scholarly journals Changes in chromatin accessibility between Arabidopsis stem cells and mesophyll cells illuminate cell type-specific transcription factor networks

2018 ◽  
Vol 94 (2) ◽  
pp. 215-231 ◽  
Author(s):  
Paja Sijacic ◽  
Marko Bajic ◽  
Elizabeth C. McKinney ◽  
Richard B. Meagher ◽  
Roger B. Deal
Keyword(s):  
Stem Cells ◽  
Transcription Factor ◽  
Chromatin Accessibility ◽  
Cell Type ◽  
Mesophyll Cells ◽  
Specific Transcription Factor ◽  
Cell Type Specific ◽  
Transcription Factor Networks

scholarly journals Chromatin accessibility changes betweenArabidopsisstem cells and mesophyll cells illuminate cell type-specific transcription factor networks

2017 ◽  
Author(s):  
Paja Sijacic ◽  
Marko Bajic ◽  
Elizabeth C. McKinney ◽  
Richard B. Meagher ◽  
Roger B. Deal
Keyword(s):  
Stem Cells ◽  
Transcription Factor ◽  
Regulatory Networks ◽  
Target Genes ◽  
Cell Types ◽  
Chromatin Accessibility ◽  
Cell Type ◽  
Mesophyll Cells ◽  
Leaf Mesophyll ◽  
Cell Type Specific

AbstractBackgroundCell differentiation is driven by changes in transcription factor (TF) activity and subsequent alterations in transcription. To study this process, differences in TF binding between cell types can be deduced by methods that probe chromatin accessibility. We used cell type-specific nuclei purification followed by the Assay for Transposase Accessible Chromatin (ATAC-seq) to delineate differences in chromatin accessibility and TF regulatory networks between stem cells of the shoot apical meristem (SAM) and differentiated leaf mesophyll cells ofArabidopsis thaliana.ResultsChromatin accessibility profiles of SAM stem cells and leaf mesophyll cells were highly similar at a qualitative level, yet thousands of regions of quantitatively different chromatin accessibility were also identified. We found that chromatin regions preferentially accessible in mesophyll cells tended to also be substantially accessible in the stem cells as compared to the genome-wide average, whereas the converse was not true. Analysis of genomic regions preferentially accessible in each cell type identified hundreds of overrepresented TF binding motifs, highlighting a set of TFs that are likely important for each cell type. Among these, we found evidence for extensive co-regulation of target genes by multiple TFs that are preferentially expressed in one cell type or the other. For example, a set of zinc-finger TFs appear to control a suite of growth-and development-related genes specifically in stem cells, while another TF set co-regulates genes involved in light responses and photosynthesis specifically in mesophyll cells. Interestingly, the TFs within both of these sets also show evidence of extensively co-regulating each other.ConclusionsQuantitative analysis of chromatin accessibility differences between stem cells and differentiated mesophyll cells allowed us to identify TF regulatory networks and downstream target genes that are likely to be functionally important in each cell type. Our findings that mesophyll cell-enriched accessible sites tend to already be substantially accessible in stem cells, but not vice versa, suggests that widespread regulatory element accessibility may be important for the developmental plasticity of stem cells. This work also demonstrates the utility of cell type-specific chromatin accessibility profiling in quickly developing testable models of regulatory control differences between cell types.

scholarly journals Leopard: fast decoding cell type-specific transcription factor binding landscape at single-nucleotide resolution

2019 ◽  
Author(s):  
Hongyang Li ◽  
Yuanfang Guan
Keyword(s):  
Transcription Factor ◽  
Network Architecture ◽  
Characteristic Curve ◽  
Cell Types ◽  
Cell Type ◽  
Single Nucleotide ◽  
Specific Transcription Factor ◽  
Cell Type Specific ◽  
Nucleotide Resolution ◽  
Single Nucleotide Resolution

AbstractDecoding the cell type-specific transcription factor (TF) binding landscape at single-nucleotide resolution is crucial for understanding the regulatory mechanisms underlying many fundamental biological processes and human diseases. However, limits on time and resources restrict the high-resolution experimental measurements of TF binding profiles of all possible TF-cell type combinations. Previous computational approaches either can not distinguish the cell-context-dependent TF binding profiles across diverse cell types, or only provide a relatively low-resolution prediction. Here we present a novel deep learning approach, Leopard, for predicting TF-binding sites at single-nucleotide resolution, achieving the median area under receiver operating characteristic curve (AUROC) of 0.994. Our method substantially outperformed state-of-the-art methods Anchor and FactorNet, improving the performance by 19% and 27% respectively despite evaluated at a lower resolution. Meanwhile, by leveraging a many-to-many neural network architecture, Leopard features hundred-fold to thousand-fold speedup compared to current many-to-one machine learning methods.

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