Proliferation dynamics of acute myeloid leukaemia and haematopoietic progenitors competing for bone marrow space

Leukaemia progressively invades bone marrow (BM), outcompeting healthy haematopoiesis by mechanisms that are not fully understood. Combining cell number measurements with a short-timescale dual pulse labelling method, we simultaneously determine the proliferation dynamics of primitive haematopoietic compartments and acute myeloid leukaemia (AML). We observe an unchanging proportion of AML cells entering S phase per hour throughout disease progression, with substantial BM egress at high levels of infiltration. For healthy haematopoiesis, we find haematopoietic stem cells (HSCs) make a significant contribution to cell production, but we phenotypically identify a quiescent subpopulation with enhanced engraftment ability. During AML progression, we observe that multipotent progenitors maintain a constant proportion entering S phase per hour, despite a dramatic decrease in the overall population size. Primitive populations are lost from BM with kinetics that are consistent with ousting irrespective of cell cycle state, with the exception of the quiescent HSC subpopulation, which is more resistant to elimination.

How leukaemia cells invade the bone marrow by outcompeting haematopoietic cells is still unclear. Here, the authors used detailed cell number measurements in conjunction with a dual pulse labelling method to determine proliferation rates and followed the in vivo dynamics of AML disease progression.

Automated Identification and Localization of Hematopoietic Stem Cells in 3D Intravital Microscopy Data

Measuring three-dimensional (3D) localization of hematopoietic stem cells (HSCs) within the bone marrow microenvironment using intravital microscopy is a rapidly expanding research theme. This approach holds the key to understanding the detail of HSC-niche interactions, which are critical for appropriate stem cell function. Due to the complex tissue architecture of the bone marrow and to the progressive introduction of scattering and signal loss at increasing imaging depths, there is no ready-made software to handle efficient segmentation and unbiased analysis of the data. To address this, we developed an automated image analysis tool that simplifies and standardizes the biological interpretation of 3D HSC microenvironment images. The algorithm identifies HSCs and measures their localization relative to surrounding osteoblast cells and bone collagen. We demonstrate here the effectiveness, consistency, and accuracy of the proposed approach compared to current manual analysis and its wider applicability to analyze other 3D bone marrow components.

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A new tool allows automated 3D image analysis of HSCs and their niche

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It performs automated segmentation of heterogeneous HSCs and bone marrow components

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This tool identifies real HSCs and eliminates false-positive signals

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3D distance measurements of HSC to the nearest osteoblast/bone are demonstrated