Evidence map›Paper›PMID 42393051›Full record

ArticleNature communications2026

Dissecting autonomous enzymatic variability in single cells.

Christian Gnann, Alina Sigaeva, Trang Le, Anthony J Cesnik, Sanem Sariyar, Diana Mahdessian, Rutger Schutten, Preethi Raghavan, Manuel D Leonetti, Cecilia Lindskog and 3 more

Abstract read
In one paragraph

Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Molecular pixelation of the CAR T cell surface proteome.bioRxiv : the preprint server for biology · 2026
    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

13 authors.

Christian GnannKTH Royal Institute of Technology, Science for Life Laboratory, Department of Protein Science, Stockholm, Sweden.
Alina SigaevaKTH Royal Institute of Technology, Science for Life Laboratory, Department of Protein Science, Stockholm, Sweden.ORCID 0000-0003-3361-3797
Trang LeKTH Royal Institute of Technology, Science for Life Laboratory, Department of Protein Science, Stockholm, Sweden.
Anthony J CesnikKTH Royal Institute of Technology, Science for Life Laboratory, Department of Protein Science, Stockholm, Sweden.ORCID 0000-0002-5326-7134
Sanem SariyarKTH Royal Institute of Technology, Science for Life Laboratory, Department of Protein Science, Stockholm, Sweden.
Diana MahdessianKTH Royal Institute of Technology, Science for Life Laboratory, Department of Protein Science, Stockholm, Sweden.ORCID 0000-0003-0750-1070
Rutger SchuttenUppsala University, Department of Immunology, Genetics and Pathology, Uppsala, Sweden.ORCID 0000-0001-8787-8868
Preethi RaghavanChan Zuckerberg Biohub, San Francisco, CA, USA.
Manuel D LeonettiChan Zuckerberg Biohub, San Francisco, CA, USA.ORCID 0000-0002-4881-405X
Cecilia LindskogUppsala University, Department of Immunology, Genetics and Pathology, Uppsala, Sweden.ORCID 0000-0001-5611-1015
Mathias UhlénKTH Royal Institute of Technology, Science for Life Laboratory, Department of Protein Science, Stockholm, Sweden.ORCID 0000-0002-4858-8056
Ulrika AxelssonKTH Royal Institute of Technology, Science for Life Laboratory, Department of Protein Science, Stockholm, Sweden.ORCID 0000-0002-0273-9306
Emma LundbergKTH Royal Institute of Technology, Science for Life Laboratory, Department of Protein Science, Stockholm, Sweden. emmalu@stanford.edu.ORCID 0000-0001-7034-0850

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Metabolic enzymes perform life-sustaining functions in various cellular compartments. Anecdotally, metabolic activity is observed to vary between genetically identical cells, which impacts drug resistance, differentiation, and immune cell activation. However, no large-scale resource systematically reporting metabolic cellular heterogeneity exists. Here, we leverage imaging-based single-cell spatial proteomics to reveal the extent of non-genetic variability of the human enzymatic proteome, as a proxy for metabolic states. Nearly two fifths of enzymes exhibit cell-to-cell variable expression, and half localize to multiple cellular compartments. Metabolic heterogeneity arises largely autonomously of cell cycling, and individual cells reestablish these myriad metabolic phenotypes over several cell divisions. We reveal through multiplexed imaging that metabolic states are continuous and that the correlation between metabolic pathways is metabolic state dependent. These results establish cell-to-cell enzymatic heterogeneity as an organizing principle of cell biology that may rewire our understanding of drug resistance, treatment design, and other aspects of medicine.

Indexed as

EnzymesProteomeSingle-Cell AnalysisHumansMetabolic Networks and PathwaysProteomicsEnzymesProteome

Identifiers

PMID42393051
PMCPMC13328634

What Socratic holds

Textmetadata
LicenceCC BY
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.