Biology subjects

Hanson, S. J.

Publications and source records attributed to Hanson, S. J..

2 recordsLinked to original sources

bioRxiv2020

Spatial analysis of ligand-receptor interactions in skin cancer at genome-wide and single-cell resolution

The ability to study cancer-immune cell communication across the whole tumor section without tissue dissociation is needed for cancer immunotherapies, to understand molecular mechanisms and to discover potential druggable targets. In this work, we developed a powerful experimental and analytical toolbox to enable genome-wide scale discovery and targeted validation of cellular communication. We assessed the utilities of five sequencing and imaging technologies to study cancer tissue, including single-cell RNA sequencing and Spatial Transcriptomic (measuring over >20,000 genes), RNA In Situ Hybridization (multiplex 4-12 genes), digital droplet PCR, and Opal multiplex protein staining (4-9 proteins). To spatially integrate multimodal data, we developed a computational method called STRISH that can automatically scan across the whole tissue section for local expression of gene and/or protein markers to recapitulate an interaction landscape across the whole tissue. We evaluated the unique ability of this toolbox to discover and validate cell-cell interaction in situ through in-depth analysis of two types of cancer, basal cell carcinoma and squamous cell carcinoma, which account for over 70% of cancer cases. We expect that the approach described here will be widely applied to discover and validate ligand receptor interaction in different types of solid cancer tumors.

cancer biology↗

bioRxiv2020

DECODING SECOND ORDER ISOMORPHISMS IN THE BRAIN: The case of colors and letters.

We introduce a new method for decoding neural data from fMRI. It is based on two assumptions, first that neural representation is distributed over networks of neurons embeded in voxel noise and second that the stimuli can be decoded as learned relations from sets of categorical stimuli. We illustrate these principles with two types of stimuli, color (wavelength) and letters (visual shape), both of which have early visual system response, but at the same time must be learned within a given function or category (color contrast, alphabet). Key to the decoding method is reducing the stimulus cross-correlation by a matched noise voxel sample by normalizing the stimulus voxel matrix thus unmasking a highly discriminative neural profile per stimulus. Projection of this new voxel space (ROI) to a smaller set of dimensions (with e.g., non-metric Multidimensional scaling), the relational information takes a unique geometric form revealing functional relationships between sets of stimuli, defined by R. Shepard, as second-order isomorphisms (SOI). In the case of colors the SOI appears as a nearly equally spaced set of wavelengths arranged in a color wheel, with a gap between the "purples" and "reds" (consistent with the gap in the original Ekmans color set). In the case of letters, a cluster space resulted from the decorrelated voxel neural profiles, which matched the phrase structure of the mnemonic used for more than 100 years to teach children the alphabet (across multiple languages), The Alphabet Song.

neuroscience↗