Evidence map›Paper›PMID 34678061›Full record

ArticleScience advances2021

Computer vision reveals hidden variables underlying NF-κB activation in single cells.

Parthiv Patel, Nir Drayman, Ping Liu, Mustafa Bilgic, Savaş Tay

Abstract read
In one paragraph

Article in Science advances, 2021. 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. Review
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  7. Paired single-cell host profiling with multiplex-tagged bacterial mutants reveals intracellular virulence-immune networks.Proceedings of the National Academy of Sciences of the United States of America · 2023
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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

5 authors.

Parthiv PatelPritzker School of Molecular Engineering, The University of Chicago, Chicago, IL, USA.ORCID 0000-0002-6868-0810
Nir DraymanPritzker School of Molecular Engineering, The University of Chicago, Chicago, IL, USA.ORCID 0000-0003-4460-9558
Ping LiuDepartment of Computer Science, Illinois Institute of Technology, Chicago, IL, USA.
Mustafa BilgicDepartment of Computer Science, Illinois Institute of Technology, Chicago, IL, USA.ORCID 0000-0001-5123-9992
Savaş TayPritzker School of Molecular Engineering, The University of Chicago, Chicago, IL, USA.ORCID 0000-0002-1912-6020

Funding

Understanding Mechanisms of Robust Information Processing by NF-kappaBR01GM128042 · NIGMS · UNIVERSITY OF CHICAGO · PI TAY, SAVAS · 2018 to 2022
$1.8M
Ultra-Sensitive Multiplexed Quantification of Proteins Secreted from Single-Cells - Resubmission 01R01GM127527 · NIGMS · UNIVERSITY OF CHICAGO · PI TAY, SAVAS · 2019 to 2022
$1.6M
NIGMS NIH HHS R01 GM127527NIGMS NIH HHS R01 GM128042
6 · The paper itself

Abstract

Individual cells are heterogeneous when responding to environmental cues. Under an external signal, certain cells activate gene regulatory pathways, while others completely ignore that signal. Mechanisms underlying cellular heterogeneity are often inaccessible because experiments needed to study molecular states destroy the very states that we need to examine. Here, we developed an image-based support vector machine learning model to uncover variables controlling activation of the immune pathway nuclear factor κB (NF-κB). Computer vision analysis predicts the identity of cells that will respond to cytokine stimulation and shows that activation is predetermined by minute amounts of “leaky” NF-κB (p65:p50) localization to the nucleus. Mechanistic modeling revealed that the ratio of NF-κB to inhibitor of NF-κB predetermines leakiness and activation probability of cells. While cells transition between molecular states, they maintain their overall probabilities for NF-κB activation. Our results demonstrate how computer vision can find mechanisms behind heterogeneous single-cell activation under proinflammatory stimuli.

Identifiers

PMID34678061
PMCPMC8535821

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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.