Orthogonal proteomics methods warrant the development of Duchenne muscular dystrophy biomarkers.

BACKGROUND: Molecular components in blood, such as proteins, are used as biomarkers to detect or predict disease states, guide clinical interventions and aid in the development of therapies. While multiplexing proteomics methods promote discovery of such biomarkers, their translation to clinical use is difficult due to the lack of substantial evidence regarding their reliability as quantifiable indicators of disease state or outcome. To overcome this challenge, a novel orthogonal strategy was developed and used to assess the reliability of biomarkers and analytically corroborate already identified serum biomarkers for Duchenne muscular dystrophy (DMD). DMD is a monogenic incurable disease characterized by progressive muscle damage that currently lacks reliable and specific disease monitoring tools. METHODS: Two technological platforms are used to detect and quantify the biomarkers in 72 longitudinally collected serum samples from DMD patients at 3 to 5 timepoints. Quantification of the biomarkers is achieved by detection of the same biomarker fragment either through interaction with validated antibodies in immuno-assays or through quantification of peptides by Parallel Reaction Monitoring Mass Spectrometry assay (PRM-MS). RESULTS: Five, out of ten biomarkers previously identified by affinity-based proteomics methods, were confirmed to be associated with DMD using the mass spectrometry-based method. Two biomarkers, carbonic anhydrase III and lactate dehydrogenase B, were quantified with two independent methods, sandwich immunoassays and PRM-MS, with Pearson correlations of 0.92 and 0.946 respectively. The median concentrations of CA3 and LDHB in DMD patients was elevated in comparison to those in healthy individuals by 35- and 3-fold, respectively. Levels of CA3 vary between 10.26 and 0.36 ng/ml in DMD patients whereas those of LDHB vary between 15.1 and 0.8 ng/ml. CONCLUSIONS: These results demonstrate that orthogonal assays can be used to assess the analytical reliability of biomarker quantification assays, providing a means to facilitate the translation of biomarkers to clinical practice. This strategy also warrants the development of the most relevant biomarkers, markers that can be reliably quantified with different proteomics methods.

Signal amplification strategies for clinical biomarker quantification using elemental mass spectrometry.

The current trends in modern medicine towards early diagnosis, or even prognosis, of different diseases have brought about the need for the corresponding biomarker detection at ever lower levels in really complex matrices. To do so, it is necessary to use proper extremely sensitive detection techniques such as elemental mass spectrometry. However, target labelling with metals for subsequent sensitive ICP-MS detection falls short nowadays even if resorting to inorganic nanoparticles containing a high number of detectable elements. Thus, new amplification strategies are being proposed to face this analytical challenge that will be critically discussed in this paper. Fundamentals of different novel strategies developed to achieve signal amplification and sensitive elemental mass spectrometry detection are here discussed. Some representative examples of relevant clinical applications are highlighted, along with future prospects and challenges.

HER2 challenge contest: a detailed assessment of automated HER2 scoring algorithms in whole slide images of breast cancer tissues.

AIMS: Evaluating expression of the human epidermal growth factor receptor 2 (HER2) by visual examination of immunohistochemistry (IHC) on invasive breast cancer (BCa) is a key part of the diagnostic assessment of BCa due to its recognized importance as a predictive and prognostic marker in clinical practice. However, visual scoring of HER2 is subjective, and consequently prone to interobserver variability. Given the prognostic and therapeutic implications of HER2 scoring, a more objective method is required. In this paper, we report on a recent automated HER2 scoring contest, held in conjunction with the annual PathSoc meeting held in Nottingham in June 2016, aimed at systematically comparing and advancing the state-of-the-art artificial intelligence (AI)-based automated methods for HER2 scoring. METHODS AND RESULTS: The contest data set comprised digitized whole slide images (WSI) of sections from 86 cases of invasive breast carcinoma stained with both haematoxylin and eosin (H&E) and IHC for HER2. The contesting algorithms predicted scores of the IHC slides automatically for an unseen subset of the data set and the predicted scores were compared with the 'ground truth' (a consensus score from at least two experts). We also report on a simple 'Man versus Machine' contest for the scoring of HER2 and show that the automated methods could beat the pathology experts on this contest data set. CONCLUSIONS: This paper presents a benchmark for comparing the performance of automated algorithms for scoring of HER2. It also demonstrates the enormous potential of automated algorithms in assisting the pathologist with objective IHC scoring.

Statistical colour models: an automated digital image analysis method for quantification of histological biomarkers.

BACKGROUND: Colour is the most important feature used in quantitative immunohistochemistry (IHC) image analysis; IHC is used to provide information relating to aetiology and to confirm malignancy. METHODS: Statistical modelling is a technique widely used for colour detection in computer vision. We have developed a statistical model of colour detection applicable to detection of stain colour in digital IHC images. Model was first trained by massive colour pixels collected semi-automatically. To speed up the training and detection processes, we removed luminance channel, Y channel of YCbCr colour space and chose 128 histogram bins which is the optimal number. A maximum likelihood classifier is used to classify pixels in digital slides into positively or negatively stained pixels automatically. The model-based tool was developed within ImageJ to quantify targets identified using IHC and histochemistry. RESULTS: The purpose of evaluation was to compare the computer model with human evaluation. Several large datasets were prepared and obtained from human oesophageal cancer, colon cancer and liver cirrhosis with different colour stains. Experimental results have demonstrated the model-based tool achieves more accurate results than colour deconvolution and CMYK model in the detection of brown colour, and is comparable to colour deconvolution in the detection of pink colour. We have also demostrated the proposed model has little inter-dataset variations. CONCLUSIONS: A robust and effective statistical model is introduced in this paper. The model-based interactive tool in ImageJ, which can create a visual representation of the statistical model and detect a specified colour automatically, is easy to use and available freely at http://rsb.info.nih.gov/ij/plugins/ihc-toolbox/index.html . Testing to the tool by different users showed only minor inter-observer variations in results.

CRISPR-Powered Aptasensor for Diagnostics of Alzheimer's Disease.

Alzheimer's disease (AD) is a neurodegenerative disorder and the most common cause of dementia, characterized by the accumulation of amyloid beta (Aß) peptides in the brain. Here, we present a simple, rapid, and affordable CRISPR-powered aptasensor for the quantitative detection of Aß40 and Aß42 biomarkers in cerebrospinal fluid (CSF) samples, enabling early and accurate diagnostics of AD patients. The aptasensor couples the high specificity of aptamers for Aß biomarkers with CRISPR-Cas12a-based fluorescence detection. The CRISPR-powered aptasensor enables us to detect Aß40 and Aß42 in CSF samples within 60 min, achieving a detection sensitivity of 1 pg/mL and 0.1 pg/mL, respectively. To validate its clinical utility, we quantitatively detected Aß40 and Aß42 biomarkers in clinical CSF samples. Furthermore, by combining CSF Aß42 levels with the c(Aß42)/c(Aß40) ratio, we achieved an accurate diagnostic classification of AD patients and healthy individuals, showing superior performance over the conventional ELISA method. We believe that our innovative aptasensor approach holds promise for the early diagnostic classification of AD patients.

HPLC-PDA Analysis of Polyacetylene Glucosides from Launaea capitata and Their Antibacterial and Antibiofilm Properties against Klebsiella pneumoniae.

Background/Objectives: Bacterial resistance and virulence are challenges in treating bacterial infections, especially in Klebsiella pneumoniae. Plants of the Launaea Cass. genus are used traditionally to address a variety of diseases, including infections, but the potential bioactive compounds are unknown. Our goals were to verify the potential contribution of two major polyacetylene glycosides isolated from our previous study, (3S,6E,12E)-6,12-tetradecadiene-8,10-diyne-1-ol 3-O-ß-D-glucopyranoside (1) and bidensyneoside A (syn. gymnasterkoreaside A) [(3R,8E)-3-hydroxy-8-decene-4,6-diyn-1-yl ß-D-glucopyranoside] (2), to the anti-infective properties of Launaea capitata and to develop a dependable HPLC method for their quantification; Methods: On a panel of K. pneumoniae clinical isolates, the antibacterial action of 1, 2, and the methanol extract of the whole L. capitata plant were evaluated by broth microdilution assay, while their antibiofilm action was evaluated by the crystal violet assay. qRT-PCR investigated luxS, mrkA, wzm, and wbbm genes that encode biofilm formation and quorum sensing (QS). The antibacterial activity of 1 was revealed by employing mice infection. Chromatographic separation was conducted using isocratic elution on a Hypersil BDS C18 column using a photodiode array (PDA) detector; Results: Compound 1 showed antibacterial activity with MIC values of 16-128 µg/mL. It remarkably reduced strong and moderate biofilm-forming bacterial isolates from 84.21% to 42.1% compared with the extract (68.42%) and 2 (78.95%). Compound 1 also downregulated the QS genes, luxS, mrkA, wzm, and wbbm, and exhibited in vivo antibacterial action through the enhancement of the histological construction of the liver and spleen, decreased TNF-α immunoreaction, bacterial burden, and the inflammatory mediators IL-1ß and IL-6. A successful HPLC-PDA approach was developed to separate the binary mixture of 1 and 2 in less than 10 min with high sensitivity, with detection limits down to 0.518 and 0.095 µg/mL for 1 and 2, respectively; Conclusions: Compound 1 exhibited remarkable antibacterial and antibiofilm properties and may contribute to the anti-infectious traditional uses of L. capitata, meriting further clinical studies and serving as a reliable quality control biomarker for the plant.

Advancement in Analytical Strategies for Quantification of Biomarkers with a Special Emphasis on Surrogate Approaches.

Accurate quantification of biomarkers has always been a challenge for many bioanalytical scientists due to their endogenous nature and low concentration in biological matrices. Different analytical approaches have been developed for quantifying biomarkers including enzyme-linked immunosorbent assay, immunohistochemistry, western blotting, and chromatographic techniques assisted with mass spectrometry. Liquid chromatography-tandem mass spectrometry-based quantification of biomarkers has gained more attention over other traditional techniques due to its higher sensitivity and selectivity. However, the primary challenge lies with this technique includes the unavailability of a blank matrix for method development. To overcome this challenge, different analytical approaches are being developed including surrogate analyte and surrogate matrix approach. Such approaches include quantification of biomarkers in a surrogate matrix or quantification of an isotopically labeled surrogate analyte in an authentic matrix. To demonstrate the authenticity of the surrogate approach, it is mandatory to establish quantitative parallelism through validation employing respective surrogate analytes and surrogate matrices. In this review, different bioanalytical approaches for biomarker quantification and recent advancements in the field aiming for improvement in the specificity of the techniques have been discussed. Liquid chromatography-tandem mass spectrometry-based surrogate approaches for biomarker quantification and significance of parallelism establishment in both surrogate matrix and surrogate analyte-based approaches have been critically discussed. In addition, different methods for demonstrating parallelism in the surrogate method have been explained.

Application of Immunoaffinity Mass Spectrometry (IA-MS) for Protein Biomarker Quantification.

Immunoaffinity mass spectrometry (IA-MS) is a powerful analytical technique for the determination of protein biomarkers with high sensitivity and unparalleled specificity. Typically, the protein antigen of interest is captured from biofluids and tissue lysates using an antibody prior to mass spectrometric analysis. Here we describe the specific steps of the protein immunoaffinity component of the IA-MS workflow that is applicable to most protein antigens.

Quantitative multiplexed analysis of MMP-11 and CD45 in metastatic breast cancer tissues by immunohistochemistry-assisted LA-ICP-MS.

Breast cancer is the leading cause of cancer death in woman and tremendous efforts are undertaken to limit its dissemination and to provide effective treatment. Various histopathological parameters are routinely assessed in breast cancer biopsies to provide valuable diagnostic and prognostic information. MMP-11 and CD45 are tumor-associated antigens and potentially valuable biomarkers for grading aggressiveness and metastatic probability. This paper presents methods for quantitative and multiplexed imaging of MMP-11 and CD45 in breast cancer tissues and investigates their potential for improved cancer characterization and patient stratification. An immunohistochemistry-assisted laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) method was successfully developed and optimized using lanthanide-tagged monoclonal antibodies as proxies to determine spatial distributions and concentrations of the two breast cancer biomarkers. The labeling degree of antibodies was determined via size exclusion-ICP-tandem mass spectrometry (SEC-ICP-MS/MS) employing online calibration via post-column isotope dilution analysis (IDA). The calibration of spatial distributions of labeled lanthanides in tissues was performed by ablating mold-prepared gelatin standards spiked with element standards. Knowledge of labeling degrees enabled the translation of lanthanide concentrations into biomarkers concentrations. The k-means clustering was used to select tissue areas for statistical analysis and mean concentrations were compared for sets of metastatic, non-metastatic and healthy samples. MMP-11 was expressed in stroma surrounding tumor areas, while CD45 was predominantly found inside tumor areas with high cell density. There was no significant correlation between CD45 and metastasis (P = 0.70); however, MMP-11 was significantly up-regulated (202%) in metastatic samples compared to non-metastatic (P = 0.0077) and healthy tissues (P = 0.0087).

A plasmonic thermal sensing based portable device for lateral flow assay detection and quantification.

Point-of-care testing (POCT) is widely used for early diagnosis and monitoring of diseases. Lateral flow assay (LFA) is a successfully commercial tool for POCT. However, LFA often suffers from a lack of quantification and analytical sensitivity. To solve these drawbacks, we have previously developed a thermal LFA using plasmonic gold nanoparticles for thermal contrast into a portable device. Although this methodology significantly improves the analytical sensitivity compared with conventional visual detection, quantification problems are still remaining. In this study, we optimized the operating conditions for the device using conduction and radiation thermal sensing modes allowing the quantification of LFA. The limit of detection of the strips merely containing nanoparticles was decreased by 5-fold (conduction mode) and 12-fold (radiation mode) compared to traditional visual detection. The effect of the ambient temperature was studied for both methods of detection showing that the radiation mode was more affected by the ambient temperature than the conduction mode. To validate the thermal sensing method, human chorionic gonadotropin (HCG) biomarker was quantified using our LFA strips, obtaining a detection limit of 2.8 mIU/mL when using the radiation method of detection.

Development of an UPLC/MS-MS method for quantification of intact IGF-I from human serum.

Aim: Developing LC-MS methods for biomolecules is often challenging due to issues with molecular size and complexity, nonspecific binding, protein binding, solubility and sensitivity. As a result, complex sample preparation workflows, including immune-affinity and/or protein digestion and lengthy analysis potentially using nano-flow LC, may be needed to achieve the required sensitivity. This work aims to provide a simple, sensitive, fast and robust method for quantification of intact IGF-I from human serum using UPLC-MS/MS. Methods: IGF-I serum samples were denatured with sodium dodecyl sulfate, followed by organic protein precipitation to effectively disrupt protein binding and subsequent SPE of the resulting supernatant for sample cleanup and enrichment prior to LC-MS/MS analysis. Separation was performed on an analytical scale LC using a reversed-phase column containing <2 µm solid core particle followed by detection on a tandem quadrupole MS in multiple reaction monitoring mode. Results: Intact IGF-I was quantified from serum using the method described above at a LLOQ of 5 ng/ml with a dynamic range 5-1000 ng/ml (r2>0.99) and mean accuracy of 101.76%. Accuracies for quality control samples were between 93.9-107.7% with RSD <7%. Conclusion: The analytical sensitivity, linear dynamic range and excellent reproducibility of this method reliably measures endogenous and elevated serum IGF-I levels, demonstrating its utility in discovery, bioanalysis and clinical research.

Compensated Interferometry Measures of CYFRA 21-1 Improve Diagnosis of Lung Cancer.

Diagnosis of lung cancer patients with indeterminate pulmonary nodules (IPNs) presents a significant clinical challenge, with morbidity and management costs of $28 billion/year. We show that a quantitative free-solution assay (FSA), coupled with a compensated interferometric reader (CIR), improves the diagnostic performance of CYFRA 21-1 as a lung cancer biomarker. FSA-CIR is a rapid, mix-and-read, isothermal, label- and enzyme-free, matrix-insensitive, and target and probe-agnostic assay. Operating FSA-CIR at ∼40, 0.75 µL samples/day delivered a serum CYFRA 21-1 limit of quantification (LOQ) of 81 pg/mL with intra-assay and interassay CVs of 4.9% and 9.6% for four-day replicate determinations. Blinded analysis of a 225 patient cohort, consisting of 75 nonmalignant nodules, 45 adenocarcinomas, 44 squamous cell carcinomas, and 61 small cell lung cancers, gave a clear separation of cases and controls, not observed in the Cobas ECL analysis. The area under the curve (AUC) for the Mayo model increased from 0.595 to 0.923 when combined with the FSA-CIR CYFRA 21-1 measurements. In a population with nodules between 6 and 30 mm, the AUC increased from 0.567 to 0.943. In this subgroup, the positive predictive value (PPV) for all tumors by the CYFRA 21-1 assay was 98.7%. Our results demonstrate increased performance of the CYFRA 21-1 assay using FSA-CIR and represents a proof of concept for redefining the performance characteristics of this important candidate biomarker.

Robust and accurate quantification of biomarkers of immune cells in lung cancer micro-environment using deep convolutional neural networks.

Recent years have seen a growing awareness of the role the immune system plays in successful cancer treatment, especially in novel therapies like immunotherapy. The characterization of the immunological composition of tumors and their micro-environment is thus becoming a necessity. In this paper we introduce a deep learning-based immune cell detection and quantification method, which is based on supervised learning, i.e., the input data for training comprises labeled images. Our approach objectively deals with staining variation and staining artifacts in immunohistochemically stained lung cancer tissue and is as precise as humans. This is evidenced by the low cell count difference to humans of 0.033 cells on average. This method, which is based on convolutional neural networks, has the potential to provide a new quantitative basis for research on immunotherapy.

Combining fluorescence-based image segmentation and automated microfluidics for ultrafast cell-by-cell assessment of biomarkers for HER2-type breast carcinoma.

Immunohistochemistry (IHC) is one of the main clinical techniques for biomarker assessment on tissue biopsies. It consists in chromogenic labeling with specific antibodies, followed by optical imaging, and it is used for diagnosis and therapeutic targeting. A well-known drawback of IHC is its limited robustness, which often precludes quantitative biomarker assessment. We combine microfluidic immunostaining, fluorescence imaging, and image-based cell segmentation to create an ultrafast procedure for accurate biomarker assessment via IHC. The experimental protocol is very simple and based on fast delivery of reagents in a microfluidic chamber created by clamping a half-chamber patterned in a silicon chip on top of a tumor tissue section. Also, the imaging procedure simply requires a standard fluorescence microscope, already widely used in clinical practice. The image processing is based on local-contrast enhancement and thresholding of the obtained fluorescence image, with subsequent Voronoi segmentation. To assess the experimental and analytical procedure on robust biological controls, we apply our method to well-characterized cell lines, which guarantee higher reproducibility than whole-tissue samples and therefore enable to disentangle the technical variability from the biological variability. To increase the potential translationality, we address the detection and quantification of the human epidermal growth factor receptor 2 (HER2) protein, which is a biomarker for HER2-type breast carcinoma diagnosis and therapy. We report both ultrafast immunofluorescence staining (5 min per sample) of two breast cancer biomarkers and ultrafast cell segmentation (1 min per sample = processing of thousands of cells). This provides a quantitative, cell-based immunofluorescent signal, with which we propose a potential diagnostic criterion to separate HER2-positive and HER2-negative breast cancer cells at high sensitivity and specificity.

To elute or not to elute in immunocapture bottom-up LC-MS.

Immunocapture-based bottom-up LC-MS is a promising technique for the quantification of low abundant proteins. Magnetic immunocapture beads provide efficient enrichment from complex samples through the highly specific interaction between the target protein and its antibody. In this article, we have performed the first thorough comparison between digestion of proteins while bound to antibody coated beads versus after elution from the beads. Two previously validated immunocapture based MS methods for the quantification of pro-gastrin releasing peptide (ProGRP) and human chorionic gonadotropin (hCG) were used as model systems. The tryptic peptide generation was shown to be protein dependent and influenced by protein folding and accessibility towards trypsin both on-beads and in the eluate. The elution of proteins bound to the beads was also shown to be incomplete. In addition, the on-beads digestion suffered from non-specific binding of the trypsin generated peptides. A combination of on-beads digestion and elution may be applied to improve both the quantitative (peak area of the signature peptides) and qualitative yield (number of missed cleavages, total number of identified peptides, coverage, signal intensity and number of zero missed cleavage peptides) of the target proteins. The quantitative yield of signature peptides was shown to be reproducible in all procedures tested.

Magnetic Materials for the Selective Analysis of Peptide and Protein Biomarkers.

This mini-review article provides an overview on the use of magnetic materials for the analysis of protein biomarkers. In particular, the advantage provided by magnetic solid phase extraction will be discussed with selected examples, considering untargeted analysis for screening new biomarker proteins and targeted investigation on known and suggested new biomarkers. Aspects, such as enrichment efficiency over conventional techniques, ease of use, functionalization versatility and automation will be considered, together with quantification and deeper structure elucidation provided by coupling selective or specific enrichment to powerful characterization techniques, such as mass spectrometry.