Evidence map›Paper›PMID 40316516›Full record

ArticleNature communications2025

snCED-seq: high-fidelity cryogenic enzymatic dissociation of nuclei for single-nucleus RNA-seq of FFPE tissues.

Yunxia Guo, Junjie Ma, Ruicheng Qi, Rongrong Ma, Xiaoying Ma, Jitao Xu, Kaiqiang Ye, Yan Huang, Xi Yang, Jianyou Zhang and 2 more

Abstract read
In one paragraph

Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

12 authors.

Yunxia Guo *State Key Laboratory of Digital Medical Engineering, School of Biological Science & Medical Engineering, Southeast University, Nanjing, China.
Junjie Ma *CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.
Ruicheng Qi *CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.
Rongrong MaDepartment of Anesthesiology, The Affiliated Hospital of Yangzhou University, Yangzhou University, Yangzhou, China.
Xiaoying MaState Key Laboratory of Digital Medical Engineering, School of Biological Science & Medical Engineering, Southeast University, Nanjing, China.
Jitao XuState Key Laboratory of Digital Medical Engineering, School of Biological Science & Medical Engineering, Southeast University, Nanjing, China.
Kaiqiang YeState Key Laboratory of Digital Medical Engineering, School of Biological Science & Medical Engineering, Southeast University, Nanjing, China.
Yan HuangState Key Laboratory of Digital Medical Engineering, School of Biological Science & Medical Engineering, Southeast University, Nanjing, China.
Xi YangState Key Laboratory of Digital Medical Engineering, School of Biological Science & Medical Engineering, Southeast University, Nanjing, China.
Jianyou ZhangDepartment of Anesthesiology, The Affiliated Hospital of Yangzhou University, Yangzhou University, Yangzhou, China. zhangjianyou@yzu.edu.cn.
Guangzhong WangCAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China. guangzhong.wang@picb.ac.cn.ORCID http://orcid.org/0000-0001-6432-8310
Xiangwei ZhaoState Key Laboratory of Digital Medical Engineering, School of Biological Science & Medical Engineering, Southeast University, Nanjing, China. xwzhao@seu.edu.cn.ORCID http://orcid.org/0000-0003-0678-5219

Funding

National Natural Science Foundation of China (National Science Foundation of China) 82361138570
6 · The paper itself

Abstract

Recent advances have shown that single-nucleus RNA sequencing (snRNA-seq) can be applied to formalin-fixed, paraffin-embedded (FFPE) tissues, opening avenues for transcriptomic analysis of archived specimens. Yet, isolating intact nuclei remains difficult due to RNA cross-linking. Here, we introduce a cryogenic enzymatic dissociation (CED) strategy for rapid, high-yield and fidelity nuclei extraction from FFPE samples and validate its utility with snRandom-seq (snCED-seq) using male C57/BL6 mice. Compared with conventional approaches, CED delivers a tenfold increase in nuclei yield with significantly reduced hands-on time, while minimizing secondary RNA degradation and preserving intranuclear transcripts. snCED-seq enhances gene detection sensitivity, lowers mitochondrial and ribosomal contamination, and increases overall gene expression quantification. In Alzheimer's disease studies, it distinguished two astrocyte subpopulations, microglia, and oligodendrocytes, revealing cellular heterogeneity. Additionally, snCED-seq identify major cell types in a single 50 μm FFPE human lung section. Our results demonstrate that snCED-seq is robust for FFPE specimens and poised to enable multi-omics analyses of clinical samples.

Indexed as

Cell NucleusRNA-SeqSequence Analysis, RNAAlzheimer DiseaseAnimalsFormaldehydeGene Expression ProfilingHumansLungMaleMiceMice, Inbred C57BLParaffin EmbeddingTissue FixationFormaldehyde

Identifiers

PMID40316516
PMCPMC12048618

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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 Socratic graph.