Systematic Comparison of Droplet-Based and Microwell-Based Platforms for Comprehensive Single-Cell Transcriptomic Analysis in Clinical Samples.

Shuai Wang, Yuxian Feng, Qiongdan Zhang, Yue Cui, Yi Qiao, Hao Huang, Xuan Pan, Jing Tu

Abstract readComparative Study

In one paragraph

Article in IET nanobiotechnology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing 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

1 citing paper in PubMed.

Systematic Comparison of Droplet-Based and Microwell-Based Platforms for Comprehensive Single-Cell Transcriptomic Analysis in Clinical Samples.IET nanobiotechnology · 2026
Article

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

8 authors.

Shuai WangState Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China, seu.edu.cn.

Yuxian FengState Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China, seu.edu.cn.

Qiongdan ZhangState Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China, seu.edu.cn.

Yue CuiState Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China, seu.edu.cn.

Yi QiaoState Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China, seu.edu.cn.

Hao HuangState Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China, seu.edu.cn.ORCID https://orcid.org/0000-0002-5570-6145

Xuan PanDepartment of Medical Oncology, Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, 210009, Jiangsu, China, njmu.edu.cn.ORCID https://orcid.org/0000-0001-9161-4267

Jing TuState Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China, seu.edu.cn.ORCID https://orcid.org/0000-0002-1310-5915

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Single-cell RNA sequencing (scRNA-seq) is widely utilized in tumor research. However, platform-specific technical biases may impact data interpretation. This study compared the performance of droplet- and microwell-based scRNA-seq platforms in the analysis of clinical samples. Despite the similarities after batch effect correction, significant differences were observed in multiple aspects, including mRNA preference, cell type restoration, and gene expression patterns. The droplet-based platform captured a higher proportion of immune cells, whereas the microwell-based platform provided a more accurate immune cell representation. Differential gene expression, pseudotime, and cell-cell communication analyses further revealed platform-dependent variations across multiple aspects. Overall, this study provides valuable insights into platform selection and optimization for cross-platform data integration in single-cell transcriptomics.

Indexed as

Gene Expression ProfilingSingle-Cell AnalysisTranscriptomeHumansSequence Analysis, RNASingle-Cell Gene Expression Analysisclinical samplesdroplet-based platformmicrowell-based platformsingle-cell RNA sequencingtechnical biases

Identifiers

PMID41755812

DOI10.1049/nbt2/9314222

PMCPMC12933413

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

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.