Publications by authors named "Mingyao Li"

199 Publications

SpaGCN: Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network.

Nat Methods 2021 Nov 28;18(11):1342-1351. Epub 2021 Oct 28.

Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.

Recent advances in spatially resolved transcriptomics (SRT) technologies have enabled comprehensive characterization of gene expression patterns in the context of tissue microenvironment. To elucidate spatial gene expression variation, we present SpaGCN, a graph convolutional network approach that integrates gene expression, spatial location and histology in SRT data analysis. Through graph convolution, SpaGCN aggregates gene expression of each spot from its neighboring spots, which enables the identification of spatial domains with coherent expression and histology. The subsequent domain guided differential expression (DE) analysis then detects genes with enriched expression patterns in the identified domains. Analyzing seven SRT datasets using SpaGCN, we show it can detect genes with much more enriched spatial expression patterns than competing methods. Furthermore, genes detected by SpaGCN are transferrable and can be utilized to study spatial variation of gene expression in other datasets. SpaGCN is computationally fast, platform independent, making it a desirable tool for diverse SRT studies.
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http://dx.doi.org/10.1038/s41592-021-01255-8DOI Listing
November 2021

Longitudinal Large-Scale Semiquantitative Proteomic Data Stability Across Multiple Instrument Platforms.

J Proteome Res 2021 Nov 20;20(11):5203-5211. Epub 2021 Oct 20.

Division of Oncology, Center for Childhood Cancer Research, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania 19104, United States.

With the rapid developments in mass spectrometry (MS)-based proteomics methods, label-free semiquantitative proteomics has become an increasingly popular tool for profiling global protein abundances in an unbiased manner. However, the reproducibility of these data across time and LC-MS platforms is not well characterized. Here, we evaluate the performance of three LC-MS platforms (Orbitrap Elite, Q Exactive HF, and Orbitrap Fusion) in label-free semiquantitative analysis of cell surface proteins over a six-year period. Sucrose gradient ultracentrifugation was used for surfaceome enrichment, following gel separation for in-depth protein identification. With our established workflow, we consistently detected and reproducibly quantified >2300 putative cell surface proteins in a human acute myeloid leukemia (AML) cell line on all three platforms. To our knowledge this is the first study reporting highly reproducible semiquantitative proteomic data collection of biological replicates across multiple years and LC-MS platforms. These data provide experimental justification for semiquantitative proteomic study designs that are executed over multiyear time intervals and on different platforms. Multiyear and multiplatform experimental designs will likely enable larger scale proteomic studies and facilitate longitudinal proteomic studies by investigators lacking access to high throughput MS facilities. Data are available via ProteomeXchange with identifier PXD022721.
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http://dx.doi.org/10.1021/acs.jproteome.1c00624DOI Listing
November 2021

Applications of single-cell genomics and computational strategies to study common disease and population-level variation.

Genome Res 2021 Oct;31(10):1728-1741

Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania 19104, USA.

The advent and rapid development of single-cell technologies have made it possible to study cellular heterogeneity at an unprecedented resolution and scale. Cellular heterogeneity underlies phenotypic differences among individuals, and studying cellular heterogeneity is an important step toward our understanding of the disease molecular mechanism. Single-cell technologies offer opportunities to characterize cellular heterogeneity from different angles, but how to link cellular heterogeneity with disease phenotypes requires careful computational analysis. In this article, we will review the current applications of single-cell methods in human disease studies and describe what we have learned so far from existing studies about human genetic variation. As single-cell technologies are becoming widely applicable in human disease studies, population-level studies have become a reality. We will describe how we should go about pursuing and designing these studies, particularly how to select study subjects, how to determine the number of cells to sequence per subject, and the needed sequencing depth per cell. We also discuss computational strategies for the analysis of single-cell data and describe how single-cell data can be integrated with bulk tissue data and data generated from genome-wide association studies. Finally, we point out open problems and future research directions.
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http://dx.doi.org/10.1101/gr.275430.121DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8494214PMC
October 2021

Intraplaque Enhancement Is Associated With Artery-to-Artery Embolism in Symptomatic Vertebrobasilar Atherosclerotic Diseases.

Front Neurol 2021 1;12:680827. Epub 2021 Sep 1.

Department of Interventional Neuroradiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.

There are limited data regarding the characteristics of intracranial plaques according to stroke mechanism in the posterior circulation. This study aims to compare whether the plaque characteristics and baseline features are different in patients with artery-to-artery (A-to-A) embolism and those with parent artery disease in the intracranial vertebrobasilar atherosclerotic disease. From September 2014 to January 2017, patients with recent posterior circulation stroke due to intracranial vertebrobasilar atherosclerotic disease were retrospectively analyzed. Patients with the following eligibility criteria were included: (1) age ≥18 years old, (2) ischemic stroke in the vertebrobasilar territory, (3) 70-99% stenosis of the intracranial vertebral artery or basilar artery, and (4) two or more atherosclerotic risk factors. Patients with concomitant ipsilateral or bilateral extracranial vertebral artery >50% stenosis, cardio-embolism, or non-atherosclerotic stenosis were excluded. The plaque characteristics, including intraplaque compositions (intraplaque hemorrhage and intraplaque calcification), intraplaque enhancement, and remodeling index, were evaluated by using 3T high-resolution magnetic resonance imaging (HRMRI). The baseline features including vascular risk factors and the involved artery were collected. Patients were divided into A-to-A embolism and parent artery disease groups based on the diffusion-weighted images, T2-weighted images, or computed tomography. The plaque characteristics and baseline features were compared between the two groups. Among consecutive 298 patients, 51 patients were included. Twenty-nine patients had A-to-A embolism and 22 patients had parent artery disease. Compared with parent artery disease, the occurrence rates of intraplaque enhancement and intracranial vertebral involvement were higher in the A-to-A embolism group (79.3 vs. 36.4%; = 0.002 and 62.1 vs. 18.2%; = 0.002, respectively). Multivariable logistic regression analysis showed that intraplaque enhancement and intracranial vertebral artery plaques were also associated with A-to-A embolism (adjusted OR, 7.31; 95% CI 1.58-33.77; = 0.011 and adjusted OR, 9.42; 95% CI 1.91-46.50; = 0.006, respectively). Intraplaque enhancement and intracranial vertebral artery plaques seem to be more closely associated with A-to-A embolism than parent artery disease in patients with symptomatic intracranial vertebrobasilar disease. http://www.clinicaltrials.gov, identifier: NCT02705599.
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http://dx.doi.org/10.3389/fneur.2021.680827DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8440987PMC
September 2021

Mapping the genetic architecture of human traits to cell types in the kidney identifies mechanisms of disease and potential treatments.

Nat Genet 2021 09 12;53(9):1322-1333. Epub 2021 Aug 12.

Department of Medicine, Renal Electrolyte and Hypertension Division, University of Pennsylvania, Philadelphia, PA, USA.

The functional interpretation of genome-wide association studies (GWAS) is challenging due to the cell-type-dependent influences of genetic variants. Here, we generated comprehensive maps of expression quantitative trait loci (eQTLs) for 659 microdissected human kidney samples and identified cell-type-eQTLs by mapping interactions between cell type abundances and genotypes. By partitioning heritability using stratified linkage disequilibrium score regression to integrate GWAS with single-cell RNA sequencing and single-nucleus assay for transposase-accessible chromatin with high-throughput sequencing data, we prioritized proximal tubules for kidney function and endothelial cells and distal tubule segments for blood pressure pathogenesis. Bayesian colocalization analysis nominated more than 200 genes for kidney function and hypertension. Our study clarifies the mechanism of commonly used antihypertensive and renal-protective drugs and identifies drug repurposing opportunities for kidney disease.
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http://dx.doi.org/10.1038/s41588-021-00909-9DOI Listing
September 2021

Implication of specific retinal cell-type involvement and gene expression changes in AMD progression using integrative analysis of single-cell and bulk RNA-seq profiling.

Sci Rep 2021 08 2;11(1):15612. Epub 2021 Aug 2.

Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA.

Age-related macular degeneration (AMD) is a blinding eye disease with no unifying theme for its etiology. We used single-cell RNA sequencing to analyze the transcriptomes of ~ 93,000 cells from the macula and peripheral retina from two adult human donors and bulk RNA sequencing from fifteen adult human donors with and without AMD. Analysis of our single-cell data identified 267 cell-type-specific genes. Comparison of macula and peripheral retinal regions found no cell-type differences but did identify 50 differentially expressed genes (DEGs) with about 1/3 expressed in cones. Integration of our single-cell data with bulk RNA sequencing data from normal and AMD donors showed compositional changes more pronounced in macula in rods, microglia, endothelium, Müller glia, and astrocytes in the transition from normal to advanced AMD. KEGG pathway analysis of our normal vs. advanced AMD eyes identified enrichment in complement and coagulation pathways, antigen presentation, tissue remodeling, and signaling pathways including PI3K-Akt, NOD-like, Toll-like, and Rap1. These results showcase the use of single-cell RNA sequencing to infer cell-type compositional and cell-type-specific gene expression changes in intact bulk tissue and provide a foundation for investigating molecular mechanisms of retinal disease that lead to new therapeutic targets.
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http://dx.doi.org/10.1038/s41598-021-95122-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8329233PMC
August 2021

Thymic stromal lymphopoietin induces adipose loss through sebum hypersecretion.

Science 2021 07;373(6554)

Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.

Emerging studies indicate that the immune system can regulate systemic metabolism. Here, we show that thymic stromal lymphopoietin (TSLP) stimulates T cells to induce selective white adipose loss, which protects against obesity, improves glucose metabolism, and mitigates nonalcoholic steatohepatitis. Unexpectedly, adipose loss was not caused by alterations in food intake, absorption, or energy expenditure. Rather, it was induced by the excessive loss of lipids through the skin as sebum. TSLP and T cells regulated sebum release and sebum-associated antimicrobial peptide expression in the steady state. In human skin, expression correlated directly with sebum-associated gene expression. Thus, we establish a paradigm in which adipose loss can be achieved by means of sebum hypersecretion and uncover a role for adaptive immunity in skin barrier function through sebum secretion.
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http://dx.doi.org/10.1126/science.abd2893DOI Listing
July 2021

Identification of 22 susceptibility loci associated with testicular germ cell tumors.

Nat Commun 2021 07 23;12(1):4487. Epub 2021 Jul 23.

Institute of Human Genetics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.

Testicular germ cell tumors (TGCT) are the most common tumor in young white men and have a high heritability. In this study, the international Testicular Cancer Consortium assemble 10,156 and 179,683 men with and without TGCT, respectively, for a genome-wide association study. This meta-analysis identifies 22 TGCT susceptibility loci, bringing the total to 78, which account for 44% of disease heritability. Men with a polygenic risk score (PRS) in the 95 percentile have a 6.8-fold increased risk of TGCT compared to men with median scores. Among men with independent TGCT risk factors such as cryptorchidism, the PRS may guide screening decisions with the goal of reducing treatment-related complications causing long-term morbidity in survivors. These findings emphasize the interconnected nature of two known pathways that promote TGCT susceptibility: male germ cell development within its somatic niche and regulation of chromosomal division and structure, and implicate an additional biological pathway, mRNA translation.
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http://dx.doi.org/10.1038/s41467-021-24334-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8302763PMC
July 2021

Statistical and machine learning methods for spatially resolved transcriptomics with histology.

Comput Struct Biotechnol J 2021 1;19:3829-3841. Epub 2021 Jul 1.

Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA.

Recent developments in spatially resolved transcriptomics (SRT) technologies have enabled scientists to get an integrated understanding of cells in their morphological context. Applications of these technologies in diverse tissues and diseases have transformed our views of transcriptional complexity. Most published studies utilized tools developed for single-cell RNA sequencing (scRNA-seq) for data analysis. However, SRT data exhibit different properties from scRNA-seq. To take full advantage of the added dimension on spatial location information in such data, new methods that are tailored for SRT are needed. Additionally, SRT data often have companion high-resolution histology information available. Incorporating histological features in gene expression analysis is an underexplored area. In this review, we will focus on the statistical and machine learning aspects for SRT data analysis and discuss how spatial location and histology information can be integrated with gene expression to advance our understanding of the transcriptional complexity. We also point out open problems and future research directions in this field.
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http://dx.doi.org/10.1016/j.csbj.2021.06.052DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8273359PMC
July 2021

LIQA: long-read isoform quantification and analysis.

Genome Biol 2021 06 17;22(1):182. Epub 2021 Jun 17.

Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA.

Long-read RNA sequencing (RNA-seq) technologies can sequence full-length transcripts, facilitating the exploration of isoform-specific gene expression over short-read RNA-seq. We present LIQA to quantify isoform expression and detect differential alternative splicing (DAS) events using long-read direct mRNA sequencing or cDNA sequencing data. LIQA incorporates base pair quality score and isoform-specific read length information in a survival model to assign different weights across reads, and uses an expectation-maximization algorithm for parameter estimation. We apply LIQA to long-read RNA-seq data from the Universal Human Reference, acute myeloid leukemia, and esophageal squamous epithelial cells and demonstrate its high accuracy in profiling alternative splicing events.
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http://dx.doi.org/10.1186/s13059-021-02399-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8212471PMC
June 2021

A joint deep learning model enables simultaneous batch effect correction, denoising, and clustering in single-cell transcriptomics.

Genome Res 2021 Oct 25;31(10):1753-1766. Epub 2021 May 25.

Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA.

Recent developments of single-cell RNA-seq (scRNA-seq) technologies have led to enormous biological discoveries. As the scale of scRNA-seq studies increases, a major challenge in analysis is batch effects, which are inevitable in studies involving human tissues. Most existing methods remove batch effects in a low-dimensional embedding space. Although useful for clustering, batch effects are still present in the gene expression space, leaving downstream gene-level analysis susceptible to batch effects. Recent studies have shown that batch effect correction in the gene expression space is much harder than in the embedding space. Methods such as Seurat 3.0 rely on the mutual nearest neighbor (MNN) approach to remove batch effects in gene expression, but MNN can only analyze two batches at a time, and it becomes computationally infeasible when the number of batches is large. Here, we present CarDEC, a joint deep learning model that simultaneously clusters and denoises scRNA-seq data while correcting batch effects both in the embedding and the gene expression space. Comprehensive evaluations spanning different species and tissues showed that CarDEC outperforms Scanorama, DCA + Combat, scVI, and MNN. With CarDEC denoising, non-highly variable genes offer as much signal for clustering as the highly variable genes (HVGs), suggesting that CarDEC substantially boosted information content in scRNA-seq. We also showed that trajectory analysis using CarDEC's denoised and batch-corrected expression as input revealed marker genes and transcription factors that are otherwise obscured in the presence of batch effects. CarDEC is computationally fast, making it a desirable tool for large-scale scRNA-seq studies.
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http://dx.doi.org/10.1101/gr.271874.120DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8494213PMC
October 2021

Potential prognostic biomarkers related to immunity in clear cell renal cell carcinoma using bioinformatic strategy.

Bioengineered 2021 12;12(1):1773-1790

Department of Medical Oncology, The Second Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China.

The clear cell renal cell carcinoma (ccRCC) is the main pathological subtype of renal cell carcinoma. Immune system evasion, one hallmark of cancer, contributes to cancer cells in escaping from the attack of immune cells. In order to identify potential prognostic biomarkers in ccRCC patients and immune cells fraction, we collected and downloaded profiles from The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database. We obtained 2 modules significantly associated with tumor stage and immune cells; functional enrichment analysis showed that genes in the module 'yellow' were significantly enriched in proteins targeting to membrane and ribosome, as well as the oxidative phosphorylation pathway, while genes in the module 'green' mainly participate in molecular functions associated with immunity like activation of T cells. Four LncRNAs (, and ) and and in the module 'yellow' and two lncRNAs ( and ) and five protein-coding genes (, and ) in the module 'green' represented independent prognostic values in patients with ccRCC. Expression of , and were significantly correlated with ratio of immune cells (like T cells CD8 and resting mast cells). , with significant correlation with immune cell fraction, shows potential prognostic value in ccRCC patients. Our findings provide a strategy in exploring biomarkers with prognostic significance and significant association with the fraction of immune cells.
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http://dx.doi.org/10.1080/21655979.2021.1924546DOI Listing
December 2021

Single cell regulatory landscape of the mouse kidney highlights cellular differentiation programs and disease targets.

Nat Commun 2021 04 15;12(1):2277. Epub 2021 Apr 15.

Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA.

Determining the epigenetic program that generates unique cell types in the kidney is critical for understanding cell-type heterogeneity during tissue homeostasis and injury response. Here, we profile open chromatin and gene expression in developing and adult mouse kidneys at single cell resolution. We show critical reliance of gene expression on distal regulatory elements (enhancers). We reveal key cell type-specific transcription factors and major gene-regulatory circuits for kidney cells. Dynamic chromatin and expression changes during nephron progenitor differentiation demonstrates that podocyte commitment occurs early and is associated with sustained Foxl1 expression. Renal tubule cells follow a more complex differentiation, where Hfn4a is associated with proximal and Tfap2b with distal fate. Mapping single nucleotide variants associated with human kidney disease implicates critical cell types, developmental stages, genes, and regulatory mechanisms. The single cell multi-omics atlas reveals key chromatin remodeling events and gene expression dynamics associated with kidney development.
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http://dx.doi.org/10.1038/s41467-021-22266-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8050063PMC
April 2021

Iterative transfer learning with neural network for clustering and cell type classification in single-cell RNA-seq analysis.

Nat Mach Intell 2020 Oct 5;2(10):607-618. Epub 2020 Oct 5.

Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA.

Clustering and cell type classification are important steps in single-cell RNA-seq (scRNA-seq) analysis. As more and more scRNA-seq data are becoming available, supervised cell type classification methods that utilize external well-annotated source data start to gain popularity over unsupervised clustering algorithms. However, the performance of existing supervised methods is highly dependent on source data quality, and they often have limited accuracy to classify cell types that are missing in the source data. To overcome these limitations, we developed ItClust, a transfer learning algorithm that borrows idea from supervised cell type classification algorithms, but also leverages information in target data to ensure sensitivity in classifying cells that are only present in the target data. Through extensive evaluations using data from different species and tissues generated with diverse scRNA-seq protocols, we show that ItClust significantly improves clustering and cell type classification accuracy over popular unsupervised clustering and supervised cell type classification algorithms.
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http://dx.doi.org/10.1038/s42256-020-00233-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8009055PMC
October 2020

Detecting cell-type-specific allelic expression imbalance by integrative analysis of bulk and single-cell RNA sequencing data.

PLoS Genet 2021 03 4;17(3):e1009080. Epub 2021 Mar 4.

Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States of America.

Allelic expression imbalance (AEI), quantified by the relative expression of two alleles of a gene in a diploid organism, can help explain phenotypic variations among individuals. Traditional methods detect AEI using bulk RNA sequencing (RNA-seq) data, a data type that averages out cell-to-cell heterogeneity in gene expression across cell types. Since the patterns of AEI may vary across different cell types, it is desirable to study AEI in a cell-type-specific manner. Although this can be achieved by single-cell RNA sequencing (scRNA-seq), it requires full-length transcript to be sequenced in single cells of a large number of individuals, which are still cost prohibitive to generate. To overcome this limitation and utilize the vast amount of existing disease relevant bulk tissue RNA-seq data, we developed BSCET, which enables the characterization of cell-type-specific AEI in bulk RNA-seq data by integrating cell type composition information inferred from a small set of scRNA-seq samples, possibly obtained from an external dataset. By modeling covariate effect, BSCET can also detect genes whose cell-type-specific AEI are associated with clinical factors. Through extensive benchmark evaluations, we show that BSCET correctly detected genes with cell-type-specific AEI and differential AEI between healthy and diseased samples using bulk RNA-seq data. BSCET also uncovered cell-type-specific AEIs that were missed in bulk data analysis when the directions of AEI are opposite in different cell types. We further applied BSCET to two pancreatic islet bulk RNA-seq datasets, and detected genes showing cell-type-specific AEI that are related to the progression of type 2 diabetes. Since bulk RNA-seq data are easily accessible, BSCET provides a convenient tool to integrate information from scRNA-seq data to gain insight on AEI with cell type resolution. Results from such analysis will advance our understanding of cell type contributions in human diseases.
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http://dx.doi.org/10.1371/journal.pgen.1009080DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7963069PMC
March 2021

Long-term stable reduction of low-density lipoprotein in nonhuman primates following in vivo genome editing of PCSK9.

Mol Ther 2021 06 18;29(6):2019-2029. Epub 2021 Feb 18.

Gene Therapy Program, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA. Electronic address:

Gene disruption via programmable, sequence-specific nucleases represents a promising gene therapy strategy in which the reduction of specific protein levels provides a therapeutic benefit. Proprotein convertase subtilisin/kexin type 9 (PCSK9), an antagonist of the low-density lipoprotein (LDL) receptor, is a suitable target for nuclease-mediated gene disruption as an approach to treat hypercholesterolemia. We sought to determine the long-term durability and safety of PCSK9 knockdown in non-human primate (NHP) liver by adeno-associated virus (AAV)-delivered meganuclease following our initial report on the feasibility of this strategy. Six previously treated NHPs and additional NHPs administered AAV-meganuclease in combination with corticosteroid treatment or an alternative AAV serotype were monitored for a period of up to 3 years. The treated NHPs exhibited a sustained reduction in circulating PCSK9 and LDL cholesterol (LDL-c) through the course of the study concomitant with stable gene editing of the PCSK9 locus. Low-frequency off-target editing remained stable, and no obvious adverse changes in histopathology of the liver were detected. We demonstrate similar on-target nuclease activity in primary human hepatocytes using a chimeric liver-humanized mouse model. These studies demonstrate that targeted in vivo gene disruption exerts a lasting therapeutic effect and provide pivotal data for safety considerations, which support clinical translation.
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http://dx.doi.org/10.1016/j.ymthe.2021.02.020DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8178442PMC
June 2021

Single-cell dissection of intratumoral heterogeneity and lineage diversity in metastatic gastric adenocarcinoma.

Nat Med 2021 01 4;27(1):141-151. Epub 2021 Jan 4.

Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.

Intratumoral heterogeneity (ITH) is a fundamental property of cancer; however, the origins of ITH remain poorly understood. We performed single-cell transcriptome profiling of peritoneal carcinomatosis (PC) from 15 patients with gastric adenocarcinoma (GAC), constructed a map of 45,048 PC cells, profiled the transcriptome states of tumor cell populations, incisively explored ITH of malignant PC cells and identified significant correlates with patient survival. The links between tumor cell lineage/state compositions and ITH were illustrated at transcriptomic, genotypic, molecular and phenotypic levels. We uncovered the diversity in tumor cell lineage/state compositions in PC specimens and defined it as a key contributor to ITH. Single-cell analysis of ITH classified PC specimens into two subtypes that were prognostically independent of clinical variables, and a 12-gene prognostic signature was derived and validated in multiple large-scale GAC cohorts. The prognostic signature appears fundamental to GAC carcinogenesis and progression and could be practical for patient stratification.
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http://dx.doi.org/10.1038/s41591-020-1125-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8074162PMC
January 2021

The Nuclear Receptor ESRRA Protects from Kidney Disease by Coupling Metabolism and Differentiation.

Cell Metab 2021 02 9;33(2):379-394.e8. Epub 2020 Dec 9.

Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USA; Institute for Diabetes, Obesity, and Metabolism, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USA. Electronic address:

Kidney disease is poorly understood because of the organ's cellular diversity. We used single-cell RNA sequencing not only in resolving differences in injured kidney tissue cellular composition but also in cell-type-specific gene expression in mouse models of kidney disease. This analysis highlighted major changes in cellular diversity in kidney disease, which markedly impacted whole-kidney transcriptomics outputs. Cell-type-specific differential expression analysis identified proximal tubule (PT) cells as the key vulnerable cell type. Through unbiased cell trajectory analyses, we show that PT cell differentiation is altered in kidney disease. Metabolism (fatty acid oxidation and oxidative phosphorylation) in PT cells showed the strongest and most reproducible association with PT cell differentiation and disease. Coupling of cell differentiation and the metabolism was established by nuclear receptors (estrogen-related receptor alpha [ESRRA] and peroxisomal proliferation-activated receptor alpha [PPARA]) that directly control metabolic and PT-cell-specific gene expression in mice and patient samples while protecting from kidney disease in the mouse model.
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http://dx.doi.org/10.1016/j.cmet.2020.11.011DOI Listing
February 2021

MicroRNA-mediated inhibition of transgene expression reduces dorsal root ganglion toxicity by AAV vectors in primates.

Sci Transl Med 2020 11;12(569)

Gene Therapy Program, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.

Delivering adeno-associated virus (AAV) vectors into the central nervous system of nonhuman primates (NHPs) via the blood or cerebral spinal fluid is associated with dorsal root ganglion (DRG) toxicity. Conventional immune-suppression regimens do not prevent this toxicity, possibly because it may be caused by high transduction rates, which can, in turn, cause cellular stress due to an overabundance of the transgene product in target cells. To test this hypothesis and develop an approach to eliminate DRG toxicity, we exploited endogenous expression of microRNA (miR) 183 complex, which is largely restricted to DRG neurons, to specifically down-regulate transgene expression in these cells. We introduced sequence targets for miR183 into the vector genome within the 3' untranslated region of the corresponding transgene messenger RNA and injected vectors into the cisterna magna of NHPs. Administration of unmodified AAV vectors resulted in robust transduction of target tissues and toxicity in DRG neurons. Consistent with the proposal that immune system activity does not mediate this neuronal toxicity, we found that steroid administration was ineffective in alleviating this pathology. However, including miR183 targets in the vectors reduced transgene expression in, and toxicity of, DRG neurons without affecting transduction elsewhere in the primate's brain. This approach might be useful in reducing DRG toxicity and the associated morbidity and should facilitate the development of AAV-based gene therapies for many central nervous system diseases.
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http://dx.doi.org/10.1126/scitranslmed.aba9188DOI Listing
November 2020

Single-Cell Genomics Reveals a Novel Cell State During Smooth Muscle Cell Phenotypic Switching and Potential Therapeutic Targets for Atherosclerosis in Mouse and Human.

Circulation 2020 11 23;142(21):2060-2075. Epub 2020 Sep 23.

Division of Cardiology, Department of Medicine (H.P., C.X., A.C.B., J.C., D.Y.Y., S.B.T., W.L., J.S., C.O.I., H.Z., M.P.R.), Columbia University Irving Medical Center, New York.

Background: Smooth muscle cells (SMCs) play significant roles in atherosclerosis via phenotypic switching, a pathological process in which SMC dedifferentiation, migration, and transdifferentiation into other cell types. Yet how SMCs contribute to the pathophysiology of atherosclerosis remains elusive.

Methods: To reveal the trajectories of SMC transdifferentiation during atherosclerosis and to identify molecular targets for disease therapy, we combined SMC fate mapping and single-cell RNA sequencing of both mouse and human atherosclerotic plaques. We also performed cell biology experiments on isolated SMC-derived cells, conducted integrative human genomics, and used pharmacological studies targeting SMC-derived cells both in vivo and in vitro.

Results: We found that SMCs transitioned to an intermediate cell state during atherosclerosis, which was also found in human atherosclerotic plaques of carotid and coronary arteries. SMC-derived intermediate cells, termed "SEM" cells (stem cell, endothelial cell, monocyte), were multipotent and could differentiate into macrophage-like and fibrochondrocyte-like cells, as well as return toward the SMC phenotype. Retinoic acid (RA) signaling was identified as a regulator of SMC to SEM cell transition, and RA signaling was dysregulated in symptomatic human atherosclerosis. Human genomics revealed enrichment of genome-wide association study signals for coronary artery disease in RA signaling target gene loci and correlation between coronary artery disease risk alleles and repressed expression of these genes. Activation of RA signaling by all-trans RA, an anticancer drug for acute promyelocytic leukemia, blocked SMC transition to SEM cells, reduced atherosclerotic burden, and promoted fibrous cap stability.

Conclusions: Integration of cell-specific fate mapping, single-cell genomics, and human genetics adds novel insights into the complexity of SMC biology and reveals regulatory pathways for therapeutic targeting of SMC transitions in atherosclerotic cardiovascular disease.
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http://dx.doi.org/10.1161/CIRCULATIONAHA.120.048378DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8104264PMC
November 2020

Assessment of Probable Opioid Use Disorder Using Electronic Health Record Documentation.

JAMA Netw Open 2020 09 1;3(9):e2015909. Epub 2020 Sep 1.

Geisinger Clinic, Geisinger, Danville, Pennsylvania.

Importance: Electronic health records are a potentially valuable source of information for identifying patients with opioid use disorder (OUD).

Objective: To evaluate whether proxy measures from electronic health record data can be used reliably to identify patients with probable OUD based on Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition) (DSM-5) criteria.

Design, Setting, And Participants: This retrospective cross-sectional study analyzed individuals within the Geisinger health system who were prescribed opioids between December 31, 2000, and May 31, 2017, using a mixed-methods approach. The cohort was identified from 16 253 patients enrolled in a contract-based, Geisinger-specific medication monitoring program (GMMP) for opioid use, including patients who maintained or violated contract terms, as well as a demographically matched control group of 16 253 patients who were prescribed opioids but not enrolled in the GMMP. Substance use diagnoses and psychiatric comorbidities were assessed using automated electronic health record summaries. A manual medical record review procedure using DSM-5 criteria for OUD was completed for a subset of patients. The analysis was conducted beginning from June 5, 2017, until May 29, 2020.

Main Outcomes And Measures: The primary outcome was the prevalence of OUD as defined by proxy measures for DSM-5 criteria for OUD as well as the prevalence of comorbidities among patients prescribed opioids within an integrated health system.

Results: Among the 16 253 patients enrolled in the GMMP (9309 women [57%]; mean [SD] age, 52 [14] years), OUD diagnoses as defined by diagnostic codes were present at a much lower rate than expected (291 [2%]), indicating the necessity for alternative diagnostic strategies. The DSM-5 criteria for OUD can be assessed using manual medical record review; a manual review of 200 patients in the GMMP and 200 control patients identifed a larger percentage of patients with probable moderate to severe OUD (GMMP, 145 of 200 [73%]; and control, 27 of 200 [14%]) compared with the prevalence of OUD assessed using diagnostic codes.

Conclusions And Relevance: These results suggest that patients with OUD may be identified using information available in the electronic health record, even when diagnostic codes do not reflect this diagnosis. Furthermore, the study demonstrates the utility of coding for DSM-5 criteria from medical records to generate a quantitative DSM-5 score that is associated with OUD severity.
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http://dx.doi.org/10.1001/jamanetworkopen.2020.15909DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7489858PMC
September 2020

Adeno-Associated Virus-Induced Dorsal Root Ganglion Pathology.

Hum Gene Ther 2020 08 31;31(15-16):808-818. Epub 2020 Jul 31.

Gene Therapy Program, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.

The administration of adeno-associated virus (AAV) vectors to nonhuman primates (NHP) via the blood or cerebrospinal fluid (CSF) can lead to dorsal root ganglion (DRG) pathology. The pathology is minimal to moderate in most cases; clinically silent in affected animals; and characterized by mononuclear cell infiltrates, neuronal degeneration, and secondary axonopathy of central and peripheral axons on histopathological analysis. We aggregated data from 33 nonclinical studies in 256 NHP and performed a meta-analysis of the severity of DRG pathology to compare different routes of administration, dose, time course, study conduct, age of the animals, sex, capsid, promoter, capsid purification method, and transgene. DRG pathology was observed in 83% of NHP that were administered AAV through the CSF, and 32% of NHP that received an intravenous (IV) injection. We show that dose and age at injection significantly affected the severity whereas sex had no impact. DRG pathology was minimal at acute time points (., <14 days), similar from one to 5 months post-injection, and was less severe after 6 months. Vector purification method had no impact, and all capsids and promoters that we tested resulted in some DRG pathology. The data presented here from five different capsids, five different promoters, and 20 different transgenes suggest that DRG pathology is almost universal after AAV gene therapy in nonclinical studies using NHP. None of the animals receiving a therapeutic transgene displayed any clinical signs. Incorporation of sensitive techniques such as nerve-conduction velocity testing can show alterations in a minority of animals that correlate with the severity of peripheral nerve axonopathy. Monitoring sensory neuropathies in human central nervous system and high-dose IV clinical studies seems prudent to determine the functional consequences of DRG pathology.
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http://dx.doi.org/10.1089/hum.2020.167DOI Listing
August 2020

APOE and TREM2 regulate amyloid-responsive microglia in Alzheimer's disease.

Acta Neuropathol 2020 10 25;140(4):477-493. Epub 2020 Aug 25.

Translational Neuropathology Research Laboratory, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, 613A Stellar Chance Laboratories, 422 Curie Blvd, Philadelphia, PA, 19104, USA.

Beta-amyloid deposition is a defining feature of Alzheimer's disease (AD). How genetic risk factors, like APOE and TREM2, intersect with cellular responses to beta-amyloid in human tissues is not fully understood. Using single-nucleus RNA sequencing of postmortem human brain with varied APOE and TREM2 genotypes and neuropathology, we identified distinct microglia subpopulations, including a subpopulation of CD163-positive amyloid-responsive microglia (ARM) that are depleted in cases with APOE and TREM2 risk variants. We validated our single-nucleus RNA sequencing findings in an expanded cohort of AD cases, demonstrating that APOE and TREM2 risk variants are associated with a significant reduction in CD163-positive amyloid-responsive microglia. Our results showcase the diverse microglial response in AD and underscore how genetic risk factors influence cellular responses to underlying pathologies.
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http://dx.doi.org/10.1007/s00401-020-02200-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7520051PMC
October 2020

Profiling Nephropathy Risk Variants in Genome-Edited Kidney Organoids with Single-Cell Transcriptomics.

Kidney360 2020 Mar;1(3):203-215

Division of Nephrology and Hypertension, Department of Medicine, Feinberg Cardiovascular and Renal Research Institute, Northwestern University Feinberg School of Medicine, Chicago, Illinois.

Background: DNA variants in associate with kidney disease, but the pathophysiologic mechanisms remain incompletely understood. Model organisms lack the gene, limiting the degree to which disease states can be recapitulated. Here we present single-cell RNA sequencing (scRNA-seq) of genome-edited human kidney organoids as a platform for profiling effects of risk variants in diverse nephron cell types.

Methods: We performed footprint-free CRISPR-Cas9 genome editing of human induced pluripotent stem cells (iPSCs) to knock in high-risk G1 variants at the native genomic locus. iPSCs were differentiated into kidney organoids, treated with vehicle, IFN-, or the combination of IFN- and tunicamycin, and analyzed with scRNA-seq to profile cell-specific changes in differential gene expression patterns, compared with isogenic G0 controls.

Results: Both G0 and G1 iPSCs differentiated into kidney organoids containing nephron-like structures with glomerular epithelial cells, proximal tubules, distal tubules, and endothelial cells. Organoids expressed detectable only after exposure to IFN-. scRNA-seq revealed cell type-specific differences in G1 organoid response to induction. Additional stress of tunicamycin exposure led to increased glomerular epithelial cell dedifferentiation in G1 organoids.

Conclusions: Single-cell transcriptomic profiling of human genome-edited kidney organoids expressing risk variants provides a novel platform for studying the pathophysiology of APOL1-mediated kidney disease.
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http://dx.doi.org/10.34067/kid.0000422019DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7351353PMC
March 2020

Detecting differential alternative splicing events in scRNA-seq with or without Unique Molecular Identifiers.

PLoS Comput Biol 2020 06 5;16(6):e1007925. Epub 2020 Jun 5.

Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States of America.

The emergence of single-cell RNA-seq (scRNA-seq) technology has made it possible to measure gene expression variations at cellular level. This breakthrough enables the investigation of a wider range of problems including analysis of splicing heterogeneity among individual cells. However, compared to bulk RNA-seq, scRNA-seq data are much noisier due to high technical variability and low sequencing depth. Here we propose SCATS (Single-Cell Analysis of Transcript Splicing) for differential splicing analysis in scRNA-seq, which achieves high sensitivity at low coverage by accounting for technical noise. SCATS models scRNA-seq data either with or without Unique Molecular Identifiers (UMIs). For non-UMI data, SCATS explicitly models technical noise by accounting for capture efficiency and amplification bias through the use of external spike-ins; for UMI data, SCATS models capture efficiency and further accounts for transcriptional burstiness. A key aspect of SCATS lies in its ability to group "exons" that originate from the same isoform(s). Grouping exons is essential in splicing analysis of scRNA-seq data as it naturally aggregates spliced reads across different exons, making it possible to detect splicing events even when sequencing depth is low. To evaluate the performance of SCATS, we analyzed both simulated and real scRNA-seq datasets and compared with existing methods including Census and DEXSeq. We show that SCATS has well controlled type I error rate, and is more powerful than existing methods, especially when splicing difference is small. In contrast, Census suffers from severe type I error inflation, whereas DEXSeq is more conservative. When applied to mouse brain scRNA-seq datasets, SCATS identified more differential splicing events with subtle difference across cell types compared to Census and DEXSeq. With the increasing adoption of scRNA-seq, we believe SCATS will be well-suited for various splicing studies. The implementation of SCATS can be downloaded from https://github.com/huyustats/SCATS.
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http://dx.doi.org/10.1371/journal.pcbi.1007925DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7299405PMC
June 2020

Single-Cell RNA Sequencing to Dissect the Immunological Network of Autoimmune Myocarditis.

Circulation 2020 07 20;142(4):384-400. Epub 2020 May 20.

State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (X.H., Y.C., Y.H., X.C., J.S.).

Background: Myocarditis can develop into dilated cardiomyopathy, which may require heart transplantation. The immunological network of myocarditis phases remains unknown. This study aimed to investigate the immunological network during the transition from myocarditis to cardiomyopathy and to identify the genes contributing to the inflammatory response to myocarditis.

Methods: Mice were treated with myosin heavy chain-α peptides to generate an experimental autoimmune myocarditis (EAM) model. We performed single-cell RNA sequencing analysis of cells extracted from mouse hearts during different EAM phases, including normal control, acute inflammatory, subacute inflammatory, and myopathy phases. Human heart tissues were collected from the surgically removed hearts of patients who had undergone heart transplantation.

Results: We identified 26 cell subtypes among 34 665 cells. Macrophages constituted the main immune cell population at all disease phases (>60%), and an inflammation-associated macrophage cluster was identified in which the expression of -regulated genes was upregulated. The neutrophil population was increased after the induction of EAM, and neutrophils then released to participate in the EAM process. T cells were observed at the highest percentage at the subacute inflammatory phase. T-helper 17 cells, in which the expression of -regulated genes was upregulated, constituted the main T-cell population detected at the acute inflammatory phase, whereas regulatory T cells were the main T-cell population detected at the subacute inflammatory phase, and γδ T cells releasing were the main T-cell population observed at the myopathy phase. Moreover, the expression level correlated with the extent of inflammation. In addition, PX-478 could alleviate the inflammatory responses of the different EAM phases. Last, was expressed at higher levels in patients with acute autoimmune myocarditis than in patients with dilated cardiomyopathy and healthy control subjects.

Conclusions: We present here a comprehensive single-cell landscape of the cardiac immune cells in different EAM phases. In addition, we elucidate the contribution of to the inflammatory response through the regulation of immune cell activity, particularly of macrophage cluster 2 and T-helper 17 cells. Moreover, an inhibitor alleviated inflammatory cell infiltration of the EAM model and may serve as a potential therapeutic target in the clinic.
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http://dx.doi.org/10.1161/CIRCULATIONAHA.119.043545DOI Listing
July 2020

Deep learning enables accurate clustering with batch effect removal in single-cell RNA-seq analysis.

Nat Commun 2020 05 11;11(1):2338. Epub 2020 May 11.

Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.

Single-cell RNA sequencing (scRNA-seq) can characterize cell types and states through unsupervised clustering, but the ever increasing number of cells and batch effect impose computational challenges. We present DESC, an unsupervised deep embedding algorithm that clusters scRNA-seq data by iteratively optimizing a clustering objective function. Through iterative self-learning, DESC gradually removes batch effects, as long as technical differences across batches are smaller than true biological variations. As a soft clustering algorithm, cluster assignment probabilities from DESC are biologically interpretable and can reveal both discrete and pseudotemporal structure of cells. Comprehensive evaluations show that DESC offers a proper balance of clustering accuracy and stability, has a small footprint on memory, does not explicitly require batch information for batch effect removal, and can utilize GPU when available. As the scale of single-cell studies continues to grow, we believe DESC will offer a valuable tool for biomedical researchers to disentangle complex cellular heterogeneity.
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http://dx.doi.org/10.1038/s41467-020-15851-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7214470PMC
May 2020

ASEP: Gene-based detection of allele-specific expression across individuals in a population by RNA sequencing.

PLoS Genet 2020 05 11;16(5):e1008786. Epub 2020 May 11.

Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States of America.

Allele-specific expression (ASE) analysis, which quantifies the relative expression of two alleles in a diploid individual, is a powerful tool for identifying cis-regulated gene expression variations that underlie phenotypic differences among individuals. Existing methods for gene-level ASE detection analyze one individual at a time, therefore failing to account for shared information across individuals. Failure to accommodate such shared information not only reduces power, but also makes it difficult to interpret results across individuals. However, when only RNA sequencing (RNA-seq) data are available, ASE detection across individuals is challenging because the data often include individuals that are either heterozygous or homozygous for the unobserved cis-regulatory SNP, leading to sample heterogeneity as only those heterozygous individuals are informative for ASE, whereas those homozygous individuals have balanced expression. To simultaneously model multi-individual information and account for such heterogeneity, we developed ASEP, a mixture model with subject-specific random effect to account for multi-SNP correlations within the same gene. ASEP only requires RNA-seq data, and is able to detect gene-level ASE under one condition and differential ASE between two conditions (e.g., pre- versus post-treatment). Extensive simulations demonstrated the convincing performance of ASEP under a wide range of scenarios. We applied ASEP to a human kidney RNA-seq dataset, identified ASE genes and validated our results with two published eQTL studies. We further applied ASEP to a human macrophage RNA-seq dataset, identified genes showing evidence of differential ASE between M0 and M1 macrophages, and confirmed our findings by results from cardiometabolic trait-relevant genome-wide association studies. To the best of our knowledge, ASEP is the first method for gene-level ASE detection at the population level that only requires the use of RNA-seq data. With the growing adoption of RNA-seq, we believe ASEP will be well-suited for various ASE studies for human diseases.
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http://dx.doi.org/10.1371/journal.pgen.1008786DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7241832PMC
May 2020

Single cell transcriptomics identifies a unique adipose lineage cell population that regulates bone marrow environment.

Elife 2020 04 14;9. Epub 2020 Apr 14.

Department of Orthopaedic Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States.

Bone marrow mesenchymal lineage cells are a heterogeneous cell population involved in bone homeostasis and diseases such as osteoporosis. While it is long postulated that they originate from mesenchymal stem cells, the true identity of progenitors and their in vivo bifurcated differentiation routes into osteoblasts and adipocytes remain poorly understood. Here, by employing large scale single cell transcriptome analysis, we computationally defined mesenchymal progenitors at different stages and delineated their bi-lineage differentiation paths in young, adult and aging mice. One identified subpopulation is a unique cell type that expresses adipocyte markers but contains no lipid droplets. As non-proliferative precursors for adipocytes, they exist abundantly as pericytes and stromal cells that form a ubiquitous 3D network inside the marrow cavity. Functionally they play critical roles in maintaining marrow vasculature and suppressing bone formation. Therefore, we name them marrow adipogenic lineage precursors (MALPs) and conclude that they are a newly identified component of marrow adipose tissue.
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http://dx.doi.org/10.7554/eLife.54695DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7220380PMC
April 2020

Different risk factors in identical features of intracranial atherosclerosis plaques in the posterior and anterior circulation in high-resolution MRI.

Ther Adv Neurol Disord 2020 13;13:1756286420909991. Epub 2020 Mar 13.

Department of Interventional Neuroradiology, Beijing Tiantan Hospital, Capital Medical University, China National Clinical Research Center for Neurological Diseases, No.119 Nansihuanxilu, Fengtai District, Beijing 100070, China.

Background: We constructed a high-volume registry to identify whether risk factors of intracranial atherosclerotic plaque (ICAP) features differ in the posterior and anterior circulation in patients with symptomatic intracranial atherosclerotic stenosis (ICAS) investigated by high-resolution magnetic resonance imaging (HRMRI).

Methods: The registry was constructed for patients with symptomatic ICAS who underwent HRMRI for culprit plaques. ICAP-vulnerable features included positive remodelling, diffuse distribution, intraplaque haemorrhage and strong enhancement.

Results: We analysed risk factors for the same ICAP features between the posterior and anterior circulation in data of 97 patients in the posterior circulation and 105 patients in the anterior circulation ICAPs. In patients with diffuse distribution, the probability of being female were lower [odds ratio (OR):0.08; 95% confidence interval (CI):0.02-0.34;  = 0.001] and having diabetes mellitus was higher (OR: 7.75; 95% CI:1.75-34.39;  = 0.007) in posterior circulation patients. In patients with strong enhancement, the probability of having diabetes was higher in posterior circulation patients (OR:6.71; 95% CI:1.37-32.81;  = 0.019).

Conclusions: Our results demonstrate more risk factors in the posterior than in the anterior circulation in patients with the same ICAP-vulnerable features, highlighting the need for stratification of risk factors in symptomatic ICAPs.

Trial Registration: URL: http://www.clinicaltrials.gov. Unique identifier: NCT02705599.
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http://dx.doi.org/10.1177/1756286420909991DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7074472PMC
March 2020
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