Pluripotency factors functionally premark cell-type-restricted enhancers in ES cells.

Enhancers for embryonic stem (ES) cell-expressed genes and lineage-determining factors are characterized by conventional marks of enhancer activation in ES cells1-3, but it remains unclear whether enhancers destined to regulate cell-type-restricted transcription units might also have distinct signatures in ES cells. Here we show that cell-type-restricted enhancers are 'premarked' and activated as transcription units by the binding of one or two ES cell transcription factors, although they do not exhibit traditional enhancer epigenetic marks in ES cells, thus uncovering the initial temporal origins of cell-type-restricted enhancers. This premarking is required for future cell-type-restricted enhancer activity in the differentiated cells, with the strength of the ES cell signature being functionally important for the subsequent robustness of cell-type-restricted enhancer activation. We have experimentally validated this model in macrophage-restricted enhancers and neural precursor cell (NPC)-restricted enhancers using ES cell-derived macrophages or NPCs, edited to contain specific ES cell transcription factor motif deletions. DNA hydroxyl-methylation of enhancers in ES cells, determined by ES cell transcription factors, may serve as a potential molecular memory for subsequent enhancer activation in mature macrophages. These findings suggest that the massive repertoire of cell-type-restricted enhancers are essentially hierarchically and obligatorily premarked by binding of a defining ES cell transcription factor in ES cells, dictating the robustness of enhancer activation in mature cells

Reimagining Retrieval Augmented Language Models for Answering Queries

We present a reality check on large language models and inspect the promise of retrieval augmented language models in comparison. Such language models are semi-parametric, where models integrate model parameters and knowledge from external data sources to make their predictions, as opposed to the parametric nature of vanilla large language models. We give initial experimental findings that semi-parametric architectures can be enhanced with views, a query analyzer/planner, and provenance to make a significantly more powerful system for question answering in terms of accuracy and efficiency, and potentially for other NLP task

Estimating the Quality of Reprogrammed Cells Using ES Cell Differentiation Expression Patterns

Somatic cells can be reprogrammed to a pluripotent state by over-expression of defined factors, and pluripotency has been confirmed by the tetraploid complementation assay. However, especially in human cells, estimating the quality of Induced Pluripotent Stem Cell(iPSC) is still difficult. Here, we present a novel supervised method for the assessment of the quality of iPSCs by estimating the gene expression profile using a 2-D “Differentiation-index coordinate”, which consists of two “developing lines” that reflects the directions of ES cell differentiation and the changes of cell states during differentiation. By applying a novel liner model to describe the differentiation trajectory, we transformed the ES cell differentiation time-course expression profiles to linear “developing lines”; and use these lines to construct the 2-D “Differentiation-index coordinate” of mouse and human. We compared the published gene expression profiles of iPSCs, ESCs and fibroblasts in mouse and human “Differentiation-index coordinate”. Moreover, we defined the Distance index to indicate the qualities of iPS cells, which based on the projection distance of iPSCs-ESCs and iPSCs-fibroblasts. The results indicated that the “Differentiation-index coordinate” can distinguish differentiation states of the different cells types. Furthermore, by applying this method to the analysis of expression profiles in the tetraploid complementation assay, we showed that the Distance index which reflected spatial distributions correlated the pluripotency of iPSCs. We also analyzed the significantly changed gene sets of “developing lines”. The results suggest that the method presented here is not only suitable for the estimation of the quality of iPS cells based on expression profiles, but also is a new approach to analyze time-resolved experimental data