Paolo Inglese

Crowdsourced estimation of cognitive decline and resilience in Alzheimer's disease

Identifying accurate biomarkers of cognitive decline is essential for advancing early diagnosis and prevention therapies in Alzheimers disease. The Alzheimers disease DREAM Challenge was designed as a computational crowdsourced project to benchmark the current state‐of‐the‐art in predicting cognitive outcomes in Alzheimers disease based on high dimensional, publicly available genetic and structural imaging data. This meta‐analysis failed to identify a meaningful predictor developed from either data modality, suggesting that alternate approaches should be considered for prediction of cognitive performance.

Medical Swab Analysis Using Desorption Electrospray Ionization Mass Spectrometry: A Noninvasive Approach for Mucosal Diagnostics

Medical swabs are routinely used worldwide to sample human mucosa for microbiological screening with culture methods. These are usually time-consuming and have a narrow focus on screening for particular microorganism species. As an alternative, direct mass spectrometric profiling of the mucosal metabolome provides a broader window into the mucosal ecosystem. We present for the first time a minimal effort/minimal-disruption technique for augmenting the information obtained from clinical swab analysis with mucosal metabolome profiling using desorption electrospray ionization mass spectrometry (DESI-MS) analysis. Ionization of mucosal biomass occurs directly from a standard rayon swab mounted on a rotating device and analyzed by DESI MS using an optimized protocol considering swab-inlet geometry, tip-sample angles and distances, rotation speeds, and reproducibility. Multivariate modeling of mass spectral fingerprints obtained in this way readily discriminate between different mucosal surfaces and display the ability to characterize biochemical alterations induced by pregnancy and bacterial vaginosis (BV). The method was also applied directly to bacterial biomass to confirm the ability to detect intact bacterial species from a swab. These results highlight the potential of direct swab analysis by DESI-MS for a wide range of clinical applications including rapid mucosal diagnostics for microbiology, immune responses, and biochemistry.

Translational utility of a hierarchical classification strategy in biomolecular data analytics

Hierarchical classification (HC) stratifies and classifies data from broad classes into more specific classes. Unlike commonly used data classification strategies, this enables the probabilistic prediction of unknown classes at different levels, minimizing the burden of incomplete databases. Despite these advantages, its translational application in biomedical sciences has been limited. We describe and demonstrate the implementation of a HC approach for “omics-driven” classification of 15 bacterial species at various taxonomic levels achieving 90–100% accuracy, and 9 cancer types into morphological types and 35 subtypes with 99% and 76% accuracy, respectively. Unknown bacterial species were probabilistically assigned with 100% accuracy to their respective genus or family using mass spectra (n = 284). Cancer types were predicted by mRNA data (n = 1960) for most subtypes with 95–100% accuracy. This has high relevance in clinical practice where complete datasets are difficult to compile with the continuous evolution of diseases and emergence of new strains, yet prediction of unknown classes, such as bacterial species, at upper hierarchy levels may be sufficient to initiate antimicrobial therapy. The algorithms presented here can be directly translated into clinical-use with any quantitative data, and have broad application potential, from unlabeled sample identification, to hierarchical feature selection, and discovery of new taxonomic variants.

A Prospective Multi-National Study of the Colorectal Cancer Mucosal Microbiome Reveals Specific Taxonomic Changes Indicative of Disease Stage and Prognosis

Introduction The colorectal cancer (CRC) microbiome is niche specific and individualised. Several putative driver organisms enriched on CRCs have been identified from human studies, but few data exist which properly account for important clinical variables in CRC. In this study, we used a meta-taxonomic approach to demonstrate how the CRC microbiome varies with disease stage, histological markers of prognosis and host molecular phenotypes. Method A prospective study was performed on patients undergoing colonoscopy and elective surgery for CRC at three hospitals in UK and Czech Republic. Tissue was sampled from tumours, adenomas, adjacent normal mucosa and mucosa from healthy colon controls. The V1-2 regions of the 16S rRNA gene were sequenced (Illumina MiSeq); data were processed in Mothur and analysed in Stamp and R. Species assignment was performed with NCBI BLAST for microbial genomes. False discovery rate p value correction accounted for multiple testing. Histological analysis and tumour molecular phenotyping were performed according to Royal College of Pathology guidelines. Results One hundred and ninety six patients were recruited: 158 CRC patients, 24 adenoma patients and 14 normal colon controls (median age 70; range 35–90). Tumours were staged as 6 T0, 4 T1, 23 T2, 97 T3, 27 T4; 99 N0, 40 N1, 27 N2; 6 M1. No significant differences were seen in diversity or taxonomy between the UK and Czech cohorts. Adenoma and healthy colon control samples were taxonomically indistinct. However, CRCs were characterised by reduced Shannon diversity (p Conclusion This large prospective analysis demonstrates that the CRC microbiome is stage-specific and appears to evolve with disease progression. We conclude that oral pathobionts which colonise advanced stage disease relate to markers of tumour prognosis, raising the possibility that they may be directly influencing tumour invasion. Disclosure of Interest None Declared

Co-localization features for classification of tumors using mass spectrometry imaging

Abstract Supervised modeling of mass spectrometry imaging (MSI) data is a crucial component for the detection of the distinct molecular characteristics of cancerous tissues. Currently, two types of supervised analyses are mainly used on MSI data: pixel-wise segmentation of sample images, and whole-sample-based classification. A large number of mass spectra associated with each MSI sample can represent a challenge for designing models that simultaneously preserve the overall molecular content while capturing valuable information contained in the MSI data. Furthermore, intensity-related batch effects can introduce biases in the statistical models. Here we introduce a method based on ion colocalization features that allows the classification of whole tissue specimens using MSI data, which naturally preserves the spatial information associated the with the mass spectra and is less sensitive to possible batch effects. Finally, we propose data visualization strategies for the inspection of the derived networks, which can be used to assess whether the correlation differences are related to co-expression/suppression or disjoint spatial localization patterns and can suggest hypotheses based on the underlying mechanisms associated with the different classes of analyzed samples.Statistical modeling of mass spectrometry imaging (MSI) data is a crucial component for the understanding of the molecular characteristics of cancerous tissues. Quantification of the abundances of metabolites or batch effect between multiple spectral acquisitions represents only a few of the challenges associated with this type of data analysis. Here we introduce a method based on ion co-localization features that allows the classification of whole tissue specimens using MSI data, which overcomes the possible batch effect issues and generates data-driven hypotheses on the underlying mechanisms associated with the different classes of analyzed samples.

SPUTNIK: an R package for filtering of spatially related peaks in mass spectrometry imaging data.

Summary: SPUTNIK is an R package consisting of a series of tools to filter mass spectrometry imaging peaks characterized by a noisy or unlikely spatial distribution. SPUTNIK can produce mass spectrometry imaging datasets characterized by a smaller but more informative set of peaks, reduce the complexity of subsequent multi‐variate analysis and increase the interpretability of the statistical results. Availability and implementation: SPUTNIK is freely available online from CRAN repository and at https://github.com/paoloinglese/SPUTNIK. The package is distributed under the GNU General Public License version 3 and is accompanied by example files and data. Supplementary information: Supplementary data are available at Bioinformatics online.

Network analysis of mass spectrometry imaging data from colorectal cancer identifies key metabolites common to metastatic development.

A deeper understanding of inter-tumor and intra-tumor heterogeneity is a critical factor for the advancement of next generation strategies against cancer. The heterogeneous morphology exhibited by solid tumors is mirrored by their metabolic heterogeneity. Defining the basic biological mechanisms that underlie tumor cell variability will be fundamental to the development of personalized cancer treatments. Variability in the molecular signatures found in local regions of cancer tissues can be captured through an untargeted analysis of their metabolic constituents. Here we demonstrate that DESI mass spectrometry imaging (MSI) combined with network analysis can provide detailed insight into the metabolic heterogeneity of colorectal cancer (CRC). We show that network modules capture signatures which differentiate tumor metabolism in the core and in the surrounding region. Moreover, module preservation analysis of network modules between patients with and without metastatic recurrence explains the inter-subject metabolic differences associated with diverse clinical outcomes such as metastatic recurrence. Significance Network analysis of DESI-MSI data from CRC human tissue reveals clinically relevant co-expression ion patterns associated with metastatic susceptibility. This delineates a more complex picture of tumor heterogeneity than conventional hard segmentation algorithms. Using tissue sections from central regions and at a distance from the tumor center, ion co-expression patterns reveal common features among patients who developed metastases (up of > 5 years) not preserved in patients who did not develop metastases. This offers insight into the nature of the complex molecular interactions associated with cancer recurrence. Presently, predicting CRC relapse is challenging, and histopathologically like-for-like cancers frequently manifest widely varying metastatic tendencies. Thus, the methodology introduced here more robustly defines the risk of metastases based on tumor biochemical heterogeneity. Author contributions P.I., Z.T., R.C.G.: designed the study, developed the workflow, analyzed the data, interpreted the results, wrote the paper; N.S. collected the MS, performed the H&E staining, wrote the paper; L.D.: interpreted the results, wrote the paper; A.M.: collected the MS; A.S.: histological assessment; L.P.: collected the tissue specimens and clinical metadata; A.D.: collected the MS; H.K.: performed the H&E staining; R.M.: collected the tissue specimens and clinical metadata. R.G.: histological assessment; J.K.N: designed the study, edited the paper.

Statistical Tools for Molecular Covariance Spectroscopy

One major application of modern spectroscopic and spectrometric techniques is to measure hundreds to thousands of molecules in biological specimens as part of a process of metabolic phenotyping. Statistical spectroscopy covers a range of techniques used for the recovery of correlated intensity patterns within and between molecules. This plays an essential role in the annotation of molecular features of potential biological or diagnostic significance. The article introduces a variety of univariate and multivariate statistical tools for molecular covariance spectroscopy.