CMPBIO 2015 - COMPUTATIONAL IMAGING FOR SPATIAL SYSTEMS BIOLOGY

Minimum Credits: 3
Maximum Credits: 3
The recent explosion of next-generation, high-content, high-throughput spatial imaging technologies for intact tissues measuring protein expressions, DNA and RNA probes for investigating systems biology challenges has attracted the interest of NIH and other international agencies in funding precision medicine efforts, including Human Tumor Atlas Network (HTAN), Immuno-Oncology Translation Network (IOTN), Human BioMolecular Atlas Program (HuBMAP), Human Cell Atlas (HCA) and Human Protein Atlas (HPA). In this course, we will study these spatial imaging technologies in greater depth, from low-resolution, spatially barcoded approaches for spatial transcriptomics to high-resolution in situ approaches for imaging proteins, DNA and RNA (CODEX, MIBI, IMC-CyTOF, Seq-FISH, MERFISH) expressions. We will focus on several key aspects of these technologies requiring evaluation and benchmarking in the context of spatial approaches: validation of reagents; comparison to gold-standard methods; comparison between related techniques; and relation to analyses on dissociated cells. We will discuss the technologies in terms of: tissue preparation and reagent validation, imaging development and optimization, and computational inference to allow for high-resolution systems biology analysis of large tissues sections. We will include experimental hands-on lectures using in-house hyperplexed immunofluorescence imaging apparatus. Prerequisites: No biological background is expected. The assignments will cover the necessary biology. Experience in programming and some software engineering is preferred. Knowledge of probability, statistics, linear algebra and algorithms will be useful. Prior introduction to machine learning and imaging is a bonus. Strong interest in cancer imaging informatics is a plus. The class is open to senior year undergraduates and graduate students.
Academic Career: Graduate
Course Component: Lecture
Grade Component: Grad Letter Grade

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