A 3D system to model human pancreas development and its reference single-cell transcriptome atlas identify signaling pathways required for progenitor expansion

Abstract Human organogenesis remains relatively unexplored for ethical and practical reasons. Here we report the establishment of a single cell transcriptome atlas of the human fetal pancreas between 7 and 10 post-conceptional weeks of development. To interrogate cell-cell interactions we developed InterCom, an R-Package for identifying receptors-ligand pairs and their downstream effects. We further report the establishment of a human pancreas culture system starting from fetal tissue or human pluripotent stem cells, enabling the long-term maintenance of pancreas progenitors in a minimal, defined medium in three-dimensions. Benchmarking the cells produced in 2D and those expanded in 3D to fetal tissue reveals that progenitors expanded in 3D are transcriptionally closer to the fetal pancreas. We further demonstrate the potential of this system as a screening platform and identify the importance of the EGF and FGF pathways controlling human pancreas progenitor expansion.

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A 3D system to model human pancreas development and its reference single-cell transcriptome atlas identify signaling pathways required for progenitor expansion

10.21203/rs.3.pex-1430/v2 ◽

2021 ◽

Author(s):

Carla A. Gonçalves ◽

Michael Larsen ◽

Sascha Jung ◽

Johannes Stratmann ◽

Akiko Nakamura ◽

...

Keyword(s):

Single Cell ◽

R Package ◽

Fetal Tissue ◽

Defined Medium ◽

Three Dimensions ◽

Practical Reasons ◽

Human Pancreas ◽

Fetal Pancreas ◽

Cell Transcriptome ◽

Single Cell Transcriptome

Abstract Human organogenesis remains relatively unexplored for ethical and practical reasons. Here we report the establishment of a single cell transcriptome atlas of the human fetal pancreas between 7 and 10 post-conceptional weeks of development. To interrogate cell-cell interactions we developed InterCom, an R-Package for identifying receptors-ligand pairs and their downstream effects. We further report the establishment of a human pancreas culture system starting from fetal tissue or human pluripotent stem cells, enabling the long-term maintenance of pancreas progenitors in a minimal, defined medium in three-dimensions. Benchmarking the cells produced in 2D and those expanded in 3D to fetal tissue reveals that progenitors expanded in 3D are transcriptionally closer to the fetal pancreas. We further demonstrate the potential of this system as a screening platform and identify the importance of the EGF and FGF pathways controlling human pancreas progenitor expansion.

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A Single-Cell Transcriptome Atlas of the Human Pancreas

Cell Systems ◽

10.1016/j.cels.2016.09.002 ◽

2016 ◽

Vol 3 (4) ◽

pp. 385-394.e3 ◽

Cited By ~ 367

Author(s):

Mauro J. Muraro ◽

Gitanjali Dharmadhikari ◽

Dominic Grün ◽

Nathalie Groen ◽

Tim Dielen ◽

...

Keyword(s):

Single Cell ◽

Human Pancreas ◽

Cell Transcriptome ◽

Single Cell Transcriptome

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Building the mega single-cell transcriptome ocular meta-atlas

GigaScience ◽

10.1093/gigascience/giab061 ◽

2021 ◽

Vol 10 (10) ◽

Author(s):

Vinay S Swamy ◽

Temesgen D Fufa ◽

Robert B Hufnagel ◽

David M McGaughey

Keyword(s):

Single Cell ◽

Biological Effects ◽

R Package ◽

Technical Noise ◽

Batch Correction ◽

Workflow System ◽

Cell Transcriptome ◽

Single Cell Transcriptome ◽

Compare And Contrast ◽

Cluster Purity

Abstract Background: The development of highly scalable single-cell transcriptome technology has resulted in the creation of thousands of datasets, >30 in the retina alone. Analyzing the transcriptomes between different projects is highly desirable because this would allow for better assessment of which biological effects are consistent across independent studies. However it is difficult to compare and contrast data across different projects because there are substantial batch effects from computational processing, single-cell technology utilized, and the natural biological variation. While many single-cell transcriptome-specific batch correction methods purport to remove the technical noise, it is difficult to ascertain which method functions best. Results: We developed a lightweight R package (scPOP, single-cell Pick Optimal Parameters) that brings in batch integration methods and uses a simple heuristic to balance batch merging and cell type/cluster purity. We use this package along with a Snakefile-based workflow system to demonstrate how to optimally merge 766,615 cells from 33 retina datsets and 3 species to create a massive ocular single-cell transcriptome meta-atlas. Conclusions: This provides a model for how to efficiently create meta-atlases for tissues and cells of interest.

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BAMM-SC: A Bayesian mixture model for clustering droplet-based single cell transcriptomic data from population studies

10.1101/392662 ◽

2018 ◽

Author(s):

Zhe Sun ◽

Li Chen ◽

Hongyi Xin ◽

Qianhui Huang ◽

Anthony R Cillo ◽

...

Keyword(s):

Single Cell ◽

Single Cells ◽

R Package ◽

Clustering Methods ◽

Model Framework ◽

Bayesian Hierarchical ◽

Bayesian Mixture ◽

Population Scale ◽

Cell Transcriptome ◽

Single Cell Transcriptome

AbstractThe recently developed droplet-based single cell transcriptome sequencing (scRNA-seq) technology makes it feasible to perform a population-scale scRNA-seq study, in which the transcriptome is measured for tens of thousands of single cells from multiple individuals. Despite the advances of many clustering methods, there are few tailored methods for population-scale scRNA-seq studies. Here, we have developed a BAyesiany Mixture Model for Single Cell sequencing (BAMM-SC) method to cluster scRNA-seq data from multiple individuals simultaneously. Specifically, BAMM-SC takes raw data as input and can account for data heterogeneity and batch effect among multiple individuals in a unified Bayesian hierarchical model framework. Results from extensive simulations and application of BAMM-SC to in-house scRNA-seq datasets using blood, lung and skin cells from humans or mice demonstrated that BAMM-SC outperformed existing clustering methods with improved clustering accuracy and reduced impact from batch effects. BAMM-SC has been implemented in a user-friendly R package with a detailed tutorial available on www.pitt.edu/~Cwec47/singlecell.html.

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scRNABatchQC: multi-samples quality control for single cell RNA-seq data

Bioinformatics ◽

10.1093/bioinformatics/btz601 ◽

2019 ◽

Vol 35 (24) ◽

pp. 5306-5308

Author(s):

Qi Liu ◽

Quanhu Sheng ◽

Jie Ping ◽

Marisol Adelina Ramirez ◽

Ken S Lau ◽

...

Keyword(s):

Single Cell ◽

R Package ◽

Supplementary Information ◽

Rna Seq ◽

Technical Artifact ◽

Multiple Sample ◽

Systematic Biases ◽

Cell Transcriptome ◽

Single Cell Transcriptome ◽

Spurious Results

Abstract Summary Single cell RNA sequencing is a revolutionary technique to characterize inter-cellular transcriptomics heterogeneity. However, the data are noise-prone because gene expression is often driven by both technical artifacts and genuine biological variations. Proper disentanglement of these two effects is critical to prevent spurious results. While several tools exist to detect and remove low-quality cells in one single cell RNA-seq dataset, there is lack of approach to examining consistency between sample sets and detecting systematic biases, batch effects and outliers. We present scRNABatchQC, an R package to compare multiple sample sets simultaneously over numerous technical and biological features, which gives valuable hints to distinguish technical artifact from biological variations. scRNABatchQC helps identify and systematically characterize sources of variability in single cell transcriptome data. The examination of consistency across datasets allows visual detection of biases and outliers. Availability and implementation scRNABatchQC is freely available at https://github.com/liuqivandy/scRNABatchQC as an R package. Supplementary information Supplementary data are available at Bioinformatics online.

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singleCellHaystack: Finding surprising genes in 2-dimensional representations of single cell transcriptome data

10.1101/557967 ◽

2019 ◽

Author(s):

Alexis Vandenbon ◽

Diego Diez

Keyword(s):

Single Cell ◽

Expression Patterns ◽

R Package ◽

Biological Knowledge ◽

Transcriptome Data ◽

Sequencing Data ◽

Single Cell Sequencing ◽

Leibler Divergence ◽

Cell Transcriptome ◽

Single Cell Transcriptome

AbstractSummarySingle-cell sequencing data is often visualized in 2-dimensional plots, including t-SNE plots. However, it is not straightforward to extract biological knowledge, such as differentially expressed genes, from these plots. Here we introduce singleCellHaystack, a methodology that addresses this problem. singleCellHaystack uses Kullback-Leibler Divergence to find genes that are expressed in subsets of cells that are non-randomly positioned on a 2D plot. We illustrate the usage of singleCellHaystack through applications on several single-cell datasets. singleCellHaystack is implemented as an R package, and includes additional functions for clustering and visualization of genes with interesting expression patterns.Availability and implementationhttps://github.com/alexisvdb/[email protected]

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Building the Mega Single Cell Transcriptome Ocular Meta-Atlas

10.1101/2021.03.26.437190 ◽

2021 ◽

Author(s):

Vinay S Swamy ◽

Temesgen D Fufa ◽

Robert B Hufnagel ◽

David M McGaughey

Keyword(s):

Single Cell ◽

Biological Effects ◽

R Package ◽

Technical Noise ◽

Batch Correction ◽

Workflow System ◽

Cell Transcriptome ◽

Single Cell Transcriptome ◽

Compare And Contrast ◽

Cluster Purity

The development of highly scalable single cell transcriptome technology has resulted in the creation of thousands of datasets, over 30 in the retina alone. Analyzing the transcriptomes between different projects is highly desirable as this would allow for better assessment of which biological effects are consistent across independent studies. However it is difficult to compare and contrast data across different projects as there are substantial batch effects from computational processing, single cell technology utilized, and the natural biological variation. While many single cell transcriptome specific batch correction methods purport to remove the technical noise it is difficult to ascertain which method functions works best. We developed a lightweight R package (scPOP) that brings in batch integration methods and uses a simple heuristic to balance batch merging and celltype/cluster purity. We use this package along with a Snakefile based workflow system to demonstrate how to optimally merge 766,615 cells from 34 retina datsets and three species to create a massive ocular single cell transcriptome meta-atlas. This provides a model how to efficiently create meta-atlases for tissues and cells of interest.

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Cell type purification by single-cell transcriptome-trained sorting

10.1101/502773 ◽

2018 ◽

Author(s):

Chloé S Baron ◽

Aditya Barve ◽

Mauro J Muraro ◽

Gitanjali Dharmadhikari ◽

Reinier van der Linden ◽

...

Keyword(s):

Single Cell ◽

Cell Sorting ◽

Cell Types ◽

Computational Method ◽

Human Pancreas ◽

Cell Type ◽

Specific Antibodies ◽

Cell Transcriptome ◽

Single Cell Transcriptome ◽

Index Sorting

Traditional cell type enrichment using fluorescence activated cell sorting (FACS) relies on methods that specifically label the cell type of interest. Here we propose GateID, a computational method that combines single-cell transcriptomics for unbiased cell type identification with FACS index sorting to purify cell types of choice. We validate GateID by purifying various cell types from the zebrafish kidney marrow and the human pancreas without resorting to specific antibodies or transgenes.

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Single cell transcriptome analysis of human pancreas reveals transcriptional signatures of aging and somatic mutation patterns

10.1101/108043 ◽

2017 ◽

Cited By ~ 3

Author(s):

Martin Enge ◽

H. Efsun Arda ◽

Marco Mignardi ◽

John Beausang ◽

Rita Bottino ◽

...

Keyword(s):

Single Cell ◽

Transcriptome Analysis ◽

Human Tissue ◽

Molecular Mechanisms ◽

Single Cells ◽

Tumor Cell Line ◽

Human Pancreas ◽

Human Brain Tissue ◽

Cell Transcriptome ◽

Single Cell Transcriptome

AbstractAs organisms age, cells accumulate genetic and epigenetic changes that eventually lead to impaired organ function or catastrophic transformation such as cancer. Since aging appears to be a stochastic process of increasing disorder1 cells in an organ will be individually affected in different ways - thus rendering bulk analyses of postmitotic adult tissues difficult to characterize. Here we directly measure the effects of aging in primary human tissue by performing single-cell transcriptome analysis of 2544 human pancreas cells from eight donors spanning six decades of life. We find that islet cells from older donors have increased levels of molecular disorder as measured both by noise in the transcriptome and by the number of cells which display inappropriate hormone expression, revealing a transcriptional instability associated with aging. By further analyzing the spectrum of somatic mutations in single cells, we found a specific age-dependent mutational signature characterized by C to A and C to G transversions. These mutations are indicators of oxidative stress and the signature is absent in single cells from human brain tissue or in a tumor cell line. We have used the single cell measurements of transcriptional noise and mutation level to identify molecular pathways correlated with these changes that could influence human disease. Our results demonstrate the feasibility of using single-cell RNA-seq data from primary cells to derive meaningful insights into the genetic processes that operate on aging human tissue and to determine molecular mechanisms coordinated with these processes.

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