Biology subjects

Salomonis, N.

Publications and source records attributed to Salomonis, N..

28 records · Page 2Linked to original sources

bioRxiv2021

Fetal maturation revealed by amniotic fluid cell-free transcriptome in rhesus macaques

Accurate estimate of fetal maturity could provide individualized guidance for delivery of complicated pregnancies. However, current methods are invasive, have low accuracy, and are limited to fetal lung maturation. To identify diagnostic gestational biomarkers, we performed transcriptomic profiling of lung and brain, as well as cell-free RNA from amniotic fluid of preterm and term rhesus macaque fetuses. These data predict new and prior associated gestational age differences in distinct lung and neuronal cell populations when compared to existing single-cell and bulk RNA-Seq data. Comparative analyses found over 200 genes coincidently induced in lung and amniotic fluid, and dozens in brain and amniotic fluid. This data enabled creation of computational models that accurately predicted lung compliance from amniotic fluid and lung transcriptome of preterm fetuses treated with antenatal corticosteroids. Cell-free RNA in amniotic fluid may provide a substrate of global fetal maturation markers for personalized management of at-risk pregnancies.

systems biology↗

bioRxiv2020

DeepImmuno: Deep learning-empowered prediction and generation of immunogenic peptides for T cell immunity

T-cells play an essential role in the adaptive immune system by seeking out, binding and destroying foreign antigens presented on the cell surface of diseased cells. An improved understanding of T-cell immunity will greatly aid in the development of new cancer immunotherapies and vaccines for life threatening pathogens. Central to the design of such targeted therapies are computational methods to predict non-native epitopes to elicit a T cell response, however, we currently lack accurate immunogenicity inference methods. Another challenge is the ability to accurately simulate immunogenic peptides for specific human leukocyte antigen (HLA) alleles, for both synthetic biological applications and to augment real training datasets. Here, we proposed a beta-binomial distribution approach to derive epitope immunogenic potential from sequence alone. We conducted systematic benchmarking of five traditional machine learning (ElasticNet, KNN, SVM, Random Forest, AdaBoost) and three deep learning models (CNN, ResNet, GNN) using three independent prior validated immunogenic peptide collections (dengue virus, cancer neoantigen and SARS-Cov-2). We chose the CNN model as the best prediction model based on its adaptivity for small and large datasets, and performance relative to existing methods. In addition to outperforming two highly used immunogenicity prediction algorithms, DeepHLApan and IEDB, DeepImmuno-CNN further correctly predicts which residues are most important for T cell antigen recognition. Our independent generative adversarial network (GAN) approach, DeepImmuno-GAN, was further able to accurately simulate immunogenic peptides with physiochemical properties and immunogenicity predictions similar to that of real antigens. We provide DeepImmuno-CNN as source code and an easy-to-use web interface. Data AvailabilityDeepImmuno Python3 code is available at https://github.com/frankligy/DeepImmuno. The DeepImmuno web portal is available from https://deepimmuno.herokuapp.com. The data in this article is available in GitHub and supplementary materials.

bioinformatics↗

bioRxiv2020

Identification and Removal of Doublets with DoubletDecon

Retention of multiplet captures in single-cell RNA-sequencing (scRNA-seq) data can hinder identification of discrete or transitional cell populations and associated marker genes. To overcome this challenge, we created DoubletDecon to identify and remove doublets, multiplets of two cells, by using a combination of deconvolution to identify putative doublets and analyses of unique gene expression. Here we provide the protocol for running DoubletDecon on scRNA-seq data. For complete details on the use of this protocol, please see DePasquale et al. (2019) (https://doi.org/10.1016/j.celrep.2019.09.082). GRAPHICAL ABSTRACT O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

bioinformatics↗

bioRxiv2020

Ontogeny and function of the circadian clock in intestinal organoids

Circadian rhythms regulate diverse aspects of gastrointestinal physiology ranging from the composition of microbiota to motility. However, development of the intestinal circadian clock and detailed molecular mechanisms regulating circadian physiology of the intestine remain largely unknown. The lack of appropriate human model systems that enable organ- and/or diseasespecific interrogation of clock functions is a major obstacle hindering advancements of translational applications using chronotherapy. In this report, we show that both pluripotent stem cell-derived human intestinal organoids engrafted into mice and patient-derived human intestinal enteroids (HIEs) possess robust circadian rhythms, and demonstrate circadian phase-dependent necrotic cell death responses to Clostridium difficile toxin B (TcdB). Intriguingly, mouse and human enteroids demonstrate anti-phasic necrotic cell death responses. RNA-Seq data show ~4% of genes are rhythmically expressed in HIEs. Remarkably, we observe anti-phasic gene expression of Rac1, a small GTPase directly inactivated by TcdB, between mouse and human enteroids. Importantly, the observed circadian time-dependent necrotic cell death response is abolished in both mouse enteroids and human intestinal organoids (HIOs) lacking robust circadian rhythms. Our findings uncover robust functions of circadian rhythms regulating critical clock-controlled genes (CCGs) in human enteroids governing organism-specific, circadian phasedependent necrotic cell death responses. Our data highlight unique differences between mouse and human enteroids, and lay a foundation for human organ- and disease-specific investigation of clock functions using human organoids for translational applications.

molecular biology↗

bioRxiv2020

Single-nucleus RNA-seq identifies transcriptional heterogeneity in multinucleated skeletal myofibers

While the majority of cells contain a single nucleus, cell types such as trophoblasts, osteoclasts, and skeletal myofibers require multinucleation. One advantage of multinucleation can be the assignment of distinct functions to different nuclei, but comprehensive interrogation of transcriptional heterogeneity within multinucleated tissues has been challenging due to the presence of a shared cytoplasm. Here, we utilized single-nucleus RNA-sequencing (snRNA-seq) to determine the extent of transcriptional diversity within multinucleated skeletal myofibers. Nuclei from mouse skeletal muscle were profiled across the lifespan, which revealed the emergence of distinct myonuclear populations in postnatal development and their reactivation in aging muscle. Our datasets also provided a platform for discovery of novel genes associated with rare specialized regions of the muscle cell, including markers of the myotendinous junction and functionally validated factors expressed at the neuromuscular junction. These findings reveal that myonuclei within syncytial muscle fibers possess distinct transcriptional profiles that regulate muscle biology.

cell biology↗

bioRxiv2020

Three-dimensional mapping identifies distinct vascular niches for myelopoiesis

In contrast to virtually all other tissues in the body the anatomy of differentiation in the bone marrow remains unknown. This is due to the lack of strategies to examine blood cell production in situ, which are required to better understand differentiation, lineage commitment decisions, and to define how spatial organizing cues inform tissue function. Here we developed imaging approaches to map all myeloid cells in whole bones and generated 3D atlases of granulocyte and monocyte/dendritic cell differentiation during homeostasis. We found that myeloid progenitors leave the hematopoietic stem cell niche during differentiation. Granulocyte and monocyte dendritic cell progenitors (MDP) do not interact, instead they localize to different sinusoids where they give rise to clusters of immature cells. MDP cluster with Ly6Clo monocytes and conventional dendritic cells; these localize to a unique subset of colony stimulating factor 1 (CSF1, the major regulator of monopoiesis1) -expressing sinusoids. Csf1 deletion in the vasculature disrupted the MDP clusters and their interaction with sinusoids, leading to reduced MDP numbers and differentiation ability, with subsequent loss of peripheral Ly6Clo monocytes and dendritic cells. These data indicate that there is a specific spatial organization of definitive hematopoiesis and that local cues produced by distinct blood vessels are responsible for this organization. These maps provide a blueprint for in situ analyses of hematopoiesis in blood disorders.

immunology↗

bioRxiv2020

Short-term exposure to intermittent hypoxia in mice leads to changes in gene expression seen in chronic pulmonary disease

Obstructive sleep apnea (OSA) results from episodes of airway collapse and intermittent hypoxia and is associated with a host of health complications including dementia, diabetes, heart failure, and stroke. Although the lung is the first organ to sense changes in inspired oxygen levels, little is known about the consequences of IH to the lung hypoxia-inducible factor (HIF)-responsive pathways. Furthermore, cellular mechanisms causing disease progression across multiple systems in OSA are unknown. We hypothesized that exposure to IH would lead to up- and down-regulation of diverse expression pathways and that individual cell populations would show distinctive responses to IH. We identify changes in circadian and immune pathways in lungs from mice exposed to IH. Among all cell types, endothelial cells show the most prominent transcriptional changes. Interestingly, up-regulated genes in endothelial, fibroblast, and myofibroblast cells were enriched for genes associated with pulmonary fibrosis and pulmonary hypertension. These genes include targets of several drugs currently used to treat chronic pulmonary diseases. Our results reveal potential candidates for cell-targeted therapy seeking to minimize pulmonary effects of OSA. A better understanding of the pathophysiologic mechanisms underlying diseases associated with OSA could improve our therapeutic approaches, directing therapies to the most relevant cells and molecular pathways.

pathology↗

bioRxiv2019

Resolving single-cell heterogeneity from hundreds of thousands of cells through sequential hybrid clustering and NMF

The rapid proliferation of single-cell RNA-Sequencing (scRNA-Seq) technologies has spurred the development of diverse computational approaches to detect transcriptionally coherent populations. While the complexity of the algorithms for detecting heterogeneity have increased, most existing algorithms require significant user-tuning, are heavily reliant on dimensionality reduction techniques and are not scalable to ultra-large datasets. We previously described a multi-step algorithm, Iterative Clustering and Guide-gene selection (ICGS), which applies intra-gene correlation and hybrid clustering to uniquely resolve novel transcriptionally coherent cell populations from an intuitive graphical user interface. Here, we describe a new iteration of ICGS that outperforms state-of-the-art scRNA-Seq detection workflows when applied to well-established benchmarks. This approach combines multiple complementary subtype detection methods (HOPACH, sparse-NMF, cluster \"fitness\", SVM) to resolve rare and common cell-states, while minimizing differences due to donor or batch effects. Using data from the Human Cell Atlas, we show that the PageRank algorithm effectively down samples ultra-large scRNA-Seq datasets, without losing extremely rare or transcriptionally similar distinct cell-types and while recovering novel transcriptionally unique cell populations. We believe this new approach holds tremendous promise in reproducibly resolving hidden cell populations in complex datasets.\n\nHighlightsO_LIICGS2 outperforms alternative approaches in small and ultra-large benchmark datasets\nC_LIO_LIIntegrates multiple solutions for cell-type detection with supervised refinement\nC_LIO_LIScales effectively to resolve rare cell-states from ultra-large datasets using PageRank sampling with a low memory footprint\nC_LIO_LIIntegrated into AltAnalyze to enable sophisticated and automated downstream analysis\nC_LI

bioinformatics↗

bioRxiv2018

CellHarmony: Cell-level matching and comparison of single-cell transcriptomes

To understand the molecular pathogenesis of human disease, precision analyses to define molecular alterations within (and between) disease-associated cell populations are desperately needed. Single-cell genomics represents an ideal platform to enable the identification and comparison of normal and diseased transcriptional cell states. We note that disease-associated perturbations usually retain cellular-identity programs (core genes), providing an appropriate reference for secondary comparison analyses. Thus, we created cellHarmony, an integrated solution for the unsupervised analysis and classification of cell types from diverse scRNA-Seq datasets. cellHarmony is an automated and easy-to-use tool that efficiently matches single-cell transcriptomes using a community clustering and alignment strategy. Utilizing core genes and community clustering to reveal disease and cell-state systems-level insights overcomes bias toward donor and disease effects that can be imposed by joint-alignment approaches. Moreover, cellHarmony directly compares cell frequencies and gene expression in a cell-type-specific manner, then produces a holistic representation of these differences across potentially dozens of cell populations and impacted regulatory networks. Using this approach, we identify gene regulatory programs that are selectively impacted in distinct hematopoietic and heart cell populations that suggest novel disease mechanisms and drug targets. Thus, this approach holds tremendous promise in revealing the molecular and cellular origins of complex diseases.

bioinformatics↗

bioRxiv2018

DoubletDecon: Cell-State Aware Removal of Single-Cell RNA-Seq Doublets

Methods for single-cell RNA sequencing (scRNA-Seq) have greatly advanced in recent years. While droplet- and well-based methods have increased the capture frequency of cells for scRNA-Seq, these technologies readily produce technical artifacts, such as doublet-cell and multiplet-cell captures. Doublets occurring between distinct cell-types can appear as hybrid scRNA-Seq profiles, but do not have distinct transcriptomes from individual cell states. We introduce DoubletDecon, an approach that detects doublets with a combination of deconvolution analyses and the identification of unique cell-state gene expression. We demonstrate the ability of DoubletDecon to identify synthetic and cell-hashing cell singlets and doublets from scRNA-Seq datasets of varying cellular complexity. DoubletDecon is able to account for cell-cycle effects and is compatible with diverse species and unsupervised population detection algorithms (e.g., ICGS, Seurat). We believe this approach has the potential to become a standard quality control step for the accurate delineation of cell states.

bioinformatics↗