Biology subjects

Parish, C. L.

Publications and source records attributed to Parish, C. L..

5 recordsLinked to original sources

bioRxiv2026

Lineage-selective suicide gene system enables post-engraftment editing of cell therapy composition

Human pluripotent stem cell (hPSC)-derived therapies are advancing rapidly toward clinical application, yet heterogeneity of transplanted cell populations remains a major barrier to safety, predictability and scalability. Existing strategies to mitigate this risk either incompletely eliminate proliferative cells or ablate the entire graft, thereby compromising therapeutic benefit. Here we present NeuroGuard, a lineage-selective suicide gene platform that decouples safety from efficacy by preserving functional neurons while enabling inducible elimination of all other cell types after transplantation. NeuroGuard integrates an inducible caspase-9 system with NEUROD1-driven Cre recombination, protecting post-mitotic neurons from apoptosis while rendering non-neuronal and proliferative populations susceptible to ablation. In vitro, activation of the system enriched neuronal content to >90% and increased dopaminergic neuron proportion >3-fold. Following transplantation of ventral midbrain progenitors, timed activation eliminated proliferative and glial populations, resulting in compact, neuron-enriched grafts without loss of dopaminergic neuron number, target innervation or behavioural recovery in Parkinsonian rodents. Single-cell transcriptomics confirmed selective removal of non-neuronal lineages while preserving neuronal identity and maturation programs. This work establishes a generalizable framework for post-engraftment editing of cell therapy composition, providing a versatile strategy to enhance the safety and functional predictability of regenerative therapies.

neuroscience↗

bioRxiv2026

Netrin-1 drives cell-type-specific plasticity of human dopaminergic neurons during circuit integration in a Parkinsonian model

The survival of transplanted ventral midbrain (vm) dopaminergic neurons (DAn) and their innervation of host striatal tissue are crucial for ameliorating motor symptoms in Parkinsons disease (PD). However, human pluripotent stem cells (hPSC) show inferior axonal plasticity compared to fetal donor tissue. While modulation of the host environment with trophic cues, such as glial cell-derived neurotrophic factor (GDNF), can improve graft outcomes, these cues lack specificity for DAn, resulting in plasticity of other neurons within the graft. Using single nuclei RNA sequencing, we identified axonal guidance pathways (Semaphorin, Netrin and Wnt) that were preferentially activated in DAn within the graft. Overexpression of Semaphorin3A/SEMA3A, Netrin1/NTN1 or WNT5A in the striatum of Parkinsonian mice following vm DA progenitor transplantation promoted A9-DA specification and selectively increased DA innervation of the host striatum, without off-target extrastriatal DA innervation observed in response to GDNF. In Parkinsonian rats, NTN1 overexpression promoted graft-induced motor recovery, selective DA plasticity and activation of postsynaptic striatal neurons without evidence of non-DAn plasticity. Further, snRNA-sequencing of NTN1 or GDNF-treated grafts confirmed the upregulation of DA-specific plasticity by NTN1, while GDNF promoted plasticity in both DA and non-DAn. These findings highlight the capacity to improve on-target integration of hPSC-derived DAn in grafts by selectively targeting DA-specific plasticity. Taken together, these results demonstrate the utility of DA-specific cues to promote functional recovery, improve graft predictability, and limit off-target innervation.

neuroscience↗

bioRxiv2026

Machine learning-based image analysis of Parkinson's disease iPS-derived neurons predicts genotype and reveals mitochondria-lysosome abnormalities

Mitochondrial and lysosomal dysfunction are central features of Parkinsons disease (PD) across major genetic forms including PRKN, SNCA, and LRRK2. We applied cell morphomics, a machine-learning-based framework combining high-content imaging with quantitative feature extraction, to analyse mitochondrial and lysosomal morphology at single-cell resolution in iPS cell-derived cortical neurons from PD patients and healthy controls (13 lines total). Supervised machine-learning models distinguished PD neurons from controls with high accuracy (AUC = 0.87) and reliably separated individual genotypes. Feature importance and attribution analysis revealed genotype-specific organelle biases, with mitochondrial features dominating classification in PRKN neurons, balanced mitochondrial and lysosomal contributions in SNCA neurons, and a greater lysosomal contribution in LRRK2 neurons. Multi-class models retained strong performance, and findings were reproduced across two independent laboratories using different dyes and imaging conditions. These results demonstrate that morphomics provides a robust and scalable framework to quantify genotype-specific organelle abnormalities in PD neurons and supports its application for cellular stratification and biomarker discovery.

neuroscience↗

bioRxiv2022

Identification of cell barcodes from long-read single-cell RNA-seq with BLAZE

Single-cell RNA sequencing (scRNA-seq) has revolutionised our ability to profile gene expression. However, short-read (SR) scRNAseq methodologies such as 10x are restricted to sequencing the 3 or 5 ends of transcripts, providing accurate gene expression but little information on the RNA isoforms expressed in each cell. Newly developed long-read (LR) scRNA-seq enables the quantification of RNA isoforms in individual cells but LR scRNA-seq using the Oxford Nanopore platform has largely relied upon matched short-read data to identify cell barcodes and allow single cell analysis. Here we introduce BLAZE (Barcode identification from long-reads for AnalyZing single-cell gene Expression), which accurately and efficiently identifies 10x cell barcodes using only nanopore LR scRNA-seq data. We compared BLAZE to existing tools, including cell barcodes identified from matched SR scRNA-seq, on differentiating stem cells and 5 cancer cell lines. BLAZE outperforms existing tools and provides a more accurate representation of the cells present in LR scRNA-seq than using matched short-reads. BLAZE provides accurate cell barcodes over a wide range of experimental read depths and sequencing accuracies, while other methodologies commonly identify false-positive barcodes and cell clusters, disrupting biological interpretation of LR scRNA-seq results. In conclusion, BLAZE eliminates the requirement for matched SR scRNA-seq to interpret LR scRNA-seq, simplifying procedures and decreasing costs while also improving LR scRNA-seq results. BLAZE is compatible with downstream tools accepting a cell barcode whitelist file and is available at https://github.com/shimlab/BLAZE.

bioinformatics↗

bioRxiv2018

Generation of four iPSC lines from peripheral blood mononuclear cells (PBMCs) of an Attention Deficit Hyperactivity Disorder (ADHD) individual and a healthy sibling in an Australia-Caucasian family

Peripheral blood mononuclear cells were donated by a male teenager with clinically diagnosed Attention Deficit Hyperactivity Disorder under the Diagnostic and Statistical Manual of Mental Disorders IV criteria and his unaffected male sibling. Induced pluripotent stem cells were developed using integration-free Sendai Reprogramming factors containing OCT, SOX2, KLF4, and c-MYC. All four iPSC lines displayed pluripotent cell morphology, pluripotency-associated factors at protein level, alkaline phosphatase enzymatic activity, male karyotype of 46, XY, and in vitro differentiation capacity into all the three germ layers and negative for Mycoplasma.

neuroscience↗