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Application of a Translational Profiling Approach for the Comparative Analysis of CNS Cell Types
Open accessAbstract
Comparative analysis can provide important insights into complex biological systems. As demonstrated in the accompanying paper, translating ribosome affinity purification (TRAP) permits comprehensive studies of translated mRNAs in genetically defined cell populations after physiological perturbations. To establish the generality of this approach, we present translational profiles for 24 CNS cell populations and identify known cell-specific and enriched transcripts for each population. We report thousands of cell-specific mRNAs that were not detected in whole-tissue microarray studies and provide examples that demonstrate the benefits deriving from comparative analysis. To provide a foundation for further biological and in silico studies, we provide a resource of 16 transgenic mouse lines, their corresponding anatomic characterization, and translational profiles for cell types from a variety of central nervous system structures. This resource will enable a wide spectrum of molecular and mechanistic studies of both well-known and previously uncharacterized neural cell populations.
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“Crossing these mice to microglia-specific Cre mice allows for the isolation of ribosome-associated mRNAs in a cell-type-specific fashion using eGFP-based immunoaffinity purification28,91,93.”
From Lymphoid gene expression supports neuroprotective microglia function (2025) · cites this paper as [93]
Experimental — accuracy not yet measuredIn the citing paper
“We further compared the cerebellar data with cerebellar cell-type-specific microarray data (Doyle et al., 2008), finding a 3- to 5-fold enrichment for astrocyte and Bergmann glia genes and a depletion of other cell types (Figure S1C).”
From The aging astrocyte transcriptome from multiple regions of the mouse brain. (2018) · cites this paper as Doyle et al., 2008
In the citing paper
“Ribo-tag restricts analysis to ribosome-associated mRNAs, which are more likely to be actively being made into proteins, providing a representation of the translational state of the cells, a similar approach to that used by others (Chai et al., 2017; Doyle et al., 2008; Morel et al., 2017).”
From The aging astrocyte transcriptome from multiple regions of the mouse brain. (2018) · cites this paper as Doyle et al., 2008
In the citing paper
“Our adult astrocyte gene expression datasets are in broad agreement with other microarray and RNA-seq profiles of astrocytes from the developing and adult brain (Cahoy et al., 2008; Chai et al., 2017; Doyle et al., 2008; Morel et al., 2017; Zhang et al., 2014).”
From The aging astrocyte transcriptome from multiple regions of the mouse brain. (2018) · cites this paper as Doyle et al., 2008
In the citing paper
“Purifying cell populations or carrying out transcriptional analyses on single cells158–163 can identify important changes that are not apparent in whole-tissue transcriptomes141,142.”
From Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015) · cites this paper as [160]
In the citing paper
“The bacTRAP technology enabled us to assay AD-relevant neuronal cell types with genome-wide coverage, measure transcripts ex vivo (as opposed to postmortem), and specifically capture actively translated (rather than all transcribed) genes.”
From Selective Neuronal Vulnerability in Alzheimer’s Disease: A Network-Based Analysis (2020) · cites this paper as Doyle et al., 2008
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Metadata
- Authors
- Doyle, J. P., Dougherty, J. D., Heiman, M., Schmidt, E. F., Stevens, T. R., Ma, G., Bupp, S., Shrestha, P., Shah, R. D., Doughty, M. L., Gong, S., Greengard, P., & Heintz, N.
- Affiliations
- Howard Hughes Medical Institute (US)
- Rockefeller University (US)
- Corresponding author
- Heintz, Nathaniel
- ORCID
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2008-11
- Volume
- 135
- Issue
- 4
- Pages
- 749-762
- ISSN
- 0092-8674 (ISSN-L)
- PMC
- PMC2763427
- OpenAlex
- W2115155255
- Cited by
- 912
- References
- 37
- Citations by year
- 2026: 19 · 2025: 39 · 2024: 55 · 2023: 35 · 2022: 50 · 2021: 44 · 2020: 62 · 2019: 55 · 2018: 66 · 2017: 68 · 2016: 48 · 2015: 88 · 2014: 85 · 2013: 60 · 2012: 47
- FWCI
- 12.47
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (vor, 1719 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Cell (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Howard Hughes Medical Institute
- National Institutes of Health (1f32da021487-01)
- National Institute on Aging (AG09464, P01AG009464)
- National Institute of Mental Health (MH-074866, P50MH074866)
- National Institute on Drug Abuse (5F32DA021487, DA10044, P01DA010044, F32 DA021487)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Research and Splicing
- Keywords
- translating ribosome affinity purification, translational profiling, transgenic mouse lines
- MeSH terms
- Animals, Brain, Central Nervous System, Genetic Techniques, Immunohistochemistry, Mice, Transgenic, Models, Biological, Neurons, Ribosomes, Protein Biosynthesis, Reverse Transcriptase Polymerase Chain Reaction, Oligonucleotide Array Sequence Analysis, Chromosomes, Artificial, Bacterial, Green Fluorescent Proteins, Mice
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.
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