ReviewFrontiers in molecular biosciences2021
Computational Methods for Single-Cell Imaging and Omics Data Integration.
Review in Frontiers in molecular biosciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
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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.
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Who cites it
21 citing papers in PubMed.
- Cellular Morphometric Analysis (CellMorph)-a comprehensive imaging-based tool for quantifying cellular phenotype heterogeneity and dynamics across biological processes.The FEBS journal · 2026Article
- Progress and new challenges in image-based profiling.Molecular systems biology · 2026Review
- Deep Learning-Enabled Multi-Omics Integration: A New Frontier in Precise Drug Target Discovery.Biology · 2026Review
- Advances in predicting omics profiles from imaging data.Briefings in bioinformatics · 2026Review
- Intervention on Modifiable Lifestyle and Physiological Factors via Variational Autoencoder Reveals Changes in Functional Connectivity-Mediated Risk for Alzheimer's Disease.medRxiv : the preprint server for health sciences · 2025Article
- Article
- AI-Driven Quality Monitoring and Control in Stem Cell Cultures: A Comprehensive Review.Biotechnology journal · 2025Review
- Integration of hyperspectral imaging and transcriptomics from individual cells with SpectralSeq.Genome research · 2025Article
- Live-cell painting: Image-based profiling in live cells using acridine orange.Molecular biology of the cell · 2025Article
- Emerging ultrafast technologies in biotechnology.3 Biotech · 2025Review
- Swift induction of human spinal lower motor neurons and robust ALS cell screening via single-cell imaging.Stem cell reports · 2025Article
- Challenges and opportunities in processing NanoString nCounter data.Computational and structural biotechnology journal · 2024Review
- Digital twins for health: a scoping review.NPJ digital medicine · 2024Article
- Integrated cellular 4D-TIMS lipidomics and transcriptomics for characterization of anti-inflammatory and anti-atherosclerotic phenotype of MyD88-KO macrophages.Frontiers in cell and developmental biology · 2024Article
- Big data in contemporary electron microscopy: challenges and opportunities in data transfer, compute and management.Histochemistry and cell biology · 2023Review
- An introduction to representation learning for single-cell data analysis.Cell reports methods · 2023Review
- Transformative Materials to Create 3D Functional Human Tissue Models In Vitro in a Reproducible Manner.Advanced healthcare materials · 2023Article
- Using Transcriptomics and Cell Morphology Data in Drug Discovery: The Long Road to Practice.ACS medicinal chemistry letters · 2023Review
- Prediction of distinct populations of innate lymphoid cells by transcriptional profiles.Frontiers in genetics · 2023Article
- Computational solutions for spatial transcriptomics.Computational and structural biotechnology journal · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Integrating single cell omics and single cell imaging allows for a more effective characterisation of the underlying mechanisms that drive a phenotype at the tissue level, creating a comprehensive profile at the cellular level. Although the use of imaging data is well established in biomedical research, its primary application has been to observe phenotypes at the tissue or organ level, often using medical imaging techniques such as MRI, CT, and PET. These imaging technologies complement omics-based data in biomedical research because they are helpful for identifying associations between genotype and phenotype, along with functional changes occurring at the tissue level. Single cell imaging can act as an intermediary between these levels. Meanwhile new technologies continue to arrive that can be used to interrogate the genome of single cells and its related omics datasets. As these two areas, single cell imaging and single cell omics, each advance independently with the development of novel techniques, the opportunity to integrate these data types becomes more and more attractive. This review outlines some of the technologies and methods currently available for generating, processing, and analysing single-cell omics- and imaging data, and how they could be integrated to further our understanding of complex biological phenomena like ageing. We include an emphasis on machine learning algorithms because of their ability to identify complex patterns in large multidimensional data.
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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.