ReviewFrontiers in immunology2021
High Throughput Multi-Omics Approaches for Clinical Trial Evaluation and Drug Discovery.
Review in Frontiers in immunology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 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
36 citing papers in PubMed, 71 citations in OpenAlex.
- Mitogen-activated protein kinase kinase 4 (MKK4) as a promising therapeutic target in liver diseases: a review.Journal of enzyme inhibition and medicinal chemistry · 2026Review
- Multi-omics-driven precision medicine.iMeta · 2026Review
- Epilepsy as a Multiscale Network Disorder: Integrating Precision Therapeutics and Emerging Experimental Platforms.Pharmaceutics · 2026Review
- Multi-omics and artificial intelligence for precision drug discovery and potential clinical applications.Signal transduction and targeted therapy · 2026Review
- Review
- From Histology to High-Resolution Mapping: The Rise of Spatial Omics in Immunology.European journal of immunology · 2025Review
- Multi-omics Quality Assessment in Personalized Medicine Through European Infrastructure for Translational Medicine (EATRIS): An Overview.Phenomics (Cham, Switzerland) · 2025Review
- Characteristic genes and immune infiltration analysis of gastric cancer based on bioinformatics analysis and machine learning.Discover oncology · 2025Article
- Cancer chemoprevention: signaling pathways and strategic approaches.Signal transduction and targeted therapy · 2025Review
- Drug Discovery and Development for Heart Failure Using Multi-Omics Approaches.International journal of molecular sciences · 2025Review
- Integrating traditional omics and AI-driven approaches for discovery and validation of novel MicroRNA biomarkers and therapeutic targets in thyroid cancer.Frontiers in pharmacology · 2025Article
- Immune snapshots along the inflammation-to-cancer road in bladder urothelium.Frontiers in immunology · 2025Review
- Highly Multiplexed Immunofluorescence PhenoCycler Panel for Murine Formalin-Fixed Paraffin-Embedded Tissues Yields Insight Into Tumor Microenvironment Immunoengineering.Laboratory investigation; a journal of technical methods and pathology · 2025Article
- Review
- Artificial Intelligence in Metabolomics: A Current Review.Trends in analytical chemistry : TRAC · 2024Article
- Deciphering metabolic crosstalk in context: lessons from inflammatory diseases.Molecular oncology · 2024Review
- Applications of Flow Cytometry in Drug Discovery and Translational Research.International journal of molecular sciences · 2024Review
- Tackling neurodegenerationFrontiers in molecular neuroscience · 2024Review
- Methods for multi-omic data integration in cancer research.Frontiers in genetics · 2024Review
- An update on the global treatment of invasive fungal infections.Future microbiology · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 1 country.
Funding
Abstract
High throughput single cell multi-omics platforms, such as mass cytometry (cytometry by time-of-flight; CyTOF), high dimensional imaging (>6 marker; Hyperion, MIBIscope, CODEX, MACSima) and the recently evolved genomic cytometry (Citeseq or REAPseq) have enabled unprecedented insights into many biological and clinical questions, such as hematopoiesis, transplantation, cancer, and autoimmunity. In synergy with constantly adapting new single-cell analysis approaches and subsequent accumulating big data collections from these platforms, whole atlases of cell types and cellular and sub-cellular interaction networks are created. These atlases build an ideal scientific discovery environment for reference and data mining approaches, which often times reveals new cellular disease networks. In this review we will discuss how combinations and fusions of different -omic workflows on a single cell level can be used to examine cellular phenotypes, immune effector functions, and even dynamic changes, such as metabolomic state of different cells in a sample or even in a defined tissue location. We will touch on how pre-print platforms help in optimization and reproducibility of workflows, as well as community outreach. We will also shortly discuss how leveraging single cell multi-omic approaches can be used to accelerate cellular biomarker discovery during clinical trials to predict response to therapy, follow responsive cell types, and define novel druggable target pathways. Single cell proteome approaches already have changed how we explore cellular mechanism in disease and during therapy. Current challenges in the field are how we share these disruptive technologies to the scientific communities while still including new approaches, such as genomic cytometry and single cell metabolomics.
Indexed as
Identifiers
What 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.