Lipidomic profiling of human serum enables detection of pancreatic cancer

Pancreatic cancer has the worst prognosis among all cancers. Cancer screening of body fluids may improve the survival time prognosis of patients, who are often diagnosed too late at an incurable stage. Several studies report the dysregulation of lipid metabolism in tumor cells, suggesting that changes in the blood lipidome may accompany tumor growth. Here we show that the comprehensive mass spectrometric determination of a wide range of serum lipids reveals statistically significant differences between pancreatic cancer patients and healthy controls, as visualized by multivariate data analysis. Three phases of biomarker discovery research (discovery, qualification, and verification) are applied for 830 samples in total, which shows the dysregulation of some very long chain sphingomyelins, ceramides, and (lyso)phosphatidylcholines. The sensitivity and specificity to diagnose pancreatic cancer are over 90%, which outperforms CA 19-9, especially at an early stage, and is comparable to established diagnostic imaging methods. Furthermore, selected lipid species indicate a potential as prognostic biomarkers.

Towards ultra-high peak capacities and peak-production rates using spatial three-dimensional liquid chromatography

In order to successfully tackle the truly complex separation problems arising from areas such as proteomics research, the development of ultra-efficient and fast separation technology is required. In spatial three-dimensional chromatography, components are separated in the space domain with each peak being characterized by its coordinates in a three-dimensional separation body. Spatial three-dimensional (3D-)LC has the potential to offer unprecedented resolving power when orthogonal retention mechanisms are applied, since the total peak capacity is the product of the three individual peak capacities. Due to parallel developments during the second- and third-dimension separations, the analysis time is greatly reduced compared to a coupled-column multi-dimensional LC approach. This communication discusses the different design aspects to create a microfluidic chip for spatial 3D-LC. The use of physical barriers to confine the flow between the individual developments, and flow control by the use of (2)D and (3)D flow distributors is discussed. Furthermore, the in situ synthesis of monolithic stationary phases is demonstrated. Finally, the potential performance of a spatial 3D-LC systems is compared with the performance obtained with state-of-the-art 1D-LC and (coupled-column) 2D-LC approaches via a Pareto-optimization approach. The proposed microfluidic device for 3D-LC featuring 16 (2)D channels and 256 (3)D channels can potentially yield a peak capacity of 8000 in a total analysis time of 10 minutes.

Isotopic and elemental profiling of ammonium nitrate in forensic explosives investigations

Ammonium nitrate (AN) is frequently encountered in explosives in forensic casework. It is widely available as fertilizer and easy to implement in explosive devices, for example by mixing it with a fuel. Forensic profiling methods to determine whether material found on a crime scene and material retrieved from a suspect arise from the same source are becoming increasingly important. In this work, we have explored the possibility of using isotopic and elemental profiling to discriminate between different batches of AN. Variations within a production batch, between different batches from the same manufacturer, and between batches from different manufacturers were studied using a total of 103 samples from 19 different fertilizer manufacturers. Isotope-ratio mass spectrometry (IRMS) was used to analyze AN samples for their (15)N and (18)O isotopic composition. The trace-elemental composition of these samples was studied using inductively coupled plasma-mass spectrometry (ICP-MS). All samples were analyzed for the occurrence of 66 elements. 32 of these elements were useful for the differentiation of AN samples. These include magnesium (Mg), calcium (Ca), iron (Fe) and strontium (Sr). Samples with a similar elemental profile may be differentiated based on their isotopic composition. Linear discriminant analysis (LDA) was used to calculate likelihood ratios and demonstrated the power of combining elemental and isotopic profiling for discrimination between different sources of AN.

Validation of biomarkers for distinguishing Mycobacterium tuberculosis from non-tuberculous mycobacteria using gas chromatography-mass spectrometry and chemometrics

Tuberculosis (TB) remains a major international health problem. Rapid differentiation of Mycobacterium tuberculosis complex (MTB) from non-tuberculous mycobacteria (NTM) is critical for decisions regarding patient management and choice of therapeutic regimen. Recently we developed a 20-compound model to distinguish between MTB and NTM. It is based on thermally assisted hydrolysis and methylation gas chromatography-mass spectrometry and partial least square discriminant analysis. Here we report the validation of this model with two independent sample sets, one consisting of 39 MTB and 17 NTM isolates from the Netherlands, the other comprising 103 isolates (91 MTB and 12 NTM) from Stellenbosch, Cape Town, South Africa. All the MTB strains in the 56 Dutch samples were correctly identified and the model had a sensitivity of 100% and a specificity of 94%. For the South African samples the model had a sensitivity of 88% and specificity of 100%. Based on our model, we have developed a new decision-tree that allows the differentiation of MTB from NTM with 100% accuracy. Encouraged by these findings we will proceed with the development of a simple, rapid, affordable, high-throughput test to identify MTB directly in sputum.