Predictive Cell-Specific Gene Regulatory Models


Asst. Prof. Hatice Ülkü Osmanbeyoğlu

Hatice Ülkü Osmanbeyoğlu is an Assistant Professor of the Biomedical Informatics Department and UPMC Hillman Cancer Center at University of Pittsburgh Medical School. Her research focuses on developing data-driven computational approaches to understand disease mechanisms in order to assist in the development of personalizing anticancer treatments. Previously, she was a postdoctoral research associate at Memorial Sloan Kettering Cancer Center (MSKCC). She obtained her Ph.D. in Biomedical Informatics from University of Pittsburgh and holds a MS degree in Electrical and Computer Engineering from Carnegie Mellon University and MS in Bioengineering from University of Pittsburgh. She completed her BS in Computer Engineering from Northeastern University (Summa Cum Laude). She is a recipient of the NIH NCI Pathway to Independence Award, Memorial Sloan Kettering Postdoctoral Research Award and the Innovation in Cancer Informatics Award.


The identity and functions of specialized cell types are dependent on the complex interplay between signaling and transcriptional networks. Recently single-cell technologies such as CITE-seq have been developed that enable simultaneous quantitative analysis of cell-surface receptor expression with transcriptional states. To date, these datasets have not been used to systematically develop cell-context-specific maps of the interface between signaling and transcriptional regulators orchestrating cellular identity and function. We present SPaRTAN (Single-cell Proteomic and RNA based Transcription factor Activity Network), a computational method to link cell-surface receptors to transcription factors (TFs) by exploiting cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) datasets with cis-regulatory information. SPaRTAN is applied to immune cell types in the blood to predict the coupling of signaling receptors with cell context-specific TFs. The predictions are validated by prior knowledge and flow cytometry analyses. SPaRTAN is then used to predict the signaling coupled TF states of tumor infiltrating CD8+ T cells in malignant peritoneal and pleural mesotheliomas. SPaRTAN greatly enhances the utility of CITE-seq datasets to uncover TF and cell-surface receptor relationships in diverse cellular states.

Date: June 11th, 2021 – 7:00 PM (GMT+3)

Language: English

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