Culture of cells using various microfluidic devices is becoming more common within experimental cell biology. At the same time, a technological radiation of microfluidic cell culture device designs is currently in progress. Ultimately, the utility of microfluidic cell culture will be determined by its capacity to permit new insights into cellular function. Especially insights that would otherwise be difficult or impossible to obtain with macroscopic cell culture in traditional polystyrene dishes, flasks or well-plates. Many decades of heuristic optimization have gone into perfecting conventional cell culture devices and protocols. In comparison, even for the most commonly used microfluidic cell culture devices, such as those fabricated from polydimethylsiloxane (PDMS), collective understanding of the differences in cellular behavior between microfluidic and macroscopic culture is still developing. Moving in vitro culture from macroscopic culture to PDMS based devices can come with unforeseen challenges. Changes in device material, surface coating, cell number per unit surface area or per unit media volume may all affect the outcome of otherwise standard protocols. In this review, we outline some of the advantages and challenges that may accompany a transition from macroscopic to microfluidic cell culture. We focus on decisive factors that distinguish macroscopic from microfluidic cell culture to encourage a reconsideration of how macroscopic cell culture principles might apply to microfluidic cell culture.

Metabolomics is a rapidly evolving analytical approach in life and health sciences. The structural elucidation of the metabolites of interest remains a major analytical challenge in the metabolomics workflow. Here, we investigate the use of ion mobility as a tool to aid metabolite identification. Ion mobility allows for the measurement of the rotationally averaged collision cross-section (CCS), which gives information about the ionic shape of a molecule in the gas phase. We measured the CCSs of 125 common metabolites using traveling-wave ion mobility-mass spectrometry (TW-IM-MS). CCS measurements were highly reproducible on instruments located in three independent laboratories (RSD < 5% for 99%). We also determined the reproducibility of CCS measurements in various biological matrixes including urine, plasma, platelets, and red blood cells using ultra performance liquid chromatography (UPLC) coupled with TW-IM-MS. The mean RSD was < 2% for 97% of the CCS values, compared to 80% of retention times. Finally, as proof of concept, we used UPLC–TW-IM-MS to compare the cellular metabolome of epithelial and mesenchymal cells, an in vitro model used to study cancer development. Experimentally determined and computationally derived CCS values were used as orthogonal analytical parameters in combination with retention time and accurate mass information to confirm the identity of key metabolites potentially involved in cancer. Thus, our results indicate that adding CCS data to searchable databases and to routine metabolomics workflows will increase the identification confidence compared to traditional analytical approaches.

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Our findings suggest that conditions associated with several CF pathogenesis hypotheses could cause the noninvasive and resistant infection phenotype, independently of the bacterial functions needed for biofilm formation.

We report a genomewide linkage study of type 2 diabetes (T2D [MIM 125853]) in the Icelandic population. A list of type 2 diabetics was cross-matched with a computerized genealogical database clustering 763 type 2 diabetics into 227 families. The diabetic patients and their relatives were genotyped with 906 microsatellite markers. A nonparametric multipoint linkage analysis yielded linkage to 5q34-q35.2 (LOD = 2.90, P=1.29 x 10(-4)) in all diabetics. Since obesity, here defined as body mass index (BMI) > or =30 kg/m(2), is a key risk factor for the development of T2D, we studied the data either independently of BMI or by stratifying the patient group as obese (BMI > or =30) or nonobese (BMI <30). A nonparametric multipoint linkage analysis yielded linkage to 5q34-q35.2 (LOD = 3.64, P=2.12 x (10)-5) in the nonobese diabetics. No linkage was observed in this region for the obese diabetics. Linkage analysis conditioning on maternal transmission to the nonobese diabetics resulted in a LOD score of 3.48 (P=3.12 x 10(-5)) in the same region, whereas conditioning on paternal transmission led to a substantial drop in the LOD score. Finally, we observed potential interactions between the 5q locus and two T2D susceptibility loci, previously mapped in other populations.