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A spontaneously occurring temperature increase in solid tumors has been reported sporadically, but is largely overlooked in terms of cancer biology. Here we show that temperature is increased in tumors of patients with pancreatic ductal adenocarcinoma (PDAC) and explore how this could affect therapy response. By mimicking this observation in PDAC cell lines, we demonstrate that through adaptive changes in lipid metabolism, the temperature increase found in human PDAC confers protection to lipid peroxidation and contributes to gemcitabine resistance. Consistent with the recently uncovered role of p38 MAPK in ferroptotic cell death, we find that the reduction in lipid peroxidation potential following adaptation to tumoral temperature allows for p38 MAPK inhibition, conferring chemoresistance. As an increase in tumoral temperature is observed in several other tumor types, our findings warrant taking tumoral temperature into account in subsequent studies related to ferroptosis and therapy resistance. More broadly, our findings indicate that tumoral temperature affects cancer biology. The development of cancer is typically accompanied by changes in the tumor microenvironment that support tumor growth and affect therapy response. Here, the authors show that increased intratumoral temperature is an inherent feature of human pancreatic cancer and contributes to therapy resistance by altering the lipid content of the tumor.

Mass spectrometry-based lipidomics and metabolomics generate extensive data sets that, along with metadata such as clinical parameters, require specific data exploration skills to identify and visualize statistically significant trends and biologically relevant differences. Besides tailored methods developed by individual labs, a solid core of freely accessible tools exists for exploratory data analysis and visualization, which we have compiled here, including preparation of descriptive statistics, annotated box plots, hypothesis testing, volcano plots, lipid maps and fatty acyl chain plots, unsupervised and supervised dimensionality reduction, dendrograms, and heat maps. This review is intended for those who would like to develop their skills in data analysis and visualization using freely available R or Python solutions. Beginners are guided through a selection of R and Python libraries for producing publication-ready graphics without being overwhelmed by the code complexity. This manuscript, along with associated GitBook code repository containing step-by-step instructions, offers readers a comprehensive guide, encouraging the application of R and Python for robust and reproducible chemometric analysis of omics data. Mass spectrometry-based lipidomics and metabolomics generate large, complex datasets requiring effective analysis. Here, authors review key statistical and visualization methods alongside widely used R and Python tools, and provide a GitBook with step-by-step code for accessible, reproducible data analysis.

Background One of the key limitations of targeted cancer therapies is the rapid onset of therapy resistance. Taking BRAF-mutant melanoma as paradigm, we previously identified the lipogenic regulator SREBP-1 as a central mediator of resistance to MAPK-targeted therapy. Reasoning that lipogenesis-mediated alterations in membrane lipid poly-unsaturation lie at the basis of therapy resistance, we targeted fatty acid synthase (FASN) as key player in this pathway to evoke an exquisite vulnerability to clinical inducers of reactive oxygen species (ROS), thereby rationalizing a novel clinically actionable combination therapy to overcome therapy resistance. Methods Using gene expression analysis and mass spectrometry-based lipidomics of BRAF-mutant melanoma cell lines, melanoma PDX and clinical data sets, we explored the association of FASN expression with membrane lipid poly-unsaturation and therapy-resistance. Next, we treated therapy-resistant models with a preclinical FASN inhibitor TVB-3664 and a panel of ROS inducers and performed ROS analysis, lipid peroxidation tests and real-time cell proliferation assays. Finally, we explored the combination of MAPK inhibitors, TVB-3664 and arsenic trioxide (ATO, as a clinically used ROS-inducer) in Mel006 BRAF mutant PDX as a gold model of therapy resistance and assessed the effect on tumor growth, survival and systemic toxicity. Results We found that FASN expression is consistently increased upon the onset of therapy resistance in clinical melanoma samples, in cell lines and in Mel006 PDX and is associated with decreased lipid poly-unsaturation. Forcing lipid poly-unsaturation in therapy-resistant models by combining MAPK inhibition with FASN inhibition attenuated cell proliferation and rendered cells exquisitely sensitive to a host of ROS inducers. In particular, the triple combination of MAPK inhibition, FASN inhibition, and the clinical ROS-inducing compound ATO dramatically increased survival of Mel006 PDX models from 15 to 72% with no associated signs of toxicity. Conclusions We conclude that under MAPK inhibition the direct pharmacological inhibition of FASN evokes an exquisite vulnerability to inducers of ROS by increasing membrane lipid poly-unsaturation. The exploitation of this vulnerability by combining MAPK and/or FASN inhibitors with inducers of ROS greatly delays the onset of therapy resistance and increases survival. Our work identifies a clinically actionable combinatorial treatment for therapy-resistant cancer.

Mass spectrometry imaging (MSI) is a powerful tool in both basic and clinical research, enabling spatial visualization of biomolecules and drugs in tissue sections. However, factors influencing mass spectrometric response during imaging are increasingly recognized for their impact on apparent molecular distribution. Quantitative mass spectrometry imaging (qMSI) addresses this variability by incorporating analytical standards that undergo the same processes as analytes. While qMSI sample preparation protocols for omics-scale quantitative lipidomics are actively evolving, software solutions for downstream data processing remain scarce. Here, we introduce LipidQMap, the first open-source platform for processing omics-scale qMSI lipidomics data. LipidQMap applies one-point calibration, normalizing annotated lipid signals against class-specific standards on a pixel-by-pixel basis, and generates concentration heat maps in pmol/mm2. The software supports centroided data import, recalibration, and lipid identification using a built-in, user-modifiable lipid database. LipidQMap resolves Na/H adduct isobaric overlaps by leveraging sodiated-to-protonated adduct ratios of standards, validated across several MSI platforms using mouse brain sections. Type II isobaric overlaps are corrected using predicted isotopic patterns. Extensive validation demonstrates that LipidQMap is robust across MSI platforms and harmonizes qMSI data, enabling more accurate and reproducible spatial lipid quantification.

Recent advances in spatial omics methods are revolutionising biomedical research by enabling detailed molecular analyses of cells and their interactions in their native state. As most technologies capture only a specific type of molecules, there is an unmet need to enable integration of multiple spatial-omics datasets. This, however, presents several challenges as these analyses typically operate on separate tissue sections at disparate spatial resolutions. Here, we established a spatial multi-omics integration pipeline enabling co-registration and granularity matching, and applied it to integrate spatial transcriptomics, mass spectrometry-based lipidomics, single nucleus RNA-seq and histomorphological information from human prostate cancer patient samples. This approach revealed unique correlations between lipids and gene expression profiles that are linked to distinct cell populations and histopathological disease states and uncovered molecularly different subregions not discernible by morphology alone. By its ability to correlate datasets that span across the biomolecular and spatial scale, the application of this novel spatial multi-omics integration pipeline provides unprecedented insight into the intricate interplay between different classes of molecules in a tissue context. In addition, it has unique hypothesis-generating potential, and holds promise for applications in molecular pathology, biomarker and target discovery and other tissue-based research fields.

Duplication of PMP22 causes Charcot-Marie-Tooth disease type 1A (CMT1A) and is known to disrupt the lipid metabolism in myelinating Schwann cells by unknown mechanisms. By using two CMT1A mouse models overexpressing human PMP22, we discovered that PMP22 dose-dependently downregulates genes that are involved in lipid and cholesterol metabolism. Lipidomic analysis on CMT1A mouse sciatic nerves confirmed lipid metabolic abnormalities primarily associated with cholesterol and sphingolipids. We observed similar lipidomic profiles and downregulation of genes associated with lipid metabolism in human CMT1A patient induced pluripotent stem cell-derived Schwann cell precursors (iPSC-SCPs). We confirmed these findings by demonstrating altered lipid raft dynamics and plasma membrane fluidity in CMT1A iPSC-SCPs. Additionally, we identified impaired cholesterol incorporation in the plasma membrane due to altered lipid storage homeostasis in CMT1A iPSC-SCPs, which could be modulated by changing the lipid composition of the cell culture medium. These findings suggest that PMP22 plays a role in regulating the lipid composition of the plasma membrane and lipid storage homeostasis. Targeting lipid metabolism may hold promise as a potential treatment for CMT1A patients. PMP22 copy number causes a dose-dependent suppression of cholesterol and lipid biosynthesis in peripheral nerves of CMT1A mice Lipid composition is altered in the sciatic nerves of CMT1A mice and in the membranes of patient derived iPSC-SCPs, with a significant reduction in sphingolipids CMT1A iPSC-SCPs show decreased plasma membrane lipids required for regulating lipid raft dynamics, membrane fluidity, and membrane order Lipid storage misregulation is key in the pathogenesis of CMT1A

Mutations and variations in and around SCN5A , encoding the major cardiac sodium channel, influence impulse conduction and are associated with a broad spectrum of arrhythmia disorders. Here, we identify an evolutionary conserved regulatory cluster with super enhancer characteristics downstream of SCN5A , which drives localized cardiac expression and contains conduction velocity-associated variants. We use genome editing to create a series of deletions in the mouse genome and show that the enhancer cluster controls the conformation of a >0.5 Mb genomic region harboring multiple interacting gene promoters and enhancers. We find that this cluster and its individual components are selectively required for cardiac Scn5a expression, normal cardiac conduction and normal embryonic development. Our studies reveal physiological roles of an enhancer cluster in the SCN5A-SCN10A locus, show that it controls the chromatin architecture of the locus and Scn5a expression, and suggest that genetic variants affecting its activity may influence cardiac function. Mutations associated with SCN5A , which encodes the major cardiac sodium channel, influence impulse conduction and are associated with arrhythmia disorders. Here, the authors identify an evolutionary conserved, super enhancer-like, regulatory cluster downstream of SCN5A  and show that it controls the chromatin architecture of the locus and Scn5a expression.

We present a comprehensive description and benchmark evaluation of the tropospheric chemistry version of the global chemistry transport model TM5 (Tracer Model 5, version TM5-chem-v3.0). A full description is given concerning the photochemical mechanism, the interaction with aerosol, the treatment of the stratosphere, the wet and dry deposition parameterizations, and the applied emissions. We evaluate the model against a suite of ground-based, satellite, and aircraft measurements of components critical for understanding global photochemistry for the year 2006. The model exhibits a realistic oxidative capacity at a global scale. The methane lifetime is ~8.9 years with an associated lifetime of methyl chloroform of 5.86 years, which is similar to that derived using an optimized hydroxyl radical field. The seasonal cycle in observed carbon monoxide (CO) is well simulated at different regions across the globe. In the Northern Hemisphere CO concentrations are underestimated by about 20 ppbv in spring and 10 ppbv in summer, which is related to missing chemistry and underestimated emissions from higher hydrocarbons, as well as to uncertainties in the seasonal variation of CO emissions. The model also captures the spatial and seasonal variation in formaldehyde tropospheric columns as observed by SCIAMACHY. Positive model biases over the Amazon and eastern United States point to uncertainties in the isoprene emissions as well as its chemical breakdown. Simulated tropospheric nitrogen dioxide columns correspond well to observations from the Ozone Monitoring Instrument in terms of its seasonal and spatial variability (with a global spatial correlation coefficient of 0.89), but TM5 fields are lower by 25-40%. This is consistent with earlier studies pointing to a high bias of 0-30% in the OMI retrievals, but uncertainties in the emission inventories have probably also contributed to the discrepancy. TM5 tropospheric nitrogen dioxide profiles are in good agreement (within ~0.1 ppbv) with in situ aircraft observations from the INTEX-B campaign over (the Gulf of) Mexico. The model reproduces the spatial and seasonal variation in background surface ozone concentrations and tropospheric ozone profiles from the World Ozone and Ultraviolet Radiation Data Centre to within 10 ppbv, but at several tropical stations the model tends to underestimate ozone in the free troposphere. The presented model results benchmark the TM5 tropospheric chemistry version, which is currently in use in several international cooperation activities, and upon which future model improvements will take place.

Variants in SCN10A, which encodes a voltage-gated sodium channel, are associated with alterations of cardiac conduction parameters and the cardiac rhythm disorder Brugada syndrome; however, it is unclear how SCN10A variants promote dysfunctional cardiac conduction. Here we showed by high-resolution 4C-seq analysis of the Scn10a-Scn5a locus in murine heart tissue that a cardiac enhancer located in Scn10a, encompassing SCN10A functional variant rs6801957, interacts with the promoter of Scn5a, a sodium channel-encoding gene that is critical for cardiac conduction. We observed that SCN5A transcript levels were several orders of magnitude higher than SCN10A transcript levels in both adult human and mouse heart tissue. Analysis of BAC transgenic mouse strains harboring an engineered deletion of the enhancer within Scn10a revealed that the enhancer was essential for Scn5a expression in cardiac tissue. Furthermore, the common SCN10A variant rs6801957 modulated Scn5a expression in the heart. In humans, the SCN10A variant rs6801957, which correlated with slowed conduction, was associated with reduced SCN5A expression. These observations establish a genomic mechanism for how a common genetic variation at SCN10A influences cardiac physiology and predisposes to arrhythmia.

The Amazon forest is fire sensitive, but, where fires were uncommon as a natural disturbance, deforestation and drought are accelerating fire occurrences, which threaten the integrity of the tropical forest, the carbon cycle and air quality. Fire emissions depend on fuel amount and type, moisture conditions and burning behaviour. Higher-resolution satellite data have helped more accurately map global burnt areas; however, the effects of fuels on the combustion process and on the composition of fire emissions remain uncertain in current fire emissions inventories. By using multiple Earth observation-based approaches, here we show that total fire emissions in the Amazon and Cerrado biomes are dominated by smouldering combustion of woody debris. The representation of woody debris and surface litter presents a critical uncertainty in fire emissions inventories and global vegetation models. For the fire season 1 August to 31 October 2020, for which all approaches are available, we found 37 2 277 605 Tg (median and range across approaches) of dry matter burnt, corresponding to carbon monoxide emissions of 39 . 1 27 59 Tg . Our results emphasize how Earth observation approaches for fuel and fire dynamics and of atmospheric trace gases reduce uncertainties of fire emission estimates. The findings enable diagnosing the representation of fuels, wildfire combustion and its effects on atmospheric composition and the carbon cycle in global vegetation–fire models. Fire emissions in the Amazon and Cerrado biomes are mainly produced from smouldering combustion of woody debris, according to observationally constrained fire emissions inventories.