ReviewNature reviews. Molecular cell biology2026
Advancing single-cell omics and cell-based therapeutics with quantum computing.
Review in Nature reviews. Molecular cell biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 7 papers.
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.
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.
Who cites it
7 citing papers in PubMed.
- The exposome and inflammatory bowel disease: a framework for primary and primordial prevention.Nature reviews. Gastroenterology & hepatology · 2026Review
- Towards 3D-oid models that integrate molecular, morphological and spatial cellular features.Nature reviews. Molecular cell biology · 2026Article
- Quantum ensembling methods for healthcare and life science.Briefings in bioinformatics · 2026Article
- Single-Cell and Spatial Omics: Methods and Applications.MedComm · 2026Review
- Remics: a redescription-based framework for multi-omics analysis.Frontiers in cell and developmental biology · 2026Article
- Machine learning in cancer imaging for enhanced precision in diagnosis and therapy.Discover computing · 2026Review
- Glioma-intrinsic SLC1A3 hijacks the vascular niche to establish an immunosuppressive microenvironment.Frontiers in immunology · 2026Article
Corrections and comments
- Erratum issued
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The generation of highly accurate models of behaviours of individual cells and cell populations through integration of high-resolution assays with advanced computational tools would transform precision medicine. Recent breakthroughs in single-cell and spatial transcriptomics and multi-omics technologies, coupled with artificial intelligence, are driving rapid progress in model development. Complementing the advances in artificial intelligence, quantum computing is maturing as a novel compute paradigm that may offer potential solutions to overcome the computational bottlenecks inherent to capturing cellular dynamics. In this Roadmap article, we discuss the advancements and challenges in spatiotemporal single-cell analysis, explore the possibility of quantum computing to address the challenges and present a case study on how quantum computing may be integrated into cell-based therapeutics. The specific confluence of quantum and classical computing with high-resolution assays may offer a crucial path towards the generation of transformative models of cellular behaviours and perturbation responses.
Indexed as
Identifiers
41478876What OpenQuestion holds
Registered trials
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.