Masson's tumor of the breast: Rare differential for new or recurrent breast cancer-Case report, pathology, and review of the literature.

A 66-year-old woman had a new left breast mass with a prior history of a stage IIIC left breast cancer. She had excision of the mass. The pathology noted intravascular papillary endothelial hyperplasia (IPEH) also known as Masson's tumor. Although a benign lesion, this remains in the differential of breast lesions with vascular morphology. IPEH has been described at multiple sites throughout the body. It must be distinguished from angiosarcoma. Although Masson's tumor has been previously documented in the breast, prior treatment for breast cancer presents a diagnostic dilemma. Treatment for the breast cancer may be a contributing event for Masson's tumor.

On comparing heterogeneity across biomarkers.

Microscopy reveals complex patterns of cellular heterogeneity that can be biologically informative. However, a limitation of microscopy is that only a small number of biomarkers can typically be monitored simultaneously. Thus, a natural question is whether additional biomarkers provide a deeper characterization of the distribution of cellular states in a population. How much information about a cell's phenotypic state in one biomarker is gained by knowing its state in another biomarker? Here, we describe a framework for comparing phenotypic states across biomarkers. Our approach overcomes the current limitation of microscopy by not requiring costaining biomarkers on the same cells; instead, we require staining of biomarkers (possibly separately) on a common collection of phenotypically diverse cell lines. We evaluate our approach on two image datasets: 33 oncogenically diverse lung cancer cell lines stained with 7 biomarkers, and 49 less diverse subclones of one lung cancer cell line stained with 12 biomarkers. We first validate our method by comparing it to the "gold standard" of costaining. We then apply our approach to all pairs of biomarkers and use it to identify biomarkers that yield similar patterns of heterogeneity. The results presented in this work suggest that many biomarkers provide redundant information about heterogeneity. Thus, our approach provides a practical guide for selecting independently informative biomarkers and, more generally, will yield insights into both the connectivity of biological networks and the complexity of the state space of biological systems.

A survey of ex vivo/in vitro transduction efficiency of mammalian primary cells and cell lines with Nine natural adeno-associated virus (AAV1-9) and one engineered adeno-associated virus serotype.

BACKGROUND: The ability to deliver a gene of interest into a specific cell type is an essential aspect of biomedical research. Viruses can be a useful tool for this delivery, particularly in difficult to transfect cell types. Adeno-associated virus (AAV) is a useful gene transfer vector because of its ability to mediate efficient gene transduction in numerous dividing and quiescent cell types, without inducing any known pathogenicity. There are now a number of natural for that designed AAV serotypes that each has a differential ability to infect a variety of cell types. Although transduction studies have been completed, the bulk of the studies have been done in vivo, and there has never been a comprehensive study of transduction ex vivo/in vitro. METHODS: Each cell type was infected with each serotype at a multiplicity of infection of 100,000 viral genomes/cell and transduction was analyzed by flow cytometry + . RESULTS: We found that AAV1 and AAV6 have the greatest ability to transduce a wide range of cell types, however, for particular cell types, there are specific serotypes that provide optimal transduction. CONCLUSIONS: In this work, we describe the transduction efficiency of ten different AAV serotypes in thirty-four different mammalian cell lines and primary cell types. Although these results may not be universal due to numerous factors such as, culture conditions and/ or cell growth rates and cell heterogeneity, these results provide an important and unique resource for investigators who use AAV as an ex vivo gene delivery vector or who work with cells that are difficult to transfect.

NPY and NPY receptors in airway structural and inflammatory cells in allergic asthma.

PURPOSE: Neuropeptide Y (NPY) level is elevated in allergic asthmatic airways and activation of NPY receptor-1 (NPY-Y1) on antigen-presenting cells (APCs) is essential for T cell priming. Paradoxically, NPY-Y1 modulates hyper-responsiveness in T cells, suggesting a bimodal role for NPY in APCs and T cells. Therefore, determination of the temporal and spatial expression pattern of NPY and its receptors in asthmatic airways is essential to further understand the role of NPY in allergic asthma. METHODS: Lungs were isolated from control and acute and chronic stages of OVA-sensitized and challenged mice (OVA). Stains, including H&E, PAS, and trichrome, were used to determine the severity of lung pathology. The expression patterns of NPY and NPY-Y receptors in the airways were determined using ELISA and immunofluorescence. Cytokine levels in the BALF were also measured. RESULTS: NPY levels were undetectable in the BALF of control mice, but significantly increased in the OVA group at day 80. Levels of IL-4, TGF-ß1 and TGF-ß2, significantly increased and peaked on day 45 and decreased on day 80 in the OVA group, exhibiting an inverse correlation with NPY levels. NPY expression was localized to macrophage-like cells in the peri-bronchial and peri-vascular areas in the lung tissue. NPY-Y1 and -Y5 receptors were constitutively expressed by both structural and inflammatory cells in the lung tissue. CONCLUSIONS: NPY produced by activated macrophage-like cells may be involved in regulating cytokine production and cellular activities of immune cells in asthma. However, it remains unclear whether such an increase in NPY is a defensive/compensatory mechanism to modulate the effects of inflammatory cytokines.

An approach for extensibly profiling the molecular states of cellular subpopulations.

Microscopy often reveals the existence of phenotypically distinct cellular subpopulations. However, additional characterization of observed subpopulations can be limited by the number of biomolecular markers that can be simultaneously monitored. Here we present a computational approach for extensibly profiling cellular subpopulations by freeing one or more imaging channels to monitor additional probes. In our approach, we trained classifiers to re-identify subpopulations accurately based on an enhanced collection of phenotypic features extracted from only a subset of the original markers. Then we constructed subpopulation profiles step-wise from replicate experiments, in which cells were labeled with different but overlapping marker sets. We applied our approach to identify molecular differences among subpopulations and to identify functional groupings of markers, in populations of differentiating mouse preadipocytes, polarizing human neutrophil-like cells and dividing human cancer cells.

Patterns of basal signaling heterogeneity can distinguish cellular populations with different drug sensitivities.

Phenotypic heterogeneity has been widely observed in cellular populations. However, the extent to which heterogeneity contains biologically or clinically important information is not well understood. Here, we investigated whether patterns of basal signaling heterogeneity, in untreated cancer cell populations, could distinguish cellular populations with different drug sensitivities. We modeled cellular heterogeneity as a mixture of stereotyped signaling states, identified based on colocalization patterns of activated signaling molecules from microscopy images. We found that patterns of heterogeneity could be used to separate the most sensitive and resistant populations to paclitaxel within a set of H460 lung cancer clones and within the NCI-60 panel of cancer cell lines, but not for a set of less heterogeneous, immortalized noncancer human bronchial epithelial cell (HBEC) clones. Our results suggest that patterns of signaling heterogeneity, characterized as ensembles of a small number of distinct phenotypic states, can reveal functional differences among cellular populations.

Diverse drug-resistance mechanisms can emerge from drug-tolerant cancer persister cells.

Cancer therapy has traditionally focused on eliminating fast-growing populations of cells. Yet, an increasing body of evidence suggests that small subpopulations of cancer cells can evade strong selective drug pressure by entering a 'persister' state of negligible growth. This drug-tolerant state has been hypothesized to be part of an initial strategy towards eventual acquisition of bona fide drug-resistance mechanisms. However, the diversity of drug-resistance mechanisms that can expand from a persister bottleneck is unknown. Here we compare persister-derived, erlotinib-resistant colonies that arose from a single, EGFR-addicted lung cancer cell. We find, using a combination of large-scale drug screening and whole-exome sequencing, that our erlotinib-resistant colonies acquired diverse resistance mechanisms, including the most commonly observed clinical resistance mechanisms. Thus, the drug-tolerant persister state does not limit--and may even provide a latent reservoir of cells for--the emergence of heterogeneous drug-resistance mechanisms.

Rapid analysis and exploration of fluorescence microscopy images.

Despite rapid advances in high-throughput microscopy, quantitative image-based assays still pose significant challenges. While a variety of specialized image analysis tools are available, most traditional image-analysis-based workflows have steep learning curves (for fine tuning of analysis parameters) and result in long turnaround times between imaging and analysis. In particular, cell segmentation, the process of identifying individual cells in an image, is a major bottleneck in this regard. Here we present an alternate, cell-segmentation-free workflow based on PhenoRipper, an open-source software platform designed for the rapid analysis and exploration of microscopy images. The pipeline presented here is optimized for immunofluorescence microscopy images of cell cultures and requires minimal user intervention. Within half an hour, PhenoRipper can analyze data from a typical 96-well experiment and generate image profiles. Users can then visually explore their data, perform quality control on their experiment, ensure response to perturbations and check reproducibility of replicates. This facilitates a rapid feedback cycle between analysis and experiment, which is crucial during assay optimization. This protocol is useful not just as a first pass analysis for quality control, but also may be used as an end-to-end solution, especially for screening. The workflow described here scales to large data sets such as those generated by high-throughput screens, and has been shown to group experimental conditions by phenotype accurately over a wide range of biological systems. The PhenoBrowser interface provides an intuitive framework to explore the phenotypic space and relate image properties to biological annotations. Taken together, the protocol described here will lower the barriers to adopting quantitative analysis of image based screens.