Biomedical subjects

Sheng Pan

Publications and source records attributed to Sheng Pan.

11 recordsLinked to original sources

PubMed2025

SLB-msSIM: A Spectral Library-Based Multiplex Segmented SIM Platform for Single-Cell Proteomic Analysis.

Mass spectrometry (MS)-based single-cell proteomics, while highly challenging, offers unique potential for a wide range of applications to interrogate cellular heterogeneity, trajectories, and phenotypes at a functional level. We report here the development of the spectral library-based multiplex segmented selected ion monitoring (SLB-msSIM) method, a conceptually unique approach with significantly enhanced sensitivity and robustness for single-cell analysis. The single-cell MS data is acquired by a multiplex segmented selected ion monitoring (msSIM) technique, which sequentially applies multiple isolation cycles with the quadrupole using a wide isolation window in each cycle to accumulate and store precursor ions in the C-trap for a single scan in the Orbitrap. Proteomic identification is achieved through spectral matching using a well-defined spectral library. We applied the SLB-msSIM method to interrogate cellular heterogeneity in various pancreatic cancer cell lines, revealing common and distinct functional traits among PANC-1, MIA-PaCa2, AsPc-1, HPAF, and normal HPDE cells. Furthermore, for the first time, our novel data revealed the diverse cell trajectories of individual PANC-1 cells during the induction and reversal of epithelial-mesenchymal transition (EMT). Collectively, our results demonstrate that SLB-msSIM is a highly sensitive and robust platform, applicable to a wide range of instruments for single-cell proteomic studies. SUMMARY: We present the SLB-msSIM method, a conceptually unique approach in mass spectrometry-based single-cell proteomics that significantly enhances sensitivity and robustness. This innovative platform enables detailed analysis of the proteome landscape, capturing cellular heterogeneity, trajectories, and phenotypes at a single-cell resolution. Utilizing the SLB-msSIM technique, we identified both common and distinct functional traits among various pancreatic cancer cell lines and normal cells. Moreover, our study unveiled new insights into the diverse cell trajectories of individual cancer cells during the induction and reversal of epithelial-mesenchymal transition (EMT). In summary, the SLB-msSIM method offers a highly sensitive and robust platform for single-cell proteomic studies, with broad applicability across different instruments.

Single-Cell Analysis↗

PubMed2007

A combined dataset of human cerebrospinal fluid proteins identified by multi-dimensional chromatography and tandem mass spectrometry.

Human cerebrospinal fluid (CSF) is an important source for studying protein biomarkers of age-related neurodegenerative diseases. Before characterizing biomarkers unique to each disease, it is necessary to categorize CSF proteins systematically and extensively. However, the enormous complexity, great dynamic range of protein concentrations, and tremendous protein heterogeneity due to post-translational modification of CSF create significant challenges to the existing proteomics technologies for an in-depth, nonbiased profiling of the human CSF proteome. To circumvent these difficulties, in the last few years, we have utilized several different separation methodologies and mass spectrometric platforms that greatly enhanced the identification coverage and the depth of protein profiling of CSF to characterize CSF proteome. In total, 2594 proteins were identified in well-characterized pooled human CSF samples using stringent proteomics criteria. This report summarizes our efforts to comprehensively characterize the human CSF proteome to date.

Cerebrospinal Fluid Proteins↗

PubMed2007

Comparison of pancreas juice proteins from cancer versus pancreatitis using quantitative proteomic analysis.

OBJECTIVES: Pancreatitis is an inflammatory condition of the pancreas. However, it often shares many molecular features with pancreatic cancer. Biomarkers present in pancreatic cancer frequently occur in the setting of pancreatitis. The efforts to develop diagnostic biomarkers for pancreatic cancer have thus been complicated by the false-positive involvement of pancreatitis. METHODS: In an attempt to develop protein biomarkers for pancreatic cancer, we previously use quantitative proteomics to identify and quantify the proteins from pancreatic cancer juice. Pancreatic juice is a rich source of proteins that are shed by the pancreatic ductal cells. In this study, we used a similar approach to identify and quantify proteins from pancreatitis juice. RESULTS: In total, 72 proteins were identified and quantified in the comparison of pancreatic juice from pancreatitis patients versus pooled normal control juice. Nineteen of the juice proteins were overexpressed, and 8 were underexpressed in pancreatitis juice by at least 2-fold compared with normal pancreatic juice. Of these 27 differentially expressed proteins in pancreatitis, 9 proteins were also differentially expressed in the pancreatic juice from pancreatic cancer patient. CONCLUSIONS: Identification of these differentially expressed proteins from pancreatitis juice provides useful information for future study of specific pancreatitis-associated proteins and to eliminate potential false-positive biomarkers for pancreatic cancer.

Biomarkers, Tumor↗

PubMed2007

Quantitative proteomics by stable isotope labeling and mass spectrometry.

The goal of quantitative proteomics is to systematically study static state or perturbation-induced changes in protein profile. Most of the recently developed mass spectrometry (MS)-based quantitative proteomic methods employ stable isotope labeling to introduce signature mass tags to peptides/proteins that can be used by a mass spectrometer to quantify each analyte and to determine the sample from which it originates. In this chapter, we discuss several methods for the introduction of mass tags to proteins and peptides for MS-based quantitative proteomic analysis, including isotope-coded affinity tags, stable isotope labeling by amino acids in cell culture, global internal standard technology, and mass-coded abundance tagging.

Isotope Labeling↗

PubMed2006

Quantitative proteomic profiling of pancreatic cancer juice.

Pancreatic juice is an exceptionally rich source of cancer-specific proteins shed from cancerous ductal cells into the pancreatic juice. Quantitative proteomic analysis of the proteins specific to pancreatic cancer juice has not previously been reported. We used isotope-code affinity tag (ICAT) technology and MS/MS to perform quantitative protein profiling of pancreatic juice from pancreatic cancer patients and normal controls. ICAT technology coupled with MS/MS allows the systematic study of the proteome and measures the protein abundance in pancreatic juice with the potential for development of biomarkers. A total of 105 proteins were identified and quantified in the pancreatic juice from a pancreatic cancer patient, of which 30 proteins showed abundance changes of at least twofold in pancreatic cancer juice compared to normal controls. Many of these proteins have been externally validated. This is the first comprehensive study of the pancreatic juice proteome by quantitative global protein profiling, and the study reveals numerous proteins that are shown for the first time to be associated with pancreatic cancer, providing candidates for diagnostic biomarkers. One of the identified proteins, insulin-like growth factor binding protein-2 was further validated by Western blotting to be elevated in pancreatic cancer juice and overexpressed in pancreatic cancer tissue.

Amino Acid Sequence↗

PubMed2006

Identification of glycoproteins in human cerebrospinal fluid with a complementary proteomic approach.

Biomarkers are pressingly needed to assist with the clinical diagnosis of neurodegenerative diseases and/or the monitoring of disease progression. Glycoproteins are enriched in bodily fluids such as human cerebrospinal fluid (CSF), an ideal source for discovering biomarkers due to its proximity to the central nervous system (CNS), and consequently can serve as diagnostic and/or therapeutic markers for CNS diseases. We report here an in-depth identification of glycoproteins in human CSF using a complementary proteomic approach which integrated hydrazide chemistry and lectin affinity column for glycoprotein enrichment, followed by multidimensional chromatography separation and tandem mass spectrometric analysis. Using stringent criteria, a total of 216 glycoproteins, including many low-abundance proteins, was identified with high confidence. Approximately one-third of these proteins was already known to be relevant to the CNS structurally or functionally. This investigation, for the first time, not only categorized many glycoproteins in human CSF but also expanded the existing overall CSF protein database.

Adult↗

PubMed2005

Proteomic profiling of pancreatic cancer for biomarker discovery.

Pancreatic cancer is a uniformly lethal disease that is difficult to diagnose at early stage and even more difficult to cure. In recent years, there has been a substantial interest in applying proteomics technologies to identify protein biomarkers for early detection of cancer. Quantitative proteomic profiling of body fluids, tissues, or other biological samples to identify differentially expressed proteins represents a very promising approach for improving the outcome of this disease. Proteins associated with pancreatic cancer identified through proteomic profiling technologies could be useful as biomarkers for the early diagnosis, therapeutic targets, and disease response markers. In this article, we discuss recent progress and challenges for applying quantitative proteomics technologies for biomarker discovery in pancreatic cancer.

Biomarkers, Tumor↗

PubMed2005

High throughput proteome screening for biomarker detection.

Mass spectrometry-based quantitative proteomics has become an important component of biological and clinical research. Current methods, while highly developed and powerful, are falling short of their goal of routinely analyzing whole proteomes mainly because the wealth of proteomic information accumulated from prior studies is not used for the planning or interpretation of present experiments. The consequence of this situation is that in every proteomic experiment the proteome is rediscovered. In this report we describe an approach for quantitative proteomics that builds on the extensive prior knowledge of proteomes and a platform for the implementation of the method. The method is based on the selection and chemical synthesis of isotopically labeled reference peptides that uniquely identify a particular protein and the addition of a panel of such peptides to the sample mixture consisting of tryptic peptides from the proteome in question. The platform consists of a peptide separation module for the generation of ordered peptide arrays from the combined peptide sample on the sample plate of a MALDI mass spectrometer, a high throughput MALDI-TOF/TOF mass spectrometer, and a suite of software tools for the selective analysis of the targeted peptides and the interpretation of the results. Applying the method to the analysis of the human blood serum proteome we demonstrate the feasibility of using mass spectrometry-based proteomics as a high throughput screening technology for the detection and quantification of targeted proteins in a complex system.

Automation↗

PubMed2005

ProbIDtree: an automated software program capable of identifying multiple peptides from a single collision-induced dissociation spectrum collected by a tandem mass spectrometer.

In MS/MS experiments with automated precursor ion, selection only a fraction of sequencing attempts lead to the successful identification of a peptide. A number of reasons may contribute to this situation. They include poor fragmentation of the selected precursor ion, the presence of modified residues in the peptide, mismatches with sequence databases, and frequently, the concurrent fragmentation of multiple precursors in the same CID attempt. Current database search engines are incapable of correctly assigning the sequences of multiple precursors to such spectra. We have developed a search engine, ProbIDtree, which can identify multiple peptides from a CID spectrum generated by the concurrent fragmentation of multiple precursor ions. This is achieved by iterative database searching in which the submitted spectra are generated by subtracting the fragment ions assigned to a tentatively matched peptide from the acquired spectrum and in which each match is assigned a tentative probability score. Tentatively matched peptides are organized in a tree structure from which their adjusted probability scores are calculated and used to determine the correct identifications. The results using MALDI-TOF-TOF MS/MS data demonstrate that multiple peptides can be effectively identified simultaneously with high confidence using ProbIDtree.

Algorithms↗

PubMed2005

Esophageal epithelial cell interaction with synthetic and natural scaffolds for tissue engineering.

As an initial step towards a tissue-engineered esophagus, rat esophageal epithelial cells (REEC) were isolated and characterized for epithelial identity, adhesion protein preference, and in vitro interaction with natural and synthetic scaffolds. The scaffolds consisted of AlloDerm (LifeCell Corporation, Branchburg, NJ), poly(L-lactic acid) (PLLA), poly(lactic-co-glycolic) acid (75:25) (PLGA75), poly(lactic-co-glycolic) acid (50:50) (PLGA50), and polycaprolactone/poly(L-lactic acid) (50:50) (PCL/PLLA). Various factors-including calcium concentration, scaffold composition, and pore size--were evaluated for their influence on epithelial growth and differentiation. By day 18, keratinocytes seeded on AlloDerm cultured under high Ca(++) (1.5mm) conditions showed a proliferating basal cell layer, epithelial stratification (5--6 layers) and a thick keratin layer. The synthetic scaffolds (PLGA, PLLA, PCL/PLLA) also showed complete surface coverage, regions of proliferating basal cells, and evidence of stratification (2--3 layers) and keratinization. The highly porous nature of the synthetic scaffolds, however, limited the formation of a continuous epithelial layer and resulted in a lack of overall spatially-defined differentiation. In conclusion, rat esophageal epithelial cells were successfully isolated and characterized, with cells seeded on AlloDerm showing superior epithelial organization and stratification compared to synthetic scaffolds. Modification of the synthetic scaffold's surface properties and pore size may be necessary to mimic epithelial behavior on natural scaffolds.

Animals↗

PubMed2005

Pancreatic cancer proteome: the proteins that underlie invasion, metastasis, and immunologic escape.

BACKGROUND & AIMS: Pancreatic cancer is a highly lethal disease that has seen little headway in diagnosis and treatment for the past few decades. The effective treatment of pancreatic cancer is critically relying on the diagnosis of the disease at an early stage, which still remains challenging. New experimental approaches, such as quantitative proteomics, have shown great potential for the study of cancer and have opened new opportunities to investigate crucial events underlying pancreatic tumorigenesis and to exploit this knowledge for early detection and better intervention. METHODS: To systematically study protein expression in pancreatic cancer, we used isotope-coded affinity tag technology and tandem mass spectrometry to perform quantitative proteomic profiling of pancreatic cancer tissues and normal pancreas. RESULTS: A total of 656 proteins were identified and quantified in 2 pancreatic cancer samples, of which 151 were differentially expressed in cancer by at least 2-fold. This study revealed numerous proteins that are newly discovered to be associated with pancreatic cancer, providing candidates for future early diagnosis biomarkers and targets for therapy. Several differentially expressed proteins were further validated by tissue microarray immunohistochemistry. Many of the differentially expressed proteins identified are involved in protein-driven interactions between the ductal epithelium and the extracellular matrix that orchestrate tumor growth, migration, angiogenesis, invasion, metastasis, and immunologic escape. CONCLUSIONS: Our study is the first application of isotope-coded affinity tag technology for proteomic analysis of human cancer tissue and has shown the value of this technology in identifying differentially expressed proteins in cancer.

Humans↗