Abstract Motivated by the growing number of single cell RNA sequencing datasets (scRNAseq) revealing the cellular heterogeneity in complex tissues, particularly in brain and induced pluripotent stem cell (iPSC)-derived brain models, we developed a high-throughput, standardized approach for reproducibly characterizing cell types in complex neuronal tissues based on protein expression levels. Our approach combines a flow cytometry (FC) antibody panel targeting brain cells with a computational pipeline called CelltypeR, with functions for aligning and transforming datasets, optimizing unsupervised clustering, annotating and quantifying cell types, and statistical comparisons. We applied this workflow to human iPSC-derived midbrain organoids and identified the expected brain cell types, including neurons, astrocytes, radial glia, and oligodendrocytes. Defining gates based on the expression levels of our protein markers, we performed Fluorescence-Activated Cell Sorting of astrocytes, radial glia, and neurons, cell types were then confirmed by scRNAseq. Among the sorted neurons, we identified three subgroups of dopamine (DA) neurons; one reminiscent of substantia nigra DA neurons, the cell type most vulnerable in Parkinson’s disease. Finally, we use our workflow to track cell types across a time course of organoid differentiation. Overall, our adaptable analysis framework provides a generalizable method for reproducibly identifying cell types across FC datasets.
Abstract 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.
Abstract 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.
Autosomal recessive mutations in either PRKN or PINK1 are associated with early-onset Parkinson's disease. The corresponding proteins, PRKN, an E3 ubiquitin ligase, and the mitochondrial serine/threonine-protein kinase PINK1 play a role in mitochondrial quality control. Using CRISPR/CAS9 technology we generated three human iPSC lines from the well characterized AIW002-02 control line. These isogenic iPSCs contain homozygous knockouts of PRKN (PRKN-KO, CBIGi001-A-1), PINK1 (PINK1-KO, CBIGi001-A-2) or both PINK1 and PRKN (PINK1-KO/PRKN-KO, CBIGi001-A-3). The knockout lines display normal karyotypes, express pluripotency markers and upon differentiation into relevant brain cells or midbrain organoids may be valuable tools to model Parkinson's disease.
Fragile X syndrome (FXS) is caused by a repression of the FMR1 gene that codes the Fragile X mental retardation protein (FMRP), an RNA binding protein involved in processes that are crucial for proper brain development. To better understand the consequences of the absence of FMRP, we analyzed gene expression profiles and activities of cortical neural progenitor cells (NPCs) and neurons obtained from FXS patients' induced pluripotent stem cells (IPSCs) and IPSC-derived cells from FMR1 knock-out engineered using CRISPR-CAS9 technology. Multielectrode array recordings revealed in FMR1 KO and FXS patient cells, decreased mean firing rates; activities blocked by tetrodotoxin application. Increased expression of presynaptic mRNA and transcription factors involved in the forebrain specification and decreased levels of mRNA coding AMPA and NMDA subunits were observed using RNA sequencing on FMR1 KO neurons and validated using quantitative PCR in both models. Intriguingly, 40% of the differentially expressed genes were commonly deregulated between NPCs and differentiating neurons with significant enrichments in FMRP targets and autism-related genes found amongst downregulated genes. Our findings suggest that the absence of FMRP affects transcriptional profiles since the NPC stage, and leads to impaired activity and neuronal differentiation over time, which illustrates the critical role of FMRP protein in neuronal development.
Abstract The lack of fragile X mental retardation protein (FMRP) protein, due to a repression of the FMR1 gene, causes Fragile X syndrome (FXS), one of the most prevalent forms of syndromic autisms. The FMR1 gene codes for an RNA binding protein involved in the regulation of gene expression through RNA processing, control of local translation, and protein-protein interactions; processes that are crucial for proper brain development. Taking advantage of induced pluripotent stem cells (iPSCs) and CRISPR-Cas9 genome editing technologies, we generated iPSC-derived cortical neural progenitors and cortical neurons from an FMR1 knock-out and patient cell line with the aim of identifying common phenotypes between the two cellular models. Using RNA sequencing, quantitative PCR and multielectrode array approaches, we assessed how the absence of the functional FMR1 gene affects the transcriptional profiles and the activities of iPSC-derived cortical neuronal progenitor cells (NPCs) and neurons with both models. We observed that FMR1 KO and FXS patient cells have a decrease in their mean firing rate; a cellular activity that can also be blocked by tetrodotoxin (TTX) application in wild-type active neurons. Relative to wild-type neurons, in FMR1 KO neurons, increased expression of presynaptic mRNA and transcription factors involved in the forebrain specification and decreased levels of mRNA coding AMPA and NMDA subunits were observed. Intriguingly, 40% of the differentially expressed genes were commonly deregulated between NPCs and differentiating neurons with significant enrichments in FMRP targets and Autism Related Genes found amongst downregulated genes. This implies that an absence of functional FMRP affects transcriptional profiles at the NPC stage, resulting in impaired activity and differentiation of the progenitors into mature neurons over time. These findings from the FMR1 KO lines were also shared with FXS patients’ iPSC-derived cells that also present with an impairment in activity and neuronal differentiation, illustrating the critical role of FMRP protein in neuronal development.
The GBA gene encodes the lysosomal enzyme glucocerebrosidase (GCase), responsible for the hydrolysis of glucocerebroside to glucose and ceramide. Heterozygous GBA mutations have been associated with the development of Parkinson's disease (PD) and dementia with Lewy bodies (DLB). We generated two induced pluripotent stem cell (iPSC) lines from PD patients carrying heterozygous GBA W378G or N370S mutations and subsequently produced isogenic control lines using CRISPR/Cas9 genome editing. The patient-derived iPSCs and isogenic control lines maintained full pluripotency, normal karyotypes, and differentiation capacity. All iPSC lines could be differentiated into dopaminergic neurons, thus providing valuable tools for studying PD pathogenesis.
Summary Cytoplasmic mislocalization and aggregation of the RNA-binding protein TDP-43 is a pathological hallmark of the motor neuron (MN) disease amyotrophic lateral sclerosis (ALS). Furthermore, while mutations in the TARDBP gene (encoding TDP-43) have been associated with ALS, the pathogenic consequences of these mutations remain poorly understood. Using CRISPR/Cas9, we engineered two homozygous knock-in iPSC lines carrying mutations in TARDBP encoding TDP-43 A382T and TDP-43 G348C , two common yet understudied ALS TDP-43 variants. MNs differentiated from knock-in iPSCs had normal viability and displayed no significant changes in TDP-43 subcellular localization, phosphorylation, solubility, or aggregation compared with isogenic control MNs. However, our results highlight synaptic impairments in both TDP-43 A382T and TDP-43 G348C MN cultures, as reflected in synapse abnormalities and alterations in spontaneous neuronal activity. Collectively, our findings suggest that MN dysfunction may precede the occurrence of TDP-43 pathology and neurodegeneration in ALS and further implicate synaptic and excitability defects in the pathobiology of this disease.
SNCA, the first gene associated with Parkinson′s disease, encodes the α-synuclein (α-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 Parkinson′s disease, and LBs have also been observed in patients with other synucleinopathies. However, the study of α-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 α-syn pathology observed in the brains of patients with synucleinopathies. In particular, we find that SNCA triplication hMOs express elevated levels of α-syn and exhibit an age-dependent increase in α-syn aggregation, manifested by the presence of both oligomeric and phosphorylated forms of α-syn. These phosphorylated α-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 Parkinson′s disease and provide a powerful system to study the pathogenesis of synucleinopathies.
