Kirstine Lykke Nielsen

Determining when bloodstains were deposited remains an unsolved challenge in forensic science, limiting investigators' ability to reconstruct events and verify suspect timelines. Here, we develop a metabolomics-based approach combining liquid chromatography-mass spectrometry (LC-MS) with machine learning to estimate bloodstain age independent of donor-specific variation. Through untargeted analysis and degradation studies, we identified 51 time-dependent biomarkers and transformed their intensities into stable ratios that normalize for individual differences and blood volume. Using samples collected under controlled environmental conditions, we achieve high accuracy for forensically relevant timeframes with prediction errors of ∼7 h for fresh bloodstains and near-perfect classification of samples as recent (60 h). Validation on two independent data sets confirms strong performance under typical indoor conditions, while highlighting sensitivity to extreme environmental fluctuations. By addressing key biological and technical sources of variability that have hindered translation to practice, this study establishes a robust analytical framework for bloodstain age estimation. The approach offers a practical foundation for future operational implementation and has the potential to substantially improve forensic timeline reconstruction.

Accurately estimating the post-mortem interval (PMI, also known as time since death) remains a major challenge in forensic science due to substantial biological and environmental variability in human cases. While animal models provide controlled conditions for identifying PMI-related metabolic changes, their translational value for human forensics is unclear. This study bridges controlled rat metabolomics with real-world human autopsy samples using LC-MS-based metabolomics and machine learning. We analyzed femoral blood from 435 medico-legal autopsies (PMI 1-10 days) and employed two complementary modeling strategies. An untargeted Lasso model using all 3450 detected features achieved R² = 0.51 and identified 51 consistently selected PMI-informative molecular features. To assess cross-species robustness, we reanalysed rat samples using the same analytical platform. Eighteen of the 51 human-derived features were detected in rats, showing clear PMI-dependent trends and strong correlations with PMI. Two metabolites achieved level 2 annotation and matched known PMI biomarkers from previous animal studies, demonstrating meaningful human-to-rat biochemical overlap. In contrast, a targeted Lasso model based on biomarkers identified in rats performed poorly in humans (R² = 0.10), underscoring limited direct rat-to-human translation. Overall, human-derived biomarkers validated robustly in the controlled rat model proved far more reliable. These findings highlight both the potential and limitations of cross-species PMI biomarker discovery and emphasize the need for improved metadata, especially temperature history, to advance PMI modeling in forensic practice.

Accurate estimation of the postmortem interval (PMI) is crucial for medico-legal investigations, providing critical timelines for criminal cases. Current PMI methods, however, often lack precision, limiting their forensic utility. In this study, we developed models to estimate PMI with high accuracy across various tissues within the first 4 days after death. Using untargeted UHPLC-qTOF-MS, we analyzed thousands of molecules in rat tissues with different PMIs. We employed machine learning on stable and highly reproducible molecules in each tissue to select candidate biomarkers and then built a second model using only the top 15 molecules. Both Lasso and Random Forest approaches yielded high cross-validation accuracy across all tissues, with the latter showing slightly superior performance. Validation was conducted using an independently collected and analyzed set of rats. The identified metabolites, including amino acids, derivatives, nucleosides, and other markers, are common to humans and mammals, underscoring their potential applicability in human forensic contexts. Our findings highlight the tissue-specific predictive potential and variability in predictive accuracy across different tissues in a rodent model.

Methylglyoxal (MG) is a reactive metabolite involved in diabetes and aging through the formation of protein adducts. Less is known about the extent that MG and its metabolic product S-d-lactoylglutathione (LGSH) form adducts with cell metabolites. Using a ‘symmetric’ isotope-labeled and reactivity-based metabolomics approach in living cells, we found over 200 adducts and, surprisingly, discovered that 10 of the most abundant are lactoylated amino acids mainly derived from LGSH. The most abundant adduct d-Lac-Cys is formed rapidly between LGSH and cysteine, whereas the diastereoisomer l-Lac-Cys is formed directly from MG and cysteine, assigning cysteine with both glyoxalase 1-like and glyoxalase 2-like activity. Cellular cysteine and MG dynamically regulate d-Lac-Cys and l-Lac-Cys levels and the adducts are increased in diabetes, suggesting their use as novel biomarkers. Lastly, cysteine amides, as proxies for protein cysteines, also undergo lactoylation by MG and LGSH, suggesting the existence of two additional pathways for nonenzymatic lactoylation of proteins.

Background: Arabinoxylan (AX) concentrates from wheat can be produced from co-products from the starch and gluten industries. These fractions are rich in AX, have high solubility, can be incorporated into bread and breakfast cereals, and have the potential to enhance functional and nutritional effects beyond what is possible with cereal species. Methods: The aim of this review is to summarize the available literature on AX concentrates from wheat in terms of production, incorporation in breads, and influence on glucose homeostasis in human subjects and animals. Results: Breads enriched in AX fiber have been found to increase the viscosity of digesta from the small intestine but with no effect on the digestibility of starch. In the large intestine, AX is, to a large extent, degraded, producing short-chain fatty acids. Across acute human and animal studies, the intake of AX concentrates has been shown to reduce the rate and extent of glucose absorption and insulin responses in a dose-dependent fashion. No general influence of AX on incretins has been identified, and the role of AX-enriched diets in appetite sensation is unclear. Medium- and long-term human and animal intervention studies demonstrated improved glucose homeostasis (fructosamine and glycated hemoglobin A1c) during the consumption of AX-enriched diets compared to placebo. Although AX concentrates across studies improved glucose homeostasis, a confounding factor to be further investigated is to what extent protein being present in relatively high concentrations in some of the produced AX-rich wheat fractions, partly or fully, is responsible for the observed homeostatic effects.

How species thrive in a wide range of environments is a major focus of evolutionary biology. For many species, limited genetic diversity or gene flow among habitats means that phenotypic plasticity must play an important role in their capacity to tolerate environmental heterogeneity and to colonize new habitats. However, we have a limited understanding of the molecular components that govern plasticity in ecologically relevant phenotypes. We examined this hypothesis in a spider species (Stegodyphus dumicola) with extremely low species-wide genetic diversity that nevertheless occupies a broad range of thermal environments. We determined phenotypic responses to temperature stress in individuals from four climatic zones using common garden acclimation experiments to disentangle phenotypic plasticity from genetic adaptations. Simultaneously, we created data sets on multiple molecular modalities: the genome, the transcriptome, the methylome, the metabolome and the bacterial microbiome to determine associations with phenotypic responses. Analyses of phenotypic and molecular associations reveal that acclimation responses in the transcriptome and metabolome correlate with patterns of phenotypic plasticity in temperature tolerance. Surprisingly, genes whose expression seemed to be involved in plasticity in temperature tolerance were generally highly methylated contradicting the idea that DNA methylation stabilizes gene expression. This suggests that the function of DNA methylation in invertebrates varies not only among species but also among genes. The bacterial microbiome was stable across the acclimation period; combined with our previous demonstrations that the microbiome is temporally stable in wild populations, this is convincing evidence that the microbiome does not facilitate plasticity in temperature tolerance. Our results suggest that population-specific variation in temperature tolerance among acclimation temperatures appears to result from the evolution of plasticity in mainly gene expression.

Fingermarks are frequently collected at crime scenes by using gelatin lifters for preservation and transport of the marks to a forensic laboratory for inspection. The gelatin lifters preserve both the imprint of the fingermark pattern necessary for identification purposes and the chemical residue of the mark potentially useful for profiling the person who left the fingermark. The fingermark patterns are traditionally recorded using photography/optical imaging, but methods for chemical analysis of fingermark residues on gelatin lifters are scarce. Here we report the first method for the chemical analysis of fingermarks on gelatin lifters using desorption electrospray ionization mass spectrometry (DESI-MS) imaging. The imaging can be done directly on the gelatin support without any sample preparation, supporting immediate operational use of the method for fingermarks collected at crime scenes. Operational use of the method is further supported by successful chemical imaging of fingermarks enhanced by traditional dusting with forensic powders and lifted off different surfaces (glass, stainless steel, painted aluminum, polystyrene, cardboard, and plastic) as well as fingermarks lifted multiple times. We also demonstrate that the present method can be used to visually separate natural overlapping powder-treated fingermarks, and the chemical composition of the fingermarks can be analyzed on the gelatin support by DESI-MS/MS. The presented method has potential for integration into the traditional workflow for fingermark analysis, and will allow more fingermarks collected at crime scenes to be evaluated both visually and chemically.

Changes in pharmacokinetics and endogenous metabolites may underlie additive biological effects of concomitant use of antipsychotics and opioids. In this study, we employed untargeted metabolomics analysis and targeted analysis to examine the changes in drug metabolites and endogenous metabolites in the prefrontal cortex (PFC), midbrain, and blood of rats following acute co-administration of quetiapine and methadone. Rats were divided into four groups and received cumulative increasing doses of quetiapine (QTP), methadone (MTD), quetiapine + methadone (QTP + MTD), or vehicle (control). All samples were analyzed using liquid chromatography-mass spectrometry (LC–MS). Our findings revealed increased levels of the quetiapine metabolites: Norquetiapine, O-dealkylquetiapine, 7-hydroxyquetiapine, and quetiapine sulfoxide, in the blood and brain when methadone was present. Our study also demonstrated a decrease in methadone and its metabolite 2-ethylidene-1,5-dimethyl-3,3-diphenylpyrrolidine (EDDP) in the rat brain when quetiapine was present. Despite these findings, there were only small differences in the levels of 225–296 measured endogenous metabolites due to co-administration compared to single administrations. For example, N-methylglutamic acid, glutaric acid, p-hydroxyphenyllactic acid, and corticosterone levels were significantly decreased in the brain of rats treated with both compounds. Accumulation of serotonin in the midbrain was additionally observed in the MTD group, but not in the QTP + MTD group. In conclusion, this study in rats suggests a few but important additive metabolic effects when quetiapine and methadone are co-administered.

Despite the advent of DNA profiling, fingerprints still play an important role in suspect identification. However, if single crime scene marks may be challenging to identify, overlapping fingermarks, understandably, pose an even greater challenge. In the last decade, mass spectrometry-imaging methods have provided a possible solution to the separation of fingermarks from two or more donors, based on the differential chemical composition. However, there are no studies attempting to separate overlapping marks from the same donor. This is important in relation to fingermark deposition at different times, which could be critical, for example, to ascertain legitimate access to the scene. In the work presented here, we investigate whether Matrix-Assisted Laser Desorption Ionisation Mass Spectrometry Imaging can separate the same donor’s fingermarks deposited at different times based on intra-donor fingermark composition variability. Additionally, the hypothesis that the different times of deposition could be also determined was investigated in the view of linking the suspect at the scene at different times; the dating window of MALDI MSI within the selected molecular range was explored. Results show that it is possible to separate overlapping fingermarks from the same donor in most cases, even from natural marks. Fresh marks (0 days) could be separated from those of fourteen days of age, though the latter could not be distinguished from the set aged for seven days. Due to the use of only one donor, these are to be considered preliminary data, though findings are interesting enough to warrant further investigation of the capabilities and limitations of this approach using a larger cohort of donors.