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Targeting cells with single vectors using multiple-feature Boolean logic

  • Lief E. Fenno
  • , Joanna Mattis
  • , Charu Ramakrishnan
  • , Minsuk Hyun
  • , Soo Yeun Lee
  • , Miao He
  • , Jason Tucciarone
  • , Aslihan Selimbeyoglu
  • , Andre Berndt
  • , Logan Grosenick
  • , Kelly A. Zalocusky
  • , Hannah Bernstein
  • , Haley Swanson
  • , Chelsey Perry
  • , Ilka Diester
  • , Frederick M. Boyce
  • , Caroline E. Bass
  • , Rachael Neve
  • , Z. Josh Huang
  • , Karl Deisseroth
  • Stanford University
  • Cold Spring Harbor Laboratory
  • Stony Brook University
  • New York University
  • Ernst Strüngmann Institute for Neuroscience
  • Massachusetts General Hospital
  • Massachusetts Institute of Technology

Research output: Contribution to journalArticlepeer-review

456 Scopus citations

Abstract

Precisely defining the roles of specific cell types is an intriguing frontier in the study of intact biological systems and has stimulated the rapid development of genetically encoded tools for observation and control. However, targeting these tools with adequate specificity remains challenging: most cell types are best defined by the intersection of two or more features such as active promoter elements, location and connectivity. Here we have combined engineered introns with specific recombinases to achieve expression of genetically encoded tools that is conditional upon multiple cell-type features, using Boolean logical operations all governed by a single versatile vector. We used this approach to target intersectionally specified populations of inhibitory interneurons in mammalian hippocampus and neurons of the ventral tegmental area defined by both genetic and wiring properties. This flexible and modular approach may expand the application of genetically encoded interventional and observational tools for intact-systems biology.

Original languageEnglish
Pages (from-to)763-772
Number of pages10
JournalNature Methods
Volume11
Issue number7
DOIs
StatePublished - Jul 2014

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