Evidence map›Paper›PMID 41488013›Full record

ArticleBlood science (Baltimore, Md.)2026

A protocol for high-quality single-cell RNA sequencing with cell surface protein quantification.

Sichong Han, Siqi Liu, Changya Chen

Abstract read
In one paragraph

Article in Blood science (Baltimore, Md.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Sichong HanState Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China.
Siqi LiuState Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China.
Changya ChenState Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) enables the simultaneous analysis of transcriptomic and proteomic data at the single-cell level, providing a comprehensive view of cellular heterogeneity and function. In this study, we present a standardized approach for high-quality single-cell RNA sequencing coupled with cell surface protein quantification. Key advantages of CITE-seq include its compatibility with existing scRNA-seq workflows, cost-efficient high-throughput protein detection, and enhanced resolution in cell type classification. Detailed steps for sample preparation, antibody-oligo conjugation, gel bead-in-emulsion (GEM) generation, complementary deoxyribonucleic acid (cDNA) amplification, and library construction are provided, ensuring reproducibility and robust data quality. This protocol facilitates the integration of multimodal single-cell data, enabling precise characterization of rare cell subsets and advancing insights in immunology, oncology, and developmental biology. The workflow is optimized for flexibility across platforms and scalable for diverse research applications.

Indexed as

CITE-SeqSingle-cell RNA sequencing

Identifiers

PMID41488013
PMCPMC12757190

What OpenQuestion holds

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Registered trials

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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.