Name: characterization of in vitro differentiation of human primary keratinocytes by rna-seq analysis
Uploaded: 2020-05-16T13:00:00
Description: Watch this Scientific Journal Video about Characterization of In Vitro Differentiation of Human Primary Keratinocytes by RNA-Seq Analysis at JoVE.com

This research was supported by Netherlands Organisation for Scientific Research (NWO/ALW/MEERVOUD/836.12.010, H.Z.) (NWO/ALW/Open Competition/ALWOP 376, H.Z., J.G.A.S.); Radboud University fellowship (H.Z.); and Chinese Scholarship Council grant 201406330059 (J.Q.).

Skin biopsies were taken from the trunk of healthy volunteers or patients with p63 mutations, to set up the primary keratinocyte culture. All procedures regarding establishing human primary keratinocytes were approved by the ethical committee of the Radboud University Nijmegen Medical Centre (&#8220;Commissie Mensgebonden Onderzoek Arnhem-Nijmegen&#8221;). Informed consent was obtained.
1. Human primary keratinocyte differentiation by contact inhibition
<ol>
	<li>Prepare keratinocyte growth ...

<ol>
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This work describes a method for inducing human keratinocyte differentiation and subsequent characterization using RNA-seq analyses. In the current literature, many studies on human keratinocyte differentiation use two other methods, with a high calcium concentration or with serum&#160;as methods to induce differentiation2,3,23. A previous report carefully compared these three different methods3 and showe...

Human primary keratinocytes derived from the human skin are often used as a cellular model to study the biology of the epidermis1,2,3,4. The stratification of the epidermis can be modeled by keratinocyte differentiation, either in a 2D submerged monolayer fashion or 3D air-lift organotypic model2,3,5,6,7. Although 3D models have become increasingly important to assess the epidermal structure and function, 2D differentiation models are still widely used, due to their convenience and the possibility to generate large numbers of cells for analyses.
Various conditions have been applied for inducing keratinocyte differentiation in 2D, including addition of serum, high concentration of calcium, lower temperature and inhibition of epidermal growth factor receptors2,3. Each of these methods has been validated by a number of keratinocyte differentiation marker genes and shown to be effective in assessing keratinocyte differentiation, including under pathological conditions. However, these induction conditions also show differences in their differentiation efficiency and kinetics when specific panels of marker genes are examined2,3.
One of these methods involves keratinocyte contact inhibition and depletion of growth factors in the culture medium8. It has been shown that keratinocytes can differentiate spontaneously when cells reach full density. &#160;Excluding growth factors in the culture medium can further enhance differentiation. The method combining contact inhibition and depleting growth factors has been shown to generate differentiated keratinocytes with gene expression patterns similar to the normal stratified epidermis when using several epidermal markers3, suggesting that this model is suitable for studying normal keratinocyte differentiation. Recently, two comprehensive gene expression analyses of keratinocyte differentiation using this model have been reported9,10. Researchers validated this model at the molecular level and showed that it can be used to study normal and diseased keratinocyte differentiation.
This protocol describes the procedure for the in vitro differentiation method and molecular analysis of differentiated cells using RNA-seq. It also illustrates characterization of the transcriptome of cells on differentiation day 0 (proliferation stage), day 2, day 4, and day 7 (early, middle, and late differentiation, respectively). It is shown that differentiated keratinocytes display gene expression patterns that largely resemble cells during epidermal stratification. To examine whether this method can be used for studying skin pathology, we applied the same experimental and analysis pipeline to investigate keratinocytes carrying mutations of the transcription factor p63 that are derived from patients with ectrodactyly, ectodermal displasia and cleft lip/palate (EEC) syndrome11,12. This protocol focuses on the in vitro differentiation of keratinocytes as well as subsequent bioinformatic analysis of RNA-seq. Other steps in the complete procedure such as RNA extraction, RNA-seq sample preparation and library construction, are well documented and can be easily followed, especially when using many commonly used commercial kits. Therefore, these steps are only briefly described in the protocol. The data show that this pipeline is suitable for studying molecular events during epidermal differentiation in healthy and diseased keratinocytes.

<table border="0" cellpadding="0" cellspacing="0" style="width:892px;" width="892">
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		<col />
		<col />
	</colgroup>
	<tbody>
		<tr height="20">
			<td height="20" style="height:20px;width:397px;">Bioanalyzer 2100</td>
			<td style="width:231px;">Agilent</td>
			<td style="width:200px;">G2929BA</td>
			<td style="width:64px;"></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Bovine pituitary extract (BPE)</td>
			<td>Lonza</td>
			<td></td>
			<td>Part of the bulletKit</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">CFX96 Real-Time system</td>
			<td>Bio-Rad</td>
			<td></td>
			<td>qPCR machine</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Dulbecco&#39;s Phosphate-Buffered Saline (DPBS)</td>
			<td>Sigma-Aldrich</td>
			<td>D8537</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Epidermal Growth Factor (EGF)</td>
			<td>Lonza</td>
			<td></td>
			<td>Part of the bulletKit</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Ethanolamine &#62;= 98%</td>
			<td>Sigma-Aldrich</td>
			<td>E9508</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">High Sensitivity DNA chips</td>
			<td>Agilent</td>
			<td>5067-4626</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Hydrocortison</td>
			<td>Lonza</td>
			<td></td>
			<td>Part of the bulletKit</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Insulin</td>
			<td>Lonza</td>
			<td></td>
			<td>Part of the bulletKit</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">iQ SYBR Green Kit</td>
			<td>BioRad</td>
			<td>170-8886</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">iScript cDNA synthesis</td>
			<td>Bio rad</td>
			<td>1708890</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">KAPA Library Quant Kit</td>
			<td>Roche</td>
			<td>07960255001</td>
			<td>Low concentration measure kit</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">KAPA RNA HyperPrep Kit with RiboErase</td>
			<td>Roche</td>
			<td>KK8540</td>
			<td>RNAseq kit</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">KGM Gold Keratinocyte Growth Medium BulletKit</td>
			<td>Lonza</td>
			<td>192060</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Nanodrop</td>
			<td>deNovix</td>
			<td>DS-11 FX (model)</td>
			<td>Nanodrop and Qbit for DNA and RNA measurements</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">NEXTflex DNA barcodes -24</td>
			<td>Illumnia</td>
			<td>NOVA-514103</td>
			<td>6 bp long primers</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Penicillin-Streptomycin</td>
			<td>Gibco</td>
			<td>15140122</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">RNA Pico Chip</td>
			<td>Agilent</td>
			<td>5067-1513</td>
			<td></td>
		</tr>
	</tbody>
</table>

characterization of in vitro differentiation of human primary keratinocytes by rna-seq analysis

Human primary keratinocytes are often used as in vitro models for studies on epidermal differentiation and related diseases. Methods have been reported for in vitro differentiation of keratinocytes cultured in two-dimensional (2D) submerged manners using various induction conditions. Described here is a procedure for 2D in vitro keratinocyte differentiation method by contact inhibition and subsequent molecular characterization by RNA-seq. In brief, keratinocytes are grown in defined keratinocyte medium supplemented with growth factors until they are fully confluent. Differentiation is induced by close contacts between the keratinocytes and further stimulated by excluding growth factors in the medium. Using RNA-seq analyses, it is shown that both 1) differentiated keratinocytes exhibit distinct molecular signatures during differentiation and 2) the dynamic gene expression pattern largely resembles cells during epidermal stratification. As for comparison to normal keratinocyte differentiation, keratinocytes carrying mutations of the transcription factor p63 exhibit altered morphology and molecular signatures, consistent with their differentiation defects. In conclusion, this protocol details the steps for 2D in vitro keratinocyte differentiation and its molecular characterization, with an emphasis on bioinformatic analysis of RNA-seq data. Because RNA extraction and RNA-seq procedures have been well-documented, it is not the focus of this protocol. The experimental procedure of in vitro keratinocyte differentiation and bioinformatic analysis pipeline can be used to study molecular events during epidermal differentiation in healthy and diseased keratinocytes.

Normal keratinocyte differentiation and RNA-seq analysis 
In this experiment, keratinocyte lines derived from five individuals were used for differentiation and RNA-seq analyses. Figure 1 summarizes the experimental procedure of differentiation and RNA-seq analysis results. An overview of in vitro differentiation procedures of normal keratinocytes and cell morphology changes during differentiation are illustrated in Figure 1A. Principle com...

Watch this Scientific Journal Video about Characterization of In Vitro Differentiation of Human Primary Keratinocytes by RNA-Seq Analysis at JoVE.com

Characterization of In Vitro Differentiation of Human Primary Keratinocytes by RNA-Seq Analysis

Presented here is a stepwise procedure for in vitro differentiation of human primary keratinocytes by contact inhibition followed by characterization at the molecular level by RNA-seq analysis.

Human primary keratinocytes are often used as in vitro models for studies on epidermal differentiation and related diseases. Methods have been reported ...

In this protocol, we describe a procedure containing two parts. First, a monolayer 2D keratinocyte differentiation method in vitro by contact inhibition. Second, its molecular characterization by RNA-seq.The 2D in vitro differentiation method is straightforward and easy to perform. It can be used to study epidermal differentiation, especially when a large number of cells are necessary. For example, in epigenomic analysis.The detailed RNA-seq analysis pipeline is clear and transparent for researchers with basic bioinformatics skills, and it can be used for any bulk RNA-seq analysis. When performing the differentiation protocol for the very first time, keep in mind that the seeding density can be tricky. Try out multiple seeding densities to find out which one works best with your cell line.Begin by preparing keratinocyte growth medium or KGM and 500 milliliters each of proliferation medium and differentiation medium according to manuscript directions. Seed primary keratinocytes at a density of 5 to 20, 000 cells per centimeter squared and add enough proliferation medium to cover the cells. Two days after seeding, refresh the cells with the proliferation medium.Check the cells regularly under the microscope and refresh medium every other day. When the cells reach 90%confluency, induce differentiation by changing the medium to the differentiation medium. To collect cells for further RNA analyses, wash the cells twice with DPBS and then add lysis buffer.Collect cells on differentiation days zero, two, four, and seven. After RNA isolation, double-stranded cDNA will be converted and RNA-seq libraries will be prepared for next generation sequencing. Following the sequencing, the sequence reads will be downloaded and mapped to the human genome.After downloading and installing R packages, generate account table from the readspergene.out. tab files and write a sample data file containing all filenames, the day of differentiation and other relevant sample data. Refer to the supplementary coding files for an example.Then use the count table and sample data to generate a deseq2 object containing both the countable and sample data. For gene expression normalization, normalize the count table in the deseq2 object using either deseq2RLD or VST normalization. While RLD normalization is preferred, VST normalization is much faster.Use the dist function in R to plot the sample distance based on the normalized read count intensities, then perform hclust clustering based on sample distance. Next, plot the heat map using pheatmap function. Finally, generate a principle component analysis or PCA plot of the normalized read count intensities using the plotPCA function of deseq2.PCA serves as a tool in exploratory data analysis and can be used to visualize distance and connection between different samples. Perform highly variable gene expression analysis with the rowVars function which extracts the top 500 highly variable genes by ordering on the variance between samples from different time points. These genes have the highest standard deviation of their normalized intensity across days of differentiation.Perform k-means clustering on the highly variable genes to cluster them by different expression patterns. Visualize the genes in a heat map with the pheatmap package. The intensity plotted in the heat map is the deseq2 normalized intensity with the median value subtracted.Generate a list of expressed background genes that includes all genes with more than 10 counts in a single sample and make a list with genes from each cluster. Finally, perform Gene Ontology analysis using GOrilla. Use the background gene list as the background set and the list of the highly variable genes in clusters as the target set, then click on search enriched GO terms.Alternatively, more advanced R users can use the package clusterProfiler to automate GO term enrichment analysis. Keratinocyte lines derived from five individuals were used for differentiation in RNA-seq analyses. Principal component analysis showed that keratinocytes undergoing differentiation had connected but distinct overall gene expression profiles.Highly variable genes were clustered to visualize gene expression dynamics and patterns during differentiation. Each cluster of genes was represented by the keratinocyte differentiation hallmark genes and Gene Ontology annotation showed a clear difference in gene functions. In the second experiment, cell morphology and gene expression differences were compared between keratinocytes from healthy controls and cell lines derived from patients carrying P63 mutations.Principal component analysis showed that the control cell lines clearly followed a differentiation pattern, but the gene expression pattern of mutant cells during differentiation stays largely similar to that of proliferating or undifferentiated cells. In the clustering analysis, genes in cluster one were downregulated in control cells and partially downregulated in patient cells carrying R204W and R279H mutations, but remained high in cells carrying R304W. These genes likely play roles in cell proliferation as shown by Gene Ontology analysis.Cluster two genes were first introduced and subsequently downregulated in control cells. These genes are likely involved in keratinocyte differentiation as epidermal differentiation and keratinization functions were highly enriched for this cluster. Genes in cluster three were only induced at the end of differentiation in control cells which indicates that they may play a role in the outermost layer of the epidermis.When analyzing RNA-seq data, it is important to know beforehand what to expect. This will help you both with interpreting the quality control and the PCA plot and in assessing if your results are making sense. Our methods can be used to study gene transcription both in epidermal biology as well as in other biological systems.

Research

JoVE Journal

Genetics

Parallel Measurement of Circadian Clock Gene Expression and Hormone Secretion in Human Primary Cell Cultures

Circadian clocks are functional in all light-sensitive organisms, allowing for an adaptation to the external world by anticipating daily environmental ...

Analysis of the Ambient Particulate Matter-induced Chromosomal Aberrations Using an In Vitro System

Analysis of the Ambient Particulate Matter-induced Chromosomal Aberrations Using an In Vitro System

Exposure to particulate matter (PM) is a major world health concern, which may damage various cellular components, including the nuclear genetic material. ...

Analysis of Cap-binding Proteins in Human Cells Exposed to Physiological Oxygen Conditions

Translational control is a focal point of gene regulation, especially during periods of cellular stress. Cap-dependent translation via the eIF4F complex ...

Genome Editing and Directed Differentiation of hPSCs for Interrogating Lineage Determinants in Human Pancreatic Development

Interrogating gene function in self-renewing or differentiating human pluripotent stem cells (hPSCs) offers a valuable platform towards understanding ...

Kinetics of Lagging-strand DNA Synthesis In Vitro by the Bacteriophage T7 Replication Proteins

Kinetics of Lagging-strand DNA Synthesis In Vitro by the Bacteriophage T7 Replication Proteins

Here we provide protocols for the kinetic examination of lagging-strand DNA synthesis in vitro by the replication proteins of bacteriophage T7. The T7 ...

Mapping Genome-wide Accessible Chromatin in Primary Human T Lymphocytes by ATAC-Seq

Assay for Transposase-Accessible Chromatin with high-throughput sequencing (ATAC-seq) is a method used for the identification of open (accessible) regions ...

Differentiation, Maintenance, and Analysis of Human Retinal Pigment Epithelium Cells: A Disease-in-a-dish Model for BEST1 Mutations

Differentiation, Maintenance, and Analysis of Human Retinal Pigment Epithelium Cells: A Disease-in-a-dish Model for BEST1 Mutations

Although over 200 genetic mutations in the human BEST1 gene have been identified and linked to retinal degenerative diseases, the pathological mechanisms ...

Isolation, Characterization and MicroRNA-based Genetic Modification of Human Dental Follicle Stem Cells

To date, several stem cell types at different developmental stages are in the focus for the treatment of degenerative diseases. Yet, certain aspects, such ...

Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

Pancreatic islets comprise of endocrine cells with distinctive hormone expression patterns. The endocrine cells show functional differences in response to ...

Improving Small RNA-seq: Less Bias and Better Detection of 2'-O-Methyl RNAs

The study of small RNAs (sRNAs) by next-generation sequencing (NGS) is challenged by bias issues during library preparation. Several types of sRNA such as ...