CoChat
Sign Up
Cited by 48

The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.

hECA: The cell-centric assembly of a cell atlas

Chen, Sijie, Luo, Yanting, Gao, Haoxiang, Li, Fanhong, Chen, Yixin, Li, Jiaqi, You, Renke, Hao, Minsheng, Bian, Haiyang, Xi, Xi, Li, Wenrui, Li, Weiyu, Ye, Mingli, Meng, Qiuchen, Zou, Ziheng, Li, Chen, Li, Haochen, Zhang, Yangyuan, Cui, Yanfei, Wei, Lei, Chen, Fufeng, Wang, Xiaowo, Lv, Hairong, Hua, Kui, Jiang, Rui, Zhang, Xuegong·2022·iScience
DOI 10.1016/j.isci.2022.104318PMC PMC9114628

Published version of a preprint

Open this paper in CoChat — free account

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.isci.2022.104318

Cites

23 of 23 references verified0 citation errors · 0 not checkable

Statistics

Figures

Graphical abstract
Graphical abstract CC BY · PubMed Central
Figure 1 Overview of hECA. Scattered data are assembled into the ensemble cell atlas using a unified informatics framework The framework includes three key components uGT, uHAF, and ECAUGT. They made hECA the first cell-centric assembled cell atlases with structured indexing and support for combinatorial searching. Based on these components, three novel functions were built on hECA: “in data” cell sorting, quantitative portraiture, and customizable reference creation. See also Figures S5, S6, and S7.
Figure 1 Overview of hECA. Scattered data are assembled into the ensemble cell atlas using a unified informatics framework The framework includes three key components uGT, uHAF, and ECAUGT. They made hECA the first cell-centric assembled cell atlases with structured indexing and support for combinatorial searching. Based on these components, three novel functions were built on hECA: “in data” cell sorting, quantitative portraiture, and customizable reference creation. See also Figures S5, S6, and S7.CC BY · PubMed Central
Figure 2 The agile construction of a draft T-cell metabolic landscape across multiple organs from hECA (A) Workflow of the in data cell sorting from hECA to build the agile T cell atlas. (B) Subtypes of selected T cells are displayed on UMAP. DN T: Double negative T cell, CD8+ Tc: CD8+ Cytotoxic T cell, CD8+ Trm: CD8+ resident memory T cell, CD4+ Th1: CD4+ T helper cell type 1, CD4+ Tem: CD4+ effector memory T cell, CD4+ Tcm: CD4+ central memory T cell. (C) Organ origins of selected T cells organ origin displayed on UMAP. (D) Gene expression signatures of the identified T cell subtypes. The color bar represents average expression level of cell type related markers with colors gray to red indicating expression low to high. The dot size represents percentage of cells expressing the marker within subtypes. (E–F) Heatmaps showing z-scores of activity scores of major metabolic pathways of the T cell subtypes in multiple organs. (E) for CD4+ T cells and (F) for CD8+ T cells. Each row in the heatmap corresponds to one selected term in the KEGG metabolism pathway database, and each column corresponds to one T cell subcluster. See also Figures S1 and S2.
Figure 2 The agile construction of a draft T-cell metabolic landscape across multiple organs from hECA (A) Workflow of the in data cell sorting from hECA to build the agile T cell atlas. (B) Subtypes of selected T cells are displayed on UMAP. DN T: Double negative T cell, CD8+ Tc: CD8+ Cytotoxic T cell, CD8+ Trm: CD8+ resident memory T cell, CD4+ Th1: CD4+ T helper cell type 1, CD4+ Tem: CD4+ effector memory T cell, CD4+ Tcm: CD4+ central memory T cell. (C) Organ origins of selected T cells organ origin displayed on UMAP. (D) Gene expression signatures of the identified T cell subtypes. The color bar represents average expression level of cell type related markers with colors gray to red indicating expression low to high. The dot size represents percentage of cells expressing the marker within subtypes. (E–F) Heatmaps showing z-scores of activity scores of major metabolic pathways of the T cell subtypes in multiple organs. (E) for CD4+ T cells and (F) for CD8+ T cells. Each row in the heatmap corresponds to one selected term in the KEGG metabolism pathway database, and each column corresponds to one T cell subcluster. See also Figures S1 and S2.CC BY · PubMed Central
Figure 3 In data experiments with hECA facilitating discoveries of side effects of targeted drugs (A) The diagram of using in data cell sorting to predict targets and off-targets of targeted therapy. Red dots and blue dots in the human body represent the intended target sites and side-effect sites, respectively. The red and blue dots in the UMAP represent the intended treatment cells and side-effect cells, respectively. (B) Visualization of CD19+ cells (expression>0.1) in UMAP, colored by organ origins of cells. CD19 is the target gene of the targeted therapy. (C) Visualization of CD19 expression levels of those CD19+ cells. (D) Visualization of CD79A expression levels of those CD19+ cells. CD79A is a marker for B cells. (E) Visualization of CD248 expression levels of those CD19+ cells. CD248 is a marker for pericytes. (F) Visualization of CD22+ (expression>0.1) cells in UMAP, colored by organ origin of cells. CD22 is the target gene of the targeted therapy. (G) Visualization of CD22 expression levels of those CD19+ cells. (H) Visualization of CD79A expression levels of those CD22+ cells. CD79A is a marker for B cells. (I) Visualization of OLIG2 expression levels of those CD22+ cells. OLIG2 is a marker for oligodendrocytes. The color bars in (C–E) represent expression levels of CD19, CD79A, and CD248, and the color bars in (G–I) represent expression levels of CD22, CD79A, and OLIG2, respectively, with colors gray to red indicating expression low to high. The red and blue ellipses in (D–E) and (H–I) line out the target cells and off-target cells, respectively. See also Figures S3 and S4.
Figure 3 In data experiments with hECA facilitating discoveries of side effects of targeted drugs (A) The diagram of using in data cell sorting to predict targets and off-targets of targeted therapy. Red dots and blue dots in the human body represent the intended target sites and side-effect sites, respectively. The red and blue dots in the UMAP represent the intended treatment cells and side-effect cells, respectively. (B) Visualization of CD19+ cells (expression>0.1) in UMAP, colored by organ origins of cells. CD19 is the target gene of the targeted therapy. (C) Visualization of CD19 expression levels of those CD19+ cells. (D) Visualization of CD79A expression levels of those CD19+ cells. CD79A is a marker for B cells. (E) Visualization of CD248 expression levels of those CD19+ cells. CD248 is a marker for pericytes. (F) Visualization of CD22+ (expression>0.1) cells in UMAP, colored by organ origin of cells. CD22 is the target gene of the targeted therapy. (G) Visualization of CD22 expression levels of those CD19+ cells. (H) Visualization of CD79A expression levels of those CD22+ cells. CD79A is a marker for B cells. (I) Visualization of OLIG2 expression levels of those CD22+ cells. OLIG2 is a marker for oligodendrocytes. The color bars in (C–E) represent expression levels of CD19, CD79A, and CD248, and the color bars in (G–I) represent expression levels of CD22, CD79A, and OLIG2, respectively, with colors gray to red indicating expression low to high. The red and blue ellipses in (D–E) and (H–I) line out the target cells and off-target cells, respectively. See also Figures S3 and S4.CC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 23 references

Research works printed but not linked: 72

  1. Aizarani, 2019, A human liver cell atlas reveals heterogeneity and epithelial progenitors, Nature, 572, 199, 10.1038/s41586-019-1373-2 · DOI · Paper page
  2. Arazi, 2019, The immune cell landscape in kidneys of patients with lupus nephritis, Nat. Immunol., 20, 902, 10.1038/s41590-019-0398-x · DOI
  3. Asp, 2019, A spatiotemporal organ-wide gene expression and cell atlas of the developing human heart, Cell, 179, 1647, 10.1016/j.cell.2019.11.025 · DOI
  4. Azizi, 2018, Single-cell map of diverse immune phenotypes in the breast tumor microenvironment, Cell, 174, 1293, 10.1016/j.cell.2018.05.060 · DOI · Paper page
  5. Baran-Gale, 2017, Experimental design for single-cell RNA sequencing, Brief. Funct. Genomics, 17, 233, 10.1093/bfgp/elx035 · DOI
  6. Bayraktar, 2020, Astrocyte layers in the mammalian cerebral cortex revealed by a single-cell in situ transcriptomic map, Nat. Neurosci., 23, 500, 10.1038/s41593-020-0602-1 · DOI · Paper page
  7. Börner, 2021, Anatomical structures, cell types and biomarkers of the human reference atlas, Nat. Cell Biol., 23, 1117, 10.1038/s41556-021-00788-6 · DOI
  8. Buck, 2015, T cell metabolism drives immunity, J. Exp. Med., 212, 1345, 10.1084/jem.20151159 · DOI
  9. Chapman, 2020, Metabolic coordination of T cell quiescence and activation, Nat. Rev. Immunol., 20, 55, 10.1038/s41577-019-0203-y · DOI
  10. Chen, 2018, BCseq: accurate single cell RNA-seq quantification with bias correction, Nucleic Acids Res., 46, e82, 10.1093/nar/gky308 · DOI
  11. Chen, 2022, Multifaceted spatial and functional zonation of cardiac cells in adult human heart, Circulation, 145, 315, 10.1161/CIRCULATIONAHA.121.055690 · DOI
  12. Chen, 2022, Toward a unified information framework for cell atlas assembly, Natl. Sci. Rev., 9, 10.1093/nsr/nwab179 · DOI
  13. Chevrier, 2017, An immune atlas of clear cell renal cell carcinoma, Cell, 169, 736, 10.1016/j.cell.2017.04.016 · DOI
  14. Cillo, 2020, Immune landscape of viral- and carcinogen-driven head and neck cancer, Immunity, 52, 183, 10.1016/j.immuni.2019.11.014 · DOI
  15. Corridoni, 2020, Single-cell atlas of colonic CD8(+) T cells in ulcerative colitis, Nat. Med., 26, 1480, 10.1038/s41591-020-1003-4 · DOI
  16. Cui, 2019, Single-cell transcriptome analysis maps the developmental track of the human heart, Cell Rep., 26, 1934, 10.1016/j.celrep.2019.01.079 · DOI
  17. Diehl, 2016, The Cell Ontology 2016: enhanced content, modularization, and ontology interoperability, J. Biomed. Semantics, 7, 44, 10.1186/s13326-016-0088-7 · DOI
  18. Fernandez, 2019, Single-cell immune landscape of human atherosclerotic plaques, Nat. Med., 25, 1576, 10.1038/s41591-019-0590-4 · DOI · Paper page
  19. Franzén, 2019, PanglaoDB: a web server for exploration of mouse and human single-cell RNA sequencing data, Database, 1
  20. Gaublomme, 2019, Nuclei multiplexing with barcoded antibodies for single-nucleus genomics, Nat. Commun., 10, 2907, 10.1038/s41467-019-10756-2 · DOI
  21. Greene, 2017, ontologyX: a suite of R packages for working with ontological data, Bioinformatics, 33, 1104, 10.1093/bioinformatics/btw763 · DOI
  22. Guo, 2018, The adult human testis transcriptional cell atlas, Cell Res., 28, 1141, 10.1038/s41422-018-0099-2 · DOI
  23. Guo, 2020, The dynamic transcriptional cell atlas of testis development during human puberty, Cell Stem Cell, 26, 262, 10.1016/j.stem.2019.12.005 · DOI
  24. Hänzelmann, 2013, GSVA: gene set variation analysis for microarray and RNA-Seq data, BMC Bioinformatics, 14, 7, 10.1186/1471-2105-14-7 · DOI · Paper page
  25. Hie, 2018, Panoramic stitching of heterogeneous single-cell transcriptomic data, bioRxiv, 371179
  26. Kanehisa, 2020, KEGG: integrating viruses and cellular organisms, Nucleic Acids Res., 49, D545, 10.1093/nar/gkaa970 · DOI · Paper page
  27. Kernfeld, 2018, A single-cell transcriptomic atlas of thymus organogenesis resolves cell types and developmental Maturation, Immunity, 48, 1258, 10.1016/j.immuni.2018.04.015 · DOI
  28. Kim, 2020, Single-cell RNA sequencing demonstrates the molecular and cellular reprogramming of metastatic lung adenocarcinoma, Nat. Commun., 11, 2285, 10.1038/s41467-020-16164-1 · DOI
  29. Kinchen, 2018, Structural remodeling of the human colonic Mesenchyme in inflammatory bowel disease, Cell, 175, 372, 10.1016/j.cell.2018.08.067 · DOI · Paper page
  30. Korsunsky, 2019, Fast, sensitive and accurate integration of single-cell data with Harmony, Nat. Methods, 16, 1289, 10.1038/s41592-019-0619-0 · DOI · Paper page
  31. La Manno, 2018, RNA velocity of single cells, Nature, 560, 494, 10.1038/s41586-018-0414-6 · DOI · Paper page
  32. Lake, 2018, Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain, Nat. Biotechnol., 36, 70, 10.1038/nbt.4038 · DOI · Paper page
  33. Li, 2021, Alterations in bile acid metabolism associated with inflammatory bowel disease, Inflamm. Bowel Dis., 27, 1525, 10.1093/ibd/izaa342 · DOI
  34. Liao, 2020, Single-cell landscape of bronchoalveolar immune cells in patients with COVID-19, Nat. Med., 26, 842, 10.1038/s41591-020-0901-9 · DOI · Paper page
  35. Lin, 2019, Off-target toxicity is a common mechanism of action of cancer drugs undergoing clinical trials, Sci. Transl. Med., 11, 10.1126/scitranslmed.aaw8412 · DOI
  36. Lukowski, 2019, A single-cell transcriptome atlas of the adult human retina, Embo J., 38, 10.15252/embj.2018100811 · DOI
  37. Madissoon, 2019, scRNA-seq assessment of the human lung, spleen, and esophagus tissue stability after cold preservation, Genome Biol., 21, 1, 10.1186/s13059-019-1906-x · DOI
  38. Martens, 2020, WikiPathways: connecting communities, Nucleic Acids Res., 49, D613, 10.1093/nar/gkaa1024 · DOI
  39. McInnes, 2018, UMAP: Uniform manifold approximation and projection for dimension reduction, arXiv
  40. Miao, 2016, Differential expression analyses for single-cell RNA-Seq: old questions on new data, Quant. Biol., 4, 243, 10.1007/s40484-016-0089-7 · DOI
  41. Miao, 2018, DEsingle for detecting three types of differential expression in single-cell RNA-seq data, Bioinformatics, 34, 3223, 10.1093/bioinformatics/bty332 · DOI
  42. Narayan, 2021, Assessing single-cell transcriptomic variability through density-preserving data visualization, Nat. Biotechnol., 39, 765, 10.1038/s41587-020-00801-7 · DOI
  43. Osumi-Sutherland, 2021, Cell type ontologies of the human cell atlas, Nat. Cell Biol., 23, 1129, 10.1038/s41556-021-00787-7 · DOI
  44. Parikh, 2019, Colonic epithelial cell diversity in health and inflammatory bowel disease, Nature, 567, 49, 10.1038/s41586-019-0992-y · DOI
  45. Park, 2020, A cell atlas of human thymic development defines T cell repertoire formation, Science, 367, 10.1126/science.aay3224 · DOI
  46. Parker, 2020, Single-cell analyses identify brain mural cells expressing CD19 as potential off-tumor targets for CAR-T Immunotherapies, Cell, 183, 126, 10.1016/j.cell.2020.08.022 · DOI
  47. Pearson, 1901, LIII. On lines and planes of closest fit to systems of points in space, Lond. Edinb. Dublin Philosophical Mag. J. Sci., 2, 559, 10.1080/14786440109462720 · DOI
  48. Phipson, 2017, Gene length and detection bias in single cell RNA sequencing protocols, F1000Res, 6, 595, 10.12688/f1000research.11290.1 · DOI · Paper page
  49. Plasschaert, 2018, A single-cell atlas of the airway epithelium reveals the CFTR-rich pulmonary ionocyte, Nature, 560, 377, 10.1038/s41586-018-0394-6 · DOI · Paper page
  50. Qiu, 2017, Single-cell mRNA quantification and differential analysis with Census, Nat. Methods, 14, 309, 10.1038/nmeth.4150 · DOI
  51. Qiu, 2017, Reversed graph embedding resolves complex single-cell trajectories, Nat. Methods, 14, 979, 10.1038/nmeth.4402 · DOI · Paper page
  52. Renthal, 2018, Characterization of human mosaic Rett syndrome brain tissue by single-nucleus RNA sequencing, Nat. Neurosci., 21, 1670, 10.1038/s41593-018-0270-6 · DOI
  53. Rubin, 2019, Neurological toxicities associated with chimeric antigen receptor T-cell therapy, Brain, 142, 1334, 10.1093/brain/awz053 · DOI
  54. Shyer, 2020, Metabolic signaling in T cells, Cell Res., 30, 649, 10.1038/s41422-020-0379-5 · DOI
  55. Smedley, 2009, BioMart--biological queries made easy, BMC Genomics, 10, 22, 10.1186/1471-2164-10-22 · DOI
  56. Soneson, 2018, Bias, robustness and scalability in single-cell differential expression analysis, Nat. Methods, 15, 255, 10.1038/nmeth.4612 · DOI · Paper page
  57. Song, 2020, Microbial bile acid metabolites modulate gut RORγ+ regulatory T cell homeostasis, Nature, 577, 410, 10.1038/s41586-019-1865-0 · DOI · Paper page
  58. Stuart, 2019, Comprehensive integration of single-cell data, Cell, 177, 1888, 10.1016/j.cell.2019.05.031 · DOI · Paper page
  59. Uhlén, 2015, Tissue-based map of the human proteome, Science, 347, 1260419, 10.1126/science.1260419 · DOI · Paper page
  60. Venteicher, 2017, Decoupling genetics, lineages, and microenvironment in IDH-mutant gliomas by single-cell RNA-seq, Science, 355, 10.1126/science.aai8478 · DOI
  61. Voigt, 2019, Single-cell transcriptomics of the human retinal pigment epithelium and choroid in health and macular degeneration, Proc. Natl. Acad. Sci. U S A., 116, 24100, 10.1073/pnas.1914143116 · DOI
  62. Wang, 2020, Single-cell reconstruction of the adult human heart during heart failure and recovery reveals the cellular landscape underlying cardiac function, Nat. Cell Biol., 22, 108, 10.1038/s41556-019-0446-7 · DOI
  63. Wang, 2020, Single-cell transcriptome analysis reveals differential nutrient absorption functions in human intestine, J. Exp. Med., 217, 10.1084/jem.20191130 · DOI · Paper page
  64. Wei, 2019, Target selection for CAR-T therapy, J. Hematol. Oncol., 12, 62, 10.1186/s13045-019-0758-x · DOI
  65. Wolf, 2018, SCANPY: large-scale single-cell gene expression data analysis, Genome Biol., 19, 15, 10.1186/s13059-017-1382-0 · DOI · Paper page
  66. Xie, 2021, Gene set knowledge discovery with enrichr, Curr. Protoc., 1, e90, 10.1002/cpz1.90 · DOI · Paper page
  67. Yáñez, 2019, CAR T cell toxicity: current Management and future directions, HemaSphere, 3, e186, 10.1097/HS9.0000000000000186 · DOI
  68. Yin, 2019, Targeting T cell metabolism in the tumor microenvironment: an anti-cancer therapeutic strategy, J. Exp. Clin. Cancer Res., 38, 403, 10.1186/s13046-019-1409-3 · DOI · Paper page
  69. Zhang, 2020, Single-cell landscape of immunological responses in patients with COVID-19, Nat. Immunol., 21, 1107, 10.1038/s41590-020-0762-x · DOI · Paper page
  70. Zhang, 2018, CellMarker: a manually curated resource of cell markers in human and mouse, Nucleic Acids Res., 47, D721, 10.1093/nar/gky900 · DOI · Paper page
  71. Zhong, 2020, Decoding the development of the human hippocampus, Nature, 577, 531, 10.1038/s41586-019-1917-5 · DOI
  72. Zhong, 2018, A single-cell RNA-seq survey of the developmental landscape of the human prefrontal cortex, Nature, 555, 524, 10.1038/nature25980 · DOI · Paper page

Cited by

Sorted by citations

See all 48

Metadata

Authors
Chen, S., Luo, Y., Gao, H., Li, F., Chen, Y., Li, J., You, R., Hao, M., Bian, H., Xi, X., Li, W., Li, W., Ye, M., Meng, Q., Zou, Z., Li, C., Li, H., Zhang, Y., Cui, Y., Wei, L., Chen, F., Wang, X., Lv, H., Hua, K., Jiang, R., & Zhang, X.
ORCID
Venue
iScience
Publisher
Elsevier BV
Published
2022-05
Volume
25
Issue
5
Pages
104318
ISSN
2589-0042
DOI
10.1016/j.isci.2022.104318
PMC
PMC9114628
Cited by
48
References
23
Language
en
Licence
Funders
  • National Natural Science Foundation of China (32000453, 42050101, 61721003, 62050178)
  • National Key Research and Development Program of China (2021YFF1200900)

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 ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: hECA: The cell-centric assembly of a cell atlas. https://app.cochat.ai/papers/10.1016/j.isci.2022.104318
The paper
Chen, S., Luo, Y., Gao, H., Li, F., Chen, Y., Li, J., You, R., Hao, M., Bian, H., Xi, X., Li, W., Li, W., Ye, M., Meng, Q., Zou, Z., Li, C., Li, H., Zhang, Y., Cui, Y., . . . Zhang, X. (2022). hECA: The cell-centric assembly of a cell atlas. iScience. https://doi.org/10.1016/j.isci.2022.104318