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Barcoding and cDNA amplification were carried out using the Chromium Single Cell 3' (vNext) Reagent Kit.

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Libraries were prepared and sequenced on the Illumina NovaSeq 6000, generating paired-end reads of 100 bp.

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The fluorescence intensity profile across the mitochondrial transverse axis was measured every 10 nm at a length of 600 nm with its center at the Bézier curve. The profiles were averaged for all selected isolated mitochondrial fragments in a single STED image. A homogeneous MIC60 distribution results in a fluorescence intensity profile with a broad plateau at the central maximum along the transversal mitochondrial axis. MIC60 accumulation at the outer edge of the mitochondria creates a central intensity dip. LS NPCs revealed a clear dip in the distribution of fluorescence intensity.

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For bulk transcriptomics of NPCs, we used four MT-ATP6 mutant NPC lines (ATP6_2, ATP6_4, ATP6_5 and ATP6_7.) and four healthy control NPC lines (CTRL_1, CTRL_2, CTRL_3 and CTRL_4) either treated for 16 h with 0.1 % of DMSO alone or with 10 μM sildenafil resuspended in 0.1 % DMSO. We used n=3 biological replicates per condition. Total RNA was isolated from pelleted NPCs using the NucleoSpin RNA Plus kit (Macherey Nagel) and eluted in 30 μl RNase-free H2O. RNA concentration was measured with NanoDrop 2000. Differential expression analysis between the two timepoints was performed using DESeq2 (Wald test, Benjamini-Hochberg p-value adjustment).

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The values depicted in the boxplots (expression, y axis) in Figures S6E and S6F are variance-stabilized counts obtained with DESeq2’s vst(), which normalized for sequencing depth and applies a transformation that is approximately log2 for large counts but reduces variance for low counts. For bulk transcriptomics of brain organoids, total RNA was isolated from day 70 cortical brain organoids generated using the first protocol.62,134 We used five pelleted organoids per sample with n=3 biological replicates per condition, grown under normal conditions (CTRL_1, CTRL_2, ATP6_4 and ATP6_7), or treated with 10 μM sildenafil for 24 h (ATP6_4 and ATP6_7). Total RNA was isolated sildenafil citrate 150 mg red pill using the RNeasy Mini Kit by Qiagen. RNA concentration as well as purity was measured at the Nanodrop Spectrophotometer ND1000 (peQlab) using ND-1000 software (V3.8.1). Total RNA was mixed with 1 μg of a DNA oligonucleotide pool comprising 50-nt long oligonucleotide mix covering the reverse complement of the entire length of each rRNA (28S rRNA, 18S rRNA, 16S rRNA, 5.8S rRNA, 5S rRNA, 12S rRNA), incubated with 1U of RNase H (Hybridase Thermostable RNase H, Epicentre), purified using RNA Cleanup XP beads (Agencourt), DNase treated using TURBO DNase rigorous treatment protocol (Thermo Fisher Scientific) and purified again with RNA Cleanup XP beads. Data processing, including demultiplexing, alignment to the reference genome (GRCh38-2020-A), and gene

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quantification, was performed using Cell Ranger (v7.2.0) from 10x Genomics with default parameters.

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After washing with DPBS, the primary antibodies were detected with secondary goat anti-rabbit antibodies labelled with Abberior STAR RED (Abberior). After washing with DPBS, the cells were mounted in Mowiol with 0.1 % 1,4-Diazabicyclo[2.2.2]octan (DABCO). Confocal and stimulated emission depletion (STED) microscopy were performed using an INFINITY platform (Abberior Instruments). For STED super-resolution microscopy, Abberior STAR RED was excited at 640 nm and depleted with 775 nm. The fluorescence was collected between 650 nm and 720 nm.

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Images were recorded with a pixel size of 20 nm and a dwell time of 30 μs per pixel. To detect structural differences between LS NPCs and control NPCs, we captured approximately 4,000 STED images and trained a neural network classifier on multiple biological replicates to distinguish control NPCs (P[healthy]-score = 1) from LS NPCs (P[healthy]-score = 0). A deep residual neuronal network (ResNet34) was trained with STED data of DMSO-treated LS NPCs (ATP6_2) and control NPCs (CTRL_1) in a supervised setting to distinguish between both conditions. For the training, STED images were percentile normalized and divided into 512 x 512 pixel-sized regions (patches). Every patch was assigned to the class “healthy” when it originated from control NPCs and to the class “ill” when it originated from LS NPCs.

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Using a mean intensity threshold, patches without a sufficient fluorescence signal were allocated to a third class (“low information”). The final training dataset consisted of equal numbers of patches from three different biological replicates and included in total 75,000 patches of the “healthy, “ill” and “low information” classes in a 0.4:0.4:0.2 ratio. The performance throughout the trainings process was monitored with a validation set of 11,000 patches. For every patch, the model predicts logits values corresponding to the three classes, that are then transformed with softmax function to receive a per class probability. The per-class accuracy computed from a confusion matrix was used as mean validation metric after each epoch and as an indicator for the best checkpoint. To generate digital gene expression (DGE) matrices for each sample. DGEs were further processed in R (v.

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4.4.1) using Seurat (v.

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The final evaluation of the neuronal network after the training was carried out with a test set of 140,000 patches with a 0.4:0.4:0.2 ratio. The trained model was used to assign a P[healthy] score from zero to sildenafil pick up one to single STED images. For each STED image with a size of 2,048 x 2,048 pixels, 49 partially overlapping patches of 512 x 512 pixels were extracted and evaluated with the model. Patches with insufficient fluorescence signal were assigned to the “low information” class and excluded from the evaluation. For the remaining patches a P[healthy]-score was predicted.

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The P[healthy]-score of the single patches were averaged to assign a P(healthy)-scores to each STED image of 2,048 x 2,048 pixels. STED images which had a proportion of “low information” patches above 90 % were excluded. After training, the classifier could reliably differentiate the two conditions on images that were not used for the training, indicating alterations of the MIC60 fluorescence signal in LS NPCs. To gain further insights, we reviewed a subset of images individually. Confocal microscopy revealed a tubular mitochondrial network in both NPC cultures, with no evident phenotypic alterations in LS NPCs.

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STED imaging showed a largely uniform distribution of MIC60 clusters along mitochondrial tubules in control NPCs, whereas in LS NPCs, MIC60 appeared to accumulate at the sides of mitochondrial tubules. To investigate this phenotype, we manually analyzed the MIC60 pattern across the mitochondrial tubules in approximately 300 images. A semi-automated MATLAB-based analysis was employed to assess the MIC60 distribution in LS NPCs (ATP6_2) and control NPCs (CTRL_1). After blinding, individual elongated mitochondria were manually analyzed. The mitochondrial centerline was determined by marking discrete points along the mid-axis, which were then fitted to a Bézier curve to approximate the mitochondrial central axis. 5.1.0).139 Filtered DGEs were imported using the function “Read10x” and only genes detected in at least 5 cells were kept for downstream analyses.

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Similarly, cells with less than 200 genes and 3,000 unique transcripts or more than 0.5 % mitochondrial transcripts were discarded.

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After merging all samples in a single Seurat object, we performed gene expression normalization and scaling for each sample

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For single-nucleus RNA sequencing (snRNAseq), we used day 72 brain organoids generated with the second protocol138 from control iPSCs (CTRL1) and LS iPSCs (ATP6_7). Brain organoids were treated with either DMSO or 10 μM sildenafil resuspended in DMSO for 45 days from day 27 to day 72. We used 15 pelleted organoids per sample (n=8 samples) with n=2 biological replicates per condition (n=120 cortical brain organoids in total). snRNA-seq was performed using the 10X Genomics Chromium system, following the manufacturer's protocol. Nuclei were isolated using mechanical dissociation with gradient centrifugation and stained with DAPI to assess nuclear integrity. independently using SCTransform.140 We performed unbiased clustering and dimensionality reduction

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rRNA-depleted RNA samples were further fragmented and processed into strand-specific cDNA libraries using TruSeq Stranded Total LT Sample Prep Kit (Illumina) and sequenced using the NovaSeq 6000 system with stranded technology, generating paired-end reads of 150 bp. Raw RNA sequencing data were processed to generate FASTQ files using Illumina bcl2fastq. Briefly, base calling was performed using Real-Time Analysis (RTA) software, and adapter sequences were trimmed. The resulting BCL files were converted to FASTQ format, followed by quality control assessment using FastQC before downstream analysis. The preprocessing of the FASTQ raw sequencing files was performed with nfcore/rnaseq (version 3.12.0) pipeline.

S1 Fig. Western blot of p-ERK1/2 in the RV myocardium.

Briefly, reads were trimmed with Trim Galore! (version 0.6.10) and mapped to the human genome (GRCh38 assembly) using STAR (version 2.7.10a) aligner. For both NPCs and brain organoids samples, we performed different comparisons: i) LS with DMSO vs. controls with DMSO (disease signature), ii) LS + sildenafil vs. LS + DMSO (sildenafil signature), iii) controls + sildenafil vs.

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controls + DMSO (negative control test). Transcript counts were determined using Salmon (version 1.10.1). DESeq2 (version 1.40.2)137 was used to identify differentially expressed genes in the NPC samples or in brain organoid samples (Table S1) (Benjamini-Hochberg-adjusted p-value 0.05). Gene Ontology (GO) enrichment analysis was performed to identify biological processes (BP) and cellular components (CC) using the enrichGO function within ClusterProfiler (4.8.3), using the annotations of org.Hs.eg.db (version 3.18.0). Visualizations were sildenafil pfizer 100g generated with ggplot2 (version 3.5.1). using the first 20 principal components on all samples together.

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