Research

We combine our passion for computational biology, cancer genomics, and neurobiology in a research program that spans two interrelated areas of activity:

Understanding the mechanisms of progression and recurrence in glioma. We make use of a diverse array of high-throughput single-cell technologies and patient-derived cell models to characterize the cellular composition, cell-to-cell signaling, and gene-regulatory circuits of gliomas and their microenvironment. Our goal is to elucidate the molecular mechanisms of tumor progression and metastasis and identify consequent targets for therapeutic intervention. We have a particular interest on pediatric ependymoma, a rare but devastating type of brain cancer in children, where we are trying to elucidate the mechanisms by which ependymal tumors metastasize to other regions of the central nervous system. Reference single-cell omics atlases of the developing brain and immune system have proven to be extremely useful in this research, and our lab also contributes to several collaborative efforts in this direction.

 
 
Read more:
  • Nasrallah, M. Santi, and P. G. Cámara. Pro-Inflammatory Cytokines Mediate the Epithelial-to-Mesenchymal-Like Transition of Pediatric Posterior Fossa Ependymoma. Nature Communications 13 (2022) 3936. DOI: 10.1038/s41467-022-31683-9. [*authors contributed equally]. [GitHub].

  • M. Haniffa, D. Taylor, S. Linnarsson, B. J. Aronow, G. D. Bader, R. A. Barker, P. G. Cámara, J. G. Camp, A. Chédotal, A. Copp, H. C. Etchevers, P. Giacobini, B. Gottgens, G. Guo, A. Hupalowska, K. R. James, E. Kirby, A. Kriegstein, J. Lundeberg, J. Marioni, K. B. Meyer, K. K. Niakan, M. Nilsson, B. Olabi, D. Pe'er, A. Regev, J. Rood, O. Rozenblatt-Rosen, R. Satija, S. A. Teichmann, B. Treutlein, R. Vento-Tormo, S. Webb, and the Human Cell Atlas Developmental Biological Network. A roadmap for the Human Developmental Cell Atlas. Nature 597 (2021) 196-205. [Paper].

  • J.-K. Lee, J. Wang, J. K. Sa, E. Ladewig, H.-O. Lee, I.-H. Lee, H.-J. Kang, D. S. Rosenbloom, P. G. Cámara, Z. Liu, P. van Nieuwenhuizen, S. W. Jung, S. W. Choi, J. Kim, A. Chen, K.-T. Kim, S. Shin, Y. J. Seo, J. M. Oh, Y. J. Shin, D.-S. Kong, H. J. Seol, A. Blumberg, J.-I. Lee, A. Iavarone, W.-Y. Park, R. Rabadán, and D.-H. Nam. Spatiotemporal genomic architecture informs precision oncology in glioblastoma. Nature Genetics 49 (2017) 594 - 599. [Paper].

Development of computational methods for studying cellular heterogeneity and its role in disease. An important part of our work consists on the development of geometry- and topology-based computational methods for the integration and analysis of single-cell multi-omics and imaging data. We make use of these methods for our research in brain cancer genomics, but also distribute and maintain them for the broad scientific community. Some of our current projects include the development of algorithms for the automated annotation of highly-multiplexed immunohistochemistry and cytometry data based on single-cell proteotranscriptomic atlases, the inference of interpretable gene-regulatory circuits based on single-cell transcriptomic and chromatin accessibility data, and the integration of single-cell omics and morphological imaging data of tissues. We also collaborate with various groups in the application of some of these methods to other important problems in cancer genomics, such as the identification of the molecular mechanisms and cell states of chimeric antigen receptor (CAR) T cells that are associated with the type and duration of clinical responses to CD19 CAR T immunotherapy.
 
 

Read more:

  • K. W. Govek*, E. C. Troisi*, Z. Miao, R. G. Aubin, S. Woodhouse, and P. G. Cámara. Single-Cell Transcriptomic Analysis of mIHC Images via Antigen Mapping. Science Advances 7 (2021) eabc5464. [*authors contributed equally]. [Paper][GitHub] [Website] [Video Poster].

  • K. W. Govek*, V. S. Yamajala*, and P. G. Cámara. Clustering-Independent Analysis of Genomic Data using Spectral Simplicial Theory. PLOS Computational Biology 15 (2019) 11. [*authors contributed equally]. [Paper] [GitHub].

  • A.H. Rizvi*, P. G. Cámara*, E. K. Kandror, T. J. Roberts, I. Scheiren, T. Maniatis, and R. Rabadán. Single-cell topological RNA-seq analysis reveals insights into cellular differentiation and development. Nature Biotechnology 35 (2017) 551-560. [*authors contributed equally]. [Paper] [GitHub].

  • P. G. Cámara. Topological methods for genomics: present and future directions. Current Opinion in Systems Biology 1 (2017) 95 - 101. [Paper].