Generation of homozygous PRKN, PINK1 and double PINK1/PRKN knockout cell lines from healthy induced pluripotent stem cells using CRISPR/Cas9 editing

Chen, Carol X.-Q.; You, Zhipeng; Abdian, Narges; Sirois, Julien; Shlaifer, Irina; Tabatabaei, Mahdieh; Boivin, Marie-Noëlle; Gaborieau, Lydiane; Karamchandani, Jason; Beitel, Lenore K.; Fon, Edward A.; Durcan, Thomas M.

doi:10.1016/j.scr.2022.102806

Cited by 11 publications

(11 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To validate the expression of the proteins targeted by the selected antibodies on known cell types, we separately differentiated iPSCs into dopaminergic neuronal precursor cells (DA NPCs), 22 dopaminergic neurons (DA neurons), 35 astrocytes, 36 and oligodendrocytes 34 ( Figure 2A ). The cultures were dissociated and the 13 antibodies in the FC panel were applied.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

CelltypeR: A flow cytometry pipeline to annotate, characterize and isolate single cells from brain organoids

Thomas

Sirois

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

With the recent surge of single cell RNA sequencing datasets (scRNAseq) the extent of cellular heterogeneity has become apparent, yet it remains poorly characterized on a protein level in brain tissue and induced pluripotent stem cell (iPSC) derived brain models. With this in mind, we developed a high-throughput, standardized approach for the reproducible characterization of cell types in complex neuronal tissues. We designed a flow cytometry (FC) antibody panel coupled with a computational pipeline to quantify cellular subtypes in human iPSC derived midbrain organoids. Our pipeline, termed CelltypeR, contains scripts to transform and align multiple datasets, optimize unsupervised clustering, annotate cell types, quantify cell types, and compare cells across conditions. We identified the expected brain cell types, then sorted neurons, astrocytes, and radial glia, confirming these cell types with scRNAseq. We present an adaptable analysis framework providing a generalizable method to reproducibly identify cell types across FC datasets.

show abstract

Section: Resultsmentioning

confidence: 99%

“…DA neurons were differentiated from DA-NPC cultures on laminin coated culture flasks in neural basal media with supplements and inhibitors as described. 35…”

Section: Dopaminergic Neural Precursor Cells (Da-npc) and Neurons (Da...mentioning

confidence: 99%

CelltypeR: A flow cytometry pipeline to annotate, characterize and isolate single cells from brain organoids

Thomas

Sirois

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The other AIW002-02 and AIW002-02-Parkin-KO iPSC cell lines were previously described. (12,17) The AIW002-02-GBA-KO and AIW002-02-SNCA-A53T lines were generated using CRISPR and standard quality control measure were performed (see supplemental documents 1 and 2).…”

Section: Methodsmentioning

confidence: 99%

“…DANs differentiated from NPCs as previously described and maintained in 96 well plates for 4 weeks. (17)…”

Section: Methodsmentioning

confidence: 99%

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

Thomas,

Cai,

Reintsch

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

Parkinson’s 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 CNN’s 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.

show abstract

“…Parkin is recruited by PINK1 to damage mitochondria in order to induce mitophagy ( Chen et al, 2022 ). The PINK1 parkin-mediated mitophagy pathway has received a lot of attention ( Quinn et al, 2020 ).…”

Section: Cognitive Disordermentioning

confidence: 99%

CHCHD2 and CHCHD10: Future therapeutic targets in cognitive disorder and motor neuron disorder

Jiang

Wang

et al. 2022

Front. Neurosci.

View full text Add to dashboard Cite

CHCHD2 and CHCHD10 are homolog mitochondrial proteins that play key roles in the neurological, cardiovascular, and reproductive systems. They are also involved in the mitochondrial metabolic process. Although previous research has concentrated on their functions within mitochondria, their functions within apoptosis, synaptic plasticity, cell migration as well as lipid metabolism remain to be concluded. The review highlights the different roles played by CHCHD2 and/or CHCHD10 binding to various target proteins (such as OPA-1, OMA-1, PINK, and TDP43) and reveals their non-negligible effects in cognitive impairments and motor neuron diseases. This review focuses on the functions of CHCHD2 and/or CHCHD10. This review reveals protective effects and mechanisms of CHCHD2 and CHCHD10 in neurodegenerative diseases characterized by cognitive and motor deficits, such as frontotemporal dementia (FTD), Lewy body dementia (LBD), Parkinson’s disease (PD) and amyotrophic lateral sclerosis (ALS). However, there are numerous specific mechanisms that have yet to be elucidated, and additional research into these mechanisms is required.

show abstract

Generation of homozygous PRKN, PINK1 and double PINK1/PRKN knockout cell lines from healthy induced pluripotent stem cells using CRISPR/Cas9 editing

Cited by 11 publications

References 4 publications

CelltypeR: A flow cytometry pipeline to annotate, characterize and isolate single cells from brain organoids

CelltypeR: A flow cytometry pipeline to annotate, characterize and isolate single cells from brain organoids

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

CHCHD2 and CHCHD10: Future therapeutic targets in cognitive disorder and motor neuron disorder

Contact Info

Product

Resources

About