Publications

The development of effective antibody therapeutics has been hampered by a lack of methods to measure drug delivery and activity within tumors at single-cell resolution. Here we introduce single-cell spatial pharmacobiology (SSP), an experimental and analytical framework that integrates in situ imaging of a systemically infused, fluorescently labeled therapeutic antibody with high-plex spatial proteomics to quantify antibody distribution, target engagement and tumor microenvironment (TME) architecture. We applied SSP to tumor tissues from participants with head and neck squamous cell carcinoma and pancreatic ductal adenocarcinoma who received the antibody panitumumab-IRDye800 in phase 1 trials. SSP identified pronounced spatial heterogeneity in single-cell drug delivery and target engagement, shaped by conserved stromal barriers, including periostin-rich extracellular matrix assemblies and fibroblast-activation-protein-positive cancer-associated fibroblast neighborhoods, which were associated with reduced antibody delivery in both tumor types. SSP measures drug-target-TME interactions in human tumors and can support studies of resistance mechanisms, dosing strategies and discovery of spatial biomarkers for precision oncology.

JTD Keywords: Cancer, Diffusion, Fibroblasts, Gemcitabine, Head, Inhibitor, Monoclonal-antibodies, Multiplex, Panitumumab-irdye800cw, Transport

Gas-Chromatography coupled to Ion Mobility Spectrometry (GC-IMS) based metabolomics is an emerging technique for obtaining fast, reliable untargeted metabolic fingerprints of biofluids. The generated raw data is highly dimensional and complex, suffers from baseline problems, misalignments, long peak tails and strong nonlinearities that must be corrected to extract chemically relevant features from samples. In this work, we present our GCIMS R package, which includes spectra loading, metadata handling, denoising, baseline correction, spectral and chromatographic alignment, peak detection, integration, and peak clustering to produce a peak table ready for multivariate data analysis. We discuss package design decisions, and, for illustration purposes, we show a case study of sex discrimination on the basis of the volatile compounds in urine samples. The GCIMS package provides a user-friendly workflow for non-code developers to process their raw data samples.

JTD Keywords: Breath analysis, Headspace, Ims, Urine

Gas chromatography—ion mobility spectrometry (GC-IMS) allows the fast, reliable, and inexpensive chemical composition analysis of volatile mixtures. This sensing technology has been successfully employed in food science to determine food origin, freshness and preventing alimentary fraud. However, GC-IMS data is highly dimensional, complex, and suffers from strong non-linearities, baseline problems, misalignments, peak overlaps, long peak tails, etc., all of which must be corrected to properly extract the relevant features from samples. In this work, a pipeline for signal pre-processing, followed by four different approaches for feature extraction in GC-IMS data, is presented. More precisely, these approaches consist of extracting data features from: (1) the total area of the reactant ion peak chromatogram (RIC); (2) the full RIC response; (3) the unfolded sample matrix; and (4) the ion peak volumes. The resulting pipelines for data processing were applied to a dataset consisting of two different quality class Iberian ham samples, based on their feeding regime. The ability to infer chemical information from samples was tested by comparing the classification results obtained from partial least-squares discriminant analysis (PLS-DA) and the samples’ variable importance for projection (VIP) scores. The choice of a feature extraction strategy is a trade-off between the amount of chemical information that is preserved, and the computational effort required to generate the data models.

JTD Keywords: authenticity, classification, electronic-nose, feature extraction, food analysis, gc-ims, headspace, least-squares, models, pld-da, pre-processing, quality, sensory analysis, wine, Discriminant analysis, Feature extraction, Food analysis, Gas chromatography-mass spectrometry, Gc-ims, Hs-gc-ims, Ion mobility spectrometry, Odorants, Pld-da, Pre-processing, Workflow

The human olfactory system remains one of the most challenging biological systems to replicate. Humans use it without thinking, where it can equally offer protection from harm and bring enjoyment in equal measure. It is the system’s ability to detect and analyze complex odors, without the need for specialized infra-structure, that is the envy of many scientists. The field of artificial olfaction has recruited and stimulated interdisciplinary research and commercial development for several applications that include malodor measurement, medical diagnostics, food and beverage quality, environment and security. Over the last century, innovative engineers and scientists have been focused on solving a range of problems associated with measurement and control of odor. The IEEE Sensors Journal has published Special Issues on olfaction in 2002 and 2012. Here we continue that coverage. In this article, we summarize early work in the 20th Century that served as the foundation upon which we have been building our odor-monitoring instrumental and measurement systems. We then examine the current state of the art that has been achieved over the last two decades as we have transitioned into the 21st Century. Much has been accomplished, but great progress is needed in sensor technology, system design, product manufacture and performance standards. In the final section, we predict levels of performance and ubiquitous applications that will be realized during in the mid to late 21st Century.

JTD Keywords: air-quality, breath analysis, calibration transfer, chemical sensor arrays, chemosensor arrays, drift compensation, electronic nose, gas sensors, headspace sampling, machine learning, machine olfaction, odor detection, plume structure, voc analysis, Artificial olfaction, Electrodes, Electronic nose, Electronic nose technology, Headspace sampling, Instruments, Machine learning, Machine olfaction, Monitoring, Odor detection, Olfactory, Sensor phenomena and characterization, Sensors, Temperature sensors, Voc analysis

Background & Aims: MicroRNAs (miRNAs) circulate in several body fluids and can be useful biomarkers. The aim of this study was to identify blood-circulating miRNAs associated with cirrhosis progression and acute-on-chronic liver failure (ACLF). Methods: Using high-throughput screening of 754 miRNAs, serum samples from 45 patients with compensated cirrhosis, decompensated cirrhosis, or ACLF were compared with those from healthy individuals (n = 15). miRNA levels were correlated with clinical parameters, organ failure, and disease progression and outcome. Dysregulated miRNAs were evaluated in portal and hepatic vein samples (n = 33), liver tissues (n = 17), and peripheral blood mononuclear cells (PBMCs) (n = 16). Results: miRNA screening analysis revealed that circulating miRNAs are dysregulated in cirrhosis progression, with 51 miRNAs being differentially expressed among all groups of patients. Unsupervised clustering and principal component analysis indicated that the main differences in miRNA expression occurred at decompensation, showing similar levels in patients with decompensated cirrhosis and those with ACLF. Of 43 selected miRNAs examined for differences among groups, 10 were differentially expressed according to disease progression. Moreover, 20 circulating miRNAs were correlated with model for end-stage liver disease and Child-Pugh scores. Notably, 11 dysregulated miRNAs were associated with kidney or liver failure, encephalopathy, bacterial infection, and poor outcomes. The most severely dysregulated miRNAs (i.e. miR-146a5p, miR-26a-5p, and miR-191-5p) were further evaluated in portal and hepatic vein blood and liver tissue, but showed no differences. However, PBMCs from patients with cirrhosis showed significant downregulation of miR-26 and miR-146a, suggesting a extrahepatic origin of some circulating miRNAs. Conclusions: This study is a repository of circulating miRNA data following cirrhosis progression and ACLF. Circulating miRNAs were profoundly dysregulated during the progression of chronic liver disease, were associated with failure of several organs and could have prognostic utility. Lay summary: Circulating miRNAs are small molecules in the blood that can be used to identify or predict a clinical condition. Our study aimed to identify miRNAs for use as biomarkers in patients with cirrhosis or acute-on-chronic liver failure. Several miRNAs were found to be dysregulated during the progression of disease, and some were also related to organ failure and disease-related outcomes. (C) 2021 The Author(s). Published by Elsevier B.V. on behalf of European Association for the Study of the Liver (EASL).

JTD Keywords: aclf, acute-on-chronic liver failure, alt, alanine aminotransferase, ast, aspartate aminotransferase, biomarkers, chronic liver disease, cxcl10, c-x-c motif chemokine ligand 10, ef clif, european foundation for the study of chronic liver failure, foxo, forkhead box o, inr, international normalised ratio, ldh, lactate dehydrogenase, liver decompensation, mapk, mitogen-activated protein kinase, meld, model for end-stage liver disease, nash, non-alcoholic steatohepatitis, non-coding rnas, pbmcs, peripheral blood mononuclear cells, pca, principal component analysis, tgf, transforming growth factor, tips, transjugular intrahepatic portosystemic shunt, Biomarkers, Chronic liver disease, Expression, Liver decompensation, Markers, Mir-146a, Non-coding rnas, Qpcr, quantitative pcr

The quality and composition of bitter orange essential oils (EOs) strongly depend on the ripening stage of the citrus fruit. The concentration of volatile compounds and consequently its organoleptic perception varies. While this can be detected by trained humans, we propose an objective approach for assessing the bitter orange from the volatile composition of their EO. The method is based on the combined use of headspace gas chromatography–mass spectrometry (HS-GC-MS) and artificial neural networks (ANN) for predictive modeling. Data obtained from the analysis of HS-GC-MS were preprocessed to select relevant peaks in the total ion chromatogram as input features for ANN. Results showed that key volatile compounds have enough predictive power to accurately classify the EO, according to their ripening stage for different applications. A sensitivity analysis detected the key compounds to identify the ripening stage. This study provides a novel strategy for the quality control of bitter orange EO without subjective methods.

JTD Keywords: Bitter orange essential oil, Headspace gas chromatography–mass spectrometry, Artificial neural network, Foodomics, Chemometrics, Feature selection