Single-Cell Mapping of Focused Ultrasound-Transfected Brain

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Abstract

Gene 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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Integrating single-cell transcriptomic data across different conditions, technologies, and species

Rahul Satija, Efthymia Papalexi, Peter Smibert … (2018)

Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to experiments representing a single condition, technology, or species to discover and define cellular phenotypes. However, identifying subpopulations of cells that are present across multiple data sets remains challenging. Here, we introduce an analytical strategy for integrating scRNA-seq data sets based on common sources of variation, enabling the identification of shared populations across data sets and downstream comparative analysis. We apply this approach, implemented in our R toolkit Seurat (http://satijalab.org/seurat/), to align scRNA-seq data sets of peripheral blood mononuclear cells under resting and stimulated conditions, hematopoietic progenitors sequenced using two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across data sets, while boosting statistical power through integrated analysis. Our approach facilitates general comparisons of scRNA-seq data sets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution.

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MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data

Greg Finak, Andrew McDavid, Masanao Yajima … (2015)

Single-cell transcriptomics reveals gene expression heterogeneity but suffers from stochastic dropout and characteristic bimodal expression distributions in which expression is either strongly non-zero or non-detectable. We propose a two-part, generalized linear model for such bimodal data that parameterizes both of these features. We argue that the cellular detection rate, the fraction of genes expressed in a cell, should be adjusted for as a source of nuisance variation. Our model provides gene set enrichment analysis tailored to single-cell data. It provides insights into how networks of co-expressed genes evolve across an experimental treatment. MAST is available at https://github.com/RGLab/MAST. Electronic supplementary material The online version of this article (doi:10.1186/s13059-015-0844-5) contains supplementary material, which is available to authorized users.

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Single-cell RNA-seq highlights intra-tumoral heterogeneity and malignant progression in pancreatic ductal adenocarcinoma

Junya Peng, Bao-Fa Sun, Chuan-Yuan Chen … (2019)

Pancreatic ductal adenocarcinoma (PDAC) is the most common type of pancreatic cancer featured with high intra-tumoral heterogeneity and poor prognosis. To comprehensively delineate the PDAC intra-tumoral heterogeneity and the underlying mechanism for PDAC progression, we employed single-cell RNA-seq (scRNA-seq) to acquire the transcriptomic atlas of 57,530 individual pancreatic cells from primary PDAC tumors and control pancreases, and identified diverse malignant and stromal cell types, including two ductal subtypes with abnormal and malignant gene expression profiles respectively, in PDAC. We found that the heterogenous malignant subtype was composed of several subpopulations with differential proliferative and migratory potentials. Cell trajectory analysis revealed that components of multiple tumor-related pathways and transcription factors (TFs) were differentially expressed along PDAC progression. Furthermore, we found a subset of ductal cells with unique proliferative features were associated with an inactivation state in tumor-infiltrating T cells, providing novel markers for the prediction of antitumor immune response. Together, our findings provide a valuable resource for deciphering the intra-tumoral heterogeneity in PDAC and uncover a connection between tumor intrinsic transcriptional state and T cell activation, suggesting potential biomarkers for anticancer treatment such as targeted therapy and immunotherapy.

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Author and article information

Journal

Journal ID (nlm-journal-id): 9421525

Journal ID (pubmed-jr-id): 8603

Journal ID (nlm-ta): Gene Ther

Journal ID (iso-abbrev): Gene Ther

Title: Gene therapy

ISSN (Print): 0969-7128

ISSN (Electronic): 1476-5462

Publication date Nihms-submitted: 27 January 2021

Publication date (Electronic): 01 February 2021

Publication date PMC-release: 01 August 2022

Electronic Location Identifier: 10.1038/s41434-021-00226-0

Affiliations

[1 ]Department of Biomedical Engineering, University of Virginia, Charlottesville, VA

[2 ]Department of Radiology & Medical Imaging, University of Virginia, Charlottesville, VA

Author notes

Author Contributions. Conceptualization - A.S.M., C.M.G., and R.J.P.; Methodology - A.S.M., C.M.G., and R.J.P.; Investigation - A.S.M., C.M.G., and R.J.P.; Formal Analysis - A.S.M., C.M.G., and R.J.P.; Writing – Original Draft Preparation, A.S.M. and R.J.P.; Writing – Review & Editing, A.S.M., C.M.G., and R.J.P.; Supervision, R.J.P.; Funding Acquisition - A.S.M. and R.J.P.

Corresponding Author: Richard J. Price, Ph.D., Department of Biomedical Engineering, Box 800759, Health System, University of Virginia, Charlottesville, VA 22908, USA, Telephone: (434) 924-0020, rprice@ 123456virginia.edu

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Manuscript ID: NIHMS1663831

DOI: 10.1038/s41434-021-00226-0

PMC ID: 8325700

PubMed ID: 33526842

SO-VID: ad8da188-db38-43d3-9b3a-36ef2037a242

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Single-Cell Mapping of Focused Ultrasound-Transfected Brain

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Abstract

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Neuronal Signaling

Most cited references 49

Integrating single-cell transcriptomic data across different conditions, technologies, and species

MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data

Single-cell RNA-seq highlights intra-tumoral heterogeneity and malignant progression in pancreatic ductal adenocarcinoma

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