Label-Free High-Throughput Leukemia Detection by Holographic Microscopy.

Complete blood count and differentiation of leukocytes (DIFF) belong to the most frequently performed laboratory diagnostic tests. Here, a flow cytometry-based method for label-free DIFF of untouched leukocytes by digital holographic microscopy on the rich phase contrast of peripheral leukocyte images, using highly controlled 2D hydrodynamic focusing conditions is reported. Principal component analysis of morphological characteristics of the reconstructed images allows classification of nine leukocyte types, in addition to different types of leukemia and demonstrates disappearance of acute myeloid leukemia cells in remission. To exclude confounding effects, the classification strategy is tested by the analysis of 20 blinded clinical samples. Here, 70% of the specimens are correctly classified with further 20% classifications close to a correct diagnosis. Taken together, the findings indicate a broad clinical applicability of the cytometry method for automated and reagent-free diagnosis of hematological disorders.

Label-free, high-throughput detection of P. falciparum infection in sphered erythrocytes with digital holographic microscopy.

Effective malaria treatment requires rapid and accurate diagnosis of infecting species and actual parasitemia. Despite the recent success of rapid tests, the analysis of thick and thin blood smears remains the gold standard for routine malaria diagnosis in endemic areas. For non-endemic regions, sample preparation and analysis of blood smears are an issue due to low microscopy expertise and few cases of imported malaria. Automation of microscopy results could be beneficial to quickly confirm suspected infections in such conditions. Here, we present a label-free, high-throughput method for early malaria detection with the potential to reduce inter-observer variation by reducing sample preparation and analysis effort. We used differential digital holographic microscopy in combination with two-dimensional hydrodynamic focusing for the label-free detection of P. falciparum infection in sphered erythrocytes, with a parasitemia detection limit of 0.01%. Moreover, the achieved differentiation of P. falciparum ring-, trophozoite- and schizont life cycle stages in synchronized cultures demonstrates the potential for future discrimination of even malaria species.

Explorative data analysis of MCL reveals gene expression networks implicated in survival and prognosis supported by explorative CGH analysis.

BACKGROUND: Mantle cell lymphoma (MCL) is an incurable B cell lymphoma and accounts for 6% of all non-Hodgkin's lymphomas. On the genetic level, MCL is characterized by the hallmark translocation t(11;14) that is present in most cases with few exceptions. Both gene expression and comparative genomic hybridization (CGH) data vary considerably between patients with implications for their prognosis. METHODS: We compare patients over and below the median of survival. Exploratory principal component analysis of gene expression data showed that the second principal component correlates well with patient survival. Explorative analysis of CGH data shows the same correlation. RESULTS: On chromosome 7 and 9 specific genes and bands are delineated which improve prognosis prediction independent of the previously described proliferation signature. We identify a compact survival predictor of seven genes for MCL patients. After extensive re-annotation using GEPAT, we established protein networks correlating with prognosis. Well known genes (CDC2, CCND1) and further proliferation markers (WEE1, CDC25, aurora kinases, BUB1, PCNA, E2F1) form a tight interaction network, but also non-proliferative genes (SOCS1, TUBA1B CEBPB) are shown to be associated with prognosis. Furthermore we show that aggressive MCL implicates a gene network shift to higher expressed genes in late cell cycle states and refine the set of non-proliferative genes implicated with bad prognosis in MCL. CONCLUSION: The results from explorative data analysis of gene expression and CGH data are complementary to each other. Including further tests such as Wilcoxon rank test we point both to proliferative and non-proliferative gene networks implicated in inferior prognosis of MCL and identify suitable markers both in gene expression and CGH data.

Genome Expression Pathway Analysis Tool--analysis and visualization of microarray gene expression data under genomic, proteomic and metabolic context.

BACKGROUND: Regulation of gene expression is relevant to many areas of biology and medicine, in the study of treatments, diseases, and developmental stages. Microarrays can be used to measure the expression level of thousands of mRNAs at the same time, allowing insight into or comparison of different cellular conditions. The data derived out of microarray experiments is highly dimensional and often noisy, and interpretation of the results can get intricate. Although programs for the statistical analysis of microarray data exist, most of them lack an integration of analysis results and biological interpretation. RESULTS: We have developed GEPAT, Genome Expression Pathway Analysis Tool, offering an analysis of gene expression data under genomic, proteomic and metabolic context. We provide an integration of statistical methods for data import and data analysis together with a biological interpretation for subsets of probes or single probes on the chip. GEPAT imports various types of oligonucleotide and cDNA array data formats. Different normalization methods can be applied to the data, afterwards data annotation is performed. After import, GEPAT offers various statistical data analysis methods, as hierarchical, k-means and PCA clustering, a linear model based t-test or chromosomal profile comparison. The results of the analysis can be interpreted by enrichment of biological terms, pathway analysis or interaction networks. Different biological databases are included, to give various information for each probe on the chip. GEPAT offers no linear work flow, but allows the usage of any subset of probes and samples as a start for a new data analysis. GEPAT relies on established data analysis packages, offers a modular approach for an easy extension, and can be run on a computer grid to allow a large number of users. It is freely available under the LGPL open source license for academic and commercial users at http://gepat.sourceforge.net. CONCLUSION: GEPAT is a modular, scalable and professional-grade software integrating analysis and interpretation of microarray gene expression data. An installation available for academic users can be found at http://gepat.bioapps.biozentrum.uni-wuerzburg.de.

Ridom TraceEdit: a DNA trace editor and viewer.

Ridom TraceEdit is a cross-platform graphical DNA trace viewer and editor. TraceEdit displays the chromatogram files from Applied Biosystems automated sequencers and files in the Staden SCF format. Incorrect base calls can be edited and saved.