Engineering an inducible leukemia-associated fusion protein enables large-scale ex vivo production of functional human phagocytes.

Ex vivo expansion of human CD34+ hematopoietic stem and progenitor cells remains a challenge due to rapid differentiation after detachment from the bone marrow niche. In this study, we assessed the capacity of an inducible fusion protein to enable sustained ex vivo proliferation of hematopoietic precursors and their capacity to differentiate into functional phagocytes. We fused the coding sequences of an FK506-Binding Protein 12 (FKBP12)-derived destabilization domain (DD) to the myeloid/lymphoid lineage leukemia/eleven nineteen leukemia (MLL-ENL) fusion gene to generate the fusion protein DD-MLL-ENL and retrovirally expressed the protein switch in human CD34+ progenitors. Using Shield1, a chemical inhibitor of DD fusion protein degradation, we established large-scale and long-term expansion of late monocytic precursors. Upon Shield1 removal, the cells lost self-renewal capacity and spontaneously differentiated, even after 2.5 y of continuous ex vivo expansion. In the absence of Shield1, stimulation with IFN-γ, LPS, and GM-CSF triggered terminal differentiation. Gene expression analysis of the obtained phagocytes revealed marked similarity with naïve monocytes. In functional assays, the novel phagocytes migrated toward CCL2, attached to VCAM-1 under shear stress, produced reactive oxygen species, and engulfed bacterial particles, cellular particles, and apoptotic cells. Finally, we demonstrated Fcγ receptor recognition and phagocytosis of opsonized lymphoma cells in an antibody-dependent manner. Overall, we have established an engineered protein that, as a single factor, is useful for large-scale ex vivo production of human phagocytes. Such adjustable proteins have the potential to be applied as molecular tools to produce functional immune cells for experimental cell-based approaches.

CIARA: a cluster-independent algorithm for identifying markers of rare cell types from single-cell sequencing data.

A powerful feature of single-cell genomics is the possibility of identifying cell types from their molecular profiles. In particular, identifying novel rare cell types and their marker genes is a key potential of single-cell RNA sequencing. Standard clustering approaches perform well in identifying relatively abundant cell types, but tend to miss rarer cell types. Here, we have developed CIARA (Cluster Independent Algorithm for the identification of markers of RAre cell types), a cluster-independent computational tool designed to select genes that are likely to be markers of rare cell types. Genes selected by CIARA are subsequently integrated with common clustering algorithms to single out groups of rare cell types. CIARA outperforms existing methods for rare cell type detection, and we use it to find previously uncharacterized rare populations of cells in a human gastrula and among mouse embryonic stem cells treated with retinoic acid. Moreover, CIARA can be applied more generally to any type of single-cell omic data, thus allowing the identification of rare cells across multiple data modalities. We provide implementations of CIARA in user-friendly packages available in R and Python.

Direct neuronal reprogramming of mouse astrocytes is associated with multiscale epigenome remodeling and requires Yy1.

Direct neuronal reprogramming is a promising approach to regenerate neurons from local glial cells. However, mechanisms of epigenome remodeling and co-factors facilitating this process are unclear. In this study, we combined single-cell multiomics with genome-wide profiling of three-dimensional nuclear architecture and DNA methylation in mouse astrocyte-to-neuron reprogramming mediated by Neurogenin2 (Ngn2) and its phosphorylation-resistant form (PmutNgn2), respectively. We show that Ngn2 drives multilayered chromatin remodeling at dynamic enhancer-gene interaction sites. PmutNgn2 leads to higher reprogramming efficiency and enhances epigenetic remodeling associated with neuronal maturation. However, the differences in binding sites or downstream gene activation cannot fully explain this effect. Instead, we identified Yy1, a transcriptional co-factor recruited by direct interaction with Ngn2 to its target sites. Upon deletion of Yy1, activation of neuronal enhancers, genes and ultimately reprogramming are impaired without affecting Ngn2 binding. Thus, our work highlights the key role of interactors of proneural factors in direct neuronal reprogramming.

CSF3R T618I Collaborates With RUNX1-RUNX1T1 to Expand Hematopoietic Progenitors and Sensitizes to GLI Inhibition.

Activating colony-stimulating factor-3 receptor gene (CSF3R) mutations are recurrent in acute myeloid leukemia (AML) with t(8;21) translocation. However, the nature of oncogenic collaboration between alterations of CSF3R and the t(8;21) associated RUNX1-RUNX1T1 fusion remains unclear. In CD34+ hematopoietic stem and progenitor cells from healthy donors, double oncogene expression led to a clonal advantage, increased self-renewal potential, and blast-like morphology and distinct immunophenotype. Gene expression profiling revealed hedgehog signaling as a potential mechanism, with upregulation of GLI2 constituting a putative pharmacological target. Both primary hematopoietic cells and the t(8;21) positive AML cell line SKNO-1 showed increased sensitivity to the GLI inhibitor GANT61 when expressing CSF3R T618I. Our findings suggest that during leukemogenesis, the RUNX1-RUNXT1 fusion and CSF3R mutation act in a synergistic manner to alter hedgehog signaling, which can be exploited therapeutically.

EpiScanpy: integrated single-cell epigenomic analysis.

EpiScanpy is a toolkit for the analysis of single-cell epigenomic data, namely single-cell DNA methylation and single-cell ATAC-seq data. To address the modality specific challenges from epigenomics data, epiScanpy quantifies the epigenome using multiple feature space constructions and builds a nearest neighbour graph using epigenomic distance between cells. EpiScanpy makes the many existing scRNA-seq workflows from scanpy available to large-scale single-cell data from other -omics modalities, including methods for common clustering, dimension reduction, cell type identification and trajectory learning techniques, as well as an atlas integration tool for scATAC-seq datasets. The toolkit also features numerous useful downstream functions, such as differential methylation and differential openness calling, mapping epigenomic features of interest to their nearest gene, or constructing gene activity matrices using chromatin openness. We successfully benchmark epiScanpy against other scATAC-seq analysis tools and show its outperformance at discriminating cell types.