Biomarker Discovery by Novel Sensors Based on Nanoproteomics Approaches

Dasilva, Noelia; Díez, Paula; Matarraz, Sergio; González‐González, María; Paradinas, Sara Sánchez; Órfão, Alberto; Fuentes, Manuel

doi:10.3390/s120202284

Cited by 60 publications

(54 citation statements)

References 74 publications

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“…Since a recent review has covered many technological advances in biosensors for biomarker discovery [27], we will limit our discussion to the most widely used technologies for molecular quantification of biomarkers in body fluids and at the single-cell level. Design and manufacturing complexity, and high operating and maintenance costs have prevented the application of most of these technologies in PM and point-of-care diagnostics.…”

Section: Introductionmentioning

confidence: 99%

Label-Free Microcavity Biosensors: Steps towards Personalized Medicine

Amarie

Glazier

2012

Sensors

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Personalized medicine has the potential to improve our ability to maintain health and treat disease, while ameliorating continuously rising healthcare costs. Translation of basic research findings to clinical applications within regulatory compliance is required for personalized medicine to become the new foundation for practice of medicine. Deploying even a few of the thousands of potential diagnostic biomarkers identified each year as part of personalized treatment workflows requires clinically efficient biosensor technologies to monitor multiple biomarkers in patients in real time. This paper discusses a critical component of a regulatory system, a microcavity optical biosensor for label-free monitoring of biomolecular interactions at physiologically-relevant concentrations. While most current biosensor research focuses on improving sensitivity, this paper emphasizes other characteristics a biosensor technology requires to be practical in a clinical setting, presenting robust microcavity biosensors which are easy to manufacture and integrate with microfluidics into flexible and redesignable platforms making the microcavity biosensors deployable for continuous monitoring of biomarkers in body fluids in the clinic, in dense 2D random arrays for high-throughput applications like drug-library screening in interactomics, and of the secretory behavior of single cells in the laboratory.

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Section: Introductionmentioning

confidence: 99%

Label-Free Microcavity Biosensors: Steps towards Personalized Medicine

Amarie

Glazier

2012

Sensors

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“…Over the last few years, several in situ expressed microarrays have been developed such as: Protein in situ arrays (PISA), printing protein arrays from DNA (DAPA) arrays and Nucleic Acids Programmable Protein Arrays (NAPPA) [9,11,17]. …”

Section: Concept Of Protein Microarrays and Current Applicationsmentioning

confidence: 99%

“…The binding between the capture agent and the analyte can be detected by label or label-free detection methods such as fluorescence, chemiluminescence, mass spectrometry, radioactivity or electrochemistry [9,11,12]. …”

Section: Feature Aspects Of Protein Microarraysmentioning

confidence: 99%

Data Analysis Strategies for Protein Microarrays

et al. 2012

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Microarrays constitute a new platform which allows the discovery and characterization of proteins. According to different features, such as content, surface or detection system, there are many types of protein microarrays which can be applied for the identification of disease biomarkers and the characterization of protein expression patterns. However, the analysis and interpretation of the amount of information generated by microarrays remain a challenge. Further data analysis strategies are essential to obtain representative and reproducible results. Therefore, the experimental design is key, since the number of samples and dyes, among others aspects, would define the appropriate analysis method to be used. In this sense, several algorithms have been proposed so far to overcome analytical difficulties derived from fluorescence overlapping and/or background noise. Each kind of microarray is developed to fulfill a specific purpose. Therefore, the selection of appropriate analytical and data analysis strategies is crucial to achieve successful biological conclusions. In the present review, we focus on current algorithms and main strategies for data interpretation.

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“…[5][6][7] Proteomics is the newest generation for biomarker analysis. The term proteome was introduced in 1994 and describes a set of all proteins expressed by a given genome.…”

Section: Introductionmentioning

confidence: 99%

Proteomic Biomarkers for Spontaneous

et al. 2014

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This review aimed to identify, synthesize, and analyze the findings of studies on proteomic biomarkers for spontaneous preterm birth (PTB). Three electronic databases (Medline, Embase, and Scopus) were searched for studies in any language reporting the use of proteomic biomarkers for PTB published between January 1994 and December 2012. Retrieved citations were screened, and relevant studies were selected for full-text reading, in triplicate. The search yielded 529 citations, 51 were selected for full-text reading and 8 studies were included in the review. A total of 64 dysregulated proteins were reported. Only 14-3-3 protein sigma, annexin A5, protein S100-A8, protein S100-A12, and inter-α-trypsin inhibitor heavy chain H4 were reported in more than 1 study, but results could not be combined due to heterogeneity in type of sample and analytical platform. In conclusion, according to the existing literature, there are no specific proteomic biomarkers capable of accurately predicting PTB.

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Biomarker Discovery by Novel Sensors Based on Nanoproteomics Approaches

Cited by 60 publications

References 74 publications

Label-Free Microcavity Biosensors: Steps towards Personalized Medicine

Label-Free Microcavity Biosensors: Steps towards Personalized Medicine

Data Analysis Strategies for Protein Microarrays

Proteomic Biomarkers for Spontaneous

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