A Powerful Method for Transcriptional Profiling of Specific Cell Types in Eukaryotes: Laser-Assisted Microdissection and RNA Sequencing

Abstract Thousands of frozen, archived tissue samples from the human central nervous system (CNS) are currently available in brain banks. As recent developments in RNA sequencing technologies are beginning to elucidate the cellular diversity present within the human CNS, it is becoming clear that an understanding of this diversity would greatly benefit from deeper transcriptional analyses. Single cell and single nucleus RNA profiling provide one avenue to decipher this heterogeneity. An alternative, complementary approach is to profile isolated, pre-defined cell types and use methods that can be applied to many archived human tissue samples that have been stored long-term. Here, we developed FIN-Seq (Frozen Immunolabeled Nuclei Sequencing), a method that accomplishes these goals. FIN-Seq uses immunohistochemical isolation of nuclei of specific cell types from frozen human tissue, followed by bulk RNA-Sequencing. We applied this method to frozen postmortem samples of human cerebral cortex and retina and were able to identify transcripts, including low abundance transcripts, in specific cell types.

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Decision letter: Probe-Seq enables transcriptional profiling of specific cell types from heterogeneous tissue by RNA-based isolation

10.7554/elife.51452.sa1 ◽

2019 ◽

Author(s):

Sydney Shaffer

Keyword(s):

Transcriptional Profiling ◽

Cell Types ◽

Specific Cell ◽

Heterogeneous Tissue

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Probe-Seq enables transcriptional profiling of specific cell types from heterogeneous tissue by RNA-based isolation

10.1101/735738 ◽

2019 ◽

Author(s):

Ryoji Amamoto ◽

Mauricio D. Garcia ◽

Emma R. West ◽

Jiho Choi ◽

Sylvain W. Lapan ◽

...

Keyword(s):

Transcriptional Profiling ◽

Cell Types ◽

Cell Populations ◽

Specific Cell ◽

Rna Profiling ◽

Complementary Method ◽

Dissociated Cells ◽

Multiple Cell ◽

Heterogeneous Tissue

ABSTRACTRecent transcriptional profiling technologies are uncovering previously-undefined cell populations and molecular markers at an unprecedented pace. While single cell RNA (scRNA) sequencing is an attractive approach for unbiased transcriptional profiling of all cell types, a complementary method to isolate and sequence specific cell populations from heterogeneous tissue remains challenging. Here, we developed Probe-Seq, which allows deep transcriptional profiling of specific cell types isolated using RNA as the defining feature. Dissociated cells are labelled using fluorescent in situ hybridization (FISH) for RNA, and then isolated by fluorescent activated cell sorting (FACS). We used Probe-Seq to purify and profile specific cell types from mouse, human, and chick retinas, as well as the Drosophila midgut. Probe-Seq is compatible with frozen nuclei, making cell types within archival tissue immediately accessible. As it can be multiplexed, combinations of markers can be used to create specificity. Multiplexing also allows for the isolation of multiple cell types from one cell preparation. Probe-Seq should enable RNA profiling of specific cell types from any organism.

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Building an RNA Sequencing Transcriptome of the Central Nervous System

The Neuroscientist ◽

10.1177/1073858415610541 ◽

2016 ◽

Vol 22 (6) ◽

pp. 579-592 ◽

Cited By ~ 12

Author(s):

Xiaomin Dong ◽

Yanan You ◽

Jia Qian Wu

Keyword(s):

Gene Expression ◽

Central Nervous System ◽

Nervous System ◽

Rna Sequencing ◽

Large Scale ◽

Expression Profiles ◽

Cell Types ◽

Specific Cell ◽

Rna Seq ◽

The Central Nervous System

The composition and function of the central nervous system (CNS) is extremely complex. In addition to hundreds of subtypes of neurons, other cell types, including glia (astrocytes, oligodendrocytes, and microglia) and vascular cells (endothelial cells and pericytes) also play important roles in CNS function. Such heterogeneity makes the study of gene transcription in CNS challenging. Transcriptomic studies, namely the analyses of the expression levels and structures of all genes, are essential for interpreting the functional elements and understanding the molecular constituents of the CNS. Microarray has been a predominant method for large-scale gene expression profiling in the past. However, RNA-sequencing (RNA-Seq) technology developed in recent years has many advantages over microarrays, and has enabled building more quantitative, accurate, and comprehensive transcriptomes of the CNS and other systems. The discovery of novel genes, diverse alternative splicing events, and noncoding RNAs has remarkably expanded the complexity of gene expression profiles and will help us to understand intricate neural circuits. Here, we discuss the procedures and advantages of RNA-Seq technology in mammalian CNS transcriptome construction, and review the approaches of sample collection as well as recent progress in building RNA-Seq-based transcriptomes from tissue samples and specific cell types.

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Bayesian estimation of cell-type-specific gene expression per bulk sample with prior derived from single-cell data

10.1101/2020.08.05.238949 ◽

2020 ◽

Cited By ~ 1

Author(s):

Jiebiao Wang ◽

Kathryn Roeder ◽

Bernie Devlin

Keyword(s):

Gene Expression ◽

Single Cell ◽

Rna Sequencing ◽

Cell Types ◽

Autism Spectrum ◽

Specific Cell ◽

Expression Data ◽

Cell Type ◽

Disease Etiology ◽

Cell Type Specific

AbstractWhen assessed over a large number of samples, bulk RNA sequencing provides reliable data for gene expression at the tissue level. Single-cell RNA sequencing (scRNA-seq) deepens those analyses by evaluating gene expression at the cellular level. Both data types lend insights into disease etiology. With current technologies, however, scRNA-seq data are known to be noisy. Moreover, constrained by costs, scRNA-seq data are typically generated from a relatively small number of subjects, which limits their utility for some analyses, such as identification of gene expression quantitative trait loci (eQTLs). To address these issues while maintaining the unique advantages of each data type, we develop a Bayesian method (bMIND) to integrate bulk and scRNA-seq data. With a prior derived from scRNA-seq data, we propose to estimate sample-level cell-type-specific (CTS) expression from bulk expression data. The CTS expression enables large-scale sample-level downstream analyses, such as detecting CTS differentially expressed genes (DEGs) and eQTLs. Through simulations, we demonstrate that bMIND improves the accuracy of sample-level CTS expression estimates and power to discover CTS-DEGs when compared to existing methods. To further our understanding of two complex phenotypes, autism spectrum disorder and Alzheimer’s disease, we apply bMIND to gene expression data of relevant brain tissue to identify CTS-DEGs. Our results complement findings for CTS-DEGs obtained from snRNA-seq studies, replicating certain DEGs in specific cell types while nominating other novel genes in those cell types. Finally, we calculate CTS-eQTLs for eleven brain regions by analyzing GTEx V8 data, creating a new resource for biological insights.

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Equivalent high-resolution identification of neuronal cell types with single-nucleus and single-cell RNA-sequencing

10.1101/239749 ◽

2017 ◽

Cited By ~ 6

Author(s):

Trygve E. Bakken ◽

Rebecca D. Hodge ◽

Jeremy M. Miller ◽

Zizhen Yao ◽

Thuc N. Nguyen ◽

...

Keyword(s):

High Resolution ◽

Single Cell ◽

Rna Sequencing ◽

Transcriptional Profiling ◽

Neuronal Cell ◽

Cell Types ◽

Matched Pair ◽

Rna Seq ◽

Nuclear Rna ◽

Single Nucleus

AbstractTranscriptional profiling of complex tissues by RNA-sequencing of single nuclei presents some advantages over whole cell analysis. It enables unbiased cellular coverage, lack of cell isolation-based transcriptional effects, and application to archived frozen specimens. Using a well-matched pair of single-nucleus RNA-seq (snRNA-seq) and single-cell RNA-seq (scRNA-seq) SMART-Seq v4 datasets from mouse visual cortex, we demonstrate that similarly high-resolution clustering of closely related neuronal types can be achieved with both methods if intronic sequences are included in nuclear RNA-seq analysis. More transcripts are detected in individual whole cells (∼11,000 genes) than nuclei (∼7,000 genes), but the majority of genes have similar detection across cells and nuclei. We estimate that the nuclear proportion of total cellular mRNA varies from 20% to over 50% for large and small pyramidal neurons, respectively. Together, these results illustrate the high information content of nuclear RNA for characterization of cellular diversity in brain tissues.

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Transcriptional Profiling of Somatostatin Interneurons in the Spinal Dorsal Horn

10.1101/215657 ◽

2017 ◽

Author(s):

Alexander Chamessian ◽

Michael Young ◽

Yawar Qadri ◽

Temugin Berta ◽

Ru-Rong Ji ◽

...

Keyword(s):

Rna Sequencing ◽

Dorsal Horn ◽

Spinal Dorsal Horn ◽

Transcriptional Profiling ◽

Expression Patterns ◽

Specific Cell ◽

Gene Repertoire ◽

Rna Seq ◽

Cell Type ◽

Spinal Dorsal

AbstractThe spinal dorsal horn (SDH) is comprised of distinct neuronal populations that process different somatosensory modalities. Somatostatin (SST)-expressing interneurons in the SDH have been implicated specifically in mediating mechanical pain. Identifying the transcriptomic profile of SST neurons could elucidate the unique genetic features of this population and enable selective analgesic targeting. To that end, we combined the Isolation of Nuclei Tagged in Specific Cell Types (INTACT) method and Fluorescence Activated Nuclei Sorting (FANS) to capture tagged SST nuclei in the SDH of adult male mice. Using RNA-sequencing (RNA-seq), we uncovered more than 13,000 genes. Differential gene expression analysis revealed more than 900 genes with at least 2-fold enrichment. In addition to many known dorsal horn genes, we identified and validated several novel transcripts from pharmacologically tractable functional classes: Carbonic Anhydrase 12 (Car12), Phosphodiesterase 11A (Pde11a), Protease-Activated Receptor 3 (F2rl2) and G-protein Coupled Receptor 26 (Gpr26). In situ hybridization of these novel genes revealed differential expression patterns in the SDH, demonstrating the presence of transcriptionally distinct subpopulations within the SST population. Pathway analysis revealed several enriched signaling pathways including cyclic AMP-mediated signaling, Nitric Oxide Synthase signaling, and voltage-gated calcium channels, highlighting the importance of these pathways to SST neuron function. Overall, our findings provide new insights into the gene repertoire of SST dorsal horn neurons and reveal several candidate targets for pharmacological modulation of this pain-mediating population.Significance StatementSomatostatin(SST)-expressing interneurons in the spinal dorsal horn (SDH) are required for the perception of mechanical pain. Identifying the distinctive genes expressed by SST neurons could facilitate the development of novel, circuit-targeting analgesics. Thus, we applied cell type-specific RNA-sequencing (RNA-seq) to provide the first transcriptional profile of SST neurons in the SDH. Bioinformatic analysis revealed hundreds of genes enriched in SST neurons, including several previously undescribed genes from druggable classes (Car12, Pde11a, F2rl2 and Gpr26). Taken together, our study unveils a comprehensive transcriptional signature for SST neurons, highlights promising candidate genes for future analgesic development, and establishes a flexible method for transcriptional profiling of any spinal cord cell type.

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RNA sequencing of kidney distal tubule cells reveals multiple mediators of chronic aldosterone action

Physiological Genomics ◽

10.1152/physiolgenomics.00084.2017 ◽

2018 ◽

Vol 50 (5) ◽

pp. 343-354 ◽

Cited By ~ 9

Author(s):

Søren Brandt Poulsen ◽

Kavee Limbutara ◽

Robert A. Fenton ◽

Trairak Pisitkun ◽

Birgitte Mønster Christensen

Keyword(s):

Rna Sequencing ◽

Collecting Duct ◽

Distal Tubule ◽

Cell Types ◽

Sodium Reabsorption ◽

Specific Cell ◽

Cortical Collecting Duct ◽

Principal Cells ◽

Mrna Transcriptome

The renal aldosterone-sensitive distal tubule (ASDT) is crucial for sodium reabsorption and blood pressure regulation. The ASDT consists of the late distal convoluted tubule (DCT2), connecting tubule (CNT), and collecting duct. Due to difficulties in isolating epithelial cells from the ASDT in large quantities, few transcriptome studies have been performed on this segment. Moreover, no studies exist on isolated DCT2 and CNT cells (excluding intercalated cells), and the role of aldosterone for regulating the transcriptome of these specific cell types is largely unknown. A mouse model expressing eGFP in DCT2/CNT/initial cortical collecting duct (iCCD) principal cells was exploited to facilitate the isolation of these cells in high number and purity. Combined with deep RNA sequencing technology, a comprehensive catalog of chronic aldosterone-regulated transcripts from enriched DCT2/CNT/iCCD principal cells was generated. There were 257 significantly downregulated and 290 upregulated transcripts in response to aldosterone ( P < 0.05). The RNA sequencing confirmed aldosterone regulation of well-described aldosterone targets including Sgk1 and Tsc22d3. Changes in selected transcripts such as S100a1 and Cldn4 were confirmed by RT-qPCR. The RNA sequencing showed downregulation of Nr3c2 encoding the mineralocorticoid receptor (MR), and cell line experiments showed a parallel decrease in MR protein. Furthermore, a large number of transcripts encoding transcription factors were downregulated. An extensive mRNA transcriptome reconstruction of an enriched CNT/iCCD principal cell population was also generated. The results provided a comprehensive database of aldosterone-regulated transcripts in the ASDT, allowing development of novel hypotheses for the action of aldosterone.

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Harnessing Single-Cell RNA Sequencing to Better Understand How Diseased Cells Behave the Way They Do in Cardiovascular Disease

Arteriosclerosis Thrombosis and Vascular Biology ◽

10.1161/atvbaha.120.314776 ◽

2020 ◽

Author(s):

Farwah Iqbal ◽

Adrien Lupieri ◽

Masanori Aikawa ◽

Elena Aikawa

Keyword(s):

Cardiovascular Disease ◽

Single Cell ◽

Rna Sequencing ◽

Molecular Mechanisms ◽

Cell Types ◽

Specific Cell ◽

Calcific Aortic Valve Disease ◽

Cardiovascular Tissues ◽

Single Cell Rna Sequencing ◽

Cell Phenotypes

The transition of healthy arteries and cardiac valves into dense, cell-rich, calcified, and fibrotic tissues is driven by a complex interplay of both cellular and molecular mechanisms. Specific cell types in these cardiovascular tissues become activated following the exposure to systemic stimuli including circulating lipoproteins or inflammatory mediators. This activation induces multiple cascades of events where changes in cell phenotypes and activation of certain receptors may trigger multiple pathways and specific alterations to the transcriptome. Modifications to the transcriptome and proteome can give rise to pathological cell phenotypes and trigger mechanisms that exacerbate inflammation, proliferation, calcification, and recruitment of resident or distant cells. Accumulating evidence suggests that each cell type involved in vascular and valvular diseases is heterogeneous. Single-cell RNA sequencing is a transforming medical research tool that enables the profiling of the unique fingerprints at single-cell levels. Its applications have allowed the construction of cell atlases including the mammalian heart and tissue vasculature and the discovery of new cell types implicated in cardiovascular disease. Recent advances in single-cell RNA sequencing have facilitated the identification of novel resident cell populations that become activated during disease and has allowed tracing the transition of healthy cells into pathological phenotypes. Furthermore, single-cell RNA sequencing has permitted the characterization of heterogeneous cell subpopulations with unique genetic profiles in healthy and pathological cardiovascular tissues. In this review, we highlight the latest groundbreaking research that has improved our understanding of the pathological mechanisms of atherosclerosis and future directions for calcific aortic valve disease.

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