A single-cell atlas identifies pretreatment features of primary imatinib resistance in chronic myeloid leukemia.

Primary resistance to tyrosine kinase inhibitors (TKIs) is a significant barrier to optimal outcomes in chronic myeloid leukemia (CML), but factors contributing to response heterogeneity remain unclear. Using single-cell RNA (scRNA) sequencing, we identified 8 statistically significant features in pretreatment bone marrow, which correlated with either sensitivity (major molecular response or MMR) or extreme resistance to imatinib (eventual blast crisis [BC] transformation). Employing machine-learning, we identified leukemic stem cell (LSC) and natural killer (NK) cell gene expression profiles predicting imatinib response with >80% accuracy, including no false positives for predicting BC. A canonical erythroid-specifying (TAL1/KLF1/GATA1) regulon was a hallmark of LSCs from patients with MMR and was associated with erythroid progenitor [ERP] expansion in vivo (P < .05), and a 2- to 10-fold (6.3-fold in group A vs 1.09-fold in group C) erythroid over myeloid bias in vitro. Notably, ERPs demonstrated exquisite TKI sensitivity compared with myeloid progenitors (P < .001). These LSC features were lost with progressive resistance, and MYC- and IRF1-driven inflammatory regulons were evident in patients who progressed to transformation. Patients with MMR also exhibited a 56-fold expansion (P < .01) of a normally rare subset of hyperfunctional adaptive-like NK cells, which diminished with progressive resistance, whereas patients destined for BC accumulated inhibitory NKG2A+ NK cells favoring NK cell tolerance. Finally, we developed antibody panels to validate our scRNA-seq findings. These panels may be useful for prospective studies of primary resistance, and in assessing the contribution of predetermined vs acquired factors in TKI response heterogeneity.

RCA2: a scalable supervised clustering algorithm that reduces batch effects in scRNA-seq data.

The transcriptomic diversity of cell types in the human body can be analysed in unprecedented detail using single cell (SC) technologies. Unsupervised clustering of SC transcriptomes, which is the default technique for defining cell types, is prone to group cells by technical, rather than biological, variation. Compared to de-novo (unsupervised) clustering, we demonstrate using multiple benchmarks that supervised clustering, which uses reference transcriptomes as a guide, is robust to batch effects and data quality artifacts. Here, we present RCA2, the first algorithm to combine reference projection (batch effect robustness) with graph-based clustering (scalability). In addition, RCA2 provides a user-friendly framework incorporating multiple commonly used downstream analysis modules. RCA2 also provides new reference panels for human and mouse and supports generation of custom panels. Furthermore, RCA2 facilitates cell type-specific QC, which is essential for accurate clustering of data from heterogeneous tissues. We demonstrate the advantages of RCA2 on SC data from human bone marrow, healthy PBMCs and PBMCs from COVID-19 patients. Scalable supervised clustering methods such as RCA2 will facilitate unified analysis of cohort-scale SC datasets.

Early Fate Defines Microglia and Non-parenchymal Brain Macrophage Development.

Central nervous system (CNS) macrophages comprise microglia and border-associated macrophages (BAMs) residing in the meninges, the choroid plexus, and the perivascular spaces. Most CNS macrophages emerge during development, with the exception of choroid plexus and dural macrophages, which are replaced by monocytes in adulthood. Whether microglia and BAMs share a developmental program or arise from separate lineages remains unknown. Here, we identified two phenotypically, transcriptionally, and locally distinct brain macrophages throughout development, giving rise to either microglia or BAMs. Two macrophage populations were already present in the yolk sac suggesting an early segregation. Fate-mapping models revealed that BAMs mostly derived from early erythro-myeloid progenitors in the yolk sac. The development of microglia was dependent on TGF-ß, whereas the genesis of BAMs occurred independently of this cytokine. Collectively, our data show that developing parenchymal and non-parenchymal brain macrophages are separate entities in terms of ontogeny, gene signature, and requirement for TGF-ß.

Costunolide inhibits proinflammatory cytokines and iNOS in activated murine BV2 microglia.

Costunolide, a sesquiterpene lactone present in Costus speciosus root exerts a variety of pharmacological activity but its effects on neuroinflammation have not been studied. Microglia, the resident phagocytic cells in the central nervous system respond to neuroinflammation and their overwhelming response in turn aggravate brain damage during infection, ischemia and neurodegenerative diseases. In this study, we report the effect of Costunolide on the production of proinflammatory mediators and mechanisms involved in BV2 microglial cells stimulated with LPS. Costunolide attenuated the expression of tumour necrosis factor-alpha, interleukin-1,6, inducible nitric oxide synthase, monocyte chemotactic protein 1 and cyclooxygenase 2 in activated microglia. This Costunolide-mediated inhibition was correspondent with the inhibition of NFkappaB activation. It has been further shown that Costunolide suppressed MAPK pathway activation by inducing MKP-1 production. Collectively our results suggest that Costunolide shows an ability to inhibit expression of multiple neuroinflammatory mediators and this is attributable to the compounds inhibition of NFkappaB and MAPK activation. This novel role of Costunolide upon investigation may aid in developing better therapeutic strategies for treatment of neuroinflammatory diseases.