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Ibrutinib is a bruton’s tyrosine kinase (BTK) inhibitor approved for the treatment of multiple B-cell malignancies, including chronic lymphocytic leukemia (CLL). In addition to blocking B-cell receptor signaling and chemokine receptor-mediated pathways in CLL cells, that are known drivers of disease, ibrutinib also affects the microenvironment in CLL via targeting BTK in myeloid cells and IL-2–inducible T-cell kinase (ITK) in T-cells. These non-BTK effects were suggested to contribute to the success of ibrutinib in CLL. By using the Eµ-TCL1 adoptive transfer mouse model of CLL, we observed that ibrutinib effectively controls leukemia development, but also results in significantly lower numbers of CD8+ effector T-cells, with lower expression of activation markers, as well as impaired proliferation and effector function. Using CD8+ T-cells from a T-cell receptor (TCR) reporter mouse, we verified that this is due to a direct effect of ibrutinib on TCR activity, and demonstrate that co-stimulation via CD28 overcomes these effects. Most interestingly, combination of ibrutinib with blocking antibodies targeting PD-1/PD-L1 axis in vivo improved CD8+ T-cell effector function and control of CLL. In sum, these data emphasize the strong immunomodulatory effects of ibrutinib and the therapeutic potential of its combination with immune checkpoint blockade in CLL.

A few years ago, it was proposed to use the simultaneous quantification of unspliced and spliced messenger RNA (mRNA) to add a temporal dimension to high-throughput snapshots of single cell RNA sequencing data. This concept can yield additional insight into the transcriptional dynamics of the biological systems under study. However, current methods for inferring cell state velocities from such data (known as RNA velocities) are afflicted by several theoretical and computational problems, hindering realistic and reliable velocity estimation. We discuss these issues and propose new solutions for addressing some of the current challenges in consistency of data processing, velocity inference and visualisation. We translate our computational conclusion in two velocity analysis tools: one detailed method κ-velo and one heuristic method eco-velo, each of which uses a different set of assumptions about the data.

Infections are a key source of stress to the hematopoietic system. While infections consume short-lived innate immune cells, their recovery depends on quiescent hematopoietic stem cells (HSCs) with long-term self-renewal capacity. Both chronic inflammatory stress and bacterial infections compromise competitive HSC capacity and cause bone marrow failure. However, our understanding of how HSCs act during acute and contained infections remains incomplete. Here, we used advanced chimeric and genetic mouse models in combination with pharmacological interventions to dissect the complex nature of the acute systemic response of HSCs to lipopolysaccharide (LPS), a well-established model for inducing inflammatory stress. Acute LPS challenge transiently induced proliferation of quiescent HSCs in vivo. This response was not only mediated via direct LPS-TLR4 conjugation on HSCs, but also involved indirect TLR4 signaling in CD115+ monocytic cells, inducing a complex pro-inflammatory cytokine cascade in the bone marrow. Downstream of LPS-TLR4 signaling, the combined action of pro-inflammatory cytokines such as IFNα, IFNγ, TNFα, IL-1α, IL-1β and many others is required to mediate full HSC activation in vivo. Together, our study reveals detailed mechanistic insights into the interplay of proinflammatory cytokine-induced molecular pathways and cell types that jointly orchestrate the complex process of emergency hematopoiesis and HSC activation upon LPS exposure in vivo.

Hematopoietic stem and progenitor cells (HSPCs) are known to respond to acute inflammation; however, little is understood about the dynamics and heterogeneity of these stress responses in HSPCs. Here, we performed single-cell sequencing of HSPCs during the sensing, response and recovery phases of the inflammatory response of HSPCs to treatment with the pro-inflammatory cytokine IFNa to investigate the HSPCs' dynamic changes during acute inflammation. For the analysis of the resulting datasets, we developed a computational pipeline for single-cell time series. Using a semi-supervised response-pseudotime inference approach, we discover a variety of different gene responses of the HSPCs to the treatment. Interestingly, we were able to associate reduced myeloid differentiation programs in HSPCs with reduced myeloid progenitor and differentiated cells following IFNa treatment. Altogether, single-cell time series analysis have allowed us to unbiasedly study the heterogeneous and dynamic impact of IFNa on the HSPCs.

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