205 Fatty Acid Oxidation in PLN R14del Cardiomyopathy 8 k-means (nstart = 200) function in R was used to partition the regions into 12 different clusters. To enable the reproducibility of identified clusters, the set.seed(10) R command was called before the clustering. Annotation of clusters with PLN-specific patterns was performed as described above (Table S4). RNA sequencing of human cardiac tissues and transcriptome analysis. RNA was isolated using ISOLATE II RNA Mini Kit (Bioline) according to the manufacturer's instructions with minor adjustments. After the selection of mRNA, libraries were prepared using the NEXTflexTM Rapid RNA-seq Kit (Bioo Scientific). Libraries were sequenced on the Nextseq500 platform (Illumina), producing single-end reads of 75bp. Reads were aligned to the human reference genome GRCh37 using STAR v2.4.2a.10 Picard’s AddOrReplaceReadGroups v1.98 (http://broadinstitute. github.io/picard/ ) was used to add read groups to the BAM files, which were sorted with Sambamba v0.4.5 and transcript abundances were quantified with HTSeq-count v0.6.1p1 using the union mode.11,12 Subsequently, reads per kilobase per million mapped reads (RPKMs) were calculated with edgeR’s RPKM function.13 DESeq2 was used to identify differentially expressed genes using the cutoff of p<0.05 in the Galaxy environment using the default settings (Table S5A).14 Gene enrichment analysis was performed using ToppFun and STRING as explained above (Table S5B and 5C).6,7 Characterization of cardiac tissues by immunofluorescence and electron microscopy. Immunofluorescence staining of Oil Red O was also performed to detect lipid droplets in frozen cardiac tissues from 4 control and 6 PLN-R14del hearts, which were included in two sequencing experiments. Electron microscopy was also performed to further investigate lipid droplets present in these cardiac tissues. Images were acquired using a Leica SP8X confocal microscope and processed using ImageJ. Processed images were compared using the Student's t-test (#p<0.1, *p < 0.05; **p < 0.01; ***p < 0.001, 0.001 and ****P ≤.0001). Besides, DAPI, ATP2A2, TNNI3, and metabolic-related proteins (HADHA and PPARA), were examined in these cardiac tissues using immunofluorescence staining. Nuclear PPARA signal was quantified by a customised pipeline using CellProfiler 4.0.6. Briefly, the images were uploaded as hyperstack images including the channels stained for DAPI, PPARA, and TNNI3. The first module is the ColortoGray module which splits the images into 3 grayscale images from each channel. Next, the nuclei were identified using the entifyPrimaryObjects module. All objects between 10 and 240 pixels in the blue channel were identified based on specific shape and intensity parameters. The boundaries of the cardiomyocytes were determined using the IdentifyPrimaryObjects module which identified all pixels that were stained with TNNI3. Cardiomyocyte nuclei were determined using the MaskObject module, in which previously identified nuclei overlapped with the identified cardiomyocytes. And those that were not overlapping were recognized as non-myocyte nuclei. Quality control
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