O'Carroll Lab - Cell Stem Cell Authors Kucinski, I., Campos, J., Barile, M., Severi, F., Bohin, N., Moreira, P.N., Allen, L., Lawson, H., Haltalli, M.L.R., Kinston, S.J., O’Carroll, D., Kranc, K.R.,and Göttgens, B. . Image This study builds a dynamic model of blood production. It provides a framework for investigating the impact of ageing, inflammation, and cancer on stem cell activity and tissue homeostasis. Summary of Paper by Cameron Finlayson In brief: Kucinski and colleagues construct a quantitative and real-time model of mouse bone marrow hematopoiesis by combining scRNA-seq and persistent HSC labeling technologies. The model reveals lineage- and stage-specific self-renewal and differentiation properties and explains how these are altered in a transplantation setting. To maintain hematopoietic homeostasis, there is continual replenishment of all blood cell-types. This is the result of various cell differentiations and replication, beginning with hematopoietic stem cells (HSCs) and then advancing through a series of progenitors, collectively known as HSPCs. Prior research identified each respective HSPC and its potential fate, enabling the full hematopoietic lineage to be classified into the archetypal hematopoietic tree model. However, even with techniques such as single-cell RNA sequencing (scRNA-seq) enabling high-resolution glimpses of contemporaneous cellular- and tissue-level gene expression, understanding was limited to the transient moment studied and the classical tree model remained unable to convey the underlying kinetics of hematopoiesis. To tackle this, scientists from six major research institutes collaborated to advance prior research by combining lineage-persistent cellular labelling with time-sequenced observation of molecular-level changes via scRNA-seq, to build a mathematical model of hematopoiesis. A tamoxifen-inducible Hoxb5-labelled mouse strain was identified as the best candidate for inducible, persistent HSC lineage labelling. Bone marrow samples were collected at nine time points from day 3 to day 269, sorted by cell-type subpopulation based upon specific markers, then examined using scRNA-seq. The results of these various cell states/fates were also compared to in vitro data. Complex mathematical modelling was then applied to this data, with numerous models developed on a lineage-by-lineage basis, aiming to collate and form the collected time-point micro- and macro-level observations of change and rates of change within the HSPC compartment into a continuous predictive program. These quantitative models for the first time linked, with high resolution, real-time single-cell gene expression changes to downstream behaviors, e.g. differentiation or proliferation, and enabled observation of real-time dynamics for steady-state hematopoiesis for the entire population within the bone marrow HSPC compartment. This enables generation of a HSPC lineage model that describes the often wildly differing kinetics observed within different branches of the lineage. The model has proved so accurate that it can be applied to third-party scRNA-seq time-series data and correctly predict lineage outcomes or identify other stressors (e.g. healthy vs diseases; transplantation) and resulting changes to kinetics. Related Links Journal URL O'Carroll Lab Website DOI This article was published on 2024-06-17
