Evidence map›Paper›PMID 27050527›Full record

ArticleCell reports2016

High-Content Quantification of Single-Cell Immune Dynamics.

Michael Junkin, Alicia J Kaestli, Zhang Cheng, Christian Jordi, Cem Albayrak, Alexander Hoffmann, Savaş Tay

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cell reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers.

0numbers the graph read from it
0cells of the map it votes in
56citing 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

56 citing papers in PubMed.

  1. Article
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  13. The Intriguing Landscape of Single-Cell Protein Analysis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022
    Review
  14. Review
  15. Review
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  18. Image-Based Live Cell Sorting.Trends in biotechnology · 2021
    Review
  19. Review
  20. Article
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

7 authors.

Michael JunkinDepartment of Biosystems Science and Engineering, ETH Zürich, 4058 Basel, Switzerland.
Alicia J KaestliDepartment of Biosystems Science and Engineering, ETH Zürich, 4058 Basel, Switzerland.
Zhang ChengInstitute for Quantitative and Computational Biosciences and Department of Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles, Los Angeles, CA 90025, USA.
Christian JordiDepartment of Biosystems Science and Engineering, ETH Zürich, 4058 Basel, Switzerland.
Cem AlbayrakDepartment of Biosystems Science and Engineering, ETH Zürich, 4058 Basel, Switzerland.
Alexander HoffmannInstitute for Quantitative and Computational Biosciences and Department of Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles, Los Angeles, CA 90025, USA.
Savaş TayDepartment of Biosystems Science and Engineering, ETH Zürich, 4058 Basel, Switzerland; Institute for Molecular Engineering, University of Chicago, Chicago, IL 60637, USA; Institute for Genomics and Systems Biology, University of Chicago, Chicago, IL 60637, USA. Electronic address: tays@uchicago.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cells receive time-varying signals from the environment and generate functional responses by secreting their own signaling molecules. Characterizing dynamic input-output relationships in single cells is crucial for understanding and modeling cellular systems. We developed an automated microfluidic system that delivers precisely defined dynamical inputs to individual living cells and simultaneously measures key immune parameters dynamically. Our system combines nanoliter immunoassays, microfluidic input generation, and time-lapse microscopy, enabling study of previously untestable aspects of immunity by measuring time-dependent cytokine secretion and transcription factor activity from single cells stimulated with dynamic inflammatory inputs. Employing this system to analyze macrophage signal processing under pathogen inputs, we found that the dynamics of TNF secretion are highly heterogeneous and surprisingly uncorrelated with the dynamics of NF-κB, the transcription factor controlling TNF production. Computational modeling of the LPS/TLR4 pathway shows that post-transcriptional regulation by TRIF is a key determinant of noisy and uncorrelated TNF secretion dynamics in single macrophages.

Indexed as

3T3 CellsAnimalsCellsCell SeparationClone CellsCytokinesGene Expression RegulationLab-On-A-Chip DevicesLipopolysaccharidesMacrophagesMiceModels, BiologicalNF-kappa BSignal TransductionSingle-Cell AnalysisToll-Like Receptor 4CytokinesLipopolysaccharidesNF-kappa BToll-Like Receptor 4Tumor Necrosis Factor-alphacytokinedynamicsinputmicrofluidicNF-κBsingle-cell analysis

Identifiers

PMID27050527
PMCPMC4835544

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.