Characterizing Migration of Specific Language Impairment and Autism Spectrum Disorders Neural Precursor Cells

Specific Language Impairment (SLI) is characterized by difficulties in learning language without any clinically obvious neurological, cognitive, emotional, sensory, or environmental defects (22). While difficulties with spoken language is a classifying characteristic of SLI, SLI is commonly associated with impairments in motor skills, cognitive function, attention, and reading (22). Moreover, the severity of these symptoms vary from individual to individual, leading to the overall clinical heterogeneity of SLI.

 Currently, the etiology of SLI is not completely understood, but studies have shown that both genetic and environmental factors play a role in its development. Some genetic factors include a dominant mutation in the FOXP2 gene, and studies have shown there is linkage to different loci on chromosomes 16 and 19 (11,15). Therefore, SLI is said to be idiopathic because there is no single or known underlying cause. Twin studies have been used to better explain the inheritance pattern of both the genetic and environmental components of SLI. These studies showed a concordance rate of 72% for monozygotic twins versus a 49% for dizygotic twins (5). However, since the concordance rate for monozygotic twins was not perfect, or not 100%, it can be assumed that environmental factors also led to the development of SLI. Some environmental factors are believed to be associated with SLI such as low birthweight, high birth order (third or older), maternal marital status (particularly if single), a shorter duration of maternal education, and late care from healthcare professionals during pregnancy (17). All of these genetic and environmental factors lead to the heterogeneity of SLI making it difficult to fully understand and treat. However, speech therapy is still helpful for patients with SLI and physicians might be able to intervene in a more effective way if they were able to understand the underlying mechanisms.

 Previous structural neuroimaging studies in the past have suggested that atypical patterns of asymmetry in language cortex, cortical dysplasia, and white-matter abnormalities may be associated with SLI (22). MRI studies have shown evidence that the brains of children with SLI are structurally different than children who have normal language. One study consisted of 35 children with SLI and 27 children without language impairments. Upon looking on their MRI scan, 12 of the 35 children with SLI had abnormalities compared to zero out of the twenty-seven with abnormalities (19). These abnormalities included ventricular enlargements, central volume loss, and white matter abnormalities (19). Mice model studies in the past have helped us to examine brain developmental processes in order to see how proliferation, migration, and differentiation contribute to neurodevelopmental disorders; this is important because it is unclear if these studies would capture the heterogeneity of SLI.  Because mice are unable to communicate with humans through language, it is not fully clear that ultrasonic vocalizations in mice are informative about language disorders. Ultrasonic vocalizations have been used to study vocal communication defects, such as the missing gene ProSAP1/Shank2 in mice (7). However, these studies do not allow clear identification of the emitter these vocalizations. If studies were able to identify the mechanism of these vocalizations, then it may be more suitable for information on language disorders. Because, to be able to equate it to spoken language we need to be able to know how they do vocalizations, not just the social reason to why they emit them.

 Autism Spectrum Disorders (ASDs) are a group of neurodevelopmental disorders characterized by difficulties in social communication and presence of repetitive behavior. ASDs affect 1 in every 59 children in the United States and is most commonly found in boys (1,2). Symptoms of ASDs include communication and motor defects such as abnormal eye contact, repetitive speech, and reduced verbal interactions, which starts becoming noticeable around age two while diagnoses occur at age four (6). Moreover, patients with ASDs tend to have comorbid symptoms, such as epilepsy, intellectual disability, anxiety, depression, and gastrointestinal problems (20). The severity of these symptoms varies from individual to individual, revealing the heterogeneity of ASDs. Just like SLI, the etiology of ASDs is not completely understood, however studies have shown that genetic and environmental factors play in role in the development. Twin studies have shown that ASDs have a strong genetic component, with monozygotic twins there is a 60-91% concordance rate (12). Furthermore, the risk in families with one child with ASDs to have another child with ASDs is as high as 20%, which is about 10X higher than the rest of the population (1). Although genetics plays a role in the development of ASDs, only 20% of ASDs have known genetics associations, meaning there is no single gene or mutation that leads to ASDs. Examples of single gene mutation ASDs include both Fragile X Syndrome and Rett Syndrome. Fragile X syndrome occurs via a trinucleotide repeat expansion of the FMRI gene, whereas Rett Syndrome is caused by mutations on the MeCP2 X-linked gene (3,8). Environmental factors such as Thalidomide and Valproic acid, may also lead to ASDs development (8). The other 80% of ASDs are idiopathic meaning that the underlying cause is still unknown. Idiopathic ASDs are believed to arise from a mix of both genetic and environmental factors, continuing the heterogeneity of the disorder.

 Mice models have been used in the past several years to understand the underlying neurobiology of ASDs. These studies have provided us insight on developmental processes and changes in ASDs, such as abnormalities in cerebellar functions that occurred due to cellular processes being disrupted (4). However, mice models are unable to capture and mimic the complexity of human development of ASDs because there are differences in the way mice and humans develop. Imaging and postmortem studies have shown us more about ASDs in the human brain itself. Imaging studies have also shown structural abnormalities in brains of ASDs patients, such as enlargements in both gray and white matter volume in all cortical lobes, and slight enlargements in temporal lobe white matter (9,10). There have been studies of postmortem ASDs brains done in order to understand the disorder, however, this method shows what the disorder is like in its terminal stage, not in its developmental stage. This highlights the notion that studies in the developmental stage are needed to understand how proliferation, migration, and differentiation occurs in ASDs patients.

 In order to better understand both the SLI and ASD developmental processes, we need to take a closer look at alterations in these developmental processes in a human model system. Patient-derived human induced pluripotent stem cells (iPSCs) technology has become a very effective and important tool for studying neurological diseases. iPSCs are created by reprogramming somatic cells, such as skin or blood cells, turned back into their pluripotent state, which can give rise to different types of cells, including neural precursor cells (NPCs) which differentiate later into Neurons. iPSCs are able to retain the specific genetics of the individual from whom they are derived, including any genetic mutations they may have, allowing us to observe the specific neurodevelopmental characteristics of that individual (16).  The DiCicco-Bloom lab has decided to focus on the SLI relative of our ongoing study between Autism Spectrum Disorders (ASD) and their siblings (SIB). Previously, our lab focused on identifying the differences and similarities between idiopathic ASD and their same sex sibling. However, there was a third component that correlated between the families selected for the study, they all had a first degree relative that had SLI. Our lab is ultimately interested in seeing the connections between the SLI, ASD, and SIB in their neurodevelopmental processes.

We will be studying proliferation, migration, and differentiation of NPCs in control culture media, to see how SLI, ASD, and SIB individuals’ neurons normally develop. We will also be studying proliferation, migration, and differentiation with the addition of extracellular factors (EFs), since the brain developmental processes are often regulated by a multitude of factors. EFs, such as growth factors and neuropeptides, are used as tools to uncover differences between out autism cell lines that would not be necessarily evident in control conditions, as shown in previous studies in our lab (13). Moreover, it helps us understand how SLI, SIB, and ASDs respond to factors that are important in regulating developmental processes.

Migration study:

Migration is an important developmental process that allows for normal brain structure. Studies on post-mortem tissue and cortical neuronal studies showed that in ASD patients there were impairments in migration (23). In the brain, developmental processes are regulated by many factors including neuropeptides, growth factors, and neuropeptides. We studied the developmental process of migration in both control conditions and under the stimulation of the EF pituitary adenylate cyclase-activating polypeptide (PACAP). PACAP is a neuropeptide that in prior studies in our lab have shown to regulate migration (21). We hypothesize that the NPCs of SLI will not have a response to PACAP, similar to older studies in our lab that showed ASD did not respond to PACAP.

Methods

Patient Population, Obtainment of tissue samples, and Obtainment of iPSCs:

Approaches and methods in this section are described in reference 14.

Severely affected idiopathic ASD male patients and their unaffected SIB were derived from eighty-five New Jersey families recruited by collaborator Dr. Linda Brzustowicz. Every family consisted of 5 individuals including one child with ASD, one unaffected same sex child, and a first degree relative with SLI. Blood cells were acquired from the individuals with ASD and SLI, as well as the unaffected SIB. T-cells were then isolated from the blood samples and reprogrammed with a non-integrating Sendai virus that contained Yamanaka factors (Oct 3/4, SOX2, Kld4, c-Myc). After infection, the cells were then replated onto feeder cells with iPSC media, after 20-25 days clumps of iPSCs formed. These clumps are known as clones, and individual clones were picked and expanded. iPSC generation and characterization were conduction by the Lu lab.

Neural Induction of iPSCs:

Approaches and methods in this section are described in reference 24.

 To create NPCs from iPSCs a commercially available kit from ThermoFisher GIBCO was used. This kit contains Neurobasal Media (NB) and Neural Induction Supplement (NIS) which are used to make Neural Induction Medium (NIM). The full protocol can be found in reference 18. When the iPSCs became 70 to 80% confluent, they were split and plated on a 6 well plate coated with Matrigel that already contained 2 mL feeder-free iPSC medium and 5 µM ROCK inhibitor (this was done at multiple densities). After one day the iPSC medium with the ROCK inhibitor was removed and then washed once with 1X PBS. Afterwards 2 mL of NIM was added to the iPSCs; this media was changed every 2 days until the cells became confluent. Once they reached confluency the media was changed daily. After 7 days in NIM the cells were plated onto a 6-well plate this was coated with Matrigel containing 2 mL of 100% expansion media. These cells were considered to be NPCs at Passage 0.

Media preparation (100% expansion media):

NPCs are maintained in 100% expansion medium. This was created by combining 24.5 mL of both DMEM/F12 and NB. 1 mL of 50X NIS was then added, followed by 100 µL of Primocin added to the solution.

Maintaining NPCs:

NPCs were maintained in a 6 well, Matrigel-coated plate. Matrigel coated plates were created by adding a Matrigel aliquot, taken from the -80°C freezer, into 6 mL of cold DMEM/F12 media. Then, 1 mL of the Matrigel-DMEM/F12 solution was added into each well and then incubated at 37°C for 30 minutes allowing for the gel to form. After this, the Matrigel-DMEM/F12 media was aspirated and replaced with 100% expansion media. The 100% expansion media was replaced every other day. NPCs were plated at a density of 1.5 million cells into wells with 2 mL of 100% expansion media and incubated at 37°C. These NPCs were split and passaged every 4-9 days depending on when confluency occurred, when the cells became densely packed together at the bottom of the well.

Media Preparation for Experimental Conditions (30% Expansion Media):

The 100% expansion media was diluted by 70% to make 30% expansion media. This is made by adding an equal amount of DMEM/F12 and NB solution and a 1:500 ration of Primocin. Then the 10nM of PACAP was added.

Migration experiment preparations and conditions:

Approaches and methods in this section are described in reference 24.

Forming Neurospheres:

Migration was studied by the formation of neurospheres. Neurospheres are formed by plating 1 million NPCs into a 35-mm dish that contains 1 mL of 100% expansion media with no coating substrate. These NPCs were then incubated at 37°C, until each sphere reaches a diameter of 100 um +/- 20 um (usually after 48-96 hours) and were collected and plated into the culture media that already had Matrigel dissolved in it. After 24 to 48 hours, the NPCs had begun migrating from the inner mass. Following migration the cells were then fixed with 1 mL of ice cold 4% paraformaldehyde (PFA) for 20 minutes. PFA was then removed and the dishes were washed three times with 1X PBS (5 minutes each time). The neurospheres were preserved in 0.05% sodium azide and were then able to be analyzed.

PACAP Media Conditions:

In order to study migration under PACAP conditions, neurospheres were formed as mentioned above. To accomplish this, cells were plated in culture gel where 10nM of PACAP was already dissolved in the 30% Matrigel mix. NPCs were incubated at 37°C , and were collected and plated once the neurospheres diameter reaches 100 um +/- 20 um. These cells were then fixed with 1 mL of ice cold 4% PFA for 20 minutes. PFA was then removed and dishes were washed three times with 1X PBS. The neurospheres were preserved in 0.05% sodium azide and were then able to be analyzed.

Microscope Imaging and Measurements:

Neurospheres were analyzed on the Zeiss microscope, where 10 spheres were imaged throughout each dish per condition. Spheres chosen to be imaged were ones that exhibited a densely packed inner mass with cells migration out as a continuous carpet. Migration is determined by finding the difference between the total area of the neurosphere with the surrounding carpet of cells by the area of the neurosphere’s inner cell mass, the program ImageJ was used for the measurements.

Statistical Tests:

T-tests were performed on Microsoft Excel. Data considered statically significant if below the 0.05 threshold.

Results (preliminary data):

Results are based on the experiments done on several clones at different passages. SIB is one clone (2135) and experiments were done on both passage 5 and passage 6 (3 wells per condition per passage). ASD consists of the average of 2 clones (2013 and 2009). Experiments on 2013 were done on passages 5, 6, and 7 (3 wells per condition per passage). Experiments on 2009 were done on passages 5 and 7 (3 wells per condition per passage). SLI is one clone (4016) and experiments were done on passages 5, 6, 7, and 8 (3 wells per condition per passage).

1. SIB, ASD, and SLI migration rate showed no significant change when 10nM PACAP was added

SIB, ASD, nor SLI showed a significant increase or decrease when 10nM of PACAP was added than when in control conditions. ASD may have significantly lower control and PACAP migration than both SIB and SLI. However, ANOVA was not performed to test for statistical significance between the three groups.

Figure 1: Migration in control and PACAP conditions show no significant response to 10nM PACAP in SIB, ASD, and SLI.

2. The individual lines of 2135 (SIB). 2013 (ASD), and 4016 (SLI) showed no significant change in migration average in 10nM PACAP conditions. 2009 (ASD) displayed significant change in migration average in 10nM PACAP conditions

The migration average of 2009 line had a 1.37-fold increase (p<0.000005) when in PACAP compared to control conditions. 2013 may have significantly lower control and PACAP migration than 2135, 2009, and 4016. However, ANOVA was not performed to test for statistical significance between the three groups.

*****

Figure 2: Migration in control and PACAP conditions show 2135, 2013, and 4016 have no significant response to 10nM PACAP, whereas 2009 have a significant response (***** p<0.000005)

3. Passage 5 and Passage 7 of 2135 (SIB) displayed no significant changes in migration average when 10nM PACAP was added.

2135 was broken down into both of its passages to test for reproducibility. In this case neither of its two passages showed a significant increase or decrease in migration average when 10nM PACAP was added versus in control conditions.

Figure 3: Migration in control and PACAP conditions show no significant response to 10nM PACAP in 2135 Passage 5 and 2135 Passage 7.

4. Passage 5, Passage 6, and Passage 7 of 2013 (ASD) displayed no significant changes in migration average when 10nM PACAP was added.

2013 was broken down into its three passages to test for reproducibility. In this case neither P5, P6, or P7 showed a significant increase or decrease in migration average when 10nM PACAP was added versus in control conditions. P6 may have significantly higher control and PACAP migration than P5 and P7. ANOVA was not performed to test for statistical significance between the three passages.

Figure 4: Migration in control and PACAP conditions show no significant response to 10nM PACAP in 2013 Passage 5, 2013 Passage 6, and 2013 Passage 7.

5. Passage 5 and Passage 7 of 2009 (ASD) displayed a significant increase in migration average when 10nM PACAP was added.

2009 was broken down into its two passages to test for reproducibility. The migration average of 2009 passage 5 had a 1.35-fold increase (p=0.00657904) when in PACAP compared to control conditions. The migration average of 2009 passage 7 had a 1.40-fold increase (p<0.00005) when in PACAP compared to control conditions.

Figure 5: Migration in control and PACAP conditions show both 2009 P5 (** p=0.00657904) and 2009 P7 (p<0.00005) have a significant response to 10nM PACAP.

6. Passage 6 and Passage 7 of 4016 (SLI) displayed no significant changes in migration average when 10nM PACAP was added. Passage 5 of 4016 displayed a significant decrease in migration average when 10nM of PACAP was added. Passage 8 of 4016 displayed a significant increase in migration average when 10nM PACAP was added.

4016 was broken down into its four passages to test for reproducibility. The migration average of 4016 passage 5 had a 1.25-fold decrease (p=0.00563938) when in PACAP compared to control conditions. The migration average of 4016 passage 8 had a 1.23-fold increase (p=0.01712381) when in PACAP compared to control conditions.

*

Figure 6: Migration in control and PACAP conditions show 4016 Passage 6 and Passage 7 have no significant response to 10nM PACAP, whereas Passage 5 (**p=0.00563938) and Passage 8 (*p=0.01712381) show a significant response. 

Discussion:

Our lab is ultimately interested in seeing the connections between SLI, ASD, and SIB in their neurodevelopmental processes. One way in which this is studied is to study of migration using neurospheres. We studied migration in both control conditions and in PACAP conditions to see if there was an increase in migration averages. Preliminary data showed that overall SIB, SLI, and ASD showed no significant change in average migration when PACAP was added. The individual line of 2009 ASD did have a significant change in average migration when PACAP was added. There was some inconsistency within the line 4016, with passage 5 and passage 8 showing significant changes to PACAP. These inconsistencies can be attributed to cell instability, the cells may have developed mutations during passages. The data does not support my hypothesis, that migration will not be increased with the addition of PACAP, since 2009 ASD did show a significant change. Although more experiments are needed to be done, this early data leads us to believe that the addition of PACAP does increase migration rate. For the migration assay, it is important to note that there were subjective decisions being made whenever the selection between neurospheres were made. These decisions were made based on the neurosphere having a densely packed inner mass surrounded by a carpet of cells. However, during this process one line, 4025, which is not included in any of the data was not able to be measured since it did not have an inner cell mas coming out of a continuous carpet. This led to counting 1 cm rows of each cell line to determine the percentages of spheres with an inner cell mass to the total number of spheres, in which the average was around 40% with lines going as low as 10% and as high as 60%. We are under the assumption that the underlying biology is the same and those that do not have an inner cell mass could mean that all the cells migrated outwards. However, after looking more closely at the cells in the pictures, it was noticed that cells within the spheres do not share the same characteristics of a typical NPCs. Recently, staining was done with PAX6 (stains the nucleus), Nestin (stains the cytoplasm of NPCs), an Oct 3/4 (stains iPSCs) was done to see if these cells were NPCs or if they were iPSCs. These stains revealed that no lines were positive for Oct 3/4, and all lines were positive for PAX 6 and Nestin stains. The DiCicco-Bloom lab is currently using these findings to determine where we should go from here to see if the migration data obtained can still be used in the overall SLI, ASD, and SIB study.

References:

1. Ardhanareeswaran, K., Coppola, G., & Vaccarino, F. (2015). The Use of Stem Cells to Study Autism Spectrum Disorder. The Yale Journal of Biology and Medicine, 88(1), 5–16.
2. Autism Spectrum Disorder (ASD). (2018, November 15). Retrieved December 12, 2018, from https://www.cdc.gov/ncbddd/autism/facts.html.
3. Beltrão-Braga, P. C., & Muotri, A. R. (2017). Modeling autism spectrum disorders with human neurons. Brain research, 1656, 49-54.
4. Belzung, C. Leman, S., Vourc’h, P., & Andres, C. (2005). Rodent models for autism: a critical review. Drug discovery today: Disease models, 2(2), 93-101
5. Bishop DVM, North T, Donlan C: Genetic basis of specific language impairment: Evidence from a twin study. Dev Med Child Neurol 1995;37:56–71.
6. DiCicco-Bloom, E., Lord, C., Zwaigenbaum, L., Courchesne, E., Dager, S. R., Schmitz, C., … & Young, L. J. (2006). The developmental neurobiology of autism spectrum disorder. Journal of Neuroscience, 26(26), 6897-6906.
7. Ferhat, A. T., Torquet, N., Le Sourd, A. M., de Chaumont, F., Olivo-Marin, J. C., Faure, P., Bourgeron, T., … Ey, E. (2016). Recording Mouse Ultrasonic Vocalizations to Evaluate Social Communication. Journal of visualized experiments : JoVE, (112), 53871. doi:10.3791/53871
8. Gadad, B. S., Hewitson, L., Young, K. A., & German, D. C. (2013). Neuropathology and animal models of autism: genetic and environmental factors. Autism research and treatment, 2013.
9. Hazlett, H. C., Poe, M. D., Gerig, G., Smith, R. G., & Piven, J. (2006). Cortical gray and white brain tissue volume in adolescents and adults with autism. Biological psychiatry, 59(1), 1-6.
10. Hazlett HC, Poe MD, Gerig G, Styner M, Chappell C, Smith RG, et al. Early brain overgrowth in autism associated with an increase in cortical surface area before age 2 years. Arch Gen Psychiatry. 2011;68:467–476.[PMC free article]  [PubMed]
11. Lai CS, Fisher SE, Hurst JA, et al: A forkhead-domain gene is mutated in a severe speech and language disorder. Nature 2001; 413:519–523.
12. Ronald A, Hoekstra RA. Autism spectrum disorders and autistic traits: a decade of new twin studies. Am J Med Genet B Neuropsychiatr Genet. 2011;156B(3):255–274.  [PubMed]
13. Rossman IT, Lin L, Morgan KM, et al. Engrailed2 modulates cerebellar granule neuron precursor proliferation, differentiation and insulin-like growth factor 1 signaling during postnatal development. Molecular Autism. 2014;5:9. doi:10.1186/2040-2392-5-9.
14. Seki T, Yuasa S, Fukuda K. Generation of induced pluripotent stem cells from a small amount of human peripheral blood using a combination of activated T cells and Sendai virus. Nat. Protoc. 2012;7:718–728.  [PubMed]
15. SLI Consortium: A genome wide scan identifies two novel loci involved in specific language impairment. Am J Hum Genet 2002; 70:384–398.

16. Sheridan, S. D., Theriault, K. M., Reis, S. A., Zhou, F., Madison, J. M., Daheron, L., … & Haggarty, S. J. (2011). Epigenetic characterization of the FMR1 gene and aberrant neurodevelopment in human induced pluripotent stem cell models of fragile X syndrome. PloS one, 6(10), e26203.
17. Stanton-Chapman, Chapman DA, Bainbridge NL, Scott KG: Iden- tification of early risk factors for language impairment. Res Dev Disabil 2002;23:390–405.
18. ThermoFisher. GIBCO Induction of Neural Stem Cells from Human Pluripotent Stem Cells Using PSC Neural Induction Medium. <https://tools.thermofisher.com/content/manuals/MAN0008031.pdf> (2017).
19. Trauner D, Wulfeck B, Tallal P, Hesselink J: Neurological and MRI profiles of children with developmental language impair- ment. Dev Med Child Neurol 2000;42:470–475
20. Volkmar FR, McPartland JC. From Kanner to DSM-5: autism as an evolving diagnostic concept. Annu Rev Clin Psychol. 2014;10:193–212.[PubMed]
21. Waschek, J. A., Dicicco-Bloom, E. M., Lelievre, V., Zhou, X., & Hu, Z. (2006). PACAP Action in Nervous System Development, Regeneration, and Neuroblastoma Cell Proliferation. Annals of the New York Academy of Sciences,921(1), 129-136. doi:10.1111/j.1749-6632.2000.tb06959.x
22. Webster, R. I., MBBS, MSc, FRACP, & Shevell, M. I., MD, CM,FRCPC. (2004). Topical Review: Neurobiology of Specific Language Impairment. Journal of Child Neurology, 19(7), 471-481. doi:10.1177/08830738040190070101
23. Wegiel, J., Kuchna, I., Nowicki, K., Imaki, H., Wegiel, J., Marchi, E., . . . Wisniewski, T. (2010). The neuropathology of autism: defects of neurogenesis and neuronal migration, and dysplastic changes. Acta Neuropathologica, 119(6), 755-770. doi:10.1007/s00401-010-0655-4
24. Williams, M., Prem, S., Zhou, X., Matteson, P., Yeung, P. L., Lu, C. W., Pang, Z., Brzustowicz, L., Millonig, J. H., Dicicco-Bloom, E. Rapid Detection of Neurodevelopmental Phenotypes in Human Neural Precursor Cells (NPCs). J. Vis. Exp. (133), e56628, doi:10.3791/56628 (2018).