Integrated functional characterization and optical tagging for cell-type identification

Donghoon Lee, Maiko Kume, Timothy E. Holy

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Imaging has become one of the most important tools for categorizing neurons based on their function. However, for a cell type identified only by its pattern of activity, the process of identifying molecular markers remains laborious. We developed physiological optical tagging sequencing (PhOTseq), a technique for tagging and ex- pression profiling of cells on the basis of their functional properties. We developed a reporter combining a green calcium indicator (GCaMP) with a photoactivatable red reporter (PAmCherry). When visualizing neuronal activity in such animals, real-time analysis allowed digital selection of cells exhibiting specific activity patterns, and photoactivation was directed specifically to those cells to tag them for later harvesting and analysis. We found that PhOTseq was capable of selecting rare cell types and enriching them by nearly 100-fold. We applied PhOTseq to the challenge of mapping receptor-ligand pairings among pheromone-sensing neurons in mice, and densely mapped the cell types responsible for encoding a specific portion of the sensory world.

Original languageEnglish
Title of host publicationIntegrated Sensors for Biological and Neural Sensing
EditorsHooman Mohseni
PublisherSPIE
ISBN (Electronic)9781510641617
DOIs
StatePublished - 2021
EventIntegrated Sensors for Biological and Neural Sensing 2021 - Virtual, Online, United States
Duration: Mar 6 2021Mar 11 2021

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume11663
ISSN (Print)1605-7422

Conference

ConferenceIntegrated Sensors for Biological and Neural Sensing 2021
Country/TerritoryUnited States
CityVirtual, Online
Period03/6/2103/11/21

Keywords

  • Calcium imaging
  • Cell types
  • Gene expression
  • Optical tagging
  • Real-time analysis
  • Single-cell sequencing

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