Advances in single-cell sequencing: insights from organ transplantation

AbstractSingle-cell RNA sequencing (scRNA-seq) is a comprehensive technical tool to analyze intracellular and intercellular interaction data by whole transcriptional profile analysis. Here, we describe the application in biomedical research, focusing on the immune system during organ transplantation and rejection. Unlike conventional transcriptome analysis, this method provides a full map of multiple cell populations in one specific tissue and presents a dynamic and transient unbiased method to explore the progression of allograft dysfunction, starting from the stress response to final graft failure. This promising sequencing technology remarkably improves individualized organ rejection treatment by identifying decisive cellular subgroups and cell-specific interactions.

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Single-cell sequencing technology in tumor research

Clinica Chimica Acta ◽

10.1016/j.cca.2021.03.013 ◽

2021 ◽

Author(s):

Xue Bai ◽

Yuxuan Li ◽

Xuemei Zeng ◽

Qiang Zhao ◽

Zhiwei Zhang

Keyword(s):

Single Cell ◽

Sequencing Technology ◽

Single Cell Sequencing ◽

Tumor Research

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Single-cell mapping of focused ultrasound-transfected brain

Gene Therapy ◽

10.1038/s41434-021-00226-0 ◽

2021 ◽

Author(s):

A. S. Mathew ◽

C. M. Gorick ◽

R. J. Price

Keyword(s):

Single Cell ◽

Focused Ultrasound ◽

Cell Types ◽

Brain Cell ◽

Therapeutic Modality ◽

Full Potential ◽

Stress Genes ◽

Multiple Cell ◽

Differential Gene

AbstractGene delivery via focused ultrasound (FUS) mediated blood-brain barrier (BBB) opening is a disruptive therapeutic modality. Unlocking its full potential will require an understanding of how FUS parameters (e.g., peak-negative pressure (PNP)) affect transfected cell populations. Following plasmid (mRuby) delivery across the BBB with 1 MHz FUS, we used single-cell RNA-sequencing to ascertain that distributions of transfected cell types were highly dependent on PNP. Cells of the BBB (i.e., endothelial cells, pericytes, and astrocytes) were enriched at 0.2 MPa PNP, while transfection of cells distal to the BBB (i.e., neurons, oligodendrocytes, and microglia) was augmented at 0.4 MPa PNP. PNP-dependent differential gene expression was observed for multiple cell types. Cell stress genes were upregulated proportional to PNP, independent of cell type. Our results underscore how FUS may be tuned to bias transfection toward specific brain cell types in vivo and predict how those cells will respond to transfection.

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PhyliCS: a Python library to explore scCNA data and quantify spatial tumor heterogeneity

BMC Bioinformatics ◽

10.1186/s12859-021-04277-3 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Marilisa Montemurro ◽

Elena Grassi ◽

Carmelo Gabriele Pizzino ◽

Andrea Bertotti ◽

Elisa Ficarra ◽

...

Keyword(s):

Spatial Heterogeneity ◽

Single Cell ◽

Tumor Heterogeneity ◽

Third Party ◽

Sequencing Technology ◽

Reduction Methods ◽

Visualization Tools ◽

Cell Subpopulations ◽

Multiple Samples ◽

Analytical Approaches

Abstract Background Tumors are composed by a number of cancer cell subpopulations (subclones), characterized by a distinguishable set of mutations. This phenomenon, known as intra-tumor heterogeneity (ITH), may be studied using Copy Number Aberrations (CNAs). Nowadays ITH can be assessed at the highest possible resolution using single-cell DNA (scDNA) sequencing technology. Additionally, single-cell CNA (scCNA) profiles from multiple samples of the same tumor can in principle be exploited to study the spatial distribution of subclones within a tumor mass. However, since the technology required to generate large scDNA sequencing datasets is relatively recent, dedicated analytical approaches are still lacking. Results We present PhyliCS, the first tool which exploits scCNA data from multiple samples from the same tumor to estimate whether the different clones of a tumor are well mixed or spatially separated. Starting from the CNA data produced with third party instruments, it computes a score, the Spatial Heterogeneity score, aimed at distinguishing spatially intermixed cell populations from spatially segregated ones. Additionally, it provides functionalities to facilitate scDNA analysis, such as feature selection and dimensionality reduction methods, visualization tools and a flexible clustering module. Conclusions PhyliCS represents a valuable instrument to explore the extent of spatial heterogeneity in multi-regional tumour sampling, exploiting the potential of scCNA data.

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Identification of heterogeneity and prognostic key genes associated with uveal melanoma using single-cell RNA-sequencing technology

Melanoma Research ◽

10.1097/cmr.0000000000000783 ◽

2021 ◽

Vol Publish Ahead of Print ◽

Author(s):

Songlin Sun ◽

Rui Shi ◽

Liang Xu ◽

Fengyuan Sun

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Uveal Melanoma ◽

Sequencing Technology ◽

Single Cell Rna Sequencing ◽

Key Genes

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Single-cell analysis reveals cellular heterogeneity and molecular determinants of hypothalamic leptin-receptor cells

10.1101/2020.07.23.217729 ◽

2020 ◽

Author(s):

N. Kakava-Georgiadou ◽

J.F. Severens ◽

A.M. Jørgensen ◽

K.M. Garner ◽

M.C.M Luijendijk ◽

...

Keyword(s):

Single Cell ◽

Leptin Receptor ◽

Single Cell Analysis ◽

Cell Types ◽

Cellular Heterogeneity ◽

Molecular Signature ◽

Neuronal Populations ◽

Hypothalamic Nuclei ◽

Satiety Hormone ◽

Multiple Cell

AbstractHypothalamic nuclei which regulate homeostatic functions express leptin receptor (LepR), the primary target of the satiety hormone leptin. Single-cell RNA sequencing (scRNA-seq) has facilitated the discovery of a variety of hypothalamic cell types. However, low abundance of LepR transcripts prevented further characterization of LepR cells. Therefore, we perform scRNA-seq on isolated LepR cells and identify eight neuronal clusters, including three uncharacterized Trh-expressing populations as well as 17 non-neuronal populations including tanycytes, oligodendrocytes and endothelial cells. Food restriction had a major impact on Agrp neurons and changed the expression of obesity-associated genes. Multiple cell clusters were enriched for GWAS signals of obesity. We further explored changes in the gene regulatory landscape of LepR cell types. We thus reveal the molecular signature of distinct populations with diverse neurochemical profiles, which will aid efforts to illuminate the multi-functional nature of leptin’s action in the hypothalamus.

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