Week 3 HW: Lab Automation

Part 1: Opentrons Artwork

This design was generated using the GUI at opentrons-art.rcdonovan.com and can be accessed through https://opentrons-art.rcdonovan.com/?id=1s7h4g7m1kn174o

Part 2: Post-Lab Questions

Although the paper is still unpublished, the paper describes a unit for drug development using brain organoids and patient-derived cells, which hasn’t been achieved before. Mainly, three automation tools are used: automated high-content imaging, automated image and PCR analysis pipelines. High-content imaging is achieved with a screening platform using 96-well plates; this way, throughput bottleneck s removed and the platform deals with variability of standard imaging. The data is processed with complementary custom image data analysis tools, created to trace specific disease hallmarks for the disease. For PCR, an automated outlier removal and relative-expression calculations are achieved through a custom Python-based algorithms. The tools allowed to have both acquisition and analysis automated and run dose-response experiments over time (identified myricetin and resveratrol as dose-dependent inhibitors of aggregate formation in a model of Parkinson’s). This is essentially the core of the drug discovery screening platform with an opportunity to scale a Parkinson’s model in human iPSC-derived neurons (typically done in rodent cells, on a smaller scale).

Han, C., Nguyen-Renou, E., Benaliouad, F., Luo, W., Chen, C. X., Alluli, A., Lorenza Villegas, Lenore K Beitel, Irina Shlaifer, Wolfgang E Reintsch, Andrea I Krahn, Esther Del Cid Pellitero2, Edward A Fon2 & Durcan, T. M. (2025). An automated workflow for quantifying the formation of synuclein aggregates in human dopaminergic neurons. bioRxiv, 2025-12. Link

For my final projects on an inducible system (as opposed to the fibril-driven pathology used in the paper), all of these automation tools (and disease hallmarks the authors are measuring) as well as the unit infrastructure, are directly transferable at the starting point and validation. However, live cell imaging will be primarily needed to trace the resulting oscillatory alpha-synuclein expression.

Part 3: Final Project Ideas

Project 1: Tunable Induction of Alpha-Synuclein Expression for Modeling Parkinson’s Disease

Aim:

This project aims to develop a tool to promote Parkinson’s disease phenotype manifestation by controllable induction of alpha-synuclein expression in dopaminergic neurons within patient-derived brain organoids.

Background:

Parkinson’s disease (PD) is driven by alpha-synuclein misfolding and accumulation in dopaminergic neurons, triggered by interconnected failures in multiple cellular processes. Current PD models using AAV-mediated alpha-synuclein overexpression and exogenous fibril seeding are effective at replicating key features of sporadic PD but lack controllability, limiting their value for investigating which cellular systems fail under pathological alpha-synuclein load in individual patients.

Patient-derived brain organoids naturally recapitulate human-specific neurodegenerative features, but their use for studying PD is constrained by the months required for PD phenotype manifestation. Tools that accelerate and standardize pathological phenotype induction in human tissue culture models are therefore needed.

Tool Description:

This project aims to develop a tool to promote Parkinson’s disease phenotype manifestation by controllable induction of alpha-synuclein expression in dopaminergic neurons within patient-derived brain organoids. The tool uses a genetic circuit for controllable oscillatory overexpression of alpha-synuclein. The circuit will employ a small molecule-activated sensor-promoter to initiate alpha-synuclein expression, a delayed negative feedback loop with a repressor to generate self-limiting oscillatory expression, and an external OFF switch to terminate the expression.

Significance:

The tool will hopefully:

• enable standardized and accelerated induction of PD phenotypes and
• allow probing patient-specific vulnerabilities and
• allow testing personalized therapeutic strategies in organoid platforms.

Project 2: Sensing α-Synuclein-Driven Mitochondrial Proteostatic Failure in Parkinson’s Disease with an RNA Toehold Switch

Aim:

This project aims to design a sensor for mitochondrial dysfunction in models of Parkinson’s disease (PD). An RNA toehold switch sensor will target mitochondrial protease (ClpP) mRNA, which is expected to rise in Parkinson’s disease models.

Background:

ClpP is a mitochondrial matrix protease that degrades misfolded or damaged proteins and recently shown to be inhibited by α-synuclein (through direct binding at the NAC domain), representing a novel mechanistic link between α-synuclein pathology and mitochondrial proteostatic failure in Parkinson’s disease. When ClpP activity is chronically suppressed by accumulating α-synuclein, the cell is expected to sense the resulting proteostatic stress through the mitochondrial unfolded protein response, which in mammals involves a nuclear transcriptional response involving ATF5-driven upregulation of ClpP as a compensatory mechanism. This creates a scenario where ClpP mRNA levels is expected to rise despite and because of functional ClpP insufficiency at the protein level.

Sensor Description:

An RNA sensor targeting ClpP mRNA would therefore report not on ClpP activity directly, but on the cell’s transcriptional response to its own proteostatic failure, serving as an indirect proxy for the α-synuclein-driven mitochondrial dysfunction that precedes late neurodegeneration and neuronal death.

Significance:

If 1) ATF5-driven ClpP upregulation in dopaminergic neurons is confirmed and 2) the sensor construct is validated in dopaminergic neurons, this sensor could provide an early, mitochondria-specific readout of PD-relevant stress in brain organoid models and become a drug screening tool to identify small molecules that restore the activity of the protease and reduce pathological α-synuclein accumulation (shift the tetrameric:monomeric α-synuclein balance), with the sensor itself as the readout.