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A new paper published in Cell Reports reveals that changes in the gene expression of blood stem cells occur across the human lifetime; an important step in the understanding and treatment of blood disorders.

A scientist in a lab coat stands on a step ladder holding binoculars. In the distance we see the outline of buildings in Oxford. She is looking into the sky at a flock of birds. The birds form a shape similar to that of the Haematopoietic Stem Cell/Progenitor Cell (HSPC) compartments studied in this new scientific article © Image created by Roger Hulley from Mediart https://mediart.com/
Artistic representation of the study by Roy, Wang et al to unravel the changes in human haematopoiesis through the human lifetime, inspired by the resemblance of single cell RNA-sequencing data projections to a starling murmuration.

The article, authored by a collaborative team from groups led by Associate Professors Andi Roy and Beth Psaila in the MRC Molecular Haematology Unit, and Dr Supat Thongjuea from the MRC WIMM Centre for Computational Biology, offers a detailed look at the gene activity shaping blood cell production in tissues sampled from four stages across the human lifetime. 

Blood cell production, or haematopoiesis, starts before birth and continues throughout life. A specific group of cells called blood stem cells continuously replenish the many cell types circulating in the blood, and this process must be dynamic to meet the changing requirements throughout human development. However, disruptions to this process can lead to blood disorders such as blood cancer.

The researchers used a new computational platform (SingCellaR), to compare the gene activity of tens of thousands of individual cells across multiple samples.

“This was a hugely enjoyable project” notes Professor Beth Psaila, a senior author on the paper. “Working collaboratively with multiple research groups enabled us to access precious samples from healthy individuals at multiple timepoints over the human lifetime, generating a unique dataset of blood stem and progenitor cells.” Sampling five distinct tissues across four stages in the human lifetime, the team generated a comprehensive dataset looking at the gene activity of over 57,000 individual blood stem cells. They found that clear transitions occur in the nature of the blood stem cells at different stages of development. For example, before birth, blood stem cells in the liver show a bias towards giving rise to red blood cells and platelets rather than white blood cells, which is the reverse of what occurs when blood cell production occurs in the bone marrow.

The team also found evidence of rapid cell turnover in the stem cells from early in development, transitioning to lower activity with increased inflammatory signalling in adult life. The gene activity ‘fingerprints’ observed at distinct stages of development showed similarities to certain blood cancers that are comment at particular ages. Stem cell samples from juvenile myelomonocytic leukaemia (JMML), a cancer that affects young children, showed similarities to prenatal stem cells, suggesting a fetal origin for the disease. In contrast, samples from myelofibrosis, a cancer typically affecting older adults, showed inflammatory signalling programs similar to those seen in normal adult bone marrow.

“We put a lot of effort into developing powerful visualisation for single-cell data and ensuring that the analysis framework made it easy to use and fast to compare results from multiple methods. We believe this will speed up the analysis process of ongoing single-cell projects, generating more robust results and enabling detailed data interrogation.” says Dr Supat Thongjuea.

Professor Andi Roy adds “The expert analysis platform developed by Drs Thongjuea and Wang sets out a roadmap for data analysis that we hope will be useful to researchers applying single cell techniques in other disciplines.” 

 A graphical abstract for the 2021 Roy, Wang et al., paper in Cell Reports. The image shows the developmental times selected for the study alongside figures from the paper.

Read the full paper here at this link

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