Table 1.
Real-time RT-qPCR designs for RG, MG candidates and GOI with expression levels.
Fig 1.
The expression stability of RG candidates among endothelial cell lines and brain samples.
Total RNA was extracted and converted into cDNA by reverse-transcription at 25 ng/ul, and followed by real-time qPCR with 25 ng/reaction. (A) Distribution of cycle threshold (Ct) values for five RG candidates by quantitative RT-PCR in BMEC and bEnd3 cell lines. The experiments were repeated 3 times in duplicate reactions. Boxes showed the range of Ct values for each candidate gene. The central line indicated the median Ct; the extended upper and lower indicate 75 and 25 percentiles. (B) The average expression stability (M value) of RG candidates in two endothelial cell lines analyzed by geNorm. RG candidates were ranked from the least stable to the most stable (left to right). (C) Ct values for four RG candidates in BrMV and CDB. Data were derived from 10 BrMV isolation experiments with 4 from WT mice, 2 from Het and 4 from Idua knock-out mice. Each sample was tested 3 times in duplicate. **, p<0.01, and ***, p<0.001. (D) The expression stability of four reference genes among all BrMV and CDB samples analyzed by geNorm. (E) RG candidates were ranked in the order of their expression stability evaluated by Bestkeeper based on coefficient of variation (CV%) and SD.
Fig 2.
Quantification of RNA input using RG standard curves derived from 3T3 or primary CDB samples.
Total RNA samples isolated from 3T3 cell line or CDB samples of C57/Bl6 mice were serially diluted, applied for reverse transcription, and followed by qPCR of 4 reference genes. (A) Standard curves of RNA amounts generated by qPCR of 4 RGs. Data were derived from 3 dilution sets with each amplified in triplicates. E, amplification efficiency for a combination of RT and qPCR steps calculated from the slope of each standard curve; R2 range from 0.986 to 0.998. (B) Quantification of total RNA inputs of BrMV and CDB isolates from 10 isolation experiments using different standard curves. Reverse transcription was conducted at 25 ng/ul (by NanoDrop) for all RNA samples, and real-time qPCR was performed using 25 ng/reaction (and indicated as dashed line). Each symbol represents mean of calculated RNA amount derived from Ct value of triplicate qPCR reactions of one sample. Short lines represent mean ± SD of RNA amounts calculated using different standard curves from each of RGs.
Fig 3.
Verification of Actb as the best reference gene by Idua expression in BrMV of WT mice and Het mice.
(A) Standard curve for absolute quantification of Idua mRNA. A plasmid containing Idua cDNA was used for generating standard curve with copy numbers by qPCR. Data was derived from 2 sets of standard samples, each amplified three times in duplicate. Error bars, standard deviation. (B, C) Idua expression in BrMV isolated from either wild-type C57/Bl6 mice (WT) or littermates of heterozygous for Idua knock-out (Het) with normalization by RG candidates. Total RNA from 4 WT and 4 Het samples were examined by RT-qPCR and calculated either by absolute Idua standard curve for copy numbers per ng RNA (B), or by ΔΔCt method for relative Idua fold changes (C).
Fig 4.
Choice of marker genes to assess the purity of BrMV samples.
(A) Distribution of threshold cycle (Ct) values for MG candidates in BrMV and CDB samples by RT-qPCR. Each sample is repeated 3 times in duplicate reactions, and each symbol represents the mean of one sample. Short lines represent mean ± SD of Ct values for each marker gene, n = 10. (B) Relative purity curves determined by relative quantitation of mRNA between different marker genes and Actb (as reference gene). One BrMV sample with relatively high marker gene expression over CDB samples was designated as “100%” relative purity and two sets of standard samples were generated by serial dilution of this BrMV with CDB samples (considered as “0%”). Data were derived from 2 sets of standard samples with 3 RT-qPCR experiments in duplicate reactions. The R2 ranges from 0.968 to 0.971.
Fig 5.
Cellular components of BrMV isolates determined by immunostaining.
(A) Representative pictures of immunofluorescence analysis for BrMV isolates. The brain BrMV isolates were stained with fluorescein-labeled (green) lectin for BBB-forming endothelial cells, and Alexa 568-conjugated (red) anti-mouse CD68 for brain macrophages, anti-mouse GFAP for astrocytes, anti-mouse NeuN for neurons, or anti-mouse PDGFR-β for pericytes, and followed by counter-staining with DAPI (blue) for nucleic. Scale bar represents 50 μm for all views. (B) Semi-quantifications of cellular composition for nine BrMV samples. Each sample was evaluated with a total of >500 nuclei. Pearson correlation coefficient between Lectin+ and other cell types are -0.862 for CD68+ cells, -0.837 for GFAP+ cells, -0.776 for NeuN+ and -0.190 for PDGFR+ cells. (C) Semi-quantification of main cell types in BrMV isolates. Data are derived from 4 BrMV samples with similar purities (ranging of 64–69%) as determined by immunostaining; error bars represent SD. CV, coefficient of variation.
Fig 6.
Correlation of purities measured by both qPCR and immunofluorescent microscopy.
The relative purities of BrMV samples were determined by real-time RT-qPCR with relative comparison of mRNA abundances using standard curves of different marker genes as described in Fig 4, as well as by immunofluorescence analysis as described in Fig 5. Each symbol represents relative purity of individual BrMV sample derived from 3 RT-qPCR experiments in duplicate reactions for qPCR, as well as from evaluation of >200 DAPI+ nuclei from cytospin slides by immunostaining analysis.