A genome-wide transcriptomic analysis of protein-coding genes in human blood cells

Mathias Uhlén(Science for Life Laboratory), Max Karlsson(Science for Life Laboratory), Wen Zhong(Science for Life Laboratory), Abdellah Tebani(Science for Life Laboratory), Christian Pou(Science for Life Laboratory), Jaromír Mikeš(Science for Life Laboratory), Tadepally Lakshmikanth(Science for Life Laboratory), Björn Forsström(Science for Life Laboratory), Fredrik Edfors(Science for Life Laboratory), Jacob Odeberg(Karolinska University Hospital), Adil Mardinoğlu(King's College London), Cheng Zhang(Science for Life Laboratory), Kalle von Feilitzen(Science for Life Laboratory), Jan Mulder(Karolinska Institutet), Evelina Sjöstedt(Karolinska Institutet), Andreas Hober(Science for Life Laboratory), Per Oksvold(Science for Life Laboratory), Martin Zwahlen(Science for Life Laboratory), Fredrik Pontén(Uppsala University), Cecilia Lindskog(Uppsala University), Åsa Sivertsson(Science for Life Laboratory), Linn Fagerberg(Science for Life Laboratory), Petter Brodin(Karolinska University Hospital)
Science
December 20, 2019
Cited by 632

Abstract

Blood is the predominant source for molecular analyses in humans, both in clinical and research settings. It is the target for many therapeutic strategies, emphasizing the need for comprehensive molecular maps of the cells constituting human blood. In this study, we performed a genome-wide transcriptomic analysis of protein-coding genes in sorted blood immune cell populations to characterize the expression levels of each individual gene across the blood cell types. All data are presented in an interactive, open-access Blood Atlas as part of the Human Protein Atlas and are integrated with expression profiles across all major tissues to provide spatial classification of all protein-coding genes. This allows for a genome-wide exploration of the expression profiles across human immune cell populations and all major human tissues and organs.


Related Papers

No related papers found

Powered by citation graph analysis