Dataset Information

Targeting cells with single vectors using multiple-feature Boolean logic.

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.

SUBMITTER: Fenno LE

PROVIDER: S-EPMC4085277 | biostudies-literature | 2014 Jul

REPOSITORIES: biostudies-literature

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Publications

Targeting cells with single vectors using multiple-feature Boolean logic.

Fenno Lief E LE Mattis Joanna J Ramakrishnan Charu C Hyun Minsuk M Lee Soo Yeun SY He Miao M Tucciarone Jason J Selimbeyoglu Aslihan A Berndt Andre A Grosenick Logan L Zalocusky Kelly A KA Bernstein Hannah H Swanson Haley H Perry Chelsey C Diester Ilka I Boyce Frederick M FM Bass Caroline E CE Neve Rachael R Huang Z Josh ZJ Deisseroth Karl K

Nature methods 20140608 7

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 recombinas ...[more]

PMID: 24908100

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Project description:Biological systems contain a large number of molecules that have diverse interactions. A fruitful path to understanding these systems is to represent them with interaction networks, and then describe flow processes in the network with a dynamic model. Boolean modeling, the simplest discrete dynamic modeling framework for biological networks, has proven its value in recapitulating experimental results and making predictions. A first step and major roadblock to the widespread use of Boolean networks in biology is the laborious network inference and construction process. Here we present a streamlined network inference method that combines the discovery of a parsimonious network structure and the identification of Boolean functions that determine the dynamics of the system. This inference method is based on a causal logic analysis method that associates a logic type (sufficient or necessary) to node-pair relationships (whether promoting or inhibitory). We use the causal logic framework to assimilate indirect information obtained from perturbation experiments and infer relationships that have not yet been documented experimentally. We apply this inference method to a well-studied process of hormone signaling in plants, the signaling underlying abscisic acid (ABA)-induced stomatal closure. Applying the causal logic inference method significantly reduces the manual work typically required for network and Boolean model construction. The inferred model agrees with the manually curated model. We also test this method by re-inferring a network representing epithelial to mesenchymal transition based on a subset of the information that was initially used to construct the model. We find that the inference method performs well for various likely scenarios of inference input information. We conclude that our method is an effective approach toward inference of biological networks and can become an efficient step in the iterative process between experiments and computations.

Dataset Information

Targeting cells with single vectors using multiple-feature Boolean logic.

Publications

Targeting cells with single vectors using multiple-feature Boolean logic.

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