Evidence map›Paper›PMID 34898038›Full record

ArticleClinical and translational medicine2021

Can single cell RNA sequencing reshape the clinical biochemistry of hematology: New clusters of circulating blood cells.

Hao Fang, Yiming Zeng, Lianzhong Zhang, Chengshui Chen, Charles A Powell, Xiangdong Wang

Abstract readEditorial
In one paragraph

Article in Clinical and translational medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Article
  2. Article
  3. Insights gained from single-cell sequencing analysis of ischemic stroke.Frontiers in cell and developmental biology · 2026
    Review
  4. Non-Malignant Granulocyte and Monocyte Disorders: An Update.British journal of biomedical science · 2025
    Review
  5. Article
  6. Review
  7. Article
  8. Article
  9. Clinical and translational values of spatial transcriptomics.Signal transduction and targeted therapy · 2022
    Review
  10. 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

6 authors.

Hao FangDepartment of Anesthesiology, Zhongshan Hospital, Fudan University, Shanghai, China.
Yiming ZengDepartment of Pulmonary and Critical Care Medicine, Clinical Center for Molecular Diagnosis and Therapy, Second Affiliated Hospital of Fujian Medical University, Respiratory Medicine Center of Fujian Province, Quanzhou, China.
Lianzhong ZhangHenan Provincial People's Hospital, People's Hospital of Henan University, People's Hospital of Zhengzhou University, Zhengzhou, China.ORCID 0000-0003-0886-5589
Chengshui ChenThe Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou, Zhejiang, China.
Charles A PowellDivision of Pulmonary, Critical Care and Sleep Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Xiangdong WangDepartment of Pulmonary and Critical Care Medicine, Shanghai Institute of Clinical Bioinformatics, Shanghai Engineering Research for AI Technology for Cardiopulmonary Diseases, Jinshan Hospital Centre for Tumor Diagnosis and Therapy, Fudan University Shanghai Medical College, Shanghai, China.ORCID 0000-0002-8406-7928

Funding

National Key Research and Development Program of Precision Medicine 2017YFC0909500Shanghai Institute of Clinical Bioinformatics and Shanghai Engineering and Technology Center for Artificial Intelligence of Lung and Heart Diseases from Zhongshan Hospital, The National Nature Science Foundation of China 81873409
6 · The paper itself

Abstract

scRNA-seq is on track for use as a routine measurement of clinical biochemistry and to assist in clinical decision-making and guide the performance of molecular medicine, but there are still a large number of challenges to be overcome. In conclusion, scRNA-seq-based clusters and differentiation of circulating blood cells have been examined and informative in patients with various diseases, although the information generated from scRNA-seq varies between different conditions, technologies, and diseases. Most of the clinical studies published have focused on the landscape of circulating immune cells, disease-specific patterns of new clusters, understanding of potential mechanisms, and potential correlation between cell clusters, differentiations, cell interactions, and circulating and migrated cells. It is clear that the information from scRNA-seq advances the understanding of the disease, identifies disease-specific target panels, and suggests new therapeutic strategies. The adaptation of scRNA-seq as a routine clinical measurement will require standardization and normalization of scRNA-seq-based comprehensive information and validation in a large population of healthy and diseased patients. The integration of public databases on human circulating cell clusters and differentiations with an application of artificial intelligence and computational science will accelerate the application of scRNA-seq for clinical practice. Thus, we call special attention from scientists and clinicians to the clinical and translational discovery, validation, and medicine opportunities of scRNA-seq development.

Indexed as

Artificial IntelligenceChemistry, ClinicalHematologyHumansSingle-Cell Analysis

Identifiers

PMID34898038
PMCPMC8666581

What OpenQuestion holds

Textmetadata
LicenceCC BY
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 OpenQuestion graph.