Sauer, T. | Search bioRxiv

bioRxiv2022

Clonally resolved single-cell multi-omics identifies routes of cellular differentiation in acute myeloid leukemia

Inter-patient variability and the similarity of healthy and leukemic stem cells have impeded the characterization of leukemic stem cells (LSCs) in acute myeloid leukemia (AML), and their differentiation landscape. Here, we introduce CloneTracer, a novel method that adds clonal resolution to single-cell RNA-seq datasets. Applied to samples from 19 AML patients, CloneTracer revealed routes of leukemic differentiation. While residual healthy cells dominated the dormant stem cell compartment, active leukemic stem cells resembled their healthy counterpart and retained erythroid capacity. By contrast, downstream myeloid progenitors were highly aberrant and constituted the disease-defining compartment: Their gene expression and differentiation state determined both chemotherapy response and the leukemias ability to differentiate to transcriptomically normal monocytes. Finally, we demonstrated the potential of CloneTracer to identify surface markers mis-regulated specifically in leukemic cells by intra-patient comparisons. Taken together, CloneTracer revealed a differentiation landscape that mimics its healthy counterpart and determines biology and therapy response in AML.

cancer biology↗

bioRxiv2022

Protein profiling of WERI RB1 and etoposide resistant WERI ETOR reveals new insights into topoisomerase inhibitor resistance in retinoblastoma

Chemotherapy resistance is one of the reasons for eye loss in patients with retinoblastoma (RB). RB chemotherapy resistance has been studied in different cell culture models such as WERI RB1. In addition, chemotherapy resistant RB subclones like the etoposide resistant WERI ETOR cell line have been established to improve the understanding of chemotherapy resistance in RB. The objective of this study was to characterize cell line models of an etoposide sensitive WERI RB1 and its etoposide resistant subclone WERI ETOR by proteomic analysis. Subsequently, quantitative proteomic data served for correlation analysis with known drug perturbation profiles. Methodically, WERI RB1 and WERI ETOR were cultured and prepared for quantitative mass spectrometry (MS). This was carried out in a data-independent acquisition (DIA) mode (Sequential Window Acquisition of All Theoretical Mass Spectra, SWATH-MS). The raw SWATH files were processed using neural networks in a library free mode along with machine learning algorithms. Pathway enrichment was performed using the REACTOME pathway resource and correlated to the Molecular Signature Database (MSigDB) hallmark gene set collections for functional annotation. Furthermore, a drug connectivity analysis using the L1000 database was used to correlate the mechanism-of-action (MOA) for different anticancer reagents to WERI RB1/WERI ETOR signatures. A total of 4,756 proteins were identified across all samples, showing a distinct clustering between the groups. Of these proteins, 64 were significantly altered (q < 0.05 & log2FC |>2|, 22% higher in WERI ETOR). Pathway analysis revealed an enriched metabolic pathway for "retinoid metabolism and transport" in WERI ETOR and for "sphingolipid de novo biosynthesis" in WERI RB1. In addition, this study revealed similar protein signatures of topoisomerase inhibitors in WERI ETOR as well as ATPase inhibitors, acetylcholine receptor antagonists and vascular endothelial growth factor receptor (VEGFR) inhibitors in WERI RB1. In this study, WERI RB1 and WERI ETOR were analyzed as a cell line model for chemotherapy resistance in RB using data-independent MS. The global proteome identified activation of "sphingolipid de novo biosynthesis" in WERI RB1 and revealed future potential treatment options for etoposide resistance in RB.

cancer biology↗