MAPPING NEURONAL CELL TYPES USING INTEGRATIVE MULTI-SPECIES MODELING OF HUMAN AND MOUSE SINGLE CELL RNA SEQUENCING

Identifying the molecular fingerprint of organismal cell types is key for understanding their function and evolution. Here, we use single cell RNA sequencing (scRNA-seq) to survey the cell types of the sea urchin early pluteus larva, representing an important developmental transition from non-feeding to feeding larva. We identify 21 distinct cell clusters, representing cells of the digestive, skeletal, immune, and nervous systems. Further subclustering of these reveal a highly detailed portrait of cell diversity across the larva, including the identification of neuronal cell types. We then validate important gene regulatory networks driving sea urchin development and reveal new domains of activity within the larval body. Focusing on neurons that co-express Pdx-1 and Brn1/2/4, we identify an unprecedented number of genes shared by this population of neurons in sea urchin and vertebrate endocrine pancreatic cells. Using differential expression results from Pdx-1 knockdown experiments, we show that Pdx1 is necessary for the acquisition of the neuronal identity of these cells. We hypothesize that a network similar to the one orchestrated by Pdx1 in the sea urchin neurons was active in an ancestral cell type and then inherited by neuronal and pancreatic developmental lineages in sea urchins and vertebrates.

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Single cell RNA sequencing of theStrongylocentrotus purpuratuslarva reveals the blueprint of major cell types and nervous system of a non-chordate deuterostome

10.1101/2021.03.16.435574 ◽

2021 ◽

Author(s):

Periklis Paganos ◽

Danila Voronov ◽

Jacob Musser ◽

Detlev Arendt ◽

Maria I. Arnone

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Sea Urchin ◽

Regulatory Networks ◽

Neuronal Cell ◽

Cell Types ◽

Molecular Fingerprint ◽

Distinct Cell ◽

Single Cell Rna Sequencing ◽

Cell Diversity

AbstractIdentifying the molecular fingerprint of organismal cell types is key for understanding their function and evolution. Here, we use single cell RNA sequencing (scRNA-seq) to survey the cell types of the sea urchin early pluteus larva, representing an important developmental transition from non-feeding to feeding larva. We identified 21 distinct cell clusters, representing cells of the digestive, skeletal, immune, and nervous systems. Further subclustering of these revealed a highly detailed portrait of cell diversity across the larva, including the identification of 12 distinct neuronal cell types. Moreover, we corroborated co-expression of key regulatory genes previously shown to drive sea urchin gene regulatory networks, and revealed additional domains in which these regulatory networks are likely to function within the larva. Lastly, we recovered a neuronal cell type co-expressingPdx-1andBrn1/2/4, which had previously been shown to share similar gene expression with vertebrate pancreas. Our results extend this finding, revealing twenty transcription factors shared by this population of neurons in sea urchin and vertebrate pancreatic cells. Using differential expression results from Pdx-1 knockdown experiments, we generate a draft of the Pdx-1 regulatory network in these cells, and hypothesize this network was present in an ancestral deuterostome neuron before being co-opted into the pancreas developmental lineage in vertebrates.

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Estimating the frequency of multiplets in single-cell RNA sequencing from cell-mixing experiments

10.1101/293639 ◽

2018 ◽

Author(s):

Jesse D. Bloom

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Cell Types ◽

Common Method ◽

Equal Proportion ◽

Single Cell Rna Sequencing ◽

Different Types ◽

Multiple Cells ◽

Human And Mouse

ABSTRACTIn single-cell RNA-sequencing, it is important to know the frequency at which the sequenced transcriptomes actually derive from multiple cells. A common method to estimate this multiplet frequency is to mix two different types of cells (e.g., human and mouse), and then determine how often the transcriptomes contain transcripts from both cell types. When the two cell types are mixed in equal proportion, the calculation of the multiplet frequency from the frequency of mixed transcriptomes is straightforward. But surprisingly, there are no published descriptions of how to calculate the multiplet frequency in the general case when the cell types are mixed unequally. Here I derive equations to analytically calculate the multiplet frequency from the numbers of observed pure and mixed transcriptomes when two cell types are mixed in arbitrary proportions, under the assumption that the loading of cells into droplets or wells is Poisson.

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Estimating the frequency of multiplets in single-cell RNA sequencing from cell-mixing experiments

PeerJ ◽

10.7717/peerj.5578 ◽

2018 ◽

Vol 6 ◽

pp. e5578 ◽

Cited By ~ 15

Author(s):

Jesse D. Bloom

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Cell Types ◽

Common Method ◽

Equal Proportion ◽

Single Cell Rna Sequencing ◽

Different Types ◽

Multiple Cells ◽

Human And Mouse

In single-cell RNA-sequencing, it is important to know the frequency at which the sequenced transcriptomes actually derive from multiple cells. A common method to estimate this multiplet frequency is to mix two different types of cells (e.g., human and mouse), and then determine how often the transcriptomes contain transcripts from both cell types. When the two cell types are mixed in equal proportion, the calculation of the multiplet frequency from the frequency of mixed transcriptomes is straightforward. But surprisingly, there are no published descriptions of how to calculate the multiplet frequency in the general case when the cell types are mixed unequally. Here, I derive equations to analytically calculate the multiplet frequency from the numbers of observed pure and mixed transcriptomes when two cell types are mixed in arbitrary proportions, under the assumption that the loading of cells into droplets or wells is Poisson.

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Single-cell data clustering based on sparse optimization and low-rank matrix factorization

G3 Genes|Genome|Genetics ◽

10.1093/g3journal/jkab098 ◽

2021 ◽

Author(s):

Yinlei Hu ◽

Bin Li ◽

Falai Chen ◽

Kun Qu

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Matrix Factorization ◽

Data Clustering ◽

Cell Types ◽

Low Rank ◽

Sequencing Data ◽

Rank Matrix ◽

Single Cell Rna Sequencing ◽

Low Rank Matrix

Abstract Unsupervised clustering is a fundamental step of single-cell RNA sequencing data analysis. This issue has inspired several clustering methods to classify cells in single-cell RNA sequencing data. However, accurate prediction of the cell clusters remains a substantial challenge. In this study, we propose a new algorithm for single-cell RNA sequencing data clustering based on Sparse Optimization and low-rank matrix factorization (scSO). We applied our scSO algorithm to analyze multiple benchmark datasets and showed that the cluster number predicted by scSO was close to the number of reference cell types and that most cells were correctly classified. Our scSO algorithm is available at https://github.com/QuKunLab/scSO. Overall, this study demonstrates a potent cell clustering approach that can help researchers distinguish cell types in single-cell RNA sequencing data.

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Comprehensive network modeling from single cell RNA sequencing of human and mouse reveals well conserved transcription regulation of hematopoiesis

BMC Genomics ◽

10.1186/s12864-020-07241-2 ◽

2020 ◽

Vol 21 (S11) ◽

Author(s):

Shouguo Gao ◽

Zhijie Wu ◽

Xingmin Feng ◽

Sachiko Kajigaya ◽

Xujing Wang ◽

...

Keyword(s):

Bone Marrow ◽

Transcription Factors ◽

Single Cell ◽

Rna Sequencing ◽

Transcription Regulation ◽

Regulatory Networks ◽

Stem Cell Differentiation ◽

Hematopoietic Stem ◽

Single Cell Rna Sequencing ◽

Human And Mouse

Abstract Background Presently, there is no comprehensive analysis of the transcription regulation network in hematopoiesis. Comparison of networks arising from gene co-expression across species can facilitate an understanding of the conservation of functional gene modules in hematopoiesis. Results We used single-cell RNA sequencing to profile bone marrow from human and mouse, and inferred transcription regulatory networks in each species in order to characterize transcriptional programs governing hematopoietic stem cell differentiation. We designed an algorithm for network reconstruction to conduct comparative transcriptomic analysis of hematopoietic gene co-expression and transcription regulation in human and mouse bone marrow cells. Co-expression network connectivity of hematopoiesis-related genes was found to be well conserved between mouse and human. The co-expression network showed “small-world” and “scale-free” architecture. The gene regulatory network formed a hierarchical structure, and hematopoiesis transcription factors localized to the hierarchy’s middle level. Conclusions Transcriptional regulatory networks are well conserved between human and mouse. The hierarchical organization of transcription factors may provide insights into hematopoietic cell lineage commitment, and to signal processing, cell survival and disease initiation.

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Defining the Cell Types That Drive Idiopathic Pulmonary Fibrosis Using Single-Cell RNA Sequencing

American Journal of Respiratory and Critical Care Medicine ◽

10.1164/rccm.201901-0197ed ◽

2019 ◽

Vol 199 (12) ◽

pp. 1454-1456 ◽

Cited By ~ 1

Author(s):

Joanna M. Poczobutt ◽

Oliver Eickelberg

Keyword(s):

Idiopathic Pulmonary Fibrosis ◽

Pulmonary Fibrosis ◽

Single Cell ◽

Rna Sequencing ◽

Cell Types ◽

Single Cell Rna Sequencing

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Localization of migraine susceptibility genes in human brain by single-cell RNA sequencing

Cephalalgia ◽

10.1177/0333102418762476 ◽

2018 ◽

Vol 38 (13) ◽

pp. 1976-1983 ◽

Cited By ~ 5

Author(s):

William Renthal

Keyword(s):

Human Brain ◽

Single Cell ◽

Rna Sequencing ◽

Expression Profiles ◽

Cell Types ◽

Susceptibility Genes ◽

Brain Cell ◽

Cell Type ◽

Single Cell Rna Sequencing ◽

Brain Cell Types

Background Migraine is a debilitating disorder characterized by severe headaches and associated neurological symptoms. A key challenge to understanding migraine has been the cellular complexity of the human brain and the multiple cell types implicated in its pathophysiology. The present study leverages recent advances in single-cell transcriptomics to localize the specific human brain cell types in which putative migraine susceptibility genes are expressed. Methods The cell-type specific expression of both familial and common migraine-associated genes was determined bioinformatically using data from 2,039 individual human brain cells across two published single-cell RNA sequencing datasets. Enrichment of migraine-associated genes was determined for each brain cell type. Results Analysis of single-brain cell RNA sequencing data from five major subtypes of cells in the human cortex (neurons, oligodendrocytes, astrocytes, microglia, and endothelial cells) indicates that over 40% of known migraine-associated genes are enriched in the expression profiles of a specific brain cell type. Further analysis of neuronal migraine-associated genes demonstrated that approximately 70% were significantly enriched in inhibitory neurons and 30% in excitatory neurons. Conclusions This study takes the next step in understanding the human brain cell types in which putative migraine susceptibility genes are expressed. Both familial and common migraine may arise from dysfunction of discrete cell types within the neurovascular unit, and localization of the affected cell type(s) in an individual patient may provide insight into to their susceptibility to migraine.

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Identifying cell types to interpret scRNA-seq data: how, why and more possibilities

Briefings in Functional Genomics ◽

10.1093/bfgp/elaa003 ◽

2020 ◽

Vol 19 (4) ◽

pp. 286-291 ◽

Cited By ~ 2

Author(s):

Ziwei Wang ◽

Hui Ding ◽

Quan Zou

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Cell Types ◽

Biological Phenomena ◽

Single Cell Rna Sequencing ◽

Numerous Data ◽

Near Future

Abstract Single-cell RNA sequencing (scRNA-seq) has generated numerous data and renewed our understanding of biological phenomena at the cellular scale. Identification of cell types has been one of the most prevalent means for interpreting scRNA-seq data, based upon which connections are made between the transcriptome and phenotype. Herein, we attempt to review the methods and tools that dedicate to the task regarding their feature and usage and look at the possibilities for scRNA-seq development in the near future.

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Transfer learning efficiently maps bone marrow cell types from mouse to human using single-cell RNA sequencing

Communications Biology ◽

10.1038/s42003-020-01463-6 ◽

2020 ◽

Vol 3 (1) ◽

Author(s):

Patrick S. Stumpf ◽

Xin Du ◽

Haruka Imanishi ◽

Yuya Kunisaki ◽

Yuichiro Semba ◽

...

Keyword(s):

Machine Learning ◽

Bone Marrow ◽

Single Cell ◽

Rna Sequencing ◽

Transfer Learning ◽

Biomedical Research ◽

Human Cell ◽

Cell Types ◽

Single Cell Rna Sequencing ◽

Using Data

AbstractBiomedical research often involves conducting experiments on model organisms in the anticipation that the biology learnt will transfer to humans. Previous comparative studies of mouse and human tissues were limited by the use of bulk-cell material. Here we show that transfer learning—the branch of machine learning that concerns passing information from one domain to another—can be used to efficiently map bone marrow biology between species, using data obtained from single-cell RNA sequencing. We first trained a multiclass logistic regression model to recognize different cell types in mouse bone marrow achieving equivalent performance to more complex artificial neural networks. Furthermore, it was able to identify individual human bone marrow cells with 83% overall accuracy. However, some human cell types were not easily identified, indicating important differences in biology. When re-training the mouse classifier using data from human, less than 10 human cells of a given type were needed to accurately learn its representation. In some cases, human cell identities could be inferred directly from the mouse classifier via zero-shot learning. These results show how simple machine learning models can be used to reconstruct complex biology from limited data, with broad implications for biomedical research.

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