Environmental stimuli often lead to heterogeneous cellular responses and transcriptional output. We developed single-cell RNA and Immunodetection (RAID) to allow combined analysis of the transcriptome and intracellular (phospho-)proteins from fixed single cells. RAID successfully recapitulated differentiation-state changes at the protein and mRNA level in human keratinocytes. Furthermore, we show that differentiated keratinocytes that retain high phosphorylated FAK levels, a feature associated with stem cells, also express a selection of stem cell associated transcripts. Our data demonstrates that RAID allows investigation of heterogeneous cellular responses to environmental signals at the mRNA and phospho-proteome level.

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.

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Advanced computational methods exploit gene expression and epigenetic datasets to predict gene regulatory networks controlled by transcription factors (TFs). These methods have identified cell fate determining TFs but require large amounts of reference data and experimental expertise. Here, we present an easy to use network-based computational framework that exploits enhancers defined by bidirectional transcription, using as sole input CAGE sequencing data to correctly predict TFs key to various human cell types. Next, we applied this Analysis Algorithm for Networks Specified by Enhancers based on CAGE (ANANSE-CAGE) to predict TFs driving red and white blood cell development, and THP-1 leukemia cell immortalization. Further, we predicted TFs that are differentially important to either cell line- or primary- associated MLL-AF9-driven gene programs, and in primary MLL-AF9 acute leukemia. Our approach identified experimentally validated as well as thus far unexplored TFs in these processes. ANANSE-CAGE will be useful to identify transcription factors that are key to any cell fate change using only CAGE-seq data as input.