Cited by 230Single-cell and spatial transcriptomicsGenomics and Phylogenetic StudiesGene expression and cancer classification

The DOI matches its Crossref record; 110 of the 110 references it cites were found in a registry or an index.

Single-cell transcriptome sequencing: recent advances and remaining challenges

Open access

Liu, Serena, Trapnell, Cole·2016·F1000Research

DOI 10.12688/f1000research.7223.1PMC PMC4758375

Open this paper in CoChat — free account

Abstract

Single-cell RNA-sequencing methods are now robust and economically practical and are becoming a powerful tool for high-throughput, high-resolution transcriptomic analysis of cell states and dynamics. Single-cell approaches circumvent the averaging artifacts associated with traditional bulk population data, yielding new insights into the cellular diversity underlying superficially homogeneous populations. Thus far, single-cell RNA-sequencing has already shown great effectiveness in unraveling complex cell populations, reconstructing developmental trajectories, and modeling transcriptional dynamics. Ongoing technical improvements to single-cell RNA-sequencing throughput and sensitivity, the development of more sophisticated analytical frameworks for single-cell data, and an increasing array of complementary single-cell assays all promise to expand the usefulness and potential applications of single-cell transcriptomic profiling.

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

Crossref · 10.12688/f1000research.7223.1

Cites

110 of 110 references verified0 citation errors · 0 not checkable

Statistics

Figure 1. Common applications of single-cell RNA sequencing. ( a) Deconvolving heterogeneous cell populations. Clustering by single-cell transcriptomic profiles can reveal population substructure and enable the identification of cell subtypes and rare cell species (e.g. red cells above). Clusters may be tight and well defined (purple, red) or diffuse (blue). ( b) Trajectory analysis of cell state transitions. Single-cell RNA sequencing time-series data can be used to map cell developmental trajectories over the course of dynamic processes such as differentiation or signaling responses to an external stimulus. Some computational suites (e.g. Monocle 6) can also accommodate branching trajectories, enabling identification of lineage-specific gene expression and key genes that drive branching events. ( c) Dissecting transcription mechanics. Genes’ expression profiles across many cells can be compared to study transcriptional bursting and to model the kinetics of stochastic gene expression. ( d) Network inference. Genes can be clustered by expression profile to identify modules of putatively co-regulated genes, and gene-gene covariation relationships can be used to infer gene regulatory networks or subnetworks.CC BY · PubMed Central

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?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

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
Circular RNAs are a large class of animal RNAs with regulatory potency
Memczak, S., Jens, M., Elefsinioti, A., Torti, F., Krueger, J., Rybak, A., Maier, L., Mackowiak, S. D., Gregersen, L. H., Munschauer, M., et al. (2013) · Nature · doi:10.1038/nature11928 · Cited by 7,990
The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells
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
Self-citation
MicroRNAs: small RNAs with a big role in gene regulation
He, L., & Hannon, G. J. (2004) · Nature Reviews Genetics · doi:10.1038/nrg1379 · Cited by 7,118
Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.
Macosko, E. Z., Basu, A., Satija, R., Nemesh, J., Shekhar, K., Goldman, M., Tirosh, I., Bialas, A. R., Kamitaki, N., Martersteck, E. M., et al. (2015) · Cell · doi:10.1016/j.cell.2015.05.002 · Cited by 7,110
Spatial reconstruction of single-cell gene expression data
Satija, R., Farrell, J. A., Gennert, D., Schier, A. F., & Regev, A. (2015) · Nature Biotechnology · doi:10.1038/nbt.3192 · Cited by 7,055
Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma
Patel, A. P., Tirosh, I., Trombetta, J. J., Shalek, A. K., Gillespie, S. M., Wakimoto, H., Cahill, D. P., Nahed, B. V., Curry, W. T., Martuza, R. L., et al. (2014) · Science · doi:10.1126/science.1254257 · Cited by 5,063
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
Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells
Klein, A. M., Mazutis, L., Akartuna, I., Tallapragada, N., Veres, A., Li, V., Peshkin, L., Weitz, D. A., & Kirschner, M. W. (2015) · Cell · doi:10.1016/j.cell.2015.04.044 · Cited by 3,356
Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq
Zeisel, A., Muñoz-Manchado, A. B., Codeluppi, S., Lönnerberg, P., La Manno, G., Juréus, A., Marques, S., Munguba, H., He, L., Betsholtz, C., et al. (2015) · Science · doi:10.1126/science.aaa1934 · Cited by 3,124

Show all 110 references

Sorted by citations

Massively parallel digital transcriptional profiling of single cells
Zheng, G. X. Y., Terry, J. M., Belgrader, P., Ryvkin, P., Bent, Z. W., Wilson, R., Ziraldo, S. B., Wheeler, T. D., McDermott, G. P., Zhu, J., et al. (2017) · Nature Communications · doi:10.1038/ncomms14049 · Cited by 7,353
Gene regulation by long non-coding RNAs and its biological functions
Statello, L., Guo, C.-J., Chen, L.-L., & Huarte, M. (2021) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-020-00315-9 · Cited by 4,991
Opportunities and obstacles for deep learning in biology and medicine
Ching, T., Himmelstein, D. S., Beaulieu-Jones, B. K., Kalinin, A. A., Do, B. T., Way, G. P., Ferrero, E., Agapow, P.-M., Zietz, M., Hoffman, M. M., et al. (2018) · Journal of The Royal Society Interface · doi:10.1098/rsif.2017.0387 · Cited by 2,310
Comprehensive single cell transcriptional profiling of a multicellular organism by combinatorial indexing
Cao, J., Packer, J. S., Ramani, V., Cusanovich, D. A., Huynh, C., Daza, R., Qiu, X., Lee, C., Furlan, S. N., Steemers, F. J., et al. (2017) · doi:10.1101/104844 · Cited by 1,402
Self-citation
A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications
Haque, A., Engel, J., Teichmann, S. A., & Lönnberg, T. (2017) · Genome Medicine · doi:10.1186/s13073-017-0467-4 · Cited by 1,071
Comprehensive functional genomic resource and integrative model for the human brain
Wang, D., Liu, S., Warrell, J., Won, H., Shi, X., Navarro, F. C. P., Clarke, D., Gu, M., Emani, P., Yang, Y. T., et al. (2018) · Science · doi:10.1126/science.aat8464 · Cited by 965
Self-citation
Wound healing, fibroblast heterogeneity, and fibrosis
desJardins-Park, H. E., Mascharak, S., Griffin, M., Wan, D. C., & Longaker, M. T. (2022) · Cell stem cell · doi:10.1016/j.stem.2022.07.006 · Cited by 835
An accurate and robust imputation method scImpute for single-cell RNA-seq data
Li, W. V., & Li, J. J. (2018) · Nature Communications · doi:10.1038/s41467-018-03405-7 · Cited by 693
Gene Regulatory Network Inference from Single-Cell Data Using Multivariate Information Measures
Chan, T. E., Stumpf, M. P. H., & Babtie, A. C. (2017) · Cell Systems · doi:10.1016/j.cels.2017.08.014 · Cited by 578
Statistics or biology: the zero-inflation controversy about scRNA-seq data
Jiang, R., Sun, T., Song, D., & Li, J. J. (2022) · Genome Biology · doi:10.1186/s13059-022-02601-5 · Cited by 614
Single-cell transcriptomes identify human islet cell signatures and reveal cell-type–specific expression changes in type 2 diabetesPreprint
Lawlor, N. T., George, J., Bolisetty, M. T., Kursawe, R., Sun, L., Vijayakumar, S., Kycia, I., Robson, P., & Stitzel, M. L. (2016) · Genome Research · doi:10.1101/gr.212720.116 · Cited by 543
A test metric for assessing single-cell RNA-seq batch correction
Büttner, M., Miao, Z., Wolf, F. A., Teichmann, S. A., & Theis, F. J. (2019) · Nature Methods · doi:10.1038/s41592-018-0254-1 · Cited by 511
Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model
Townes, F. W., Hicks, S. C., Aryee, M. J., & Irizarry, R. A. (2019) · Genome Biology · doi:10.1186/s13059-019-1861-6 · Cited by 471
Membrane and Nuclear Estrogen Receptor Alpha Actions: From Tissue Specificity to Medical Implications
Arnal, J., Lenfant, F., Métivier, R., Flouriot, G., Henrion, D., Adlanmérini, M., Fontaine, C., Gourdy, P., Chambon, P., Katzenellenbogen, B. S., et al. (2017) · Physiological Reviews · doi:10.1152/physrev.00024.2016 · Cited by 453
The causes of evolvability and their evolution
Payne, J. L., & Wagner, A. (2018) · Nature Reviews Genetics · doi:10.1038/s41576-018-0069-z · Cited by 392
Single-Cell Transcriptome Analysis Reveals Dynamic Cell Populations and Differential Gene Expression Patterns in Control and Aneurysmal Human Aortic Tissue
Li, Y., Ren, P., Dawson, A., Vasquez, H. G., Ageedi, W., Zhang, C., Luo, W., Chen, R., Li, Y., Kim, S., et al. (2020) · Circulation · doi:10.1161/circulationaha.120.046528 · Cited by 344
Molecular Anatomy of the Developing Human Retina
Hoshino, A., Ratnapriya, R., Brooks, M. J., Chaitankar, V., Wilken, M. S., Zhang, C., Starostik, M. R., Gieser, L. M., Torre, A. L., Nishio, M., et al. (2017) · Developmental Cell · doi:10.1016/j.devcel.2017.10.029 · Cited by 254
SINCERITIES: inferring gene regulatory networks from time-stamped single cell transcriptional expression profiles
Papili Gao, N., Ud-Dean, S. M. M., Gandrillon, O., & Gunawan, R. (2018) · Bioinformatics · doi:10.1093/bioinformatics/btx575 · Cited by 235
Benchmarking Computational Doublet-Detection Methods for Single-Cell RNA Sequencing Data
Xi, N. M., & Li, J. J. (2021) · Cell Systems · doi:10.1016/j.cels.2020.11.008 · Cited by 208
High-Throughput Transcriptome Profiling in Drug and Biomarker Discovery
Yang, X., Kui, L., Tang, M., Li, D., Wei, K., Chen, W., Miao, J., & Dong, Y. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.00019 · Cited by 231
Tutorial: guidelines for annotating single-cell transcriptomic maps using automated and manual methods
Clarke, Z. A., Andrews, T. S., Atif, J., Pouyabahar, D., Innes, B. T., MacParland, S. A., & Bader, G. D. (2021) · Nature Protocols · doi:10.1038/s41596-021-00534-0 · Cited by 234
Variation among intact tissue samples reveals the core transcriptional features of human CNS cell classes
Kelley, K. W., Nakao-Inoue, H., Molofsky, A. V., & Oldham, M. C. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0216-z · Cited by 198
Barcoded solid-phase RNA capture for Spatial Transcriptomics profiling in mammalian tissue sections
Salmén, F., Ståhl, P. L., Mollbrink, A., Navarro, J. F., Vicković, S., Frisén, J., & Lundeberg, J. (2018) · Nature Protocols · doi:10.1038/s41596-018-0045-2 · Cited by 196
Alternative Splicing in Neurogenesis and Brain Development
Su, C.-H., Dhananjaya, D., & Tarn, W. (2018) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2018.00012 · Cited by 204
Single-cell transcriptomics in cancer: computational challenges and opportunities
Fan, J., Slowikowski, K., & Zhang, F. (2020) · Experimental & Molecular Medicine · doi:10.1038/s12276-020-0422-0 · Cited by 195
Inferring Causal Gene Regulatory Networks from Coupled Single-Cell Expression Dynamics Using Scribe
Qiu, X., Rahimzamani, A., Wang, L., Ren, B., Mao, Q., Durham, T., McFaline-Figueroa, J. L., Saunders, L., Trapnell, C., & Kannan, S. (2020) · Cell Systems · doi:10.1016/j.cels.2020.02.003 · Cited by 185
Self-citation
scClassify: sample size estimation and multiscale classification of cells using single and multiple reference
Lin, Y., Cao, Y., Kim, H. J., Salim, A., Speed, T. P., Lin, D., Yang, P., & Yang, J. Y. H. (2020) · Molecular Systems Biology · doi:10.15252/msb.20199389 · Cited by 168
The relationship between the gut microbiome and host gene expression: a review
Nichols, R. G., & Davenport, E. (2020) · Human Genetics · doi:10.1007/s00439-020-02237-0 · Cited by 177
Platforms for Single-Cell Collection and Analysis
Valihrach, L., Androvic, P., & Kubista, M. (2018) · International Journal of Molecular Sciences · doi:10.3390/ijms19030807 · Cited by 168
Recent advances in the characterization of plant transcriptomes in response to drought, salinity, heat, and cold stress
Bashir, K., Matsui, A., Rasheed, S., & Seki, M. (2019) · F1000Research · doi:10.12688/f1000research.18424.1 · Cited by 163
Transcriptomics in aquaculture: current status and applications
Chandhini, S., & Rejish Kumar, V. J. (2018) · Reviews in Aquaculture · doi:10.1111/raq.12298 · Cited by 150
Recapitulation and Reversal of Schizophrenia-Related Phenotypes in Setd1a-Deficient Mice
Mukai, J., Cannavò, E., Crabtree, G. W., Sun, Z., Diamantopoulou, A., Thakur, P., Chang, C., Cai, Y., Lomvardas, S., Takata, A., et al. (2019) · Neuron · doi:10.1016/j.neuron.2019.09.014 · Cited by 146
Artificial Intelligence in Pharmaceutical Sciences
Lu, M., Yin, J., Zhu, Q., Lin, G., Mou, M., Liu, F., Pan, Z., You, N., Lian, X., Li, F., et al. (2023) · Engineering · doi:10.1016/j.eng.2023.01.014 · Cited by 145
Fibroblasts – the cellular choreographers of wound healing
Knoedler, S. A., Broichhausen, S., Guo, R., Dai, R., Knoedler, L., Kauke‐Navarro, M., Diatta, F. H., Pomahač, B., Machens, H., Jiang, D., et al. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1233800 · Cited by 141
UMI-count modeling and differential expression analysis for single-cell RNA sequencing
Chen, W., Li, Y., Easton, J., Finkelstein, D., Wu, G., & Chen, X. (2018) · Genome Biology · doi:10.1186/s13059-018-1438-9 · Cited by 113
Applied Bioinformatics
Hendrix, D. A. (2019) · doi:10.5399/osu/1140 · Cited by 100
Impact of Tumor Purity on Immune Gene Expression and Clustering Analyses across Multiple Cancer Types
Rhee, J., Jung, Y. C., Kim, K. R., Yoo, J., Kim, J., Lee, Y., Ko, Y. H., Lee, H. H., Cho, B. C., & Kim, T. (2017) · Cancer Immunology Research · doi:10.1158/2326-6066.cir-17-0201 · Cited by 98
The Future of Cell Biology: Emerging Model Organisms
Goldstein, B., & King, N. (2016) · Trends in Cell Biology · doi:10.1016/j.tcb.2016.08.005 · Cited by 97
TooManyCells identifies and visualizes relationships of single-cell clades
Schwartz, G. W., Zhou, Y., Petrovic, J., Fasolino, M., Xu, L., Shaffer, S. M., Pear, W. S., Vahedi, G., & Faryabi, R. B. (2020) · Nature Methods · doi:10.1038/s41592-020-0748-5 · Cited by 90
Continuous Variation within Cell Types of the Nervous System
Cembrowski, M. S., & Menon, V. (2018) · Trends in Neurosciences · doi:10.1016/j.tins.2018.02.010 · Cited by 85
Quantitative Clonal Analysis and Single-Cell Transcriptomics Reveal Division Kinetics, Hierarchy, and Fate of Oral Epithelial Progenitor Cells
Jones, K. B., Furukawa, S., Marangoni, P., Ma, H., Pinkard, H., D’Urso, R. S., Žilionis, R., Klein, A. M., & Klein, O. D. (2018) · Cell stem cell · doi:10.1016/j.stem.2018.10.015 · Cited by 75
Meeting the Challenges of High-Dimensional Single-Cell Data Analysis in Immunology
Palit, S., Heuser-Loy, C., de Almeida, G. P., Theis, F. J., & Zielinski, C. E. (2019) · Frontiers in Immunology · doi:10.3389/fimmu.2019.01515 · Cited by 82
Highly parallel and efficient single cell mRNA sequencing with paired picoliter chambers
Zhang, M., Zou, Y. W., Xu, X. J., Zhang, X., Gao, M., Song, J. T., Huang, P., Chen, Q., Zhu, Z., Lin, W., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-15765-0 · Cited by 80
Reference Transcriptomes of Porcine Peripheral Immune Cells Created Through Bulk and Single-Cell RNA Sequencing
Herrera-Uribe, J., Wiarda, J. E., Sivasankaran, S. K., Daharsh, L., Liu, H., Byrne, K. A., Smith, T. P. L., Lunney, J. K., Loving, C. L., & Tuggle, C. K. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.689406 · Cited by 82
Impact of sequencing depth and read length on single cell RNA sequencing data of T cells
Rizzetto, S., Eltahla, A. A., Lin, P., Bull, R. A., Lloyd, A. R., Ho, J. W. K., Venturi, V., & Luciani, F. (2017) · Scientific Reports · doi:10.1038/s41598-017-12989-x · Cited by 76
Statistical and Machine Learning Approaches to Predict Gene Regulatory Networks From Transcriptome Datasets
Mochida, K., Koda, S., Inoue, K., & Nishii, R. (2018) · Frontiers in Plant Science · doi:10.3389/fpls.2018.01770 · Cited by 80
Autoimmunity and autoinflammation: A systems view on signaling pathway dysregulation profiles
Arakelyan, A., Nersisyan, L., Poghosyan, D., Khondkaryan, L., Hakobyan, A., Loeffler‐Wirth, H., Melanitou, E., & Binder, H. (2017) · PLoS ONE · doi:10.1371/journal.pone.0187572 · Cited by 84
Variation in Activity State, Axonal Projection, and Position Define the Transcriptional Identity of Individual Neocortical Projection Neurons
Chevée, M., Robertson, J. D., Cannon, G. H., Brown, S. P., & Goff, L. A. (2018) · Cell Reports · doi:10.1016/j.celrep.2017.12.046 · Cited by 76
A contemporary review on the important role of in silico approaches for managing different aspects of COVID-19 crisis
Moradi, M., Golmohammadi, R., Najafi, A., Moghaddam, M. M., Fasihi‐Ramandi, M., & Mirnejad, R. (2022) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2022.100862 · Cited by 70
Prospects for the Study and Improvement of Abiotic Stress Tolerance in Date Palms in the Post-genomics Era
Hazzouri, K. M., Flowers, J. M., Nelson, D. R., Lemansour, A., Masmoudi, K., & Amiri, K. M. A. (2020) · Frontiers in Plant Science · doi:10.3389/fpls.2020.00293 · Cited by 76
Genomics of Islet (Dys)function and Type 2 Diabetes
Lawlor, N. T., Khetan, S., Ucar, D., & Stitzel, M. L. (2017) · Trends in Genetics · doi:10.1016/j.tig.2017.01.010 · Cited by 64
Cellular and molecular basis of liver regeneration
Bangru, S., & Kalsotra, A. (2020) · Seminars in Cell and Developmental Biology · doi:10.1016/j.semcdb.2019.12.004 · Cited by 67
scRNA‐seq data analysis method to improve analysis performance
Lu, J., Sheng, Y., Qian, W., Pan, M., Zhao, X., & Ge, Q. (2023) · IET Nanobiotechnology · doi:10.1049/nbt2.12115 · Cited by 66
Leukocyte diversity in resolving and nonresolving mechanisms of cardiac remodeling
Tourki, B., & Halade, G. V. (2017) · The FASEB Journal · doi:10.1096/fj.201700109r · Cited by 65
Characterization of Diabetic and Non-Diabetic Foot Ulcers Using Single-Cell RNA-Sequencing
Januszyk, M., Chen, K., Henn, D., Foster, D. S., Borrelli, M. R., Bonham, C. A., Sivaraj, D., Wagh, D. A., Longaker, M. T., Wan, D. C., et al. (2020) · Micromachines · doi:10.3390/mi11090815 · Cited by 64
Roles of Non-Coding RNA in Alzheimer’s Disease Pathophysiology
Olufunmilayo, E. O., & Holsinger, R. M. D. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms241512498 · Cited by 68
Dissecting the initiation of female meiosis in the mouse at single-cell resolution
Ge, W., Wang, J., Zhang, R., Tan, S., Zhang, F., Liu, W., Li, L., Sun, X.-F., Cheng, S., Dyce, P. W., et al. (2020) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-020-03533-8 · Cited by 58
Single‐cell RNA sequencing in Drosophila: Technologies and applications
Li, H. (2020) · Wiley Interdisciplinary Reviews Developmental Biology · doi:10.1002/wdev.396 · Cited by 55
Goals and approaches for each processing step for single-cell RNA sequencing data
Zhang, Z., Cui, F., Wang, C., Zhao, L., & Zou, Q. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa314 · Cited by 54
The rise of the distributions: why non-normality is important for understanding the transcriptome and beyond
Mar, J. C. (2019) · Biophysical Reviews · doi:10.1007/s12551-018-0494-4 · Cited by 55
Novel insights into adipose tissue heterogeneity
Wang, T., Sharma, A. K., & Wolfrum, C. (2021) · Reviews in Endocrine and Metabolic Disorders · doi:10.1007/s11154-021-09703-8 · Cited by 51
Single-cell transcriptome analysis of Physcomitrella leaf cells during reprogramming using microcapillary manipulation
Kubo, M., Nishiyama, T., Tamada, Y., Sano, R., Ishikawa, M., Murata, T., Imai, A., Lang, D., Demura, T., Reski, R., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz181 · Cited by 51
Interrogating islets in health and disease with single-cell technologies
Carrano, A. C., Mulas, F., Zeng, C., & Sander, M. (2017) · Molecular Metabolism · doi:10.1016/j.molmet.2017.04.012 · Cited by 49
Identification of differentially expressed genes by single‐cell transcriptional profiling of umbilical cord and synovial fluid mesenchymal stem cells
Jia, Z., Wang, S., & Liu, Q. (2019) · Journal of Cellular and Molecular Medicine · doi:10.1111/jcmm.14891 · Cited by 48
Differential Expression Analysis of Single-Cell RNA-Seq Data: Current Statistical Approaches and Outstanding Challenges
Das, S., Rai, A. K., & Rai, S. N. (2022) · Entropy · doi:10.3390/e24070995 · Cited by 47
A comparison of methods accounting for batch effects in differential expression analysis of UMI count based single cell RNA sequencing
Chen, W., Zhang, S., Williams, J., Ju, B., Shaner, B., Easton, J., Wu, G., & Chen, X. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.03.026 · Cited by 44
Advances in genomics and genome editing for breeding next generation of fruit and nut crops
Savadi, S., Mangalassery, S., & Sandesh, M. S. (2021) · Genomics · doi:10.1016/j.ygeno.2021.09.001 · Cited by 47
Macrophage Colony Stimulating Factor Derived from CD4+ T Cells Contributes to Control of a Blood-Borne Infection
Fontana, M. F., de Melo, G. L., Anidi, C. M., Hamburger, R., Kim, C. Y., Lee, S. Y., Pham, J., & Kim, C. C. (2016) · PLoS Pathogens · doi:10.1371/journal.ppat.1006046 · Cited by 44
Analysis of circulating breast cancer cell heterogeneity and interactions with peripheral blood mononuclear cells
Brechbuhl, H. M., Vinod-Paul, K., Gillen, A. E., Kopin, E. G., Gibney, K., ELIAS, A. D., Hayashi, M., Sartorius, C. A., & Kabos, P. (2020) · Molecular Carcinogenesis · doi:10.1002/mc.23242 · Cited by 44
scTPA: a web tool for single-cell transcriptome analysis of pathway activation signatures
Zhang, Y., Zhang, Y., Hu, J. S., Zhang, J., Guo, F., Zhou, M., Zhang, G., Yu, F., & Su, J. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa532 · Cited by 39
Alternative Polyadenylation in Stem Cell Self-Renewal and Differentiation
Sommerkamp, P., Cabezas‐Wallscheid, N., & Trumpp, A. (2021) · Trends in Molecular Medicine · doi:10.1016/j.molmed.2021.04.006 · Cited by 40
Multi-omics approaches to improve malaria therapy
Zhou, M., Varol, A., & Efferth, T. (2021) · Pharmacological Research · doi:10.1016/j.phrs.2021.105570 · Cited by 41
Kinetic Foundation of the Zero-Inflated Negative Binomial Model for Single-Cell RNA Sequencing Data
Jia, C. (2020) · SIAM Journal on Applied Mathematics · doi:10.1137/19m1253198 · Cited by 35
Imputation of spatially-resolved transcriptomes by graph-regularized tensor completion
Li, Z., Song, T., Yong, J., & Kuang, R. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008218 · Cited by 37
New Neurons in the Post-ischemic and Injured Brain: Migrating or Resident?
Nemirovich-Danchenko, N. M., & Khodanovich, M. Y. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00588 · Cited by 38
High-dimension single-cell analysis applied to cancer
Wang, L., Livak, K. J., & Wu, C. J. (2017) · Molecular Aspects of Medicine · doi:10.1016/j.mam.2017.08.005 · Cited by 34
Single-Cell Analysis in Immuno-Oncology
Christodoulou, M., & Zaravinos, A. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24098422 · Cited by 35
Single-cell RNA sequencing to track novel perspectives in HSC heterogeneity
Zhang, P., Li, X., Pan, C., Zheng, X., Hu, B., Xie, R., Hu, J., Shang, X., & Yang, H. (2022) · Stem Cell Research & Therapy · doi:10.1186/s13287-022-02718-1 · Cited by 44
The impact of microRNAs on transcriptional heterogeneity and gene co-expression across single embryonic stem cells
Gambardella, G., Carissimo, A., Chen, A. F., Cutillo, L., Nowakowski, T. J., di Bernardo, D., & Blelloch, R. H. (2017) · Nature Communications · doi:10.1038/ncomms14126 · Cited by 35
Cell Type-Selective Expression of Circular RNAs in Human Pancreatic Islets
Kaur, S., Mirza, A. H., & Pociot, F. (2018) · Non-Coding RNA · doi:10.3390/ncrna4040038 · Cited by 34
Understanding the complexity of retina and pluripotent stem cell derived retinal organoids with single cell RNA sequencing: current progress, remaining challenges and future prospective
Zerti, D., Collin, J., Queen, R. A., Cockell, S., & Lako, M. (2019) · Current Eye Research · doi:10.1080/02713683.2019.1697453 · Cited by 32
Identification of new marker genes from plant single‐cell RNA‐seq data using interpretable machine learning methods
Yan, H., Lee, J., Song, Q., Li, Q., Schiefelbein, J., Zhao, B., & Li, S. (2022) · New Phytologist · doi:10.1111/nph.18053 · Cited by 34
Techniques for investigating lncRNA transcript functions in neurodevelopment
Srinivas, T., Soares, E. S., & Guil, S. (2023) · Molecular Psychiatry · doi:10.1038/s41380-023-02377-5 · Cited by 37
Microdroplet-based one-step RT-PCR for ultrahigh throughput single-cell multiplex gene expression analysis and rare cell detection
Ma, J., Tran, G., Wan, A. M. D., Young, E. W. K., Kumacheva, E., Iscove, N. N., & Zandstra, P. W. (2021) · Scientific Reports · doi:10.1038/s41598-021-86087-4 · Cited by 30
Single cell transcriptome sequencing: A new approach for the study of mammalian sex determination
Stévant, I., & Nef, S. (2018) · Molecular and Cellular Endocrinology · doi:10.1016/j.mce.2018.01.013 · Cited by 29
Tracing production instability in a clonally derived CHO cell line using single‐cell transcriptomics
Tzani, I., Herrmann, N., Carillo, S., Spargo, C. A., Hagan, R., Barron, N., Bones, J., Dillmore, W. S., & Clarke, C. (2021) · Biotechnology and Bioengineering · doi:10.1002/bit.27715 · Cited by 28
Technical Advances in Single-Cell RNA Sequencing and Applications in Normal and Malignant Hematopoiesis
Huang, X., Li, X., Qin, P., Zhu, Y., Xu, S., & Chen, J. (2018) · Frontiers in Oncology · doi:10.3389/fonc.2018.00582 · Cited by 27
Absolute scaling of single-cell transcriptomes identifies pervasive hypertranscription in adult stem and progenitor cells
Kim, Y., Cho, B., Cook, D. P., Trcka, D., Wrana, J. L., & Ramalho‐Santos, M. (2023) · Cell Reports · doi:10.1016/j.celrep.2022.111978 · Cited by 31
An ultra high‐throughput, massively multiplexable, single‐cell RNA‐seq platform in yeasts
Brettner, L. M., Eder, R., Schmidlin, K., & Geiler‐Samerotte, K. (2024) · Yeast · doi:10.1002/yea.3927 · Cited by 26
CALLR: a semi-supervised cell-type annotation method for single-cell RNA sequencing data
Wei, Z., & Zhang, S. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab286 · Cited by 25
Single Cell RNA Sequencing in Autoimmune Inflammatory Rheumatic Diseases: Current Applications, Challenges and a Step Toward Precision Medicine
Kuret, T., SodinŠemrl, S., Leskošek, B., & Ferk, P. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2021.822804 · Cited by 25
The ‘TranSeq’ 3′‐end sequencing method for high‐throughput transcriptomics and gene space refinement in plant genomes
Tzfadia, O., Bocobza, S. E., Defoort, J., Almekias‐Siegl, E., Panda, S., Levy, M., Storme, V., Rombauts, S., Jaitin, D. A., Keren‐Shaul, H., et al. (2018) · The Plant Journal · doi:10.1111/tpj.14015 · Cited by 26
Replication Domains: Genome Compartmentalization into Functional Replication Units
Zhao, P. A., Rivera‐Mulia, J. C., & Gilbert, D. M. (2017) · Advances in experimental medicine and biology · doi:10.1007/978-981-10-6955-0_11 · Cited by 22
Functional genomic approaches to elucidate the role of enhancers during development
Ryan, G. E., & Farley, E. K. (2019) · WIREs Systems Biology and Medicine · doi:10.1002/wsbm.1467 · Cited by 22
Illuminating Messengers: An Update and Outlook on RNA Visualization in Bacteria
van Gijtenbeek, L. A., & Kok, J. (2017) · Frontiers in Microbiology · doi:10.3389/fmicb.2017.01161 · Cited by 24
Single-Cell mRNA Sequencing of the Mouse Brain Vasculature
Vanlandewijck, M., & Betsholtz, C. (2018) · Methods in molecular biology · doi:10.1007/978-1-4939-8712-2_21 · Cited by 21
Early Transcriptional Landscapes of Chlamydia trachomatis-Infected Epithelial Cells at Single Cell Resolution
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
Single-cell transcriptome reveals core cell populations and androgen-RXFP2 axis involved in deer antler full regeneration
Ba, H., Wang, X., Wang, D., Ren, J., Wang, Z., Sun, H., Hu, P., Zhang, G., Wang, S., Ma, C., et al. (2022) · Cell Regeneration · doi:10.1186/s13619-022-00153-4 · Cited by 21
Unmasking cellular response of a bloom-forming alga to viral infection by resolving expression profiles at a single-cell level
Rosenwasser, S., Sheyn, U., Frada, M. J., Pilzer, D., Rotkopf, R., & Vardi, A. (2019) · PLoS Pathogens · doi:10.1371/journal.ppat.1007708 · Cited by 22
Precision monitoring of immunotherapies in solid organ and hematopoietic stem cell transplantation
DiLoreto, R., Khush, K. K., & Vlaminck, I. D. (2017) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2017.06.009 · Cited by 21

Authors: Liu, S., & Trapnell, C.
Affiliations: University of Washington (US)
Corresponding author: Trapnell, Cole
ORCID: Liu, Serena
Trapnell, Cole
Venue: F1000Research
Publisher: F1000 Research Ltd
Published: 2016-02-17
Volume: 5
Pages: 182
ISSN: 2046-1402 (ISSN-L)
DOI: 10.12688/f1000research.7223.1
PMC: PMC4758375
OpenAlex: W2288153033
Cited by: 230
References: 110
Citations by year: 2026: 14 · 2025: 12 · 2024: 14 · 2023: 22 · 2022: 13 · 2021: 21 · 2020: 35 · 2019: 27 · 2018: 32 · 2017: 34 · 2016: 6
FWCI: 2.99
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (unspecified)
Locations: F1000Research (publishedVersion, cc-by)
Europe PMC (PubMed Central) (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders: National Science Foundation (1258485)
Damon Runyon Cancer Research Foundation
National Institutes of Health (NIH DP2)
Eunice Kennedy Shriver National Institute of Child Health and Human Development (DP2HD088158)
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, cellular heterogeneity, cell state dynamics, developmental trajectories, transcriptional dynamics, single-cell data analysis, single cell testing

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

Single-cell transcriptome sequencing: recent advances and remaining challenges

Abstract

Overview

Integrity signals

Figures

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks