scAAVengr: Single-cell transcriptome-based quantification of engineered AAVs in non-human primate retina

AbstractAdeno-associated virus (AAV)-mediated gene therapies are rapidly advancing to the clinic, and AAV engineering has resulted in vectors with increased ability to deliver therapeutic genes. Although the choice of vector is critical, quantitative comparison of AAVs, especially in large animals, remains challenging. Here, we developed an efficient single-cell AAV engineering pipeline (scAAVengr) to quantify efficiency of AAV-mediated gene expression across all cell types. scAAVengr allows for definitive, head-to-head comparison of vectors in the same animal. To demonstrate proof-of-concept for the scAAVengr workflow, we quantified – with cell-type resolution – the abilities of naturally occurring and newly engineered AAVs to mediate gene expression in primate retina following intravitreal injection. A top performing variant, K912, was used to deliver SaCas9 and edit the rhodopsin gene in macaque retina, resulting in editing efficiency similar to infection rates detected by the scAAVengr workflow. These results validate scAAVengr as a powerful method for development of AAV vectors.

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Quantitative single-cell transcriptome-based ranking of engineered AAVs in human retinal explants

10.1101/2021.09.27.461256 ◽

2021 ◽

Author(s):

Zhouhuan Xi ◽

Bilge E. Ozturk ◽

Molly E. Johnson ◽

Leah C. Byrne

Keyword(s):

Single Cell ◽

Viral Vector ◽

Cell Types ◽

Adeno Associated Virus ◽

Altered Expression ◽

Gene Therapies ◽

Retinal Tissue ◽

Retinal Explants ◽

Cell Transcriptome ◽

Single Cell Transcriptome

Gene therapy is a rapidly developing field, and adeno-associated virus (AAV) is a leading viral vector candidate for therapeutic gene delivery. Newly engineered AAVs with improved abilities are now entering the clinic. It has proven challenging, however, to predict the translational potential of gene therapies developed in animal models, due to cross-species differences. Human retinal explants are the only available model of fully developed human retinal tissue, and are thus important for the validation of candidate AAV vectors. In this study, we evaluated 18 wildtype and engineered AAV capsids in human retinal explants using a recently developed single-cell RNA-Seq AAV engineering pipeline (scAAVengr). Human retinal explants retained the same major cell types as fresh retina, with similar expression of cell-specific markers, except for a cone population with altered expression of cone-specific genes. The efficiency and tropism of AAVs in human explants were quantified, with single-cell resolution. The top performing serotypes, K91, K912, and 7m8, were further validated in non-human primate and human retinal explants. Together, this study provides detailed information about the transcriptome profiles of retinal explants, and quantifies the infectivity of leading AAV serotypes in human retina, accelerating the translation of retinal gene therapies to the clinic.

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scAAVengr, a transcriptome-based pipeline for quantitative ranking of engineered AAVs with single-cell resolution

eLife ◽

10.7554/elife.64175 ◽

2021 ◽

Vol 10 ◽

Author(s):

Bilge E Öztürk ◽

Molly E Johnson ◽

Michael Kleyman ◽

Serhan Turunç ◽

Jing He ◽

...

Keyword(s):

Single Cell ◽

Genetic Research ◽

Cell Types ◽

Infection Rates ◽

Adeno Associated Virus ◽

Systemic Injection ◽

Gene Therapies ◽

Naturally Occurring ◽

Large Animals ◽

Therapy Center

Background:Adeno-associated virus (AAV)-mediated gene therapies are rapidly advancing to the clinic, and AAV engineering has resulted in vectors with increased ability to deliver therapeutic genes. Although the choice of vector is critical, quantitative comparison of AAVs, especially in large animals, remains challenging. Methods:Here, we developed an efficient single-cell AAV engineering pipeline (scAAVengr) to simultaneously quantify and rank efficiency of competing AAV vectors across all cell types in the same animal. Results:To demonstrate proof-of-concept for the scAAVengr workflow, we quantified - with cell-type resolution - the abilities of naturally occurring and newly engineered AAVs to mediate gene expression in primate retina following intravitreal injection. A top performing variant identified using this pipeline, K912, was used to deliver SaCas9 and edit the rhodopsin gene in macaque retina, resulting in editing efficiency similar to infection rates detected by the scAAVengr workflow. scAAVengr was then used to identify top-performing AAV variants in mouse brain, heart and liver following systemic injection. Conclusions:These results validate scAAVengr as a powerful method for development of AAV vectors. Funding:This work was supported by funding from the Ford Foundation, NEI/NIH, Research to Prevent Blindness, Foundation Fighting Blindness, UPMC Immune Transplant and Therapy Center, and the Van Sloun fund for canine genetic research.

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A Single-Cell Transcriptome Atlas for Zebrafish Development

10.1101/738344 ◽

2019 ◽

Cited By ~ 1

Author(s):

Dylan R. Farnsworth ◽

Lauren Saunders ◽

Adam C. Miller

Keyword(s):

Gene Expression ◽

Single Cell ◽

Cell Types ◽

Developmental Time ◽

Cell Type ◽

Specific Expression ◽

Transcriptional Profiles ◽

Larval Stages ◽

Cell Transcriptome ◽

Single Cell Transcriptome

ABSTRACTThe ability to define cell types and how they change during organogenesis is central to our understanding of animal development and human disease. Despite the crucial nature of this knowledge, we have yet to fully characterize all distinct cell types and the gene expression differences that generate cell types during development. To address this knowledge gap, we produced an Atlas using single-cell RNA-sequencing methods to investigate gene expression from the pharyngula to early larval stages in developing zebrafish. Our single-cell transcriptome Atlas encompasses transcriptional profiles from 44,102 cells across four days of development using duplicate experiments that confirmed high reproducibility. We annotated 220 identified clusters and highlighted several strategies for interrogating changes in gene expression associated with the development of zebrafish embryos at single-cell resolution. Furthermore, we highlight the power of this analysis to assign new cell-type or developmental stage-specific expression information to many genes, including those that are currently known only by sequence and/or that lack expression information altogether. The resulting Atlas is a resource of biologists to generate hypotheses for genetic (mutant) or functional analysis, to launch an effort to define the diversity of cell-types during zebrafish organogenesis, and to examine the transcriptional profiles that produce each cell type over developmental time.

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Single-Cell RNA-seq Identification of the Cellular Molecular Characteristics of Sporadic Bilateral Clear Cell Renal Cell Carcinoma

Frontiers in Oncology ◽

10.3389/fonc.2021.659251 ◽

2021 ◽

Vol 11 ◽

Author(s):

Zhenyuan Yu ◽

Wenhao Lu ◽

Cheng Su ◽

Yufang Lv ◽

Yu Ye ◽

...

Keyword(s):

Gene Expression ◽

Renal Cell Carcinoma ◽

Single Cell ◽

Renal Cell ◽

Clear Cell ◽

Cell Types ◽

Molecular Characteristics ◽

Left And Right ◽

Cell Transcriptome ◽

Single Cell Transcriptome

Bilateral renal cell carcinoma (RCC) is a rare disease that can be classified as either familial or sporadic. Studying the cellular molecular characteristics of sporadic bilateral RCC is important to provide guidance for clinical treatment. Cellular molecular characteristics can be expressed at the RNA level, especially at the single-cell degree. Single-cell RNA sequencing (scRNA-seq) was performed on bilateral clear cell RCC (ccRCC). A total of 3,575 and 3,568 high-quality single-cell transcriptome data were captured from the left and right tumour tissues, respectively. Gene characteristics were identified by comparing left and right tumours at the scRNA level. The complex cellular environment of bilateral ccRCC was presented by using scRNA-seq. Single-cell transcriptomic analysis revealed high similarity in gene expression among most of the cell types of bilateral RCCs but significant differences in gene expression among different site tumour cells. Additionally, the potential biological function of different tumour cell types was determined by gene ontology (GO) analysis. The transcriptome characteristics of tumour tissues in different locations at the single-cell transcriptome level were revealed through the scRNA-seq of bilateral sporadic ccRCC. This work provides new insights into the diagnosis and treatment of bilateral RCC.

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DSAVE: Detection of misclassified cells in single-cell RNA-Seq data

PLoS ONE ◽

10.1371/journal.pone.0243360 ◽

2020 ◽

Vol 15 (12) ◽

pp. e0243360

Author(s):

Johan Gustafsson ◽

Jonathan Robinson ◽

Juan S. Inda-Díaz ◽

Elias Björnson ◽

Rebecka Jörnsten ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Cell Types ◽

Rna Seq ◽

Cell Type ◽

Log Likelihood ◽

Single Cell Rna Sequencing ◽

Cell Transcriptome ◽

Average Gene ◽

Single Cell Transcriptome

Single-cell RNA sequencing has become a valuable tool for investigating cell types in complex tissues, where clustering of cells enables the identification and comparison of cell populations. Although many studies have sought to develop and compare different clustering approaches, a deeper investigation into the properties of the resulting populations is lacking. Specifically, the presence of misclassified cells can influence downstream analyses, highlighting the need to assess subpopulation purity and to detect such cells. We developed DSAVE (Down-SAmpling based Variation Estimation), a method to evaluate the purity of single-cell transcriptome clusters and to identify misclassified cells. The method utilizes down-sampling to eliminate differences in sampling noise and uses a log-likelihood based metric to help identify misclassified cells. In addition, DSAVE estimates the number of cells needed in a population to achieve a stable average gene expression profile within a certain gene expression range. We show that DSAVE can be used to find potentially misclassified cells that are not detectable by similar tools and reveal the cause of their divergence from the other cells, such as differing cell state or cell type. With the growing use of single-cell RNA-seq, we foresee that DSAVE will be an increasingly useful tool for comparing and purifying subpopulations in single-cell RNA-Seq datasets.

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CellView: Interactive exploration of high dimensional single cell RNA-seq data

10.1101/123810 ◽

2017 ◽

Cited By ~ 6

Author(s):

Mohan T. Bolisetty ◽

Michael L. Stitzel ◽

Paul Robson

Keyword(s):

Gene Expression ◽

Single Cell ◽

Web Application ◽

Cell Types ◽

High Dimensional ◽

Transcriptome Data ◽

Rna Seq ◽

Cell Transcriptome ◽

Single Cell Transcriptome ◽

Measure Gene Expression

Advances in high-throughput single cell transcriptomics technologies have revolutionized the study of complex tissues. It is now possible to measure gene expression across thousands of individual cells to define cell types and states. While powerful computational and statistical frameworks are emerging to analyze these complex datasets, a gap exists between this data and a biologist’s insight. The CellView web application fills this gap by providing easy and intuitive exploration of single cell transcriptome data.

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In search of a core cellular network in single cell transcriptome data

10.1101/2021.09.19.460857 ◽

2021 ◽

Author(s):

Ming Yang ◽

Benjamin R. Harrison ◽

Daniel E.L. Promislow

Keyword(s):

Gene Expression ◽

Single Cell ◽

Cellular Network ◽

Cell Types ◽

Specific Gene ◽

Transcriptome Data ◽

Cell Type ◽

Cell Type Specific ◽

Cell Transcriptome ◽

Single Cell Transcriptome

AbstractBackgroundAlong with specialized functions, cells of multicellular organisms also perform essential functions common to most if not all cells. Whether diverse cells do this by using the same set of genes, interacting in a fixed coordinated fashion to execute essential functions, remains a central question in biology. Single-cell RNA-sequencing (scRNA-seq) measures gene expression of individual cells, enabling researchers to discover gene expression patterns that contribute to the diversity of cell functions. Current analyses focus primarily on identifying differentially expressed genes across cells. However, patterns of co-expression between genes are probably more indicative of biological processes than are the expression of individual genes. Using single cell transcriptome data from the fly brain, here we focus on gene co-expression to search for a core cellular network.ResultsIn this study, we constructed cell type-specific gene co-expression networks using single cell transcriptome data of brains from the fruit fly, Drosophila melanogaster. We detected a set of highly coordinated genes preserved across cell types in fly brains and defined this set as the core cellular network. This core is very small compared with cell type-specific gene co-expression networks and shows dense connectivity. Modules within this core are enriched for basic cellular functions, such as translation and ATP metabolic processes, and gene members of these modules have distinct evolutionary signatures.ConclusionsOverall, we demonstrated that a core cellular network exists in diverse cell types of fly brains and this core exhibits unique topological, structural, functional and evolutionary properties.

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Single-cell Transcriptome Mapping Identifies Common and Cell-type Specific Genes Affected by Acute Delta9-tetrahydrocannabinol in Humans

10.1101/638254 ◽

2019 ◽

Author(s):

Ying Hu ◽

Mohini Ranganathan ◽

Chang Shu ◽

Xiaoyu Liang ◽

Suhas Ganesh ◽

...

Keyword(s):

Gene Expression ◽

Immune Response ◽

Single Cell ◽

Immune Cells ◽

Cell Types ◽

Gene Set Enrichment Analysis ◽

Cell Type ◽

Cell Type Specific ◽

Cell Transcriptome ◽

Single Cell Transcriptome

AbstractDelta 9-tetrahydrocannabinol (THC), the principal psychoactive constituent of cannabis, is also known to modulate immune response in peripheral cells. The mechanisms of THC’s effects on gene expression in human immune cells remains poorly understood. Combining a within-subject design with single cell transcriptome mapping, we report that administration of THC acutely alters gene expression in 15,973 human blood immune cells. Controlled for high inter-individual transcriptomic variability, we identified 294 transcriptome-wide significant genes among eight cell types including 69 common genes and 225 cell-type specific genes affected by acute THC administration, including those genes involving not only in immune response, cytokine production, but signal transduction, and cell proliferation and apoptosis. We revealed distinct transcriptomic sub-clusters affected by THC in major immune cell types where THC perturbed cell type-specific intracellular gene expression correlations. Gene set enrichment analysis further supports the findings of THC’s common and cell-type specific effects on immune response and cell toxicity. We found that THC alters the correlation of cannabinoid receptor gene, CNR2, with other genes in B cells, in which CNR2 showed the highest level of expression. This comprehensive cell-specific transcriptomic profiling identified novel genes regulated by THC and provides important insights into THC’s acute effects on immune function that may have important medical implications.

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Single cell transcriptome profiling of the human developing spinal cord reveals a conserved genetic programme with human specific features

10.1101/2021.04.12.439474 ◽

2021 ◽

Author(s):

Teresa Rayon ◽

Rory J. Maizels ◽

Christopher Barrington ◽

James Briscoe

Keyword(s):

Gene Expression ◽

Spinal Cord ◽

Single Cell ◽

Motor Neurons ◽

Neural Tube ◽

Transcriptome Profiling ◽

Cell Types ◽

Human Embryos ◽

Neuronal Populations ◽

Cell Transcriptome

AbstractThe spinal cord receives input from peripheral sensory neurons and controls motor output by regulating muscle innervating motor neurons. These functions are carried out by neural circuits comprising molecularly and physiologically distinct neuronal subtypes that are generated in a characteristic spatial-temporal arrangement from progenitors in the embryonic neural tube. The systematic mapping of gene expression in mouse embryos has provided insight into the diversity and complexity of cells in the neural tube. For human embryos, however, less information has been available. To address this, we used single cell mRNA sequencing to profile cervical and thoracic regions in four human embryos of Carnegie Stages (CS) CS12, CS14, CS17 and CS19 from Gestational Weeks (W) 4-7. In total we recovered the transcriptomes of 71,219 cells. Analysis of progenitor and neuronal populations from the neural tube, as well as cells of the peripheral nervous system, in dorsal root ganglia adjacent to the neural tube, identified dozens of distinct cell types and facilitated the reconstruction of the differentiation pathways of specific neuronal subtypes. Comparison with existing mouse datasets revealed the overall similarity of mouse and human neural tube development while highlighting specific features that differed between species. These data provide a catalogue of gene expression and cell type identity in the developing neural tube that will support future studies of sensory and motor control systems and can be explored at https://shiny.crick.ac.uk/scviewer/neuraltube/.

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