Cited by 920Single-cell and spatial transcriptomicsGenomics and Phylogenetic StudiesGene expression and cancer classification

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Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex

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Pollen, Alex A, Nowakowski, Tomasz J, Shuga, Joe, Wang, Xiaohui, Leyrat, Anne A, Lui, Jan H, Li, Nianzhen, Szpankowski, Lukasz, Fowler, Brian, Chen, Peilin, Ramalingam, Naveen, Sun, Gang, Thu, Myo, Norris, Michael, Lebofsky, Ronald, Toppani, Dominique, Kemp, Darnell W, Wong, Michael, Clerkson, Barry, Jones, Brittnee N, Wu, Shiquan, Knutsson, Lawrence, Alvarado, Beatriz, Wang, Jing, Weaver, Lesley S, May, Andrew P, Jones, Robert C, Unger, Marc A, Kriegstein, Arnold R, West, Jay A A·2014·Nature Biotechnology

DOI 10.1038/nbt.2967PMC PMC4191988

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Abstract

Large-scale surveys of single-cell gene expression have the potential to reveal rare cell populations and lineage relationships but require efficient methods for cell capture and mRNA sequencing. Although cellular barcoding strategies allow parallel sequencing of single cells at ultra-low depths, the limitations of shallow sequencing have not been investigated directly. By capturing 301 single cells from 11 populations using microfluidics and analyzing single-cell transcriptomes across downsampled sequencing depths, we demonstrate that shallow single-cell mRNA sequencing (~50,000 reads per cell) is sufficient for unbiased cell-type classification and biomarker identification. In the developing cortex, we identify diverse cell types, including multiple progenitor and neuronal subtypes, and we identify EGR1 and FOS as previously unreported candidate targets of Notch signaling in human but not mouse radial glia. Our strategy establishes an efficient method for unbiased analysis and comparison of cell populations from heterogeneous tissue by microfluidic single-cell capture and low-coverage sequencing of many cells.

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Ultrafast and memory-efficient alignment of short DNA sequences to the human genome.
Langmead, B., Trapnell, C., Pop, M., & Salzberg, S. L. (2009) · Genome Biology · doi:10.1186/gb-2009-10-3-r25 · Cited by 21,849
TopHat: discovering splice junctions with RNA-Seq
Trapnell, C., Pachter, L. S., & Salzberg, S. L. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp120 · Cited by 11,682
Pvclust: an R package for assessing the uncertainty in hierarchical clustering
Suzuki, R., & Shimodaira, H. (2006) · Bioinformatics · doi:10.1093/bioinformatics/btl117 · Cited by 2,456
The UCSC Genome Browser database: update 2011
Fujita, P. A., Rhead, B., Zweig, A. S., Hinrichs, A. S., Karolchik, D., Cline, M., Goldman, M. J., Barber, G. P., Clawson, H., Coelho, A., et al. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq963 · Cited by 1,843
Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types
Jaitin, D. A., Kenigsberg, E., Keren-Shaul, H., Elefant, N., Paul, F., Zaretsky, I., Mildner, A., Cohen, N., Jung, S., Tanay, A., et al. (2014) · Science · doi:10.1126/science.1247651 · Cited by 1,795
Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells
Ramsköld, D., Luo, S., Wang, Y.-C., Li, R., Deng, Q., Faridani, O. R., Daniels, G. A., Khrebtukova, I., Loring, J. F., Laurent, L. C., et al. (2012) · Nature Biotechnology · doi:10.1038/nbt.2282 · Cited by 1,609
Sequencing depth and coverage: key considerations in genomic analyses
Sims, D., Sudbery, I. M., Ilott, N. E., Heger, A., & Ponting, C. P. (2014) · Nature Reviews Genetics · doi:10.1038/nrg3642 · Cited by 1,488
Single-Cell RNA-Seq Reveals Dynamic, Random Monoallelic Gene Expression in Mammalian Cells
Deng, Q., Ramsköld, D., Reinius, B., & Sandberg, R. (2014) · Science · doi:10.1126/science.1245316 · Cited by 1,345
Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq
Treutlein, B., Brownfield, D. G., Wu, A. R., Neff, N. F., Mantalas, G. L., Espinoza, F. H., Desai, T. J., Krasnow, M. A., & Quake, S. R. (2014) · Nature · doi:10.1038/nature13173 · Cited by 1,342
Transcriptional landscape of the prenatal human brain
Miller, J. A., Ding, S.-L., Sunkin, S. M., Smith, K. A., Ng, L., Szafer, A., Ebbert, A., Riley, Z. L., Royall, J. J., Aiona, K., et al. (2014) · Nature · doi:10.1038/nature13185 · Cited by 1,325

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Gan, S., Deng, H., Qiu, Y., Alshahrani, M., & Liu, S. (2022) · Current Bioinformatics · doi:10.2174/1574893617666220330151024 · Cited by 6
Single-Cell Transcriptome Analysis of Developing and Regenerating Spiral Ganglion Neurons
Kwan, K. Y. (2016) · Current Pharmacology Reports · doi:10.1007/s40495-016-0064-z · Cited by 6
Neocortex Expansion in Development and Evolution: The Cell Biology of Neural Stem and Progenitor Cells and the Impact of Human-Specific Gene Expression
Kalebic, N., Long, K. R., & Huttner, W. Β. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-804042-3.00136-6 · Cited by 6
Single cell analysis of the inner ear sensory organs
Barnea, O. Y., & Avraham, K. B. (2017) · The International Journal of Developmental Biology · doi:10.1387/ijdb.160453ka · Cited by 6
Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
Stratton, J. A., Sinha, S., Shin, W., Labit, E., Chu, T., Shah, P. T., Midha, R., & Biernaskie, J. A. (2019) · Journal of Visualized Experiments · doi:10.3791/58709 · Cited by 6
Single-cell RNA-Sequencing in Neuroscience
Mayer, S., Khakipoor, S., Droemer, M. A., & Cozetto, D. A. (2019) · e-Neuroforum · doi:10.1515/nf-2019-0021 · Cited by 6
scLINE: A multi-network integration framework based on network embedding for representation of single-cell RNA-seq data
Li, H., Xiao, X., Wu, X., Ye, L., & Ji, G. (2021) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2021.103899 · Cited by 6
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Azim, R., Wang, S., & Dipu, S. A. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105658 · Cited by 6
Differential Pathway Analysis
Fan, J. (2019) · Methods in molecular biology · doi:10.1007/978-1-4939-9057-3_7 · Cited by 5
Detection of Cell Types from Single-cell RNA-seq Data using Similarity via Kernel Preserving Learning Embedding
Liu, Z., Liu, F., Hong, C., Gao, M., Chen, Y. P., Liu, S., & Zhang, W. (2019) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm47256.2019.8983395 · Cited by 6
MAPS-seq: magnetic bead-assisted parallel single-cell gene expression profiling
Park, M.-S., Lee, D., Bang, D., & Lee, J. H. (2020) · Experimental & Molecular Medicine · doi:10.1038/s12276-020-0433-x · Cited by 6
CellSIUS provides sensitive and specific detection of rare cell populations from complex single cell RNA-seq dataPreprint
Wegmann, R., Neri, M., Schuierer, S., Bilican, B., Hartkopf, H., Nigsch, F., Mapa, F., Waldt, A., Cuttat, R., Salick, M. R., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/514950 · Cited by 6
Probabilistic Count Matrix Factorization for Single Cell Expression Data AnalysisPreprint
Durif, G., Modolo, L., Mold, J. E., Lambert‐Lacroix, S., & Picard, F. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/211938 · Cited by 6
Spatial RNA sequencing identifies robust markers of vulnerable and resistant human midbrain dopamine neurons and their expression in Parkinson’s DiseasePreprint
Benitez, J. A., Cheng, S., Kee, N., Cao, M., Wang, M., Deng, Q., & Hedlund, E. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/334417 · Cited by 6
Generating realistic cell samples for gene selection in scRNA-seq data: A novel generative frameworkPreprint
Lall, S., Ray, S., & Bandyopadhyay, S. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.04.29.441920 · Cited by 6
Barcode-free prediction of cell lineages from scRNA-seq datasetsPreprint
Eisele, A. S., Tarbier, M., Dormann, A. A., Pelechano, V., & Suter, D. M. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.09.20.508646 · Cited by 6
An integrated single cell and spatial omics atlas of human prenatal developmentPreprint
Webb, S., Rose, A., Xu, C., Steele, L., Kuri, M. A., Stephenson, E., İnecik, K., Jafree, D. J., Foster, A. R., Basurto-Lozada, D., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.03.30.714220 · Cited by 7
OrgaCCC: Orthogonal graph autoencoders for constructing cell-cell communication networks on spatial transcriptomics data
Feng, X., Zhang, S., & Li, L. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013212 · Cited by 7
Single-molecule peptide sequencing through reverse translation of peptides into DNA
Zheng, L., Sun, Y., Hein, L. A., Eisenstein, M. S., & Soh, H. T. (2026) · Nature Biotechnology · doi:10.1038/s41587-026-03061-z · Cited by 5
Applications of single‐cell multi‐omics sequencing in deep understanding of brain diseases
Zhang, X., Lü, P., & Shen, X. (2022) · Clinical and Translational Discovery · doi:10.1002/ctd2.95 · Cited by 6
KGLRR: A low-rank representation K-means with graph regularization constraint method for Single-cell type identification
Wang, L., Liu, J., Shang, J., Kong, X.-Z., Guan, B., & Wang, J. (2023) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2023.107862 · Cited by 5
Self-citation
Imputation method for single-cell RNA-seq data using neural topic model
Qi, Y., Han, S., Tang, L., & Liu, L. (2022) · GigaScience · doi:10.1093/gigascience/giad098 · Cited by 4
Clustering scRNA-seq data with the cross-view collaborative information fusion strategy
Lou, Z., Wei, X., Hu, Y., Hu, S., Wu, Y., & Tian, Z. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae511 · Cited by 7
Vertical flow array chips reliably identify cell types from single-cell mRNA sequencing experiments
Shirai, M., Arikawa, K., Taniguchi, K., Tanabe, M., & Sakai, T. (2016) · Scientific Reports · doi:10.1038/srep36014 · Cited by 5
The Significance of Single-Cell Biomedicine in Stem Cells
Zhuge, W., Yan, F., Zhu, Z., & Wang, X. (2018) · Advances in experimental medicine and biology · doi:10.1007/978-981-13-0502-3_16 · Cited by 5
Self-citation
Density peaks based clustering for single-cell interpretation via multikernel learning
Kausar, S., Mehmood, R., Iqbal, M. S., Bie, R., Ali, S., & Shabir, M. Y. (2019) · Procedia Computer Science · doi:10.1016/j.procs.2019.01.187 · Cited by 5
scASK: A Novel Ensemble Framework for Classifying Cell Types Based on Single-cell RNA-seq Data
Liu, B., Wu, F., & Zou, X. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3050963 · Cited by 5
Glycine Receptor Inhibition Differentially Affect Selected Neuronal Populations of the Developing Embryonic Cortex, as Evidenced by the Analysis of Spontaneous Calcium Oscillations
Ávila, D., Aedo, E., Sánchez-Hechavarría, M. E., Ávila, C., & Ávila, A. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21218013 · Cited by 6
Effectively Clustering Single Cell RNA Sequencing Data by Sparse Representation
Li, R.-Y., Wang, Z., Guan, J., & Zhou, S. (2021) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2021.3128576 · Cited by 5
Quantifying protein abundance on single cells using split-pool sequencing on DNA-barcoded antibodies for diagnostic applications
Sheng, J., Hod, E. A., Vlad, G., & Chavez, A. (2022) · Scientific Reports · doi:10.1038/s41598-022-04842-7 · Cited by 6
Uncertainty measurement for a gene space based on class-consistent technology: an application in gene selection
Li, Z., Zhang, Q., Wang, P., Song, Y., & Wen, C. (2022) · Applied Intelligence · doi:10.1007/s10489-022-03657-3 · Cited by 5
Building blocks of the human brain
Nowakowski, T. J. (2018) · Science · doi:10.1126/science.aav1252 · Cited by 5
Self-citation
Feature Selection for Topological Proximity Prediction of Single-Cell Transcriptomic Profiles in Drosophila Embryo Using Genetic Algorithm
Gupta, S., Verma, A. K., & Ahmad, S. (2020) · Genes · doi:10.3390/genes12010028 · Cited by 5
Seq-ing the cortex one neuron at a time
Chen, H.-H., & Arlotta, P. (2016) · Nature Neuroscience · doi:10.1038/nn.4230 · Cited by 5
MGKA: A genetic algorithm-based clustering technique for genomic data
Nguyen, H., Louis, S. J., & Nguyen, T. (2019) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.2019.8790225 · Cited by 5
ClusterMine: A knowledge-integrated clustering approach based on expression profiles of gene sets
Li, H., Xu, Y., Zhu, X., Liu, Q., Omenn, G. S., & Wang, J. (2020) · Journal of Bioinformatics and Computational Biology · doi:10.1142/s0219720020400090 · Cited by 5
Self-citation
Molecular organization of CA1 interneuron classesPreprint
Harris, K. D., Magno, L., Katona, L., Lönnerberg, P., Muñoz‐Manchado, A. B., Somogyi, P., Kessaris, N., Linnarsson, S., & Hjerling‐Leffler, J. (2015) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/034595 · Cited by 5
Heterogeneous progenitor cell behaviors underlie the assembly of neocortical cytoarchitecturePreprint
Llorca, A., Ciceri, G., Beattie, R., Wong, F. K., Diana, G., Serafeimidou‐Pouliou, E., Fernández-Otero, M., Streicher, C., Arnold, S. J., Meyer, M. P., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/494088 · Cited by 5
Age-associated changes in lineage composition of the enteric nervous system regulate gut health and diseasePreprint
Kulkarni, S., Saha, M., Slosberg, J., Singh, A., Nagaraj, S., Becker, L. S., Zhang, C., Bukowski, A., Wang, Z., Liu, G., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.08.25.262832 · Cited by 5
scMINER: a mutual information-based framework for identifying hidden drivers from single-cell omics dataPreprint
Ding, L., Shi, H., Qian, C., Burdyshaw, C., Veloso, J. P., Khatamian, A., Pan, Q., Dhungana, Y., Xie, Z., Risch, I., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.01.26.523391 · Cited by 5
scEM: A New Ensemble Framework for Predicting Cell Type Composition Based on scRNA-Seq Data
Cai, X., Zhang, W., Zheng, X., Xu, Y., & Li, Y. (2024) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-023-00601-y · Cited by 4
Cauchy hyper-graph Laplacian nonnegative matrix factorization for single-cell RNA-sequencing data analysis
Wang, G.-F., & Shen, L. (2024) · BMC Bioinformatics · doi:10.1186/s12859-024-05797-4 · Cited by 4
Uncertainty measurement for single cell RNA-seq data based on class-consistent technology with application to semi-supervised gene selection
Zhang, Q., Zhao, Z., Liu, F., & Li, Z. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110645 · Cited by 4
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Wei, X., Wu, J., Li, G., Liu, J., Wu, X., & He, C. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012924 · Cited by 4
Single-cell RNA-Seq: a next generation sequencing tool for a high-resolution view of the individual cell
Stevenson, K. B., & Uversky, V. N. (2019) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2019.1659859 · Cited by 4
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Liu, Y., Warren, J. L., & Zhao, H. (2019) · The Annals of Applied Statistics · doi:10.1214/19-aoas1250 · Cited by 3
Applications of CyTOF in Brain Immune Component Studies
Wang, Y., Xu, B., & Xue, L. (2021) · Engineering · doi:10.1016/j.eng.2021.06.022 · Cited by 5
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Jiang, H.-J., Huang, Y., & Li, Q. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac113 · Cited by 4
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Hao, Y., Zhang, S., Shao, C., Li, J., Zhao, G., Zhang, D., & Fu, X. (2022) · Genome biology · doi:10.1186/s13059-022-02729-4 · Cited by 4
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Self-citation
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Self-citation
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FEED: a feature selection method based on gene expression decomposition for single cell clustering
Zhang, C., Duan, Z.-W., Xu, Y., Liu, J., & Li, H. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad389 · Cited by 4
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Ye, Y., Song, H., Zhang, J., & Shi, S. (2018) · Kidney Diseases · doi:10.1159/000492470 · Cited by 4
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Park, S., & Zhao, H. (2019) · The Annals of Applied Statistics · doi:10.1214/18-aoas1220 · Cited by 4
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Zhao, X., Han, Y., Liang, Y., Nie, C., & Wang, J. (2016) · PLoS ONE · doi:10.1371/journal.pone.0149171 · Cited by 4
Self-citation
Literature Search and Review
Auld, D., Simeonov, A. M., Lea, W., & Thomas, C. J. (2010) · Assay and Drug Development Technologies · doi:10.1089/adt.2010.0805.lr · Cited by 4
BROCKMAN: Deciphering variance in epigenomic regulators by k -mer factorizationPreprint
de Boer, C. G., & Regev, A. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/129247 · Cited by 4
Neural Data Visualization for Scalable and Generalizable Single Cell AnalysisPreprint
Cho, H., Berger, B., & Peng, J. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/289223 · Cited by 4
SSCC: a novel computational framework for rapid and accurate clustering large single cell RNA-seq dataPreprint
Ren, X., Zheng, L., & Zhang, Z. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/344242 · Cited by 4
Scalable probabilistic matrix factorization for single-cell RNA-seq analysisPreprint
Ferreira, P., Carvalho, A. M., & Vinga, S. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/496810 · Cited by 4
CNLLRR: A Novel Low-Rank Representation Method for Single-cell RNA-seq Data AnalysisPreprint
Yu, N., Liu, J., Gao, Y.-L., Zheng, C.-H., Shang, J., & Cai, H. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/818062 · Cited by 4
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Böttcher, M. E., Tada, Y., Moody, J., Kondo, M., Ura, H., Abugessaisa, I., Kasukawa, T., Hon, C.-C., Nagao, K., Carninci, P., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.09.25.313239 · Cited by 4
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Millard, N., Korsunsky, I., Weinand, K., Fonseka, C. Y., Nathan, A., Kang, J. B., & Raychaudhuri, S. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.11.23.390682 · Cited by 4
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Self-citation
Global identification of mammalian host and nested gene pairs reveal tissue-specific transcriptional interplayPreprint
Montibus, B., Cain, J. A., Martínez-Nuñez, R. T., & Oakey, R. J. (2024) · Genome Research · doi:10.1101/gr.279430.124 · Cited by 4
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Tong, L., Tian, P., Wang, R., Niu, S., Wang, R., Ye, Y., Wu, Y., Zhang, W., Wang, Y., Foggetti, A., et al. (2025) · Cell & Bioscience · doi:10.1186/s13578-025-01515-6 · Cited by 3
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Gong, L., Xiong, C., Liu, Y., Cai, L., & Gao, Z. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108225 · Cited by 3
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Lan, J., Zhuo, X., Ye, S., & Deng, J. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.112982 · Cited by 3
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Reig‐Viader, R., & Bayés, À. (2017) · Neuromethods · doi:10.1007/978-1-4939-7119-0_16 · Cited by 3
Sparse Hyper-graph Non-negative Matrix Factorization by Maximizing Correntropy
Jiao, C.-N., Liu, J., Gao, Y.-L., Kong, X.-Z., Zheng, C.-H., & Xianzi, Y. (2021) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm52615.2021.9669357 · Cited by 3
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Du, J., Gu, X.-R., Yu, X., Cao, Y.-J., & Hou, J. (2023) · Blood Science · doi:10.1097/bs9.0000000000000172 · Cited by 3
ScLSTM: single-cell type detection by siamese recurrent network and hierarchical clustering
Jiang, H.-J., Huang, Y., Li, Q., & Feng, B. (2023) · BMC Bioinformatics · doi:10.1186/s12859-023-05494-8 · Cited by 4
A Clustering Method for Single-Cell RNA-Seq Data Based on Automatic Weighting Penalty and Low-Rank Representation
Wang, J., Wang, Z., Yuan, S., Zheng, C.-H., Liu, J., & Shang, J. (2024) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2024.3362472 · Cited by 4
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Mapping and differential expression analysis from short‐read RNA‐Seq data in model organisms
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Deep generative autoencoder for low-dimensional embeding extraction from single-cell RNAseq data
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Moore's Law in Single Cell TranscriptomicsPreprint
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Bues, J., Biočanin, M., Pezoldt, J., Dainese, R., Chrisnandy, A., Rezakhani, S., Saelens, W., Gardeux, V., Gupta, R., Russeil, J., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.05.19.103812 · Cited by 3
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Human Developmental Cell Atlas: milestones achieved and the roadmap aheadPreprint
Haniffa, M. A., Taylor, D. M., Linnarsson, S., Aronow, B. J., Bader, G. D., Cámara, P. G., Camp, J. G., Chédotal, A., Copp, A. J., Etchevers, H., et al. (2020) · Research Square · doi:10.21203/rs.3.rs-73986/v1 · Cited by 3
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MARS-seq2.0: an experimental and analytical pipeline for indexed sorting combined with single-cell RNA sequencing v1Preprint
Shaul, H. K., Keren‐Shaul, H., Kenigsberg, E., Jaitin, D. A., David, E., Paul, F., Tanay, A., & Amit, I. (2019) · doi:10.17504/protocols.io.7hkhj4w · Cited by 2
Peptide sequencing via reverse translation of peptides into DNAPreprint
Zheng, L., Sun, Y., Eisenstein, M. S., & Soh, H. T. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.05.31.596913 · Cited by 3
ANTIPODE Provides a Global View of Cell Type Homology and Transcriptomic Divergence in the Developing Mammalian BrainPreprint
Schmitz, M. T., Ding, J. W., Nolbrant, S., McMullen, R., Kim, C. N., Pavlovic, B. J., Nowakowski, T. J., Bakken, T. E., Ye, C., & Pollen, A. A. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.10.18.683238 · Cited by 3
Self-citation

Authors: Pollen, A. A., Nowakowski, T. J., Shuga, J., Wang, X., Leyrat, A. A., Lui, J. H., Li, N., Szpankowski, L., Fowler, B., Chen, P., Ramalingam, N., Sun, G., Thu, M., Norris, M., Lebofsky, R., Toppani, D., Kemp, D. W., Wong, M., Clerkson, B., Jones, B. N., Wu, S., Knutsson, L., Alvarado, B., Wang, J., Weaver, L. S., May, A. P., Jones, R. C., Unger, M. A., Kriegstein, A. R., & West, J. A. A.
Affiliations: University of California, San Francisco (US)
Standard BioTools (United States) (US)
Corresponding author: Pollen, Alex A
Kriegstein, Arnold R
ORCID: Pollen, Alex A
Nowakowski, Tomasz J
Wang, Xiaohui
Lui, Jan H
Fowler, Brian
Chen, Peilin
Ramalingam, Naveen
Sun, Gang
Lebofsky, Ronald
Wu, Shiquan
Alvarado, Beatriz
Wang, Jing
May, Andrew P
Jones, Robert C
Kriegstein, Arnold R
West, Jay A A
Venue: Nature Biotechnology
Publisher: Springer Science and Business Media LLC
Published: 2014-08-03
Volume: 32
Issue: 10
Pages: 1053-1058
ISSN: 0733-222X (ISSN-L)
1087-0156
1546-1696
DOI: 10.1038/nbt.2967
PMC: PMC4191988
OpenAlex: W2051658465
Cited by: 920
References: 38
Citations by year: 2026: 33 · 2025: 51 · 2024: 54 · 2023: 62 · 2022: 78 · 2021: 110 · 2020: 110 · 2019: 105 · 2018: 93 · 2017: 91 · 2016: 74 · 2015: 52 · 2014: 5
FWCI: 30.36
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: www.springer.com/tdm (tdm)
Locations: Nature Biotechnology (publishedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
eScholarship (California Digital Library) (submittedVersion)
Funders: National Institute of Neurological Disorders and Stroke (R01NS072630, R01NS075998)
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 transcriptomics, low-pass sequencing, cellular heterogeneity, cortical development, microfluidic cell capture, cell type classification, biomarker discovery, Notch signaling, radial glia, EGR1, FOS, human brain
MeSH terms: Animals, Cerebral Cortex, Equipment Design, Humans, RNA, Messenger, Signal Transduction, Sequence Analysis, RNA, Computational Biology, Gene Expression Profiling, Microfluidic Analytical Techniques, Mice

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Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex

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