Microdissection (LCM) and biological mass spectrometry (MS). With the use of LCM, homogeneous regions of cells exhibiting uniform histology were isolated and captured from fresh frozen tissue specimens, which were obtained from a human lymph node containing breast carcinoma metastasis. Six specimens ∼50 000 cell each (three from tumor proper and three from tumor stroma) were collected by LCM. Specimens were processed directly on LCM caps, using sonication in buffered methanol to lyse captured cells, solubilize, and digest extracted proteins. Prepared samples were analyzed by LC/MS/MS resulting in more than 500 unique protein identifications. Decoy database searching revealed a false-positive rate between 5 and 10%. Subcellular localization analysis for stromal cells revealed plasma membrane 14%, cytoplasm 39%, nucleus 11%, extracellular space 27%, and unknown 9%; and tumor cell results were 5%, 58%, 26%, 4%, and 7%, respectively. Western blot analysis confirmed specific linkage of validated proteins to underlying pathology and their potential role in solid tumor heterogeneity. With continued research and optimization of this method including analysis of additional clinical specimens, this approach may lead to an improved understanding of tumor heterogeneity, and serve as a platform for solid tumor biomarker discovery

Optimized Method for Computing ¹⁸O/¹⁶O Ratios of Differentially Stable-Isotope Labeled Peptides in the Context of Postdigestion ¹⁸O Exchange/Labeling

Differential 18O/16O stable isotope labeling of peptides that relies on enzyme-catalyzed oxygen exchange at their carboxyl termini in the presence of H218O has been widely used for relative quantitation of peptides/proteins. The role of tryptic proteolysis in bottom-up shotgun proteomics and low reagent costs have made trypsin-catalyzed 18O postdigestion exchange a convenient and affordable stable isotope labeling approach. However, it is known that trypsin-catalyzed 18O exchange at the carboxyl terminus is in many instances inhomogeneous/incomplete. The extent of the 18O exchange/incorporation fluctuates from peptide to peptide mostly due to variable enzyme−substrate affinity. Thus, accurate calculation and interpretation of peptide ratios are analytically complicated and in some regard deficient. Therefore, a computational approach capable of improved measurement of actual 18O incorporation for each differentially labeled peptide pair is needed. In this regard, we have developed an algorithmic method that relies on the trapezoidal rule to integrate peak intensities of all detected isotopic species across a particular peptide ion over the retention time, which fits the isotopic manifold to Poisson distributions. Optimal values for manifold fitting were calculated and then 18O/16O ratios derived via evolutionary programming. The algorithm is tested using trypsin-catalyzed 18O postdigestion exchange to differentially label bovine serum albumin (BSA) at a priori determined ratios. Both accuracy and precision are improved utilizing this rigorous mathematical approach. We further demonstrate the effectiveness of this method to accurately calculate 18O/16O ratios in a large scale proteomic quantitation of detergent resistant membrane microdomains (DRMMs) isolated from cells expressing wild-type HIV-1 Gag and its nonmyristylated mutant

Optimized Method for Computing ¹⁸O/¹⁶O Ratios of Differentially Stable-Isotope Labeled Peptides in the Context of Postdigestion ¹⁸O Exchange/Labeling

Differential 18O/16O stable isotope labeling of peptides that relies on enzyme-catalyzed oxygen exchange at their carboxyl termini in the presence of H218O has been widely used for relative quantitation of peptides/proteins. The role of tryptic proteolysis in bottom-up shotgun proteomics and low reagent costs have made trypsin-catalyzed 18O postdigestion exchange a convenient and affordable stable isotope labeling approach. However, it is known that trypsin-catalyzed 18O exchange at the carboxyl terminus is in many instances inhomogeneous/incomplete. The extent of the 18O exchange/incorporation fluctuates from peptide to peptide mostly due to variable enzyme−substrate affinity. Thus, accurate calculation and interpretation of peptide ratios are analytically complicated and in some regard deficient. Therefore, a computational approach capable of improved measurement of actual 18O incorporation for each differentially labeled peptide pair is needed. In this regard, we have developed an algorithmic method that relies on the trapezoidal rule to integrate peak intensities of all detected isotopic species across a particular peptide ion over the retention time, which fits the isotopic manifold to Poisson distributions. Optimal values for manifold fitting were calculated and then 18O/16O ratios derived via evolutionary programming. The algorithm is tested using trypsin-catalyzed 18O postdigestion exchange to differentially label bovine serum albumin (BSA) at a priori determined ratios. Both accuracy and precision are improved utilizing this rigorous mathematical approach. We further demonstrate the effectiveness of this method to accurately calculate 18O/16O ratios in a large scale proteomic quantitation of detergent resistant membrane microdomains (DRMMs) isolated from cells expressing wild-type HIV-1 Gag and its nonmyristylated mutant

Optimized Method for Computing ¹⁸O/¹⁶O Ratios of Differentially Stable-Isotope Labeled Peptides in the Context of Postdigestion ¹⁸O Exchange/Labeling

Differential 18O/16O stable isotope labeling of peptides that relies on enzyme-catalyzed oxygen exchange at their carboxyl termini in the presence of H218O has been widely used for relative quantitation of peptides/proteins. The role of tryptic proteolysis in bottom-up shotgun proteomics and low reagent costs have made trypsin-catalyzed 18O postdigestion exchange a convenient and affordable stable isotope labeling approach. However, it is known that trypsin-catalyzed 18O exchange at the carboxyl terminus is in many instances inhomogeneous/incomplete. The extent of the 18O exchange/incorporation fluctuates from peptide to peptide mostly due to variable enzyme−substrate affinity. Thus, accurate calculation and interpretation of peptide ratios are analytically complicated and in some regard deficient. Therefore, a computational approach capable of improved measurement of actual 18O incorporation for each differentially labeled peptide pair is needed. In this regard, we have developed an algorithmic method that relies on the trapezoidal rule to integrate peak intensities of all detected isotopic species across a particular peptide ion over the retention time, which fits the isotopic manifold to Poisson distributions. Optimal values for manifold fitting were calculated and then 18O/16O ratios derived via evolutionary programming. The algorithm is tested using trypsin-catalyzed 18O postdigestion exchange to differentially label bovine serum albumin (BSA) at a priori determined ratios. Both accuracy and precision are improved utilizing this rigorous mathematical approach. We further demonstrate the effectiveness of this method to accurately calculate 18O/16O ratios in a large scale proteomic quantitation of detergent resistant membrane microdomains (DRMMs) isolated from cells expressing wild-type HIV-1 Gag and its nonmyristylated mutant

Optimized Method for Computing ¹⁸O/¹⁶O Ratios of Differentially Stable-Isotope Labeled Peptides in the Context of Postdigestion ¹⁸O Exchange/Labeling

Differential 18O/16O stable isotope labeling of peptides that relies on enzyme-catalyzed oxygen exchange at their carboxyl termini in the presence of H218O has been widely used for relative quantitation of peptides/proteins. The role of tryptic proteolysis in bottom-up shotgun proteomics and low reagent costs have made trypsin-catalyzed 18O postdigestion exchange a convenient and affordable stable isotope labeling approach. However, it is known that trypsin-catalyzed 18O exchange at the carboxyl terminus is in many instances inhomogeneous/incomplete. The extent of the 18O exchange/incorporation fluctuates from peptide to peptide mostly due to variable enzyme−substrate affinity. Thus, accurate calculation and interpretation of peptide ratios are analytically complicated and in some regard deficient. Therefore, a computational approach capable of improved measurement of actual 18O incorporation for each differentially labeled peptide pair is needed. In this regard, we have developed an algorithmic method that relies on the trapezoidal rule to integrate peak intensities of all detected isotopic species across a particular peptide ion over the retention time, which fits the isotopic manifold to Poisson distributions. Optimal values for manifold fitting were calculated and then 18O/16O ratios derived via evolutionary programming. The algorithm is tested using trypsin-catalyzed 18O postdigestion exchange to differentially label bovine serum albumin (BSA) at a priori determined ratios. Both accuracy and precision are improved utilizing this rigorous mathematical approach. We further demonstrate the effectiveness of this method to accurately calculate 18O/16O ratios in a large scale proteomic quantitation of detergent resistant membrane microdomains (DRMMs) isolated from cells expressing wild-type HIV-1 Gag and its nonmyristylated mutant

sj-docx-1-jhc-10.1369_00221554221124163 – Supplemental material for Effect of Antigen Retrieval on Genomic DNA From Immunodissected Samples

Supplemental material, sj-docx-1-jhc-10.1369_00221554221124163 for Effect of Antigen Retrieval on Genomic DNA From Immunodissected Samples by Donald J. Johann, Ik Jae Shin, Adam Roberge, Sarah Laun, Erich A. Peterson, Meei Liu, Matthew A. Steliga, Jason Muesse, Michael R. Emmert-Buck and Michael A. Tangrea in Journal of Histochemistry & Cytochemistry</p

DataSheet1_An advanced molecular medicine case report of a rare human tumor using genomics, pathomics, and radiomics.zip

Background: Pulmonary Sclerosing Pneumocytoma (PSP) is a rare tumor of the lung with a low malignant potential that primarily affects females. Initial studies of PSP focused primarily on analyzing features uncovered using conventional X-ray or CT imaging. In recent years, because of the widespread use of next-generation sequencing (NGS), the study of PSP at the molecular-level has emerged.Methods: Analytical approaches involving genomics, radiomics, and pathomics were performed. Genomics studies involved both DNA and RNA analyses. DNA analyses included the patient’s tumor and germline tissues and involved targeted panel sequencing and copy number analyses. RNA analyses included tumor and adjacent normal tissues and involved studies covering expressed mutations, differential gene expression, gene fusions and molecular pathways. Radiomics approaches were utilized on clinical imaging studies and pathomics techniques were applied to tumor whole slide images.Results: A comprehensive molecular profiling endeavor involving over 50 genomic analyses corresponding to 16 sequencing datasets of this rare neoplasm of the lung were generated along with detailed radiomic and pathomic analyses to reveal insights into the etiology and molecular behavior of the patient’s tumor. Driving mutations (AKT1) and compromised tumor suppression pathways (TP53) were revealed. To ensure the accuracy and reproducibility of this study, a software infrastructure and methodology known as NPARS, which encapsulates NGS and associated data, open-source software libraries and tools including versions, and reporting features for large and complex genomic studies was used.Conclusion: Moving beyond descriptive analyses towards more functional understandings of tumor etiology, behavior, and improved therapeutic predictability requires a spectrum of quantitative molecular medicine approaches and integrations. To-date this is the most comprehensive study of a patient with PSP, which is a rare tumor of the lung. Detailed radiomic, pathomic and genomic molecular profiling approaches were performed to reveal insights regarding the etiology and molecular behavior. In the event of recurrence, a rational therapy plan is proposed based on the uncovered molecular findings.</p

Combined Blood/Tissue Analysis for Cancer Biomarker Discovery: Application to Renal Cell Carcinoma

A method that relies on subtractive tissue-directed shot-gun proteomics to identify tumor proteins in the blood of a patient newly diagnosed with cancer is described. To avoid analytical and statistical biases caused by physiologic variability of protein expression in the human population, this method was applied on clinical specimens obtained from a single patient diagnosed with nonmetastatic renal cell carcinoma (RCC). The proteomes extracted from tumor, normal adjacent tissue and preoperative plasma were analyzed using 2D-liquid chromatography−mass spectrometry (LC−MS). The lists of identified proteins were filtered to discover proteins that (i) were found in the tumor but not normal tissue, (ii) were identified in matching plasma, and (iii) whose spectral count was higher in tumor tissue than plasma. These filtering criteria resulted in identification of eight tumor proteins in the blood. Subsequent Western-blot analysis confirmed the presence of cadherin-5, cadherin-11, DEAD-box protein-23, and pyruvate kinase in the blood of the patient in the study as well as in the blood of four other patients diagnosed with RCC. These results demonstrate the utility of a combined blood/tissue analysis strategy that permits the detection of tumor proteins in the blood of a patient diagnosed with RCC

Combined Blood/Tissue Analysis for Cancer Biomarker Discovery: Application to Renal Cell Carcinoma

A method that relies on subtractive tissue-directed shot-gun proteomics to identify tumor proteins in the blood of a patient newly diagnosed with cancer is described. To avoid analytical and statistical biases caused by physiologic variability of protein expression in the human population, this method was applied on clinical specimens obtained from a single patient diagnosed with nonmetastatic renal cell carcinoma (RCC). The proteomes extracted from tumor, normal adjacent tissue and preoperative plasma were analyzed using 2D-liquid chromatography−mass spectrometry (LC−MS). The lists of identified proteins were filtered to discover proteins that (i) were found in the tumor but not normal tissue, (ii) were identified in matching plasma, and (iii) whose spectral count was higher in tumor tissue than plasma. These filtering criteria resulted in identification of eight tumor proteins in the blood. Subsequent Western-blot analysis confirmed the presence of cadherin-5, cadherin-11, DEAD-box protein-23, and pyruvate kinase in the blood of the patient in the study as well as in the blood of four other patients diagnosed with RCC. These results demonstrate the utility of a combined blood/tissue analysis strategy that permits the detection of tumor proteins in the blood of a patient diagnosed with RCC