Biology subjects

Reintsch, W.

Publications and source records attributed to Reintsch, W..

4 recordsLinked to original sources

bioRxiv2023

A deep learning convolutional neural network distinguishes neuronal models of Parkinson's disease from matched controls

Parkinsons disease (PD) is a neurodegenerative disorder that results in the loss of dopaminergic neurons in the substantia nigra pars compacta. Despite advances in understanding PD, there is a critical need for novel therapeutics that can slow or halt its progression. Induced pluripotent stem cell (iPSC)-derived dopaminergic neurons have been used to model PD but measuring differences between PD and control cells in a robust, reproducible, and scalable manner remains a challenge. In this study, we developed a binary classifier convolutional neural network (CNN) to accurately classify microscopy images of PD models and matched control cells. We acquired images of iPSC-derived neural precursor cells (NPCs) and dopaminergic (DANs) and trained multiple CNN models comparing control cells to genetic and chemical models of PD. Our CNN accurately predicted whether control NPC cells were treated with the PD-inducing pesticide rotenone with 97.60% accuracy. We also compared control to a genetic model of PD (deletion of the Parkin gene) and found a predictive accuracy of 86.77% and 95.47% for NPC and DAN CNNs, respectively. Our cells were stained for nuclei, mitochondria, and plasma membrane, and we compared the contribution of each to the CNNs accuracy. Using all three features together produced the best accuracy, but nuclear staining alone produced a highly predictive CNN. Our study demonstrates the power of deep learning and computer vision for analyzing complex PD-related phenotypes in DANs and suggests that these tools hold promise for identifying new targets for therapy and improving our understanding of PD.

neuroscience↗

bioRxiv2023

Transcriptional dysregulation and impaired neuronal activity in FMR1 knock-out and Fragile X patients iPSC-derived models.

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.

neuroscience↗

bioRxiv2023

Assessing the performance of commercial reagent antibodies

Antibodies are critical reagents to detect and characterize proteins. It is commonly understood that many commercial antibodies do not recognize their intended targets, but information on the scope of the problem remains largely anecdotal, and as such, feasibility of the goal of at least one potent and specific antibody targeting each protein in a proteome cannot be assessed. Focusing on antibodies for human proteins, we have scaled a standardized characterization approach using parental and knockout cell lines (Laflamme et al., 2019) to assess the performance of 614 commercial antibodies for 65 neuroscience-related proteins. Side-by-side comparisons of all antibodies against each target, obtained from multiple commercial partners, demonstrates that: i) more than 50% of all antibodies failed in one or more tests, ii) yet, [~]50-75% of the protein set was covered by at least one high-performing antibody, depending on application, suggesting that coverage of human proteins by commercial antibodies is significant; and iii) recombinant antibodies performed better than monoclonal or polyclonal antibodies. The hundreds of underperforming antibodies identified in this study were found to have been used in a large number of published articles, which should raise alarm. Encouragingly, more than half of the underperforming commercial antibodies were reassessed by the manufacturers, and many had alterations to their recommended usage or were removed from the market. This first such study helps demonstrate the scale of the antibody specificity problem but also suggests an efficient strategy toward achieving coverage of the human proteome; mine the existing commercial antibody repertoire, and use the data to focus new renewable antibody generation efforts.

molecular biology↗

bioRxiv2021

Microfabricated disk technology: rapid scale up in midbrain organoid generation

By providing a three-dimensional in vitro culture system with key features of the substantia nigra region in the brain, 3D neuronal organoids derived from human induced pluripotent stem cells (iPSCs) provide living neuronal tissue resembling the midbrain region of the brain. However, a major limitation of conventional brain organoid culture is that it is often labor-intensive, requiring highly specialized personnel for moderate throughput. Additionally, the methods published for long-term cultures require time-consuming maintenance to generate brain organoids in large numbers. With the increasing need for human midbrain organoids (hMOs) to better understand and model Parkinsons disease (PD) in a dish, there is a need to implement new workflows and methods to both generate and maintain hMOs, while minimizing batch to batch variation. In this study, we developed a method with microfabricated disks to scale up the generation of hMOs. This opens up the possibility to generate larger numbers of hMOs, in a manner that minimizes the amount of labor required, while decreasing variability and maintaining the viability of these hMOs over time. Taken together, producing hMOs in this manner opens up the potential for these to be used to further PD studies.

neuroscience↗