ArticleScientific reports2022
Titration of 124 antibodies using CITE-Seq on human PBMCs.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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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
25 citing papers in PubMed, 28 citations in OpenAlex.
- Single-cell Transcriptomics in Autoimmune Diseases: Mechanistic Insights, Cross-disease Convergence, and Translational Challenges in Rheumatoid Arthritis, Systemic Lupus Erythematosus, Type 1 Diabetes, and Multiple Sclerosis.Clinical reviews in allergy & immunology · 2026Review
- Cell-based therapies of autoimmune diseases in the context of artificial intelligence development.Clinical and experimental medicine · 2026Review
- Multi-omics-driven precision medicine.iMeta · 2026Review
- A method to unravel complexity in human peripheral blood B-cell subsets through multiomic cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) analyses.ImmunoHorizons · 2026Article
- ImmunoPheno: A Computational Framework for Data-Driven Design and Analysis of Immunophenotyping Experiments.bioRxiv : the preprint server for biology · 2026Article
- Decoding Treg diversity and dysfunction to advance Treg-based therapies in autoimmune and inflammatory diseases.Frontiers in immunology · 2026Review
- Methodologies for Sample Multiplexing and Computational Deconvolution in Single-Cell Sequencing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Characterization and classification of chronic kidney disease by spatial MIST and deep learning algorithm.American journal of physiology. Renal physiology · 2025Article
- Key Considerations on CITE-Seq for Single-Cell Multiomics.Proteomics · 2025Review
- Automated denoising of CITE-seq data with ThresholdR.Cell reports methods · 2025Article
- scPDA: denoising protein expression in droplet-based single-cell data.Genome biology · 2025Article
- ADTnorm: robust integration of single-cell protein measurement across CITE-seq datasets.Nature communications · 2025Article
- Spectral Flow Cytometry: The Current State and Future of the Technology.International journal of molecular sciences · 2025Review
- scMINER: a mutual information-based framework for clustering and hidden driver inference from single-cell transcriptomics data.Nature communications · 2025Article
- CITE-Seq Analysis Reveals a Differential Natural Killer CellInternational journal of molecular sciences · 2025Article
- Single-cell sequencing reveals cellular heterogeneity and molecular mechanisms in tendon and enthesis injury repair.Frontiers in physiology · 2025Review
- Personalised immunotherapy strategies informed by single cell profiling in thyroid cancer: a mini review.Frontiers in immunology · 2025Review
- Quantitative measures to assess the quality of cellular indexing of transcriptomes and epitopes by sequencing data.Frontiers in bioinformatics · 2025Article
- Transcriptomic profiling of thyroid eye disease orbital fat demonstrates differences in adipogenicity and IGF-1R pathway.JCI insight · 2024Article
- ADTnorm: Robust Integration of Single-cell Protein Measurement across CITE-seq Datasets.Research square · 2024Article
Corrections and comments
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Authors and funding
8 authors at 1 institution in 3 countries.
Funding
Abstract
Single-cell RNA-sequencing (scRNA-Seq) is widely used to characterize immune cell populations. However, mRNA levels correlate poorly with expression of surface proteins, which are well established to define immune cell types. CITE-Seq (cellular indexing of transcriptomes and epitopes by sequencing) utilizes oligonucleotide-tagged antibodies to simultaneously analyze surface phenotypes and transcriptomes. Considering the high costs of adding surface phenotyping to scRNA-Seq, we aimed to determine which of 188 tested CITE-Seq antibodies can detect their antigens on human peripheral blood mononuclear cells (PBMCs), a commonly interrogated cell population in immunology, and find the optimal concentration for staining. The recommended concentration was optimal for 76 antibodies, whereas staining quality of 7 antibodies improved when the concentration was doubled. 33 and 8 antibodies still worked well when the concentration was reduced to 1/5 or 1/25, respectively. 64 antigens were not detected at any antibody concentration. Optimizing the antibody panel by removing antibodies not able to detect their target antigens and adjusting concentrations of the remaining antibodies will improve the analysis and may reduce costs. In conclusion, our data are a resource for building an informative and cost-effective panel of CITE-Seq antibodies and use them at their optimal concentrations in future CITE-seq experiments on human PBMCs.
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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.