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. Barcoding and cDNA amplification were carried out using the Chromium Single Cell 3' (vNext) Reagent Kit. Libraries were prepared and sequenced on the Illumina NovaSeq 6000, generating paired-end reads of 100 bp.

Cost information

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. Data processing, including demultiplexing, alignment to the reference genome (GRCh38-2020-A), and gene quantification, was performed using Cell Ranger (v7.2.0) from 10x Genomics with default parameters. To generate digital gene expression (DGE) matrices for each sample. DGEs were further processed in R (v. 4.4.1) using Seurat (v. 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.

11 DESCRIPTION

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 generated with ggplot2 (version 3.5.1). Similarly, cells with less than 200 genes and 3,000 unique transcripts or more than 0.5 % mitochondrial transcripts were discarded. After merging all samples in a single Seurat object, we performed gene expression normalization and scaling for each sample independently using SCTransform.140 We performed unbiased clustering and dimensionality reduction using the first 20 principal components on all samples together. Clusters were manually annotated in 7 cell types analyzing their marker genes (Table S1). To identify differential genes induced by MT-ATP6 variants and sildenafil treatment in a robust and reproducible way, we performed a pseudo-bulk analysis using the DESeq2 package140 (v.

Erectile dysfunction drugs can have nasty side effects, so always consult your doctor before taking them

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. 7.5.1), as described previously.141 Pseudo-bulk analysis was used to determine differentially expressed genes within the individual populations (Table S1). For each population, we generated a pseudo-bulk expression profile for each organoid by summing the expression of 250 randomly selected cells. We filtered genes with less than 50 counts in at least 2 samples, normalized expression values by library size, estimated negative binomial dispersions. We performed different comparisons: i) LS + DMSO vs. controls + DMSO (disease signature), ii) LS + sildenafil vs. LS + DMSO (sildenafil signature), iii) controls + sildenafil vs.

Uses of Vega Oral Jelly

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 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. controls + DMSO (negative control test). For all comparisons, we identified differentially expressed genes and adjusted p value was calculated using Bonferroni correction for multiple testing correction. To estimate glycolytic pathway activity in single cells, we leveraged the AUCell package (v. 1.26.0)142 and the ‘Glycolysis’ gene set in the ‘Hallmark’ database accessed from the MsigDB package (v 7.5.1).143 Plots were generated with ggplot2 (v. 3.5.1) and piping using dplyr (v. 1.1.4) and visualized with uniform manifold approximation and projection (UMAP). We carried out proteomics using mass spectrometry (MS) for 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) with n=3 biological replicates per condition that were treated with either DMSO or 10 μM sildenafil in DMSO for 16 h. Cells were washed three times with ice-cold DPBS, detached using a cell scraper, transferred to pre-chilled 1.5 ml tubes and centrifuged at 1,000-3,000 rpm for 10 min at 4 °C. Subsequently, the supernatant was removed, and intact cells were snap-frozen. Lysates were denatured at 95 °C for 10 min shaking at 1,000 rpm in a thermal shaker and sonicated in a water bath for 10 min. The protein concentration of each sample was measured with a BCA protein assay kit (23252, Thermo Fisher Scientific). 500 ng protein was used per sample and diluted with a dilution buffer containing 10 % acetonitrile and 25 mM Tris-HCl, pH 8.0, to reach a 1 M GdmCl concentration. Then, proteins were digested with LysC (Roche; enzyme to protein ratio 1:50, MS-grade) shaking at 800 rpm at 37 °C for 3.5 hours. The digestion mixture was diluted again with the same dilution buffer to reach 0.5 M GdmCl, followed by tryptic digestion (Roche, enzyme to protein ratio 1:50, MS-grade) and incubation at 37 °C overnight in a thermal shaker at 800 rpm. Peptides were acidified with formic acid to a final concentration of 2 %. LC-MS/MS was performed by nanoflow reversed-phase liquid chromatography (Dionex Ultimate 3000, Thermo Fisher Scientific) coupled online to a timsTOF SCP mass spectrometer (Bruker Daltonics) using the data-independent acquisition (DIA) method with parallel accumulation serial fragmentation (PASEF). 150 ng of each desalted digest was applied to the column and peptides were eluted using a gradient of 3.8 to 38 % solvent B in solvent A over 60 min (total run time) at a flow rate of 400 nl per minute. The "match between run" feature was used and the mass search range was set to m/z 400 to 1,000. MS data were processed with Dia-NN (v1.8.1) and searched against an in silico predicted human spectral library (Table S1). Metabolites were extracted from cells with 500 μl of cold extraction solvent (Acetonitrile:Methanol:MilliQ; 40:40:20, Thermo Fischer Scientific). Subsequently, samples were processed with three cycles of sonication (60 s) and vortexing (120 s) followed by centrifugation at 14,000 rpm at 4 °C for 5 min. Next, the samples were centrifuged, the supernatants transferred to evaporation tube and evaporated to dry under nitrogen stream. Samples were reconstituted in 40 μl extraction buffer (Acetonitrile:Methanol:MilliQ; 40:40:20) and transferred to LC-MS vials. 2 μl of the samples were analyzed with Thermo Vanquish UHPLC coupled with Q-Exactive Orbitrap mass spectrometer equipped with a heated electrospray ionization (H-ESI) source probe (Thermo Fischer Scientific). A SeQuant ZIC-pHILIC (2.1 × 100 mm, 5 μm particle) column (Merck) was used for chromatographic separation. The gradient elution was carried out with a flow rate of 0.1 ml/min and mobile phase gradient with 20 mM ammonium hydrogen carbonate, adjusted to pH 9.4 with ammonium solution (25 %) as mobile phase A and acetonitrile as mobile phase B, 0-2 min 80 % B, 2-17 min 80-20 % B, 17-24 min 80 % B. The column oven and auto-sampler temperatures were set to 40 ± 3 °C and 5 ± 3 °C, respectively. Following setting were used for MS: full scan range: 55-825 m/z, polarity switching; resolution of 35,000, the spray voltages: 4250 V for positive and 3250 V for negative mode; the sheath gas: 25 arbitrary units (AU); the auxiliary gas: 15 AU; sweep gas flow 0; capillary temperature: 275 °C; S-lens RF level: 50.0. Instrument control was operated with the Xcalibur software (Thermo Fischer Scientific).