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.
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“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]
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“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
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“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.”
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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 ›
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