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Southwell, D. G.

Publications and source records attributed to Southwell, D. G..

2 recordsLinked to original sources

Programmable RNA Sensing for Cell Monitoring and Manipulation

RNAs are the central and universal mediator of genetic information underlying the diversity of cell types and cell states, which together shape tissue organization and organismal function across species and life spans. Despite advances in RNA sequencing and massive accumulation of transcriptome datasets across life sciences, the dearth of technologies that leverage RNAs to observe and manipulate cell types remains a prohibitive bottleneck in biology and medicine. Here, we describe CellREADR (Cell access through RNA sensing by Endogenous ADAR), a programmable RNA sensing technology that leverages RNA editing mediated by ADAR (adenosine deaminase acting on RNA) for coupling the detection of cell-defining RNAs with translation of effector proteins. Viral delivery of CellREADR conferred specific cell type access in mouse and rat brains and in ex vivo human brain tissues. Furthermore, CellREADR enabled recording and control of neuron types in behaving mice. CellREADR thus highlights the potential for RNA-based monitoring and editing of animal cells in ways that are specific, versatile, easy, and generalizable across organ systems and species, with broad applications in biology, biotechnology, and programmable RNA medicine.

molecular biology↗

Accurate speech decoding requires high-resolution neural interfaces

Patients suffering from debilitating neurodegenerative diseases often lose the ability to communicate, detrimentally affecting their quality of life. One promising solution to restore communication is to decode signals directly from the brain to enable neural speech prostheses. However, decoding has been limited by coarse neural recordings which inadequately capture the rich spatio-temporal structure of human brain signals. To resolve this limitation, we performed novel, high-resolution, micro-electrocorticographic (ECoG) neural recordings during intra-operative speech production. We obtained neural signals with 57x higher spatial resolution and 48% higher signal-to-noise ratio compared to standard invasive recordings. This increased signal quality improved phoneme decoding by 35% compared to standard intracranial signals. Accurate decoding was dependent on the high-spatial resolution of the neural interface. Non-linear decoding models designed to utilize enhanced spatio-temporal neural information produced better results than linear techniques. We show for the first time that ECoG can enable high-quality speech decoding, demonstrating its ability to improve neural interfaces for neural speech prostheses.

neuroscience↗