BACKGROUND: Myeloproliferative neoplasms (MPNs) are blood malignancies manifested in increased production of red blood cells, white blood cells, and/or platelets. Myelofibrosis is a subtype of MPNs characterized by the formation of scar-like tissues in the bone marrow due to abnormal hematopoiesis. It is considered a disease of both hematopoietic stem cells and stem cell niches. Patients with myelofibrosis have very poor prognosis, and there is no effective treatment so far. Myelofibrosis has routinely been detected by using histochemical staining methods which produce qualitative rather than quantitative results. In this study, we developed a quantitative assay of bone marrow myelofibrosis in JAK2V617F transgenic mice by determining hydroxyproline. METHODS: The JAK2V617F transgenic mice's tissue was collected to detect the bone marrow myelofibrosis. Statistical analyses were performed using the GraphPad Prism program. Differences of samples between two groups were accessed using t tests. P values less than 0.05 (2-tailed) were considered significantly different. RESULTS: We developed a quantitative method for detecting myelofibrosis by analyzing the content of hydroxyproline, a modified amino acid largely restricted to collagen which forms the fibrotic structure in bone marrow tissues. Our study also demonstrated age-dependent development of bone marrow myelofibrosis in JAK2V617F transgenic mice. CONCLUSIONS: In the present study, we have developed a new method for detecting bone marrow myelofibrosis by analyzing hydroxyproline contents. The method is highly sensitive and accurate. It provides more accurate, representative, and quantitative information than histochemical analyses. We believe that this method should find wide applications for analyzing the progression of myelofibrosis and efficacy of drug treatment.
BACKGROUND: Myeloproliferative neoplasms (MPNs) are blood malignancies manifested in increased production of red blood cells, white blood cells, and/or platelets. Myelofibrosis is a subtype of MPNs characterized by the formation of scar-like tissues in the bone marrow due to abnormal hematopoiesis. It is considered a disease of both hematopoietic stem cells and stem cell niches. Patients with myelofibrosis have very poor prognosis, and there is no effective treatment so far. Myelofibrosis has routinely been detected by using histochemical staining methods which produce qualitative rather than quantitative results. In this study, we developed a quantitative assay of bone marrow myelofibrosis in JAK2V617F transgenic mice by determining hydroxyproline. METHODS: The JAK2V617F transgenic mice's tissue was collected to detect the bone marrow myelofibrosis. Statistical analyses were performed using the GraphPad Prism program. Differences of samples between two groups were accessed using t tests. P values less than 0.05 (2-tailed) were considered significantly different. RESULTS: We developed a quantitative method for detecting myelofibrosis by analyzing the content of hydroxyproline, a modified amino acid largely restricted to collagen which forms the fibrotic structure in bone marrow tissues. Our study also demonstrated age-dependent development of bone marrow myelofibrosis in JAK2V617F transgenic mice. CONCLUSIONS: In the present study, we have developed a new method for detecting bone marrow myelofibrosis by analyzing hydroxyproline contents. The method is highly sensitive and accurate. It provides more accurate, representative, and quantitative information than histochemical analyses. We believe that this method should find wide applications for analyzing the progression of myelofibrosis and efficacy of drug treatment.
Authors: Ross L Levine; Martha Wadleigh; Jan Cools; Benjamin L Ebert; Gerlinde Wernig; Brian J P Huntly; Titus J Boggon; Iwona Wlodarska; Jennifer J Clark; Sandra Moore; Jennifer Adelsperger; Sumin Koo; Jeffrey C Lee; Stacey Gabriel; Thomas Mercher; Alan D'Andrea; Stefan Fröhling; Konstanze Döhner; Peter Marynen; Peter Vandenberghe; Ruben A Mesa; Ayalew Tefferi; James D Griffin; Michael J Eck; William R Sellers; Matthew Meyerson; Todd R Golub; Stephanie J Lee; D Gary Gilliland Journal: Cancer Cell Date: 2005-04 Impact factor: 31.743
Authors: Juan Li; Dominik Spensberger; Jong Sook Ahn; Shubha Anand; Philip A Beer; Cedric Ghevaert; Edwin Chen; Ariel Forrai; Linda M Scott; Rita Ferreira; Peter J Campbell; Steve P Watson; Pentao Liu; Wendy N Erber; Brian J P Huntly; Katrin Ottersbach; Anthony R Green Journal: Blood Date: 2010-05-20 Impact factor: 22.113