The Schuele lab is interested in the understanding of genetic causes and risk factors for neurodegeneration and the pathophysiological consequences leading to the clinical expression of Parkinson’s disease and related disorders.
Our projects range from clinical genetic family studies and human stem cell modeling of neuronal cell types or neurocircuits to translational approaches to ultimately treat Parkinson’s disease.
The Schuele lab has extensive expertise and a long history of human induced pluripotent stem cell modeling and gene discovery and has been a long-standing collaborator in several MJFF-funded LRRK2 and alpha-synuclein consortia.
Current work relates to genetic modifiers of LRRK2, gene dosage levels of alpha-synuclein, and ATXN-10 as a rare form of Parkinson’s disease/spinocerebellar ataxia.
Our projects range from clinical genetic family studies and human stem cell modeling of neuronal cell types or neurocircuits to translational approaches to ultimately treat Parkinson’s disease.
The Schuele lab has extensive expertise and a long history of human induced pluripotent stem cell modeling and gene discovery and has been a long-standing collaborator in several MJFF-funded LRRK2 and alpha-synuclein consortia.
Current work relates to genetic modifiers of LRRK2, gene dosage levels of alpha-synuclein, and ATXN-10 as a rare form of Parkinson’s disease/spinocerebellar ataxia.
Developing human iPSC resources for the scientific community
Roughly 10,000 human iPSC lines were created from 2008 to 2016, spanning diverse clinical and genetic conditions. Over the past decade, in vitro modeling utilizing iPSCs has displayed substantial potential.
However, variability and reproducibility is a critical challenge in human iPSC research. Biological factors, such as individual differences, genetic background, and cellular heterogeneity, contribute to this variability. Technical aspects, including variations in reprogramming efficiency, differentiation protocols, and culture conditions lead to reproducibility issues. This variability poses a substantial challenge in the accurate interpretation of data generated from iPSC models.
To overcome these barriers, we implement: 1. derivation of high-quality iPSC lines from consistent starting material with uniform non-integrating reprogramming techniques, 2. establishment of a high-quality protocols for iPSC differentiation along with clear guidelines for maintenance and storage, 3. define experimental endpoints that represent the benchmarks that a differentiating human iPSC must achieve to qualify as a differentiated neuron and 4. define robust indicators of the desired cell type, including disease biomarkers.
Since 2009, the Schuele lab has established a repository of human skin cells and fibroblasts derived from over 100 patients and healthy controls with genetic forms of Parkinson’s disease and related neurodegenerative diseases. This invaluable collection is currently housed within the Schuele lab and owned by the Michael J. Fox Foundation committed to promoting research accessibility and facilitating the broad dissemination of these invaluable cell lines across the scientific community.
The Schuele lab is affiliated with the Stanford Alzheimer Research Center (ADRC) and is supported by NIA to derive a human iPSC bank for the Stanford ADRC cohort from patients with monogenic forms of Alzheimer disease, ApoE4 genotypes with and without Alzheimer disease to study resistance and resilience, and matched healthy controls.
All human iPSCs are shared with the NINDS Human Cell and Data Repository (NHCDR), American Type Culture Collection (ATCC) or National Cell Repository for Alzheimer’s Disease (NCRAD).
However, variability and reproducibility is a critical challenge in human iPSC research. Biological factors, such as individual differences, genetic background, and cellular heterogeneity, contribute to this variability. Technical aspects, including variations in reprogramming efficiency, differentiation protocols, and culture conditions lead to reproducibility issues. This variability poses a substantial challenge in the accurate interpretation of data generated from iPSC models.
To overcome these barriers, we implement: 1. derivation of high-quality iPSC lines from consistent starting material with uniform non-integrating reprogramming techniques, 2. establishment of a high-quality protocols for iPSC differentiation along with clear guidelines for maintenance and storage, 3. define experimental endpoints that represent the benchmarks that a differentiating human iPSC must achieve to qualify as a differentiated neuron and 4. define robust indicators of the desired cell type, including disease biomarkers.
Since 2009, the Schuele lab has established a repository of human skin cells and fibroblasts derived from over 100 patients and healthy controls with genetic forms of Parkinson’s disease and related neurodegenerative diseases. This invaluable collection is currently housed within the Schuele lab and owned by the Michael J. Fox Foundation committed to promoting research accessibility and facilitating the broad dissemination of these invaluable cell lines across the scientific community.
The Schuele lab is affiliated with the Stanford Alzheimer Research Center (ADRC) and is supported by NIA to derive a human iPSC bank for the Stanford ADRC cohort from patients with monogenic forms of Alzheimer disease, ApoE4 genotypes with and without Alzheimer disease to study resistance and resilience, and matched healthy controls.
All human iPSCs are shared with the NINDS Human Cell and Data Repository (NHCDR), American Type Culture Collection (ATCC) or National Cell Repository for Alzheimer’s Disease (NCRAD).
Gene therapy strategies for Parkinson's disease and related neurodegenerative disorders
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Gene therapy offers potentially a multifaceted approach to develop innovative and curative therapies Parkinson's disease. Firstly, it encompasses the rescue of degenerating neuronal cells by introducing genes such as glucocerebrosidase or strategies to inhibit overproduction of alpha-synuclein. Second, gene therapy approaches seek to revitalize the dopamine system by introducing genes that encode growth factors to stimulate brain cells and instigate the regrowth of the dopamine system. Lastly, gene therapies for dopamine restoration through gene-induced cell conversion, achieved by injecting a gene that orchestrates the transformation of existing cells into dopamine producing cells.
We develop gene therapies for PD and related neurodegenerative diseases that uses gene-engineering strategies. In particular, we pursue a CRISPR interference approach to regulate alpha-synuclein expression levels . We combine patient-derived iPSC models and in vivo models to test proof-of-concept and off-target effects, address efficacy and safety, and immunological responses of the virus and transgenes. |
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Characterization of Human iPSC with Optical Genome Mapping
written and illustrated by Kamilla Sedov Previous studies have established that prolonged in vitro culture of iPSCs can lead to chromosomal instabilities that can contribute to genetic losses and gains, decreased differentiation potential, and reproducibility issues. Therefore, the international standards for human stem cell research emphasize the importance of iPSC characterization with karyotyping after extended periods of cell culture. Optical Genome Mapping (OGM) allows to detect structural variants (SVs) such as deletions, insertions, duplications, inversions, and translocations at high resolution (over 500 bp) and low allele frequencies (over 5%), bridging the size gap between sequencing and karyotyping and providing insights into chromosomal changes that occur during iPSC derivation and routine culture. We use OGM for several projects, including characterization of iPSCs derived from the Stanford ADRC cohort, identification of insertions in genetically engineered cell lines and mice, and detection of genetic CNV mutations in samples with SNCA mutations or ATXN10 repeat expansions. We benchmarked OGM in a reference iPSC line KOLF2.1J and confirmed previously reported SVs with more details due to enhanced resolution of this technique (LINK). We also established a workflow to determine SVs introduced by reprogramming and their potential pathogenic burden. We use this workflow to filter our cell lines for other projects by selecting the clones without deleterious structural variants, thus contributing to rigorous and reproducible studies with iPSCs. |
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Studying Repeat Expansions with OGM and Long-Read Sequencing
OGM also allows us to confirm a specific type of SVs called repeat expansions which occur when short repetitive sequences of DNA expand abnormally. Spinocerebellar ataxia type10 (SCA10) arises due to a 5-nucleotide repeat expansion in an intron of the ATXN10 gene. Our previous work showed that the pathogenicity and disease severity depend upon whether the repeat is pure (ATTCT) or mixed (ATTCT-ATTCC). While OGM can accurately detect the size, type, and location of SV, we cannot resolve the exact sequence of any SVs with this technique alone.
Therefore, we complement OGM with long-read sequencing and use the PureTarget protocol by PacBio to identify specific sequences of these repeat expansions in a cohort of SCA10 patients.
Both methods utilize ultra-high molecular weight DNA extracted using Nanobind technology, allowing us to preserve the length and high quality of DNA molecules for downstream applications. For OGM, we label DNA with an enzymatic label that binds every ~5 kbp and take high-quality images of molecules that further undergo a series of computational analyses to confirm the exact size of repeat expansions. For long-read sequencing, we enrich target regions with CRISPR/Cas9 and follow the standard library prep workflow that barcodes individual samples before pooling them for sequencing.
OGM also allows us to confirm a specific type of SVs called repeat expansions which occur when short repetitive sequences of DNA expand abnormally. Spinocerebellar ataxia type10 (SCA10) arises due to a 5-nucleotide repeat expansion in an intron of the ATXN10 gene. Our previous work showed that the pathogenicity and disease severity depend upon whether the repeat is pure (ATTCT) or mixed (ATTCT-ATTCC). While OGM can accurately detect the size, type, and location of SV, we cannot resolve the exact sequence of any SVs with this technique alone.
Therefore, we complement OGM with long-read sequencing and use the PureTarget protocol by PacBio to identify specific sequences of these repeat expansions in a cohort of SCA10 patients.
Both methods utilize ultra-high molecular weight DNA extracted using Nanobind technology, allowing us to preserve the length and high quality of DNA molecules for downstream applications. For OGM, we label DNA with an enzymatic label that binds every ~5 kbp and take high-quality images of molecules that further undergo a series of computational analyses to confirm the exact size of repeat expansions. For long-read sequencing, we enrich target regions with CRISPR/Cas9 and follow the standard library prep workflow that barcodes individual samples before pooling them for sequencing.
Characterization of rodent models of neurological diseases with machine learning
Written and illustrated by Madison James Yang
Rodent behavior analysis is pivotal in neuroscience research, with a major goal in identifying behavioral changes due to introduction of a transgene or pharmacological intervention. Methods to judge mice have been limited by subjectivity or imprecision that can make it challenging to robustly identify behavioral differences between models. This issue can potentially be resolved via quantitative classification through neural-network based machine-learning. We are keen on characterizing mice carrying human alpha-synuclein (α-syn) gene, which is implicated in Parkinson's disease, and identifying distinct behavioral changes in transgenic models.
We optimized a workflow utilizing two machine learning models to perform high-volume quantitative classifications of mouse behavior. Free behavior of mice as they change through aging is recorded, and labeled with key kinematic data using supervised DeepLabCut (DLC)models. This data is further interpreted by Variational Animal Motion Embedding (VAME), which classifies every moment of mouse activity as a specific behavior, allowing us to quantify the rates and relationships of mouse behaviors, including exploration, maintenance, and inactivity. These models can distinguish transgenic mice from controls, even at early timepoints, but it can also demonstrate the effects of age on behaviors, manifesting as a decrease in locomotion as illustrated below. Our DLC/VAME workflow enhances the precision of motor assessments in PD mouse models, to a degree impossible for human raters alone.
Written and illustrated by Madison James Yang
Rodent behavior analysis is pivotal in neuroscience research, with a major goal in identifying behavioral changes due to introduction of a transgene or pharmacological intervention. Methods to judge mice have been limited by subjectivity or imprecision that can make it challenging to robustly identify behavioral differences between models. This issue can potentially be resolved via quantitative classification through neural-network based machine-learning. We are keen on characterizing mice carrying human alpha-synuclein (α-syn) gene, which is implicated in Parkinson's disease, and identifying distinct behavioral changes in transgenic models.
We optimized a workflow utilizing two machine learning models to perform high-volume quantitative classifications of mouse behavior. Free behavior of mice as they change through aging is recorded, and labeled with key kinematic data using supervised DeepLabCut (DLC)models. This data is further interpreted by Variational Animal Motion Embedding (VAME), which classifies every moment of mouse activity as a specific behavior, allowing us to quantify the rates and relationships of mouse behaviors, including exploration, maintenance, and inactivity. These models can distinguish transgenic mice from controls, even at early timepoints, but it can also demonstrate the effects of age on behaviors, manifesting as a decrease in locomotion as illustrated below. Our DLC/VAME workflow enhances the precision of motor assessments in PD mouse models, to a degree impossible for human raters alone.
