Evidence map›Paper›PMID 28858614›Full record

ArticleNeuron2017

Closed-Loop Real-Time Imaging Enables Fully Automated Cell-Targeted Patch-Clamp Neural Recording In Vivo.

Ho-Jun Suk, Ingrid van Welie, Suhasa B Kodandaramaiah, Brian Allen, Craig R Forest, Edward S Boyden

Erratum issuedAbstract read
In one paragraph

Article in Neuron, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

26 citing papers in PubMed.

  1. Article
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  3. Review
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  11. Multiscale, multi-perspective imaging assisted robotic microinjection of 3D biological structures.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2021
    Article
  12. Article
  13. Article
  14. Review
  15. Harnessing non-destructive 3D pathology.Nature biomedical engineering · 2021
    Review
  16. Review
  17. Grand Challenge at the Frontiers of Synaptic Neuroscience.Frontiers in synaptic neuroscience · 2021
    Article
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Ho-Jun SukHealth Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Media Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; McGovern Institute, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Ingrid van WelieMedia Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; McGovern Institute, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Suhasa B KodandaramaiahMedia Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; McGovern Institute, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Brian AllenMedia Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; McGovern Institute, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Craig R ForestG.W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA.
Edward S BoydenMedia Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; McGovern Institute, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. Electronic address: esb@media.mit.edu.

Funding

Recording neural activities onto DNAR01MH103910 · NIMH · UNIVERSITY OF PENNSYLVANIA · PI BOYDEN, EDWARD S., CHURCH, GEORGE M · 2013 to 2017
$9.5M
High-throughput robotic analysis of integrated neuronal phenotypesR01EY023173 · NEI · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI BOYDEN, EDWARD S., FOREST, CRAIG · 2012 to 2016
$4.3M
Millisecond-Timescale Whole-Brain Neural Activity Mapping in Health and DiseaseDP1NS087724 · NINDS · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI BOYDEN, EDWARD S. · 2013 to 2017
$3.9M
Scalable Cell- and Circuit-Targeted ElectrophysiologyR01NS102727 · NINDS · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI BOYDEN, EDWARD S., FOREST, CRAIG · 2017 to 2020
$2.2M
NEI NIH HHS R01 EY023173NIMH NIH HHS R01 MH103910NINDS NIH HHS DP1 NS087724NINDS NIH HHS R01 NS102727
6 · The paper itself

Abstract

Targeted patch-clamp recording is a powerful method for characterizing visually identified cells in intact neural circuits, but it requires skill to perform. We previously developed an algorithm that automates "blind" patching in vivo, but full automation of visually guided, targeted in vivo patching has not been demonstrated, with currently available approaches requiring human intervention to compensate for cell movement as a patch pipette approaches a targeted neuron. Here we present a closed-loop real-time imaging strategy that automatically compensates for cell movement by tracking cell position and adjusting pipette motion while approaching a target. We demonstrate our system's ability to adaptively patch, under continuous two-photon imaging and real-time analysis, fluorophore-expressing neurons of multiple types in the living mouse cortex, without human intervention, with yields comparable to skilled human experimenters. Our "imagepatching" robot is easy to implement and will help enable scalable characterization of identified cell types in intact neural circuits.

Indexed as

Computer SystemsAnimalsAutomationFluorescent DyesFunctional NeuroimagingMiceNeuronsPatch-Clamp TechniquesFluorescent Dyesautomationcell typescortexfluorescent object detectionfluorescent proteinsimagingin vivo electrophysiologymousepatch clamptwo-photon microscopy

Identifiers

PMID28858614
PMCPMC5586501

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.