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. 2017 Mar 9;10(1):121.
doi: 10.1186/s13104-017-2447-6.

RNA Isolation From Precision-Cut Lung Slices (PCLS) From Different Species

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Free PMC article

RNA Isolation From Precision-Cut Lung Slices (PCLS) From Different Species

Monika Niehof et al. BMC Res Notes. .
Free PMC article

Abstract

Background: Functional 3D organ models such as precision-cut lung slices (PCLS) have recently captured the attention of biomedical research. To enable wider implementation in research and development, these new biologically relevant organ models are being constantly refined. A very important issue is to improve the preparation of high-quality RNA (ribonucleic acid) from PCLS for drug discovery and development of new therapies. Gene expression analysis at different levels is used as an important experimental readout. Genome-wide analysis using microarrays is mostly applied for biomarker selection in disease models or in comprehensive toxicological studies. Specific biomarker testing by reverse transcriptase quantitative polymerase chain reaction (RTqPCR) is often used in efficacy studies. Both applications require high-quality RNA as starting material for the generation of reliable data. Additionally, a small number of slices should be sufficient for satisfactory RNA isolation to allow as many experimental conditions as possible to be covered with a given tissue sample. Unfortunately, the vast amount of agarose in PCLS impedes RNA extraction according to the standard procedures.

Results: We established an optimized protocol for RNA isolation from PCLS from humans, rats, mice, marmosets, and rhesus macaques based on the separation of lysis and precipitation steps and a magnetic-bead cleanup procedure. The resulting RNA is of high purity and possesses a high degree of integrity. There are no contaminations affecting RTqPCR efficiency or any enzymatic step in sample preparation for microarray analysis.

Conclusions: In summary, we isolated RNA from PCLS from different species that is well suited for RTqPCR and for microarray analysis as downstream applications.

Keywords: Ex vivo; Lung material; Lung tissue; Microarray; Organotypic tissue; RNA extraction; RNA quality; RTqPCR.

Figures

Fig. 1
Fig. 1
Assessment of RNA quality for human PCLS. Representative bioanalyzer results showing the quality of RNA isolated from 10 different human PCLS samples (virtual RNA gel format and electropherogram depicting fluorescence units versus run time in seconds)
Fig. 2
Fig. 2
Relationship between RNA yield and 260/230 absorbance. a Concentration, yield, RIN, and absorbance ratios of RNA from A459 cells and from PCLS from different species (two slices each). b Correlation between RNA concentration and absorbance ratio 260/230. c RNA yield and absorbance ratio prior to and after the clean up procedure. Results represent the means of ten human PCLS samples
Fig. 3
Fig. 3
RTqPCR as a follow-up endpoint of RNA from human PCLS. a Efficiency calculation for B2M primer using several A549 cDNA dilutions. b Efficiency analysis for different RNA preparations from A549 cells and human PCLS (different donors). Results represent the means of at least three samples (RIN > 7.0). c MUC5AC expression in human PCLS as an example of lung-specific gene expression (calibration curve, melting curve, gel image)
Fig. 4
Fig. 4
Genome-wide gene expression analysis as a follow-up endpoint of RNA from human PCLS. Bioinformatic quality assessment of 36 microarrays. Legend color red belongs to donor 1, blue to donor 2, and green to donor 3. a Box plots of array signal intensity distributions, where each box corresponds to one array. Signal intensities are in log2 scale (after RMA normalization). Medians are shown as a blue line within the boxes. One array (array 8) exceeded the threshold; it was considered an outlier and marked with an asterisk. b Density histograms of the microarrays. Signal intensities are in log2 scale (after RMA normalization). The curves of the individual donors are superimposed. Density distribution of one array (array 8, donor 1), marked with an asterisk, shows a shift to the right. c Eight MA plots of normalized gene expression data. The figure shows the four highest values of Hoeffding’s statistic and the four lowest ones. Da values are given in the Ma plot headers. No array showed a Da > 0.15, and none were marked as outliers. d Principal component analysis of four different substance groups (ad). The two-dimensional scatter plot shows homogeneity of all groups for the first two principal components

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