The DOI matches its Crossref record; 63 of the 63 references it cites were found in a registry or an index.
Mapping the Mouse Cell Atlas by Microwell-Seq
Open accessAbstract
Single-cell RNA sequencing (scRNA-seq) technologies are poised to reshape the current cell-type classification system. However, a transcriptome-based single-cell atlas has not been achieved for complex mammalian systems. Here, we developed Microwell-seq, a high-throughput and low-cost scRNA-seq platform using simple, inexpensive devices. Using Microwell-seq, we analyzed more than 400,000 single cells covering all of the major mouse organs and constructed a basic scheme for a mouse cell atlas (MCA). We reveal a single-cell hierarchy for many tissues that have not been well characterized previously. We built a web-based "single-cell MCA analysis" pipeline that accurately defines cell types based on single-cell digital expression. Our study demonstrates the wide applicability of the Microwell-seq technology and MCA resource.
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In the citing paper
“Their data contains four times as many cells and included several sample types not present in our data, such as neonatal and fetal organs, cell lines, and young adult ovary, peripheral blood, placenta, prostate, small intestine, stomach, testis, and uterus.”
From Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris (2018) · cites this paper as Han et al.20
In the citing paper
“Recently, Han et al.20 published a complementary scRNA-seq study across mouse organs.”
From Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris (2018) · cites this paper as Han et al.20
In the citing paper
“For example, the median number of genes detected in bladder is ~4,900 (FACS), 2,900 (droplet), and 900 (microwell-seq), while in kidney it is ~1,400 (FACS), 1,900 (droplet), and 500 (microwell-seq).”
From Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris (2018) · cites this paper as Han et al.20
In the citing paper
“However, our FACS data contains four times as many genes per cell, and we analyzed several organs not present in the Han et al. data set, such as aorta, four brain regions, diaphragm, four fat types, four adult heart chambers, adult skin, tongue, and trachea.”
From Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris (2018) · cites this paper as Han et al.20
In the citing paper
“Canonical CNS cell type markers were compiled from previous studies66–69.”
From APOE4 to APOE2 allelic switching in mice improves Alzheimer’s disease-related metabolic signatures, neuropathology and cognition (2025) · cites this paper as [69]
In the citing paper
“These clusters were identified based on the expression of known cell type–specific markers: microglia (clusters 0 to 6 and 10), T cells (cluster 7), macrophages (clusters 8 and 9), neutrophils (cluster 12), and B cells (cluster 14) (Fig. 8A and table S5) (40, 41).”
From Deletion of Abi3 gene locus exacerbates neuropathological features of Alzheimer’s disease in a mouse model of Aβ amyloidosis (2021) · cites this paper as [41]
In the citing paper
“Abi3 is predominately expressed in myeloid derived monocytic and microglial immune cells (Zhang et al., 2014; Han et al., 2018).”
From Deletion of the Alzheimer’s disease risk gene Abi3 locus results in obesity and systemic metabolic disruption in mice (2022) · cites this paper as Han et al., 2018
In the citing paper
“However, although two scRNA-seq atlases of mouse were recently released10,11, they are mostly restricted to adult organs, and do not attempt to characterize the emergence and dynamics of cell types during development.”
From The single-cell transcriptional landscape of mammalian organogenesis (2019) · cites this paper as [10]
In the citing paper
“We compared our subtypes against 130 fetal cell types annotated in the MCA10 with cell type correlation analysis, matching 96 MCA-defined cell types (rows) to 58 subtypes in our mouse embryo atlas (columns).”
From The single-cell transcriptional landscape of mammalian organogenesis (2019) · cites this paper as [10]
In the citing paper
“The cellular identity of the sorted endothelium, epithelium and fibroblasts was further confirmed by RNA-seq data analysis, which showed the expected organ-specific (Extended Data Fig. 3a) and cell-type-specific (Extended Data Fig. 3b) patterns of gene expression compared to published multi-tissue expression profiles18, 19.”
From Structural cells are key regulators of organ-specific immune responses (2020) · cites this paper as [19]
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Self-citationDroplet barcoding for single-cell transcriptomics applied to embryonic stem cells
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Encoding Method of Single-cell Spatial Transcriptomics Sequencing
Zhou, Y., Jia, E., Pan, M., Zhao, X., & Ge, Q. (2020) · International Journal of Biological Sciences · doi:10.7150/ijbs.43887 · Cited by 32
Self-citationKim, B., Dabin, L. C., Tate, M. D., Karahan, H., Sharify, A. D., Acri, D. J., Al-Amin, M. M., Philtjens, S., Smith, D. C., Wijeratne, H. R. S., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-48484-x · Cited by 28
“We annotated each cluster using the single-cell Mouse Cell Atlas (scMCA) package for single-cell mapping 29 and con fi rmed the enrichment of microglia (M) in our dataset.”
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Comparative analysis of nuclei isolation methods for brain single-nucleus RNA sequencing
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“Abi3 is predominately expressed in myeloid derived monocytic and microglial immune cells (Zhang et al., 2014; Han et al., 2018).”
BackgroundPark, J. H., Kim, B., Al‐Amin, M. M., Tate, M. D., Sharify, A. D., John, S. K., Karahan, H., Lu, H., Dabin, L. C., & Kim, J. (2026) · Alzheimer's & Dementia · doi:10.1002/alz.71742
scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis
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Self-citation
Metadata
- Authors
- Han, X., Wang, R., Zhou, Y., Fei, L., Sun, H., Lai, S., Saadatpour, A., Zhou, Z., Chen, H., Ye, F., Huang, D., Xu, Y., Huang, W., Jiang, M., Jiang, X., Mao, J., Chen, Y., Lu, C., Xie, J., Fang, Q., Wang, Y., Yue, R., Li, T., Huang, H., Orkin, S. H., Yuan, G.-C., Chen, M., & Guo, G.
- Affiliations
- Zhejiang University (CN)
- Harvard University (US)
- Women's Hospital, School of Medicine, Zhejiang University (CN)
- State Key Laboratory Fluid Power and Mechatronic Systems (CN)
- Tongji University (CN)
- First Affiliated Hospital Zhejiang University (CN)
- Boston Children's Hospital (US)
- Corresponding author
- Han, Xiaoping
- Guo, Guoji
- ORCID
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2018-02
- Volume
- 172
- Issue
- 5
- Pages
- 1091-1107.e17
- ISSN
- 0092-8674 (ISSN-L)
- OpenAlex
- W2788348358
- Cited by
- 1,597
- References
- 63
- Citations by year
- 2026: 80 · 2025: 161 · 2024: 180 · 2023: 185 · 2022: 238 · 2021: 257 · 2020: 257 · 2019: 157 · 2018: 80
- FWCI
- 67.74
- Type
- journal article
- Language
- en
- Update notices
- erratum (2018-05-17)
- Open access
- bronze
- doi.org
- Licence
- Locations
- Cell (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (31722027, 81770188, 31701290)
- National Key Program on Stem Cell and Translational Research (2017YFA0103401)
- 973 Program (2015CB964900)
- Zhejiang Provincial Natural Science Foundation of China (R17H080001)
- Fundamental Research Funds for the Central Universities (2016XZZX002-04)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- single-cell RNA sequencing, cell type classification, mouse organs, low-cost microfluidics, digital gene expression
- MeSH terms
- Animals, Costs and Cost Analysis, Female, Organ Specificity, Reproducibility of Results, 3T3 Cells, Sequence Analysis, RNA, Mice, Single-Cell Analysis, High-Throughput Nucleotide Sequencing
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