Investments
The Advanced Single Cell Technology Innovation Core (ASCeTIC) will provide state-of-the-art technology for single-cell genomic analysis, multiplexed single-cell molecular perturbation, and in situ validation, leveraging robust experimental pipelines for large-scale profiling of human and murine solid tumors. A key Center deliverable is the identification of Master Regulator (MR) proteins underlying drug response and cell-cell interactions in the tumor microenvironment (TME), by interrogating tumor-specific regulatory networks. ASCeTIC will combine scRNA-seq with multiplexed CRISPR perturbations of putative MRs for direct validation of their downstream targets. To this end, we have successfully implemented CRISPR droplet sequencing (CROP-seq) for multiplexed, pooled gene perturbation with direct read-out by scRNA-seq (1). The Core will generate pooled lentiviral gRNA constructs for multiplexed perturbation and validation of master regulators with CROP-seq.
For more information, visit the Advanced Single Cell Technology Innovation Core website here.
The Columbia University Cryo-Electron Microscopy Center (CEC) provides researchers with training and access to the advanced instrumentation, data collection capacity, and processing support required to incorporate cryo-electron microscopy into their studies. The CEC supports instruments at the Medical Center, Zuckerman Institute, and New York Structural Biology Center campuses, but operates as a single core facility, allowing users to use instruments across locations.
Use of the Cryo-Electron Microscopy Center is available to all Columbia-affiliated researchers, although users must undergo training and receive project approval before instrument time can be scheduled. External users may utilize the resources of the facility if scanning time is available. New users should contact the facility(link sends e-mail) for more information.
The Columbia Precision Medicine Initiative invests in infrastructure and is supporting OpenFold. Developed by Dr. Mohammed AlQuraishi and colleagues at the Program for Mathematical genomics, directed by Dr. Raul Rabadan, OpenFold is a world-wide, community-driven effort to build an open-source protein structure prediction system. This system will utilize the latest advances in machine learning developed by Columbia’s scientists. Crucially, it will be built in a modular manner to serve as a platform for developing life science applications, similar to how software platforms enable numerous applications. Running at full capacity the initial cluster of 100 GPUs will enable training of a protein structure prediction system in one-to-two months of computing time and, subsequent to training, will predict ~6 protein structures per day.
For more information, visit the OpenFold website here.