Publications by authors named "Winzi M"

Developmental cell fate specification is a unidirectional process that can be reverted in response to injury or experimental reprogramming. Whether differentiation and de-differentiation trajectories intersect mechanistically is unclear. Here, we performed comparative screening in lineage-related mouse naïve embryonic stem cells (ESCs) and primed epiblast stem cells (EpiSCs), and identified the constitutively expressed zinc finger transcription factor (TF) Zfp281 as a bidirectional regulator of cell state interconversion.

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The throughput of cell mechanical characterization has recently approached that of conventional flow cytometers. However, this very sensitive, label-free approach still lacks the specificity of molecular markers. Here we developed an approach that combines real-time 1D-imaging fluorescence and deformability cytometry in one instrument (RT-FDC), thus opening many new research avenues.

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RNA interference (RNAi) screens have been shown to be valuable to study embryonic stem cell (ESC) self-renewal and they have been successfully applied to identify coding as well as noncoding genes required for maintaining pluripotency. Here, we used an RNAi library targeting >640 long noncoding RNAs (lncRNA) to probe for their role in early cell differentiation. Utilizing a Sox1-GFP ESC reporter cell line, we identified the lncRNA lncR492 as lineage-specific inhibitor of neuroectodermal differentiation.

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Cellular reprogramming is a dedifferentiation process during which cells continuously undergo phenotypical remodeling. Although the genetic and biochemical details of this remodeling are fairly well understood, little is known about the change in cell mechanical properties during the process. In this study, we investigated changes in the mechanical phenotype of murine fetal neural progenitor cells (fNPCs) during reprogramming to induced pluripotent stem cells (iPSCs).

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Hepatocellular carcinoma (HCC) is the fifth most frequent cancer worldwide and the third leading cause of cancer-related death. However, therapy options are limited leaving an urgent need to develop new strategies. Currently, targeting cancer cell lipid and cholesterol metabolism is gaining interest especially regarding HCC.

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We combine a genome-scale RNAi screen in mouse epiblast stem cells (EpiSCs) with genetic interaction, protein localization, and "protein-level dependency" studies-a systematic technique that uncovers post-transcriptional regulation-to delineate the network of factors that control the expression of Oct4, a key regulator of pluripotency. Our data signify that there are similarities, but also fundamental differences in Oct4 regulation in EpiSCs versus embryonic stem cells (ESCs). Through multiparametric data analyses, we predict that Tox4 is associating with the Paf1C complex, which maintains cell identity in both cell types, and validate that this protein-protein interaction exists in ESCs and EpiSCs.

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Pluripotent mouse embryonic stem cells (mESCs) show heterogeneous expression levels of transcription factors (TFs) involved in pluripotency regulation, among them Nanog and Rex1. The expression of both TFs can change dynamically between states of high and low activity, correlating with the cells' capacity for self-renewal. Stochastic fluctuations as well as sustained oscillations in gene expression are possible mechanisms to explain this behaviour, but the lack of suitable data hampered their clear distinction.

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The mechanical properties of cells are known to be a label-free, inherent marker of biological function in health and disease. Wide-spread utilization has so far been impeded by the lack of a convenient measurement technique with sufficient throughput. To address this unmet need, we have recently introduced real-time deformability cytometry (RT-DC) for continuous mechanical single-cell classification of heterogeneous cell populations at rates of several hundred cells per second.

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Somatic cells can be reprogrammed to induced pluripotent stem cells via exogenous expression of a small set of transcription factors, but the regulatory mechanisms controlling this cell transition are poorly understood. Two recent reports demonstrate the value of RNAi screens as a tool to uncover roadblocks in this inefficient process.

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TGFß signaling patterns the primitive streak, yet little is known about transcriptional effectors that mediate the cell fate choices during streak-like development in mammalian embryos and in embryonic stem (ES) cells. Here we demonstrate that cross-antagonistic actions of EVEN-SKIPPED HOMEOBOX 1 (EVX1) and GOOSECOID (GSC) regulate cell fate decisions in streak-like progenitors derived from human ES cells exposed to BMP4 and/or activin. We found that EVX1 repressed GSC expression and promoted formation of posterior streak-like progeny in response to BMP4, and conversely that GSC repressed EVX1 expression and was required for development of anterior streak-like progeny in response to activin.

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The homeobox gene Noto is expressed in the node and its derivative the notochord. Here we use a targeted Noto-GFP reporter to isolate and characterize node/notochord-like cells derived from mouse embryonic stem cells. We find very few Noto-expressing cells after spontaneous differentiation.

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Here we examine how BMP, Wnt, and FGF signaling modulate activin-induced mesendodermal differentiation of mouse ES cells grown under defined conditions in adherent monoculture. We monitor ES cells containing reporter genes for markers of primitive streak (PS) and its progeny and extend previous findings on the ability of increasing concentrations of activin to progressively induce more ES cell progeny to anterior PS and endodermal fates. We find that the number of Sox17- and Gsc-expressing cells increases with increasing activin concentration while the highest number of T-expressing cells is found at the lowest activin concentration.

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A key challenge of functional genomics today is to generate well-annotated data sets that can be interpreted across different platforms and technologies. Large-scale functional genomics data often fail to connect to standard experimental approaches of gene characterization in individual laboratories. Furthermore, a lack of universal annotation standards for phenotypic data sets makes it difficult to compare different screening approaches.

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