Risk assessment of latent tuberculosis infection through a multiplexed cytokine biosensor assay and machine learning feature selection.

Accurate detection and risk stratification of latent tuberculosis infection (LTBI) remains a major clinical and public health problem. We hypothesize that multiparameter strategies that probe immune responses to Mycobacterium tuberculosis can provide new diagnostic insights into not only the status of LTBI infection, but also the risk of reactivation. After the initial proof-of-concept study, we developed a 13-plex immunoassay panel to profile cytokine release from peripheral blood mononuclear cells stimulated separately with Mtb-relevant and non-specific antigens to identify putative biomarker signatures. We sequentially enrolled 65 subjects with various risk of TB exposure, including 32 subjects with diagnosis of LTBI. Random Forest feature selection and statistical data reduction methods were applied to determine cytokine levels across different normalized stimulation conditions. Receiver Operator Characteristic (ROC) analysis for full and reduced feature sets revealed differences in biomarkers signatures for LTBI status and reactivation risk designations. The reduced set for increased risk included IP-10, IL-2, IFN-γ, TNF-α, IL-15, IL-17, CCL3, and CCL8 under varying normalized stimulation conditions. ROC curves determined predictive accuracies of > 80% for both LTBI diagnosis and increased risk designations. Our study findings suggest that a multiparameter diagnostic approach to detect normalized cytokine biomarker signatures might improve risk stratification in LTBI.

Identification of lipid biomarkers of metastatic potential and gene expression (HER2/p53) in human breast cancer cell cultures using ambient mass spectrometry.

In breast cancer, overexpression of human epidermal growth factor receptor 2 (HER2) correlates with overactivation of lipogenesis, mutation of tumor suppressor p53, and increased metastatic potential. The mechanisms through which lipids mediate p53, HER2, and metastatic potential are largely unknown. We have developed a desorption electrospray ionization mass spectrometry (DESI-MS) method to identify lipid biomarkers of HER2/p53 expression, metastatic potential, and disease state (viz. cancer vs. non-cancerous) in monolayer and suspension breast cancer cell cultures (metastatic potential: MCF-7, T-47D, MDA-MB-231; HER2/p53: HCC2218 (HER2+++/p53+), HCC1599 (HER2-/p53-), HCC202 (HER2++/p53-), HCC1419 (HER2+++/p53-) HCC70 (HER2-/p53+++); non-cancerous: MCF-10A). Unsupervised principal component analysis (PCA) of DESI-MS spectra enabled identification of twelve lipid biomarkers of metastatic potential and disease state, as well as ten lipids that distinguish cell lines based on HER2/p53 expression levels (> 200 lipids were identified per cell line). In addition, we developed a DESI-MS imaging (DESI-MSI) method for mapping the spatial distribution of lipids in metastatic spheroids (MDA-MB-231). Of the twelve lipids that correlate with changes in the metastatic potential of monolayer cell cultures, three were localized to the necrotic core of spheroids, indicating a potential role in promoting cancer cell survival in nutrient-deficient environments. One lipid species, which was not detected in monolayer MDA-MB-231 cultures, was spatially localized to the periphery of the spheroid, suggesting a potential role in invasion and/or proliferation. These results demonstrate that combining DESI-MS/PCA of monolayer and suspension cell cultures with DESI-MSI of spheroids is a promising approach for identifying lipid biomarkers of specific genotypes and phenotypes, as well as elucidating the potential function of these biomarkers in breast cancer. Graphical Absract.

Precision immunoprofiling to reveal diagnostic signatures for latent tuberculosis infection and reactivation risk stratification.

Latent tuberculosis infection (LTBI) is estimated in nearly one quarter of the world's population, and of those immunocompetent and infected ~10% will proceed to active tuberculosis (TB). Current diagnostics cannot definitively identify LTBI and provide no insight into reactivation risk, thereby defining an unmet diagnostic challenge of incredible global significance. We introduce a new machine-learning-driven approach to LTBI diagnostics that leverages a high throughput, multiplexed cytokine detection technology and powerful bioinformatics to reveal multi-marker signatures for LTBI diagnosis and risk stratification. This approach is enabled through an individualized normalization procedure that allows disease-relevant biomarker signatures to be revealed despite heterogeneity in basal immune response. Specifically, cytokines secreted from antigen-challenged peripheral blood mononuclear cells were detected using silicon photonic sensor arrays and multidimensional data correlation of individually-normalized immune responses revealed signatures important for LTBI status. These results demonstrate a powerful combination of multiplexed biomarker detection technologies, precision immune normalization, and feature selection algorithms that revealed positively correlated multi-biomarker signatures for LTBI status and reactivation risk stratification from a relatively simple blood-based assay.

Recent advances in environmental and clinical analysis using microring resonator-based sensors.

Progress in the development of biosensors has dramatically improved analytical techniques. Biosensors have advantages over more conventional analytical techniques arising from attributes such as straightforward analyses, higher throughput, miniaturization, smaller sample input, and lower cost. Microring optical resonators have emerged in the area of optical sensors as an exceptional choice due to their sensitivity, ease of fabrication, multiplexity capability and label-free detection. In this paper, the sensing principle of these sensors is described. In addition, we summarize and highlight their most recent and relevant applications in environmental and clinical detection analysis.

A Guide to Quantitative Biomarker Assay Development using Whispering Gallery Mode Biosensors.

Whispering gallery mode (WGM) sensors are a class of powerful analytical techniques defined by the measurement of changes in the local refractive index at or near the sensor surface. When functionalized with target-specific capture agents, analyte binding can be measured with very low limits of detection. There are many geometric manifestations of WGM sensors, with chip-integrated silicon photonic devices first commercialized because of the robust, wafer-scale device fabrication, facile optical interrogation, and amenability to the creation of multiplexed sensor arrays. Using these arrays, a number of biomolecular targets have been detected in both label-free and label-enhanced assay formats. For example, sub-picomolar detection limits for multiple cytokines were achieved using an enzymatically enhanced sandwich immunoassay that showed high analyte specificity suitable for detection in complex, clinical matrices. This protocol describes a generalizable approach for the development of quantitative, multiplexed immunoassays using silicon photonic microrings as an example WGM platform. © 2017 by John Wiley & Sons, Inc.