Evidence map›Paper›PMID 39334490›Full record

ReviewBiomarker research2024

Single-cell sequencing to multi-omics: technologies and applications.

Xiangyu Wu, Xin Yang, Yunhan Dai, Zihan Zhao, Junmeng Zhu, Hongqian Guo, Rong Yang

Abstract readReview
In one paragraph

Review in Biomarker research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 112 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
112citing papers in PubMed, 1 pooled it
–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

112 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. scCADI: Cell-Aware Diffusion Model for Single-Cell RNA-seq Imputation.Interdisciplinary sciences, computational life sciences · 2026
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52 more citing papers are in PubMed but not listed here.

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

7 authors.

Xiangyu Wu *Department of Urology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, 321 Zhongshan Road, Nanjing, 210008, Jiangsu, China.
Xin Yang *Department of Urology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, 321 Zhongshan Road, Nanjing, 210008, Jiangsu, China.
Yunhan DaiMedical School, Nanjing University, Nanjing, China.
Zihan ZhaoDepartment of Urology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, 321 Zhongshan Road, Nanjing, 210008, Jiangsu, China.
Junmeng ZhuDepartment of Oncology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.
Hongqian GuoDepartment of Urology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, 321 Zhongshan Road, Nanjing, 210008, Jiangsu, China. dr.ghq@nju.edu.cn.
Rong YangDepartment of Urology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, 321 Zhongshan Road, Nanjing, 210008, Jiangsu, China. doctoryr@gmail.com.

Funding

Jiangsu Provincial Healthcare Commission Scientific Research Grant Top Project M2020093Jiangsu Provincial Social Development Project BE2023659National Natural Science Foundation of China 81772710National Natural Science Foundation of China 82172691
6 · The paper itself

Abstract

Cells, as the fundamental units of life, contain multidimensional spatiotemporal information. Single-cell RNA sequencing (scRNA-seq) is revolutionizing biomedical science by analyzing cellular state and intercellular heterogeneity. Undoubtedly, single-cell transcriptomics has emerged as one of the most vibrant research fields today. With the optimization and innovation of single-cell sequencing technologies, the intricate multidimensional details concealed within cells are gradually unveiled. The combination of scRNA-seq and other multi-omics is at the forefront of the single-cell field. This involves simultaneously measuring various omics data within individual cells, expanding our understanding across a broader spectrum of dimensions. Single-cell multi-omics precisely captures the multidimensional aspects of single-cell transcriptomes, immune repertoire, spatial information, temporal information, epitopes, and other omics in diverse spatiotemporal contexts. In addition to depicting the cell atlas of normal or diseased tissues, it also provides a cornerstone for studying cell differentiation and development patterns, disease heterogeneity, drug resistance mechanisms, and treatment strategies. Herein, we review traditional single-cell sequencing technologies and outline the latest advancements in single-cell multi-omics. We summarize the current status and challenges of applying single-cell multi-omics technologies to biological research and clinical applications. Finally, we discuss the limitations and challenges of single-cell multi-omics and potential strategies to address them.

Indexed as

Computational biologyMetabolomeMicrobiomeProteomicsscBCR-seqscRNA-seqscTCR-seqSingle-cell multi-omicsSpatial transcriptomics

Identifiers

PMID39334490
PMCPMC11438019

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.