Evidence map›Paper›PMID 32437685›Full record

ArticleCell systems2020

Lattice Light-Sheet Microscopy Multi-dimensional Analyses (LaMDA) of T-Cell Receptor Dynamics Predict T-Cell Signaling States.

Jillian Rosenberg, Guoshuai Cao, Fernanda Borja-Prieto, Jun Huang

Abstract read
In one paragraph

Article in Cell systems, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

  1. Article
  2. Review
  3. Cell dynamics revealed by microscopy advances.Current opinion in cell biology · 2024
    Review
  4. Article
  5. Review
  6. Article
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  8. Article
  9. Article
  10. Review
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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

4 authors.

Jillian RosenbergCommittee on Cancer Biology, University of Chicago, Chicago, IL 60637, USA.
Guoshuai CaoPritzker School of Molecular Engineering, University of Chicago, Chicago, IL 60637, USA.
Fernanda Borja-PrietoPritzker School of Molecular Engineering, University of Chicago, Chicago, IL 60637, USA.
Jun HuangCommittee on Cancer Biology, University of Chicago, Chicago, IL 60637, USA; Pritzker School of Molecular Engineering, University of Chicago, Chicago, IL 60637, USA. Electronic address: huangjun@uchicago.edu.

Funding

Molecular mechanism of natural killer cell recognitionDP2AI144245 · NIAID · UNIVERSITY OF CHICAGO · PI HUANG, JUN · 2018 to 2022
$2.9M
NIAID NIH HHS DP2 AI144245
6 · The paper itself

Abstract

Lattice light-sheet microscopy provides large amounts of high-dimensional, high-spatiotemporal resolution imaging data of cell surface receptors across the 3D surface of live cells, but user-friendly analysis pipelines are lacking. Here, we introduce lattice light-sheet microscopy multi-dimensional analyses (LaMDA), an end-to-end pipeline comprised of publicly available software packages that combines machine learning, dimensionality reduction, and diffusion maps to analyze surface receptor dynamics and classify cellular signaling states without the need for complex biochemical measurements or other prior information. We use LaMDA to analyze images of T-cell receptor (TCR) microclusters on the surface of live primary T cells under resting and stimulated conditions. We observe global spatial and temporal changes of TCRs across the 3D cell surface, accurately differentiate stimulated cells from unstimulated cells, precisely predict attenuated T-cell signaling after CD4 and CD28 receptor blockades, and reliably discriminate between structurally similar TCR ligands. All instructions needed to implement LaMDA are included in this paper.

Indexed as

Cell MembraneImage Processing, Computer-AssistedImaging, Three-DimensionalMicroscopyMicroscopy, FluorescenceReceptors, Antigen, T-CellSignal TransductionT-LymphocytesReceptors, Antigen, T-Cellcomputational biologylattice light-sheet microscopymachine learningT cell receptor

Identifiers

PMID32437685
PMCPMC7250142

What Socratic 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 Socratic graph.