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Randolph Ashton: "RosetteArray® Platform: AI-enabled High-Throughput Screening of Human Neurodevelopmental for Toxicology and Precision Medicine"

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ABSTRACTS

RosetteArray® Platform: AI-enabled High-Throughput Screening of Human Neurodevelopmental for Toxicology and Precision Medicine

Authors: B. F. Lundin, G. T. Knight, N. J. Fedorchak, Kevin Krucki, M. Birtele, N. Iyer, J. F. Robinson, B. J. Iskandar, G. Quadrato, Kelly Carstens, Rebecca Willett, and R. S. Ashton.

Background and Purpose: Human pluripotent stem cell (hPSC)-derived neural organoids are prominent in vitro models of human central nervous system (CNS) development. Formed through spontaneous self-organization of neurally differentiating cell aggregates, these organoids recapitulate extensive aspects of CNS tissue morphogenesis. As such, they are excellent tools for toxicological screening, disease modeling, and drug discovery. Yet, emergence of their 3D structure and morphology is variable limiting their reproducible use as a scalable screening platform. This has prevented widespread implementation of neural organoid technology as a quantitative high-throughput screening (qHTS) tool for assessing the risk of chemicals/drugs to cause human developmental neurotoxicity (DNT) or neurodevelopmental disorders. 

Methods: We present use of micropatterned culture substrates to standardize formation of 3D, forebrain and spinal cord, neural rosette tissues, i.e., the incipient structure of neural organoids (Knight et al. ELife 2018). 96-, 24-, and 6-well RosetteArray plates can be manufacture with micropatterned culture substrates as the plate bottom. Then, the plates can be used to standardize derivation of 100’s-1000’s of singularly polarized neural rosette tissues per well from direct seeding of cryopreserved hPSCs or hPSC-derived neuromesodermal progenitors (NMPs; Iyer et al. Sci. Adv. 2022). After 8 (forebrain) or 5 (spinal) days of culture, RosetteArray plates are analyzed via standard fixation, immunostaining, and confocal imaging steps followed by image analysis using artificial intelligence-based RosetteDetect™ software to quantify RosetteArray metrics: # of tissues per well, # of cells per tissue; % of Pax6⁺ neural stem cells per tissue; % of tissues per well displaying a singularly polarized neural rosette structure. 

Results: TheRosetteArray Platform has been validated for scalable qHTS of DNT and modeling of chemical and genetic risk factors known to cause Spina Bifida (Lundin et al. BioarXiv 2024) and Autism Spectrum Disorder (Birtele et al. Nat. Neurosci. 2023). Reproducibility has been demonstrated across seventeen forebrain RosetteArrays (n=40 randomly selected tissues/well analyzed across 60 wells/plate; error is stdev), where media control wells yielded 86.3% ± 9.06% singularly polarized neural rosette emergence efficiency (SRE) and the Z-score comparison between media (negative control) and 1.0mM Methotrexate (positive control) wells was 0.532 (>0.5 indicative of an effective qHTS assay). Also, analysis of two spinal RosetteArrays yielded 93.8%± 4.1% SRE. Both region-specific RosetteArrays were tolerant of 0.1% DMSO-containing media and up to 0.002mg/mL S9-pooled liver fraction, indicating the ability to screen DMSO-solvated chemical libraires and integrate simulated human metabolism. To evaluate the forebrain RosetteArray’s utility for DNT screening, a blinded screen of the EPA’s ToxCast Phase 1/2 library, focusing on compounds cross-listed in the OECD’s Initial Recommendation of a DNT In Vitro Testing Battery (No. 377), was conducted. The forebrain RosetteArray demonstrated 87.1% sensitivity (n=31) and 100% specificity (n=3), which is on par with the DNT In Vitro Testing Battery’s performance. With additional DNT validation testing ongoing, the RosetteArray Platform was also used to model Spina Bifida risk caused by both chemical and genetic disruption of folate metabolism (Lundin et al. BioarXiv 2024) and Autism Spectrum Disorder risk caused by a SYNGAP1 genetic mutation (Birtele et al. Nat. Neurosci. 2023). 

Conclusions: Collectively, these results support the RosetteArray Platform’s utility for qHTS of chemical DNT and modeling risk factors that cause neurodevelopmental disorders such as Spina Bifida and Autism Spectrum Disorder. The Platform’s scalability and cost-effectiveness has the potential to transform implementation of neural organoid-based toxicological screening, disease modeling, and drug discovery. Moreover, the Forebrain RosetteArray’s performance motivates its inclusion in the OECD’s DNT In Vitro Testing Battery.

 
TempO-seq and RNA-seq gene expression levels are highly correlated for most genes: A comparison using 39 human cell lines

Recent advances in transcriptomics technologies allow for whole transcriptome gene expression profiling using targeted sequencing techniques, which is becoming increasingly popular due to logistical ease of data acquisition and analysis. As data from these targeted sequencing platforms accumulates, it is important to evaluate their similarity to traditional whole transcriptome RNA-seq. Thus, we evaluated the comparability of TempO-seq data from cell lysates to traditional RNA-Seq from purified RNA using baseline gene expression profiles. First, two TempO-seq data sets that were generated several months apart at different read depths were compared for six human cell lines. The average Pearson correlation was 0.93 (95% CI: 0.90 – 0.96) and principal component analysis (PCA) showed that these two TempO-seq data sets were highly reproducible and could be combined. Next, TempO-seq data was compared to RNA-Seq data for 39 human cell lines. The log2 normalized expression data for 19,290 genes within both platforms were well correlated between TempO-seq and RNA-seq (Pearson correlation 0.77, 95% CI: 0.76 – 0.78), and the majority of genes (15,480 genes, 80%) had concordant gene expression levels. PCA showed a platform divergence, but this was readily resolved by calculating relative log2 expression (RLE) of genes compared to the average expression across cell lines in each platform. Application of gene ontology analysis revealed that ontologies associated with histone and ribosomal functions were enriched for the 20% of genes with non-concordant expression levels (3,810 genes). On the other hand, gene ontologies annotated to cellular structure functions were enriched for genes with concordant expression levels between the platforms. In conclusion, we found TempO-seq baseline expression data to be reproducible at different read depths and found TempO-seq RLE data from lysed cells to be comparable to RNA-seq RLE data from purified RNA across 39 cell lines, even though the datasets were generated by different laboratories using different cell stocks.

 

Laura Word, PhD is a Lead Toxicologist at ICF International, where she applies her expertise in toxicology to inform risk assessments that protect human health and the environment. She previously completed her postdoctoral research at the U.S. Environmental Protection Agency (US EPA), where she advanced new approach methodologies (NAMs) by using high-throughput transcriptomic data to prioritize chemicals for further safety testing. She holds a Master’s degree in Toxicology and a PhD in Environmental Sciences and Engineering from the University of North Carolina at Chapel Hill, and has built extensive experience in genetics, epigenetics, transcriptomics, and exposure science. Through her work in computational toxicology, Dr. Word strives to continue advancing innovative methodologies that enhance environmental health and safety decision-making.

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Recordings and other materials from this webinar will be posted on the ASCCT webinar archive: https://www.ascctox.org/webinar-archive