Biology subjects

Bernardinelli, G.

Publications and source records attributed to Bernardinelli, G..

2 recordsLinked to original sources

bioRxiv2019

A DNA nanoassembly-based approach to map membrane protein nanoenvironments

Super-resolution imaging has revealed that most proteins at the plasma membrane are not uniformly distributed but localize to dynamic domains of nanoscale dimensions. To investigate their functional relevance, there is a need for methods that enable comprehensive mapping of the compositions and spatial organizations of membrane protein nanodomains in cell populations. However, current superresolution methods are limited to analysing small, preselected subsets of proteins, at very low sampling fractions. Here we describe the development of a non-microscopy based super-resolution method for unbiased ensemble analysis of membrane protein nanodomains. The method, termed NANOscale DEciphEring of membrane Protein nanodomains (NanoDeep), is based on the use of DNA nanoassemblies to translate membrane protein organization information into a DNA sequencing readout. Using NanoDeep, we characterized the nanoenvironments of Her2, a membrane receptor of critical relevance in cancer. We found that the occupancies of Her2, Her3 and EGFR in the nanoenvironments surrounding Her2 were similar in two cell lines with vastly different expression levels of Her2. Further, we found that adding Heregulin-{beta}1 to cancer cells led to increased occupancy of Her2 and Her3, and to a lesser extent EGFR, in Her2 nanoenvironments. NanoDeep has the potential to provide new insights into the roles of the composition and spatial organization of protein nanoenvironments in the regulation of membrane protein function.

bioengineering↗

bioRxiv2018

A Computational Framework for DNA Sequencing-Based Microscopy

Barcoded DNA polony amplification techniques provide a means to impart a unique sequence identity onto specific locations of a surface wafer or chip. We describe a method whereby micro-scale spatial information such as the relative positions of biomolecules on a surface can be transferred to a sequence-based format and reconstructed into images without the use of conventional optics. The principle is based on the pair-wise association of uniquely tagged and spatially adjacenct polonies. The network of polonies connected by shared borders forms a graph whose topology can be reconstructed from a set of edges derived from pairs of barcodes associated with each other during a polony crosslinking phase, the sequences of which could be determined by isolation of the DNA and next-gen sequencing. We developed a mathematical and computational framework for this principle called Polony Adjacency Reconstruction for Spatial Inference and Topology and show that Euclidean spatial data may be partially stored and transmitted in the form of untethered graph topology. This effect may be exploited to form images by transferring molecular information from a surface of interest, which we demonstrated in silico by reconstructing images formed from stochastic transfer of hypothetical red, green, and blue molecular markers. The theory developed here could serve as a guide for a highly automated, multiplexable, and potentially super resolution imaging method based on molecular information encoding and transmission.

biophysics↗