Biomarker Discovery Applications Research Articles

Urinary metabolic variation analysis during pregnancy and application in Gestational Diabetes Mellitus and spontaneous abortion biomarker discovery

Pregnancy is associated with the onset of many adaptation processes that are likely to change over the course of gestation. Understanding normal metabolites’ variation with pregnancy progression is crucial for gaining insights of the key nutrients for normal fetal growth, and for comparative research of pregnancy-related complications. This work presents liquid chromatography-mass spectrum-based urine metabolomics study of 50 health pregnant women at three time points during pregnancy. The influence of maternal physiological factors, including age, BMI, parity and gravity to urine metabolome was explored. Additionally, urine metabolomics was applied for early prediction of two pregnancy complications, gestational diabetes mellitus and spontaneous abortion. Our results suggested that during normal pregnancy progression, pathways of steroid hormone biosynthesis and tyrosine metabolism were significantly regulated. BMI is a factor that should be considered during cross-section analysis. Application analysis discovered potential biomarkers for GDM in the first trimester with AUC of 0.89, and potential biomarkers for SA in the first trimester with AUC of 0.90. In conclusion, our study indicated that urine metabolome could reflect variations during pregnancy progression, and has potential value for pregnancy complications early prediction. The clinical trial number for this study is NCT03246295.

General functional connectivity: Shared features of resting-state and task fMRI drive reliable and heritable individual differences in functional brain networks

Intrinsic connectivity, measured using resting-state fMRI, has emerged as a fundamental tool in the study of the human brain. However, due to practical limitations, many studies do not collect enough resting-state data to generate reliable measures of intrinsic connectivity necessary for studying individual differences. Here we present general functional connectivity (GFC) as a method for leveraging shared features across resting-state and task fMRI and demonstrate in the Human Connectome Project and the Dunedin Study that GFC offers better test-retest reliability than intrinsic connectivity estimated from the same amount of resting-state data alone. Furthermore, at equivalent scan lengths, GFC displayed higher estimates of heritability than resting-state functional connectivity. We also found that predictions of cognitive ability from GFC generalized across datasets, performing as well or better than resting-state or task data alone. Collectively, our work suggests that GFC can improve the reliability of intrinsic connectivity estimates in existing datasets and, subsequently, the opportunity to identify meaningful correlates of individual differences in behavior. Given that task and resting-state data are often collected together, many researchers can immediately derive more reliable measures of intrinsic connectivity through the adoption of GFC rather than solely using resting-state data. Moreover, by better capturing heritable variation in intrinsic connectivity, GFC represents a novel endophenotype with broad applications in clinical neuroscience and biomarker discovery.

Tools for Enhanced NMR-Based Metabolomics Analysis.

Metabolomics is the study of profiles of small molecules in biological fluids, cells, or organs. These profiles can be thought of as the "fingerprints" left behind from chemical processes occurring in biological systems. Because of its potential for groundbreaking applications in disease diagnostics, biomarker discovery, and systems biology, metabolomics has emerged as a rapidly growing area of research. Metabolomics investigations often, but not always, involve the identification and quantification of endogenous and exogenous metabolites in biological samples. Software tools and databases play a crucial role in advancing the rigor, robustness, reproducibility, and validation of these studies. Specifically, the establishment of a robust library of spectral signatures with unique compound descriptors and atom identities plays a key role in profiling studies based on data from nuclear magnetic resonance (NMR) spectroscopy. Here, we discuss developments leading to a rigorous basis for unique identification of compounds, reproducible numbering of atoms, the compact representation of NMR spectra of metabolites and small molecules, tools for improved compound identification, quantification and visualization, and approaches toward the goal of rigorous analysis of metabolomics data.

The proteomic and metabolomic characterization of exercise-induced sweat for human performance monitoring: A pilot investigation.

Sweat is a biofluid with several attractive attributes. However, investigation into sweat for biomarker discovery applications is still in its infancy. To add support for the use of sweat as a non-invasive media for human performance monitoring, volunteer participants were subjected to a physical exertion model using a treadmill. Following exercise, sweat was collected, aliquotted, and analyzed for metabolite and protein content via high-resolution mass spectrometry. Overall, the proteomic analysis illustrates significant enrichment steps will be required for proteomic biomarker discovery from single sweat samples as protein abundance is low in this medium. Furthermore, the results indicate a potential for protein degradation, or a large number of low molecular weight protein/peptides, in these samples. Metabolomic analysis shows a strong correlation in the overall abundance among sweat metabolites. Finally, hierarchical clustering of participant metabolite abundances show trends emerging, although no significant trends were observed (alpha = 0.8, lambda = 1 standard error via cross validation). However, these data suggest with a greater number of biological replicates, stronger, statistically significant results, can be obtained. Collectively, this study represents the first to simultaneously use both proteomic and metabolomic analysis to investigate sweat. These data highlight several pitfalls of sweat analysis for biomarker discovery applications.

Proteomics Applications in Health: Biomarker and Drug Discovery and Food Industry.

Advancing in genome sequencing has greatly propelled the understanding of the living world; however, it is insufficient for full description of a biological system. Focusing on proteomics has emerged as another large-scale platform for improving the understanding of biology. Proteomic experiments can be used for different aspects of clinical and health sciences such as food technology, biomarker discovery and drug target identification. Since proteins are main constituents of foods, proteomic technology can monitor and characterize protein content of foods and their change during production process. The proteomic biomarker discovery is advanced in various diseases such as cancer, cardiovascular diseases, AIDS, and renal diseases which provide non-invasive methods by the use of body fluids such as urine and serum. Proteomics is also used in drug target identification using different approaches such as chemical proteomics and protein interaction networks. The development and application of proteomics has increased tremendously over the last decade. Advances in proteomics methods offer many promising new directions of studying in clinical fields. In this regard, we want to discuss proteomics technology application in food investigations, drug, and biomarker discovery.

Abstract 3665: Longitudinal monitoring of prostate cancer evolution by plasma genome sequencing

Abstract Castration-resistant prostate cancer (CRPC) is associated with poor survival and frequent metastasis to the bone. Disease progression and evolution of therapy resistance have been challenging to study, in part due to the difficulty in collecting invasive core biopsy samples longitudinally over time. While liquid biopsy methods hold great potential for longitudinal monitoring, they are currently limited to targeted measurements of single genes, or small cancer gene panels. To address this challenge, we developed an unbiased whole-genome sequencing method called PEGASUS (Plasma Exome and Genome Analysis by Size-Selection and Unbiased Sequencing) that enables the detection of copy number aberrations and exome mutations from circulating-tumor DNA (ctDNA). We applied PEGASUS to 15 CRPC patients, which identified common driver mutations and copy number aberrations in plasma DNA including AR, MYC, RB1 and PTEN. We compared matched ctDNA and metastatic tumor samples in 8 CRPC patients, which showed a high correlation (r=0.79) of copy number profiles and moderate concordance of exome mutations (~56%), but also revealed a number of ctDNA and metastasis-specific mutations. We further applied PEGASUS to analyze longitudinal samples from 10 prostate cancer patients that were treated with chemotherapy or androgen inhibition to delineate clonal evolution in response to therapy. Clonal subpopulations and dynamics were inferred from mutation frequencies, revealing both chemoresistant and chemosensitive clones. These data showed that while a subset of point mutations were under positive and negative selection; most copy number aberrations remained stable and did not change in response to therapy. Our data show the technical feasibility of performing whole-genome and exome profiling of plasma DNA and pave the way for future clinical applications in biomarker discovery and informing clinical treatment decisions in prostate cancer patients. Citation Format: Naveen Ramesh, Emi Sei, Ruli Gao, Pei Ching Tsai, Christopher Logothetis, Amado J. Zurita, Nicholas E. Navin. Longitudinal monitoring of prostate cancer evolution by plasma genome sequencing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3665.

Profiling of soluble immune checkpoint proteins as potential non-invasive biomarkers in colorectal cancer and sepsis

Abstract In recent years, targeting immune checkpoint proteins has been shown to be an innovation for treatment of cancer and immune-related adverse events. Many checkpoint molecules have been identified on immune cells and/or tumor cells as activating receptors or inhibitory receptors. Many immune checkpoint proteins are also expressed as soluble forms in circulation, easily accessible as potential biomarker candidates. Here we report the development of a bead-based Luminex® multiplex assay for the quantitative profiling of a panel of co-inhibitory and co-stimulating immune checkpoint proteins: BTLA, CD27, CD28, CD40, CD80/B7-1, CD86/B7-2, CTLA-4, GITR, GITRL, HVEM, ICOS, LAG-3, PD-1, PD-L1, TIM-3, and TLR-2. The aim of this study was to investigate a potential role for serum circulating immune-checkpoint proteins as putative biomarkers in colorectal cancer (CRC) and sepsis patients. We have used this novel 16-plex multiplex assay to measure multiple checkpoint protein expression levels in serum samples from CRC patients (n=20), sepsis patients (n=20), and a corresponding set of normal serum samples (n=20). Analysis of the soluble checkpoint protein signatures generated from this multiplex approach revealed differential expression of 9 soluble form of proteins, sBTLA, sCD27, sTIM-3, sHVEM, sCD40, sPD-1, sPD-L1, sCD86, and sTLR-2, in the CRC or sepsis serum samples, compared to the healthy serum controls. In summary, our results demonstrate that the multiplex immunoassay we developed is a useful research tool for the simultaneous quantitation of circulating immune checkpoint proteins, as well as its potential application in biomarker discovery and translational research.

Investigation of the urinary metabolic variations and the application in bladder cancer biomarker discovery

Urine metabolomics have been used to identify biomarkers for clinical diseases. However, inter-individual variations and effect factors need to be further evaluated. In our study, we explored the urine metabolome in a cohort of 203 health adults, 6 patients with benign bladder lesions, and 53 patients with bladder cancer (BCa) using liquid chromatography coupled with high resolution mass spectrometry. Inter-individual analysis of both healthy controls and BCa patients showed that the urine metabolome was relatively stable. Further analysis indicated that sex and age affect inter-individual variations in urine metabolome. Metabolic pathways such as tryptophan metabolism, the citrate cycle, and pantothenate and CoA biosynthesis were found to be related to sex and age. To eliminate age and sex interference, additional BCa urine metabolomic biomarkers were explored using age and sex-matched urine samples (Test group: 44 health adults vs. 33 patients with BCa). Metabolic profiling of urine could significantly differentiate the cases with cancer from the controls and high-grade from low-grade BCa. A metabolite panel consisting of trans-2-dodecenoylcarnitine, serinyl-valine, feruloyl-2-hydroxyputrescine, and 3-hydroxynonanoyl carnitine were discovered to have good predictive ability for BCa with an area under the curve (AUC) of 0.956 (cross validation: AUC = 0.924). A panel of indolylacryloylglycine, N2 -galacturonyl-L-lysine, and aspartyl-glutamate was used to establish a robust model for high- and low-grade BCa distinction with AUC of 0.937 (cross validation: AUC = 0.891). External sample (26 control vs. 20 BCa) validation verified the acceptable accuracy of these models for BCa detection. Our study showed that urinary metabolomics is a useful strategy for differential analysis and biomarker discovery.

Heuristic algorithms for feature selection under Bayesian models with block-diagonal covariance structure

BackgroundMany bioinformatics studies aim to identify markers, or features, that can be used to discriminate between distinct groups. In problems where strong individual markers are not available, or where interactions between gene products are of primary interest, it may be necessary to consider combinations of features as a marker family. To this end, recent work proposes a hierarchical Bayesian framework for feature selection that places a prior on the set of features we wish to select and on the label-conditioned feature distribution. While an analytical posterior under Gaussian models with block covariance structures is available, the optimal feature selection algorithm for this model remains intractable since it requires evaluating the posterior over the space of all possible covariance block structures and feature-block assignments. To address this computational barrier, in prior work we proposed a simple suboptimal algorithm, 2MNC-Robust, with robust performance across the space of block structures. Here, we present three new heuristic feature selection algorithms.ResultsThe proposed algorithms outperform 2MNC-Robust and many other popular feature selection algorithms on synthetic data. In addition, enrichment analysis on real breast cancer, colon cancer, and Leukemia data indicates they also output many of the genes and pathways linked to the cancers under study.ConclusionsBayesian feature selection is a promising framework for small-sample high-dimensional data, in particular biomarker discovery applications. When applied to cancer data these algorithms outputted many genes already shown to be involved in cancer as well as potentially new biomarkers. Furthermore, one of the proposed algorithms, SPM, outputs blocks of heavily correlated genes, particularly useful for studying gene interactions and gene networks.

Abstract 2214: Adaptive dynamic artificial polyligand targeting (ADAPT): a method to identify exosomal proteins from a prostate cancer cell line

Abstract In the recent years it was demonstrated that a multitude of body fluids contains substantial amounts of exosomes, extracellular vesicles with sizes ranging between 40 and 100 nm. Those vesicles have protein profiles characteristic of their cells of origin. It was shown that exosomes play a role in cell-to-cell communication making them attractive targets to identify early disease stage biomarkers. Cancer heterogeneity is known for a long time to be an important clinical determinant of patient outcome. We developed the highly multiplexed ADAPT platform to capture systems-based biological signatures that may reflect the molecular heterogeneity of various cancer types and help to improve diagnosis of the disease. Exosomes from two prostate cancer cell lines, VCaP and LNCaP, were used to train ssDNA libraries to discriminate them. A highly diverse library of 1012 oligonucleotides (ODNs) was subjected to five rounds of positive and negative selection against exosomes from VCaP and LNCaP prostate cancer cell lines. Individual ODNs that bound preferably to exosomes from VCaP cells were identified by NGS, resynthesized and binding of co-precipitated ODNs to VCaP exosomes was verified by qPCR. LC-MS/MS was used to identify binding partners of ODNs bound to VCaP exosomes. Several of those binding partners (CHMP1b/2a/4b, VPS28, Syntenin-1) were found to be part of the ESCRT machinery, which participates in exosomes biogenesis. It was found that those proteins are overexpressed in human cancers. In addition, we identified the chemokine I-TAC, which is overexpressed in blood and tissue of men with advanced prostate adenocarcinomas. Finally, we found hnRNP-1, a cancer associated splicing factor, and the cold shock proteins RNPL and A18 hnRNP. Knock-down of these cold shock proteins has been shown to enhance chemotherapeutic cell killing of prostate cells. ADAPT is an unbiased profiling platform that identifies cancer associated proteins expressed on exosomes. This platform can be deployed against multiple cancer types and offers broad potential applications in biomarker discovery. Citation Format: Tassilo Hornung, Stephen Logie, Aniket S. Bondre, Varun Maher, Melissa N. Richards, Jelena Zarkovic, Teresa T. Tinder, Heather A. O'Neill, Mark R. Miglarese, David B. Spetzler. Adaptive dynamic artificial polyligand targeting (ADAPT): a method to identify exosomal proteins from a prostate cancer cell line [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2214. doi:10.1158/1538-7445.AM2017-2214

Abstract 2218: Quantitative multiplex analysis of immune checkpoint protein expression in circulation and in the tumor microenvironment

Abstract Immune checkpoint inhibitors have been proven to be an effective method in improving antitumor immune response. Many immune checkpoint proteins are expressed as soluble forms in circulation and in the tumor and tumor microenvironment. Here we report the development of bead-based Luminex multiplex assays for the quantitative profiling of co-inhibitory and co-stimulating immune checkpoint proteins CTLA-4, PD-1, TIM-3, LAG-3, HVEM. GITRL, BTLA, CD27, CD28, CD40, GITR, PD-L1, B7-1/CD80, B7-2/CD86, and ICOS. In order to explore the use of soluble immune checkpoint proteins as putative cancer biomarkers, we used these multiplex assays to measure checkpoint protein levels in serum samples from breast cancer patients, colon cancer patients, and a corresponding set of normal serum samples. Analysis of the soluble checkpoint protein signatures generated from this multiplex approach revealed a significantly elevated level of soluble TIM-3 protein in the breast cancer serum samples and in the colon cancer serum samples compared to the healthy serum controls (p<0.001). In addition, we analyzed the immune checkpoint protein expression profiles in lysates of tumor and adjacent normal tissues from 3 patients with metastatic breast cancer and 2 patients with colorectal cancer. Differential expression of multiple checkpoint proteins, including BTLA, CD27, TIM-3, HVEM, CD40, GITR, LAG3, CTLA-4, B7-1, PD-L1, or ICOS, were detected in these matched lysates, indicating the roles and complexity of checkpoint proteins in tumor microenvironment. In summary, our results demonstrate that the multiplex assays we developed are useful research tools for the simultaneously quantitation of immune checkpoint proteins, as well as its potential application in cancer biomarker discovery and translational research in cancer immunotherapy. Citation Format: Wen-Rong Lie, Jehangir Mistry. Quantitative multiplex analysis of immune checkpoint protein expression in circulation and in the tumor microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2218. doi:10.1158/1538-7445.AM2017-2218

SWATH-MS as a tool for biomarker discovery: From basic research to clinical applications.

In the era of quantitative proteomics, where mass spectrometry plays a pivotal role, in particular associated with the use of data-independent acquisition, it is time to perform an overview of this growing field with special focus on one of the most promising approaches: SWATH-MS, and to present future perspectives for its application as a translational tool. Therefore, a summary of this technique is presented focusing on two key relevant concepts associated with its application in biomarker discovery: the protein library and the individual digital maps concepts. It is also the purpose of this review to document the likely impact of SWATH-MS in both fundamental and translational research including biomarker identification and creation of diagnostic tools. To that end, the two concepts referred above were integrated with ongoing technical developments. Finally, some of the current restrictions for the implementation of SWATH-MS on a large scale are identified, and potential solutions presented, namely protocol standardization combined with the use of the proper standards.

Mass spectrometry-based analysis of glycoproteins and its clinical applications in cancer biomarker discovery

Most proteins are glycosylated, glycosylation is one of the most important posttranslational modifications of proteins and plays essential roles in various biological processes. Aberration in the glycan moieties of glycoproteins is associated with many diseases. It is especially critical to develop the rapid and sensitive methods for analysis of aberrant glycoproteins associated with diseases. With recent advances in proteomics, analytical and computational technologies, glycoproteomics, the global analysis of glycoproteins, is rapidly emerging as a subfield of proteomics with high biological and clinical relevance. Glycoproteomics integrates glycoprotein enrichment and proteomics technologies to support the systematic identification and quantification of glycoproteins in a complex sample. It is especially critical to develop the rapid and sensitive methods for analysis of aberrant glycoproteins associated with diseases. Mass spectrometry (MS) has become a powerful tool for mapping glycoprotein glycosylation and detailed glycan structural determination. Especially, tandem mass spectrometry can provide highly informative fragments for structural identification of glycoproteins. This review provides an overview of the development of MS technologies and their applications in identification of abnormal glycoproteins and glycans in human serum to screen cancer biomarkers in recent years.

Genome-wide epigenomic profiling for biomarker discovery.

A myriad of diseases is caused or characterized by alteration of epigenetic patterns, including changes in DNA methylation, post-translational histone modifications, or chromatin structure. These changes of the epigenome represent a highly interesting layer of information for disease stratification and for personalized medicine. Traditionally, epigenomic profiling required large amounts of cells, which are rarely available with clinical samples. Also, the cellular heterogeneity complicates analysis when profiling clinical samples for unbiased genome-wide biomarker discovery. Recent years saw great progress in miniaturization of genome-wide epigenomic profiling, enabling large-scale epigenetic biomarker screens for disease diagnosis, prognosis, and stratification on patient-derived samples. All main genome-wide profiling technologies have now been scaled down and/or are compatible with single-cell readout, including: (i) Bisulfite sequencing to determine DNA methylation at base-pair resolution, (ii) ChIP-Seq to identify protein binding sites on the genome, (iii) DNaseI-Seq/ATAC-Seq to profile open chromatin, and (iv) 4C-Seq and HiC-Seq to determine the spatial organization of chromosomes. In this review we provide an overview of current genome-wide epigenomic profiling technologies and main technological advances that allowed miniaturization of these assays down to single-cell level. For each of these technologies we evaluate their application for future biomarker discovery. We will focus on (i) compatibility of these technologies with methods used for clinical sample preservation, including methods used by biobanks that store large numbers of patient samples, and (ii) automation of these technologies for robust sample preparation and increased throughput.

High-Sensitivity and Low-Toxicity Fucose Probe for Glycan Imaging and Biomarker Discovery

Fucose, a terminal sugar in glycoconjugates, criticallyregulates various physiological and pathological phenomena, including cancer development andinflammation. However, there are currently no probes for efficient labeling and detection of this sugar. We chemically synthesized a novel series of alkynyl-fucose analogs as probe candidates and found that 7-alkynyl-fucose gave the highest labeling efficiency and low cytotoxicity. Among the fucose analogs, 7-alkynyl-fucose was the best substrate against all five fucosyltransferases examined. We confirmed its conversion to the corresponding guanosine diphosphate derivative in cells and foundthat cellular glycoproteins were labeled much moreefficiently with 7-alkynyl-fucose than with an existing probe. 7-Alkynyl-fucose was detected in the N-glycan core by mass spectrometry, and 7-alkynyl-fucose-modified proteins mostly disappeared in core-fucose-deficient mouse embryonic fibroblasts, suggesting that this analog mainly labeled core fucose in these cells. These results indicate that 7-alkynyl-fucose is a highly sensitive and powerful tool for basic glycobiology research and clinical application for biomarker discovery.

HPTLC-MALDI MS for (glyco)sphingolipid multiplexing in tissues and blood: A promising strategy for biomarker discovery and clinical applications.

Sphingolipids have hydrophilic and hydrophobic properties, different saturation and combination of the oligosaccharide chains and mass homology of species located in a narrow m/z region hampering their recognition. To target sphingolipids for diagnostic purposes, standardized methods for lipid extraction, quali- and quantitative assessments are required. In this study, HPTLC-MALDI MS was adopted to establish sphingolipid and glycosphingolipid profiles in muscle, brain and serum to create a database of molecules to be searched in the preclinical and clinical investigations. Specific protocols for lipid extraction were set up based on the characteristics of the tissue or/and fluids; this approach maximizes the HPTLC-MALDI MS analytical throughput both for lipids extracted in organic and aqueous phase. This study indicates that alkaline hydrolysis is necessary for the detection of low abundant species such as Gb3Cer and ceramides in serum and Gb4Cer, CerP and HexCer in muscle tissue. The high hydrophobicity of ceramides has been overcome by the development of HPTLC plate in chloroform:methanol/50:3.5, which increases the number and the intensity of low abundant Cer species. MS/MS analysis has been conducted directly on HPTLC plate allowing the molecular recognition; furthermore a dataset of spectra was acquired to create a database for future profiling of these molecules.

Joint analysis of histopathology image features and gene expression in breast cancer.

BackgroundGenomics and proteomics are nowadays the dominant techniques for novel biomarker discovery. However, histopathology images contain a wealth of information related to the tumor histology, morphology and tumor-host interactions that is not accessible through these techniques. Thus, integrating the histopathology images in the biomarker discovery workflow could potentially lead to the identification of new image-based biomarkers and the refinement or even replacement of the existing genomic and proteomic signatures. However, extracting meaningful and robust image features to be mined jointly with genomic (and clinical, etc.) data represents a real challenge due to the complexity of the images.ResultsWe developed a framework for integrating the histopathology images in the biomarker discovery workflow based on the bag-of-features approach – a method that has the advantage of being assumption-free and data-driven. The images were reduced to a set of salient patterns and additional measurements of their spatial distribution, with the resulting features being directly used in a standard biomarker discovery application. We demonstrated this framework in a search for prognostic biomarkers in breast cancer which resulted in the identification of several prognostic image features and a promising multimodal (imaging and genomic) prognostic signature. The source code for the image analysis procedures is freely available.ConclusionsThe framework proposed allows for a joint analysis of images and gene expression data. Its application to a set of breast cancer cases resulted in image-based and combined (image and genomic) prognostic scores for relapse-free survival.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1072-z) contains supplementary material, which is available to authorized users.

Use of Time-Resolved Fluorescence To Improve Sensitivity and Dynamic Range of Gel-Based Proteomics.

Limitations in the sensitivity and dynamic range of two-dimensional gel electrophoresis (2-DE) are currently hampering its utility in global proteomics and biomarker discovery applications. In the current study, we present proof-of-concept analyses showing that introducing time-resolved fluorescence in the image acquisition step of in-gel protein quantification provides a sensitive and accurate method for subtracting confounding background fluorescence at the photon level. In-gel protein detection using the minimal difference gel electrophoresis workflow showed improvements in lowest limit of quantification in terms of CyDye molecules per pixel of 330-fold in the blue-green region (Cy2) and 8000-fold in the red region (Cy5) over conventional state-of-the-art image acquisition instrumentation, here represented by the Typhoon 9400 instrument. These improvements make possible the detection of low-abundance proteins present at sub-attomolar levels, thereby representing a quantum leap for the use of gel-based proteomics in biomarker discovery. These improvements were achieved using significantly lower laser powers and overall excitation times, thereby drastically decreasing photobleaching during repeated scanning. The single-fluorochrome detection limits achieved by the cumulative time-resolved emission two-dimensional electrophoresis (CuTEDGE) technology facilitates in-depth proteomics characterization of very scarce samples, for example, primary human tissue materials collected in clinical studies. The unique information provided by high-sensitivity 2-DE, including positional shifts due to post-translational modifications, may increase the chance to detect biomarker signatures of relevance for identification of disease subphenotypes.

The Importance and Clinical Relevance of Surfaces in Tissue Culture.

Cell and tissue culture has evolved from the use of simple glassware for the propagation of cells and tissues into a comprehensive platform for interrogating complex biological systems, directing cell fate, and deriving products with clinical and therapeutic value. However, despite significant advances, current in vitro culture approaches remain limited in their capacity to model the clinical/biological complexities of disease, in part at least, due to the deficiencies of existing culture materials. The challenge is therefore to identify innovative materials-based solutions that have greater control over cells in vitro, while better representing biological systems in vivo. Such platforms would be suitable for biomarker discovery and tissue engineering applications. This review examines the development of tissue culture materials, advances in our understanding of cell-surface interactions, and the application of this knowledge toward the development of new approaches for better examining biological events.

Cancer biomarker discovery using DNA aptamers.

Biomarkers are signature molecules able to indicate specific physiological states of cells. Identification of reliable biomarkers is essential for early diagnosis and adaptive treatment of diseases, especially cancer. Aptamers are single-stranded oligonucleotides generated by an in vitro screening method called Systematic Evolution of Ligands by Exponential Enrichment (SELEX). They can recognize their cognate targets with selectivity and affinity comparable to protein antibodies. In addition, aptamers have superiorities including easy synthesis, high chemical stability, convenient modification and flexible design. As such, these DNA molecules show great promise as powerful molecular probes for biomarker discovery and biomarker-based clinical applications. Using complex samples as targets, a panel of aptamers can be systematically generated for comprehensive recognition of disease-specific proteins, which can potentially serve as biomarkers. This review describes the current methods for biomarker discovery using aptamers.