Steven W. Lane

The proliferation, differentiation, and survival of cells of the macrophage lineage depends on signals from the macrophage colony-stimulating factor receptor (CSF1R). On a C57BL/6J background homozygous kinase-dead Csf1r mutation (Csf1rE631K/E631K–E631Km/m) causes perinatal lethality. By contrast, E631Km/m mice on a mixed genetic background (C57 × BALB/c) were osteoclast-deficient/osteopetrotic and growth retarded but did not develop hydrocephalus or other major structural brain abnormalities seen in inbred Csf1r−/− mice, and they were viable as adults. Outbred E631Km/m mice lacked tissue-resident macrophages detected with a Csf1r-EGFP transgene or F4/80 in most major organs, including the brain, but aside from reproductive organs, which were underdeveloped, no gross histological abnormalities were observed. E631Km/m genotype was associated with reduced blood monocytes, the loss of circulating C1Q, reduced IGF1, and increased CSF1. In mice, F4/80+/CD169+ resident marrow macrophages are believed to be essential components of erythroblastic and hematopoietic island niches. These cells were undetectable in E631Km/m marrow. Their absence was associated with granulocytosis and B-cell deficiency in marrow, blood, and spleen, whereas the relative abundance of pluripotent and committed progenitors was not affected. Erythropoietic homeostasis was also maintained; erythroblasts were physically associated with a minor residual population of CSF1R-independent CD169−/F4/80+ marrow macrophages. We conclude that many developmental and homeostatic functions attributed to mouse resident-tissue macrophages are redundant, species-specific, or restricted to inbred strains.

Myeloproliferative neoplasms (MPNs) are caused by acquired mutations in hematopoietic stem and progenitor cells (HSPCs). The acquisition of additional mutations like TP53 and the overall mutational burden influence a patient's risk of disease progression toward lethal post-MPN acute myeloid leukemia (AML). Recent technological advancements in linking single-cell gene expression with genotype have improved our understanding of tumor heterogeneity. However, current methodologies have limitations in simultaneously genotyping low-expression genes (such as JAK2) alongside other pathogenic loci. To address this, we developed a novel long read genotyping pipeline of cDNA transcripts called LOTR-Seq, which can genotype the full length of expressed transcripts of 30 genes at once. Using LOTR-Seq, we genotyped HSPCs at the JAK2V617 locus in 9,075 single cells from eight patients with chronic phase MPN (CP-MPN) and in 5,016 cells from four patients with post-MPN AML. We then linked the mutations to the single cell transcriptome of 29,712 JAK2V617F-driven CP-MPN cells and 16,895 post-MPN AML cells. In our analysis of post-MPN AMLs, we identified nine mutated loci across six genes (JAK2, IDH1/2, TP53, SRSF2, U2AF1) and linked these mutations to specific transcriptional phenotypes. Overall, LOTR-Seq provides novel insights into the evolution of post-MPN AML.

In this issue of Blood, Yan et al1 have identified and characterized a unique subset of proinflammatory monocytes, in both murine models of myelofibrosis (

Myeloproliferative neoplasms (MPNs) are caused by acquired mutations in hematopoietic stem and progenitor cells (HSPCs). The acquisition of additional mutations like TP53 and the overall mutational burden influence a patient’s risk of disease progression toward lethal post-MPN acute myeloid leukemia (AML). Recent technological advancements in linking single-cell gene expression with genotype have improved our understanding of tumor heterogeneity. However, current methodologies have limitations in simultaneously genotyping low-expression genes (such as JAK2 ) alongside other pathogenic loci. To address this, we developed a novel lo ng read genotyping pipeline of cDNA tr anscripts called LOTR-Seq, which can genotype the full length of expressed transcripts of 30 genes at once. Using LOTR-Seq, we genotyped HSPCs at the JAK2 V617 locus in 9,075 single cells from eight patients with chronic phase MPN (CP-MPN) and in 5,016 cells from four patients with post-MPN AML. We then linked the mutations to the single cell transcriptome of 29,712 JAK2 V617F-driven CP-MPN cells and 16,895 post-MPN AML cells. In our analysis of post-MPN AMLs, we identified nine mutated loci across six genes ( JAK2, IDH1/2, TP53, SRSF2, U2AF1 ) and linked these mutations to specific transcriptional phenotypes. Overall, LOTR-Seq provides novel insights into the evolution of post-MPN AML.

Purpose: Febrile neutropenia (FN) is a life-threatening complication of chemotherapy. Although practice guidelines suggest the use of existing prediction models when making decisions to prevent and treat FN, recent evidence suggests that these models are limited in their discriminative ability. This study aims to systematically review and critically evaluate the recent literature to assess the question: what evidence-based clinical prediction models can be used to predict FN or its outcomes? Methods: PubMed, EMBASE, Web of Science, and SCOPUS were searched for primary journal articles that developed or validated models that predicted FN or outcomes in patients with FN. Risk of bias was critically evaluated using the Prediction model Risk of Bias Assessment Tool (PROBAST). Results: Five thousand nine hundred nineteen articles were identified, of which 90 met inclusion criteria. Twenty-five studies predicted FN, and 65 studies predicted outcomes in patients with FN, including 28 that predicted mortality, 35 that predicted microbiological outcomes, and 35 that predicted other complications. Eight studies used machine learning methods in their development, and few studies were externally validated. All 90 studies were graded as high risk of bias using PROBAST. Conclusion: Prediction models for FN and its outcomes demonstrate promising discriminatory ability; however, several limitations have prevented these from translating clinically. These limitations include variable FN definitions, high ROB in current models, limited external validation, and heterogeneous cohorts. Future work is needed to further develop and validate robust, well-evidenced models that can translate into clinical practice. This may best be achieved through machine learning and electronic medical record integration.