Evidence map›Paper›PMID 32778050›Full record

ArticleBMC bioinformatics2020

ImmunoGlobe: enabling systems immunology with a manually curated intercellular immune interaction network.

Michelle B Atallah, Varun Tandon, Kamir J Hiam, Hunter Boyce, Michelle Hori, Waleed Atallah, Matthew H Spitzer, Edgar Engleman, Parag Mallick

Abstract read
In one paragraph

Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
–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

7 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. A single-cell cytokine dictionary of human peripheral blood.bioRxiv : the preprint server for biology · 2025
    Article
  5. Article
  6. Article
  7. 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

9 authors.

Michelle B AtallahCanary Center at Stanford, Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA.
Varun TandonStanford University, Stanford, CA, USA.
Kamir J HiamDepartments of Otolaryngology and Microbiology & Immunology, Helen Diller Family Comprehensive Cancer Center, Parker Institute for Cancer Immunotherapy, Chan Zuckerberg Biohub, University of California, San Francisco, CA, USA.
Hunter BoyceCanary Center at Stanford, Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA.
Michelle HoriCanary Center at Stanford, Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA.
Waleed AtallahCanary Center at Stanford, Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA.
Matthew H SpitzerDepartments of Otolaryngology and Microbiology & Immunology, Helen Diller Family Comprehensive Cancer Center, Parker Institute for Cancer Immunotherapy, Chan Zuckerberg Biohub, University of California, San Francisco, CA, USA.
Edgar EnglemanDepartment of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
Parag MallickCanary Center at Stanford, Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA. paragm@stanford.edu.ORCID http://orcid.org/0000-0002-0505-2032

Funding

PREDOCTORAL TRAINING IN BIOMEDICAL SCIENCEST32GM008568 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI ANSEL, KARL MARK · 1995 to 2019
$6.3M
Quantitatively modeling immune responses to cancerDP5OD023056 · OD · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI SPITZER, MATTHEW · 2016 to 2020
$1.9M
UCSF DVS CyTOF Mass CytometerS10OD018040 · OD · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI LANIER, LEWIS LEE · 2014 to 2014
$600k
NIGMS NIH HHS T32 GM008568NIH HHS 1R01GM11709701NIH HHS DP5OD023056NIH HHS S10 OD018040NIH HHS S10OD018040
6 · The paper itself

Abstract

backgroundWhile technological advances have made it possible to profile the immune system at high resolution, translating high-throughput data into knowledge of immune mechanisms has been challenged by the complexity of the interactions underlying immune processes. Tools to explore the immune network are critical for better understanding the multi-layered processes that underlie immune function and dysfunction, but require a standardized network map of immune interactions. To facilitate this we have developed ImmunoGlobe, a manually curated intercellular immune interaction network extracted from Janeway's Immunobiology textbook.

resultsImmunoGlobe is the first graphical representation of the immune interactome, and is comprised of 253 immune system components and 1112 unique immune interactions with detailed functional and characteristic annotations. Analysis of this network shows that it recapitulates known features of the human immune system and can be used uncover novel multi-step immune pathways, examine species-specific differences in immune processes, and predict the response of immune cells to stimuli. ImmunoGlobe is publicly available through a user-friendly interface at www.immunoglobe.org and can be downloaded as a computable graph and network table.

conclusionWhile the fields of proteomics and genomics have long benefited from network analysis tools, no such tool yet exists for immunology. ImmunoGlobe provides a ground truth immune interaction network upon which such tools can be built. These tools will allow us to predict the outcome of complex immune interactions, providing mechanistic insight that allows us to precisely modulate immune responses in health and disease.

Indexed as

Cell CommunicationData CurationProtein Interaction MapsSoftwareSystems BiologyAnimalsExtracellular SpaceHumansImmune SystemMiceModels, ImmunologicalBioinformaticsCytokinesImmune network analysisImmunobiologyProtein networksSystems immunology

Identifiers

PMID32778050
PMCPMC7430879

What OpenQuestion holds

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Registered trials

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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.