Dataset Information

Transfection via whole-cell recording in vivo: bridging single-cell physiology, genetics and connectomics.

ABSTRACT: Single-cell genetic manipulation is expected to substantially advance the field of systems neuroscience. However, existing gene delivery techniques do not allow researchers to electrophysiologically characterize cells and to thereby establish an experimental link between physiology and genetics for understanding neuronal function. In the mouse brain in vivo, we found that neurons remained intact after 'blind' whole-cell recording, that DNA vectors could be delivered through the patch-pipette during such recordings and that these vectors drove protein expression in recorded cells for at least 7 d. To illustrate the utility of this approach, we recorded visually evoked synaptic responses in primary visual cortical cells while delivering DNA plasmids that allowed retrograde, monosynaptic tracing of each neuron's presynaptic inputs. By providing a biophysical profile of a cell before its specific genetic perturbation, this combinatorial method captures the synaptic and anatomical receptive field of a neuron.

SUBMITTER: Rancz EA

PROVIDER: S-EPMC3501644 | biostudies-literature | 2011 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Transfection via whole-cell recording in vivo: bridging single-cell physiology, genetics and connectomics.

Rancz Ede A EA Franks Kevin M KM Schwarz Martin K MK Pichler Bruno B Schaefer Andreas T AT Margrie Troy W TW

Nature neuroscience 20110220 4

Single-cell genetic manipulation is expected to substantially advance the field of systems neuroscience. However, existing gene delivery techniques do not allow researchers to electrophysiologically characterize cells and to thereby establish an experimental link between physiology and genetics for understanding neuronal function. In the mouse brain in vivo, we found that neurons remained intact after 'blind' whole-cell recording, that DNA vectors could be delivered through the patch-pipette dur ...[more]

PMID: 21336272

Similar Datasets

Project description:In this paper we describe how to efficiently record the entire genetic history of a population in forwards-time, individual-based population genetics simulations with arbitrary breeding models, population structure and demography. This approach dramatically reduces the computational burden of tracking individual genomes by allowing us to simulate only those loci that may affect reproduction (those having non-neutral variants). The genetic history of the population is recorded as a succinct tree sequence as introduced in the software package msprime, on which neutral mutations can be quickly placed afterwards. Recording the results of each breeding event requires storage that grows linearly with time, but there is a great deal of redundancy in this information. We solve this storage problem by providing an algorithm to quickly 'simplify' a tree sequence by removing this irrelevant history for a given set of genomes. By periodically simplifying the history with respect to the extant population, we show that the total storage space required is modest and overall large efficiency gains can be made over classical forward-time simulations. We implement a general-purpose framework for recording and simplifying genealogical data, which can be used to make simulations of any population model more efficient. We modify two popular forwards-time simulation frameworks to use this new approach and observe efficiency gains in large, whole-genome simulations of one to two orders of magnitude. In addition to speed, our method for recording pedigrees has several advantages: (1) All marginal genealogies of the simulated individuals are recorded, rather than just genotypes. (2) A population of N individuals with M polymorphic sites can be stored in O(N log N + M) space, making it feasible to store a simulation's entire final generation as well as its history. (3) A simulation can easily be initialized with a more efficient coalescent simulation of deep history. The software for recording and processing tree sequences is named tskit.

Dataset Information

Transfection via whole-cell recording in vivo: bridging single-cell physiology, genetics and connectomics.

Publications

Transfection via whole-cell recording in vivo: bridging single-cell physiology, genetics and connectomics.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure