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.

SRSF1 mediates cytokine-induced impaired imatinib sensitivity in chronic myeloid leukemia.

Patients with chronic myeloid leukemia (CML) who are treated with tyrosine kinase inhibitors (TKIs) experience significant heterogeneity regarding depth and speed of responses. Factors intrinsic and extrinsic to CML cells contribute to response heterogeneity and TKI resistance. Among extrinsic factors, cytokine-mediated TKI resistance has been demonstrated in CML progenitors, but the underlying mechanisms remain obscure. Using RNA-sequencing, we identified differentially expressed splicing factors in primary CD34+ chronic phase (CP) CML progenitors and controls. We found SRSF1 expression to be increased as a result of both BCR-ABL1- and cytokine-mediated signaling. SRSF1 overexpression conferred cytokine independence to untransformed hematopoietic cells and impaired imatinib sensitivity in CML cells, while SRSF1 depletion in CD34+ CP CML cells prevented the ability of extrinsic cytokines to decrease imatinib sensitivity. Mechanistically, PRKCH and PLCH1 were upregulated by elevated SRSF1 levels, and contributed to impaired imatinib sensitivity. Importantly, very high SRSF1 levels in the bone marrow of CML patients at presentation correlated with poorer clinical TKI responses. In summary, we find SRSF1 levels to be maintained in CD34+ CP CML progenitors by cytokines despite effective BCR-ABL1 inhibition, and that elevated levels promote impaired imatinib responses. Together, our data support an SRSF1/PRKCH/PLCH1 axis in contributing to cytokine-induced impaired imatinib sensitivity in CML.