Semi-supervised integration of single-cell transcriptomics data

Batch effects in single-cell RNA-seq data pose a significant challenge for comparative analyses across samples, individuals, and conditions. Although batch effect correction methods are routinely applied, data integration often leads to overcorrection and can result in the loss of biological variability. In this work we present STACAS, a batch correction method for scRNA-seq that leverages prior knowledge on cell types to preserve biological variability upon integration. Through an open-source benchmark, we show that semi-supervised STACAS outperforms state-of-the-art unsupervised methods, as well as supervised methods such as scANVI and scGen. STACAS scales well to large datasets and is robust to incomplete and imprecise input cell type labels, which are commonly encountered in real-life integration tasks. We argue that the incorporation of prior cell type information should be a common practice in single-cell data integration, and we provide a flexible framework for semi-supervised batch effect correction.

Noncanonical EZH2 drives retinoic acid resistance of variant acute promyelocytic leukemias

Cancer cell heterogeneity is a major driver of therapy resistance. To characterize resistant cells and their vulnerabilities, we studied the PLZF-RARA variant of acute promyelocytic leukemia, resistant to retinoic acid (RA), using single-cell multiomics. We uncovered transcriptional and chromatin heterogeneity in leukemia cells. We identified a subset of cells resistant to RA with proliferation, DNA replication, and repair signatures that depend on a fine-tuned E2F transcriptional network targeting the epigenetic regulator enhancer of zeste homolog 2 (EZH2). Epigenomic and functional analyses validated the driver role of EZH2 in RA resistance. Targeting pan-EZH2 activities (canonical/noncanonical) was necessary to eliminate leukemia relapse-initiating cells, which underlies a dependency of resistant cells on an EZH2 noncanonical activity and the necessity to degrade EZH2 to overcome resistance. Our study provides critical insights into the mechanisms of RA resistance that allow us to eliminate treatment-resistant leukemia cells by targeting EZH2, thus highlighting a potential targeted therapy approach. Beyond RA resistance and acute promyelocytic leukemia context, our study also demonstrates the power of single-cell multiomics to identify, characterize, and clear therapy-resistant cells.

A novel Boolean network inference strategy to model early hematopoiesis aging

Hematopoietic stem cell (HSC) aging is a multifactorial event leading to changes in HSC properties and functions, which are intrinsically coordinated and affect the early hematopoiesis. To better understand the mechanisms and factors controlling these changes, we developed an original strategy to construct a Boolean model of HSC differentiation. Based on our previous scRNA-seq data, we exhaustively characterized active transcription modules or regulons along the differentiation trajectory and constructed an influence graph between 15 selected components involved in the dynamics of the process. Then we defined dynamical constraints between observed cellular states along the trajectory and using answer set programming with in silico perturbation analysis, we obtained a Boolean model explaining the early priming of HSCs. Finally, perturbations of the model based on age-related changes revealed important deregulations, such as the overactivation of Egr1 and Junb or the loss of Cebpa activation by Gata2. These new regulatory mechanisms were found to be relevant for the myeloid bias of aged HSC and explain the decreased transcriptional priming of HSCs to all mature cell types except megakaryocytes.

Single Cell Transcriptomics to Understand HSC Heterogeneity and Its Evolution upon Aging

Single-cell transcriptomic technologies enable the uncovering and characterization of cellular heterogeneity and pave the way for studies aiming at understanding the origin and consequences of it. The hematopoietic system is in essence a very well adapted model system to benefit from this technological advance because it is characterized by different cellular states. Each cellular state, and its interconnection, may be defined by a specific location in the global transcriptional landscape sustained by a complex regulatory network. This transcriptomic signature is not fixed and evolved over time to give rise to less efficient hematopoietic stem cells (HSC), leading to a well-documented hematopoietic aging. Here, we review the advance of single-cell transcriptomic approaches for the understanding of HSC heterogeneity to grasp HSC deregulations upon aging. We also discuss the new bioinformatics tools developed for the analysis of the resulting large and complex datasets. Finally, since hematopoiesis is driven by fine-tuned and complex networks that must be interconnected to each other, we highlight how mathematical modeling is beneficial for doing such interconnection between multilayered information and to predict how HSC behave while aging.

Single-cell RNA-seq reveals a concomitant delay in differentiation and cell cycle of aged hematopoietic stem cells

BACKGROUND

Hematopoietic stem cells (HSCs) are the guarantor of the proper functioning of hematopoiesis due to their incredible diversity of potential. During aging, heterogeneity of HSCs changes, contributing to the deterioration of the immune system. In this study, we revisited mouse HSC compartment and its transcriptional plasticity during aging at unicellular scale.

RESULTS

Through the analysis of 15,000 young and aged transcriptomes, we identified 15 groups of HSCs revealing rare and new specific HSC abilities that change with age. The implantation of new trajectories complemented with the analysis of transcription factor activities pointed consecutive states of HSC differentiation that were delayed by aging and explained the bias in differentiation of older HSCs. Moreover, reassigning cell cycle phases for each HSC clearly highlighted an imbalance of the cell cycle regulators of very immature aged HSCs that may contribute to their accumulation in an undifferentiated state.

CONCLUSIONS

Our results establish a new reference map of HSC differentiation in young and aged mice and reveal a potential mechanism that delays the differentiation of aged HSCs and could promote the emergence of age-related hematologic diseases.

PLZF limits enhancer activity during hematopoietic progenitor aging

PLZF (promyelocytic leukemia zinc finger) is a transcription factor acting as a global regulator of hematopoietic commitment. PLZF displays an epigenetic specificity by recruiting chromatin-modifying factors but little is known about its role in remodeling chromatin of cells committed toward a given specific hematopoietic lineage. In murine myeloid progenitors, we decipher a new role for PLZF in restraining active genes and enhancers by targeting acetylated lysine 27 of Histone H3 (H3K27ac). Functional analyses reveal that active enhancers bound by PLZF are involved in biological processes related to metabolism and associated with hematopoietic aging. Comparing the epigenome of young and old myeloid progenitors, we reveal that H3K27ac variation at active enhancers is a hallmark of hematopoietic aging. Taken together, these data suggest that PLZF, associated with active enhancers, appears to restrain their activity as an epigenetic gatekeeper of hematopoietic aging.