scholarly journals Isolation of murine large intestinal crypt cell populations with flow sorting

2020 ◽  
Author(s):  
Amber N. Habowski ◽  
Jennifer M. Bates ◽  
Jessica L. Flesher ◽  
Robert A. Edwards ◽  
Marian L. Waterman
Keyword(s):  
Antigen Expression ◽  
Cell Types ◽  
Cell Number ◽  
Intestinal Cell ◽  
Cell Populations ◽  
Rna Seq ◽  
Daughter Cells ◽  
Tissue Dissociation ◽  
Flow Sorting ◽  
Downstream Analysis

Abstract Here we present a high-resolution sorting protocol for colon crypt stem cells, their daughter cells and mature, differentiated cell types. We used freshly dissected mouse colons and validated intestinal cell surface markers amenable to Flow Activated Cell Sorting (FACS). This 5-7 hour protocol enables isolation of six distinct cryptal cell populations (Stem, AbsPro, SecPDG, Tuft, Ent, and EEC) from any mouse strain/background (Figure 1 and 2) . Downstream analysis of sorted cells (Transcriptomics = bulk RNA-seq and Proteomics = small cell number LC/MS) validated the identity of cell populations. An important strength of this protocol is the independence from any trans-genic labeling of cell types and the flexibility for users to add additional markers for a variety of downstream applications. The absence of proteases during dissociation increases antigen expression resolving the six cell types but also decreases cell yield (Figure 3). The main steps of this protocol include: Tissue Dissection, Tissue Dissociation, Preparation for FACS, and Performing FACS.

scholarly journals Enhancing droplet-based single-nucleus RNA-seq resolution using the semi-supervised machine learning classifier DIEM

2019 ◽  
Author(s):  
Marcus Alvarez ◽  
Elior Rahmani ◽  
Brandon Jew ◽  
Kristina M. Garske ◽  
Zong Miao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cell Types ◽  
Supervised Machine Learning ◽  
Data Sets ◽  
Rna Seq ◽  
Novel Approach ◽  
Single Nucleus ◽  
Downstream Analysis

AbstractSingle-nucleus RNA sequencing (snRNA-seq) measures gene expression in individual nuclei instead of cells, allowing for unbiased cell type characterization in solid tissues. Contrary to single-cell RNA seq (scRNA-seq), we observe that snRNA-seq is commonly subject to contamination by high amounts of extranuclear background RNA, which can lead to identification of spurious cell types in downstream clustering analyses if overlooked. We present a novel approach to remove debris-contaminated droplets in snRNA-seq experiments, called Debris Identification using Expectation Maximization (DIEM). Our likelihood-based approach models the gene expression distribution of debris and cell types, which are estimated using EM. We evaluated DIEM using three snRNA-seq data sets: 1) human differentiating preadipocytes in vitro, 2) fresh mouse brain tissue, and 3) human frozen adipose tissue (AT) from six individuals. All three data sets showed various degrees of extranuclear RNA contamination. We observed that existing methods fail to account for contaminated droplets and led to spurious cell types. When compared to filtering using these state of the art methods, DIEM better removed droplets containing high levels of extranuclear RNA and led to higher quality clusters. Although DIEM was designed for snRNA-seq data, we also successfully applied DIEM to single-cell data. To conclude, our novel method DIEM removes debris-contaminated droplets from single-cell-based data fast and effectively, leading to cleaner downstream analysis. Our code is freely available for use at https://github.com/marcalva/diem.

scholarly journals Full-Length Transcriptome: A Reliable Alternative for Single-Cell RNA-Seq Analysis in the Spleen of Teleost Without Reference Genome

2021 ◽  
Vol 12 ◽  
Author(s):  
Lixing Huang ◽  
Ying Qiao ◽  
Wei Xu ◽  
Linfeng Gong ◽  
Rongchao He ◽  
...  
Keyword(s):  
Data Analysis ◽  
B Cells ◽  
Single Cell ◽  
Reference Genome ◽  
Immune Cell ◽  
Cell Types ◽  
Full Length ◽  
Cell Populations ◽  
Epinephelus Coioides ◽  
Rna Seq

Fish is considered as a supreme model for clarifying the evolution and regulatory mechanism of vertebrate immunity. However, the knowledge of distinct immune cell populations in fish is still limited, and further development of techniques advancing the identification of fish immune cell populations and their functions are required. Single cell RNA-seq (scRNA-seq) has provided a new approach for effective in-depth identification and characterization of cell subpopulations. Current approaches for scRNA-seq data analysis usually rely on comparison with a reference genome and hence are not suited for samples without any reference genome, which is currently very common in fish research. Here, we present an alternative, i.e. scRNA-seq data analysis with a full-length transcriptome as a reference, and evaluate this approach on samples from Epinephelus coioides-a teleost without any published genome. We show that it reconstructs well most of the present transcripts in the scRNA-seq data achieving a sensitivity equivalent to approaches relying on genome alignments of related species. Based on cell heterogeneity and known markers, we characterized four cell types: T cells, B cells, monocytes/macrophages (Mo/MΦ) and NCC (non-specific cytotoxic cells). Further analysis indicated the presence of two subsets of Mo/MΦ including M1 and M2 type, as well as four subsets in B cells, i.e. mature B cells, immature B cells, pre B cells and early-pre B cells. Our research will provide new clues for understanding biological characteristics, development and function of immune cell populations of teleost. Furthermore, our approach provides a reliable alternative for scRNA-seq data analysis in teleost for which no reference genome is currently available.

scholarly journals MarkerCount: A stable, count-based cell type identifier for single cell RNA-Seq experiments

2021 ◽  
Author(s):  
Hanbyeol Kim ◽  
Joongho Lee ◽  
Keunsoo Kang ◽  
Seokhyun Yoon
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cell Types ◽  
Batch Effect ◽  
Expression Level ◽  
Rna Seq ◽  
Cell Type ◽  
Stable Performance ◽  
Downstream Analysis

Abstract Cell type identification is a key step to downstream analysis of single cell RNA-seq experiments. Indispensible information for this is gene expression, which is used to cluster cells, train the model and set rejection thresholds. Problem is they are subject to batch effect arising from different platforms and preprocessing. We present MarkerCount, which uses the number of markers expressed regardless of their expression level to initially identify cell types and, then, reassign cell type in cluster-basis. MarkerCount works both in reference and marker-based mode, where the latter utilizes only the existing lists of markers, while the former required pre-annotated dataset to train the model. The performance was evaluated and compared with the existing identifiers, both marker and reference-based, that can be customized with publicly available datasets and marker DB. The results show that MarkerCount provides a stable performance when comparing with other reference-based and marker-based cell type identifiers.

scholarly journals Reference Transcriptomes of Porcine Peripheral Immune Cells Created Through Bulk and Single-Cell RNA Sequencing

2021 ◽  
Vol 12 ◽  
Author(s):  
Juber Herrera-Uribe ◽  
Jayne E. Wiarda ◽  
Sathesh K. Sivasankaran ◽  
Lance Daharsh ◽  
Haibo Liu ◽  
...  
Keyword(s):  
T Cells ◽  
Single Cell ◽  
Immune Cells ◽  
Peripheral Blood ◽  
Cell Types ◽  
Cell Populations ◽  
Rna Seq ◽  
Cell Type ◽  
Transcriptomic Data ◽  
Single Cell Rna Sequencing

Pigs are a valuable human biomedical model and an important protein source supporting global food security. The transcriptomes of peripheral blood immune cells in pigs were defined at the bulk cell-type and single cell levels. First, eight cell types were isolated in bulk from peripheral blood mononuclear cells (PBMCs) by cell sorting, representing Myeloid, NK cells and specific populations of T and B-cells. Transcriptomes for each bulk population of cells were generated by RNA-seq with 10,974 expressed genes detected. Pairwise comparisons between cell types revealed specific expression, while enrichment analysis identified 1,885 to 3,591 significantly enriched genes across all 8 cell types. Gene Ontology analysis for the top 25% of significantly enriched genes (SEG) showed high enrichment of biological processes related to the nature of each cell type. Comparison of gene expression indicated highly significant correlations between pig cells and corresponding human PBMC bulk RNA-seq data available in Haemopedia. Second, higher resolution of distinct cell populations was obtained by single-cell RNA-sequencing (scRNA-seq) of PBMC. Seven PBMC samples were partitioned and sequenced that produced 28,810 single cell transcriptomes distributed across 36 clusters and classified into 13 general cell types including plasmacytoid dendritic cells (DC), conventional DCs, monocytes, B-cell, conventional CD4 and CD8 αβ T-cells, NK cells, and γδ T-cells. Signature gene sets from the human Haemopedia data were assessed for relative enrichment in genes expressed in pig cells and integration of pig scRNA-seq with a public human scRNA-seq dataset provided further validation for similarity between human and pig data. The sorted porcine bulk RNAseq dataset informed classification of scRNA-seq PBMC populations; specifically, an integration of the datasets showed that the pig bulk RNAseq data helped define the CD4CD8 double-positive T-cell populations in the scRNA-seq data. Overall, the data provides deep and well-validated transcriptomic data from sorted PBMC populations and the first single-cell transcriptomic data for porcine PBMCs. This resource will be invaluable for annotation of pig genes controlling immunogenetic traits as part of the porcine Functional Annotation of Animal Genomes (FAANG) project, as well as further study of, and development of new reagents for, porcine immunology.

scholarly journals Differential functional roles of fibroblasts and pericytes in the formation of tissue-engineered microvascular networks in vitro

2019 ◽  
Author(s):  
Natalia Kosyakova ◽  
Derek D. Kao ◽  
Francesc López-Giráldez ◽  
Susann Spindler ◽  
Morven Graham ◽  
...  
Keyword(s):  
Stromal Cells ◽  
Self Assembly ◽  
Stromal Cell ◽  
Cell Types ◽  
Cell Populations ◽  
Rna Seq ◽  
Fibrin Gels ◽  
Endothelial Colony Forming Cells ◽  
Paracrine Growth Factors

AbstractAimsFormation of a perfusable microvascular network (μVN) is critical for tissue engineering of solid organs. Stromal cells can support endothelial cell (EC) self-assembly into a μVN, but distinct stromal cell populations may play different roles in this process. Here we investigated the effects that two widely used stromal cells populations, fibroblasts (FBs) and pericytes (PCs), have on μVN formation.Methods and resultsWe examined the effects of adding defined stromal cell populations on the self-assembly of ECs derived from human endothelial colony forming cells (ECFCs) into perfusable μVNs in fibrin gels cast within a microfluidics chamber. ECs alone fail to fully assemble a perfusable μVN. Human lung FBs stimulate the formation of EC lined μVNs within microfluidic devices. RNA-seq analysis suggested that FBs produce high levels of hepatocyte growth factor (HGF), and addition of recombinant HGF improved μVN formation within devices. Human placental PCs could not substitute for FBs, but in the presence of FBs, PCs closely associated with ECs, formed a common basement membrane, extended microfilaments intercellularly, and reduced microvessel diameters.ConclusionsDifferent stromal cell types provide different functions in microvessel assembly by ECs. FBs support μVN formation by providing paracrine growth factors whereas PCs directly interact with ECs to modify microvascular morphology.Statement of ContributionNatalia Kosyakova, Derek Kao, William G. Chang were primarily responsible for the conception, design, interpretation of experiments, and drafting of the manuscript. Francesc López-Giráldez carried out analysis of RNA-seq data. Susann Spindler and Gregory Tietjen assisted with microvessel analysis software. Morven Graham and Xinran Liu assisted with the electron microscopy. Kevin J. James and Jee Won Shin assisted with data collection. Jordan Pober assisted with a critical review of manuscript and experimental design.

scholarly journals Isolation of high yield and quality RNA from human precision-cut lung slices for RNA-sequencing and computational integration with larger patient cohorts

2020 ◽  
Author(s):  
John Stegmayr ◽  
Hani N Alsafadi ◽  
Wojciech Langwinski ◽  
Anna Niroomand ◽  
Sandra Lindstedt ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Human Tissue ◽  
Immune Cell ◽  
High Yield ◽  
Cell Populations ◽  
Rna Seq ◽  
Yield And Quality ◽  
Novel Approach ◽  
Downstream Analysis ◽  
Precision Cut Lung Slices

Precision-cut lung slices (PCLS) have gained increasing interest as a model to study lung biology/disease and screening novel therapeutics. In particular, PCLS derived from human tissue can better recapitulate some aspects of lung biology/disease as compared to animal models. Several experimental readouts have been established for use with PCLS, but obtaining high yield and quality RNA for downstream analysis has remained challenging. This is particularly problematic for utilizing the power of next-generation sequencing techniques, such as RNA-sequencing (RNA-seq), for non-biased and high through-put analysis of PCLS human cohorts. In the current study, we present a novel approach for isolating high quality RNA from a small amount of tissue, including diseased human tissue, such as idiopathic pulmonary fibrosis (IPF). We show that the RNA isolated using this method has sufficient quality for RT-qPCR and RNA-seq analysis. Furthermore, the RNA-seq data from human PCLS could be used in several established computational pipelines, including deconvolution of bulk RNA-seq data using publicly available single-cell RNA-seq data. Deconvolution using Bisque revealed a diversity of cell populations in human PCLS, including several immune cell populations, which correlated with cell populations known to be present and aberrant in human disease.

scholarly journals SDImpute: A statistical block imputation method based on cell-level and gene-level information for dropouts in single-cell RNA-seq data

2021 ◽  
Vol 17 (6) ◽  
pp. e1009118
Author(s):  
Jing Qi ◽  
Yang Zhou ◽  
Zicen Zhao ◽  
Shuilin Jin
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Differential Expression Analysis ◽  
Cell Types ◽  
Rna Seq ◽  
Cell Level ◽  
Gene Level ◽  
Level Information ◽  
Downstream Analysis ◽  
Gene Expression Levels

The single-cell RNA sequencing (scRNA-seq) technologies obtain gene expression at single-cell resolution and provide a tool for exploring cell heterogeneity and cell types. As the low amount of extracted mRNA copies per cell, scRNA-seq data exhibit a large number of dropouts, which hinders the downstream analysis of the scRNA-seq data. We propose a statistical method, SDImpute (Single-cell RNA-seq Dropout Imputation), to implement block imputation for dropout events in scRNA-seq data. SDImpute automatically identifies the dropout events based on the gene expression levels and the variations of gene expression across similar cells and similar genes, and it implements block imputation for dropouts by utilizing gene expression unaffected by dropouts from similar cells. In the experiments, the results of the simulated datasets and real datasets suggest that SDImpute is an effective tool to recover the data and preserve the heterogeneity of gene expression across cells. Compared with the state-of-the-art imputation methods, SDImpute improves the accuracy of the downstream analysis including clustering, visualization, and differential expression analysis.

scholarly journals The Application of Single-Cell RNA Sequencing in Mammalian Meiosis Studies

2021 ◽  
Vol 9 ◽  
Author(s):  
Yiheng Peng ◽  
Huanyu Qiao
Keyword(s):  
Single Cell ◽  
Molecular Mechanisms ◽  
Cell Types ◽  
Marker Genes ◽  
Rna Seq ◽  
Cell Level ◽  
Cellular Division ◽  
Division Process ◽  
Complex Events ◽  
Downstream Analysis

Meiosis is a cellular division process that produces gametes for sexual reproduction. Disruption of complex events throughout meiosis, such as synapsis and homologous recombination, can lead to infertility and aneuploidy. To reveal the molecular mechanisms of these events, transcriptome studies of specific substages must be conducted. However, conventional methods, such as bulk RNA-seq and RT-qPCR, are not able to detect the transcriptional variations effectively and precisely, especially for identifying cell types and stages with subtle differences. In recent years, mammalian meiotic transcriptomes have been intensively studied at the single-cell level by using single-cell RNA-seq (scRNA-seq) approaches, especially through two widely used platforms, Smart-seq2 and Drop-seq. The scRNA-seq protocols along with their downstream analysis enable researchers to accurately identify cell heterogeneities and investigate meiotic transcriptomes at a higher resolution. In this review, we compared bulk RNA-seq and scRNA-seq to show the advantages of the scRNA-seq in meiosis studies; meanwhile, we also pointed out the challenges and limitations of the scRNA-seq. We listed recent findings from mammalian meiosis (male and female) studies where scRNA-seq applied. Next, we summarized the scRNA-seq analysis methods and the meiotic marker genes from spermatocytes and oocytes. Specifically, we emphasized the different features of the two scRNA-seq protocols (Smart-seq2 and Drop-seq) in the context of meiosis studies and discussed their strengths and weaknesses in terms of different research purposes. Finally, we discussed the future applications of scRNA-seq in the meiosis field.

scholarly journals DIANA-miTED: a microRNA tissue expression database

2021 ◽  
Author(s):  
Ioannis Kavakiotis ◽  
Athanasios Alexiou ◽  
Spyros Tastsoglou ◽  
Ioannis S Vlachos ◽  
Artemis G Hatzigeorgiou
Keyword(s):  
Mirna Expression ◽  
Cell Types ◽  
Tissue Expression ◽  
The Cancer Genome Atlas ◽  
Rna Seq ◽  
Starting Point ◽  
Metadata Quality ◽  
Cancer Genome Atlas ◽  
Downstream Analysis ◽  
Systematic Collection

Abstract microRNAs (miRNAs) are short (∼23nt) single-stranded non-coding RNAs that act as potent post-transcriptional gene expression regulators. Information about miRNA expression and distribution across cell types and tissues is crucial to the understanding of their function and for their translational use as biomarkers or therapeutic targets. DIANA-miTED is the most comprehensive and systematic collection of miRNA expression values derived from the analysis of 15 183 raw human small RNA-Seq (sRNA-Seq) datasets from the Sequence Read Archive (SRA) and The Cancer Genome Atlas (TCGA). Metadata quality maximizes the utility of expression atlases, therefore we manually curated SRA and TCGA-derived information to deliver a comprehensive and standardized set, incorporating in total 199 tissues, 82 anatomical sublocations, 267 cell lines and 261 diseases. miTED offers rich instant visualizations of the expression and sample distributions of requested data across variables, as well as study-wide diagrams and graphs enabling efficient content exploration. Queries also generate links towards state-of-the-art miRNA functional resources, deeming miTED an ideal starting point for expression retrieval, exploration, comparison, and downstream analysis, without requiring bioinformatics support or expertise. DIANA-miTED is freely available at http://www.microrna.gr/mited.

scholarly journals Single-Cell Transcriptomic Analysis of mIHC Images via Antigen Mapping

2019 ◽  
Author(s):  
Kiya W. Govek ◽  
Emma C. Troisi ◽  
Zhen Miao ◽  
Steven Woodhouse ◽  
Pablo G. Camara
Keyword(s):  
Single Cell ◽  
Spatial Patterns ◽  
Tissue Section ◽  
Cell Types ◽  
Level Of Detail ◽  
Cell Populations ◽  
Rna Seq ◽  
Spatially Resolved ◽  
Murine Spleen ◽  
Antibody Panel

Highly-multiplexed immunohistochemistry (mIHC) enables the staining and quantification of dozens of antigens in a tissue section with single-cell resolution. However, annotating cell populations that differ little in the profiled antigens or for which the antibody panel does not include specific markers is challenging. To overcome this obstacle, we have developed an approach for enriching mIHC images with single-cell RNA-seq data, building upon recent experimental procedures for augmenting single-cell transcriptomes with concurrent antigen measurements. Spatially-resolved Transcriptomics via Epitope Anchoring (STvEA) performs transcriptome-guided annotation of highly-multiplexed cytometry datasets. It increases the level of detail in histological analyses by enabling annotation of subtle cell populations, spatial patterns of transcription, and interactions between cell types. More generally, it enables the systematic annotation of cell populations in cytometry data. We demonstrate the utility of STvEA by uncovering the architecture of poorly characterized cell types in the murine spleen using published highly-multiplexed cytometry and mIHC data.

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