Evidence map›Paper›PMID 38689046›Full record

ReviewMolecular biology reports2024

Single-cell transcriptomics: background, technologies, applications, and challenges.

Lucky Duhan, Deepika Kumari, Mohammad Naime, Virinder S Parmar, Anil K Chhillar, Mehak Dangi, Ritu Pasrija

Abstract readReview
PubMed Publisher
In one paragraph

Review in Molecular biology reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

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  16. Single-Cell RNA Sequencing Reveals an Atlas of Hezuo Pig Testis Cells.International journal of molecular sciences · 2024
    Article
  17. 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

7 authors.

Lucky DuhanDepartment of Biochemistry, Maharshi Dayanand University, Rohtak, Haryana, 124001, India.
Deepika KumariDepartment of Biochemistry, Maharshi Dayanand University, Rohtak, Haryana, 124001, India.
Mohammad NaimeCentral Research Institute of Unani Medicine (Under Central Council for Research in Unani Medicine, Ministry of Ayush, Govt of India), Uttar Pradesh, Lucknow, India.
Virinder S ParmarCUNY-Graduate Center and Departments of Chemistry, Nanoscience Program, City College & Medgar Evers College, The City University of New York, 1638 Bedford Avenue, Brooklyn, NY, 11225, USA.
Anil K ChhillarCentre for Biotechnology, Maharshi Dayanand University, Rohtak, Haryana, 124001, India.
Mehak DangiCentre for Bioinformatics, Maharshi Dayanand University, Rohtak, Haryana, 124001, India.
Ritu PasrijaDepartment of Biochemistry, Maharshi Dayanand University, Rohtak, Haryana, 124001, India. ritupasrija.biochem@mdurohtak.ac.in.

Funding

Council of Scientific and Industrial Research, India 09/382(0249)/2019-EMR-ICouncil of Scientific and Industrial Research, India 09/382(0264)/2020-EMR-IScience & Engineering Research Board (SERB) CRG/2020/004986
6 · The paper itself

Abstract

Single-cell sequencing was developed as a high-throughput tool to elucidate unusual and transient cell states that are barely visible in the bulk. This technology reveals the evolutionary status of cells and differences between populations, helps to identify unique cell subtypes and states, reveals regulatory relationships between genes, targets and molecular mechanisms in disease processes, tumor heterogeneity, the state of the immune environment, etc. However, the high cost and technical limitations of single-cell sequencing initially prevented its widespread application, but with advances in research, numerous new single-cell sequencing techniques have been discovered, lowering the cost barrier. Many single-cell sequencing platforms and bioinformatics methods have recently become commercially available, allowing researchers to make fascinating observations. They are now increasingly being used in various industries. Several protocols have been discovered in this context and each technique has unique characteristics, capabilities and challenges. This review presents the latest advancements in single-cell transcriptomics technologies. This includes single-cell transcriptomics approaches, workflows and statistical approaches to data processing, as well as the potential advances, applications, opportunities and challenges of single-cell transcriptomics technology. You will also get an overview of the entry points for spatial transcriptomics and multi-omics.

Indexed as

Computational BiologyGene Expression ProfilingSingle-Cell AnalysisTranscriptomeAnimalsHigh-Throughput Nucleotide SequencingHumansApplicationsBioinformatic methodsMulti-omicsSingle-cell sequencingSpatial transcriptomicsTranscriptome

Identifiers

PMID38689046

What OpenQuestion holds

Textmetadata
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.