DOI: 10.5061/dryad.b2rbnzsmm
title: Cross-species analysis identifies mitochondrial dysregulation as a functional consequence of the schizophrenia-associated 3q29 deletion

description: The 1.6Mb deletion at chromosome 3q29 (3q29Del) is the strongest
 identified genetic risk factor for schizophrenia, but the effects of this
 variant on neurodevelopment are not well understood. We interrogated the
 developing neural transcriptome in two experimental model systems with
 complementary advantages: isogenic human cortical organoids and isocortex
 from the 3q29Del mouse model. We profiled transcriptomes from isogenic
 cortical organoids that were aged for 2 months and 12 months, as well as
 perinatal mouse isocortex, all at single-cell resolution. Systematic
 pathway analysis implicated dysregulation of mitochondrial function and
 energy metabolism. These molecular signatures were supported by analysis
 of oxidative phosphorylation protein complex expression in mouse brains
 and assays of mitochondrial function in engineered cell lines. Together
 these data indicate that metabolic disruption is associated with 3q29Del
 and is conserved across species.
description: <em>Cell Culture and Genome
 Engineering</em> Whole blood samples of 5-10 mL
 were collected in EDTA Vacutainer test tubes and processed for the
 isolation of erythroid progenitor cells (EPCs) using the Erythroid
 Progenitor Kit (StemCell Technologies). EPCs were reprogrammed using
 Sendai particles (CytoTune-iPS 2.0 Reprogramming kit, Invitrogen) and
 plated onto Matrigel-coated six-well plates (Corning). Cultures were
 transitioned from erythroid expansion media to ReproTesR (StemCell
 Technologies) and then fed daily with ReproTesR until clones were
 isolated. iPSCs were maintained on Matrigel-coated tissue culture plates
 with mTeSR Plus (StemCell Technologies). Cell lines
 were characterized for stem cell markers by RT-PCR and immunocytochemistry
 after at least 10 passages in culture. Total RNA was isolated from each
 cell line with the RNeasy Plus Kit (Qiagen) according to the
 manufacturer’s protocol. mRNA was reverse transcribed into cDNA using the
 High Capacity cDNA Synthesis Kit (Applied Biosystems). Expression of
 pluripotency genes <em>OCT4</em>, <em>SOX2</em>,
 <em>REX1,</em> and <em>NANOG</em> was determined
 by RT-PCR. Sendai virus inactivity was confirmed using Sendai
 genome-specific primers. Isogenic 3q29Del iPSC and HEK
 cell lines were generated using the SCORE method
 (<em>11</em>). To identify low-copy repeat (LCR) target
 sequences, the reference sequence (hg38) between <em>TNK2 –
 TFRC</em> (centromeric) and <em>BDH1 – RUBCN
 </em>(telomeric) was downloaded and aligned in NCBI BLAST. A ~20 Kb
 segment was found to be 97% identical and was searched for gRNA sequences
 using CHOPCHOP (https://chopchop.cbu.uib.no) (<em>14</em>).
 Three single gRNA sequences (IDT) that were predicted to each cut at a
 single site in both LCRs were identified and cloned into
 pSpCas9(BB)-2A-Puro (PX459) V2.0, which was a gift from Feng Zhang
 (Addgene plasmid #62988; http://n2t.net/addgene:62988; RRID:
 Addgene_62988) (<em>42</em>). Single gRNA
 plasmids were transfected into a neurotypical control iPSC line
 (IRB#CR002-IRB00088012, maintained in mTeSR or mTeSR+ (STEMCELL,
 Vancouver) on Matrigel (Corning)-coated plates using a reverse
 transfection method and Mirus TransIT-LT1 reagent (Mirus Bio, Madison, WI)
 and transfected cells were transiently selected for puromycin resistance.
 Genome cleavage efficiency for each gRNA was calculated using the GeneArt
 Genomic Cleavage Detection Kit (Thermo) and gRNA_2
 (5’-CAGTCTTGGCTACATGACAA-3’, directed to -strand, hg38 chr3:195,996,820 -
 chr3:197,634,397) was found to be the most efficient with cleaved bands at
 the predicted sizes. Cells transfected with gRNA_2 were dissociated and
 cloned out by limiting dilution in mTeSR supplemented with 10% CloneR
 (STEMCELL). Putative clonal colonies were manually transferred to
 Matrigel-coated 24-well plates for expansion and screened for change in
 copy number of the 3q29Del locus gene <em>PAK2
 </em>(Hs03456434_cn) using TaqMan Copy Number Assays (Thermo). Three
 (of 100) clones showed an apparent loss of one copy of
 <em>PAK2</em> and were subsequently screened for loss of the
 3q29 genes <em>TFRC </em>(Hs03499383_cn),<em> DLG1
 </em>(Hs04250494_cn)<em>, </em>and <em>BDH1
 </em>(Hs03458594_cn) and for no change in copy number to external
 (non-deleted) 3q29 genes <em>TNK2 </em>(Hs03499383_cn) and
 <em>RUBCN </em>(Hs03499806_cn) all referenced to
 <em>RNASEP</em> (Thermo #4401631). All cell lines retained
 normal karyotypes (WiCell, Madison, WI) and were free of mycoplasma
 contamination (LookOut, Sigma). To generate 3q29Del
 HEK-293T cell lines, HEK cells (RRID:CVCL_0063) were transfected with
 either empty px459 or px459+gRNA_1 (5’-ttagatgtatgccccagacg-3’, directed
 to the +strand) and screened and verified with TaqMan copy number assays
 as described above. <em>PAK2</em> was deleted from a control
 HEK-293T line as detailed above (<em>PAK2</em> gRNA
 5’-TTTCGTATGATCCGGTCGCG-3’, directed to -strand). Clones were screened by
 Western blot (Rabbit monoclonal PAK2 from Abcam; ab76293; RRID AB_1524149;
 1:5000 dilution) and confirmed by Sanger sequencing PCR-amplified gDNA.
 HEK cell lines were also negative for mycoplasma contamination.
 <em>Genome-wide Optical Mapping</em>
 1.5E6 iPSCs were pelleted, washed with DPBS, and frozen at -80°C
 following aspiration of all visible supernatant. 750ng of DNA was labeled,
 stained, and homogenized using the DNA Labeling Kit-DLS (Bionano; 80005).
 Stained DNA was loaded onto the Saphyr chip G1.2 and the chip was scanned
 in order to image the labeled DNA using the Saphyr System. Structural
 variants were called relative to the reference genome (hg38) using Bionano
 Solve. Structural variants were compared to the parent (unedited) cell
 line using the Bionano Solve Variant Annotation Pipeline.
 <em>Cortical Organoid Differentiation</em>
 Engineered isogenic 3q29Del iPSC lines and the unedited parent
 line, along with two additional clonal lines from the same donor, were
 expanded in mTeSR or mTeSR+ on Matrigel-coated plates. On DIV 0, colonies
 were gently released from plates in 0.35mg/ml Dispase according to an
 established protocol (<em>15</em>). Floating colonies were
 re-suspended in mTeSR supplemented with 10uM Y-27632 (Reprocell,
 Beltsville, MD) in ultra-low attachment 10cm dishes (Corning). After 48hr,
 spheroids were transitioned to Neural Induction Medium (20% Knockout Serum
 Replacement, 1% Non-essential amino acids, 100U/mL Pen/Strep, 0.5%
 GlutaMAX, 0.1mM 2-mercaptoethanol in DMEM/F12 w/ HEPES), supplemented with
 5uM Dorsomorphin and 10uM SB-431542 (added fresh) with daily media changes
 through DIV 6. On DIV 7, Neural Induction Medium was replaced with Neural
 Medium (Neurobasal-A with 2% B-27 w/o vitamin A, 1% GlutaMAX, 100U/mL
 Pen/Strep) supplemented with fresh EGF (20ng/ml, R&D Systems) and
 FGF (20ng/ml, R&D Systems) for daily media changes through day 16.
 From day 17 to 25, organoids were fed Neural Medium with EGF and FGF every
 two days. From day 26-42, Neural Medium was supplemented with BDNF
 (20ng/ml, R&D Systems) and NT-3 (20ng/ml, R&D Systems)
 every two days. From day 43 onwards, organoids were fed Neural Medium
 without supplements twice weekly. <em>Mouse
 Genotyping and Maintenance</em> All animal
 experiments were performed under guidelines approved by the Emory
 University Institutional Animal Care and Use Committee. Mice were
 genotyped as described previously (<em>6</em>) and noted as
 either Control (wild-type, C57BL/6 N Charles River Laboratories) or
 3q29Del (B6.Del16<sup>+/<em>Bdh1-Tfrc</em></sup>,
 MGI:6241487). Male 3q29Del mice and Control littermates were included in
 the scRNA-seq study. Both male and female mice were included in
 mitochondrial fractionation experiments.
 <em>Tissue Dissociation and Sorting</em>
 Single-cell suspensions from cortical organoids (DIV 50 =
 “2-month” N=2 Control, N=2 3q29Del, and DIV 360 = “12-month” N=2 Control,
 N=2 3q29Del) and postnatal day 7 (P7) mouse cortices (N=4 Control, N=4
 3q29Del) were produced by a papain dissociation method based on a
 published protocol (<em>43</em>). Organoids were dissociated
 in three batches that were each balanced for genotype and “age”. Mouse
 samples were also dissociated in three batches each balanced by genotype.
 In both sets the experimenter was blinded to genotype. Tissue was coarsely
 chopped with a sterile scalpel and digested for 1hr at 34°C in a
 pH-equilibrated papain solution (Worthington, Lakewood, NJ) with constant
 CO<sub>2</sub>flow over the enzyme solution. Digested tissue
 was gently spun out of papain, through ovomucoid solutions, and
 sequentially triturated with P1000 and P200 pipet tips. Live cells were
 counted by manual and automated methods (Countess II, Thermo) and in
 organoid samples were isolated from cellular debris by
 fluorescence-activated cell sorting on a FACSAria-II instrument (calcein
 AM-high, Ethidium Homodimer-1 low).
 <em>Single-cell Library Prep and
 RNA-Sequencing</em> Single-cell suspensions were
 loaded into the 10X Genomics Controller chip for the Chromium Next GEM
 Single Cell 3’ kit workflow as instructed by the manufacturer with a goal
 capture of 10,000 cells per sample. The resulting 10X libraries were
 sequenced using Illumina chemistry. Mouse samples and libraries were
 prepared and sequenced at a separate time from human samples.
 <em>Single-cell
 RNA-seq</em> To
 quantify gene expression at single-cell resolution, the standard Cell
 Ranger (10x Genomics) and Seurat data processing pipelines were followed
 for demultiplexing base call files into FASTQ files, alignment of
 scRNA-seq reads to species-specific reference transcriptomes with STAR
 (mouse: mm10, human: GRCh38), cellular barcode and unique molecular
 identifier (UMI) counting, and gene- and cell-level quality control (QC).
 To filter out low-quality cells, empty droplets, and multiplets, genes
 expressed in &lt;10 cells, cells with &gt;30% reads mapping to the
 mitochondrial genome, and cells with unique feature (gene) counts
 &gt;7,000 were removed based on manual inspection of the distributions
 of each QC metric individually and jointly. Outlier cells with low unique
 feature counts were further removed via sample-specific thresholding of
 corresponding distributions (&lt;250 for mice; &lt;700 for
 organoids). Thresholds were set as permissive as possible to avoid
 filtering out viable cell populations, consistent with current
 best-practice recommendations. The <em>sctransform</em>
 function in Seurat was used for normalization and variance stabilization
 of raw UMI counts based on regularized negative binomial regression models
 of the count by cellular sequencing depth relationship for each gene while
 controlling for mitochondrial mapping percentage as a confounding source
 of variation. Resulting Pearson’s residuals were used to identify the most
 variable features in each dataset (n=3,000 by default), followed by
 dimensionality reduction by PCA and UMAP, shared nearest neighbor (SNN)
 graph construction based on the Euclidean distance between cells in
 principal component space, and unbiased clustering of cells by Louvain
 modularity optimization. Optimal clustering solutions for each dataset
 were determined by building cluster trees and evaluating the SC3 stability
 index for every cluster iteratively at ten different clustering
 resolutions with the <em>clustree</em> function in R. The
 effect of cell-cycle variation on clustering was examined by calculating
 and regressing out cell-cycle phase scores in a second iteration of
 <em>sctransform</em>, based on the expression of canonical
 G2/M and S phase markers. Consistent with the developmental context of the
 interrogated datasets, cell-cycle differences were found to covary with
 cell-type and retained in final analyses as biologically relevant sources
 of heterogeneity. Cluster compositions were checked to confirm comparable
 distributions of experimental batch, replicate ID, and genotype metadata.
 Cluster annotations for cell-type were determined based on the expression
 of known cell-type and cortical layer markers curated from the literature.
 Clusters exhibiting cell-type ambiguity were further sub-clustered to
 refine annotations or dropped from downstream analysis in case of
 inconclusive results (human cl.7 and cl.16; mouse cl. 25 and cl.
 27). <em>Seahorse
 Mitochondrial Stress Assay</em> HEK-293T cells that had been engineered to
 carry the 3q29Del (<em>3q29Del</em>), PAK2 knockout
 (<em>PAK2</em>), and mock-edited control cells
 (<em>CTRL</em>) were plated on poly-D-lysine coated 96-well
 Seahorse assay plates (XF96, Agilent) in DMEM (Gibco A144300) supplemented
 with 10% FBS, 2mM L-glutamine, 1mM sodium pyruvate, and either 10mM
 D-(+)-glucose (“Glu”, 7.5E3 cells/well) or 10mM galactose (“Gal”,15E3
 cells/well). After 48 hours, cells were washed twice in XF DMEM Assay
 Medium (Agilent) with either glucose or galactose (10mM) supplemented with
 1mM pyruvate, and 2mM glutamine.  Neural progenitor cells (NPCs) were plated
 at 5E4 cells/well in poly-L-ornithine and laminin-coated 96-well Seahorse
 assay plates in STEMdiff Neural Progenitor Medium. After 24 hours, all
 media was aspirated and exchanged for Neural Medium containing 2mM
 L-glutamine, 1mM sodium pyruvate, and either 17.5 mM glucose or galactose
 for 48hrs. Mitochondrial
 stress test compounds were loaded into injection ports as indicated by the
 manufacturer to achieve the following final concentrations for :
 HEK cells: 1uM oligomycin,
 0.25uM FCCP, 0.5uM rotenone, 0.5uM antimycin A (all sourced from Sigma).
 NPC assays were performed with 2uM oligomycin, 0.5uM FCCP, 1uM rotenone,
 and 1um antimycin A. Cells equilibrated at 37°C with ambient
 CO<sub>2 </sub>for approximately 1hr prior to assay
 initiation. At the end of the experiment, cells were washed twice in
 PBS+Ca<sup>2+</sup>+Mg<sup>2+</sup> and lysed at
 4C for 30 min in 0.5% Triton X-100 protein buffer (150mM NaCl, 10mM HEPES,
 0.1mM MgCl<sub>2</sub>, 1mM EGTA, 1x HALT Protease +
 Phosphatase inhibitor). Protein concentrations in each well were
 determined by BCA (Pierce) to normalize oxygen consumption rate data. Data
 were analyzed in Wave (Agilent).  <em>Statistical
 Analysis</em> Differential gene expression testing for
 genotype was performed on log normalized expression values
 (scale.factor=10,000) of each cluster separately with a two-part
 generalized linear model that parameterizes the bimodal expression
 distribution and stochastic dropout characteristic of scRNA-seq data,
 using the MAST algorithm, while controlling for cellular detection rate. A
 threshold of 0.1 was implemented as the minimum cut off for average
 log-fold change (logfc.threshold) and detection rates (min.pct) of each
 gene in either genotype to increase the stringency of differential
 expression analysis. Multiple hypothesis testing correction was applied
 conservatively using the Bonferroni method to reduce the likelihood of
 type 1 errors, based on the total number of genes in the dataset. To
 facilitate comparative transcriptomics, human homologs (including multiple
 paralogs) were identified for all differentially-expressed genes (DEGs) in
 the mouse dataset via the NCBI’s HomoloGene database
 (ncbi.nlm.nih.gov/homologene/). Data processing and analysis pipelines
 were harmonized across the mouse and organoid datasets, yielding parallel
 computational approaches for cross-species comparison of differential
 expression signals. The BrainSpan Developmental Transcriptome dataset used
 for developmental stage estimations was obtained by bulk RNA sequencing of
 postmortem human brain specimens collected from donors with no known
 history of neurological or psychiatric disorders, as described previously.
 This large-scale resource is accessible via the Allen Brain Atlas data
 portal (https://www.brainspan.org/static/download/, file name: “RNA-Seq
 Gencode v10 summarized to genes”); dbGaP accession number:
 phs000755.v2.p1. All statistical analyses of scRNA-seq data were performed
 in R (v.4.0.3). To
 interpret differential gene expression results, pathways likely impacted
 by the 3q29Del were determined based on statistically over-represented
 gene-sets with known functions using g:Profiler. DEGs (Bonferroni adj.
 p&lt;0.05) for each cluster were identified as described above and
 input with an experiment-specific background gene set (genes with min.pct
 &gt; 0.1 in any cluster). GO:Biological Process (GO:BP) and Reactome
 (REAC) databases were searched with 10 &lt; term size &lt; 2000.
 Significantly enriched pathways below a threshold of g:SCS &lt; 0.05
 were compiled and filtered in Revigo to reduce redundancy and determine
 umbrella terms. Western
 blot analysis of mouse brain OXPHOS complex components: (Fig. 4B) one
 sample Wilcoxon signed-rank test (two-tailed), *p&lt;0.05,
 **p&lt;0.01, N = 5. Seahorse Mitochondrial Stress Test
 Analysis: (Fig. 4F) two-way ANOVA, main effect of medium F(1, 6)=23.99,
 **P=0.0027; (Fig. 4G) one-way ANOVA, effect of genotype F(2, 9)=17.24,
 P=0.0008; <em>CTRL vs. 3q29</em> ***P=0.0005, *<em>CTRL
 vs PAK2</em> P=0.0332; (Fig. 4H) one-way ANOVA, effect of genotype
 F(2, 9)=8.838, P=0.0075; <em>CTRL vs. 3q29</em> **P=0.0075,
 <em>CTRL vs PAK2</em> *P=0.0138; (Fig. 4I) two-way ANOVA
 effect of genotype, F(2, 18)=448.0, P=0.0003; <em>CTRL vs
 3q29</em> **P=0.0047; (Fig. 4J) two-way ANOVA effect of genotype,
 F(2, 18)=4.309, P=0.0296; <em>CTRL vs 3q29</em>**P=0.0079;
 (Fig. 4K) two-way ANOVA, main effect of genotype F(2, 18)=31.16,
 P&lt;0.0001; <em>CTRL vs 3q29</em> ****P&lt;0.0001,
 galactose <em>CTRL vs PAK2</em>***P=0.0007; (Fig. 4L) two-way
 ANOVA interaction of genotype and medium, F(2, 18)=4.219, P=0.0314;
 <em>CTRL vs 3q29</em> *P=0.0364. N=4 for all HEK Seahorse
 experiments. (Fig. 5E) two-way ANOVA main effect of medium, F(1,
 28)=9.295, **P=0.0050; (Fig. 5F) two-way ANOVA, medium effect F(1,
 28)=0.01219, P=0.9129; (Fig. 5G) two-tailed ratio paired t-test, P=0.7015;
 (Fig. 5H) two-tailed ratio paired t-test, **P=0.0009; (Fig. 5l) two-tailed
 ratio paired t-test, P=0.0935; (Fig. 5J) two-tailed ratio paired t-test,
 P=0.5028; (Fig. 5K) two-tailed ratio paired t-test, ***P=0.0007; (Fig. 5L)
 one sample two-tailed t-test, **P=0.0026; (Fig. 5M) two-way ANOVA genotype
 effect F(1, 56)=0.5930, P=0.4445. N=15 for all NPC Seahorse
 experiments.
description: # Cross-species analysis identifies mitochondrial dysregulation as a
 functional consequence of the schizophrenia-associated 3q29 deletion ---
 Use the following code and input data sets to generate the figures in this
 paper. Create a directory for each figure, copy the code and input files
 into that folder, and put all folders in a directory named "Purcell
 2023 dataset". Place the Purcell 2023 dataset directory on the
 desktop for file paths in R scripts to work smoothly. ## Description of
 the data and file structure ### Fig. 1 Cross-species single-cell
 sequencing. (A) A single-cell RNA-sequencing experiment was performed in
 isogenic human induced pluripotent stem cell (iPSC)-derived cortical
 organoids at two time points (2 months and 12 months in vitro) and in
 postnatal day 7 mouse isocortex. An overview of the strategy to collect
 and filter differential gene expression data from both model systems is
 illustrated. (B) The human 3q29 deletion locus is nearly perfectly
 syntenic with a region of mouse chromosome 16, with the same gene order
 inverted. Corresponding loci are illustrated in the same orientation to
 facilitate clearer cross-species comparison. *Bex6* (in gray) is the only
 gene present in the mouse, not in the human locus. (C and E) UMAP
 dimensionality reduction plots colored by the main cell types identified
 in human (C) and mouse (E) experiments. Human and mouse cells showed no
 obvious difference in gross distribution by genotype (D, F) but human
 cells were divided in their transcriptomic clustering patterns by time
 point (d, top). The average expression profile of each sample was
 correlated (Spearman) to BrainSpan gene expression data profiling the
 human brain transcriptome in postmortem specimens across the lifespan (G).
 Abbreviations: pcw, post-conception weeks (prenatal); m, months
 (postnatal); y, years (postnatal).  * Fig. 1 code.R &gt; used to generate
 panels in Figure 1 * anno_data.csv &gt; input data file for code *
 Annotation data for heat map visualization * brainspan_heatmap.csv &gt;
 input data file for code * Columns: scRNA-seq samples (human cortical
 organoids and mouse cortex) * Rows: Age (pcw = post-conception week; m =
 month; y = year) * scRNA-seq Seurat Objects (contain all scRNA-seq data,
 required for R script above). Refer to Methods for more details on the
 generation of samples and data processing. * Mouse isocortex Seurat
 Object: ms_obj.rds  * Human cortical organoid Seurat Object: sub_obj2.rds
 ### Fig. 2  Transcriptomic evidence of metabolic changes in 3q29Del. The
 umbrella pathways are most frequently found to be differentially expressed
 based on up- (B) and down- (C) regulated genes in cortical organoids (A).
 Oxidative phosphorylation (OXPHOS) was enriched among both increased and
 decreased genes, but all clusters contributing to up-regulated OXPHOS were
 from 2-month organoids and all clusters contributing to down-regulated
 OXPHOS were from 12-month organoids. (D) Example violin plots visualizing
 log-normalized expression data of genes dysregulated in 2-month organoid
 clusters: MT-CO3 (increased in 3q29Del) encodes the respiratory chain
 complex IV subunit COX3, LDHA (decreased in 3q29Del) is a key enzyme in
 glycolysis. (E) Example violin plots visualizing log-normalized expression
 data of genes dysregulated in 12-month organoid clusters: MT-ND1
 (decreased in 3q29Del) encodes a component of respiratory chain complex I
 and MT-ATP6 (decreased in 3q29Del) encodes a component of the ATP Synthase
 complex. The most frequently up- (G) and down- (I) regulated umbrella
 pathways in mouse isocortex (F) are shown. Treemaps derived by Revigo
 analysis (H and J) display the hierarchical organization of specific Gene
 Ontology Biological Processes (GO:BP) identified in pathway analysis.
 Similar colors denote semantic similarity. The size of each rectangle is
 proportional to the number of clusters exhibiting over-representation of a
 given GO:BP term. Abbreviations: OPC, oligodendrocyte progenitor cells;
 NSC, neural stem cells; cl, cluster. * Fig. 2 code.R &gt; used to generate
 panels in Figure 2 * mouse_down.csv &gt; input data * Columns: * Term -
 gene ontology pathway * Cluster - Seurat cluster * Direction - Up or Down
 * Cell_Type - Annotation of cluster cell composition * Cell_Class -
 Broader grouping of cell types * source - gene ontology database (GO:BP =
 Gene Ontology: Biological Process) * term_id --&gt; intersections - output
 of g:Profiler analysis * human_pathways_combined_timepoints.csv &gt; input
 data * Output of g:Profiler analysis * human_common_down.csv &gt; input
 data * human_common_up.csv &gt; input data * mouse_up.csv &gt; input data
 ### Fig. 3  Common patterns of differential gene expression in two major
 mouse and human cell types. Astrocytes were identified in human cortical
 organoids (12 months) and mouse isocortex. Corresponding clusters are
 color-coded in blue in UMAP projections (A). The human homologs of mouse
 DEGs identified by MAST analysis were compared to organoid DEGs based on
 direction of change and a significant overlap was observed between the
 down-regulated DEGs of mouse and organoid astrocyte clusters (B). Pathway
 analysis of overlapping DEGs showed that all significantly enriched Gene
 Ontology: Biological Process (GO:BP) and Reactome (REAC) terms were
 related to mitochondrial function and metabolism (C). Upper and deep-layer
 excitatory neuron DEGs were pooled and unique organoid DEGs were compared
 to the human homologs of mouse DEGs based on the direction of change.
 Corresponding clusters are color-coded in red in UMAP projections (D).
 There was a significant overlap between the DEGs of mouse and organoid
 excitatory neuron clusters for both up-regulated and down-regulated genes
 (E). Decreased genes were heavily enriched for GO:BP and REAC terms
 related to mitochondrial function and cellular respiration (F). * Fig. 3
 code.R &gt; use to generate panels in Figure 3 * mm_hs_cell_type_overlaps
 -Contains input files for the R script above ### Fig. 4  Mitochondrial
 phenotypes in 3q29 mice and engineered cell lines. Mitochondrial fractions
 from adult mouse brain lysates were found to have selective decreases in
 components of OXPHOS Complexes II and IV (A, quantified in B, N=5).  At
 least 7 3q29-encoded proteins interact with mitochondria-localized
 proteins (C, from Antonicka et al. 2020). Symbol size reflects topological
 coefficients. HEK cell lines were engineered to carry either the
 heterozygous 3q29Del or completely lack PAK2 as shown by Western blot.
 Control HEK-293T cells (CTRL) transition from a glycolytic to a more
 aerobic cellular respiration state in galactose medium (E). Oxygen
 consumption rate (OCR) is significantly increased by 48-hour galactose
 medium challenge in CTRL cells (F) but not in 3q29 or PAK2 cells. Both
 3q29 and PAK2 cells displayed increased baseline OCR (G) and decreased
 response to galactose (H). In the glucose medium, 3q29 cells showed
 reduced spare capacity (I) and increased ATP production (J). Proton leak
 (K) was found to be increased in 3q29 cells in glucose and decreased in
 PAK2 cells in galactose. Maximal respiration was significantly elevated in
 3q29 cells in glucose but was unchanged from CTRL in galactose conditions.
 * Fig. 4 code.R &gt; used to generate panels in Figure 4 * blot images *
 Contains mouse OXPHOS blot images * mouse_oxphos_blots.csv &gt; input
 data, mouse blot quantifications * hek_protein_pak2.csv &gt; input data,
 PAK2 blot quantifications (note: PAK2 blot images are in the Supplemental
 Figures folder) * hek_seahorse_ocr_ecar.csv &gt; input data * Columns: *
 Experiment (number) * Assay_date * Cell_Line (CTRL, 1D7, or 3F2) *
 Genotype (Control, del3q29, or PAK2) * Medium (Glucose or Galactose) *
 Time (of the assay in minutes) * OCR (Oxygen Consumption Rate per minute
 per microgram of protein) * ECAR (ExtraCellular Acidification Rate per
 minute per microgram of protein) * hek_seahorse_measures.csv &gt; input
 data * Columns: * Metadata (Experiment, Assay_date) * Cell_Line (CTRL,
 1D7, or 3F2) * Genotype (Control, del3q29, or PAK2) * Medium (Glucose or
 Galactose) * Seahorse Mitochondrial Stress Test output (Basal (OCR) --&gt;
 Coupling Efficiency) * Additional measures: * Spare Respiratory Capacity
 (pct) - percent of baseline * Galactose Response = fold change of Basal
 OCR in Galactose medium over Basal OCR in Glucose medium * Change in ATP
 Production = ATP production in Galactose medium over ATP Production in
 Glucose medium * Max_Ratio = Maximal Respiration in 3q29 or PAK2 / Maximal
 Respiration in CTRL ### Fig. 5  Lack of metabolic flexibility in 3q29Del
 neural progenitor cells. A) Control and 3q29Del neural progenitor cells
 (NPCs) exhibited normal morphology and stained positive for the
 neurofilament protein Nestin, multipotency marker SOX2, and NPC marker
 PAX6 (scale = 50um, quantified in Fig. S15). B) Illustration of
 experimental design. NPCs were challenged for 48-hr in a neural medium
 containing glucose (GLU) or galactose (GAL). C) Table of cell lines used
 in this experiment. Data from three separate cohorts was combined in plots
 D-M. N=15 from 6 independent NPC lines for all experiments. D) The energy
 map indicates that galactose treatment pushes cells from a more glycolytic
 to a more aerobic metabolic profile. E) Control NPCs significantly
 increase oxygen consumption rate (OCR) in galactose medium. F) 3q29Del
 NPCs exhibited no significant change in OCR in the galactose medium. G) No
 significant difference in baseline OCR mean in glucose medium was
 observed, but 3q29 NPCs displayed a significantly lower baseline OCR mean
 in galactose medium (H). I) Galactose response (i.e., basal OCR fold
 change over glucose) was unchanged in 3q29Del NPCs. J) Maximal respiration
 was unchanged in the glucose medium, but was significantly decreased in
 3q29Del NPCs in the galactose medium (K). Similarly, (L) the maximal
 respiration ratio of 3q29Del:Control NPCs was unchanged in glucose medium
 (GLU) but was significantly reduced in galactose conditions (GAL). There
 was no significant change in spare capacity in 3q29Del NPCs (M). * Fig. 5
 code.R &gt; used to generate panels in Figure 5 * all_measures.csv &gt;
 input data * See above for column descriptions * all_ocr_ecar.csv &gt;
 input data * See above for column descriptions ## Code/Software All
 visualization code is written in R. Single-cell RNA-seq analysis is
 performed in the Seurat R package.