scholarly journals A method for rapid flow-cytometric isolation of endothelial nuclei and RNA from archived frozen brain tissue

2021 ◽  
Author(s):  
Amy L. Kimble ◽  
Jordan Silva ◽  
Omar M. Omar ◽  
Melissa Murphy ◽  
Jessica A. Hensel ◽  
...  
Keyword(s):  
Endothelial Cells ◽  
Human Brain ◽  
Single Cell ◽  
Single Cell Analysis ◽  
Specific Marker ◽  
Bulk Analysis ◽  
Fresh Tissue ◽  
Rna Transcripts ◽  
Tissue Digestion ◽  
Brain Tissues

AbstractEndothelial cells are important contributors to brain development, physiology, and disease. Although RNA sequencing has contributed to the understanding of brain endothelial cell diversity, bulk analysis and single-cell approaches have relied on fresh tissue digestion protocols for the isolation of single endothelial cells and flow cytometry-based sorting on surface markers or transgene expression. These approaches are limited in the analysis of the endothelium in human brain tissues, where fresh samples are difficult to obtain. Here, we developed an approach to examine endothelial RNA expression by using an endothelial-specific marker to isolate nuclei from abundant archived frozen brain tissues. We show that this approach rapidly and reliably extracts endothelial nuclei from frozen mouse brain samples, and importantly, from archived frozen human brain tissues. Furthermore, isolated RNA transcript levels are closely correlated with expression in whole cells from tissue digestion protocols and are enriched in endothelial markers and depleted of markers of other brain cell types. As high-quality RNA transcripts could be obtained from as few as 100 nuclei in archived frozen human brain tissues, we predict that this approach should be useful for both bulk analysis of endothelial RNA transcripts in human brain tissues as well as single-cell analysis of endothelial sub-populations.

scholarly journals A Method for Rapid Flow-cytometric Isolation of Endothelial Nuclei and RNA from Archived Frozen Brain Tissue

2021 ◽  
Author(s):  
Amy L Kimble ◽  
Jordan Silva ◽  
Melissa Murphy ◽  
Jessica A Hensel ◽  
Sarah-Anne E Nicholas ◽  
...  
Keyword(s):  
Endothelial Cells ◽  
Human Brain ◽  
Single Cell ◽  
Single Cell Analysis ◽  
Specific Marker ◽  
Bulk Analysis ◽  
Fresh Tissue ◽  
Rna Transcripts ◽  
Tissue Digestion ◽  
Brain Tissues

Endothelial cells are important contributors to brain development, physiology, and disease. Although RNA sequencing has contributed to the understanding of brain endothelial cell diversity, bulk analysis and single-cell approaches have relied on fresh tissue digestion protocols for the isolation of single endothelial cells and flow cytometry-based sorting on surface markers or transgene expression. These approaches are limited in the analysis of the endothelium in human brain tissues, where fresh samples are difficult to obtain. Here, we developed an approach to examine endothelial RNA expression by using an endothelial-specific marker to isolate nuclei from abundant archived frozen brain tissues. We show that this approach rapidly and reliably extracts endothelial nuclei from frozen mouse brain samples, and importantly, from archived frozen human brain tissues. Furthermore, isolated RNA transcript levels are closely correlated with expression in whole cells from tissue digestion protocols and are enriched in endothelial markers and depleted of markers of other brain cell types. As high-quality RNA transcripts could be obtained from as few as 100 nuclei in archived frozen human brain tissues, we predict that this approach should be useful for both bulk analysis of endothelial RNA transcripts in human brain tissues as well as single-cell analysis of endothelial sub-populations.

scholarly journals Microfluidic platform accelerates tissue processing into single cells for molecular analysis and primary culture models

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Jeremy A. Lombardo ◽  
Marzieh Aliaghaei ◽  
Quy H. Nguyen ◽  
Kai Kessenbrock ◽  
Jered B. Haun
Keyword(s):  
Single Cell ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Cell Analysis ◽  
Processing Technologies ◽  
Microfluidic Platform ◽  
Tissue Processing ◽  
Single Cell Rna Sequencing ◽  
Culture Models ◽  
Tissue Digestion

AbstractTissues are complex mixtures of different cell subtypes, and this diversity is increasingly characterized using high-throughput single cell analysis methods. However, these efforts are hindered, as tissues must first be dissociated into single cell suspensions using methods that are often inefficient, labor-intensive, highly variable, and potentially biased towards certain cell subtypes. Here, we present a microfluidic platform consisting of three tissue processing technologies that combine tissue digestion, disaggregation, and filtration. The platform is evaluated using a diverse array of tissues. For kidney and mammary tumor, microfluidic processing produces 2.5-fold more single cells. Single cell RNA sequencing further reveals that endothelial cells, fibroblasts, and basal epithelium are enriched without affecting stress response. For liver and heart, processing time is dramatically reduced. We also demonstrate that recovery of cells from the system at periodic intervals during processing increases hepatocyte and cardiomyocyte numbers, as well as increases reproducibility from batch-to-batch for all tissues.

scholarly journals Connectivity analysis of single cell RNA-sequencing derived transcriptional signature of lymphangioleiomyomatosis

2020 ◽  
Author(s):  
Naim Al Mahi ◽  
Erik Y. Zhang ◽  
Susan Sherman ◽  
Jane J. Yu ◽  
Mario Medvedovic
Keyword(s):  
Single Cell ◽  
Single Cell Analysis ◽  
Connectivity Analysis ◽  
Rna Seq ◽  
Childbearing Age ◽  
Bulk Analysis ◽  
Integrated Network ◽  
Transcriptional Signature ◽  
Disease Signatures ◽  
Novel Drug

ABSTRACTLymphangioleiomyomatosis (LAM) is a rare pulmonary disease affecting women of childbearing age that is characterized by the aberrant proliferation of smooth-muscle (SM)-like cells and emphysema-like lung remodeling. In LAM, mutations in TSC1 or TSC2 genes results in the activation of the mechanistic target of rapamycin complex 1 (mTORC1) and thus sirolimus, an mTORC1 inhibitor, has been approved by FDA to treat LAM patients. Sirolimus stabilizes lung function and improves symptoms. However, the disease recurs with discontinuation of the drug, potentially because of the sirolimus-induced refractoriness of the LAM cells. Therefore, there is a critical need to identify remission inducing cytocidal treatments for LAM. Recently released Library of Integrated Network-based Cellular Signatures (LINCS) L1000 transcriptional signatures of chemical perturbations has opened new avenues to study cellular responses to existing drugs and new bioactive compounds. Connecting transcriptional signature of a disease to these chemical perturbation signatures to identify bioactive chemicals that can “revert” the disease signatures can lead to novel drug discovery. We developed methods for constructing disease transcriptional signatures and performing connectivity analysis using single cell RNA-seq data. The methods were applied in the analysis of scRNA-seq data of naïve and sirolimus-treated LAM cells. The single cell connectivity analyses implicated mTORC1 inhibitors as capable of reverting the LAM transcriptional signatures while the corresponding standard bulk analysis did not. This indicates the importance of using single cell analysis in constructing disease signatures. The analysis also implicated other classes of drugs, CDK, MEK/MAPK and EGFR/JAK inhibitors, as potential therapeutic agents for LAM.

Single-Cell Analysis of Costimulation by B Cells, Endothelial Cells, and Fibroblasts Demonstrates Heterogeneity in Responses of CD4+ Memory T Cells

1999 ◽  
Vol 194 (2) ◽  
pp. 150-161 ◽  
Author(s):  
Lisa L. Salazar Murphy ◽  
Melissa M. Mazanet ◽  
Angela C. Taylor ◽  
Javier Mestas ◽  
Christopher C.W. Hughes
Keyword(s):  
T Cells ◽  
Endothelial Cells ◽  
B Cells ◽  
Single Cell ◽  
Single Cell Analysis ◽  
Memory T Cells ◽  
Cell Analysis

scholarly journals ASAP: A web-based platform for the analysis and interactive visualization of single-cell RNA-seq data

2016 ◽  
Author(s):  
Vincent Gardeux ◽  
Fabrice David ◽  
Adrian Shajkofci ◽  
Petra C Schwalie ◽  
Bart Deplancke
Keyword(s):  
Single Cell ◽  
Single Cell Analysis ◽  
Transcriptome Profiling ◽  
Cell Types ◽  
Complete Analysis ◽  
Marker Genes ◽  
Specific Marker ◽  
Rna Seq ◽  
Web Based ◽  
Wide Range

AbstractMotivationSingle-cell RNA-sequencing (scRNA-seq) allows whole transcriptome profiling of thousands of individual cells, enabling the molecular exploration of tissues at the cellular level. Such analytical capacity is of great interest to many research groups in the world, yet, these groups often lack the expertise to handle complex scRNA-seq data sets.ResultsWe developed a fully integrated, web-based platform aimed at the complete analysis of scRNA-seq data post genome alignment: from the parsing, filtering, and normalization of the input count data files, to the visual representation of the data, identification of cell clusters, differentially expressed genes (including cluster-specific marker genes), and functional gene set enrichment. This Automated Single-cell Analysis Pipeline (ASAP) combines a wide range of commonly used algorithms with sophisticated visualization tools. Compared with existing scRNA-seq analysis platforms, researchers (including those lacking computational expertise) are able to interact with the data in a straightforward fashion and in real time. Furthermore, given the overlap between scRNA-seq and bulk RNA-seq analysis workflows, ASAP should conceptually be broadly applicable to any RNA-seq dataset. As a validation, we demonstrate how we can use ASAP to simply reproduce the results from a single-cell study of 91 mouse cells involving five distinct cell types.AvailabilityThe tool is freely available at http://[email protected]

scholarly journals Single cell analysis of population-wide nuclear and cytosolic drug responses using high-content FRET imaging: measuring protein kinase activation in rat primary striatal neurons

2021 ◽  
Author(s):  
Jace Jones-Tabah ◽  
Ryan D. Martin ◽  
Jason C. Tanny ◽  
Paul B.S. Clarke ◽  
Terence E. Hébert
Keyword(s):  
Single Cell ◽  
Single Cell Analysis ◽  
Fluorescent Proteins ◽  
Rank Order ◽  
Drug Application ◽  
Population Level ◽  
Cell Analysis ◽  
Striatal Neurons ◽  
Bulk Analysis ◽  
Multiple Drugs

AbstractGenetically-encoded biosensors are used to track biochemical activities in living cells by measuring changes in fluorescence emitted by one or more fluorescent proteins. In the present article, we describe the application of genetically-encoded FRET biosensors with high content microscopy to image the signaling responses of thousands of neurons in response to drug treatments. We applied this approach to reveal intercellular variation in signaling responses among cultured striatal neurons stimulated with multiple drugs. The striatum is largely composed of medium-spiny GABAergic neurons which are divided into two broad sub-types based in part on their expression of dopamine D1 vs. D2 receptors. Using high content FRET imaging and immunofluorescence, we identified neuronal sub-populations with unique responses to pharmacological manipulation. Focusing on dopamine- and glutamate-regulated PKA and ERK1/2 signaling in both the cytoplasm and nucleus, we identified pronounced intercellular differences, in both the magnitude and kinetics of signaling responses to drug application. Importantly, we found that a conventional “bulk” analysis that included all cells in culture yielded a different rank order of drug potency than that revealed by our single-cell analysis. The high degree of heterogeneity that we observed at the single cell level would not have been detectable using common population-level analyses, derived for example from western blotting or plate reader-based measurements. In conclusion, our single-cell analytical approach highlights the limitations of population-level analyses, and provides a novel way to study signaling biology.

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