Non-invasive laminar inference with MEG Comparison of methods and source inversion algorithms, bioRxiv, 2017-06-08

AbstractMagnetoencephalography (MEG) is a direct measure of neuronal current flow; its anatomical resolution is therefore not constrained by physiology but rather by data quality and the models used to explain these data. Recent simulation work has shown that it is possible to distinguish between signals arising in the deep and superficial cortical laminae given accurate knowledge of these surfaces with respect to the MEG sensors. This previous work has focused around a single inversion scheme (multiple sparse priors) and a single global parametric fit metric (free energy). In this paper we use several different source inversion algorithms and both local and global, as well as parametric and non-parametric fit metrics in order to demonstrate the robustness of the discrimination between layers. We find that only algorithms with some sparsity constraint can successfully be used to make laminar discrimination. Importantly, local t-statistics, global cross-validation and free energy all provide robust and mutually corroborating metrics of fit. We show that discrimination accuracy is affected by patch size estimates, cortical surface features, and lead field strength, which suggests several possible future improvements to this technique. This study demonstrates the possibility of determining the laminar origin of MEG sensor activity, and thus directly testing theories of human cognition that involve laminar- and frequency- specific mechanisms. This possibility can now be achieved using recent developments in high precision MEG, most notably the use of subject-specific head-casts, which allow for significant increases in data quality and therefore anatomically precise MEG recordings.

biorxiv neuroscience 0-100-users 2017

Resetting the yeast epigenome with human nucleosomes, bioRxiv, 2017-06-08

SummaryHumans and yeast are separated by a billion years of evolution, yet their conserved core histones retain central roles in gene regulation. Here, we “reset” yeast to use core human nucleosomes in lieu of their own, an exceedingly rare event which initially took twenty days. The cells adapt, however, by acquiring suppressor mutations in cell-division genes, or by acquiring certain aneuploidy states. Robust growth was also restored by converting five histone residues back to their yeast counterparts. We reveal that humanized nucleosomes in yeast are positioned according to endogenous yeast DNA sequence and chromatin-remodeling network, as judged by a yeast-like nucleosome repeat length. However, human nucleosomes have higher DNA occupancy and reduce RNA content. Adaptation to new biological conditions presented a special challenge for these cells due to slower chromatin remodeling. This humanized yeast poses many fundamental new questions about the nature of chromatin and how it is linked to many cell processes, and provides a platform to study histone variants via yeast epigenome reprogramming.Highlights<jatslist list-type=simple><jatslist-item>- Only 1 in 107 yeast survive with fully human nucleosomes, but they rapidly evolve<jatslist-item><jatslist-item>- Nucleosome positioning and nucleosome repeat length is not influenced by histone type<jatslist-item><jatslist-item>- Human nucleosomes remodel slowly and delay yeast environmental adaptation<jatslist-item><jatslist-item>- Human core nucleosomes are more repressive and globally reduce transcription in yeast<jatslist-item>

biorxiv synthetic-biology 0-100-users 2017

SCENIC Single-cell regulatory network inference and clustering, bioRxiv, 2017-06-01

AbstractSingle-cell RNA-seq allows building cell atlases of any given tissue and infer the dynamics of cellular state transitions during developmental or disease trajectories. Both the maintenance and transitions of cell states are encoded by regulatory programs in the genome sequence. However, this regulatory code has not yet been exploited to guide the identification of cellular states from single-cell RNA-seq data. Here we describe a computational resource, called SCENIC (Single Cell rEgulatory Network Inference and Clustering), for the simultaneous reconstruction of gene regulatory networks (GRNs) and the identification of stable cell states, using single-cell RNA-seq data. SCENIC outperforms existing approaches at the level of cell clustering and transcription factor identification. Importantly, we show that cell state identification based on GRNs is robust towards batch-effects and technical-biases. We applied SCENIC to a compendium of single-cell data from the mouse and human brain and demonstrate that the proper combinations of transcription factors, target genes, enhancers, and cell types can be identified. Moreover, we used SCENIC to map the cell state landscape in melanoma and identified a gene regulatory network underlying a proliferative melanoma state driven by MITF and STAT and a contrasting network controlling an invasive state governed by NFATC2 and NFIB. We further validated these predictions by showing that two transcription factors are predominantly expressed in early metastatic sentinel lymph nodes. In summary, SCENIC is the first method to analyze scRNA-seq data using a network-centric, rather than cell-centric approach. SCENIC is generic, easy to use, and flexible, and allows for the simultaneous tracing of genomic regulatory programs and the mapping of cellular identities emerging from these programs. Availability SCENIC is available as an R workflow based on three new RBioconductor packages GENIE3, RcisTarget and AUCell. As scalable alternative to GENIE3, we also provide GRNboost, paving the way towards the network analysis across millions of single cells.

biorxiv bioinformatics 0-100-users 2017

 

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