Evidence map›Paper›PMID 25609410›Full record

ArticleLab on a chip2015

An automated programmable platform enabling multiplex dynamic stimuli delivery and cellular response monitoring for high-throughput suspension single-cell signaling studies.

Luye He, Ariel Kniss, Adriana San-Miguel, Tel Rouse, Melissa L Kemp, Hang Lu

Abstract read
In one paragraph

Article in Lab on a chip, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Modeling immune cell behavior across scales in cancer.Wiley interdisciplinary reviews. Systems biology and medicine · 2020
    Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Single-cell resolution of intracellular T cell CaIntegrative biology : quantitative biosciences from nano to macro · 2017
    Article
  10. New insights into mammalian signaling pathways using microfluidic pulsatile inputs and mathematical modeling.Integrative biology : quantitative biosciences from nano to macro · 2017
    Review
  11. Review
  12. Review
  13. Microfluidics in systems biology-hype or truly useful?Current opinion in biotechnology · 2016
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Luye HeSchool of Chemical & Biomolecular Engineering, Georgia Institute of Technology, 311 Ferst Dr. NW, Atlanta, GA, USA 30332-0100. hang.lu@gatech.edu.
Ariel Kniss
Adriana San-Miguel
Tel Rouse
Melissa L Kemp
Hang Lu

Funding

Spatiotemporal control of reactive oxygen species in T cellsR01AI088023 · NIAID · GEORGIA INSTITUTE OF TECHNOLOGY · PI KEMP, MELISSA LAMBETH · 2011 to 2014
$1.4M
A Computational Biology and Predictive Health Genomics Training Program at GTT32GM105490 · NIGMS · GEORGIA INSTITUTE OF TECHNOLOGY · PI GIBSON, GREGORY C · 2014 to 2018
$901k
NIAID NIH HHS R01 AI088023NIAID NIH HHS R01AI088023NIGMS NIH HHS T32 GM105490NIGMS NIH HHS T32GM105490
6 · The paper itself

Abstract

Cell signaling events are orchestrated by dynamic external biochemical cues. By rapidly perturbing cells with dynamic inputs and examining the output from these systems, one could study the structure and dynamic properties of a cellular signaling network. Conventional experimental techniques limit the implementation of these systematic approaches due to the lack of sophistication in manipulating individual cells and the fluid microenvironment around them; existing microfluidic technologies thus far are mainly targeting adherent cells. In this paper we present an automated platform to interrogate suspension cells with dynamic stimuli while simultaneously monitoring cellular responses in a high-throughput manner at single-cell resolution. We demonstrate the use of this platform in an experiment to measure Jurkat T cells in response to distinct dynamic patterns of stimuli; we find cells exhibit highly heterogeneous responses under each stimulation condition. More interestingly, these cells act as low-pass filters, only entrained to the low frequency stimulus signals. We also demonstrate that this platform can be easily programmed to actively generate arbitrary dynamic signals. We envision our platform to be useful in other contexts to study cellular signaling dynamics, which may be difficult using conventional experimental methods.

Indexed as

Signal TransductionAutomationCalcium SignalingDiffusionHigh-Throughput Screening AssaysHumansHydrogen PeroxideJurkat CellsMicrofluidic Analytical TechniquesSingle-Cell AnalysisSuspensionsTime FactorsT-LymphocytesHydrogen PeroxideSuspensions

Identifiers

PMID25609410
PMCPMC4362087

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.