Simulating multiple faceted variability in single cell RNA sequencing.

The abundance of new computational methods for processing and interpreting transcriptomes at a single cell level raises the need for in silico platforms for evaluation and validation. Here, we present SymSim, a simulator that explicitly models the processes that give rise to data observed in single cell RNA-Seq experiments. The components of the SymSim pipeline pertain to the three primary sources of variation in single cell RNA-Seq data: noise intrinsic to the process of transcription, extrinsic variation indicative of different cell states (both discrete and continuous), and technical variation due to low sensitivity and measurement noise and bias. We demonstrate how SymSim can be used for benchmarking methods for clustering, differential expression and trajectory inference, and for examining the effects of various parameters on their performance. We also show how SymSim can be used to evaluate the number of cells required to detect a rare population under various scenarios

Mycobacterium tuberculosis Rv0928 protein facilitates macrophage control of mycobacterium infection by promoting mitochondrial intrinsic apoptosis and ROS-mediated inflammation

Macrophages are the main target cells for Mycobacterium tuberculosis (Mtb) infection. Previous studies have shown that Mtb actively upregulates phosphorus transport proteins, such as Rv0928 protein (also known as PstS3), to increase inorganic phosphate uptake and promote their survival under low phosphorus culture conditions in vitro. However, it is unclear whether this upregulation of PstS3 affects the intracellular survival of Mtb, as the latter is also largely dependent on the immune response of infected macrophages. By using Rv0928-overexpressing Mycobacterium smegmatis (Ms::Rv0928), we unexpectedly found that Rv0928 not only increased apoptosis, but also augmented the inflammatory response of infected macrophages. These enhanced cellular defense mechanisms ultimately led to a dramatic reduction in intracellular bacterial load. By investigating the underlying mechanisms, we found that Rv0928 interacted with the macrophage mitochondrial phosphate carrier protein SLC25A3, reduced mitochondrial membrane potential and caused mitochondrial cytochrome c release, which ultimately activated caspase-9-mediated intrinsic apoptosis. In addition, Rv0928 amplified macrophage mitochondrial ROS production, further enhancing pro-inflammatory cytokine production by promoting activation of NF-κB and MAPK pathways. Our study suggested that Mtb Rv0928 up-regulation enhanced the immune defense response of macrophages. These findings may help us to better understand the complex process of mutual adaptation and mutual regulation between Mtb and macrophages during infection

Inference of single-cell phylogenies from lineage tracing data using Cassiopeia.

The pairing of CRISPR/Cas9-based gene editing with massively parallel single-cell readouts now enables large-scale lineage tracing. However, the rapid growth in complexity of data from these assays has outpaced our ability to accurately infer phylogenetic relationships. First, we introduce Cassiopeia-a suite of scalable maximum parsimony approaches for tree reconstruction. Second, we provide a simulation framework for evaluating algorithms and exploring lineage tracer design principles. Finally, we generate the most complex experimental lineage tracing dataset to date, 34,557 human cells continuously traced over 15 generations, and use it for benchmarking phylogenetic inference approaches. We show that Cassiopeia outperforms traditional methods by several metrics and under a wide variety of parameter regimes, and provide insight into the principles for the design of improved Cas9-enabled recorders. Together, these should broadly enable large-scale mammalian lineage tracing efforts. Cassiopeia and its benchmarking resources are publicly available at www.github.com/YosefLab/Cassiopeia

Genetic variants in the TIRAP gene are associated with increased risk of sepsis-associated acute lung injury

<p>Abstract</p> <p>Background</p> <p>Toll like receptors (TLRs) signaling pathways, including the adaptor protein Mal encoded by the TIRAP gene, play a central role in the development of acute lung injury (ALI). Recently, the <it>TIRAP </it>variants have been described association with susceptibility to inflammatory diseases. The aim of this study was to investigate whether genetic variants in <it>TIRAP </it>are associated with the development of ALI.</p> <p>Methods</p> <p>A case-control collection from Han Chinese of 298 healthy subjects, 278 sepsis-associated ALI and 288 sepsis alone patients were included. Three tag single nucleotide polymorphisms (SNPs) of the TIRAP gene and two additional SNPs that have previously showed association with susceptibility to other inflammatory diseases were genotyped by direct sequencing. The differences of allele, genotype and haplotype frequencies were evaluated between three groups.</p> <p>Results</p> <p>The minor allele frequencies of both rs595209 and rs8177375 were significantly increased in ALI patients compared with both healthy subjects (odds ratio (OR) = 1.47, 95% confidence interval (CI):1.15-1.88, P = 0.0027 and OR = 1.97, 95% CI: (1.38-2.80), P = 0.0001, respectively) and sepsis alone patients (OR = 1.44, 95% CI: 1.12-1.85, P = 0.0041 and OR = 1.82, 95% CI: 1.28-2.57, P = 0.00079, respectively). Haplotype consisting of these two associated SNPs strengthened the association with ALI susceptibility. The frequency of haplotype AG (rs595209A, rs8177375G) in the ALI samples was significantly higher than that in the healthy control group (OR = 2.13, 95% CI: 1.46-3.09, P = 0.00006) and the sepsis alone group (OR = 2.24, 95% CI: 1.52-3.29, P = 0.00003). Carriers of the haplotype CA (rs595209C, rs8177375A) had a lower risk for ALI compared with healthy control group (OR = 0.69, 95% CI: 0.54-0.88, P = 0.0003) and sepsis alone group (OR = 0.71, 95% CI: 0.55-0.91, P = 0.0006). These associations remained significant after adjustment for covariates in multiple logistic regression analysis and for multiple comparisons.</p> <p>Conclusions</p> <p>These results indicated that genetic variants in the TIRAP gene might be associated with susceptibility to sepsis-associated ALI in Han Chinese population. However, the association needs to be replicated in independent studies.</p

Recognizing Cell Identity: Classifying cell types in scRNAseq data

The classification of cell type is one of the first steps in scRNAseq analysis for translating observed transcriptional variation to biological insights. The same cell types can be sampled from different environment and using different technologies and their transcriptional profile can differ. Thus, defining cell types in scRNAseq data is much more than a matter of identifying clusters of cells that are similar to each other. In chapter 1, we developed a simulation method SymSim in order to understand the different facets of variability in scRNAseq. In Chapter 2, we applied a Bayesian Variational Inference method scVI for the harmonization scRNAseq datasets and propose a new method scANVI in the same frame work for the annotation of these datasets. We tested the performance of scVI and scANVI using both SymSim and experimental data. In Chapter 3 we applied our data harmonization method scVI to a Multiple Sclerosis (MS) case-control study using scRNAseq data to profile immune cells. We identified cellular changes associated with MS in tissue-specific cell type abundance and transcriptional changes after being able to identify shared cell types in both blood and CSF in multiple donors. In Chapter 4 we apply a number of scRNAseq harmonization and annotation including scVI and scANVI to a large consortium cell atlas project Tabula Sapiens. Tabula Sapiens aims to provide a comprehensive reference scRNAseq dataset for the scientific community. We developed an automatic annotation pipeline named PopularVote to facilitate the in-house data annotation process, and to be published for using as a public tool for other scientists to annotate their own data. This dissertation presents a set of tools that we developed or used in cell type annotation in a diverse set of scRNAseq applications (identifying rare cell types, comparing cell types across conditions, generating automatic data annotations). The potential of scRNAseq is best realized by generating a well-curated dataset that everyone in the research community can use and contribute to, and the ability to classify cells in an automatic manner will enable such efforts in the future

Simulating multiple faceted variability in single cell RNA sequencing.

The abundance of new computational methods for processing and interpreting transcriptomes at a single cell level raises the need for in silico platforms for evaluation and validation. Here, we present SymSim, a simulator that explicitly models the processes that give rise to data observed in single cell RNA-Seq experiments. The components of the SymSim pipeline pertain to the three primary sources of variation in single cell RNA-Seq data: noise intrinsic to the process of transcription, extrinsic variation indicative of different cell states (both discrete and continuous), and technical variation due to low sensitivity and measurement noise and bias. We demonstrate how SymSim can be used for benchmarking methods for clustering, differential expression and trajectory inference, and for examining the effects of various parameters on their performance. We also show how SymSim can be used to evaluate the number of cells required to detect a rare population under various scenarios

Leveraging Multiple Populations across Time Helps Define Accurate Models of Human Evolution: A Reanalysis of the Lactase Persistence Adaptation

Access to a geographically diverse set of modern human samples from the present time and from ancient remains, combined with archaic hominin samples, provides an unprecedented level of resolution to study both human history and adaptation. The amount and quality of ancient human data continue to improve and enable tracking the trajectory of genetic variation over time. These data have the potential to help us redefijine or generate new hypotheses of how human evolution occurred and to revise previous conjectures. In this article, we argue that leveraging all these data will help us better detail adaptive histories in humans. As a case in point, we focus on one of the most celebrated examples of human adaptation: the evolution of lactase persistence. We briefly review this dietary adaptation and argue that, effectively, the evolutionary history of lactase persistence is still not fully resolved. We propose that, by leveraging data from multiple populations across time and space, we will find evidence of a more nuanced history than just a simple selective sweep. We support our hypotheses with simulation results and make some cautionary notes regarding the use of haplotype-based summary statistics to estimate evolutionary parameters

Probabilistic harmonization and annotation of single‐cell transcriptomics data with deep generative models

Abstract As the number of single‐cell transcriptomics datasets grows, the natural next step is to integrate the accumulating data to achieve a common ontology of cell types and states. However, it is not straightforward to compare gene expression levels across datasets and to automatically assign cell type labels in a new dataset based on existing annotations. In this manuscript, we demonstrate that our previously developed method, scVI, provides an effective and fully probabilistic approach for joint representation and analysis of scRNA‐seq data, while accounting for uncertainty caused by biological and measurement noise. We also introduce single‐cell ANnotation using Variational Inference (scANVI), a semi‐supervised variant of scVI designed to leverage existing cell state annotations. We demonstrate that scVI and scANVI compare favorably to state‐of‐the‐art methods for data integration and cell state annotation in terms of accuracy, scalability, and adaptability to challenging settings. In contrast to existing methods, scVI and scANVI integrate multiple datasets with a single generative model that can be directly used for downstream tasks, such as differential expression. Both methods are easily accessible through scvi‐tools

Phylomitogenomics reconfirm the phylogenetic position of the genus Metaplax inferred from the two grapsid crabs (Decapoda: Brachyura: Grapsoidea).

Two new complete mitogenomes of the grapsids, Metaplax longipes Stimpson, 1858 and Nanosesarma minutum (De Man, 1887) were sequenced using next-generation sequencing (NGS). By analyzing a combination of 75 Brachyura taxa, our phylomitogenomic inferences suggested that Metaplax crab seperated earlier from the sesarmid crabs and closely related to the varunids with respect to Nanosesarma crab. It reconfirmed that the Metaplax should be removed from the Sesarmidae and assinged to the Varunidae. Additional mitogenomic comparisons including gene rearrangement and genomic organization were conducted among the 33 taxa of Grapsoidea and Ocypodoidea, and a shared rearrangement pattern between Metaplax longipes and the varunids were recovered, which also strongly supported the inference for the phylogenetic position of the Metaplax

Data from: Natural selection interacts with recombination to shape the evolution of hybrid genomes

To investigate the consequences of hybridization between species, we studied three replicate hybrid populations that formed naturally between two swordtail fish species, estimating their fine-scale genetic map and inferring ancestry along the genomes of 690 individuals. In all three populations, ancestry from the “minor” parental species is more common in regions of high recombination and where there is linkage to fewer putative targets of selection. The same patterns are apparent in a reanalysis of human and archaic admixture. These results support models in which ancestry from the minor parental species is more likely to persist when rapidly uncoupled from alleles that are deleterious in hybrids. Our analyses further indicate that selection on swordtail hybrids stems predominantly from deleterious combinations of epistatically-interacting alleles