Evidence map›Paper›PMID 38328114›Full record

ArticlebioRxiv : the preprint server for biology2024

Comprehensive single-cell atlas of the mouse retina.

Jin Li, Jongsu Choi, Xuesen Cheng, Justin Ma, Shahil Pema, Joshua R Sanes, Graeme Mardon, Benjamin J Frankfort, Nicholas M Tran, Yumei Li and 1 more

Open access · greenAbstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 12 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors at 2 institutions in 1 country.

Jin LiDepartment of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030, USA.
Jongsu ChoiDepartment of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, Texas 77030, USA.
Xuesen ChengDepartment of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030, USA.
Justin MaDepartment of Pathology and Immunology, Baylor College of Medicine, Houston, Texas 77030, USA.
Shahil PemaDepartment of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030, USA.
Joshua R SanesCenter for Brain Science and Department of Molecular and Cellular Biology, Harvard University, Cambridge, Massachusetts 02130, USA.
Graeme MardonDepartment of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030, USA.
Benjamin J FrankfortDepartments of Ophthalmology and Neuroscience, Baylor College of Medicine, Houston, Texas 77030, USA.
Nicholas M TranDepartment of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030, USA.
Yumei LiDepartment of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030, USA.
Rui ChenDepartment of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030, USA.
Baylor College of Medicine · USHarvard University · US

Funding

GENETICS OF EARLY ONSET RETINAL DISEASESR01EY018571 · NEI · UNIVERSITY OF CALIFORNIA-IRVINE · PI RUI CHEN · 2008 to 2026
$7.2M
Molecular Basis of Human Visual System DisordersR01EY022356 · NEI · UNIVERSITY OF CALIFORNIA-IRVINE · PI RUI CHEN · 2012 to 2026
$6.1M
Single Cell Spatial Transcriptomics Shared Instrument at the BCM Core FacilityS10OD032189 · OD · BAYLOR COLLEGE OF MEDICINE · PI CHEN, RUI · 2022 to 2022
$304k
NEI NIH HHS R01 EY018571NEI NIH HHS R01 EY022356NIH HHS S10 OD032189
6 · The paper itself

Abstract

Single-cell RNA sequencing (scRNA-seq) has advanced our understanding of cellular heterogeneity at the single-cell resolution by classifying and characterizing cell types in multiple tissues and species. While several mouse retinal scRNA-seq reference datasets have been published, each dataset either has a relatively small number of cells or is focused on specific cell classes, and thus is suboptimal for assessing gene expression patterns across all retina types at the same time. To establish a unified and comprehensive reference for the mouse retina, we first generated the largest retinal scRNA-seq dataset to date, comprising approximately 190,000 single cells from C57BL/6J mouse whole retinas. This dataset was generated through the targeted enrichment of rare population cells via antibody-based magnetic cell sorting. By integrating this new dataset with public datasets, we conducted an integrated analysis to construct the Mouse Retina Cell Atlas (MRCA) for wild-type mice, which encompasses over 330,000 single cells. The MRCA characterizes 12 major classes and 138 cell types. It captured consensus cell type characterization from public datasets and identified additional new cell types. To facilitate the public use of the MRCA, we have deposited it in CELLxGENE, UCSC Cell Browser, and the Broad Single Cell Portal for visualization and gene expression exploration. The comprehensive MRCA serves as an easy-to-use, one-stop data resource for the mouse retina communities.

Identifiers

PMID38328114
PMCPMC10849744
OpenAlexW4391291311

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.