Single-cell transcriptome sequencing: recent advances and remaining challenges

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MicroRNAs: small RNAs with a big role in gene regulation

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Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.

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Spatial reconstruction of single-cell gene expression data

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Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma

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mRNA-Seq whole-transcriptome analysis of a single cell

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Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells

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Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq

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Performance comparison of high throughput single-cell RNA-Seq platforms in complex tissues

Colino‐Sanguino, Y., de la Fuente, L. R., Gloss, B., Law, A. M. K., Handler, K., Pajic, M., Salomon, R., Gallego‐Ortega, D., & Valdés‐Mora, F. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e37185 · Cited by 22

An in vitro model of intestinal infection reveals a developmentally regulated transcriptome of Toxoplasma sporozoites and a NF-κB-like signature in infected host cells

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Navigating single-cell RNA-sequencing: protocols, tools, databases, and applications

Arya, A., Tripathi, P., Dubey, N., Aier, I., & Varadwaj, P. K. (2025) · Genomics & Informatics · doi:10.1186/s44342-025-00044-5 · Cited by 20

The Use of Non-Apoptotic Sperm Selected by Magnetic Activated Cell Sorting (MACS) to Enhance Reproductive Outcomes: What the Evidence Says

Garrido, N., & Juliá, M. G. (2024) · Biology · doi:10.3390/biology13010030 · Cited by 19

Visualizing omics and clinical data: Which challenges for dealing with their variety?

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Novel application of single-cell next-generation sequencing for determination of intratumoral heterogeneity of canine osteosarcoma cell lines

Ayers, J., Milner, R. J., Cortés-Hinojosa, G., Riva, A., Axiak‐Bechtel, S. M., Sahay, B., Cascio, M., Lejeune, A. T., Shiomitsu, K., Souza, C. H. M., et al. (2021) · Journal of Veterinary Diagnostic Investigation · doi:10.1177/1040638720985242 · Cited by 19

New Technologies for Monitoring Marine Mammal Health

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Uncovering transcriptional dark matter via gene annotation independent single-cell RNA sequencing analysis

Wang, M. F. Z., Mantri, M., Chou, S., Scuderi, G. J., McKellar, D. W., Butcher, J. T., Danko, C. G., & Vlaminck, I. D. (2021) · Nature Communications · doi:10.1038/s41467-021-22496-3 · Cited by 20

Decoding cellular plasticity and niche regulation of limbal stem cells during corneal wound healing

Sun, D., Zhang, X., Chen, R., Sang, T., Li, Y., Wang, Q., Xie, L., Zhou, Q., & Dou, S. (2024) · Stem Cell Research & Therapy · doi:10.1186/s13287-024-03816-y · Cited by 16

Comprehensive multi-omics single-cell data integration reveals greater heterogeneity in the human immune system

Xu, C., Yang, J., Kosters, A., Babcock, B. R., Qiu, P., & Ghosn, E. (2022) · iScience · doi:10.1016/j.isci.2022.105123 · Cited by 18

Advances and applications of immune receptor sequencing in systems immunology

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Single cell RNA sequencing improves the next generation of approaches to AML treatment: challenges and perspectives

Khosroabadi, Z., Azaryar, S., Dianat‐Moghadam, H., Amoozgar, Z., & Sharifi, M. (2025) · Molecular Medicine · doi:10.1186/s10020-025-01085-w · Cited by 18

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

Frontline Science: Cxxc5 expression alters cell cycle and myeloid differentiation of mouse hematopoietic stem and progenitor cells

Joshi, H. R., Hill, H. R., Zhou, Z., He, X., Voelkerding, K. V., & Kumánovics, A. (2020) · Journal of Leukocyte Biology · doi:10.1002/jlb.1hi0120-169r · Cited by 14

FIN-Seq: transcriptional profiling of specific cell types from frozen archived tissue of the human central nervous system

Amamoto, R., Zuccaro, E., Curry, N. C., Khurana, S., Chen, H.-H., Cepko, C. L., & Arlotta, P. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz968 · Cited by 16

Mouse models in the era of large human tumour sequencing studies

de Ruiter, J. R., Wessels, L. F. A., & Jonkers, J. (2018) · Open Biology · doi:10.1098/rsob.180080 · Cited by 16

Dimensionality reduction for single cell RNA sequencing data using constrained robust non-negative matrix factorization

Zhang, S., Liu, Y., Yang, J., Lin, Z., & Ng, M. K. (2020) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqaa064 · Cited by 15

Single-cell transcriptome provides novel insights into antler stem cells, a cell type capable of mammalian organ regeneration

Ba, H., Wang, D., Wu, W., Sun, H., & Li, C. (2019) · Functional & Integrative Genomics · doi:10.1007/s10142-019-00659-2 · Cited by 15

A Tool for Visualization and Analysis of Single-Cell RNA-Seq Data Based on Text Mining

Gambardella, G., & di Bernardo, D. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00734 · Cited by 15

A descriptive marker gene approach to single-cell pseudotime inferencePreprint

Campbell, K. R., & Yau, C. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/060442 · Cited by 15

Integrated analyses reveal evolutionarily conserved and specific injury response genes in dorsal root ganglion

Xu, L., Chen, Z., Li, X., Xu, H., Zhang, Y., Yang, W., Chen, J., Zhang, S., Xu, L., Zhou, S., et al. (2022) · Scientific Data · doi:10.1038/s41597-022-01783-8 · Cited by 15

Single-cell transcriptomic analysis reveals transcriptional and cell subpopulation differences between human and pig immune cells

Li, J., Xu, Y., Zhang, J., Zhang, Z., Guo, H., Wei, D., Wu, C., Hai, T., Sun, H., & Zhao, Y. X. (2023) · Genes & Genomics · doi:10.1007/s13258-023-01456-9 · Cited by 16

Identifying prognostic biomarkers in oral squamous cell carcinoma: an integrated single-cell and bulk RNA sequencing study on mitophagy-related genes

Li, M., Wei, Y., Huang, W., Wang, C., He, S., Bi, S., Hu, S., You, L., & Huang, X. (2024) · Scientific Reports · doi:10.1038/s41598-024-70498-0 · Cited by 14

MP3RNA‐seq: Massively parallel 3′ end RNA sequencing for high‐throughput gene expression profiling and genotyping

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Genetic and Molecular Contributors in Eosinophilic Esophagitis

Sato, H., Osonoi, K., Sharlin, C. S., & Shoda, T. (2023) · Current Allergy and Asthma Reports · doi:10.1007/s11882-023-01075-0 · Cited by 14

Integrating phylogenies into single-cell RNA sequencing analysis allows comparisons across species, genes, and cells

Church, S. H., Mah, J. L., & Dunn, C. W. (2024) · PLoS Biology · doi:10.1371/journal.pbio.3002633 · Cited by 13

Psychological stress increases skin infection through the action of TGFβ to suppress immune-acting fibroblasts

Chan, H., Li, F., Dokoshi, T., Cavagnero, K. J., Li, Q., Chen, Y., Aguilera, C., Nakatsuji, T., Liu, E., Indra, A., et al. (2025) · Science Immunology · doi:10.1126/sciimmunol.ads0519 · Cited by 13

Nucleotide and structural label identification in single RNA molecules with quantum tunneling spectroscopy

Abel, G. R., Korshoj, L. E., Otoupal, P. B., Khan, S. A., Chatterjee, A., & Nagpal, P. (2018) · Chemical Science · doi:10.1039/c8sc03354d · Cited by 10

scRNAss: a single-cell RNA-seq assembler via imputing dropouts and combing junctions

Liu, J., Liu, X., Ren, X., & Li, G. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz240 · Cited by 10

Single-cell RNA sequencing in cornea research: Insights into limbal stem cells and their niche regulation

Sun, D., Shi, W., & Dou, S. (2023) · World Journal of Stem Cells · doi:10.4252/wjsc.v15.i5.466 · Cited by 12

Single-cell RNA-seq analysis maps the development of human fetal retinaPreprint

Lu, Y., Yi, W., Wu, Q., Zhong, S., Zuo, Z., Zhao, F., Zhang, M., Tsai, N. Y., Zhuo, Y., He, S., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/423830 · Cited by 12

The Secret Life of RNA: Lessons from Emerging Methodologies

Médioni, C., & Besse, F. (2017) · Methods in molecular biology · doi:10.1007/978-1-4939-7213-5_1 · Cited by 11

Improved downstream functional analysis of single-cell RNA-sequence data using DGAN

Pandey, D., & Perumal, O. (2023) · Scientific Reports · doi:10.1038/s41598-023-28952-y · Cited by 10

Elucidating the Role of Host Long Non-Coding RNA during Viral Infection: Challenges and Paths Forward

Lemler, D. J., Brochu, H. N., Yang, F., Harrell, E. A., & Peng, X. (2017) · Vaccines · doi:10.3390/vaccines5040037 · Cited by 11

Altered visual processing in the mdx52 mouse model of Duchenne muscular dystrophy

Barboni, M. T. S., Liber, A. M. P., Joachimsthaler, A., Saoudi, A., Goyenvalle, A., Rendón, Á., Roger, J. E., Ventura, D. F., Kremers, J., & Vaillend, C. (2021) · Neurobiology of Disease · doi:10.1016/j.nbd.2021.105288 · Cited by 11

The generation of midbrain dopaminergic neurons

Blaess, S., Stott, S., & Ang, S. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-814405-3.00017-5 · Cited by 12

A sparse differential clustering algorithm for tracing cell type changes via single-cell RNA-sequencing data

Barron, M., Zhang, S., & Li, J. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx1113 · Cited by 11

I-Impute: a self-consistent method to impute single cell RNA sequencing data

Feng, X., Chen, L., Wang, Z., & Li, S. C. (2020) · BMC Genomics · doi:10.1186/s12864-020-07007-w · Cited by 11

Single-cell transcriptome landscape of the kidney reveals potential innate immune regulation mechanisms in hybrid yellow catfish after Aeromonas hydrophila infection

Guo, S., Zeng, M., Wang, Z., Zhang, C., Fan, Y., Ran, M., Shi, Q., & Song, Z. (2024) · Fish & Shellfish Immunology · doi:10.1016/j.fsi.2024.109866 · Cited by 10

Lung Immune Cell Niches and the Discovery of New Cell Subtypes

Shan, Q., Qiu, J., Dong, Z., Xu, X., Zhang, S., Ma, J., & Liu, S. (2024) · Advanced Science · doi:10.1002/advs.202405490 · Cited by 10

A heterogeneous cellular response to ionizing radiation revealed by single cell transcriptome sequencing.

Gao, Y., Duan, Q., Wu, N., & Xu, B. (2021) · PubMed Central · Cited by 10

Optimal transport reveals dynamic gene regulatory networks via gene velocity estimation

Zhao, W., Larschan, E., Sandstede, B., & Singh, R. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012476 · Cited by 12

Big data analytics in single‐cell transcriptomics: Five grand opportunities

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RIA: a novel Regression-based Imputation Approach for single-cell RNA sequencing

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Amalgam regulates the receptor tyrosine kinase pathway through Sprouty in glial cell development

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Executable models of immune signaling pathways in HIV-associated atherosclerosis

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Integrated analysis of plasma proteome and cortex single-cell transcriptome reveals the novel biomarkers during cortical aging

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Binomial models uncover biological variation during feature selection of droplet-based single-cell RNA sequencing

Sparta, B., Hamilton, T., Natesan, G., Aragones, S. D., & Deeds, E. J. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012386 · Cited by 7

Simultaneous Measurement of Surface Proteins and Gene Expression from Single Cells

Luo, J., Erb, C., & Chen, K. (2020) · Methods in molecular biology · doi:10.1007/978-1-0716-0266-9_3 · Cited by 8

SCHiRM: Single Cell Hierarchical Regression Model to detect dependencies in read count dataPreprint

Intosalmi, J., Mannerström, H., Hiltunen, S., & Lähdesmäki, H. J. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/335695 · Cited by 7

Normalizing need not be the norm: count-based math for analyzing single-cell data

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Recent advances and future perspectives in vector-omics

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Advancing towards a global mammalian gene regulation model through single-cell analysis and synthetic biology

Tycko, J., Van, M. V., Elowitz, M. B., & Bintu, L. (2017) · Current Opinion in Biomedical Engineering · doi:10.1016/j.cobme.2017.10.011 · Cited by 7

Single cell transcriptome analysis of MCF-7 reveals consistently and inconsistently expressed gene groups each associated with distinct cellular localization and functions

Chiang, Y., Huang, Y., Midha, M. K., Chen, T.-H., Shiau, H.-C., & Chiu, K. (2018) · PLoS ONE · doi:10.1371/journal.pone.0199471 · Cited by 7

Multiparameter cell characterization using nanofluidic technology facilitates real-time phenotypic and genotypic elucidation of intratumor heterogeneityPreprint

Beaumont, K. G., Hamou, W., Bozinovic, N., Silvers, T. R., Shah, H., Dave, A., Allette, K., Strahl, M., Wang, Y., Arib, H., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/457010 · Cited by 7

Identification of new marker genes from plant single-cell RNA-seq data using interpretable machine learning methodsPreprint

Yan, H., Lee, J., Song, Q., Li, Q., Schiefelbein, J., Zhao, B., & Li, S. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.11.22.393165 · Cited by 7

Bacterial Siderophore Production in Metal-Rich Environments: Underexplored Sources of Siderophores and Insights into Bioremediation

Knapp, M., & Giddings, L. (2026) · Journal of Natural Products · doi:10.1021/acs.jnatprod.5c01586 · Cited by 6

Enabling high-throughput single-animal gene-expression studies with molecular and micro-scale technologies

Wan, J., & Lu, H. (2020) · Lab on a Chip · doi:10.1039/d0lc00881h · Cited by 7

Drug Design for Malaria with Artificial Intelligence (AI)

Ghosh, B., & Choudhuri, S. (2021) · IntechOpen eBooks · doi:10.5772/intechopen.98695 · Cited by 6

SMURF: embedding single-cell RNA-seq data with matrix factorization preserving self-consistency

Pu, J., Wang, B., Liu, X., Chen, L., & Li, S. C. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad026 · Cited by 7

Isolation of Insect Bacteriocytes as a Platform for Transcriptomic Analyses

Lopes, M. R., Simonet, P., Duport, G., Gaget, K., Balmand, S., Sugio, A., Simon, J., Parisot, N., & Calevro, F. (2020) · Methods in molecular biology · doi:10.1007/978-1-0716-0743-5_13 · Cited by 6

Single-Cell Transcriptome Analysis of Human Adipose-Derived Stromal Cells Identifies a Contractile Cell Subpopulation

Wolmarans, E., Mellet, J., Durandt, C., Joubert, F., & Pepper, M. (2021) · Stem Cells International · doi:10.1155/2021/5595172 · Cited by 5

A Data-Distribution and Successive Spline Points based discretization approach for evolving gene regulatory networks from scRNA-Seq time-series data using Cartesian Genetic Programming

da Silva, J. E. H., de Carvalho, P. C., Camata, J. J., de Oliveira, I. L., & Bernardino, H. S. (2024) · Biosystems · doi:10.1016/j.biosystems.2024.105126 · Cited by 5

Quantitation of nonspecific protein adsorption at solid–liquid interfaces for single-cell proteomics

Lee, M. C. G., & Sun, B. (2017) · Canadian Journal of Chemistry · doi:10.1139/cjc-2017-0304 · Cited by 4

Single‐cell RNA Sequencing Contributes to the Treatment of Acute Myeloid Leukaemia With Hematopoietic Stem Cell Transplantation, Chemotherapy, and Immunotherapy

Saleh, R. O., Hjazi, A. M., Rab, S. O., Uthirapathy, S., Ganesan, S., Shankhyan, A., Kumar, M. R., Sharma, G. C., Kariem, M., & Ahmed, J. K. (2025) · Journal of Biochemical and Molecular Toxicology · doi:10.1002/jbt.70218 · Cited by 4

Gene Regulatory Networks from Single Cell Data for Exploring Cell Fate Decisions

Chan, T. E., Stumpf, M. P. H., & Babtie, A. C. (2019) · Methods in molecular biology · doi:10.1007/978-1-4939-9224-9_10 · Cited by 4

Cultivation of Hair Matrix Cells from Cashmere Goat Skins and Exemplified Applications

Ma, S., Wang, L., Zong, B., Wang, Y., Wang, X., Shi, Y., Yang, Y., & Chen, Y. (2020) · Animals · doi:10.3390/ani10081400 · Cited by 4

Using DenseFly algorithm for cell searching on massive scRNA-seq datasets

Chen, Y., Chen, S., & Zhang, X. (2020) · BMC Genomics · doi:10.1186/s12864-020-6651-8 · Cited by 4

On the Analysis of CGP Mutation Operators When Inferring Gene Regulatory Networks Using ScRNA-Seq Time Series Data

da Silva, J. E. H., Bernardino, H. S., de Oliveira, I. L., Vieira, A. B., & Barbosa, H. J. C. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-91702-9_18 · Cited by 4

Comparison of Principal Component Analysis and t-Stochastic Neighbor Embedding with Distance Metric Modifications for Single-cell RNA-sequencing Data AnalysisPreprint

Kwon, H., Fan, J., & Kharchenko, P. V. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/102780 · Cited by 4

A survey of the methodological process of modeling, inference, and evaluation of gene regulatory networks using scRNA-Seq data

da Silva, J. E. H., Bernardino, H. S., de Oliveira, I. L., & Camata, J. J. (2025) · Biosystems · doi:10.1016/j.biosystems.2025.105464 · Cited by 4

Twin Scholarships of Glycomedicine and Precision Medicine in Times of Single-Cell Multiomics

Oh, S., Cao, W., & Song, M. (2024) · OMICS A Journal of Integrative Biology · doi:10.1089/omi.2024.0111 · Cited by 3

Advances in analyzing RNA diversity in eukaryotic transcriptomes: peering through the Omics lens

Bangru, S., & Kalsotra, A. (2016) · F1000Research · doi:10.12688/f1000research.9511.1 · Cited by 3

LLE Based K-Nearest Neighbor Smoothing for scRNA-Seq Data Imputation

Feng, Y., Ai, Y., & Jiang, H. (2023) · Financial Mathematics and Fintech · doi:10.1007/978-981-99-2366-3_11 · Cited by 3

scCrab: A Reference-Guided Cancer Cell Identification Method based on Bayesian Neural Networks

Hua, H., Long, W., Pan, Y., Li, S., Zhou, J., Wang, H., & Chen, S. (2024) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-024-00655-6 · Cited by 3

Functional genomics approaches for combating the effect of abiotic stresses

Hada, A., Jaabir, M. S. M., Singh, N., Changwal, C., & Kumar, A. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-822849-4.00013-9 · Cited by 3

Decoding nervous system by single‐cell RNA sequencing

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RNA sequencing and machine learning as molecular scalpels

Lewis, M., & Barnes, M. R. (2018) · Nature Reviews Rheumatology · doi:10.1038/s41584-018-0012-x · Cited by 3

Deep generative autoencoder for low-dimensional embeding extraction from single-cell RNAseq data

Sun, S., Liu, Y., & Shang, X. (2019) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm47256.2019.8983289 · Cited by 2

Urn models for stochastic gene expression yield intuitive insights into the probability distributions of single-cell mRNA and protein counts

Choudhary, K., & Narang, A. (2020) · Physical Biology · doi:10.1088/1478-3975/aba50f · Cited by 3

Single-cell aging clocks: A precision tool for dissecting and targeting the aging process

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Application of a Modified Smart-seq2 Sample Preparation Protocol for Rare Cell Full-length Single-cell mRNA Sequencing to Mouse Oocytes

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Proceedings of the Second International Forum on Financial Mathematics and Financial Technology

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Systems Biology: Methods and Applications

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Role of Transcriptomics in Elucidating Mechanism of Abiotic Stress Tolerance in Plants

Rizvi, M. Z., Abid, M., Pandey, S. N., & Khan, M. M. A. A. (2024) · Microorganisms for sustainability · doi:10.1007/978-981-97-2355-3_7 · Cited by 2

SMURF: embedding single-cell RNA-seq data with matrix factorization preserving self-consistencyPreprint

Wang, B., Pu, J., Chen, L., & Li, S. C. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.04.22.489140 · Cited by 2

Screening and Validation of Genes Associated With Lysosomal‐Dependent Cell Death in Colorectal Cancer

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Comprehensive multi-omics analysis identifies transferrin receptor as a therapeutic target in esophageal cancer

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