Cellular and Molecular Probing of Intact Transparent Human Organs, bioRxiv, 2019-05-21

SUMMARYOptical tissue transparency permits cellular and molecular investigation of complex tissues in 3D, a fundamental need in biomedical sciences. Adult human organs are particularly challenging for this approach, owing to the accumulation of dense and sturdy molecules in decades-aged human tissues. Here, we introduce SHANEL method utilizing a new tissue permeabilization approach to clear and label stiff human organs. We used SHANEL to generate the first intact transparent adult human brain and kidney, and perform 3D histology using antibodies and dyes in centimeters depth. Thereby, we revealed structural details of sclera, iris and suspensory ligament in the human eye, and the vessels and glomeruli in the human kidney. We also applied SHANEL on transgenic pig organs to map complex structures of EGFP expressing beta cells in &gt;10 cm size pancreas. Overall, SHANEL is a robust and unbiased technology to chart the cellular and molecular architecture of intact large mammalian organs.Graphical Abstract<jatsfig id=ufig1 position=float orientation=portrait fig-type=figure><jatsgraphic xmlnsxlink=httpwww.w3.org1999xlink xlinkhref=643908v1_ufig1 position=float orientation=portrait >Supplementary Movies of SHANEL are available at <jatsext-link xmlnsxlink=httpwww.w3.org1999xlink ext-link-type=uri xlinkhref=httpdiscotechnologies.orgSHANEL>httpdiscotechnologies.orgSHANEL<jatsext-link>

biorxiv cell-biology 500+-users 2019

Is adaptation limited by mutation? A timescale-dependent effect of genetic diversity on the adaptive substitution rate in animals, bioRxiv, 2019-05-21

ABSTRACTWhether adaptation is limited by the beneficial mutation supply is a long-standing question of evolutionary genetics, which is more generally related to the determination of the adaptive substitution rate and its relationship with the effective population size Ne. Empirical evidence reported so far is equivocal, with some but not all studies supporting a higher adaptive substitution rate in large-Ne than in small-Ne species.We gathered coding sequence polymorphism data and estimated the adaptive amino-acid substitution rate ωa, in 50 species from ten distant groups of animals with markedly different population mutation rate θ. We reveal the existence of a complex, timescale dependent relationship between species adaptive substitution rate and genetic diversity. We find a positive relationship between ωa and θ among closely related species, indicating that adaptation is indeed limited by the mutation supply, but this was only true in relatively low-θ taxa. In contrast, we uncover a weak negative correlation between ωa and θ at a larger taxonomic scale. This result is consistent with Fisher’s geometrical model predictions and suggests that the proportion of beneficial mutations scales negatively with species’ long-term Ne.

biorxiv evolutionary-biology 0-100-users 2019

Next-generation sequencing of double stranded RNA is greatly improved by treatment with the inexpensive denaturing reagent DMSO, bioRxiv, 2019-05-21

AbstractDouble stranded RNA (dsRNA) is the genetic material of important viruses and a key component of RNA interference-based immunity in eukaryotes. Previous studies have noted difficulties in determining the sequence of dsRNA molecules that have affected studies of immune function and estimates of viral diversity in nature. Dimethyl sulfoxide (DMSO) has been used to denature dsRNA prior to the reverse transcription stage to improve RT-PCR and Sanger sequencing. We systematically tested the utility of DMSO to improve sequencing yield of a dsRNA virus (Φ6) in a short-read next generation sequencing platform. DMSO treatment improved sequencing read recovery by over two orders of magnitude, even when RNA and cDNA concentrations were below the limit of detection. We also tested the effects of DMSO on a mock eukaryotic viral community and found that dsRNA virus reads increased with DMSO treatment. Furthermore, we provide evidence that DMSO treatment does not adversely affect recovery of reads from a single-stranded RNA viral genome (Influenza ACalifornia072009). We suggest that up to 50% DMSO treatment be used prior to cDNA synthesis when samples of interest are composed of or may contain dsRNA.Data SummarySequence data was deposited in the NCBI Short Read Archive (accession numbers PRJNA527100, PRJNA527101, PRJNA527098). Data and code for analysis is available on GitHub (<jatsext-link xmlnsxlink=httpwww.w3.org1999xlink ext-link-type=uri xlinkhref=httpsgithub.comawilcox83dsRNA-sequencing>httpsgithub.comawilcox83dsRNA-sequencing<jatsext-link>. doi10.5281zenodo.1453423). Protocol for dsRNA sequencing is posted on protocols.io (doi10.17504protocols.io.ugnetve).

biorxiv microbiology 0-100-users 2019

Npas1+-Nkx2.1+ Neurons Are an Integral Part of the Cortico-pallido-cortical Loop, bioRxiv, 2019-05-21

AbstractWithin the basal ganglia circuit, the external globus pallidus (GPe) is critically involved in motor control. Aside from Foxp2+ neurons and ChAT+ neurons that have been established as unique neuron types, there is little consensus on the classification of GPe neurons. Properties of the remaining neuron types are poorly-defined. In this study, we leverage new mouse lines, viral tools, and molecular markers to better define GPe neuron subtypes. We found that Sox6 represents a novel, defining marker for GPe neuron subtypes. Lhx6+ neurons that lack the expression of Sox6 were devoid of both parvalbumin and Npas1. This result confirms previous assertions of the existence of a unique Lhx6+ population. Neurons that arise from the Dbx1+ lineage were similarly abundant in the GPe and displayed a heterogeneous makeup. Importantly, tracing experiments revealed that Npas1+-Nkx2.1+ neurons represent the principal non-cholinergic, cortically-projecting neurons. In other words, they form the pallido-cortical arm of the cortico-pallido-cortical loop. Our data further described that pyramidal-tract neurons in the cortex collateralized within the GPe, forming a closed-loop system between the two brain structures. Overall, our findings reconcile some of the discrepancies that arose from differences in techniques or the reliance on pre-existing tools. While spatial distribution and electrophysiological properties of GPe neurons reaffirm the diversification of GPe subtypes, statistical analyses strongly support the notion that these neuron subtypes can be categorized under the two principal neuron classes—i.e., PV+ neurons and Npas1+ neurons.Significance statementThe poor understanding of the neuronal composition in the GPe undermines our ability to interrogate its precise behavioral and disease involvements. In this study, twelve different genetic crosses were used, hundreds of neurons were electrophysiologically-characterized, and over 100,000 neurons were histologically- andor anatomically-profiled. Our current study further establishes the segregation of GPe neuron classes and illustrates the complexity of GPe neurons in adult mice. Our results support the idea that Npas1+-Nkx2.1+ neurons are a distinct GPe neuron subclass. By providing a detailed analysis of the organization of the cortico-pallidal-cortical projection, our findings establish the cellular and circuit substrates that can be important for motor function and dysfunction.

biorxiv neuroscience 0-100-users 2019

Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data, bioRxiv, 2019-05-20

AbstractWe present a comprehensive evaluation of state-of-the-art algorithms for inferring gene regulatory networks (GRNs) from single-cell gene expression data. We develop a systematic framework called BEELINE for this purpose. We use synthetic networks with predictable cellular trajectories as well as curated Boolean models to serve as the ground truth for evaluating the accuracy of GRN inference algorithms. We develop a strategy to simulate single-cell gene expression data from these two types of networks that avoids the pitfalls of previously-used methods. We selected 12 representative GRN inference algorithms. We found that the accuracy of these methods (measured in terms of AUROC and AUPRC) was moderate, by and large, although the methods were better in recovering interactions in the synthetic networks than the Boolean models. Techniques that did not require pseudotime-ordered cells were more accurate, in general. The observation that the endpoints of many false positive edges were connected by paths of length two in the Boolean models suggested that indirect effects may be predominant in the outputs of the algorithms we tested. The predicted networks were considerably inconsistent with each other, indicating that combining GRN inference algorithms using ensembles is likely to be challenging. Based on the results, we present some recommendations to users of GRN inference algorithms, including suggestions on how to create simulated gene expression datasets for testing them. BEELINE, which is available at <jatsext-link xmlnsxlink=httpwww.w3.org1999xlink ext-link-type=uri xlinkhref=httpgithub.commurali-groupBEELINE>httpgithub.commurali-groupBEELINE<jatsext-link> under an open-source license, will aid in the future development of GRN inference algorithms for single-cell transcriptomic data.

biorxiv bioinformatics 0-100-users 2019

Benchmarking principal component analysis for large-scale single-cell RNA-sequencing, bioRxiv, 2019-05-20

Principal component analysis (PCA) is an essential method for analyzing single-cell RNA-seq (scRNA-seq) dataset but for large-scale scRNA-seq datasets, the computation consumes a long time and large memory space. In this work, we review the existing fast and memory-efficient PCA algorithms and implementations and evaluate their practical application to large-scale scRNA-seq dataset. Our benchmark showed that some PCA algorithms based on Krylov subspace and randomized singular value decomposition are fast, memory-efficient, and accurate than the other algorithms. Considering the difference of computational environment of users and developers, we also developed the guideline to select the appropriate PCA implementations.

biorxiv bioinformatics 100-200-users 2019