Characterization of transcript enrichment and detection bias in single-nuclei RNA-seq for mapping of distinct human adipocyte lineages

ABSTRACTSingle-cell RNA-sequencing (scRNA-seq) enables molecular characterization of complex biological tissues at high resolution. The requirement of single-cell extraction, however, makes it challenging for profiling tissues such as adipose tissue where collection of intact single adipocytes is complicated by their fragile nature. For such tissues, single-nuclei extraction is often much more efficient and therefore single-nuclei RNA-sequencing (snRNA-seq) presents an alternative to scRNA-seq. However, nuclear transcripts represent only a fraction of the transcriptome in a single cell, with snRNA-seq marked with inherent transcript enrichment and detection biases. Therefore, snRNA-seq may be inadequate for mapping important transcriptional signatures in adipose tissue. In this study, we compare the transcriptomic landscape of single nuclei isolated from preadipocytes and mature adipocytes across human white and brown adipocyte lineages, with whole-cell transcriptome. We demonstrate that snRNA-seq is capable of identifying the broad cell types present in scRNA-seq at all states of adipogenesis. However, we also explore how and why the nuclear transcriptome is biased and limited, and how it can be advantageous. We robustly characterize the enrichment of nuclear-localized transcripts and adipogenic regulatory lncRNAs in snRNA-seq, while also providing a detailed understanding for the preferential detection of long genes upon using this technique. To remove such technical detection biases, we propose a normalization strategy for a more accurate comparison of nuclear and cellular data. Finally, we demonstrate successful integration of scRNA-seq and snRNA-seq datasets with existing bioinformatic tools. Overall, our results illustrate the applicability of snRNA-seq for characterization of cellular diversity in the adipose tissue.

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The Tabula Sapiens: a single cell transcriptomic atlas of multiple organs from individual human donors

10.1101/2021.07.19.452956 ◽

2021 ◽

Author(s):

◽

Stephen R Quake

Keyword(s):

Single Cell ◽

Molecular Characterization ◽

Rna Splicing ◽

Detailed Comparison ◽

Cell Types ◽

Proliferative Potential ◽

Tissue Specific ◽

Multiple Organs ◽

Cell Transcriptome

In recent years there has been tremendous progress towards deep molecular characterization of cell types using single cell transcriptome sequencing. Here we report a single cell transcriptomic atlas comprising nearly 500,000 cells from 24 different human tissues and organs. In several instances multiple organs were analyzed from the same donor. Analyzing organs from the same individual controls for genetic background, age, environment, and epigenetic effects, and enables a detailed comparison of cell types that are shared between tissues. This resource provides a rich molecular characterization of more than 400 cell types, their distribution across tissues, and detailed information about tissue specific variation in gene expression. We have used the fact that multiple tissues came from the same donor to study the clonal distribution of T cells between tissues, to understand the tissue specific mutation rate in B cells, and to analyze the cell cycle state and proliferative potential of shared cell types across tissues. Finally, we have also used this data to characterize cell type specific RNA splicing and how such splicing varies across tissues within an individual.

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Novel Molecular Subgroups of Intrahepatic Cholangiocarcinoma Discovered by Single-Cell RNA Sequencing-Assisted Multi-Omics Analysis

10.21203/rs.3.rs-729739/v1 ◽

2021 ◽

Author(s):

Xuanwen Bao ◽

Qiong Li ◽

Jinzhang Chen ◽

Diyu Chen ◽

Chanqi Ye ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Intrahepatic Cholangiocarcinoma ◽

Poor Prognosis ◽

Molecular Subtypes ◽

Omics Analysis ◽

Single Cell Rna Sequencing ◽

Cell Transcriptome ◽

Single Cell Transcriptome

Abstract Intrahepatic cholangiocarcinoma (ICC) is a relatively rare but highly aggressive tumour type that responds poorly to chemotherapy and immunotherapy. Comprehensive molecular characterization of ICC is essential for the development of novel therapeutics. We performed a comprehensive multi-omics analysis of ICC via proteomic, whole-exon sequencing (WES) and single-cell RNA sequencing (scRNA-seq). We identified three molecular subtypes with deteriorating prognosis in ICC: chromatin remodelling, metabolism, and inflammation. The inflammation subtype was associated with a poor prognosis. Our random forest algorithm revealed that the mutation of KMT2D frequently occurred in the metabolism subtype and were associated with lower inflammatory activity. scRNA-seq further identified a novel APOE+C1QB+ macrophage subtype, which showed the capacity of promoting the inflammation subtype and contributing to a poor prognosis in ICC. Taking together, with a single-cell transcriptome-assisted multi-omics analysis, we identified novel molecular subtypes of ICC and valided APOE+C1QB+ TAMs as potential novel immunotherapy targets against ICC.

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Single-cell transcriptomic characterization of 20 organs and tissues from individual mice creates a Tabula Muris

10.1101/237446 ◽

2017 ◽

Cited By ~ 10

Author(s):

Nicholas Schaum ◽

Jim Karkanias ◽

Norma F Neff ◽

Andrew P. May ◽

Stephen R. Quake ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Cell Biology ◽

Model Organism ◽

High Sensitivity ◽

Cell Types ◽

Cell Transcriptome ◽

Full Length Transcript ◽

Low Coverage

The Tabula Muris ConsortiumWe have created a compendium of single cell transcriptome data from the model organism Mus musculus comprising more than 100,000 cells from 20 organs and tissues. These data represent a new resource for cell biology, revealing gene expression in poorly characterized cell populations and allowing for direct and controlled comparison of gene expression in cell types shared between tissues, such as T-lymphocytes and endothelial cells from distinct anatomical locations. Two distinct technical approaches were used for most tissues: one approach, microfluidic droplet-based 3’-end counting, enabled the survey of thousands of cells at relatively low coverage, while the other, FACS-based full length transcript analysis, enabled characterization of cell types with high sensitivity and coverage. The cumulative data provide the foundation for an atlas of transcriptomic cell biology.

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How to design a single-cell RNA-sequencing experiment: pitfalls, challenges and perspectives

Briefings in Bioinformatics ◽

10.1093/bib/bby007 ◽

2018 ◽

Vol 20 (4) ◽

pp. 1384-1394 ◽

Cited By ~ 21

Author(s):

Alessandra Dal Molin ◽

Barbara Di Camillo

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Messenger Rna ◽

Single Cells ◽

Cell Types ◽

Experimental Designs ◽

Sequencing Data ◽

Single Cell Rna Sequencing ◽

Sequencing Experiment ◽

Cell Transcriptome

Abstract The sequencing of the transcriptome of single cells, or single-cell RNA-sequencing, has now become the dominant technology for the identification of novel cell types in heterogeneous cell populations or for the study of stochastic gene expression. In recent years, various experimental methods and computational tools for analysing single-cell RNA-sequencing data have been proposed. However, most of them are tailored to different experimental designs or biological questions, and in many cases, their performance has not been benchmarked yet, thus increasing the difficulty for a researcher to choose the optimal single-cell transcriptome sequencing (scRNA-seq) experiment and analysis workflow. In this review, we aim to provide an overview of the current available experimental and computational methods developed to handle single-cell RNA-sequencing data and, based on their peculiarities, we suggest possible analysis frameworks depending on specific experimental designs. Together, we propose an evaluation of challenges and open questions and future perspectives in the field. In particular, we go through the different steps of scRNA-seq experimental protocols such as cell isolation, messenger RNA capture, reverse transcription, amplification and use of quantitative standards such as spike-ins and Unique Molecular Identifiers (UMIs). We then analyse the current methodological challenges related to preprocessing, alignment, quantification, normalization, batch effect correction and methods to control for confounding effects.

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Transcriptional profiling of mouse peripheral nerves to the single-cell level to build a sciatic nerve ATlas (SNAT)

eLife ◽

10.7554/elife.58591 ◽

2021 ◽

Vol 10 ◽

Author(s):

Daniel Gerber ◽

Jorge A Pereira ◽

Joanne Gerber ◽

Ge Tan ◽

Slavica Dimitrieva ◽

...

Keyword(s):

Sciatic Nerve ◽

Single Cell ◽

Rna Sequencing ◽

Schwann Cells ◽

Peripheral Nerves ◽

Transcriptional Profiling ◽

Cell Types ◽

Cell Transcriptome ◽

Transcriptome Resource ◽

Single Cell Transcriptome

Peripheral nerves are organ-like structures containing diverse cell types to optimize function. This interactive assembly includes mostly axon-associated Schwann cells, but also endothelial cells of supporting blood vessels, immune system-associated cells, barrier-forming cells of the perineurium surrounding and protecting nerve fascicles, and connective tissue-resident cells within the intra-fascicular endoneurium and inter-fascicular epineurium. We have established transcriptional profiles of mouse sciatic nerve-inhabitant cells to foster the fundamental understanding of peripheral nerves. To achieve this goal, we have combined bulk RNA sequencing of developing sciatic nerves up to the adult with focused bulk and single-cell RNA sequencing of Schwann cells throughout postnatal development, extended by single-cell transcriptome analysis of the full sciatic nerve both perinatally and in the adult. The results were merged in the transcriptome resource Sciatic Nerve ATlas (SNAT:https://www.snat.ethz.ch). We anticipate that insights gained from our multi-layered analysis will serve as valuable interactive reference point to guide future studies.

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MBRS-46. CHARTING NEOPLASTIC AND IMMUNE CELL HETEROGENEITY IN HUMAN AND GEM MODELS OF MEDULLOBLASTOMA USING scRNAseq

Neuro-Oncology ◽

10.1093/neuonc/noaa222.555 ◽

2020 ◽

Vol 22 (Supplement_3) ◽

pp. iii406-iii406

Author(s):

Andrew Donson ◽

Kent Riemondy ◽

Sujatha Venkataraman ◽

Ahmed Gilani ◽

Bridget Sanford ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Immune Cell ◽

Genetically Engineered ◽

Cellular Heterogeneity ◽

Cell Heterogeneity ◽

Transcriptomic Data ◽

Single Cell Rna Sequencing ◽

Transcript Profiles

Abstract We explored cellular heterogeneity in medulloblastoma using single-cell RNA sequencing (scRNAseq), immunohistochemistry and deconvolution of bulk transcriptomic data. Over 45,000 cells from 31 patients from all main subgroups of medulloblastoma (2 WNT, 10 SHH, 9 GP3, 11 GP4 and 1 GP3/4) were clustered using Harmony alignment to identify conserved subpopulations. Each subgroup contained subpopulations exhibiting mitotic, undifferentiated and neuronal differentiated transcript profiles, corroborating other recent medulloblastoma scRNAseq studies. The magnitude of our present study builds on the findings of existing studies, providing further characterization of conserved neoplastic subpopulations, including identification of a photoreceptor-differentiated subpopulation that was predominantly, but not exclusively, found in GP3 medulloblastoma. Deconvolution of MAGIC transcriptomic cohort data showed that neoplastic subpopulations are associated with major and minor subgroup subdivisions, for example, photoreceptor subpopulation cells are more abundant in GP3-alpha. In both GP3 and GP4, higher proportions of undifferentiated subpopulations is associated with shorter survival and conversely, differentiated subpopulation is associated with longer survival. This scRNAseq dataset also afforded unique insights into the immune landscape of medulloblastoma, and revealed an M2-polarized myeloid subpopulation that was restricted to SHH medulloblastoma. Additionally, we performed scRNAseq on 16,000 cells from genetically engineered mouse (GEM) models of GP3 and SHH medulloblastoma. These models showed a level of fidelity with corresponding human subgroup-specific neoplastic and immune subpopulations. Collectively, our findings advance our understanding of the neoplastic and immune landscape of the main medulloblastoma subgroups in both humans and GEM models.

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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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Molecular characteristics and spatial distribution of adult human corneal cell subtypes

Scientific Reports ◽

10.1038/s41598-021-94933-8 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Ann J. Ligocki ◽

Wen Fury ◽

Christian Gutierrez ◽

Christina Adler ◽

Tao Yang ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Cross Sections ◽

Cell Types ◽

Marker Genes ◽

Molecular Characteristics ◽

Transcriptional Level ◽

Human Cornea ◽

Adult Human ◽

And Migration

AbstractBulk RNA sequencing of a tissue captures the gene expression profile from all cell types combined. Single-cell RNA sequencing identifies discrete cell-signatures based on transcriptomic identities. Six adult human corneas were processed for single-cell RNAseq and 16 cell clusters were bioinformatically identified. Based on their transcriptomic signatures and RNAscope results using representative cluster marker genes on human cornea cross-sections, these clusters were confirmed to be stromal keratocytes, endothelium, several subtypes of corneal epithelium, conjunctival epithelium, and supportive cells in the limbal stem cell niche. The complexity of the epithelial cell layer was captured by eight distinct corneal clusters and three conjunctival clusters. These were further characterized by enriched biological pathways and molecular characteristics which revealed novel groupings related to development, function, and location within the epithelial layer. Moreover, epithelial subtypes were found to reflect their initial generation in the limbal region, differentiation, and migration through to mature epithelial cells. The single-cell map of the human cornea deepens the knowledge of the cellular subsets of the cornea on a whole genome transcriptional level. This information can be applied to better understand normal corneal biology, serve as a reference to understand corneal disease pathology, and provide potential insights into therapeutic approaches.

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