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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The paper grouped cells into 565 subclusters using network-based clustering on biological independent component cell loadings.
“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
The Methods section (page unavailable) supports the use of graph-based clustering, but says cells were clustered using scores on 37 significant principal components, not biological independent-component loadings. It reports 30 initial Louvain-Jaccard clusters, while the Results section (page unavailable) reports 26 putative cell-type clusters—not 565 subclusters. (LLM, conf=0.88)
Experimental — accuracy not yet measuredA modified GLM including a batch indicator variable was sufficient to correct for technical differences arising from two profiled batches of murine bipolar cells, though successful application requires all cell types to share a similar batch effect.
“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 cellular transcriptomic state space can be modeled as a smooth, low-dimensional manifold or a set of manifolds.
“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]
Clustering 13,313 nuclei from frozen adult mouse hippocampus and PFC revealed groups corresponding to known cell types and anatomically distinct brain regions.
“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]
Massively parallel scRNA-seq methods such as Drop-seq can be applied at scale cost-effectively but require intact single-cell suspensions.
“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]
A small number of genes, detectable even with shallow sequencing, may be sufficient to encode cell-type identity in the brain.
“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]
Small differences can give a subset of cells a new property within a general I/O transformation, which may justify calling them a distinct subtype, as seen with the 15 retinal bipolar cell types.
“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]
Recent scRNA-seq studies sampled >20,000-750,000 cells in individual mouse brain regions.
“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
The number of cells needed to generate a comprehensive atlas of a tissue depends on multiple factors, including the number of cell types and molecular differences between cell types.
“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 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.
“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
Phenograph is a modularity-based clustering algorithm that produces robust clusterings on scRNA-seq data.
“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
Iterative PCA-Louvain clustering with stepwise cluster robustness assessment identified 14 distinct cell clusters with a minimum of 8 cells per cluster.
“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]
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Deep generative modeling for single-cell transcriptomics
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Self-citationShared and distinct transcriptomic cell types across neocortical areas
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Self-citationA single-cell survey of the small intestinal epithelium
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Self-citationRecovering Gene Interactions from Single-Cell Data Using Data Diffusion
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Molecular diversity and specializations among the cells of the adult mouse brain
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Self-citationSingle-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding
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A benchmark of batch-effect correction methods for single-cell RNA sequencing data
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Massively parallel single-nucleus RNA-seq with DroNc-seq
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Self-citationA revised airway epithelial hierarchy includes CFTR-expressing ionocytes
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The art of using t-SNE for single-cell transcriptomics
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Individual brain organoids reproducibly form cell diversity of the human cerebral cortex
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Self-citationSpatiotemporal gene expression trajectories reveal developmental hierarchies of the human cortex
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Visualizing structure and transitions in high-dimensional biological data
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Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain
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Self-citationSingle-cell reconstruction of developmental trajectories during zebrafish embryogenesis
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Self-citationAn atlas of the aging lung mapped by single cell transcriptomics and deep tissue proteomics
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Self-citationEstablishing Cerebral Organoids as Models of Human-Specific Brain Evolution
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Self-citationCheckpoint Blockade Immunotherapy Induces Dynamic Changes in PD-1-CD8+ Tumor-Infiltrating T Cells
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The Human and Mouse Enteric Nervous System at Single-Cell Resolution
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Spatially resolved cell atlas of the mouse primary motor cortex by MERFISH
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Single-Cell Transcriptomics of Regulatory T Cells Reveals Trajectories of Tissue Adaptation
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A Comprehensive Roadmap of Murine Spermatogenesis Defined by Single-Cell RNA-Seq
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Transcriptional Architecture of Synaptic Communication Delineates GABAergic Neuron Identity
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Single-cell analysis of experience-dependent transcriptomic states in the mouse visual cortex
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What is a cell type and how to define it?
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Self-citationCell type atlas and lineage tree of a whole complex animal by single-cell transcriptomics
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Self-citationSABER amplifies FISH: enhanced multiplexed imaging of RNA and DNA in cells and tissues
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Self-citationGenetic Dissection of Neural Circuits: A Decade of Progress
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Self-citationOuter Radial Glia-like Cancer Stem Cells Contribute to Heterogeneity of Glioblastoma
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- 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 · 2012: 1
- 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
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › 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
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