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07, 2021;7 (31):

A single-cell type transcriptomics map of human tissues.

Max Karlsson, Cheng Zhang, Loren Méar, Wen Zhong, Andreas Digre, Borbala Katona, Evelina Sjöstedt, Lynn Butler, Jacob Odeberg, Philip Dusart, Fredrik Edfors, Per Oksvold, Kalle von Feilitzen, Martin Zwahlen, Muhammad Arif, Ozlem Altay, Xiangyu Li, Mehmet Ozcan, Adil Mardinoglu, Linn Fagerberg, Jan Mulder, Yonglun Luo, Fredrik Ponten, Mathias Uhlén, Cecilia Lindskog
Author Information
  1. Max Karlsson: Science for Life Laboratory, Department of Protein Science, KTH-Royal Institute of Technology, Stockholm, Sweden. ORCID
  2. Cheng Zhang: Science for Life Laboratory, Department of Protein Science, KTH-Royal Institute of Technology, Stockholm, Sweden. ORCID
  3. Loren Méar: Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden. ORCID
  4. Wen Zhong: Science for Life Laboratory, Department of Protein Science, KTH-Royal Institute of Technology, Stockholm, Sweden. ORCID
  5. Andreas Digre: Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden. ORCID
  6. Borbala Katona: Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden.
  7. Evelina Sjöstedt: Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden. ORCID
  8. Lynn Butler: Science for Life Laboratory, Department of Protein Science, KTH-Royal Institute of Technology, Stockholm, Sweden.
  9. Jacob Odeberg: Science for Life Laboratory, Department of Protein Science, KTH-Royal Institute of Technology, Stockholm, Sweden. ORCID
  10. Philip Dusart: Science for Life Laboratory, Department of Protein Science, KTH-Royal Institute of Technology, Stockholm, Sweden.
  11. Fredrik Edfors: Science for Life Laboratory, Department of Protein Science, KTH-Royal Institute of Technology, Stockholm, Sweden. ORCID
  12. Per Oksvold: Science for Life Laboratory, Department of Protein Science, KTH-Royal Institute of Technology, Stockholm, Sweden. ORCID
  13. Kalle von Feilitzen: Science for Life Laboratory, Department of Protein Science, KTH-Royal Institute of Technology, Stockholm, Sweden. ORCID
  14. Martin Zwahlen: Science for Life Laboratory, Department of Protein Science, KTH-Royal Institute of Technology, Stockholm, Sweden. ORCID
  15. Muhammad Arif: Science for Life Laboratory, Department of Protein Science, KTH-Royal Institute of Technology, Stockholm, Sweden. ORCID
  16. Ozlem Altay: Science for Life Laboratory, Department of Protein Science, KTH-Royal Institute of Technology, Stockholm, Sweden.
  17. Xiangyu Li: Science for Life Laboratory, Department of Protein Science, KTH-Royal Institute of Technology, Stockholm, Sweden. ORCID
  18. Mehmet Ozcan: Science for Life Laboratory, Department of Protein Science, KTH-Royal Institute of Technology, Stockholm, Sweden. ORCID
  19. Adil Mardinoglu: Science for Life Laboratory, Department of Protein Science, KTH-Royal Institute of Technology, Stockholm, Sweden. ORCID
  20. Linn Fagerberg: Science for Life Laboratory, Department of Protein Science, KTH-Royal Institute of Technology, Stockholm, Sweden. ORCID
  21. Jan Mulder: Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden. ORCID
  22. Yonglun Luo: Lars Bolund Institute of Regenerative Medicine and Qingdao-Europe Advanced Institute for Life Sciences, BGI-Qingdao, BGI-Shenzhen, Qingdao, China. ORCID
  23. Fredrik Ponten: Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden. ORCID
  24. Mathias Uhlén: Science for Life Laboratory, Department of Protein Science, KTH-Royal Institute of Technology, Stockholm, Sweden. mathias.uhlen@scilifelab.se. ORCID
  25. Cecilia Lindskog: Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden. ORCID
PMID: 34321199 DOI: 10.1126/sciadv.abh2169

Abstract

Advances in molecular profiling have opened up the possibility to map the expression of genes in cells, tissues, and organs in the human body. Here, we combined single-cell transcriptomics analysis with spatial antibody-based protein profiling to create a high-resolution single-cell type map of human tissues. An open access atlas has been launched to allow researchers to explore the expression of human protein-coding genes in 192 individual cell type clusters. An expression specificity classification was performed to determine the number of genes elevated in each cell type, allowing comparisons with bulk transcriptomics data. The analysis highlights distinct expression clusters corresponding to cell types sharing similar functions, both within the same organs and between organs.

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MeSH Term

Antibodies
Gene Expression Profiling
Humans
Proteome
Proteomics
Transcriptome

Chemicals

Antibodies
Proteome
Journal Article Research Support, Non-U.S. Gov't
Article has an altmetric score of 153

Word Cloud

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