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Thomas, R. A.

Publications and source records attributed to Thomas, R. A..

3 recordsLinked to original sources

Midbrain organoids with an SNCA gene triplication model key features of synucleinopathy

SNCA, the first gene associated with Parkinsons disease, encodes the -synuclein (-syn) protein, the predominant component within pathological inclusions termed Lewy bodies (LBs). The presence of LBs is one of the classical hallmarks found in the brain of patients with Parkinsons disease, and LBs have also been observed in patients with other synucleinopathies. However, the study of -syn pathology in cells has relied largely on two-dimensional culture models, which typically lack the cellular diversity and complex spatial environment found in the brain. Here, to address this gap, we use 3D midbrain organoids (hMOs), differentiated from human induced pluripotent stem cells derived from patients carrying a triplication of the SNCA gene and from CRISPR/Cas9 corrected isogenic control iPSCs. These hMOs recapitulate key features of -syn pathology observed in the brains of patients with synucleinopathies. In particular, we find that SNCA triplication hMOs express elevated levels of -syn and exhibit an age-dependent increase in -syn aggregation, manifested by the presence of both oligomeric and phosphorylated forms of -syn. These phosphorylated -syn aggregates were found in both neurons and glial cells and their time-dependent accumulation correlated with a selective reduction in dopaminergic neuron numbers. Thus, hMOs from patients carrying SNCA gene multiplication can reliably model key pathological features of Parkinsons disease and provide a powerful system to study the pathogenesis of synucleinopathies.

neuroscience

Auto-qPCR: A Python based web app for automated and reproducible analysis of qPCR data

Quantifying changes in DNA and RNA levels is essential in numerous molecular biology protocols. Quantitative real time PCR (qPCR) techniques have evolved to become commonplace, however, data analysis includes many time-consuming and cumbersome steps, which can lead to mistakes and misinterpretation of data. To address these bottlenecks, we have developed an open-source Python software to automate processing of result spreadsheets from qPCR machines, employing calculations usually performed manually. Auto-qPCR is a tool that saves time when computing qPCR data, helping to ensure reproducibility of qPCR experiment analyses. Our web-based app (https://auto-q-pcr.com/) is easy to use and does not require programming knowledge or software installation. Using Auto-qPCR, we provide examples of data treatment, display and statistical analyses for four different data processing modes within one program: (1) DNA quantification to identify genomic deletion or duplication events; (2) assessment of gene expression levels using an absolute model, and relative quantification (3) with or (4) without a reference sample. Our open access Auto-qPCR software saves the time of manual data analysis and provides a more systematic workflow, minimizing the risk of errors. Our program constitutes a new tool that can be incorporated into bioinformatic and molecular biology pipelines in clinical and research labs.

molecular biology

Standardized quality control workflow to evaluate the reproducibility and differentiation potential of human iPSCs into neurons

Induced pluripotent stem cells (iPSCs) derived from human somatic cells have created new opportunities to generate disease-relevant cells. Thus, as the use of patient-derived stem cells has become more widespread, having a workflow to monitor each line is critical. This ensures iPSCs pass a suite of quality control measures, promoting reproducibility across experiments and between labs. With this in mind, we established a multistep workflow to assess our newly generated iPSCs for variations and reproducibility relative to each other and iPSCs obtained from external sources. Our benchmarks for evaluating iPSCs include examining iPSC morphology and proliferation in two different media conditions and evaluating their ability to differentiate into each of the three germ layers, with a particular focus on neurons. Genomic integrity in the human iPSCs was analyzed by G-band karyotyping and a qPCR-based test for the detection of hotspot mutations test. Cell-line identity was authenticated by Short Tandem Repeat (STR) analysis. Using standardized dual SMAD inhibition methods, all iPSC lines gave rise to neural progenitors that could subsequently be differentiated into cortical neurons. Neural differentiation was analyzed qualitatively by immunocytochemistry and quantitatively by qPCR for progenitor, neuronal, cortical, and glial markers. Taken together, we present a multistep quality control workflow to evaluate variability and reproducibility across and between iPSCs.

cell biology