The separation and purification of human blood cell subpopulations is an essential step in many biomedical applications. New dielectrophoretic fractionation methods have great potential for cell discrimination and manipulation, both for microscale diagnostic applications and for much larger scale clinical problems. To discover whether human leukocyte subpopulations might be separable by such methods, the dielectric characteristics of the four main leukocyte subpopulations, namely, B- and T-lymphocytes, monocytes, and granulocytes, were measured by electrorotation over the frequency range 1 kHz to 120 MHz. The subpopulations were derived from human peripheral blood by magnetically activated cell sorting (MACS) and sheep erythrocyte rosetting methods, and the quality of cell fractions was checked by flow cytometry. Mean specific membrane capacitance values were calculated from the electrorotation data as 10.5 (+/- 3.1), 12.6 (+/- 3.5), 15.3 (+/- 4.3), and 11.0 (+/- 3.2) mF/m2 for T- and B-lymphocytes, monocytes, and granulocytes, respectively, according to a single-shell dielectric model. In agreement with earlier findings, these values correlated with the richness of the surface morphologies of the different cell types, as revealed by scanning electron microscopy (SEM). The data reveal that dielectrophoretic cell sorters should have the ability to discriminate between, and to separate, leukocyte subpopulations under appropriate conditions.

Understanding active proinflammatory mechanisms at and before type 1 diabetes mellitus (T1DM) onset is hindered in humans, given that the relevant tissues are inaccessible and pancreatic immune responses are difficult to measure in the periphery by traditional approaches. Therefore, we investigated the use of a sensitive and comprehensive genomics strategy to investigate the presence of proinflammatory factors in serum. The sera of recent onset diabetes patients (n = 15, 12 possessing and 3 lacking islet cell autoantibodies), long-standing diabetes patients (n = 12), “at risk” siblings of diabetes patients (n = 9), and healthy controls (n = 12) were used to induce gene expression in unrelated, healthy PBMC. After culture, gene expression was measured with microarrays and normalized expression data were subjected to hierarchical clustering and multidimensional scaling. All recent onset sera induced an expression signature (192 UniGenes; fold change: >1.5, p < 0.01; false discovery rate: <0.01) that included IL-1 cytokine family members and chemokines involved in monocyte/macrophage and neutrophil chemotaxis, as well as numerous receptors and signaling molecules. This molecular signature was not induced with the sera of healthy controls or long standing diabetes patients, where longitudinal analysis of “at risk” siblings (n = 3) before and after onset support the hypothesis that the signature emerges years before onset. This study supports prior investigations of serum that reflect disease processes associated with progression to T1DM. Identification of unique inflammatory mediators may improve disease prediction beyond current islet autoantibodies. Furthermore, proinflammatory serum markers may be used as inclusion criteria or endpoint measures in clinical trials aimed at preventing T1DM.

A new integrated image analysis package with quantitative quality control schemes is described for cDNA microarray technology. The package employs an iterative algorithm that utilizes both intensity characteristics and spatial information of the spots on a microarray image for signal-background segmentation and defines five quality scores for each spot to record irregularities in spot intensity, size and background noise levels. A composite score q(com) is defined based on these individual scores to give an overall assessment of spot quality. Using q(com) we demonstrate that the inherent variability in intensity ratio measurements is closely correlated with spot quality, namely spots with higher quality give less variable measurements and vice versa. In addition, gauging data by q(com) can improve data reliability dramatically and efficiently. We further show that the variability in ratio measurements drops exponentially with increasing q(com) and, for the majority of spots at the high quality end, this improvement is mainly due to an improvement in correlation between the two dyes. Based on these studies, we discuss the potential of quantitative quality control for microarray data and the possibility of filtering and normalizing microarray data using a quality metrics-dependent scheme.