scANANSE gene regulatory network and motif analysis of single-cell clusters.

The recent development of single-cell techniques is essential to unravel complex biological systems. By measuring the transcriptome and the accessible genome on a single-cell level, cellular heterogeneity in a biological environment can be deciphered. Transcription factors act as key regulators activating and repressing downstream target genes, and together they constitute gene regulatory networks that govern cell morphology and identity. Dissecting these gene regulatory networks is crucial for understanding molecular mechanisms and disease, especially within highly complex biological systems. The gene regulatory network analysis software ANANSE and the motif enrichment software GimmeMotifs were both developed to analyse bulk datasets. We developed scANANSE, a software pipeline for gene regulatory network analysis and motif enrichment using single-cell RNA and ATAC datasets. The scANANSE pipeline can be run from either R or Python. First, it exports data from standard single-cell objects. Next, it automatically runs multiple comparisons of cell cluster data. Finally, it imports the results back to the single-cell object, where the result can be further visualised, integrated, and interpreted. Here, we demonstrate our scANANSE pipeline on a publicly available PBMC multi-omics dataset. It identifies well-known cell type-specific hematopoietic factors. Importantly, we also demonstrated that scANANSE combined with GimmeMotifs is able to predict transcription factors with both activating and repressing roles in gene regulation.

Enhanced pro-apoptosis gene signature following the activation of TAp63α in oocytes upon γ irradiation.

Specialized surveillance mechanisms are essential to maintain the genetic integrity of germ cells, which are not only the source of all somatic cells but also of the germ cells of the next generation. DNA damage and chromosomal aberrations are, therefore, not only detrimental for the individual but affect the entire species. In oocytes, the surveillance of the structural integrity of the DNA is maintained by the p53 family member TAp63α. The TAp63α protein is highly expressed in a closed and inactive state and gets activated to the open conformation upon the detection of DNA damage, in particular DNA double-strand breaks. To understand the cellular response to DNA damage that leads to the TAp63α triggered oocyte death we have investigated the RNA transcriptome of oocytes following irradiation at different time points. The analysis shows enhanced expression of pro-apoptotic and typical p53 target genes such as CDKn1a or Mdm2, concomitant with the activation of TAp63α. While DNA repair genes are not upregulated, inflammation-related genes become transcribed when apoptosis is initiated by activation of STAT transcription factors. Furthermore, comparison with the transcriptional profile of the ΔNp63α isoform from other studies shows only a minimal overlap, suggesting distinct regulatory programs of different p63 isoforms.

Proteomics of resistance to Notch1 inhibition in acute lymphoblastic leukemia reveals targetable kinase signatures.

Notch1 is a crucial oncogenic driver in T-cell acute lymphoblastic leukemia (T-ALL), making it an attractive therapeutic target. However, the success of targeted therapy using γ-secretase inhibitors (GSIs), small molecules blocking Notch cleavage and subsequent activation, has been limited due to development of resistance, thus restricting its clinical efficacy. Here, we systematically compare GSI resistant and sensitive cell states by quantitative mass spectrometry-based phosphoproteomics, using complementary models of resistance, including T-ALL patient-derived xenografts (PDX) models. Our datasets reveal common mechanisms of GSI resistance, including a distinct kinase signature that involves protein kinase C delta. We demonstrate that the PKC inhibitor sotrastaurin enhances the anti-leukemic activity of GSI in PDX models and completely abrogates the development of acquired GSI resistance in vitro. Overall, we highlight the potential of proteomics to dissect alterations in cellular signaling and identify druggable pathways in cancer.

The acute myeloid leukemia associated AML1-ETO fusion protein alters the transcriptome and cellular progression in a single-oncogene expressing in vitro induced pluripotent stem cell based granulocyte differentiation model.

Acute myeloid leukemia (AML) is characterized by recurrent mutations that affect normal hematopoiesis. The analysis of human AMLs has mostly been performed using end-point materials, such as cell lines and patient derived AMLs that also carry additional contributing mutations. The molecular effects of a single oncogenic hit, such as expression of the AML associated oncoprotein AML1-ETO on hematopoietic development and transformation into a (pre-) leukemic state still needs further investigation. Here we describe the development and characterization of an induced pluripotent stem cell (iPSC) system that allows in vitro differentiation towards different mature myeloid cell types such as monocytes and granulocytes. During in vitro differentiation we expressed the AML1-ETO fusion protein and examined the effects of the oncoprotein on differentiation and the underlying alterations in the gene program at 8 different time points. Our analysis revealed that AML1-ETO as a single oncogenic hit in a non-mutated background blocks granulocytic differentiation, deregulates the gene program via altering the acetylome of the differentiating granulocytic cells, and induces t(8;21) AML associated leukemic characteristics. Together, these results reveal that inducible oncogene expression during in vitro differentiation of iPS cells provides a valuable platform for analysis of aberrant regulation in disease.