Evidence map›Paper›PMID 33571119›Full record

ArticleScience advances2021

Spatially resolved 3D metabolomic profiling in tissues.

Shambavi Ganesh, Thomas Hu, Eric Woods, Mayar Allam, Shuangyi Cai, Walter Henderson, Ahmet F Coskun

Open access · goldAbstract 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 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed
2.9field-weighted citation impact, top 8% of its field
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

27 citing papers in PubMed, 46 citations in OpenAlex.

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  6. Dissecting inflammation in the immunemetabolomic era.Cellular and molecular life sciences : CMLS · 2025
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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

7 authors at 1 institution in 1 country.

Shambavi GaneshWallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA.ORCID 0000-0003-0577-2600
Thomas HuWallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA.ORCID 0000-0001-7801-7070
Eric WoodsSchool of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA.ORCID 0000-0002-1169-893X
Mayar AllamWallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA.ORCID 0000-0002-1550-2316
Shuangyi CaiWallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA.ORCID 0000-0002-2653-6335
Walter HendersonSchool of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA.ORCID 0000-0002-7999-3388
Ahmet F CoskunWallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA. ahmet.coskun@bme.gatech.edu.ORCID 0000-0002-5797-1524
Georgia Institute of Technology · US

Funding

Spatial Epigenomic Profiling of Immune Cell Signatures at Subcellular Resolution in Health and DiseaseK25AI140783 · NIAID · STANFORD UNIVERSITY · PI COSKUN, AHMET F. · 2018 to 2022
$539k
NIAID NIH HHS K25 AI140783
6 · The paper itself

Abstract

Spatially resolved RNA and protein molecular analyses have revealed unexpected heterogeneity of cells. Metabolic analysis of individual cells complements these single-cell studies. Here, we present a three-dimensional spatially resolved metabolomic profiling framework (3D-SMF) to map out the spatial organization of metabolic fragments and protein signatures in immune cells of human tonsils. In this method, 3D metabolic profiles were acquired by time-of-flight secondary ion mass spectrometry to profile up to 189 compounds. Ion beams were used to measure sub-5-nanometer layers of tissue across 150 sections of a tonsil. To incorporate cell specificity, tonsil tissues were labeled by an isotope-tagged antibody library. To explore relations of metabolic and cellular features, we carried out data reduction, 3D spatial correlations and classifications, unsupervised K-means clustering, and network analyses. Immune cells exhibited spatially distinct lipidomic fragment distributions in lymphatic tissue. The 3D-SMF pipeline affects studying the immune cells in health and disease.

Indexed as

MetabolomeMetabolomicsCluster AnalysisHumansSpectrometry, Mass, Secondary Ion

Identifiers

PMID33571119
PMCPMC7840140
OpenAlexW3123258065

What OpenQuestion holds

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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.