Publications
36 results found
Huang HX, Inglese P, Tang J, et al., 2024, Mass spectrometry imaging highlights dynamic patterns of lipid co-expression with Aβ plaques in mouse and human brains., J Neurochem
Lipids play crucial roles in the susceptibility and brain cellular responses to Alzheimer's disease (AD) and are increasingly considered potential soluble biomarkers in cerebrospinal fluid (CSF) and plasma. To delineate the pathological correlations of distinct lipid species, we conducted a comprehensive characterization of both spatially localized and global differences in brain lipid composition in AppNL-G-F mice with spatial and bulk mass spectrometry lipidomic profiling, using human amyloid-expressing (h-Aβ) and WT mouse brains controls. We observed age-dependent increases in lysophospholipids, bis(monoacylglycerol) phosphates, and phosphatidylglycerols around Aβ plaques in AppNL-G-F mice. Immunohistology-based co-localization identified associations between focal pro-inflammatory lipids, glial activation, and autophagic flux disruption. Likewise, in human donors with varying Braak stages, similar studies of cortical sections revealed co-expression of lysophospholipids and ceramides around Aβ plaques in AD (Braak stage V/VI) but not in earlier Braak stage controls. Our findings in mice provide evidence of temporally and spatially heterogeneous differences in lipid composition as local and global Aβ-related pathologies evolve. Observing similar lipidomic changes associated with pathological Aβ plaques in human AD tissue provides a foundation for understanding differences in CSF lipids with reported clinical stage or disease severity.
Curran L, Simoes Monteiro de Marvao A, Inglese P, et al., 2023, Genotype-phenotype taxonomy of hypertrophic cardiomyopathy, Circulation: Genomic and Precision Medicine, Vol: 16, Pages: 559-570, ISSN: 2574-8300
Background:Hypertrophic cardiomyopathy (HCM) is an important cause of sudden cardiac death associated with heterogeneous phenotypes but there is no systematic framework for classifying morphology or assessing associated risks. Here we quantitatively survey genotype-phenotype associations in HCM to derive a data-driven taxonomy of disease expression.Methods:We enrolled 436 HCM patients (median age 60 years; 28.8% women) with clinical, genetic and imaging data. Anindependent cohort of 60 HCM patients from Singapore (median age 59 years; 11% women) and a reference population from UK Biobank (n = 16,691, mean age 55 years; 52.5% women) were also recruited. We used machine learning to analyse the three-dimensional structure of the left ventricle from cardiac magnetic resonance imaging and build a tree-based classification of HCM phenotypes. Genotype and mortality risk distributions were projected on the tree.Results:Carriers of pathogenic or likely pathogenic variants (P/LP) for HCM had lower left ventricular mass, but greater basalseptal hypertrophy, with reduced lifespan (mean follow-up 9.9 years) compared to genotype negative individuals(hazard ratio: 2.66; 95% confidence interval [CI]: 1.42-4.96; P < 0.002). Four main phenotypic branches were identified using unsupervised learning of three-dimensional shape: 1) non-sarcomeric hypertrophy with co-existing hypertension; 2) diffuse and basal asymmetric hypertrophy associated with outflow tract obstruction; 3) isolated basal hypertrophy; 4) milder non-obstructive hypertrophy enriched for familial sarcomeric HCM (odds ratio for P/LP variants: 2.18 [95% CI: 1.93-2.28, P = 0.0001]). Polygenic risk for HCM was also associated with different patterns and degrees of disease expression. The model was generalisable to an independent cohort (trustworthiness M1: 0.86-0.88).Conclusions:We report a data-driven taxonomy of HCM for identifying groups of patients with similar morphology while preserving a continuum of disease severi
Kreuzaler P, Inglese P, Ghanate A, et al., 2023, Vitamin B5 supports MYC oncogenic metabolism and tumor progression in breast cancer., Nat Metab, Vol: 5, Pages: 1870-1886
Tumors are intrinsically heterogeneous and it is well established that this directs their evolution, hinders their classification and frustrates therapy1-3. Consequently, spatially resolved omics-level analyses are gaining traction4-9. Despite considerable therapeutic interest, tumor metabolism has been lagging behind this development and there is a paucity of data regarding its spatial organization. To address this shortcoming, we set out to study the local metabolic effects of the oncogene c-MYC, a pleiotropic transcription factor that accumulates with tumor progression and influences metabolism10,11. Through correlative mass spectrometry imaging, we show that pantothenic acid (vitamin B5) associates with MYC-high areas within both human and murine mammary tumors, where its conversion to coenzyme A fuels Krebs cycle activity. Mechanistically, we show that this is accomplished by MYC-mediated upregulation of its multivitamin transporter SLC5A6. Notably, we show that SLC5A6 over-expression alone can induce increased cell growth and a shift toward biosynthesis, whereas conversely, dietary restriction of pantothenic acid leads to a reversal of many MYC-mediated metabolic changes and results in hampered tumor growth. Our work thus establishes the availability of vitamins and cofactors as a potential bottleneck in tumor progression, which can be exploited therapeutically. Overall, we show that a spatial understanding of local metabolism facilitates the identification of clinically relevant, tractable metabolic targets.
Dannhorn A, Doria ML, McKenzie J, et al., 2023, Targeted Desorption Electrospray Ionization Mass Spectrometry Imaging for Drug Distribution, Toxicity, and Tissue Classification Studies, METABOLITES, Vol: 13
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Loukas I, Simeoni F, Milan M, et al., 2023, Selective advantage of epigenetically disrupted cancer cells via phenotypic inertia, Cancer Cell, Vol: 41, Pages: 70-87.e14, ISSN: 1535-6108
The evolution of established cancers is driven by selection of cells with enhanced fitness. Subclonal mutations in numerous epigenetic regulator genes are common across cancer types, yet their functional impact has been unclear. Here, we show that disruption of the epigenetic regulatory network increases the tolerance of cancer cells to unfavorable environments experienced within growing tumors by promoting the emergence of stress-resistant subpopulations. Disruption of epigenetic control does not promote selection of genetically defined subclones or favor a phenotypic switch in response to environmental changes. Instead, it prevents cells from mounting an efficient stress response via modulation of global transcriptional activity. This "transcriptional numbness" lowers the probability of cell death at early stages, increasing the chance of long-term adaptation at the population level. Our findings provide a mechanistic explanation for the widespread selection of subclonal epigenetic-related mutations in cancer and uncover phenotypic inertia as a cellular trait that drives subclone expansion.
Inglese P, Huang HX, Wu V, et al., 2022, Mass recalibration for desorption electrospray ionization mass spectrometry imaging using endogenous reference ions, BMC Bioinformatics, Vol: 23, Pages: 1-17, ISSN: 1471-2105
BackgroundMass spectrometry imaging (MSI) data often consist of tens of thousands of mass spectra collected from a sample surface. During the time necessary to perform a single acquisition, it is likely that uncontrollable factors alter the validity of the initial mass calibration of the instrument, resulting in mass errors of magnitude significantly larger than their theoretical values. This phenomenon has a two-fold detrimental effect: (a) it reduces the ability to interpret the results based on the observed signals, (b) it can affect the quality of the observed signal spatial distributions.ResultsWe present a post-acquisition computational method capable of reducing the observed mass drift by up to 60 ppm in biological samples, exploiting the presence of typical molecules with a known mass-to-charge ratio. The procedure, tested on time-of-flight and Orbitrap mass spectrometry analyzers interfaced to a desorption electrospray ionization (DESI) source, improves the molecular annotation quality and the spatial distributions of the detected ions.ConclusionThe presented method represents a robust and accurate tool for performing post-acquisition mass recalibration of DESI-MSI datasets and can help to increase the reliability of the molecular assignment and the data quality.
MacIntyre DA, Pruski P, Correia G, et al., 2022, Rapid Assessment of Vaginal Microbiota Host Interactions During Pregnancy and Preterm Birth by Direct On-Swab Desorption Electrospray Ionization Mass Spectrometry, Publisher: SPRINGER HEIDELBERG, Pages: 53-53, ISSN: 1933-7191
Inglese P, Huang X, Wu V, et al., 2021, Mass recalibration for desorption electrospray ionization mass spectrometry imaging using endogenous reference ions, Publisher: Cold Spring Harbor Laboratory
BackgroundMass spectrometry imaging (MSI) data often consist of tens of thousands of mass spectra collected from a sample surface. During the time necessary to perform a single acquisition, it is likely that uncontrollable factors alter the validity of the initial mass calibration of the instrument, resulting in mass errors of magnitude significantly larger than their theoretical values. This phenomenon has a two-fold detrimental effect: a) it reduces the ability to interpret the results based on the observed signals, b) it can affect the quality of the observed signal spatial distributions. ResultsWe present a post-acquisition computational method capable of reducing the observed mass drift by up to 60 ppm in biological samples, exploiting the presence of typical molecules with a known mass-to-charge ratio. The procedure, tested on time-of-flight (TOF) and Orbitrap mass spectrometry analyzers interfaced to a desorption electrospray ionization (DESI) source, improves the molecular annotation quality and the spatial distributions of the detected ions.ConclusionThe presented method represents a robust and accurate tool for performing post-acquisition mass recalibration of DESI-MSI datasets and can help to increase the reliability of the molecular assignment and the data quality.
Strittmatter N, Kanvatirth P, Inglese P, et al., 2021, Holistic characterization of a salmonella typhimurium infection model using integrated molecular imaging., Journal of the American Society for Mass Spectrometry, Vol: 32, Pages: 2791-2802, ISSN: 1044-0305
A more complete and holistic view on host-microbe interactions is needed to understand the physiological and cellular barriers that affect the efficacy of drug treatments and allow the discovery and development of new therapeutics. Here, we developed a multimodal imaging approach combining histopathology with mass spectrometry imaging (MSI) and same section imaging mass cytometry (IMC) to study the effects of Salmonella Typhimurium infection in the liver of a mouse model using the S. Typhimurium strains SL3261 and SL1344. This approach enables correlation of tissue morphology and specific cell phenotypes with molecular images of tissue metabolism. IMC revealed a marked increase in immune cell markers and localization in immune aggregates in infected tissues. A correlative computational method (network analysis) was deployed to find metabolic features associated with infection and revealed metabolic clusters of acetyl carnitines, as well as phosphatidylcholine and phosphatidylethanolamine plasmalogen species, which could be associated with pro-inflammatory immune cell types. By developing an IMC marker for the detection of Salmonella LPS, we were further able to identify and characterize those cell types which contained S. Typhimurium.
Pruski P, Dos Santos Correia G, Lewis H, et al., 2021, Direct on-swab metabolic profiling of vaginal microbiome host interactions during pregnancy and preterm birth, Nature Communications, Vol: 12, ISSN: 2041-1723
The pregnancy vaginal microbiome contributes to risk of preterm birth, the primary cause of death in children under 5 years of age. Here we describe direct on-swab metabolic profiling by Desorption Electrospray Ionization Mass Spectrometry (DESI-MS) for sample preparation-free characterisation of the cervicovaginal metabolome in two independent pregnancy cohorts (VMET, n = 160; 455 swabs; VMET II, n = 205; 573 swabs). By integrating metataxonomics and immune profiling data from matched samples, we show that specific metabolome signatures can be used to robustly predict simultaneously both the composition of the vaginal microbiome and host inflammatory status. In these patients, vaginal microbiota instability and innate immune activation, as predicted using DESI-MS, associated with preterm birth, including in women receiving cervical cerclage for preterm birth prevention. These findings highlight direct on-swab metabolic profiling by DESI-MS as an innovative approach for preterm birth risk stratification through rapid assessment of vaginal microbiota-host dynamics.
Kazanc E, Karali E, Wu V, et al., 2020, A multimodal analysis in breast cancer: Revealing metabolic heterogeneity using DESI-MS imaging with Laser-microdissection coupled transcriptome approach., AACR Virtual Special Conference on Tumor Heterogeneity - From Single Cells to Clinical Impact, Publisher: AMER ASSOC CANCER RESEARCH, ISSN: 0008-5472
Dannhorn A, Kazanc E, Ling S, et al., 2020, Universal Sample Preparation Unlocking Multimodal Molecular Tissue Imaging, ANALYTICAL CHEMISTRY, Vol: 92, Pages: 11080-11088, ISSN: 0003-2700
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- Citations: 41
Koundouros N, Karali E, Tripp A, et al., 2020, Metabolic fingerprinting links oncogenic PIK3CA with enhanced arachidonic acid-derived eicosanoids, Cell, Vol: 181, Pages: 1596-1611.e27, ISSN: 0092-8674
Oncogenic transformation is associated with profound changes in cellular metabolism, but whether tracking these can improve disease stratification or influence therapy decision-making is largely unknown. Using the iKnife to sample the aerosol of cauterized specimens, we demonstrate a new mode of real-time diagnosis, coupling metabolic phenotype to mutant PIK3CA genotype. Oncogenic PIK3CA results in an increase in arachidonic acid and a concomitant overproduction of eicosanoids, acting to promote cell proliferation beyond a cell-autonomous manner. Mechanistically, mutant PIK3CA drives a multimodal signaling network involving mTORC2-PKCζ-mediated activation of the calcium-dependent phospholipase A2 (cPLA2). Notably, inhibiting cPLA2 synergizes with fatty acid-free diet to restore immunogenicity and selectively reduce mutant PIK3CA-induced tumorigenicity. Besides highlighting the potential for metabolic phenotyping in stratified medicine, this study reveals an important role for activated PI3K signaling in regulating arachidonic acid metabolism, uncovering a targetable metabolic vulnerability that largely depends on dietary fat restriction.
Inglese P, Correia G, Pruski P, et al., 2019, Colocalization features for classification of tumors using desorption electrospray ionization mass spectrometry imaging, Analytical Chemistry, Vol: 91, Pages: 6530-6540, ISSN: 0003-2700
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 coexpression/suppression or disjoint spatial localization patterns and can suggest hypotheses based on the underlying mechanisms associated with the different classes of analyzed samples.
Koundouros N, Tripp A, Karali E, et al., 2019, Near real-time stratification of PIK3CA mutant breast cancers using the iKnife, 211th Meeting of the Pathological-Society-of-Great-Britain-and-Ireland, Publisher: Wiley, Pages: S8-S8, ISSN: 0022-3417
Inglese P, Correia G, Takats Z, et al., 2019, SPUTNIK: an R package for filtering of spatially related peaks in mass spectrometry imaging data, Bioinformatics, Vol: 35, Pages: 178-180, ISSN: 1367-4803
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: 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.
Inglese P, Dos Santos Correia G, Pruski P, et al., 2018, Co-localization features for classification of tumors using mass spectrometry imaging
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.
Inglese P, Strittmatter N, Doria L, et al., 2018, Mass spectrometry: from imaging to metabolic networks
A deeper understanding of inter-tumorand intra-tumorheterogeneity is a critical factor for the advancement of next generation strategies against cancer. Under the hypothesis that heterogeneous progression of tumorsis mirrored by their metabolic heterogeneity, detection of biochemical mechanisms responsible of the local metabolism becomes crucial.We show that network analysis of co-localized ions from mass spectrometry imaging data provides a detailed chemo-spatial insightinto the metabolic heterogeneity of tumor. Furthermore, module preservation analysis between colorectal cancer patients with and without metastatic recurrence suggests hypotheses on the nature of the different local metabolic pathways.
Alexander JL, Scott A, Poynter LR, et al., 2018, Sa1840 - The colorectal cancer mucosal microbiome is defined by disease stage and the tumour metabonome, Digestive Disease Week 2018, Publisher: Elsevier, Pages: S415-S415, ISSN: 0016-5085
Alexander JL, Scott A, Poynter LR, et al., 2018, THE COLORECTAL CANCER MUCOSAL MICROBIOME IS DEFINED BY DISEASE STAGE AND THE TUMOUR METABONOME, Annual Meeting of the American-Society-for-Gastrointestinal-Endoscopy / Digestive Disease Week, Publisher: W B SAUNDERS CO-ELSEVIER INC, Pages: S415-S415, ISSN: 0016-5085
Inglese P, Strittmatter N, Doria L, et al., 2017, Network analysis of mass spectrometry imaging data from colorectal cancer identifies key metabolites common to metastatic development
<jats:title>Abstract</jats:title><jats:p>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.</jats:p><jats:sec><jats:title>Significance</jats:title><jats:p>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 freque
Galea D, Inglese P, Cammack L, et al., 2017, Translational utility of a hierarchical classification strategy in biomolecular data analytics., Scientific Reports, Vol: 7, ISSN: 2045-2322
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.
Inglese P, Alexander JL, Mroz A, et al., 2017, Variational autoencoders for tissue heterogeneity exploration from (almost) no preprocessed mass spectrometry imaging data, arXiv
The paper presents the application of Variational Autoencoders (VAE) for datadimensionality reduction and explorative analysis of mass spectrometry imagingdata (MSI). The results confirm that VAEs are capable of detecting the patternsassociated with the different tissue sub-types with performance than standardapproaches.
Kinross JM, Alexander J, Perdones-Monter A, et al., 2017, A Prospective Multi-National Study of the Colorectal Cancer Mucosal Microbiome Reveals Specific Taxonomic Changes Indicative of Disease Stage and Prognosis, DDW
Lewis H, Pruski P, Kindinger L, et al., 2017, Desorption electrospray ionisation mass spectrometry permits identification of vaginal mucosal metabolome signatures associated with preterm birth risk, Publisher: WILEY, Pages: 135-135, ISSN: 1470-0328
Inglese P, McKenzie JS, Mroz A, et al., 2017, Deep learning and 3D-DESI imaging reveal the hidden metabolic heterogeneity of cancer, Chemical Science, Vol: 8, Pages: 3500-3511, ISSN: 2041-6539
Visual inspection of tumour tissues does not reveal the complex metabolic changes that differentiate cancer and its sub-types from healthy tissues. Mass spectrometry imaging, which quantifies the underlying chemistry, represents a powerful tool for the molecular exploration of tumour tissues. A 3-dimensional topological description of the chemical properties of the tumour permits the formulation of hypotheses about the biological composition and interactions and the possible causes of its heterogeneous structure. The large amount of information contained in such datasets requires powerful tools for its analysis, visualisation and interpretation. Linear methods for unsupervised dimensionality reduction, such as PCA, are inadequate to capture the complex non-linear relationships present in these data. For this reason, a deep unsupervised neural network based technique, parametric t-SNE, is adopted to map a 3D-DESI-MS dataset from a human colorectal adenocarcinoma biopsy onto a 2-dimensional manifold. This technique allows the identification of clusters not visible with linear methods. The unsupervised clustering of the tumour tissue results in the identification of sub-regions characterised by the abundance of identified metabolites, making possible the formulation of hypotheses to account for their significance and the underlying biological heterogeneity in the tumour.
Pruski P, MacIntyre DA, Lewis HV, et al., 2016, Medical swab analysis using desorption electrospray ionization mass spectrometry (DESI-MS) – a non-invasive approach for mucosal diagnostics, Analytical Chemistry, Vol: 89, Pages: 1540-1550, ISSN: 0003-2700
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.
Veselkov KA, Inglese, Galea D, et al., 2016, Statistical Tools for Molecular Covariance Spectroscopy, Encyclopedia of Spectroscopy and Spectrometry, Editors: Lindon, Tranter, Koppenaal, Publisher: Elsevier B.V., Pages: 243-249, ISBN: 978-0-12-803224-4
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.
Allen GI, Amoroso N, Anghel C, et al., 2016, Crowdsourced estimation of cognitive decline and resilience in Alzheimer's disease, Alzheimer's & Dementia, Vol: 12, Pages: 645-653, ISSN: 1552-5260
Identifying accurate biomarkers of cognitive decline is essential for advancing early diagnosis and prevention therapies in Alzheimer's disease. The Alzheimer's disease DREAM Challenge was designed as a computational crowdsourced project to benchmark the current state-of-the-art in predicting cognitive outcomes in Alzheimer's 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 to prediction of cognitive performance.
Veselkov KA, Inglese P, Galea D, et al., 2016, Statistical tools for molecular covariance spectroscopy, Encyclopedia of Spectroscopy and Spectrometry, Pages: 243-249, ISBN: 9780128032244
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.
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