The DOI matches its Crossref record; 167 of the 167 references it cites were found in a registry or an index.
Single-cell RNA-seq: advances and future challenges
Open accessAbstract
Phenotypically identical cells can dramatically vary with respect to behavior during their lifespan and this variation is reflected in their molecular composition such as the transcriptomic landscape. Single-cell transcriptomics using next-generation transcript sequencing (RNA-seq) is now emerging as a powerful tool to profile cell-to-cell variability on a genomic scale. Its application has already greatly impacted our conceptual understanding of diverse biological processes with broad implications for both basic and clinical research. Different single-cell RNA-seq protocols have been introduced and are reviewed here-each one with its own strengths and current limitations. We further provide an overview of the biological questions single-cell RNA-seq has been used to address, the major findings obtained from such studies, and current challenges and expected future developments in this booming field.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“new methodologies, such as Hi-C and single-cell sequencing, have yet to be fully integrated at scale with brain genomics data (13–16).”
From Comprehensive functional genomic resource and integrative model for the human brain (2018) · cites this paper as [15]
In the citing paper
“This includes 43,181 proximal and 42,681 distal linkages involving 11,573 protein-encoding target genes (TF–to–target gene via promoter for proximal versus via enhancer–target gene connection for distal) (Fig. 5A) (15, 21).”
From Comprehensive functional genomic resource and integrative model for the human brain (2018) · cites this paper as [15]
In the citing paper
“Using it, we determined which modules were prioritized, as well as the sets of genes associated with latent nodes that were found at each hidden layer (Fig. 8A and table S10) (15, 21).”
From Comprehensive functional genomic resource and integrative model for the human brain (2018) · cites this paper as [15]
In the citing paper
“In addition, current single-cell techniques suffer from low sensitivity and dropouts; thus, it remains challenging to reliably quantify low-abundance transcripts (15, 53).”
From Comprehensive functional genomic resource and integrative model for the human brain (2018) · cites this paper as [15]
In the citing paper
“This is particularly the case for specific brain cell substructures, such as axons and dendrites (15).”
From Comprehensive functional genomic resource and integrative model for the human brain (2018) · cites this paper as [15]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
RNA-Seq: a revolutionary tool for transcriptomics
Wang, Z., Gerstein, M., & Snyder, M. (2009) · Nature Reviews Genetics · doi:10.1038/nrg2484 · Cited by 13,174
Trapnell, C., Cacchiarelli, D., Grimsby, J., Pokharel, P., Li, S., Morse, M., Lennon, N. J., Livak, K. J., Mikkelsen, T. S., & Rinn, J. L. (2014) · Nature Biotechnology · doi:10.1038/nbt.2859 · Cited by 7,314
Sequencing technologies – the next generation
Metzker, M. L. (2010) · Nature Reviews Genetics · doi:10.1038/nrg2626 · Cited by 7,066
Landscape of transcription in human cells
Djebali, S., Davis, C. A., Merkel, A., Dobin, A., Lassmann, T., Mortazavi, A., Tanzer, A., Lagarde, J., Lin, W., Schlesinger, F., et al. (2012) · Nature · doi:10.1038/nature11233 · Cited by 5,187
Derrien, T., Johnson, R., Bussotti, G., Andrea Tanzer, A., Djebali, S., Tilgner, H., Guernec, G., Martin, D., Merkel, A., Knowles, D. G., et al. (2012) · Genome Research · doi:10.1101/gr.132159.111 · Cited by 5,135
miRBase: annotating high confidence microRNAs using deep sequencing data
Kozomara, A., & Griffiths-Jones, S. (2014) · Nucleic Acids Research · doi:10.1093/nar/gkt1181 · Cited by 5,073
Manipulating the mouse embryo: A laboratory manual
Hogan, B. L. M., Costantini, F. D., & Lacy, E. (1986) · Cited by 4,460
Full-length RNA-seq from single cells using Smart-seq2
Picelli, S., Faridani, O. R., Björklund, Å. K., Winberg, G., Sagasser, S., & Sandberg, R. (2014) · Nature Protocols · doi:10.1038/nprot.2014.006 · Cited by 4,137
Tissue microarrays for high-throughput molecular profiling of tumor specimens
Kononen, J., Bubendorf, L., Kallionimeni, A., Barlund, M. T., Schraml, P., Leighton, S. B., Torhorst, J. K. H., Mihatsch, M. J., Sauter, G., & Kallionimeni, O.-P. (1998) · Nature Medicine · doi:10.1038/nm0798-844 · Cited by 4,101
mRNA-Seq whole-transcriptome analysis of a single cell
Tang, F., Barbacioru, C., Wang, Y., Nordman, E., Lee, C., Xu, N., Wang, X., Bodeau, J., Tuch, B. B., Siddiqui, A., et al. (2009) · Nature Methods · doi:10.1038/nmeth.1315 · Cited by 3,969
Flagged first, then by citations
Research works printed but not linked: 4
- 10.1038/nmeth.2639 · DOI · Paper page
- Trapnell C. Cacchiarelli D. Grimsby J. Pokharel P. Li S. Morse M. Lennon N.J. Livak K.J. Mikkelsen T.S. Rinn J.L. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol 2014;32. · DOI · Paper page
- 10.1371/journal.pone.0021208 · DOI
- 10.1038/nature12352 · DOI · Paper page
Cited by
Sorted by citations
Items 301–400 of 854
Qin, Y., Liu, Z., Gao, S., Martínez-Vasallo, C., Long, Y., Zhu, X., Liu, B., Gao, Y., Xu, X., Nohales, M. A., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-59424-8 · Cited by 27
Havird, J. C., & Santos, S. R. (2016) · Integrative and Comparative Biology · doi:10.1093/icb/icw061 · Cited by 22
Hayward, R. J., Marsh, J. W., Humphrys, M. S., Huston, W. M., & Myers, G. S. A. (2019) · Frontiers in Cellular and Infection Microbiology · doi:10.3389/fcimb.2019.00392 · Cited by 22
Applications of Single-Cell Sequencing for Multiomics
Xu, Y., & Zhou, X. (2018) · Methods in molecular biology · doi:10.1007/978-1-4939-7717-8_19 · Cited by 22
Wang, Z., Feng, X., & Li, S. C. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00903 · Cited by 22
Wu, F., Olson, B. G., & Yao, J. (2016) · Journal of Visualized Experiments · doi:10.3791/53620 · Cited by 22
Baul, S., Ahmed, K. T., Jiang, Q., Wang, G., Li, Q., Yong, J., & Zhang, W. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae316 · Cited by 20
Li, Y., He, X., Li, M., Li, B., Yang, M., Xie, Y., Zhang, Y., & Ma, X. (2020) · Virus Genes · doi:10.1007/s11262-020-01746-4 · Cited by 21
Kim, S. H., Lee, K. Y., & Chang, K. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24065556 · Cited by 21
Dynamic mechanobiology of cardiac cells and tissues: Current status and future perspective
Wang, C., Ramahdita, G., Genin, G. M., Huebsch, N., & Ma, Z. (2023) · Biophysics Reviews · doi:10.1063/5.0141269 · Cited by 20
Kim, T., Lim, C., & Kaang, B. (2015) · BMB Reports · doi:10.5483/bmbrep.2015.48.7.218 · Cited by 21
Circulating Tumor Cells: The Importance of Single Cell Analysis
Heymann, D., & Gabriel, M. T. (2018) · Advances in experimental medicine and biology · doi:10.1007/978-981-13-0502-3_5 · Cited by 21
Area cooling enables thermal positioning and manipulation of single cells
Shen, Y., Yalikun, Y., Aishan, Y., Tanaka, N., Sato, A., & Tanaka, Y. (2020) · Lab on a Chip · doi:10.1039/d0lc00523a · Cited by 21
Phylogenomics of the Epigenetic Toolkit Reveals Punctate Retention of Genes across Eukaryotes
Weiner, A. K. M., Cerón-Romero, M. A., Yan, Y., & Katz, L. A. (2020) · Genome Biology and Evolution · doi:10.1093/gbe/evaa198 · Cited by 20
CoolMPS: evaluation of antibody labeling based massively parallel non-coding RNA sequencing
Li, Y., Fehlmann, T., Borcherding, A., Drmanac, S., Liu, S., Groeger, L., Xu, C., Callow, M., Villarosa, C., Jorjorian, A., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkaa1122 · Cited by 21
Massive single-cell RNA-seq analysis and imputation via deep learningPreprint
Deng, Y. C., Bao, F., Dai, Q., Wu, L. F., & Altschuler, S. J. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/315556 · Cited by 21
DREAMSeq: An Improved Method for Analyzing Differentially Expressed Genes in RNA-seq Data
Gao, Z., Zhao, Z., & Tang, W. (2018) · Frontiers in Genetics · doi:10.3389/fgene.2018.00588 · Cited by 22
Progress in mimicking brain microenvironments to understand and treat neurological disorders
Ngo, M. T., & Harley, B. A. C. (2021) · APL Bioengineering · doi:10.1063/5.0043338 · Cited by 21
Single-Cell Toolkits Opening a New Era for Cell Engineering
Lee, S., Kim, J., & Park, J.-E. (2021) · Molecules and Cells · doi:10.14348/molcells.2021.0002 · Cited by 20
A review on gene regulatory network reconstruction algorithms based on single cell RNA sequencing
Kim, H., Choi, H. K., Lee, D., & Kim, J. (2023) · Genes & Genomics · doi:10.1007/s13258-023-01473-8 · Cited by 21
Mechanisms of fate decision and lineage commitment during haematopoiesis
Cvejic, A. (2015) · Immunology and Cell Biology · doi:10.1038/icb.2015.96 · Cited by 20
Booth, K. T. A., Azaiez, H., Jahan, I., Smith, R. J., & Fritzsch, B. (2018) · Frontiers in Genetics · doi:10.3389/fgene.2018.00156 · Cited by 20
Mass Cytometry Moving Forward in Support of Clinical Research: Advantages and Considerations
Nassar, A. F., Wisnewski, A. V., & Raddassi, K. (2016) · Bioanalysis · doi:10.4155/bio.15.257 · Cited by 20
Decoding brain memory formation by single-cell RNA sequencing
Sardoo, A. M., Zhang, S., Ferraro, T. N., Keck, T. M., & Chen, Y. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac412 · Cited by 20
Nassar, A. F., Wisnewski, A. V., & Raddassi, K. (2017) · Analytical and Bioanalytical Chemistry · doi:10.1007/s00216-017-0182-4 · Cited by 19
Systems biology: perspectives on multiscale modeling in research on endocrine-related cancers
Clarke, R. C., Tyson, J. J., Tan, M. T., Baumann, W. T., Lu, J., Xuan, J., & Wang, Y. (2019) · Endocrine Related Cancer · doi:10.1530/erc-18-0309 · Cited by 21
Kalb, D., Vo, H. D., Adikari, S., Hong‐Geller, E., Munsky, B. E., & Werner, J. H. (2021) · Scientific Reports · doi:10.1038/s41598-021-92846-0 · Cited by 21
Zheng, Y., Chen, Z.-C., Shi, Z., Hu, K.-H., Zhong, J., Wang, C., Shi, W., Chen, Y., Xie, S., Luo, F., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.07.27.222349 · Cited by 18
Shu, Z., Xia, M., Tan, K., Zhang, Y., & Yu, Z. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae562 · Cited by 21
Xu, H., Liu, Z. Y., Zhou, X., Ji, X., Liu, X., Zhu, X., Lu, L., Dalbeth, N., He, R., & Hua, Y. (2025) · Annals of the Rheumatic Diseases · doi:10.1016/j.ard.2025.09.003 · Cited by 20
Conrad, S., Azizi, H., & Skutella, T. (2017) · Advances in experimental medicine and biology · doi:10.1007/5584_2017_117 · Cited by 18
Nicolas, A., Deplanche, M., Commère, P., Diot, A., Genthon, C., da Silva, W. M., Azevedo, V., Germon, P., Jamme, H., Guédon, É., et al. (2022) · Frontiers in Cellular and Infection Microbiology · doi:10.3389/fcimb.2022.854242 · Cited by 18
Gundogdu, P., Loucera, C., Álamo-Álvarez, I., Dopazo, J., & Nepomuceno, I. (2022) · BioData Mining · doi:10.1186/s13040-021-00285-4 · Cited by 22
Single-cell RNA sequencing in the context of neuropathic pain: progress, challenges, and prospects
Chen, L., Li, Y., Zhu, L., Jin, H., Kang, X., & Feng, Z. (2022) · Translational research · doi:10.1016/j.trsl.2022.07.004 · Cited by 19
Trego, A. C., Keating, C., Nzeteu, C., Graham, A., O’Flaherty, V., & Ijaz, U. Z. (2022) · Microorganisms · doi:10.3390/microorganisms10101961 · Cited by 17
Wen, N., Wu, J., Li, H., Liao, J., Lan, L., Yang, X., Zhu, G., Lei, Z., Dong, J., & Sun, X. (2023) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2023.1208566 · Cited by 21
Data-driven classification of individual cells by their non-Markovian motion
Klimek, A., Mondal, D., Block, S., Sharma, P., & Netz, R. R. (2024) · Biophysical Journal · doi:10.1016/j.bpj.2024.03.023 · Cited by 18
Mahmood, O., Mahmood, O., Bonneau, R., & Cho, K. (2024) · Genome biology · doi:10.1186/s13059-024-03226-6 · Cited by 20
Daniela Gerovska, D., & Araúzo-Bravo, M. J. (2016) · Molecular Human Reproduction · doi:10.1093/molehr/gav072 · Cited by 18
Molecular phenotyping of infection-associated small non-coding RNAs
Barquist, L., Westermann, A. J., & Vogel, J. (2016) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2016.0081 · Cited by 18
Self-citationSingle-Cell Non-coding RNA in Embryonic Development
Fu, Q., Liu, C., Zhai, Z., Zhang, X., Qin, T., & Zhang, H. (2018) · Advances in experimental medicine and biology · doi:10.1007/978-981-13-0502-3_3 · Cited by 18
Yu, L., Sa, S., Wang, L., Dulmage, K., Bhagwat, N., Yee, S. S., Sen, M., Pletcher, C. H., Moore, J. S., Saksena, S., et al. (2018) · Cytometry Part A · doi:10.1002/cyto.a.23599 · Cited by 18
Between Fate Choice and Self-Renewal—Heterogeneity of Adult Neural Crest-Derived Stem Cells
Höving, A. L., Windmöller, B. A., Knabbe, C., Kaltschmidt, B., Kaltschmidt, C., & Greiner, J. F. W. (2021) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2021.662754 · Cited by 18
Puyvelde, S. V., Vanderleyden, J., & Keersmaecker, S. C. J. D. (2015) · MicrobiologyOpen · doi:10.1002/mbo3.263 · Cited by 18
Identification of gene expression changes in rabbit uterus during embryo implantation
Liu, J., Zhao, M., Peng, Y., & Fu, Y.-S. (2016) · Genomics · doi:10.1016/j.ygeno.2016.03.005 · Cited by 18
Bioinformatics for RNA‐Seq Data Analysis
Zhao, S., Zhang, B., Zhang, Y., Gordon, W., Du, S., Paradis, T., Vincent, M. S., & von Schack, D. (2016) · InTech eBooks · doi:10.5772/63267 · Cited by 20
Huang, L., Chen, W., Tan, Z., Huang, Y., Gu, X., Liu, L., Zhang, H., Shi, Y., Ding, J., Zheng, C., et al. (2024) · Theranostics · doi:10.7150/thno.89174 · Cited by 17
Khnouf, R., Shore, S., Han, C. M., Henderson, J. M., Munro, S. A., McCaffrey, A. P., Shintaku, H., & Santiago, J. G. (2018) · Analytical Chemistry · doi:10.1021/acs.analchem.8b02773 · Cited by 17
Quantitative single cell gene expression profiling in the avian embryo
Morrison, J. A., Box, A., McKinney, M. C., McLennan, R., & Kulesa, P. M. (2015) · Developmental Dynamics · doi:10.1002/dvdy.24274 · Cited by 17
Reverse Transcription Errors and RNA–DNA Differences at Short Tandem Repeats
Fungtammasan, A., Tomaszkiewicz, M., Campos-Sánchez, R., Eckert, K. A., DeGiorgio, M., & Makova, K. D. (2016) · Molecular Biology and Evolution · doi:10.1093/molbev/msw139 · Cited by 16
Khosroabadi, Z., Azaryar, S., Dianat‐Moghadam, H., Amoozgar, Z., & Sharifi, M. (2025) · Molecular Medicine · doi:10.1186/s10020-025-01085-w · Cited by 18
Next-generation sequencing applications in food science: fundamentals and recent advances
Tigrero-Vaca, J., Díaz, B., Gu, G., & Cevallos‐Cevallos, J. M. (2025) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2025.1638957 · Cited by 21
Exploring the Complexity of Cortical Development Using Single-Cell Transcriptomics
Jeong, H., & Tiwari, V. K. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00031 · Cited by 16
Respiratory epithelial cell types, states and fates in the era of single-cell RNA-sequencing
Dudchenko, O., Ordovás-Montañés, J., & Bingle, C. D. (2023) · Biochemical Journal · doi:10.1042/bcj20220572 · Cited by 18
Single-Cell Transcriptomics Revealed White Matter Repair Following Subarachnoid Hemorrhage
Wang, X., Wen, D., Xia, F., Fang, M., Zheng, J., You, C., & Ma, L. (2024) · Translational Stroke Research · doi:10.1007/s12975-024-01265-6 · Cited by 17
Single-cell SNP analyses and interpretations based on RNA-Seq data for colon cancer research
Chen, J., Zhou, Q., Wang, Y., & Ning, K. (2016) · Scientific Reports · doi:10.1038/srep34420 · Cited by 17
Abraham, Y., Gerrits, B., Ludwig, M., Rebhan, M., & Keller, C. G. (2016) · Cytometry Part B Clinical Cytometry · doi:10.1002/cyto.b.21499 · Cited by 16
Identification of cell type specific ACE2 modifiers by CRISPR screening
Sherman, E. J., Mirabelli, C., Tang, V. T., Khan, T. G., Leix, K., Kennedy, A. A., Graham, S. E., Willer, C. J., Tai, A. W., Sexton, J. Z., et al. (2022) · PLoS Pathogens · doi:10.1371/journal.ppat.1010377 · Cited by 18
Expansion of human pluripotent stem cells
Adil, M. M., & Schaffer, D. V. (2016) · Current Opinion in Chemical Engineering · doi:10.1016/j.coche.2016.11.002 · Cited by 15
The vagal ganglia transcriptome identifies candidate therapeutics for airway hyperreactivity
Reznikov, L. R., Meyerholz, D. K., Alaiwa, M. H. A., Kuan, S., Liao, Y.-S. J., Bormann, N. L., Bair, T., Price, M. P., Stoltz, D. A., & Welsh, M. J. (2018) · American Journal of Physiology-Lung Cellular and Molecular Physiology · doi:10.1152/ajplung.00557.2017 · Cited by 16
Lieberman, B., Kusi, M., Hung, C., Chou, C., Ning, H., Ho, Y., Taverna, J. A., Huang, T. H. ‐., & Chen, C. (2020) · Journal of Translational Genetics and Genomics · doi:10.20517/jtgg.2020.51 · Cited by 15
SLRRSC: Single-Cell Type Recognition Method Based on Similarity and Graph Regularization Constraints
Zhang, N.-N., Liu, J., Zheng, C.-H., & Wang, J. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3148286 · Cited by 16
Zheng, S., & Wang, W. (2023) · Fish & Shellfish Immunology · doi:10.1016/j.fsi.2023.109070 · Cited by 15
Emergence of Bias During the Synthesis and Amplification of cDNA for scRNA-seq
Luo, Q., & Zhang, H. (2018) · Advances in experimental medicine and biology · doi:10.1007/978-981-13-0502-3_12 · Cited by 15
RNA Regulatory Networks as a Control of Stochasticity in Biological Systems
Vandevenne, M. S., Delmarcelle, M., & Galleni, M. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00403 · Cited by 16
mRNA detection in budding yeast with single fluorophores
Wadsworth, G. M., Parikh, R. Y., Choy, J. S., & Kim, H. D. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx568 · Cited by 14
Zhang, S., Liu, Y., Yang, J., Lin, Z., & Ng, M. K. (2020) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqaa064 · Cited by 15
A gentle introduction to understanding preclinical data for cancer pharmaco-omic modeling
Piyawajanusorn, C., Nguyen, L. C., Ghislat, G., & Ballester, P. J. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab312 · Cited by 18
Jia, B., Jin, J., Han, M., Li, B., & Yuan, Y. (2022) · Science China Life Sciences · doi:10.1007/s11427-021-2084-1 · Cited by 15
Saiz, N., Płusa, B., & Hadjantonakis, A. (2015) · Seminars in Cell and Developmental Biology · doi:10.1016/j.semcdb.2015.06.004 · Cited by 15
Lin, C., Huang, S.-C., Tung, C.-C., Chou, C., Gau, S. S., & Huang, H. (2016) · PLoS ONE · doi:10.1371/journal.pone.0163663 · Cited by 15
Progress of Single-Cell RNA Sequencing Technology in Myocardial Infarction Research
Li, L., Wang, M., Ma, Q., Li, Y., Ye, J., Sun, X., & Sun, G. (2022) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2022.768834 · Cited by 13
Advanced sequencing-based high-throughput and long-read single-cell transcriptome analysis
Huang, S., Shi, W., Li, S., Fan, Q., Yang, C. J., Cao, J., & Wu, L. (2024) · Lab on a Chip · doi:10.1039/d4lc00105b · Cited by 14
A CRISPR/Cas9-based enhancement of high-throughput single-cell transcriptomics
Pandey, A. C., Bezney, J., DeAscanis, D., Kirsch, E., Ahmed, F., Crinklaw, A., Choudhary, K. S., Mandala, T., Deason, J., Hamidi, J., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-59880-2 · Cited by 16
Las técnicas de secuenciación masiva en el estudio de la diversidad biológica
de Heredia, U. L. (2016) · Munibe Ciencias Naturales · doi:10.21630/mcn.2016.64.07 · Cited by 14
Patir, A., Gossner, A. G., Ramachandran, P., Alves, J., Freeman, T. C., Henderson, N. C., Watson, M., & Hassan, M. A. (2020) · Scientific Reports · doi:10.1038/s41598-020-78250-0 · Cited by 14
Pinkney, H. R., Black, M. A., & Diermeier, S. D. (2021) · Biology · doi:10.3390/biology10100987 · Cited by 14
A novel method for single-cell data imputation using subspace regression
Tran, D., Tran, B., Nguyen, H., & Nguyen, T. (2022) · Scientific Reports · doi:10.1038/s41598-022-06500-4 · Cited by 14
scCNC: a method based on capsule network for clustering scRNA-seq data
Wang, H., Zhao, J., Zheng, C.-H., & Su, Y. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac393 · Cited by 14
ISOdb: A Comprehensive Database of Full-Length Isoforms Generated by Iso-Seq
Xie, S., Han, Y., Chen, X., Cao, T.-Y., Ji, K.-K., Zhu, J., Ling, P., & Xiao, C. (2018) · International Journal of Genomics · doi:10.1155/2018/9207637 · Cited by 14
Yang, C., Song, Z.-Q., Pei, S., Yu, X., Miao, J., Liang, H., Miao, Y., & Du, Z. (2020) · Theriogenology · doi:10.1016/j.theriogenology.2020.08.020 · Cited by 13
Targeting individual cells by barcode in pooled sequence libraries
Ranu, N., Villani, A.-C., Hacohen, N., & Blainey, P. C. (2019) · Nucleic Acids Research · doi:10.1093/nar/gky856 · Cited by 13
Understanding breast cancer heterogeneity through non-genetic heterogeneity
Barough, N. B., Sajjadian, F., Jalilzadeh, N., Shafaei, H., & Velaei, K. (2021) · Breast Cancer · doi:10.1007/s12282-021-01237-w · Cited by 13
Radford, R. A. W., Rayner, S. L., Szwaja, P., Morsch, M., Cheng, F., Zhu, T., Widagdo, J., Anggono, V., Pountney, D. L., Chung, R. S., et al. (2023) · Journal of Neurochemistry · doi:10.1111/jnc.15796 · Cited by 12
Moon, J.-H., Roh, D., Kwack, K. H., & Lee, J. (2023) · Japanese Dental Science Review · doi:10.1016/j.jdsr.2023.08.001 · Cited by 13
Li, Z., Gu, H., Xu, X., Tian, Y., Huang, X., & Du, Y. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1288027 · Cited by 14
Current Applications of Single-Cell RNA Sequencing in Glioblastoma: A Scoping Review
Ordóñez-Rubiano, E. G., Rincón-Arias, N., Shelton, W. J., Salazar, A. F., Sierra, M. A., Bertani, R., Gómez-Amarillo, D. F., Hakim, F., Baldoncini, M., Payán‐Gómez, C., et al. (2025) · Brain Sciences · doi:10.3390/brainsci15030309 · Cited by 13
Single-cell sequencing in cancer research
Prado, M. M., Frampton, A. E., Stebbing, J., & Krell, J. (2015) · Expert Review of Molecular Diagnostics · doi:10.1586/14737159.2016.1115345 · Cited by 13
Improving single-cell transcriptome sequencing efficiency with a microfluidic phase-switch device
Zhang, B., Xu, H., Huang, Y., Shu, W., Feng, H., Cai, J., Zhong, J. F., & Chen, Y. (2019) · The Analyst · doi:10.1039/c9an00823c · Cited by 13
DNF: A differential network flow method to identify rewiring drivers for gene regulatory networks
Xie, J., Yang, F., Wang, J., Karikomi, M., Yin, Y., Sun, J., Wen, T., & Nie, Q. H. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.05.028 · Cited by 12
A brief guide for the measurement and interpretation of microbial functional diversity
Johnson, D. R., & Pomati, F. (2020) · Environmental Microbiology · doi:10.1111/1462-2920.15147 · Cited by 14
Single-cell nascent transcription reveals sparse genome usage and plasticity
Ma, S., Hong, Y., Chen, J., Xu, J., & Shen, X. (2025) · Cell · doi:10.1016/j.cell.2025.09.003 · Cited by 12
CDCP: a visualization and analyzing platform for single-cell datasets
Li, Y., Yang, T., Lai, T., You, L., Yang, F., Qiu, J., Wang, L., Du, W., Hua, C., Xu, Z., et al. (2021) · Journal of genetics and genomics/Journal of Genetics and Genomics · doi:10.1016/j.jgg.2021.12.004 · Cited by 12
Chen, W., Xu, D., Liu, Q., Wu, Y., Wang, Y., & Yang, J. (2023) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2023.114697 · Cited by 12
Fang, C., Zhu, S., Zhong, R., Yu, G., Lü, S., Liu, Z., Gao, J., Yan, C., Wang, Y., & Feng, X. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e27466 · Cited by 12
Quantitative Assessment of Intracellular Effectors and Cellular Response in RAGE Activation
Deepu, V., Rai, V., & Agrawal, D. K. (2024) · Archives of Internal Medicine Research · doi:10.26502/aimr.0168 · Cited by 14
CircaKB: a comprehensive knowledgebase of circadian genes across multiple species
Zhu, X., Han, X., Li, Z., Zhou, X., Yoo, S. H., Chen, Z., & Ji, Z. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae817 · Cited by 12
Model-based dimensionality reduction for single-cell RNA-seq using generalized bilinear models
Nicol, P. B., & Miller, J. W. (2024) · Biostatistics · doi:10.1093/biostatistics/kxaf024 · Cited by 12
The Application of Single-Cell RNA Sequencing in Mammalian Meiosis Studies
Peng, Y., & Qiao, H. (2021) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2021.673642 · Cited by 12
Single-Nucleotide Polymorphism to Associate Cancer Risk
Shaw, V. E., Bullock, K., & Greenhalf, W. (2015) · Methods in molecular biology · doi:10.1007/978-1-4939-3204-7_6 · Cited by 13
Metadata
- Authors
- Saliba, A.-E., Westermann, A. J., Gorski, S. A., & Vogel, J.
- Affiliations
- University of Würzburg (DE)
- Corresponding author
- Vogel, Jörg
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2014-07-22
- Volume
- 42
- Issue
- 14
- Pages
- 8845-8860
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- PMC
- PMC4132710
- OpenAlex
- W2069295116
- Cited by
- 847
- References
- 167
- Citations by year
- 2026: 38 · 2025: 81 · 2024: 92 · 2023: 78 · 2022: 74 · 2021: 69 · 2020: 58 · 2019: 63 · 2018: 67 · 2017: 91 · 2016: 84 · 2015: 46 · 2014: 6
- FWCI
- 7.00
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nucleic Acids Research (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- MDC Repository (Max-Delbrueck-Center for Molecular Medicine) (acceptedVersion)
- Online Publication Service of Würzburg University (Würzburg University) (submittedVersion, cc-by)
- Funders
- Deutsche Forschungsgemeinschaft
- Bundesministerium für Bildung und Forschung
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- single-cell RNA sequencing, single-cell transcriptomics, cell-to-cell variability, transcriptional landscape
- MeSH terms
- Cell Separation, Sequence Analysis, RNA, Gene Expression Profiling, Single-Cell Analysis, High-Throughput Nucleotide Sequencing
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review