ADTnorm: robust integration of single-cell protein measurement across CITE-seq datasets

Abstract Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) enables paired measurement of surface protein and mRNA expression in single cells using antibodies conjugated to oligonucleotide tags. Due to the high copy number of surface protein molecules, sequencing antibody-deri...

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Main Authors: Ye Zheng, Daniel P. Caron, Ju Yeong Kim, Seong-Hwan Jun, Yuan Tian, Florian Mair, Kenneth D. Stuart, Peter A. Sims, Raphael Gottardo
Format: Article
Language:English
Published: Nature Portfolio 2025-07-01
Series:Nature Communications
Online Access:https://doi.org/10.1038/s41467-025-61023-6
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author Ye Zheng
Daniel P. Caron
Ju Yeong Kim
Seong-Hwan Jun
Yuan Tian
Florian Mair
Kenneth D. Stuart
Peter A. Sims
Raphael Gottardo
author_facet Ye Zheng
Daniel P. Caron
Ju Yeong Kim
Seong-Hwan Jun
Yuan Tian
Florian Mair
Kenneth D. Stuart
Peter A. Sims
Raphael Gottardo
author_sort Ye Zheng
collection DOAJ
description Abstract Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) enables paired measurement of surface protein and mRNA expression in single cells using antibodies conjugated to oligonucleotide tags. Due to the high copy number of surface protein molecules, sequencing antibody-derived tags (ADTs) allows for robust protein detection, improving cell-type identification. However, variability in antibody staining leads to batch effects in the ADT expression, obscuring biological variation, reducing interpretability, and obstructing cross-study analyses. Here, we present ADTnorm, a normalization and integration method designed explicitly for ADT abundance. Benchmarking against 14 existing scaling and normalization methods, we show that ADTnorm accurately aligns populations with negative- and positive-expression of surface protein markers across 13 public datasets, effectively removing technical variation across batches and improving cell-type separation. ADTnorm enables efficient integration of public CITE-seq datasets, each with unique experimental designs, paving the way for atlas-level analyses. Beyond normalization, ADTnorm includes built-in utilities to aid in automated threshold-gating as well as assessment of antibody staining quality for titration optimization and antibody panel selection. Applying ADTnorm to an antibody titration study, a published COVID-19 CITE-seq dataset, and a human hematopoietic progenitors study allowed for identifying previously undetected phenotype-associated markers, illustrating a broad utility in biological applications.
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spelling doaj-art-37bb22c42fd74fa68d3616dd603643402025-08-20T03:45:31ZengNature PortfolioNature Communications2041-17232025-07-0116111310.1038/s41467-025-61023-6ADTnorm: robust integration of single-cell protein measurement across CITE-seq datasetsYe Zheng0Daniel P. Caron1Ju Yeong Kim2Seong-Hwan Jun3Yuan Tian4Florian Mair5Kenneth D. Stuart6Peter A. Sims7Raphael Gottardo8Department of Bioinformatics and Computational Biology, Department of Systems Biology, University of Texas MD Anderson Cancer CenterDepartment of Microbiology and Immunology, Columbia UniversityVaccine and Infectious Disease Division, Fred Hutchinson Cancer CenterVaccine and Infectious Disease Division, Fred Hutchinson Cancer CenterVaccine and Infectious Disease Division, Fred Hutchinson Cancer CenterFlow Cytometry Core Facility, ETH ZürichDepartment of Pediatrics, University of Washington and Center for Global Infectious Disease Research, Seattle Children’s Research InstituteDepartment of Systems Biology, Columbia UniversityVaccine and Infectious Disease Division, Fred Hutchinson Cancer CenterAbstract Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) enables paired measurement of surface protein and mRNA expression in single cells using antibodies conjugated to oligonucleotide tags. Due to the high copy number of surface protein molecules, sequencing antibody-derived tags (ADTs) allows for robust protein detection, improving cell-type identification. However, variability in antibody staining leads to batch effects in the ADT expression, obscuring biological variation, reducing interpretability, and obstructing cross-study analyses. Here, we present ADTnorm, a normalization and integration method designed explicitly for ADT abundance. Benchmarking against 14 existing scaling and normalization methods, we show that ADTnorm accurately aligns populations with negative- and positive-expression of surface protein markers across 13 public datasets, effectively removing technical variation across batches and improving cell-type separation. ADTnorm enables efficient integration of public CITE-seq datasets, each with unique experimental designs, paving the way for atlas-level analyses. Beyond normalization, ADTnorm includes built-in utilities to aid in automated threshold-gating as well as assessment of antibody staining quality for titration optimization and antibody panel selection. Applying ADTnorm to an antibody titration study, a published COVID-19 CITE-seq dataset, and a human hematopoietic progenitors study allowed for identifying previously undetected phenotype-associated markers, illustrating a broad utility in biological applications.https://doi.org/10.1038/s41467-025-61023-6
spellingShingle Ye Zheng
Daniel P. Caron
Ju Yeong Kim
Seong-Hwan Jun
Yuan Tian
Florian Mair
Kenneth D. Stuart
Peter A. Sims
Raphael Gottardo
ADTnorm: robust integration of single-cell protein measurement across CITE-seq datasets
Nature Communications
title ADTnorm: robust integration of single-cell protein measurement across CITE-seq datasets
title_full ADTnorm: robust integration of single-cell protein measurement across CITE-seq datasets
title_fullStr ADTnorm: robust integration of single-cell protein measurement across CITE-seq datasets
title_full_unstemmed ADTnorm: robust integration of single-cell protein measurement across CITE-seq datasets
title_short ADTnorm: robust integration of single-cell protein measurement across CITE-seq datasets
title_sort adtnorm robust integration of single cell protein measurement across cite seq datasets
url https://doi.org/10.1038/s41467-025-61023-6
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