The lineage-specific basic helix-loop-helix transcription factor Ptf1a is a critical driver for development of both the pancreas and nervous system. How one transcription factor controls diverse programs of gene expression is a fundamental question in developmental biology. To uncover molecular strategies for the program-specific functions of Ptf1a, we identified bound genomic regions in vivo during development of both tissues. Most regions bound by Ptf1a are specific to each tissue, lie near genes needed for proper formation of each tissue, and coincide with regions of open chromatin. The specificity of Ptf1a binding is encoded in the DNA surrounding the Ptf1a-bound sites, because these regions are sufficient to direct tissue-restricted reporter expression in transgenic mice. Fox and Sox factors were identified as potential lineage-specific modifiers of Ptf1a binding, since binding motifs for these factors are enriched in Ptf1a-bound regions in pancreas and neural tube, respectively. Of the Fox factors expressed during pancreatic development, Foxa2 plays a major role. Indeed, Ptf1a and Foxa2 colocalize in embryonic pancreatic chromatin and can act synergistically in cell transfection assays. Together, these findings indicate that lineage-specific chromatin landscapes likely constrain the DNA binding of Ptf1a, and they identify Fox and Sox gene families as part of this process.

Unusually high aneuploidy is a hallmark of epithelial serous ovarian cancer (SOC). Previous analyses have focused on aneuploidy on average across all tumor cells. With the expansion of single-cell sequencing technologies, however, an analysis of copy number heterogeneity cell-tocell is now technically feasible. Here, we describe an analysis of single-cell RNA sequencing (scRNA-seq) data to infer arm-level aneuploidy in individual serous ovarian cancer cells. By first clustering high-quality sequenced epithelial versus non-epithelial cells, high-confidence tumor cell populations were identified. InferCNV was used to predict segmented copy-number alterations (CNAs), which were then used to determine arm-level aneuploidy at the single-cell level. Control comparisons of normal cells to normal cells showed zero arm-level aneuploidy, whereas a median of four aneuploid events were detectable in cancer cells. A heterogeneity analysis of high-grade tumor cells compared to low-grade tumor cells showed similar levels of cell-to-cell variation between cancer grades. Metastatic tumors potentially showed selection pressure with reduced cell-to-cell variation compared to cells from primary tumors. Minor cell populations with CNAs similar to metastatic cells were identified within the matched primary tumors. Taken together, these results provide a minimum estimate for single-cell aneuploidy in serous ovarian cancer and demonstrate the utility of single-cell sequencing for CNA analysis.

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To address the current need of innovative technologies that blend rapid data processing capabilities of computers with intuitive decision making skills of humans, we have developed a prototype of Cloud Enabled Brain Computer Interface (CEB) decision making technologies. The implemented architecture integrates cloud enabled big data analytics capabilities, networked BCI (Brain Computer Interface) devices, and Decision Making Engine. The novel CEB technology comprises of 1. Cloud-enabled BCI (Brain-Computer Interface) headsets, which is developed and networked in a cloud to enable rapid decision making and 2. Genetic algorithm based decision making engine, to intelligently assist the users in decision making; Advantage of our architecture is that when CEB loads the data, it will automatically recommend the best applicable Machine Learning (ML) algorithms after being evaluated to solve a given problem. Hence, with such automated machine learning techniques, CEB users workload is significantly reduced. Our experiments on DARPA dataset indicate that CEB technologies performed 10 times faster and about 4 times less false negative rate than current computational methods in seeking and understanding information. Our results demonstrate that these CEB technologies would enable humans to accurately and quickly detect meaningful information from a mass amount of data. The novel CEB technologies ensure that the reduced manpower does not result in reduced performance.