Core C

Core C (Informatics and Computation Core) will support bioinformatic analysis, computational modeling, and training in computational modeling for personnel in this program project grant. The overall goal of Core C is to provide centralized support in generation of bulk and single-cell RNA-Seq data and its bioinformatic analysis, and training in genomic data analysis and network visualization tools to trainees and PIs in all Projects and Cores.

This core will oversee bulk and single cell RNA-Seq studies and analyze their results to (i) develop intra- and inter-cellular signaling networks, (ii) identify critical transitions in cell fate predictive of disease onset, and (iii) generate novel hypotheses for the individual Projects.

A secondary role of the core will be to provide training to investigators and trainees in use of computational tools for bulk and single-cell RNA-Seq data analysis, signaling network visualization, and analysis.

about Core C

Mission

Core C will integrate, analyze, and visualize bulk and single-cell genomic data generated by individual Projects, and provide training in use of cutting-edge data analysis, visualization, and network analysis tools.

Service: Integrate, analyze, and visualize bulk and single-cell genomic data generated by individual Projects.

  • Projects I-IV will generate a variety of tissue-specific, bulk and single-cell RNA-Seq data. Building on preliminary analysis, Core C will identify significantly dysregulated genes between PVAT in healthy and hypertensive animals, and computationally infer the upstream regulators of these genes to reconstruct transcriptional and signaling networks responsible for health-to-disease transition.

Training: Provide training to all Projects and Cores in use of cutting-edge data analysis, visualization, and network analysis tools.

  • A crucial function of Core C will be to provide extensive training to PPG members in use of various computational tools for analysis and visualization of bulk and single-cell RNA-Seq data and downstream network analysis. This training will be provided in the form of regular hands-on tutorials and occasional day-long workshops. In this way, Project leaders and trainees will have a truly informed understanding of their RNA sequencing data.

Development: Develop a predictive computational model based on single-cell RNA-Seq data of the transition of PVAT from a healthy state to a diseased state in high fat diet-associated hypertension.

  • Given the variety of PVAT cell types potentially involved in the transition from a healthy to hypertensive state, Core C will develop a predictive computational model of cell-specific transitions based on single-cell RNA-Seq data. This model will make use of recent developments in the theory of critical state transitions.

Core C Team

Vishal Kumar Panda

Assistant Professor, Dept of Pharmacology and Toxicology
Core B Leader

I recently joined the PPG project and am enthusiastic about contributing to the project.  In my role, I specialize in analyzing data from adipocytes – the cells in this tissue. A significant part of my work involves employing a range of tools and methods, including advanced machine learning algorithms, to decipher how these cells interact with each other. This process includes using various data classifiers to detect and interpret patterns in the perivascular adipose tissue (PVAT) data. My role represents a fusion of biology and data science. Through computational techniques and machine learning, I delve into the complexities of cell-to-cell communication within adipose tissue. This approach is vital in deepening our knowledge of how these tissues operate and their interactions with other bodily systems.

Sudin Bhattacharya

Assistant Professor, Depts of Biomedical Engineering, Pharmacology and Toxicology
Core C Leader

Dr. Sudin Bhattacharya will lead Core C and coordinate the effort in integrating computational modeling with analysis of high-throughput sequencing experiments and computational training. He has a background in various types of systems biology and bioinformatic modeling, including ordinary differential equation-based kinetic models, mapping transcriptional regulatory networks from functional genomics data, and multiscale “virtual tissue” modeling.

Rance Nault

Assistant Professor, Dept of Biochemistry
Core C Co-Leader

Dr. Rance Nault, co-lead of Core C, has over a decade of experience in carrying out high-throughput functional genomics experiments from study design to data generation including both bulk and single-cell RNA-Seq. He also has a strong background in computational analysis of RNA-Seq data, and will support Dr. Bhattacharya as co-Investigator of Core C. Dr. Nault will also aid in providing computational training to PPG trainees and PIs as part of Core C.

Leah Terrian

Graduate Student
Core C Member

Ms. Leah Terrian, PhD student in Biomedical Engineering and a graduate research assistant in the Bhattacharya lab, has a background in mathematics and computational modeling. She will take part in both single cell RNA-Seq data generation in collaboration with PPG Projects, and computational analysis of the data. Specifically, she will work on prediction of health to disease transitions in PVAT cell types using pseudo-time analysis of single-cell data with critical state transition theory.

Integration with other projects

Core C will play a three-pronged role in this Program Project Grant (PPG) investigating the role of perivascular adipose tissue (PVAT) as a central integrator of vascular health: i) service, ii) training and iii) development. Under the service component,

Core C will oversee generation of, analyze, and visualize all bulk and single-cell genomic data required by Projects I-IV. Voluntary training will be provided to all Project and Core personnel and trainees in use of cutting-edge tools in biological data visualization and network analysis. A predictive computational model of the transition of PVAT from a healthy state to an inflamed state in diet-associated hypertension, will be developed based on critical state transition theory.

The model will integrate findings on: i) mechanisms of communication of vascular pressure to PVAT and the response of PVAT to this pressure (Project I); ii) the extent of innervation and the role of the nervous system in the functioning of PVAT in health and disease (Project II); iii) the effect of the PVAT microenvironment on immune function in the vascular neighborhood (Project III); and iv) the effect of mechanical forces on the adipogenic potential of PVAT (Project IV). This predictive computational model will provide a unified quantitative framework to generate and test novel hypotheses about the role of PVAT in health and under hypertension.

Publications

A cell atlas of thoracic aortic perivascular adipose tissue: a focus on mechanotransducers

Janice M Thompson , Stephanie W Watts , Leah Terrian 2, G Andres Contreras , Cheryl Rockwell , C Javier Rendon , Emma Wabel  Lizbeth Lockwood , Sudin Bhattacharya, Rance Nault Am J Physiol Heart Circ Physiol 2024 May 1;326(5):H1252-H1265. doi: 10.1152/ajpheart.00040.2024. Epub 2024 Mar 22. ...

Integrins play a role in stress relaxation of perivascular adipose tissue

Stephanie W Watts, Janice M Thompson, Sudin Bhattacharya, Vishal Panda, Leah Terrian, Andres Contreras, Rance Nault Pharmacol Res 2024 Jun 14:206:107269. doi: 10.1016/j.phrs.2024.107269. Online ahead of print. ...

Perivascular Adipose Tissue Remodels Only after Elevation of Blood Pressure in the Dahl SS Rat Fed a High-Fat Diet

J Vasc Res. 2023 Dec 19:1-12. doi: 10.1159/000535513. Online ahead of print. Caitlin Wilson , Janice M Thompson , Leah Terrian , Adam D Lauver , Emma D Flood , Gregory D Fink , Lisa Sather , Sudin Bhattacharya , G Andres Contreras , Stephanie W Watts  ...

A Cell Atlas of Thoracic Aortic Perivascular Adipose Tissue: a focus on mechanotransducers

Janice M Thompson , Stephanie W Watts, Leah Terrian, G Andres Contreras , Cheryl Rockwell , C Javier Rendon , Emma Wabel , Lizabeth Lockwood , Sudin Bhattacharya , Rance Nault bioRxiv 2023 Oct 9:2023.10.09.561581. doi: 10.1101/2023.10.09.561581. Preprint ...

Protocols

Available Soon.