Biology subjects

Choi, P. S.

Publications and source records attributed to Choi, P. S..

4 recordsLinked to original sources

bioRxiv2024

A low-cost, open-source 3D printer for multimaterial and high-throughput direct ink writing of soft and living materials

Direct ink writing is a 3D printing method that is compatible with a wide range of structural, elastomeric, electronic, and living materials, and it continues to expand its uses into physics, engineering, and biology laboratories. However, the large footprint, closed hardware and software ecosystems, and expense of commercial systems often hamper widespread adoption. Here, we present a compact, simple-to-build, low-cost, multimaterial, and high-throughput direct ink writing 3D printer platform with detailed assembly files and instructions provided freely online. In contrast to existing low-cost 3D printers and bioprinters, which typically modify off-the-shelf plastic 3D printers, this system is built from scratch, offering a lower cost and full customizability. Despite its low cost, we demonstrate advanced active mixing and multimaterial multinozzle 3D (MM3D) printing methods, which previously have relied on expensive and custom motion control platforms. We finally introduce embedded multinozzle and 3D gradient nozzle designs that offer high throughput and graded 3D parts. This powerful, easy-to-build, and customizable printing platform can help stimulate a vibrant biomaker community of engineers, biologists, and educators.

bioengineering↗

bioRxiv2024

Machine learning-optimized targeted detection of alternative splicing

RNA-sequencing (RNA-seq) is widely adopted for transcriptome analysis but has inherent biases which hinder the comprehensive detection and quantification of alternative splicing. To address this, we present an efficient targeted RNA-seq method that greatly enriches for splicing-informative junction-spanning reads. Local Splicing Variation sequencing (LSV-seq) utilizes multiplexed reverse transcription from highly scalable pools of primers anchored near splicing events of interest. Primers are designed using Optimal Prime, a novel machine learning algorithm trained on the performance of thousands of primer sequences. In experimental benchmarks, LSV-seq achieves high on-target capture rates and concordance with RNA-seq, while requiring significantly lower sequencing depth. Leveraging deep learning splicing code predictions, we used LSV-seq to target events with low coverage in GTEx RNA-seq data and newly discover hundreds of tissue-specific splicing events. Our results demonstrate the ability of LSV-seq to quantify splicing of events of interest at high-throughput and with exceptional sensitivity.

genomics↗

bioRxiv2023

BEERS2: RNA-Seq simulation through high fidelity in silico modeling

Simulation of RNA-seq reads is critical in the assessment, comparison, benchmarking, and development of bioinformatics tools. Yet the field of RNA-seq simulators has progressed little in the last decade. To address this need we have developed BEERS2, which combines a flexible and highly configurable design with detailed simulation of the entire library preparation and sequencing pipeline. BEERS2 takes input transcripts (typically fully-length mRNA transcripts with polyA tails) from either customizable input or from CAMPAREE simulated RNA samples. It produces realistic reads of these transcripts as FASTQ, SAM, or BAM formats with the SAM or BAM formats containing the true alignment to the reference genome. It also produces true transcript-level quantification values. BEERS2 combines a flexible and highly configurable design with detailed simulation of the entire library preparation and sequencing pipeline and is designed to include the effects of polyA selection and RiboZero for ribosomal depletion, hexamer priming sequence biases, GC-content biases in PCR amplification, barcode read errors, and errors during PCR amplification. These characteristics combine to make BEERS2 the most complete simulation of RNA-seq to date. Finally, we demonstrate the use of BEERS2 by measuring the effect of several settings on the popular Salmon pseudoalignment algorithm.

bioinformatics↗

bioRxiv2020

Genome-scale genetic screening identifies PRMT1 as a critical vulnerability in castration-resistant prostate cancer

Androgen receptor (AR) signaling is the central driver of prostate cancer across disease states. While androgen deprivation therapy (ADT) is effective in the initial treatment of prostate cancer, resistance to ADT or to next-generation androgen pathway inhibitors invariably arises, most commonly through re-activation of the AR axis. Thus, orthogonal approaches to inhibit AR signaling in advanced prostate cancer are essential. Here, via genome-scale CRISPR/Cas9 screening, we identify protein arginine methyltransferase 1 (PRMT1) as a critical mediator of AR expression and signaling. PRMT1 regulates recruitment of AR to genomic target sites and inhibition of PRMT1 impairs AR binding at lineage-specific enhancers, leading to decreased expression of key oncogenes, including AR itself. Additionally, AR-driven prostate cancer cells are uniquely susceptible to combined AR and PRMT1 inhibition. Our findings implicate PRMT1 as a key regulator of AR output and provide a preclinical framework for co-targeting of AR and PRMT1 in advanced prostate cancer.

cancer biology↗