A 1H NMR metabolomic approach for the estimation of the time since death using aqueous humour: an animal model

Locci, Emanuela; Stocchero, Matteo; Noto, Antonio; Chighine, Alberto; Natali, Luca; Napoli, Pietro Emanuele; Caria, Roberto; De-Giorgio, Fabio; Nioi, Matteo; d’Aloja, Ernesto

doi:10.1007/s11306-019-1533-2

Cited by 38 publications

(42 citation statements)

References 84 publications

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“…The experimental protocol was previously described [19]. AH samples were collected from 36 sheep (Ovis aries) heads of young adult females belonging to the same herd obtained from a local slaughterhouse after animal sacrifice.…”

Section: Sample Collection and Preparationmentioning

confidence: 99%

“…The majority of the studies describe modifications occurring after death without proposing appropriate prediction models. We recently employed ovine aqueous humour (AH) 1 H NMR metabolomics to generate and validate a regression model for PMI estimation [19]. A total of 59 samples were collected at different times after death up to 24 h (from 118 to 1429 min) from very homogeneous animals.…”

Section: Introductionmentioning

confidence: 99%

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Comparative use of aqueous humour 1H NMR metabolomics and potassium concentration for PMI estimation in an animal model

et al. 2020

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Estimation of the post-mortem interval (PMI) remains a matter of concern in the forensic scenario. Traditional and novel approaches are not yet able to fully address this issue, which relies on complex biological phenomena triggered by death. For this purpose, eye compartments may be chosen for experimental studies because they are more resistant to post-mortem modifications. Vitreous humour, in particular, has been extensively investigated, with potassium concentration ([K+]) being the marker that is better correlated with PMI estimation. Recently, a 1H nuclear magnetic resonance (NMR) metabolomic approach based on aqueous humour (AH) from an animal model was proposed for PMI estimation, resulting in a robust and validated regression model. Here we studied the variation in [K+] in the same experimental setup. [K+] was determined through capillary ion analysis (CIA) and a regression analysis was performed. Moreover, it was investigated whether the PMI information related to potassium could improve the metabolome predictive power in estimating the PMI. Interestingly, we found that a part of the metabolomic profile is able to explain most of the information carried by potassium, suggesting that the rise in both potassium and metabolite concentrations relies on a similar biological mechanism. In the first 24-h PMI window, the AH metabolomic profile shows greater predictive power than [K+] behaviour, suggesting its potential use as an additional tool for estimating the time since death.

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Section: Sample Collection and Preparationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparative use of aqueous humour 1H NMR metabolomics and potassium concentration for PMI estimation in an animal model

et al. 2020

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“…Very recently, 1 H NMR metabolomics data were successfully employed for building a regression model for PMI estimation using AH samples from an ovine model (Locci et al 2019 ). A total of 59 samples collected at different times after death up to 24 h (from 118 to 1429 min) were taken from as many eyes, half of which were kept with the eyelids closed and the other half with the eyelids open.…”

Section: Metabolome Modifications After Deathmentioning

confidence: 99%

“…, Heart, Kidney, Liver, Serum, Skin, Spleen, Plasma, Urine, VH, WAT Human, Calf, Goat, Mice, SheepLocci et al (2014Locci et al ( , 2019,Snytnikova et al (2017a, b),Jawor et al 2019, andMora-Ortiz et al 2019 Alanine Cornea, Kidney, Plasma, Spleen, Serum, Skin, Urine, VH, WAT Human, Calf, Goat, MiceLocci et al (2014),Rosa et al (2015),Snytnikova et al (2017aSnytnikova et al ( , 2017b, Jawor et al(2019), and Mora-Ortiz et al (2019) Ascorbate AH, Cornea, Kidney, Serum, Spleen, Urine, VH Human, Calf, Goat, Mice, Sheep Locci et al (2014, 2019), Snytnikova et al (2017a, b), Jawor et al (2019), and Mora-Ortiz et al (2019) BCAA AH, Cornea, Heart, Kidney, Liver, Plasma, Serum, Spleen, Urine, VH, WAT Human, Calf, Goat, Mice, Sheep Locci et al (2014, 2019), Snytnikova et al (2017a, 2017b), Jawor et al (2019), Mora-Ortiz et al (2019) Betaine Kidney, AH, VH, Serum, Urine Human, Calf, Goat, Mice, Sheep Locci et al (2014, 2019) Snytnikova et al (2017a), Jawor et al (2019), and Mora-Ortiz et al (2019) Carnitine Cornea, Heart, Serum Human, Mice Snytnikova et al (2017a, b) and Mora-Ortiz et al (2019) Creatine AH, Cornea, Heart, Kidney, Liver, Plasma, Skin, Serum, Spleen, Urine, WAT, VH Human, Calf, Goat, Mice, Sheep Locci et al (2014, 2019), Rosa et al (2015), Snytnikova et al (2017b), Jawor et al (2019), and Mora-Ortiz et al (2019) Creatinine Cornea, AH, Plasma, Serum, Urine Human, Calf, Sheep, Snytnikova et al (2017a, 2017b), Jawor et al (2019), and Locci et al (2019) Choline AH, Cornea, Heart, Kidney, Plasma, Serum, Spleen, Urine, VH Human, Calf, Goat, Mice, Rabbit, Sheep Locci et al (2014, 2019), Rosa et al (2015), Zelentsova et al (2016), Snytnikova et al (2017a, b), Jawor et al (2019), and Mora-Ortiz et al (2019) Formate Cornea, Spleen, AH, Plasma, Urine Human, Calf, Mice, Sheep Snytnikova et al (2017a, b), Jawor et al (2019), Locci et al (2019), and Mora-Ortiz et al (2019) Glucose Heart, Kidney, Liver, Plasma, Skin, Spleen, Urine, VH, WAT Calf, Goat, Mice Locci et al (2014), Rosa et al (2015), Jawor et al (2019), and Mora-Ortiz et al (2019) Glutamate AH, Heart, Kidney, Liver, Plasma, Serum, Skin, Spleen, VH, WAT Human, Calf, Mice, Rabbit, Sheep Zelentsova et al (2016), Snytnikova et al (2017a), Jawor et al (2019), Locci et al 2019, and Mora-Ortiz et al (2019) Glutamine Heart, Plasma, Serum, Spleen, Urine, VH Human, Calf, Goat, Mice Locci et al (2014), Snytnikova et al (2017a), Mora-Ortiz et al (2019), and Jawor et al (2019) Glycerol AH, Cornea, Heart, Serum, Spleen, VH Human, Goat, Mice, Rabbit Locci et al (2014), Zelentsova et al (2016), Snytnikova et al (2017a, b), and Mora-Ortiz et al (2019) Glycine AH, Cornea, Serum, VH Human, Rabbit Zelentsova et al (2016), and Snytnikova et al (2017a, b) 3-OH-butyrate AH, Urine, VH Calf, Goat, Sheep Rosa et al (2015), Jawor et al (2019), and Locci et al (2019) Histidine Kidney, Serum, Urine Human, Calf, Mice Snytnikova et al (2017a), Mora-Ortiz et al (2019), and Jawor et al (2019) Hypoxanthine AH, Cornea, Heart, Serum, Urine, VH Human, Calf, Goat, Mice, Rabbit, Sheep Rosa et al (2015), Zelentsova et al (2016), Snytnikova et al (2017a, b), Jawor et al (2019), Mora-Ortiz et al (2019), and Locci et al (2019) Lactate AH, Cornea, Heart, Kidney, Plasma, Serum,...…”

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confidence: 99%

Forensic NMR metabolomics: one more arrow in the quiver

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Introduction NMR metabolomics is increasingly used in forensics, due to the possibility of investigating both endogenous metabolic profiles and exogenous molecules that may help to describe metabolic patterns and their modifications associated to specific conditions of forensic interest. Objectives The aim of this work was to review the recent literature and depict the information provided by NMR metabolomics. Attention has been devoted to the identification of peculiar metabolic signatures and specific ante-mortem and post-mortem profiles or biomarkers related to different conditions of forensic concern, such as the identification of biological traces, the estimation of the time since death, and the exposure to drugs of abuse. Results and Conclusion The results of the described studies highlight how forensics can benefit from NMR metabolomics by gaining additional information that may help to shed light in several forensic issues that still deserve to be further elucidated.

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“…Data were mean centered prior to performing data analysis. More details about sample collection, experimental procedure and data pre-processing can be found in [39].…”

Section: Applications To Metabolomicsmentioning

confidence: 99%

PLS2 in Metabolomics

et al. 2019

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Metabolomics is the systematic study of the small-molecule profiles of biological samples produced by specific cellular processes. The high-throughput technologies used in metabolomic investigations generate datasets where variables are strongly correlated and redundancy is present in the data. Discovering the hidden information is a challenge, and suitable approaches for data analysis must be employed. Projection to latent structures regression (PLS) has successfully solved a large number of problems, from multivariate calibration to classification, becoming a basic tool of metabolomics. PLS2 is the most used implementation of PLS. Despite its success, PLS2 showed some limitations when the so called ‘structured noise’ affects the data. Suitable methods have been recently introduced to patch up these limitations. In this study, a comprehensive and up-to-date presentation of PLS2 focused on metabolomics is provided. After a brief discussion of the mathematical framework of PLS2, the post-transformation procedure is introduced as a basic tool for model interpretation. Orthogonally-constrained PLS2 is presented as strategy to include constraints in the model according to the experimental design. Two experimental datasets are investigated to show how PLS2 and its improvements work in practice.

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A 1H NMR metabolomic approach for the estimation of the time since death using aqueous humour: an animal model

Cited by 38 publications

References 84 publications

Comparative use of aqueous humour 1H NMR metabolomics and potassium concentration for PMI estimation in an animal model

Comparative use of aqueous humour 1H NMR metabolomics and potassium concentration for PMI estimation in an animal model

Forensic NMR metabolomics: one more arrow in the quiver

PLS2 in Metabolomics

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