Allelic alterations of chromosomes 1 and 19 are frequent events in human di use gliomas and have recently proven to be strong predictors of chemotherapeutic response and prolonged survival in oligodendrogliomas (Cairncross et al., 1998; Smith et al., submitted). Using 115 human di use gliomas, we localized regions of common allelic loss on chromosomes 1 and 19 and assessed the association of these deletion intervals with glioma histological subtypes. Further, we evaluated the capacity of multiple modalities to detect these alterations, including loss of heterozygosity (LOH),¯uorescence in situ hybridization (FISH), and comparative genomic hybridization (CGH). The correlation coe cients for detection of 1p and 19q alterations, respectively, between modalities were: 0.98 and 0.87 for LOH and FISH, 0.79 and 0.60 for LOH and CGH, and 0.79 and 0.53 for FISH and CGH. Minimal deletion regions were de®ned on 19q13.3 (D19S412-D19S596) and 1p (D1S468-D1S1612). Loss of the 1p36 region was found in 18% of astrocytomas (10/55) and in 73% (24/33) of oligodendrogliomas (P50.0001), and loss of the 19q13.3 region was found in 38% (21/55) of astrocytomas and 73% (24/33) of oligodendrogliomas (P=0.0017). Loss of both regions was found in 11% (6/55) of astrocytomas and in 64% (21/33) of oligodendrogliomas (P50.0001). All gliomas with LOH on either 1p or 19q demonstrated loss of the corresponding FISH probe, 1p36 or 19q13.3, suggesting not only locations of putative tumor suppressor genes, but also a simple assay for assessment of 1p and 19q alterations as diagnostic and prognostic markers.

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The ability to image large numbers of cells at high resolution enhances flow cytometric analysis of cells and cell populations. In particular, the ability to image intracellular features adds a unique aspect to analyses, and can enable correlation between molecular phenomena resulting in alterations in cellular phenotype. Unicellular microalgae are amenable to high-throughput analysis to capture the diversity of cell types in natural samples, or diverse cellular responses in clonal populations, especially using imaging cytometry. Using examples from our laboratory, we review applications of imaging cytometry, specifically using an Amnis(®) ImageStream(®)X instrument, to characterize photosynthetic microalgae. Some of these examples highlight advantages of imaging flow cytometry for certain research objectives, but we also include examples that would not necessarily require imaging and could be performed on a conventional cytometer to demonstrate other concepts in cytometric evaluation of microalgae. We demonstrate the value of these approaches for (1) analysis of populations, (2) documentation of cellular features, and (3) analysis of gene expression.

Virus-host interactions are frequently studied in bulk cell populations, obscuring cell-to-cell variation. Here we investigate endogenous herpesvirus gene expression at the single-cell level, combining a sensitive and robust fluorescent in situ hybridization platform with multiparameter flow cytometry, to study the expression of gammaherpesvirus non-coding RNAs (ncRNAs) during lytic replication, latent infection and reactivation in vitro. This method allowed robust detection of viral ncRNAs of murine gammaherpesvirus 68 (γHV68), Kaposi’s sarcoma associated herpesvirus and Epstein-Barr virus, revealing variable expression at the single-cell level. By quantifying the inter-relationship of viral ncRNA, viral mRNA, viral protein and host mRNA regulation during γHV68 infection, we find heterogeneous and asynchronous gene expression during latency and reactivation, with reactivation from latency identified by a distinct gene expression profile within rare cells. Further, during lytic replication with γHV68, we find many cells have limited viral gene expression, with only a fraction of cells showing robust gene expression, dynamic RNA localization, and progressive infection. Lytic viral gene expression was enhanced in primary fibroblasts and by conditions associated with enhanced viral replication, with multiple subpopulations of cells present in even highly permissive infection conditions. These findings, powered by single-cell analysis integrated with automated clustering algorithms, suggest inefficient or abortive γHV infection in many cells, and identify substantial heterogeneity in viral gene expression at the single-cell level.