Atypical Neurogenesis in Induced Pluripotent Stem Cells From Autistic Individuals

Background Autism is a heterogeneous collection of disorders with a complex molecular underpinning. Evidence from postmortem brain studies have indicated that early prenatal development may be altered in autism. Induced pluripotent stem cells (iPSCs) generated from individuals with autism with macrocephaly also indicate prenatal development as a critical period for this condition. But little is known about early altered cellular events during prenatal stages in autism. Methods iPSCs were generated from 9 unrelated individuals with autism without macrocephaly and with heterogeneous genetic backgrounds, and 6 typically developing control individuals. iPSCs were differentiated toward either cortical or midbrain fates. Gene expression and high throughput cellular phenotyping was used to characterize iPSCs at different stages of differentiation. Results A subset of autism-iPSC cortical neurons were RNA-sequenced to reveal autism-specific signatures similar to postmortem brain studies, indicating a potential common biological mechanism. Autism-iPSCs differentiated toward a cortical fate displayed impairments in the ability to self-form into neural rosettes. In addition, autism-iPSCs demonstrated significant differences in rate of cell type assignment of cortical precursors and dorsal and ventral forebrain precursors. These cellular phenotypes occurred in the absence of alterations in cell proliferation during cortical differentiation, differing from previous studies. Acquisition of cell fate during midbrain differentiation was not different between control- and autism-iPSCs. Conclusions Taken together, our data indicate that autism-iPSCs diverge from control-iPSCs at a cellular level during early stage of neurodevelopment. This suggests that unique developmental differences associated with autism may be established at early prenatal stages.


Study participants and neuronal differentiation
Keratinocytes were collected from autistic participants, and typical controls without an autism diagnosis (Ethics approved, 13/LO/1218) as part of a larger European studies (EU-AIMS, STEMBANCC). All participants were Caucasian, while controls were selected if they did not have diagnosis of any psychiatric conditions. These were reprogrammed into iPSCs using previously described methods (1,2). IPS cells were cultured in E8 medium (Life

Immunocytochemistry
Cultures were fixed in 4% formaldehyde followed by ice-cold 100% methanol and processed for immunofluorescence staining, confocal microscopy and high throughput imaging. Secondary antibodies used for primary antibody detection were species-specific Alexa-dye conjugates (Invitrogen). We used the following primary antibodies to Ki67 (Thermo Harmony Software v4.9, which is based on the CellProfiler high throughput image analysis system (7). Cell nuclei were first identified based on DAPI staining. For nuclear proteins, only the nuclear area was selected. For cytoplasmic protein, the area around the nucleus was selected. Thresholds of fluorescent intensity was selected after background subtraction.
Threshold for each probe remained unchanged for every sample imaged. Antibodies, dilutions used and fluorescence threshold information in Supplementary Table S5.
Cortical spheroids were first washed in PBS and then fixed in 4% formaldehyde in 4% sucrose-PBS for 45 minutes. Following fixation, spheroids were washed in PBS and stored in sucrose 30% (weight/volume); sucrose sinking improves their preservation. After sucrose sinking, spheroids were permeabilised in 2% normal goat serum (NGS) in 0.1% Triton X100 in PBS for 60 minutes. Spheroids were incubated in a solution of permeabilization-blocking solution containing the primary antibodies for 48 hours (Supplementary Table S5). The samples were washed three times for three minutes in PBS and incubated with permeabilisation-blocking solution containing the secondary antibodies and HOECHST (nuclear staining) for two hours and kept in darkness to prevent bleaching of the fluorophores.
Cortical spheroids were mounted as whole tissues (without sectioning), due to their small size (1-5 mm). Cortical spheroids were imaged using a Lecia SP5 confocal microscope using a 40x oil immersion objective. Imagines were acquired as Z stacks at resolution 1024x1024px and employing multiple channels: 405 (DAPI/HOECHST, blue), 488nm (green), 561nm (red) and 633nm (far-red). A line average of three was used in each channel during point scanning to prevent random background signal due to light scattering.

EdU labelling
IPSCs at specific stages of differentiation were labelled with EdU (5-ethynyl-2´-deoxyuridine) using the Click-iT EdU Assay (Invitrogen). Cells were incubated with EdU for 4 hours at 37°C, then an additional 4 hours with EdU-free media. After incubation, labelled cells were fixed and prepared for detection using the Click-iT reaction cocktail. Nuclei were labelled using Hoechst 33342. Number of EdU-labelled cells were recorded as a percentage over total number of live nuclei. Imaging and analysis were done using the Opera Phenix HCS and Harmony Analysis Software.

RNA isolation and sequencing
RNA from 2 technical replicates was extracted using 2 clones from each participant (total: 4 samples per participant). TRIzol (Thermo Fischer) method was used, replacing chloroform with 1-bromo-3-chloropropane (BCP; Sigma). To remove genomic DNA during processing, turbo DNase (Thermo Fischer) was used. RNA concentration was quantified using Ribogreen assay (Invitrogen).
Starting with 500ng of total RNA, poly(A) containing mRNA was purified and libraries were prepared using TruSeq Stranded mRNA kit (Illumina). Unstranded libraries were constructed and underwent 50bp single ended sequencing on an Illumina HiSeq 2500 machine.
To analyse iPSC mRNA-seq data, the raw reads were mapped to the human genome GRCh37.75 (UCSC version hg19) using STAR: RNA-seq aligner (8). Aligned reads were sorted using samtools (9), while biases were removed using Picard tools (Broad Institute).
Quality control was performed using Picard tools (Broad Institute) and QoRTs (10). Gene expression levels were quantified using an union exon model with HTSeq (11), which uses uniquely aligned reads. Only the genes with >10 reads and expressed in 80% of the samples, were kept. The resulting read counts were log2 transformed and GC content, gene length, and library size normalised using the cqn package (12) in R.

mRNA weighted co-expression network analysis
Co-expression network analysis was performed using the R library, WGCNA (13). We wanted to investigate autism-specific iPSC-neuronal culture co-expressed genes (or modules).
Biweighted mid-correlations were calculated for all pairs of genes, then a signed similarity matrix was created. In the signed network, the similarity between genes reflects the sign of the correlation of their expression profiles. The signed similarity matrix was then raised to power β to emphasize strong correlations on an exponential scale. The resulting matrix (known as adjacency matrix) was then transformed into a topological overlap matrix. Since we are primarily interested in exploring co-expressed genes conserved across our cohort, we created consensus networks correlated to autism as previously published (14,15 corresponding ME, and genes with kME < 0.7 were removed from the module. Network 7 visualisation was done using iGraph package in R (16). Differentially expressed genes, and gene module assignments in Supplementary Table S6.

Enrichment analysis for gene sets
Two types of gene set enrichments were performed. For autism-correlated module enrichment, logistic regression was performed using already published gene modules (14,15,17) to control for gene length and gene expression level. A two-sided Fisher exact test with 95% confidence interval was performed for cell-type enrichment analysis using published human brain dataset (18).
Module genes were characterised using GO Elite (version 1.2.5) (19) using total expressed genes as background. GO Elite uses a Z-score approximation of hypergeometric distribution to assess term enrichment, and removes redundant GO or KEGG terms to give a concise output.
10,000 permutations were used, and required at least 10 genes to be enriched in a given pathway at a Z-score of at least 2. Only biological process and molecular function categories are reported. Pipeline schematic in Supplementary Figure S2.   Table S3).  Table S6).

Transcriptomic analysis of iPSCs reveal enrichment of gene modules associated with autism
The three most upregulated and three most downregulated gene modules were strongly enriched respectively in autism post-mortem brain gene modules (Supplementary Figure   S3D). These gene modules showed little to no enrichment in schizophrenia or cancer gene modules (Supplementary Figure S3E) indicating that the gene expression patterns in our 9 samples were autism-specific. From this we concluded that altered gene expression in adult autism brains was also found in prenatal neurons generated from iPSCs, and that gene expression patterns were specific to autism.  16 show degree of variance within the non-syndromic and syndromic autism groups. All parameters measured show significant variance (p < 0.05) across both groups, and each group shows greater variance compared to the other in equal number of parameters, 5 out of 10.