MarrowQuant Across Aging and Aplasia: A Digital Pathology Workflow for Quantification of Bone Marrow Compartments in Histological Sections

Abstract

in mid-sections of murine C57BL/6 bones in homeostatic conditions, including quantification of the highly predictable red-to-yellow transitions in the proximal section of the caudal tail and in the proximal-to-distal tibia. Additionally, we present a comparative skeletal map induced by lethal irradiation, with longitudinal quantification of the "red-to-yellow-to-red" transition over 2 months in C57BL/6 femurs and tibiae. We find that, following BM transplantation, BM adiposity inversely correlates with kinetics of hematopoietic recovery and that a proximal to distal gradient is conserved. Analysis of in vivo recovery through magnetic resonance imaging (MRI) reveals comparable kinetics. On human trephine biopsies MarrowQuant successfully recognizes the BM compartments, opening avenues for its application in experimental, or clinical contexts that require standardized human BM evaluation.

Using deep learning for quantification of cellularity and cell lineages in bone marrow biopsies and comparison to normal age-related variation

“…For red marrow, they did not distinguish between erythropoiesis, myelopoiesis, and megakaryocytes, prohibiting direct comparison of the segmentation performance. Most recently, Tratwal et al 11 developed a semi-automatic technique for determining cellularity via segmenting biopsies into bone, nucleated cells (haematopoiesis), lipocytes and interstitium and found good correlation (R 2 =0.85 for human trephine biopsies). In contrast to our approach, theirs requires manual background/artifact delineation as a preprocessing step in addition to ROI selection.…”

“…Several recent studies reported cellularity measurement using digital image analysis techniques on digitised slides that show good agreement with references of visual estimate or point counting. 4,[10][11][12] These studies have used traditional machine learning techniques, while the field of medical image analysis has shown a shift towards generally better performing deep learning systems. 13 Deep learning has already been applied to the segmentation of erythropoiesis and myelopoiesis, 14 but no work has been published on the simultaneous segmentation of all major cell types in bone marrow, which would allow for a more detailed analysis of the tissue.…”

Report From the 6th International Meeting on Bone Marrow Adiposity (BMA2020)

“…Last, Josefine Tratwal gave an overview of a new injectable, three-dimensional tissue engineered model of bone marrow adipogenesis and hematopoiesis that can be used to accelerate studies on the regeneration of the bone marrow niche for treatment of blood and other marrow-related disorders (23). (24). This presentation concluded that marrow adiposity correlates inversely with hematopoiesis in health and pathologic conditions, and that the degree of adipocyte maturation correlates inversely with the proliferation of hematopoietic precursors.…”

Bone Marrow Adipocytes: A Link between Obesity and Bone Cancer

“…A recent review has discussed the roles of BMAds (and leptin, adiponectin, and Sam68 specifically) in bone metastasis from breast cancer [19]. There are conflicting results when analyzing the influence of age in the development of bone metastasis for breast cancer patients [74].…”

“…In contrast, they found that PPARγ expression in MSCs is important for cortical bone integrity and limiting cortical porosity during aging [18]. Most recently, a population of cells termed marrow adipogenic lineage precursors (MALPs), cells that are adiponectin-positive but without lipid droplets, have been defined using single-cell transcriptomics from the Ling Qin laboratory [19]. Overall, although we have learned a great deal about BMAT, BMAd progenitors, and the BMAT-bone relationship, many factors such as sex, hormones, sympathetic nerve signaling, temperature, and age contribute to this relationship, demonstrating that more research is needed to fully understand BMAT biology.…”