Biology subjects

Paull, D.

Publications and source records attributed to Paull, D..

12 recordsLinked to original sources

bioRxiv2026

Genome writing and Targeted Delivery of the NKX6-3/ANK1 gene cluster and its Type 2 Diabetes GWAS Variants to Human iPSCs

Genome-wide association studies (GWAS) identified over 600 loci containing single-nucleotide polymorphisms (SNPs) associated with type 2 diabetes (T2D), most of which reside in non-coding regions. Among the set of T2D SNPs, linking causal genome variants to disease risk experimentally has remained a challenge; however, advances in synthetic mammalian genome writing techniques now enable the delivery of multiple haplotypes to human induced pluripotent stem cells (hiPSCs) to create a series of isogenic cell lines that can be differentiated and phenotyped in vitro. Here, to begin efforts in dissecting a T2D GWAS locus, we engineered an NKX6-3/ANK1 gene cluster knockout hiPSC line and introduced a landing pad facilitating the delivery of synthetic haplotype payloads. We built four haplotypes, including several that are not observed in nature, containing risk SNPs spanning the NKX6-3/ANK1 gene cluster using a method called "variant Switching Auxotrophic markers for Integration" (vSwAP-In), and integrated them precisely into hiPSCs. NKX6-3/ANK1 deletion blocked pancreatic progenitor and skeletal muscle differentiation, suggesting that NKX6-3 and ANK1 are required for early pancreatic and skeletal muscle development, and perhaps related to the existence of two nonoverlapping sets of SNPs in linkage disequilibrium that associate with the expression of the two adjacent genes. When NKX6-3/ANK1 T2D "Risk" haplotypes were reintroduced, skeletal muscle and pancreatic progenitor differentiation capabilities were restored. ANK1 expression was elevated in the ANK1 Risk and All-Risk haplotypes compared to the NKX6-3 Risk and Non-Risk haplotypes, establishing a functional experimental platform to examine risk SNP clusters in their native contexts. Overall, this work establishes a platform for the dissection of GWAS loci using synthetic haplotype genomics in hiPSCs. Significance StatementGenome-wide association studies have been used to identify disease-associated SNPs; however, most SNPs lie in non-coding regions, making functional experimentation difficult to perform. Using vSwAP-In, a yeast-based DNA variant-building method, and mSwAP-In, a mammalian genome engineering approach, we establish a platform for functional GWAS dissection in hiPSCs. This platform allows us to build DNA harboring virtually any combination of disease-risk SNPs, allowing for functional characterization of SNPs without the limitations of linkage disequilibrium. We demonstrate this approach using a Type 2 diabetes GWAS gene cluster, NKX6-3/ANK1.

genetics↗

bioRxiv2025

Patient induced pluripotent stem cells identify specificities of a reticular pseudodrusen phenotype in age-related macular degeneration

BackgroundAge-related macular degeneration (AMD) is a leading cause of vision loss. Reticular pseudodrusen (RPD), deposits on the apical side of the retinal pigment epithelium (RPE), signify a distinctive and critical AMD phenotype. Yet, their molecular basis and relationship to the conventional drusen seen in AMD remain unclear. ResultsWe generated induced pluripotent stem cell-derived RPE cells from a clinically phenotyped cohort comprising only individuals with conventional drusen (AMD/RPD-) or drusen coexisting with RPD (AMD/RPD+). From these cells, we generated single-cell transcriptomics, proteomics, and functional data to identify differences between the two cohorts. We show that AMD/RPD+ RPE cells exhibit enrichment in extracellular matrix (ECM) remodelling, cytoskeletal, and hypoxia-responsive programs, whereas AMD/RPD- RPE cells display a relatively greater representation of mitochondrial and protein homeostasis pathways. Both subtypes engaged pathways classically linked to ageing, including ECM remodelling and mitochondrial function, but differed in the direction and extent of these changes. Expression and protein quantitative trait loci (QTLs) highlight shared genetic influences on mitochondrial and iron-handling pathways, while disease-interacting eQTLs and transcriptome-wide association study identify regulatory signals that are distinctive of the RPD subtype within AMD, including through regulation of ECM. Functionally, all iPSC-derived RPE formed drusen-like deposits in vitro: AMD/RPD-lines generated more basal deposits, whereas AMD/RPD+ cells exhibited greater structural instability under bisretinoid-induced stress. ConclusionsThese findings indicate that AMD with and without RPD represent mechanistically distinct entities and provide novel insight into the molecular mechanisms underlying disease heterogeneity in AMD.

genetics↗

bioRxiv2025

Genetics of growth rate in induced pluripotent stem cells

Human induced pluripotent stem cells (iPSCs) have transformed biomedical research by enabling the generation of diverse cell types from accessible somatic tissues. However, certain fundamental biological properties, such as the genetic and epigenetic determinants of iPSC proliferation, remain poorly characterized. We measured the growth of iPSC lines derived from 602 unique donors using high-throughput time-lapse imaging, quantified proliferation through a growth Area-Under-the-Curve (gAUC) phenotype, and correlated gAUC with the gene expression and genotype of the cell lines. We identified 3,091 genes associated with gAUC, many of which are well established regulators of cell proliferation. We also found that rare deleterious variants in WDR54 were associated with reduced iPSC growth and that WDR54 was differentially expressed with respect to gAUC. Although no common variants showed a genome-wide association with gAUC, iPSC lines from monozygotic twins were highly correlated, and common genetic variation explained approximately 71-75% of the variance in iPSC growth rates. These results indicate a complex genetic architecture of iPSC growth rates, where rare, large-effect variants in important growth regulators, including WDR54, are layered onto a highly polygenic background. These findings have important implications for the design of pooled iPSC-based studies and disease models, which may be confounded by intrinsic growth differences.

developmental biology↗

bioRxiv2024

A repository of Ogden syndrome (NAA10) and NAA15 patient derived iPSC lines and isogenic pairs by X-chromosome screening and genome-editing.

Amino-terminal (Nt-) acetylation (NTA) is a common protein modification, affecting 80% of cytosolic proteins in humans. The human essential gene, NAA10, encodes the enzyme NAA10, as the catalytic subunit for the N-terminal acetyltransferase A (NatA) complex, including the accessory protein, NAA15. The first human disease directly involving NAA10 was discovered in 2011, and it was named Ogden syndrome (OS), after the location of the first affected family residing in Ogden, Utah, USA. Since that time, other variants have been found in NAA10 and NAA15. Here we describe the generation of 31 iPSC lines, with 16 from females and 15 from males. This cohort includes CRISPR-mediated correction to the wild-type genotype in 4 male lines, along with editing one female line to generate homozygous wild-type or mutant clones. Following the monoclonalizaiton and screening for X-chromosome activation status in female lines, 3 additional pairs of female lines, in which either the wild type allele is on the active X chromosome (Xa) or the pathogenic variant allele is on Xa, have been generated. Subsets of this cohort have been successfully used to make cardiomyocytes and neural progenitor cells (NPCs). These cell lines are made available to the community via the NYSCF Repository.

genetics↗

bioRxiv2023

Common genetic variation impacts molecular stress response in the brain

To explain why individuals exposed to identical stressors experience divergent clinical outcomes, we determine how molecular encoding of stress modifies genetic risk for brain disorders. Analysis of post-mortem brain (n=304) revealed 8557 stress-interactive expression quantitative trait loci (eQTLs) that dysregulate expression of 915 eGenes in response to stress, and lie in stress-related transcription factor binding sites. Response to stress is robust across experimental paradigms: up to 50% of stress-interactive eGenes validate in glucocorticoid treated hiPSC-derived neurons (n=39 donors). Stress-interactive eGenes show brain region- and cell type-specificity, and, in post-mortem brain, implicate glial and endothelial mechanisms. Stress dysregulates long-term expression of disorder risk genes in a genotype-dependent manner; stress-interactive transcriptomic imputation uncovered 139 novel genes conferring brain disorder risk only in the context of traumatic stress. Molecular stress-encoding explains individualized responses to traumatic stress; incorporating trauma into genomic studies of brain disorders is likely to improve diagnosis, prognosis, and drug discovery. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=71 SRC="FIGDIR/small/573459v1_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@1e07fa9org.highwire.dtl.DTLVardef@1dd530forg.highwire.dtl.DTLVardef@17822a5org.highwire.dtl.DTLVardef@2a889f_HPS_FORMAT_FIGEXP M_FIG C_FIG

genetics↗

bioRxiv2023

Patient iPSC models reveal glia-intrinsic phenotypes in multiple sclerosis

Multiple sclerosis (MS) is considered an inflammatory and neurodegenerative disease of the central nervous system, typically resulting in significant neurological disability that worsens over time. While considerable progress has been made in defining the immune systems role in MS pathophysiology, the contribution of intrinsic CNS-cell dysfunction remains unclear. Here, we generated the largest reported collection of iPSC lines from people with MS spanning diverse clinical subtypes and differentiated them into glia-enriched cultures. Using single-cell transcriptomic profiling, we observed several distinguishing characteristics of MS cultures pointing to glia-intrinsic disease mechanisms. We found that iPSC-derived cultures from people with primary progressive MS contained fewer oligodendrocytes. Moreover, iPSC-oligodendrocyte lineage cells and astrocytes from people with MS showed increased expression of immune and inflammatory genes that match those of glial cells from MS postmortem brains. Thus, iPSC-derived MS models provide a unique platform for dissecting glial contributions to disease phenotypes independent of the peripheral immune system and identify potential glia-specific targets for therapeutic intervention.

neuroscience↗

bioRxiv2023

FocA: A deep learning tool for reliable, near-real-time imaging focus analysis in automated cell assay pipelines

The increasing use of automation in cellular assays and cell culture presents significant opportunities to enhance the scale and throughput of imaging assays, but to do so, reliable data quality and consistency are critical. Realizing the full potential of automation will thus require the design of robust analysis pipelines that span the entire workflow in question. Here we present FocA, a deep learning tool that, in near real-time, identifies in-focus and out-of-focus images generated on a fully automated cell biology research platform, the NYSCF Global Stem Cell Array(R). The tool is trained on small patches of downsampled images to maximize computational efficiency without compromising accuracy, and optimized to make sure no sub-quality images are stored and used in downstream analyses. The tool automatically generates balanced and maximally diverse training sets to avoid bias. The resulting model correctly identifies 100% of out-of-focus and 98% of in-focus images in under 4 seconds per 96-well plate, and achieves this result even in heavily downsampled data ([~]30 times smaller than native resolution). Integrating the tool into automated workflows minimizes the need for human verification as well as the collection and usage of low-quality data. FocA thus offers a solution to ensure reliable image data hygiene and improve the efficiency of automated imaging workflows using minimal computational resources.

bioinformatics↗

bioRxiv2023

ScaleFExSM: a lightweight and scalable method to extract fixed features from single cells in high-content imaging screens

Leveraging artificial intelligence (AI) in image-based morphological profiling of cell populations is proving increasingly valuable for identifying diseased states and drug responses in high-content imaging (HCI) screens. When the differences between populations (such as a healthy and diseased) are completely unknown and undistinguishable by the human eye, it is crucial that HCI screens are large in scale, allowing numerous replicates for developing reliable models, as well as accounting for confounding factors such as individual (donor) and intra-experimental variation. However, as screen sizes increase, challenges arise including the lack of scalable solutions for analyzing high-dimensional datasets and processing the results in a timely manner. For this purpose, many tools have been developed to reduce images into a set of features using unbiased methods, such as embedding vectors extracted from pre-trained neural networks or autoencoders. While these methods preserve most of the predictive power contained in each image despite reducing the dimensionality significantly, they do not provide easily interpretable information. Alternatively, techniques to extract specific cellular features from data are typically slow, difficult to scale, and often produce redundant outputs, which can lead to the model learning from irrelevant data, which might distort future predictions. Here we present ScaleFExSM, a memory efficient and scalable open-source Python pipeline that extracts biologically meaningful features from large high-content imaging datasets. It requires only modest computational resources but can also be deployed on high-powered cloud computing infrastructure. ScaleFExSM can be used in conjunction with AI models to cluster data and subsequently explore, identify, and rank features to provide insights into the morphological hallmarks of the phenotypic categories. We demonstrate the performance of this tool on a dataset consisting of control and drug-treated cells from a cohort of 20 donors, benchmarking it against the state-of-the-art tool, CellProfiler, and analyze the features underlying the phenotypic shift induced by chemical compounds. In addition, the tools generalizability and utility is shown in the analysis of publicly available datasets. Overall, ScaleFExSM constitutes a robust and compact pipeline for identifying the effects of drugs on morphological phenotypes and defining interpretable features that can be leveraged in disease profiling and drug discovery.

cell biology↗

bioRxiv2023

Reprogramming method does not impact the neuronal differentiation potential of 16p11.2 deletion patient iPSCs

A major impediment to the actualization of the induced pluripotent stem cell (iPSC)-based personalized medicine revolution is the lack of widely accepted standard operating procedures (SOPs) across different groups and institutions. The various methods employed can include choice of starting materials, reprogramming agents, and culture conditions, with each of these factors hypothesized to influence the reprogramming efficiency and transcriptional identity of iPSCs. As such, we systematically compared iPSC reprogramming procedures using cells derived from the somatic cells of three patients with 16p11.2 deletion syndrome (16p11.2del) and found remarkable similarity among the different methods. FACS analysis revealed that regardless of somatic cell type (fibroblast, lymphocyte, erythroblast), route of reprogramming factor introduction (mRNA, Sendai virus, episome), donor sex, or facility (Rutgers, NYSCF), 16p11.2del patient iPSCs were viable as high purity cultures expressing pluripotency marker proteins. This observation was supported at the transcript level by qPCR analysis, which demonstrated the ability for the iPSCs to differentiate into all three embryonic germ cell lineages after 12 days in culture as embryoid bodies. NGN2-mediated differentiation of these iPSCs produced functional neurons that formed active synaptic networks as revealed by multi-electrode array (MEA) recordings. Importantly, no group-wise comparisons among the reprogramming methods yielded consistent statistically significant differences, indicating that these procedures are equally capable of producing pluripotent stem cells that can efficiently differentiate into mature, functional neurons. This work highlights the utility of these reprogramming methods and supports the use of differentially reprogrammed iPSCs for direct comparative studies of human neurodevelopment.

developmental biology↗

bioRxiv2021

Modeling gene x environment interactions in PTSD using glucocorticoid-induced transcriptomics in human neurons

Post-traumatic stress disorder (PTSD) results from severe trauma exposure, but the extent to which genetic and epigenetic risk factors impact individual clinical outcomes is unknown. We assessed the impact of genomic differences following glucocorticoid administration by examining the transcriptional profile of human induced pluripotent stem cell (hiPSC)-derived glutamatergic neurons and live cultured peripheral blood mononuclear cells from combat veterans with PTSD (n=5) and without PTSD (n=5). This parallel examination in baseline and glucocorticoid-treated conditions resolves cell-type specific and diagnosis-dependent elements of stress response, and permits discrimination of gene expression signals associated with PTSD risk from those induced by stress. Computational analyses revealed neuron-specific glucocorticoid-response expression patterns that were enriched for transcriptomic patterns observed in clinical PTSD samples. PTSD-specific signatures, albeit underpowered, accurately stratify veterans with PTSD relative to combat-exposed controls. Overall, in vitro PTSD and glucocorticoid response signatures in blood and brain cells represent exciting new platforms with which to test the genetic and epigenetic mechanisms underlying PTSD, identify biomarkers of PTSD risk and onset, and conduct drug-screening to identify novel therapeutics to prevent or ameliorate clinical phenotypes.

pathology↗

bioRxiv2020

Modular deep learning enables automated identification of monoclonal cell lines

Monoclonalization refers to the isolation and expansion of a single cell derived from a cultured population. This is a valuable step in cell culture so as to minimize a cell lines technical variability downstream of cell-altering events, such as reprogramming or gene editing, as well as for processes such as monoclonal antibody development. However, traditional methods for verifying clonality do not scale well, posing a critical obstacle to studies involving large cohorts. Without automated, standardized methods for assessing clonality post-hoc, methods involving monoclonalization cannot be reliably upscaled without exacerbating the technical variability of cell lines. We report the design of a deep learning workflow that automatically detects colony presence and identifies clonality from cellular imaging. The workflow, termed Monoqlo, integrates multiple convolutional neural networks and, critically, leverages the chronological directionality of the cell culturing process. Our algorithm design provides a fully scalable, highly interpretable framework, capable of analyzing industrial data volumes in under an hour using commodity hardware. In the present study, we focus on monoclonalization of human induced pluripotent stem cells (HiPSCs) as a case example. Monoqlo standardizes the monoclonalization process, enabling colony selection protocols to be infinitely upscaled while minimizing technical variability.

cell biology↗

bioRxiv2020

Deep learning and automated Cell Painting reveal Parkinson's disease-specific signatures in primary patient fibroblasts

Drug discovery for diseases such as Parkinsons disease are impeded by the lack of screenable cellular phenotypes. We present an unbiased phenotypic profiling platform that combines automated cell culture, high-content imaging, Cell Painting, and deep learning. We applied this platform to primary fibroblasts from 91 Parkinsons disease patients and matched healthy controls, creating the largest publicly available Cell Painting image dataset to date at 48 terabytes. We use fixed weights from a convolutional deep neural network trained on ImageNet to generate deep embeddings from each image and train machine learning models to detect morphological disease phenotypes. Our platforms robustness and sensitivity allow the detection of individual-specific variation with high fidelity across batches and plate layouts. Lastly, our models confidently separate LRRK2 and sporadic Parkinsons disease lines from healthy controls (receiver operating characteristic area under curve 0.79 (0.08 standard deviation)), supporting the capacity of this platform for complex disease modeling and drug screening applications.

cell biology↗