Transcriptional and Cellular Diversity of the Human Heart

Background: The human heart requires a complex ensemble of specialized cell types to perform its essential function. A greater knowledge of the intricate cellular milieu of the heart is critical to increase our understanding of cardiac homeostasis and pathology. As recent advances in low-input RNA sequencing have allowed definitions of cellular transcriptomes at single-cell resolution at scale, we have applied these approaches to assess the cellular and transcriptional diversity of the nonfailing human heart. Methods: Microfluidic encapsulation and barcoding was used to perform single nuclear RNA sequencing with samples from 7 human donors, selected for their absence of overt cardiac disease. Individual nuclear transcriptomes were then clustered based on transcriptional profiles of highly variable genes. These clusters were used as the basis for between-chamber and between-sex differential gene expression analyses and intersection with genetic and pharmacologic data. Results: We sequenced the transcriptomes of 287 269 single cardiac nuclei, revealing 9 major cell types and 20 subclusters of cell types within the human heart. Cellular subclasses include 2 distinct groups of resident macrophages, 4 endothelial subtypes, and 2 fibroblast subsets. Comparisons of cellular transcriptomes by cardiac chamber or sex reveal diversity not only in cardiomyocyte transcriptional programs but also in subtypes involved in extracellular matrix remodeling and vascularization. Using genetic association data, we identified strong enrichment for the role of cell subtypes in cardiac traits and diseases. Intersection of our data set with genes on cardiac clinical testing panels and the druggable genome reveals striking patterns of cellular specificity. Conclusions: Using large-scale single nuclei RNA sequencing, we defined the transcriptional and cellular diversity in the normal human heart. Our identification of discrete cell subtypes and differentially expressed genes within the heart will ultimately facilitate the development of new therapeutics for cardiovascular diseases.

Download Full-text

Transcriptional and Cellular Diversity of the Human Heart

10.1101/2020.01.06.896076 ◽

2020 ◽

Cited By ~ 2

Author(s):

Nathan R. Tucker ◽

Mark Chaffin ◽

Stephen J. Fleming ◽

Amelia W. Hall ◽

Victoria A. Parsons ◽

...

Keyword(s):

Rna Sequencing ◽

Human Heart ◽

Large Scale ◽

Cell Types ◽

Matrix Remodeling ◽

Cellular Specificity ◽

Normal Human ◽

Differential Gene ◽

Association Data

AbstractIntroductionThe human heart requires a complex ensemble of specialized cell types to perform its essential function. A greater knowledge of the intricate cellular milieu of the heart is critical to increase our understanding of cardiac homeostasis and pathology. As recent advances in low input RNA-sequencing have allowed definitions of cellular transcriptomes at single cell resolution at scale, here we have applied these approaches to assess the cellular and transcriptional diversity of the non-failing human heart.MethodsMicrofluidic encapsulation and barcoding was used to perform single nuclear RNA sequencing with samples from seven human donors, selected for their absence of overt cardiac disease. Individual nuclear transcriptomes were then clustered based upon transcriptional profiles of highly variable genes. These clusters were used as the basis for between-chamber and between-sex differential gene expression analyses and intersection with genetic and pharmacologic dataResultsWe sequenced the transcriptomes of 287,269 single cardiac nuclei, revealing a total of 9 major cell types and 20 subclusters of cell types within the human heart. Cellular subclasses include two distinct groups of resident macrophages, four endothelial subtypes, and two fibroblasts subsets. Comparisons of cellular transcriptomes by cardiac chamber or sex reveal diversity not only in cardiomyocyte transcriptional programs, but also in subtypes involved in extracellular matrix remodeling and vascularization. Using genetic association data, we identified strong enrichment for the role of cell subtypes in cardiac traits and diseases. Finally, intersection of our dataset with genes on cardiac clinical testing panels and the druggable genome reveals striking patterns of cellular specificity.ConclusionsUsing large-scale single nuclei RNA sequencing, we have defined the transcriptional and cellular diversity in the normal human heart. Our identification of discrete cell subtypes and differentially expressed genes within the heart will ultimately facilitate the development of new therapeutics for cardiovascular diseases.

Download Full-text

Production, crystallization and preliminary X-ray analysis of the human integrin \alpha_1 I domain

Acta Crystallographica Section D Biological Crystallography ◽

10.1107/s0907444999006009 ◽

1999 ◽

Vol 55 (7) ◽

pp. 1365-1367 ◽

Cited By ~ 10

Author(s):

Tiina A. Salminen ◽

Yvonne Nymalm ◽

Jussi Kankare ◽

Jarmo Käpylä ◽

Jyrki Heino ◽

...

Keyword(s):

Large Scale ◽

Cell Types ◽

Diffusion Method ◽

Scale Production ◽

Data Set ◽

Cell Parameters ◽

Collagen Receptors ◽

Large Scale Production ◽

Peg 4000 ◽

Reservoir Solution

Integrin α1β1 is one of the main collagen receptors in many cell types. A fast large-scale production, purification and crystallization method for the integrin α1 I domain is reported here. The α1 I domain was crystallized using the vapour-diffusion method with a reservoir solution containing a mixture of PEG 4000, sodium acetate, glycerol and Tris–HCl buffer. The crystals beong to the C2 space group, with unit-cell parameters a = 74.5, b = 81.9, c = 37.3 Å, α = γ = 90.0, β = 90.8°. The crystals diffract to 2.0 Å and a 94.2% complete data set to 2.2 Å has been collected from a single crystal with an R merge of 5.8%.

Download Full-text

Information flow, cell types and stereotypy in a full olfactory connectome

10.1101/2020.12.15.401257 ◽

2020 ◽

Cited By ~ 1

Author(s):

Philipp Schlegel ◽

Alexander Shakeel Bates ◽

Tomke Stürner ◽

Sridhar R. Jagannathan ◽

Nikolas Drummond ◽

...

Keyword(s):

Information Flow ◽

Olfactory System ◽

Mushroom Body ◽

Antennal Lobe ◽

Large Scale ◽

Cell Types ◽

Neuronal Morphology ◽

Striking Similarity ◽

Data Set ◽

Lateral Horn

AbstractThe hemibrain connectome (Scheffer et al., 2020) provides large scale connectivity and morphology information for the majority of the central brain of Drosophila melanogaster. Using this data set, we provide a complete description of the most complex olfactory system studied at synaptic resolution to date, covering all first, second and third-order neurons of the olfactory system associated with the antennal lobe and lateral horn (mushroom body neurons are described in a parallel paper, (Li et al., 2020)). We develop a generally applicable strategy to extract information flow and layered organisation from synaptic resolution connectome graphs, mapping olfactory input to descending interneurons. This identifies a range of motifs including highly lateralised circuits in the antennal lobe and patterns of convergence downstream of the mushroom body and lateral horn. We also leverage a second data set (FAFB, (Zheng et al., 2018)) to provide a first quantitative assessment of inter- versus intra-individual stereotypy. Complete reconstruction of select developmental lineages in two brains (three brain hemispheres) reveals striking similarity in neuronal morphology across brains for >170 cell types. Within and across brains, connectivity correlates with morphology. Notably, neurons of the same morphological type show similar connection variability within one brain as across brains; this property should enable a rigorous quantitative approach to cell typing.

Download Full-text

deepMNN: Deep Learning-Based Single-Cell RNA Sequencing Data Batch Correction Using Mutual Nearest Neighbors

Frontiers in Genetics ◽

10.3389/fgene.2021.708981 ◽

2021 ◽

Vol 12 ◽

Author(s):

Bin Zou ◽

Tongda Zhang ◽

Ruilong Zhou ◽

Xiaosen Jiang ◽

Huanming Yang ◽

...

Keyword(s):

Deep Learning ◽

Single Cell ◽

Rna Sequencing ◽

Large Scale ◽

Cell Types ◽

Batch Effect ◽

Batch Effects ◽

Batch Correction ◽

Single Cell Rna Sequencing ◽

Identical Cell

It is well recognized that batch effect in single-cell RNA sequencing (scRNA-seq) data remains a big challenge when integrating different datasets. Here, we proposed deepMNN, a novel deep learning-based method to correct batch effect in scRNA-seq data. We first searched mutual nearest neighbor (MNN) pairs across different batches in a principal component analysis (PCA) subspace. Subsequently, a batch correction network was constructed by stacking two residual blocks and further applied for the removal of batch effects. The loss function of deepMNN was defined as the sum of a batch loss and a weighted regularization loss. The batch loss was used to compute the distance between cells in MNN pairs in the PCA subspace, while the regularization loss was to make the output of the network similar to the input. The experiment results showed that deepMNN can successfully remove batch effects across datasets with identical cell types, datasets with non-identical cell types, datasets with multiple batches, and large-scale datasets as well. We compared the performance of deepMNN with state-of-the-art batch correction methods, including the widely used methods of Harmony, Scanorama, and Seurat V4 as well as the recently developed deep learning-based methods of MMD-ResNet and scGen. The results demonstrated that deepMNN achieved a better or comparable performance in terms of both qualitative analysis using uniform manifold approximation and projection (UMAP) plots and quantitative metrics such as batch and cell entropies, ARI F1 score, and ASW F1 score under various scenarios. Additionally, deepMNN allowed for integrating scRNA-seq datasets with multiple batches in one step. Furthermore, deepMNN ran much faster than the other methods for large-scale datasets. These characteristics of deepMNN made it have the potential to be a new choice for large-scale single-cell gene expression data analysis.

Download Full-text

Large scale similarity search across digital reconstructions of neural morphology

10.1101/2021.12.17.473026 ◽

2021 ◽

Author(s):

Bengt Ljungquist ◽

Masood A Akram ◽

Giorgio A Ascoli

Keyword(s):

Similarity Search ◽

Large Scale ◽

Principal Component ◽

Software Tool ◽

Cell Types ◽

Brain Regions ◽

Morphological Comparison ◽

Data Set ◽

Qualitative Performance ◽

The Impact

Most functions of the nervous system depend on neuronal and glial morphology. Continuous advances in microscopic imaging and tracing software have provided an increasingly abundant availability of 3D reconstructions of arborizing dendrites, axons, and processes, allowing their detailed study. However, efficient, large-scale methods to rank neural morphologies by similarity to an archetype are still lacking. Using the NeuroMorpho.Org database, we present a similarity search software enabling fast morphological comparison of hundreds of thousands of neural reconstructions from any species, brain regions, cell types, and preparation protocols. We compared the performance of different morphological measurements: 1) summary morphometrics calculated by L-Measure, 2) persistence vectors, a vectorized descriptor of branching structure, 3) the combination of the two. In all cases, we also investigated the impact of applying dimensionality reduction using principal component analysis (PCA). We assessed qualitative performance by gauging the ability to rank neurons in order of visual similarity. Moreover, we quantified information content by examining explained variance and benchmarked the ability to identify occasional duplicate reconstructions of the same specimen. The results indicate that combining summary morphometrics and persistence vectors with applied PCA provides an information rich characterization that enables efficient and precise comparison of neural morphology. The execution time scaled linearly with data set size, allowing seamless live searching through the entire NeuroMorpho.Org content in fractions of a second. We have deployed the similarity search function as an open-source online software tool both through a user-friendly graphical interface and as an API for programmatic access.

Download Full-text

A Method for Cryopreservation and Single Nucleus RNA-sequencing of Normal Adult Human Interventricular Septum Heart Tissue Reveals Cellular Diversity and Function

10.21203/rs.3.rs-136136/v1 ◽

2020 ◽

Author(s):

Amy Larson ◽

Michael T. Chin

Keyword(s):

Single Cell ◽

Human Heart ◽

Expression Patterns ◽

Interventricular Septum ◽

Cell Types ◽

Heart Tissue ◽

Gene Expression Patterns ◽

High Quality ◽

Normal Human ◽

Human Heart Tissue

Abstract Background: Single cell sequencing of human heart tissue is technically challenging and methods to cryopreserve heart tissue for obtaining single cell information have not been standardized. Studies published to date have used varying methods to preserve and process human heart tissue, and have generated interesting datasets, but development of a biobanking standard has not yet been achieved. Heart transcription patterns are known to be regionally diverse, and there are few single cell datasets for normal human heart tissue. Methods: Using pig tissue, we developed a rigorous and reproducible method for tissue mincing and cryopreservation that allowed recovery of high quality single nuclei RNA. We subsequently tested this protocol on normal human heart tissue obtained from organ donors and were able to recover high quality nuclei for generation of single nuclei RNA-seq datasets, using a commercially available platform from 10x Genomics. We analyzed these datasets using standard software packages such as CellRanger and Seurat. Results: Human heart tissue preserved with our method consistently yielded nuclear RNA with RNA Integrity Numbers of greater than 8.5. We demonstrate the utility of this method for single nuclei RNA-sequencing of the normal human interventricular septum and delineating its cellular diversity. The human IVS showed unexpected diversity with detection of 23 distinct cell clusters that were subsequently categorized into different cell types. Cardiomyocytes and fibroblasts were the most commonly identified cell types and could be further subdivided into 5 different cardiomyocyte subtypes and 6 different fibroblast subtypes that differed by gene expression patterns. Ingenuity Pathway analysis of these gene expression patterns suggested functional diversity in these cell subtypes. Conclusions: Here we report a simple technical method for cryopreservation and subsequent nuclear isolation of human interventricular septum tissue that can be done with common laboratory equipment. We show how this method can be used to generate single nuclei transcriptomic datasets that rival those already published by larger groups in terms of cell diversity and complexity and suggest that this simple method can provide guidance for biobanking of human myocardial tissue for complex genomic analysis.

Download Full-text

The single-cell eQTLGen consortium

eLife ◽

10.7554/elife.52155 ◽

2020 ◽

Vol 9 ◽

Cited By ~ 18

Author(s):

MGP van der Wijst ◽

DH de Vries ◽

HE Groot ◽

G Trynka ◽

CC Hon ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Large Scale ◽

Cell Types ◽

Eqtl Analysis ◽

Sequencing Data ◽

Scale Population ◽

Trait Locus ◽

Different Cell Types ◽

Affect Gene Expression

In recent years, functional genomics approaches combining genetic information with bulk RNA-sequencing data have identified the downstream expression effects of disease-associated genetic risk factors through so-called expression quantitative trait locus (eQTL) analysis. Single-cell RNA-sequencing creates enormous opportunities for mapping eQTLs across different cell types and in dynamic processes, many of which are obscured when using bulk methods. Rapid increase in throughput and reduction in cost per cell now allow this technology to be applied to large-scale population genetics studies. To fully leverage these emerging data resources, we have founded the single-cell eQTLGen consortium (sc-eQTLGen), aimed at pinpointing the cellular contexts in which disease-causing genetic variants affect gene expression. Here, we outline the goals, approach and potential utility of the sc-eQTLGen consortium. We also provide a set of study design considerations for future single-cell eQTL studies.

Download Full-text

Detecting and profiling tissue-selective genes

Physiological Genomics ◽

10.1152/physiolgenomics.00313.2005 ◽

2006 ◽

Vol 26 (2) ◽

pp. 158-162 ◽

Cited By ~ 35

Author(s):

Shuang Liang ◽

Yizheng Li ◽

Xiaobing Be ◽

Steve Howes ◽

Wei Liu

Keyword(s):

Drug Targets ◽

Tissue Specificity ◽

Statistical Approach ◽

Cell Types ◽

Expression Data ◽

Data Set ◽

Normal Human ◽

Tissue Selectivity ◽

Error Rate Control ◽

Shed Light

The widespread use of DNA microarray technologies has generated large amounts of data from various tissue and/or cell types. These data set the stage to answer the question of tissue specificity of human transcriptome in a comprehensive manner. Our focus is to uncover the tissue-gene relationship by identifying genes that are preferentially expressed in a small number of tissue types. The tissue selectivity would shed light on the potential physiological functions of these genes and provides an indispensable reference to compare against disease pathophysiology and to identify or validate tissue-specific drug targets. Here we describe a systematic computational and statistical approach to profile gene expression data to identify tissue-selective genes with the use of a more extensive data set and a well-established multiple-comparison procedure with error rate control. Expression data of 35,152 probe sets in 97 normal human tissue types were analyzed, and 3,919 genes were identified to be selective to one or a few tissue types. We presented results of these tissue-selective genes and compared them to those identified by other studies.

Download Full-text

Single-cell RNA sequencing of adultDrosophilaovary identifies transcriptional programs governing oogenesis

10.1101/802314 ◽

2019 ◽

Author(s):

Allison Jevitt ◽

Deeptiman Chatterjee ◽

Gengqiang Xie ◽

Xian-Feng Wang ◽

Taylor Otwell ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Large Scale ◽

Developmental Process ◽

Expression Profiles ◽

Follicle Cell ◽

Cell Types ◽

Broad Perspective ◽

Single Cell Rna Sequencing ◽

Egg Shell

AbstractOogenesis is a complex developmental process that involves spatiotemporally regulated coordination between the germline and supporting, somatic cell populations. This process has been modelled extensively using theDrosophilaovary. While different ovarian cell types have been identified through traditional means, the large-scale expression profiles underlying each cell type remain unknown. Using single-cell RNA sequencing technology, we have built a transcriptomic dataset for the adultDrosophilaovary and connected tissues. This dataset captures the entire transcriptional trajectory of the developing follicle cell population over time. Our findings provide detailed insight into processes such as cell-cycle switching, migration, symmetry breaking, nurse cell engulfment, egg-shell formation, and signaling during corpus luteum formation, marking a newly identified oogenesis-to-ovulation transition. Altogether, these findings provide a broad perspective on oogenesis at a single-cell resolution while revealing new genetic markers and fate-specific transcriptional signatures to facilitate future studies.

Download Full-text

Building an RNA Sequencing Transcriptome of the Central Nervous System

The Neuroscientist ◽

10.1177/1073858415610541 ◽

2016 ◽

Vol 22 (6) ◽

pp. 579-592 ◽

Cited By ~ 12

Author(s):

Xiaomin Dong ◽

Yanan You ◽

Jia Qian Wu

Keyword(s):

Gene Expression ◽

Central Nervous System ◽

Nervous System ◽

Rna Sequencing ◽

Large Scale ◽

Expression Profiles ◽

Cell Types ◽

Specific Cell ◽

Rna Seq ◽

The Central Nervous System

The composition and function of the central nervous system (CNS) is extremely complex. In addition to hundreds of subtypes of neurons, other cell types, including glia (astrocytes, oligodendrocytes, and microglia) and vascular cells (endothelial cells and pericytes) also play important roles in CNS function. Such heterogeneity makes the study of gene transcription in CNS challenging. Transcriptomic studies, namely the analyses of the expression levels and structures of all genes, are essential for interpreting the functional elements and understanding the molecular constituents of the CNS. Microarray has been a predominant method for large-scale gene expression profiling in the past. However, RNA-sequencing (RNA-Seq) technology developed in recent years has many advantages over microarrays, and has enabled building more quantitative, accurate, and comprehensive transcriptomes of the CNS and other systems. The discovery of novel genes, diverse alternative splicing events, and noncoding RNAs has remarkably expanded the complexity of gene expression profiles and will help us to understand intricate neural circuits. Here, we discuss the procedures and advantages of RNA-Seq technology in mammalian CNS transcriptome construction, and review the approaches of sample collection as well as recent progress in building RNA-Seq-based transcriptomes from tissue samples and specific cell types.

Download Full-text