The DOI matches its Crossref record; 75 of the 75 references it cites were found in a registry or an index.
Comprehensive Classification of Retinal Bipolar Neurons by Single-Cell Transcriptomics
Open accessAbstract
Patterns of gene expression can be used to characterize and classify neuronal types. It is challenging, however, to generate taxonomies that fulfill the essential criteria of being comprehensive, harmonizing with conventional classification schemes, and lacking superfluous subdivisions of genuine types. To address these challenges, we used massively parallel single-cell RNA profiling and optimized computational methods on a heterogeneous class of neurons, mouse retinal bipolar cells (BCs). From a population of ∼25,000 BCs, we derived a molecular classification that identified 15 types, including all types observed previously and two novel types, one of which has a non-canonical morphology and position. We validated the classification scheme and identified dozens of novel markers using methods that match molecular expression to cell morphology. This work provides a systematic methodology for achieving comprehensive molecular classification of neurons, identifies novel neuronal types, and uncovers transcriptional differences that distinguish types within a class.
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“We then grouped cells based on combinations of “biological IC” cell loadings into 565 subclusters using network-based clustering”
From Molecular diversity and specializations among the cells of the adult mouse brain (2018) · cites this paper as Shekhar et al., 2016
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Indeed, we observed that a modified GLM including a batch indicator variable was sufficient to correct for technical differences arising from two profiled batches of murine bipolar cells [32], though successful application requires all cell types to share a similar batch effect (Additional file 2: Figure S8).”
From Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression (2019) · cites this paper as [32]
The cited source says what the claim attributes to it.
In the citing paper
“we ran an iterative PCA-Louvain clustering approach15,16 with stepwise cluster robustness assessment and identified 14 distinct cell clusters with a minimum of 8 cells per cluster”
From Single cell RNA sequencing of human microglia uncovers a subset associated with Alzheimer’s disease (2020) · cites this paper as [16]
In the citing paper
“We begin by modeling the cellular transcriptomic state space as a smooth, low-dimensional manifold or set of manifolds.”
From Quantifying the effect of experimental perturbations at single-cell resolution (2021) · cites this paper as [5]
In the citing paper
“Clustering11 of 13,313 nuclei profiled from frozen adult mouse hippocampus (n=4 mice) and prefrontal cortex (PFC, n=4)”
From Massively parallel single-nucleus RNA-seq with DroNc-seq (2017) · cites this paper as [11]
In the citing paper
“massively parallel scRNA-seq methods, such as Drop-seq7 and related methods8–10 can be readily applied at scale11 in a cost-effective manner12, but require intact single cell suspension as input.”
From Massively parallel single-nucleus RNA-seq with DroNc-seq (2017) · cites this paper as [11]
In the citing paper
“suggesting that the cell-type identity in the brain can be encoded by a small set of genes, easily detected with shallow sequencing, as previously observed in other systems11.”
From Massively parallel single-nucleus RNA-seq with DroNc-seq (2017) · cites this paper as [11]
In the citing paper
“In some cases, seemingly subtle variations may endow novel properties of a generic type of I/O transformation to a subset of cells, thereby warranting their classification as a separate subtype (for example, the 15 types of retinal bipolar cell69).”
From The diversity of GABAergic neurons and neural communication elements (2019) · cites this paper as [69]
In the citing paper
“Recent scRNA-seq studies sampled >20,000–750,000 cells in individual mouse brain regions (Kozareva et al., 2020; Shekhar et al., 2016; Tasic et al., 2018; Yao et al., 2020a).”
From Single-Cell Sequencing of Brain Cell Transcriptomes and Epigenomes (2021) · cites this paper as Shekhar et al., 2016
In the citing paper
“The number of cells that are needed to generate a comprehensive atlas of a tissue depends on multiple factors, which include the number of cell types and the molecular differences between the cell types (Shekhar et al., 2016).”
From Single cell transcriptomics reveals spatial and temporal dynamics of gene expression in the developing mouse spinal cord (2019) · cites this paper as Shekhar et al., 2016
In the citing paper
“In neural circuits where cell types have been well studied by anatomical and physiological methods, there is an excellent correspondence between cell types defined by transcriptomes and the classical methods (Li et al., 2017; Shekhar et al., 2016).”
From Single-cell transcriptomes and whole-brain projections of serotonin neurons in the mouse dorsal and median raphe nuclei (2019) · cites this paper as Shekhar et al., 2016
In the citing paper
“Clustering was performed on the resulting k-nearest neighbors graph using Phenograph (Levine et al., 2015), a modularity-based clustering algorithm that produces robust clusterings on scRNA-seq data (Shekhar et al., 2016).”
From Single-Cell Analysis of Regional Differences in Adult V-SVZ Neural Stem Cell Lineages (2019) · cites this paper as Shekhar et al., 2016
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
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Fast gapped-read alignment with Bowtie 2
Langmead, B., & Salzberg, S. L. (2012) · Nature Methods · doi:10.1038/nmeth.1923 · Cited by 56,400
STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709
(2008) · Journal of Machine Learning Research · Cited by 33,051
RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome
Li, B., & Dewey, C. N. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-323 · Cited by 22,179
Fast unfolding of communities in large networks
Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008) · Journal of Statistical Mechanics: Theory and Experiment · doi:10.1088/1742-5468/2008/10/p10008 · Cited by 21,424
Fortunato, S. (2010) · Physics Reports · doi:10.1016/j.physrep.2009.11.002 · Cited by 11,293
Adjusting batch effects in microarray expression data using empirical Bayes methods
Johnson, W. E., Li, C., & Rabinovic, A. (2007) · Biostatistics · doi:10.1093/biostatistics/kxj037 · Cited by 8,658
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
Self-citationA robust and high-throughput Cre reporting and characterization system for the whole mouse brain
Madisen, L., Zwingman, T. A., Sunkin, S. M., Oh, S. W., Zariwala, H. A., Gu, H., Ng, L. L., Palmiter, R. D., Hawrylycz, M. J., Jones, A. R., et al. (2010) · Nature Neuroscience · doi:10.1038/nn.2467 · Cited by 6,907
Flagged first, then by citations
Research works printed but not linked: 1
- Matutano, J.C., Unanue, R.M., and Avanzada, M.U.E.I. (2014). New clustering proposals for Twitter based on the Louvain algorithm, Master’s Thesis (Universidad Nacional de Educación a Distancia).
Cited by
Sorted by citations
Comprehensive Integration of Single-Cell Data
Stuart, T., Butler, A., Hoffman, P., Hafemeister, C., Papalexi, E., Mauck, W. M., Hao, Y., Stoeckius, M., Smibert, P., & Satija, R. (2019) · Cell · doi:10.1016/j.cell.2019.05.031 · Cited by 15,453
Integrating single-cell transcriptomic data across different conditions, technologies, and species
Butler, A., Hoffman, P., Smibert, P., Papalexi, E., & Satija, R. (2018) · Nature Biotechnology · doi:10.1038/nbt.4096 · Cited by 13,174
Hafemeister, C., & Satija, R. (2019) · Genome Biology · doi:10.1186/s13059-019-1874-1 · Cited by 4,479
“Indeed, we observed that a modified GLM containing a batch indicator variable was sufficient to correct for technical differences arising across from two profiled batches of murine bipolar cells [Shekhar et al., 2016], though successful application requires all cell types to share a similar batch effect (Supp.”
MethodMolecular Architecture of the Mouse Nervous System
Zeisel, A., Hochgerner, H., Lönnerberg, P., Johnsson, A., Memic, F., van der Zwan, J., Häring, M., Braun, E., Borm, L. E., La Manno, G., et al. (2018) · Cell · doi:10.1016/j.cell.2018.06.021 · Cited by 2,552
Deep generative modeling for single-cell transcriptomics
Lopez, R., Regier, J., Cole, M. B., Jordan, M. I., & Yosef, N. (2018) · Nature Methods · doi:10.1038/s41592-018-0229-2 · Cited by 2,530
Regev, A., Teichmann, S. A., Lander, E. S., Amit, I., Benoist, C., Birney, E., Bodenmiller, B., Campbell, P., Carninci, P., Clatworthy, M., et al. (2017) · eLife · doi:10.7554/elife.27041 · Cited by 2,232
Self-citationShared and distinct transcriptomic cell types across neocortical areas
Tasic, B., Yao, Z., Graybuck, L. T., Smith, K. A., Nguyen, T. N., Bertagnolli, D., Goldy, J., Garren, E., Economo, M. N., Viswanathan, S., et al. (2018) · Nature · doi:10.1038/s41586-018-0654-5 · Cited by 1,746
Dixit, A., Parnas, O., Li, B., Chen, J., Fulco, C. P., Jerby-Arnon, L., Marjanovic, N. D., Dionne, D., Burks, T., Raychowdhury, R., et al. (2016) · Cell · doi:10.1016/j.cell.2016.11.038 · Cited by 1,863
Self-citationRecovering Gene Interactions from Single-Cell Data Using Data Diffusion
van Dijk, D., Sharma, R., Nainys, J., Yim, K., Kathail, P., Carr, A. J., Burdziak, C., Moon, K. R., Chaffer, C. L., Pattabiraman, D., et al. (2018) · Cell · doi:10.1016/j.cell.2018.05.061 · Cited by 1,829
Molecular diversity and specializations among the cells of the adult mouse brain
Saunders, A., Macosko, E. Z., Wysoker, A., Goldman, M., Krienen, F. M., de Rivera, H., Bien, E., Baum, M., Bortolin, L., Wang, S., et al. (2018) · Cell · doi:10.1016/j.cell.2018.07.028 · Cited by 1,531
“Inference of Cell Types and States Using Independent Components Analysis The recognition of subtle variation among cells of the same class presents a formidable analytical challenge for unsupervised analysis (Mayer et al., 2015; Satija et al., 2015; Shekhar et al., 2016; Tanay and Regev, 2017; Tasic et al., 2016).”
BackgroundMethodSelf-citationThree-dimensional intact-tissue sequencing of single-cell transcriptional states
Wang, X., Allen, W. E., Wright, M. A., Sylwestrak, E. L., Samusik, N., Vesuna, S., Evans, K., Liu, C., Ramakrishnan, C., Liu, J., et al. (2018) · Science · doi:10.1126/science.aat5691 · Cited by 1,620
A single-cell survey of the small intestinal epithelium
Haber, A. L., Biton, M., Rogel, N., Herbst, R. H., Shekhar, K., Smillie, C., Burgin, G., Delorey, T. M., Howitt, M. R., Katz, Y., et al. (2017) · Nature · doi:10.1038/nature24489 · Cited by 1,750
Self-citationSingle-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding
Rosenberg, A. B., Roco, C. M., Muscat, R. A., Kuchina, A., Sample, P., Yao, Z., Graybuck, L. T., Peeler, D. J., Mukherjee, S., Chen, W., et al. (2018) · Science · doi:10.1126/science.aam8999 · Cited by 1,422
Regev, A., Teichmann, S. A., Lander, E. S., Amit, I., Benoist, C. O., Birney, E., Bodenmiller, B., & al, E. (2017) · Zurich Open Repository and Archive (University of Zurich) · doi:10.5167/uzh-162597 · Cited by 1,457
Self-citationMolecular, spatial, and functional single-cell profiling of the hypothalamic preoptic region
Moffitt, J. R., Bambah-Mukku, D., Eichhorn, S. W., Vaughn, E., Shekhar, K., Perez, J. D., Rubinstein, N. D., Hao, J., Regev, A., Dulac, C., et al. (2018) · Science · doi:10.1126/science.aau5324 · Cited by 1,365
Self-citationDistinct cellular mechanisms underlie anti-CTLA-4 and anti-PD-1 checkpoint blockade
Wei, S. C., Levine, J. H., Cogdill, A. P., Zhao, Y., Anang, N.-A. A. S., Andrews, M. C., Sharma, P., Wang, J., Wargo, J. A., Pe’er, D., et al. (2017) · Cell · doi:10.1016/j.cell.2017.07.024 · Cited by 1,302
A benchmark of batch-effect correction methods for single-cell RNA sequencing data
Tran, H. T. N., Ang, K. S., Chevrier, M., Zhang, X., Lee, N. Y. S., Goh, M., & Chen, J. (2020) · Genome Biology · doi:10.1186/s13059-019-1850-9 · Cited by 1,113
Massively parallel single-nucleus RNA-seq with DroNc-seq
Habib, N., Avraham-Davidi, I., Basu, A., Burks, T., Shekhar, K., Hofree, M., Choudhury, S. R., Aguet, F., Gelfand, E., Ardlie, K., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4407 · Cited by 1,069
Self-citationA revised airway epithelial hierarchy includes CFTR-expressing ionocytes
Montoro, D. T., Haber, A. L., Biton, M., Vinarsky, V., Lin, B., Birket, S. E., Yuan, F., Chen, S., Leung, H. M., Villoria, J., et al. (2018) · Nature · doi:10.1038/s41586-018-0393-7 · Cited by 1,206
Self-citationBrann, D. H., Tsukahara, T., Weinreb, C., Lipovsek, M., Van den Berge, K., Gong, B., Chance, R., Macaulay, I. C., Chou, H.-J., Fletcher, R. B., et al. (2020) · Science Advances · doi:10.1126/sciadv.abc5801 · Cited by 1,198
A high-resolution transcriptomic and spatial atlas of cell types in the whole mouse brain
Yao, Z., van Velthoven, C. T. J., Kunst, M., Zhang, M., McMillen, D., Lee, C., Jung, W., Goldy, J., Abdelhak, A., Aitken, M., et al. (2023) · Nature · doi:10.1038/s41586-023-06812-z · Cited by 1,203
The art of using t-SNE for single-cell transcriptomics
Kobak, D., & Berens, P. (2019) · Nature Communications · doi:10.1038/s41467-019-13056-x · Cited by 1,161
Individual brain organoids reproducibly form cell diversity of the human cerebral cortex
Velasco, S., Kedaigle, A. J., Simmons, S. K., Nash, A., Rocha, M., Quadrato, G., Paulsen, B., Nguyen, L., Adiconis, X., Regev, A., et al. (2019) · Nature · doi:10.1038/s41586-019-1289-x · Cited by 1,094
Self-citationSpatiotemporal gene expression trajectories reveal developmental hierarchies of the human cortex
Nowakowski, T. J., Bhaduri, A., Pollen, A. A., Alvarado, B., Mostajo-Radji, M. A., Di Lullo, E., Haeussler, M., Sandoval-Espinosa, C., Liu, S. J., Velmeshev, D., et al. (2017) · Science · doi:10.1126/science.aap8809 · Cited by 964
Visualizing structure and transitions in high-dimensional biological data
Moon, K. R., van Dijk, D., Wang, Z., Gigante, S., Burkhardt, D. B., Chen, W. S., Yim, K., Elzen, A. V. D., Hirn, M. J., Coifman, R. R., et al. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0336-3 · Cited by 1,065
Stoeckius, M., Zheng, S., Houck-Loomis, B., Hao, S., Yeung, B. Z., Mauck, W. M., Smibert, P., & Satija, R. (2018) · Genome Biology · doi:10.1186/s13059-018-1603-1 · Cited by 1,046
Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain
Lake, B. B., Chen, S., Sos, B. C., Fan, J., Kaeser, G. E., Yung, Y. C., Duong, T. E., Gao, D., Chun, J., Kharchenko, P. V., et al. (2018) · Nature Biotechnology · doi:10.1038/nbt.4038 · Cited by 983
Neuronal cell-type classification: challenges, opportunities and the path forward
Zeng, H., & Sanes, J. R. (2017) · Nature Reviews Neuroscience · doi:10.1038/nrn.2017.85 · Cited by 841
Self-citationSingle-cell genomics identifies cell type–specific molecular changes in autism
Velmeshev, D., Schirmer, L., Jung, D., Haeussler, M., Perez, Y., Mayer, S., Bhaduri, A., Goyal, N., Rowitch, D. H., & Kriegstein, A. R. (2019) · Science · doi:10.1126/science.aav8130 · Cited by 927
Spatially organized multicellular immune hubs in human colorectal cancer
Pelka, K., Hofree, M., Chen, J. H., Sarkizova, S., Pirl, J. D., Jorgji, V., Bejnood, A., Dionne, D. A., Ge, W. H., Xu, K. H., et al. (2021) · Cell · doi:10.1016/j.cell.2021.08.003 · Cited by 917
Self-citationSingle-cell reconstruction of developmental trajectories during zebrafish embryogenesis
Farrell, J. A., Wang, Y., Riesenfeld, S. J., Shekhar, K., Regev, A., & Schier, A. F. (2018) · Science · doi:10.1126/science.aar3131 · Cited by 876
Self-citationSzabo, P. A., Levitin, H. M., Miron, M., Snyder, M. E., Senda, T., Yuan, J., Cheng, Y. L., Bush, E. C., Dogra, P., Thapa, P., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-12464-3 · Cited by 756
Scaling single-cell genomics from phenomenology to mechanism
Tanay, A., & Regev, A. (2017) · Nature · doi:10.1038/nature21350 · Cited by 792
Self-citationA single-cell and single-nucleus RNA-Seq toolbox for fresh and frozen human tumors
Slyper, M., Porter, C. B. M., Ashenberg, O., Waldman, J., Drokhlyansky, E., Wakiro, I., Smillie, C. S., Smith-Rosario, G., Wu, J., Dionne, D. A., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0844-1 · Cited by 758
Self-citationWu, H., Kirita, Y., Donnelly, E. L., & Humphreys, B. D. (2019) · Journal of the American Society of Nephrology · doi:10.1681/asn.2018090912 · Cited by 752
scmap: projection of single-cell RNA-seq data across data sets
(2018) · doi:10.1038/nmeth.4644 · Cited by 735
Single cell RNA sequencing of human microglia uncovers a subset associated with Alzheimer’s disease
Olah, M., Menon, V., Habib, N., Taga, M. F., Ma, Y., Yung, C. J., Cimpean, M., Khairallah, A., Coronas-Samano, G., Sankowski, R., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-19737-2 · Cited by 722
Self-citationAn atlas of the aging lung mapped by single cell transcriptomics and deep tissue proteomics
Angelidis, I., Simon, L. M., Fernandez, I. E., Strunz, M., Mayr, C. H., Greiffo, F. R., Tsitsiridis, G., Ansari, M., Graf, E., Strom, T.-M., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-08831-9 · Cited by 637
Cell Types of the Human Retina and Its Organoids at Single-Cell Resolution
Cowan, C. S., Renner, M., De Gennaro, M., Gross-Scherf, B., Goldblum, D., Hou, Y., Munz, M., Rodrigues, T. M., Krol, J., Szikra, T., et al. (2020) · Cell · doi:10.1016/j.cell.2020.08.013 · Cited by 654
Tran, N. M., Shekhar, K., Whitney, I. E., Jacobi, A., Benhar, I., Hong, G., Yan, W., Adiconis, X., Arnold, M. E., Lee, J. M., et al. (2019) · Neuron · doi:10.1016/j.neuron.2019.11.006 · Cited by 627
Self-citationEstablishing Cerebral Organoids as Models of Human-Specific Brain Evolution
Pollen, A. A., Bhaduri, A., Andrews, M. G., Nowakowski, T. J., Meyerson, O. S., Mostajo-Radji, M. A., Di Lullo, E., Alvarado, B., Bedolli, M., Dougherty, M. L., et al. (2019) · Cell · doi:10.1016/j.cell.2019.01.017 · Cited by 600
The neuropeptide NMU amplifies ILC2-driven allergic lung inflammation
Wallrapp, A., Riesenfeld, S. J., Burkett, P. R., Abdulnour, R.-E. E., Nyman, J., Dionne, D. A., Hofree, M., Cuoco, M. S., Rodman, C., Farouq, D., et al. (2017) · Nature · doi:10.1038/nature24029 · Cited by 652
Self-citationNeuronal vulnerability and multilineage diversity in multiple sclerosis
Schirmer, L., Velmeshev, D., Holmqvist, S., Kaufmann, M., Werneburg, S., Jung, D., Vistnes, S., Stockley, J. H., Young, A., Steindel, M., et al. (2019) · Nature · doi:10.1038/s41586-019-1404-z · Cited by 638
The Human Cell Atlas: from vision to reality
Rozenblatt-Rosen, O., Stubbington, M. J. T., Regev, A., & Teichmann, S. A. (2017) · Nature · doi:10.1038/550451a · Cited by 638
Self-citationCheckpoint Blockade Immunotherapy Induces Dynamic Changes in PD-1-CD8+ Tumor-Infiltrating T Cells
Kurtulus, S., Madi, A., Escobar, G., Klapholz, M., Nyman, J., Christian, E., Pawlak, M., Dionne, D., Xia, J., Rozenblatt-Rosen, O., et al. (2019) · Immunity · doi:10.1016/j.immuni.2018.11.014 · Cited by 636
Self-citationRevealing the vectors of cellular identity with single-cell genomics
Wagner, A., Regev, A., & Yosef, N. (2016) · Nature Biotechnology · doi:10.1038/nbt.3711 · Cited by 632
Self-citationCell stress in cortical organoids impairs molecular subtype specification
Bhaduri, A., Andrews, M. G., Mancia Leon, W., Jung, D., Shin, D., Allen, D., Jung, D., Schmunk, G., Haeussler, M., Salma, J., et al. (2020) · Nature · doi:10.1038/s41586-020-1962-0 · Cited by 624
Temporal evolution of cortical ensembles promoting remote memory retrieval
DeNardo, L. A., Liu, C. D., Allen, W. E., Adams, E. L., Friedmann, D., Dadgar‐Kiani, E., Fu, L., Guenthner, C. J., Lee, J. H., Tessier‐Lavigne, M., et al. (2019) · Nature Neuroscience · doi:10.1038/s41593-018-0318-7 · Cited by 646
Simultaneous single-cell profiling of lineages and cell types in the vertebrate brain
Raj, B., Wagner, D. E., McKenna, A., Pandey, S., Klein, A. M., Shendure, J., Gagnon, J. A., & Schier, A. F. (2018) · Nature Biotechnology · doi:10.1038/nbt.4103 · Cited by 610
Single-nucleus and single-cell transcriptomes compared in matched cortical cell types
Bakken, T. E., Hodge, R. D., Miller, J. A., Yao, Z., Nguyen, T. N., Aevermann, B., Barkan, E., Bertagnolli, D., Casper, T., Dee, N., et al. (2018) · PLoS ONE · doi:10.1371/journal.pone.0209648 · Cited by 608
Wu, H., Uchimura, K., Donnelly, E. L., Kirita, Y., Morris, S. A., & Humphreys, B. D. (2018) · Cell stem cell · doi:10.1016/j.stem.2018.10.010 · Cited by 623
Winnubst, J., Bas, E., Ferreira, T. A., Wu, Z., Economo, M. N., Edson, P., Arthur, B. J., Bruns, C., Rokicki, K., Schauder, D., et al. (2019) · Cell · doi:10.1016/j.cell.2019.07.042 · Cited by 538
Clonal analysis of lineage fate in native haematopoiesis
Rodriguez-Fraticelli, A. E., Wolock, S. L., Weinreb, C. S., Panero, R., Patel, S. H., Jankovic, M., Sun, J., Calogero, R. A., Klein, A. M., & Camargo, F. D. (2018) · Nature · doi:10.1038/nature25168 · Cited by 596
A Single-Cell Transcriptomic Atlas of Human Neocortical Development during Mid-gestation
Polioudakis, D., de la Torre-Ubieta, L., Langerman, J., Elkins, A. G., Shi, X., Stein, J. L., Vuong, C. K., Nichterwitz, S., Gevorgian, M., Opland, C. K., et al. (2019) · Neuron · doi:10.1016/j.neuron.2019.06.011 · Cited by 513
The Human and Mouse Enteric Nervous System at Single-Cell Resolution
Drokhlyansky, E., Smillie, C. S., Wittenberghe, N. V., Ericsson, M., Griffin, G. K., Eraslan, G., Dionne, D. A., Cuoco, M. S., Goder-Reiser, M. N., Sharova, T. Y., et al. (2020) · Cell · doi:10.1016/j.cell.2020.08.003 · Cited by 606
Self-citationIntegrated Morphoelectric and Transcriptomic Classification of Cortical GABAergic Cells
Gouwens, N. W., Sorensen, S. A., Baftizadeh, F., Budzillo, A., Lee, B. R., Jarsky, T., Alfiler, L., Baker, K., Barkan, E., Berry, K., et al. (2020) · Cell · doi:10.1016/j.cell.2020.09.057 · Cited by 538
“Recent advances in the collection and analysis of single cell RNA-sequencing (scRNAseq) data provide a promising new way to define cell types and how they relate to one another (Saunders et al., 2018; Tasic et al., 2018; Zeisel et al., 2018; Zeisel et al., 2015; Tasic et al., 2016; Shekhar et al., 2016).”
Spatially resolved cell atlas of the mouse primary motor cortex by MERFISH
Zhang, M., Eichhorn, S. W., Zingg, B., Yao, Z., Cotter, K., Zeng, H., Dong, H., & Zhuang, X. (2021) · Nature · doi:10.1038/s41586-021-03705-x · Cited by 498
Single-Cell Transcriptomics of Regulatory T Cells Reveals Trajectories of Tissue Adaptation
Miragaia, R. J., Gomes, T., Chomka, A., Jardine, L., Riedel, A., Hegazy, A. N., Whibley, N., Tucci, A., Chen, X., Lindeman, I., et al. (2019) · Immunity · doi:10.1016/j.immuni.2019.01.001 · Cited by 499
Fast interpolation-based t-SNE for improved visualization of single-cell RNA-seq data
Linderman, G. C., Rachh, M., Hoskins, J. G., Steinerberger, S., & Kluger, Y. (2019) · Nature Methods · doi:10.1038/s41592-018-0308-4 · Cited by 521
Clark, B. S., Stein-O’Brien, G., Shiau, F., Cannon, G. H., Davis-Marcisak, E. F., Sherman, T. D., Santiago, C. P., Hoang, T. V., Rajaii, F., James-Esposito, R. E., et al. (2019) · Neuron · doi:10.1016/j.neuron.2019.04.010 · Cited by 527
A Comprehensive Roadmap of Murine Spermatogenesis Defined by Single-Cell RNA-Seq
Green, C. D., Ma, Q., Manske, G. L., Shami, A. N., Zheng, X., Marini, S., Moritz, L., Sultan, C., Gurczynski, S. J., Moore, B. B., et al. (2018) · Developmental Cell · doi:10.1016/j.devcel.2018.07.025 · Cited by 525
Molecularly defined and spatially resolved cell atlas of the whole mouse brain
Zhang, M., Pan, X., Jung, W., Halpern, A. R., Eichhorn, S. W., Lei, Z., Cohen, L., Smith, K. A., Tasic, B., Yao, Z., et al. (2023) · Nature · doi:10.1038/s41586-023-06808-9 · Cited by 531
Tosches, M. A., Yamawaki, T. M., Naumann, R. K., Jacobi, A. A., Tushev, G., & Laurent, G. (2018) · Science · doi:10.1126/science.aar4237 · Cited by 506
Transcriptional Architecture of Synaptic Communication Delineates GABAergic Neuron Identity
Paul, A., Crow, M., Raudales, R., He, M., Gillis, J., & Huang, Z. J. (2017) · Cell · doi:10.1016/j.cell.2017.08.032 · Cited by 421
Single-cell analysis of experience-dependent transcriptomic states in the mouse visual cortex
Hrvatin, S., Hochbaum, D. R., Nagy, M. A., Cicconet, M., Robertson, K., Cheadle, L., Zilionis, R., Ratner, A., Borges-Monroy, R., Klein, A. M., et al. (2018) · Nature Neuroscience · doi:10.1038/s41593-017-0029-5 · Cited by 497
Developmental diversification of cortical inhibitory interneurons
Mayer, C., Hafemeister, C., Bandler, R. C., Machold, R., Batista Brito, R., Jaglin, X., Allaway, K., Butler, A., Fishell, G., & Satija, R. (2018) · Nature · doi:10.1038/nature25999 · Cited by 492
What is a cell type and how to define it?
Zeng, H. (2022) · Cell · doi:10.1016/j.cell.2022.06.031 · Cited by 439
scPred: accurate supervised method for cell-type classification from single-cell RNA-seq data
Alquicira-Hernández, J., Sathe, A., Ji, H. P., Nguyen, Q. H., & Powell, J. E. (2019) · Genome biology · doi:10.1186/s13059-019-1862-5 · Cited by 474
Molecular Classification and Comparative Taxonomics of Foveal and Peripheral Cells in Primate Retina
Peng, Y., Shekhar, K., Yan, W., Herrmann, D., Sappington, A., Bryman, G. S., van Zyl, T. A., Do, M. T. H., Regev, A., & Sanes, J. R. (2019) · Cell · doi:10.1016/j.cell.2019.01.004 · Cited by 470
Self-citationCell type atlas and lineage tree of a whole complex animal by single-cell transcriptomics
Plass, M., Solana, J., Wolf, F. A., Ayoub, S., Misios, A., Glažar, P., Obermayer, B., Theis, F. J., Kocks, C., & Rajewsky, N. (2018) · Science · doi:10.1126/science.aaq1723 · Cited by 474
Induction and transcriptional regulation of the co-inhibitory gene module in T cells
Chihara, N., Madi, A., Kondo, T., Zhang, H., Acharya, N., Singer, M., Nyman, J., Marjanovic, N. D., Kowalczyk, M. S., Wang, C., et al. (2018) · Nature · doi:10.1038/s41586-018-0206-z · Cited by 438
Self-citationSABER amplifies FISH: enhanced multiplexed imaging of RNA and DNA in cells and tissues
Kishi, J. Y., Lapan, S. W., Beliveau, B. J., West, E. R., Zhu, A., Sasaki, H. M., Saka, S. K., Wang, Y., Cepko, C. L., & Yin, P. (2019) · Nature Methods · doi:10.1038/s41592-019-0404-0 · Cited by 446
Self-citationGenetic Dissection of Neural Circuits: A Decade of Progress
Luo, L., Callaway, E. M., & Svoboda, K. (2018) · Neuron · doi:10.1016/j.neuron.2018.03.040 · Cited by 454
Crow, M., Paul, A., Ballouz, S., Huang, Z. J., & Gillis, J. (2018) · Nature Communications · doi:10.1038/s41467-018-03282-0 · Cited by 365
Detecting Activated Cell Populations Using Single-Cell RNA-Seq
Wu, Y. E., Pan, L., Zuo, Y., Li, X., & Hong, W. (2017) · Neuron · doi:10.1016/j.neuron.2017.09.026 · Cited by 438
The promise of spatial transcriptomics for neuroscience in the era of molecular cell typing
Lein, E., Borm, L. E., & Linnarsson, S. (2017) · Science · doi:10.1126/science.aan6827 · Cited by 383
A Distinct Gene Module for Dysfunction Uncoupled from Activation in Tumor-Infiltrating T Cells
Singer, M., Wang, C., Cong, L., Marjanovic, N. D., Kowalczyk, M. S., Zhang, H., Nyman, J., Sakuishi, K., Kurtuluş, S., Gennert, D. G., et al. (2016) · Cell · doi:10.1016/j.cell.2016.08.052 · Cited by 422
Self-citationOuter Radial Glia-like Cancer Stem Cells Contribute to Heterogeneity of Glioblastoma
Bhaduri, A., Di Lullo, E., Jung, D., Müller, S., Crouch, E. E., Espinosa, C. S., Ozawa, T., Alvarado, B., Spatazza, J., Cadwell, C. R., et al. (2020) · Cell Stem Cell · doi:10.1016/j.stem.2019.11.015 · Cited by 419
Deep learning enables accurate clustering with batch effect removal in single-cell RNA-seq analysis
Li, X., Wang, K., Lyu, Y., Pan, H., Zhang, J., Stambolian, D. E., Suszták, K., Reilly, M. P., Hu, G., & Li, M. (2020) · Nature Communications · doi:10.1038/s41467-020-15851-3 · Cited by 419
Emergence of a High-Plasticity Cell State during Lung Cancer Evolution
Marjanovic, N. D., Hofree, M., Chan, J. E., Canner, D. A., Wu, K., Trakala, M., Hartmann, G. G., Smith, O. C. G., Kim, J. Y., Evans, K. V., et al. (2020) · Cancer Cell · doi:10.1016/j.ccell.2020.06.012 · Cited by 407
Self-citationDelile, J., Rayon, T., Melchionda, M., Edwards, A., Briscoe, J., & Sagner, A. (2019) · Development · doi:10.1242/dev.173807 · Cited by 356
Single-cell mapping of lineage and identity in direct reprogramming
Biddy, B. A., Kong, W., Kamimoto, K., Guo, C., Waye, S. E., Sun, T., & Morris, S. A. (2018) · Nature · doi:10.1038/s41586-018-0744-4 · Cited by 390
Embracing the dropouts in single-cell RNA-seq analysis
Qiu, P. (2020) · Nature Communications · doi:10.1038/s41467-020-14976-9 · Cited by 400
The Drosophila embryo at single-cell transcriptome resolution
Karaiskos, N., Wahle, P., Alles, J., Boltengagen, A., Ayoub, S., Kipar, C., Kocks, C., Rajewsky, N., & Zinzen, R. P. (2017) · Science · doi:10.1126/science.aan3235 · Cited by 382
Population snapshots predict early haematopoietic and erythroid hierarchies
Tusi, B. K., Wolock, S. L., Weinreb, C., Hwang, Y., Hidalgo, D., Zilionis, R., Waisman, A., Huh, J. R., Klein, A. M., & Socolovsky, M. (2018) · Nature · doi:10.1038/nature25741 · Cited by 384
Szabo, P. A., Dogra, P., Gray, J. I., Wells, S. B., Connors, T. J., Weisberg, S. P., Krupska, I., Matsumoto, R., Poon, M. M. L., Idzikowski, E., et al. (2021) · Immunity · doi:10.1016/j.immuni.2021.03.005 · Cited by 378
Voigt, A. P., Mulfaul, K., Mullin, N. K., Flamme‐Wiese, M. J., Giacalone, J. C., Stone, E. M., Tucker, B. A., Scheetz, T. E., & Mullins, R. F. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1914143116 · Cited by 375
Stephenson, W., Donlin, L. T., Butler, A., Rozo, C. T., Bracken, B., Rashidfarrokhi, A., Goodman, S. M., Ivashkiv, L. B., Bykerk, V. P., Orange, D. E., et al. (2018) · Nature Communications · doi:10.1038/s41467-017-02659-x · Cited by 370
Anatomically and Functionally Distinct Lung Mesenchymal Populations Marked by Lgr5 and Lgr6
Lee, J., Tammela, T., Hofree, M., Choi, J., Marjanovic, N. D., Han, S., Canner, D. A., Wu, K., Paschini, M., Bhang, D. H., et al. (2017) · Cell · doi:10.1016/j.cell.2017.07.028 · Cited by 363
Self-citationHigh-resolution comparative analysis of great ape genomes
Kronenberg, Z. N., Fiddes, I. T., Gordon, D., Murali, S., Cantsilieris, S., Meyerson, O. S., Underwood, J. G., Nelson, B. J., Chaisson, M. J. P., Dougherty, M. L., et al. (2018) · Science · doi:10.1126/science.aar6343 · Cited by 350
Endothelial TGF-β signalling drives vascular inflammation and atherosclerosis
Chen, P., Qin, L., Li, G., Wang, Z., Dahlman, J. E., Malagon-Lopez, J., Gujja, S., Cilfone, N. A., Kauffman, K. J., Sun, L., et al. (2019) · Nature Metabolism · doi:10.1038/s42255-019-0102-3 · Cited by 348
A single‐cell transcriptome atlas of the adult human retina
Lukowski, S. W., Lo, C. Y.-W., Sharov, A. A., Nguyen, Q. H., Fang, L., Hung, S. S. C., Zhu, L., Zhang, T., Grünert, U., Nguyen, T., et al. (2019) · The EMBO Journal · doi:10.15252/embj.2018100811 · Cited by 347
Challenges and emerging directions in single-cell analysis
Yuan, G., Cai, L., Elowitz, M. B., Enver, T., Fan, G., Guo, G., Irizarry, R. A., Kharchenko, P. V., Kim, J., Orkin, S. H., et al. (2017) · Genome biology · doi:10.1186/s13059-017-1218-y · Cited by 349
Interpretable dimensionality reduction of single cell transcriptome data with deep generative models
Ding, J., Condon, A., & Shah, S. P. (2018) · Nature Communications · doi:10.1038/s41467-018-04368-5 · Cited by 337
Mouse Retinal Cell Atlas: Molecular Identification of over Sixty Amacrine Cell Types
Yan, W., Laboulaye, M. A., Tran, N. M., Whitney, I. E., Benhar, I., & Sanes, J. R. (2020) · Journal of Neuroscience · doi:10.1523/jneurosci.0471-20.2020 · Cited by 343
Self-citationKalluri, A. S., Vellarikkal, S. K., Edelman, E. R., Nguyễn, L., Subramanian, A., Ellinor, P. T., Regev, A., Kathiresan, S., & Gupta, R. M. (2019) · Circulation · doi:10.1161/circulationaha.118.038362 · Cited by 335
Self-citationMenon, M., Mohammadi, S., Davila-Velderrain, J., Goods, B. A., Cadwell, T. D., Xing, Y., Stemmer-Rachamimov, A., Shalek, A. K., Love, J. C., Kellis, M., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-12780-8 · Cited by 331
Cieslak, M. C., Castelfranco, A. M., Roncalli, V., Lenz, P. H., & Hartline, D. K. (2019) · Marine Genomics · doi:10.1016/j.margen.2019.100723 · Cited by 312
Molecular landscapes of human hippocampal immature neurons across lifespan
Zhou, Y. F., Su, Y., Li, S., Kennedy, B. C., Zhang, D. Y., Bond, A. M., Sun, Y. Y., Jacob, F., Lu, L., Hu, P., et al. (2022) · Nature · doi:10.1038/s41586-022-04912-w · Cited by 314
Ma, P., Amemiya, H. M., He, L. L., Gandhi, S. J., Nicol, R., Bhattacharyya, R. P., Smillie, C. S., & Hung, D. T. (2023) · Cell · doi:10.1016/j.cell.2023.01.002 · Cited by 291
Metadata
- Authors
- Shekhar, K., Lapan, S. W., Whitney, I. E., Tran, N. M., Macosko, E. Z., Kowalczyk, M., Adiconis, X., Levin, J. Z., Nemesh, J., Goldman, M., McCarroll, S. A., Cepko, C. L., Regev, A., & Sanes, J. R.
- Affiliations
- Broad Institute (US)
- Howard Hughes Medical Institute (US)
- Harvard University (US)
- Corresponding author
- Cepko, Constance L.
- Regev, Aviv
- Sanes, Joshua R.
- ORCID
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2016-08
- Volume
- 166
- Issue
- 5
- Pages
- 1308-1323.e30
- ISSN
- 0092-8674 (ISSN-L)
- PMC
- PMC5003425
- OpenAlex
- W2510746232
- Cited by
- 1,176
- References
- 75
- Citations by year
- 2026: 71 · 2025: 89 · 2024: 101 · 2023: 113 · 2022: 105 · 2021: 159 · 2020: 158 · 2019: 157 · 2018: 135 · 2017: 79 · 2016: 7
- FWCI
- 65.68
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Cell (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
- eScholarship (California Digital Library) (submittedVersion, cc-by-nc-nd)
- eScholarship (California Digital Library) (submittedVersion)
- PMC
- Funders
- National Institute of Mental Health (R25MH094612, U01MH105960)
- National Eye Institute (F32EY024833)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Health Sciences › Medicine › Ophthalmology › Retinal Development and Disorders
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Keywords
- retinal bipolar cells, neuron classification, single-cell transcriptomics, cell morphology, transcriptional heterogeneity, mouse retina
- MeSH terms
- Animals, Female, Genetic Markers, Male, Mice, Inbred Strains, Mice, Transgenic, Transcription, Genetic, Cluster Analysis, Sequence Analysis, RNA, Amacrine Cells, Retinal Bipolar Cells, Mice, Single-Cell Analysis, Transcriptome
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
