Biology subjects

Lynch, M. P.

Publications and source records attributed to Lynch, M. P..

4 recordsLinked to original sources

bioRxiv2026

Paired CRISPR screens identify mitochondrial metabolism and UBE2H as aneuploid-specific dependencies in human cancer cell lines

Aneuploidy is a hallmark of cancer and imposes widespread cellular stress, including proteotoxicity, transcriptional dysregulation, and increased metabolic demand. Although these stresses are predicted to create therapeutic vulnerabilities, the genetic dependencies of aneuploid cells remain incompletely characterized. Here, we performed paired CRISPR loss-of-function screens in isogenic aneuploid and near-euploid cancer cell line models to systematically identify aneuploidy-specific dependencies. Seven genome-wide paired screens identified ribosomes, rRNA processing, spliceosome-mediated RNA processing, proteasome subunits, and mitochondrial metabolism as top aneuploid-specific dependency gene groups. To identify therapeutically targetable aneuploid dependencies, we performed 18 additional paired CRISPR screens using a focused druggable genome library. This analysis identified the ubiquitin-conjugating enzyme UBE2H as a top aneuploid-selective dependency. Functional validation confirmed aneuploid cell dependency on UBE2H, and mechanistic analyses linked UBE2H to mitochondrial protein abundance, suggesting a role in maintaining mitochondrial proteostasis under aneuploid stress. Together, these findings define core cellular systems that support the viability of aneuploid cells and identify UBE2H as a potential therapeutic vulnerability connecting ubiquitin signaling to mitochondrial homeostasis.

cancer biology↗

bioRxiv2025

Massively parallel evolution reveals a biophysical scaling law between cell size and internal cell density

Using a massively parallel evolution platform, we selected Saccharomyces cerevisiae for increased cell size to test how cellular architecture adapts to biophysical constraints. As cells evolved larger size, they became less spherical and showed reduced carrying capacity without changes in maximum growth rate. Optical diffraction tomography revealed that individual cells with greater volume consistently exhibited lower internal density, a relationship that persisted across replicate populations and evolved isolates. The largest cells often contained enlarged vacuoles, suggesting that vacuole expansion may sometimes contribute to reduced density. Extending this analysis beyond yeast, 68 species spanning major phylogenetic clades also demonstrate a negative scaling between cell density and cell size. Together, these results suggest that decreased internal density is a conserved consequence of increasing cell size, revealing a fundamental cellular trade-off between volume expansion and material concentration.

biophysics↗

bioRxiv2025

Enabling reproducible and reusable genetic demultiplexing benchmarking with Nextflow and Apptainer

Reproducibility, reusability, and portability were identified as key requirements for analysis pipelines in genomics. A previous study identified that less than 4% of notebooks used for biomedical research were fully reproducible. Reusable and portable software are required to ensure compatibility with federated and cloud computing. The field requires computational workflows that meet high standards for reproducibility, reusability, and portability to allow benchmarking to be repeated and updated periodically. This is of particular importance in areas where new methods are frequently released, such as demultiplexing scRNA-seq, a critical early step in most scRNA-seq analysis pipelines. To address this, we developed demux_bench for benchmarking genetic demultiplexing methods in single-cell RNA sequencing, which meets the gold standard for reproducibility of computational workflows, and incorporates best practices for reusability and portability. We used workflow manager Nextflow to enable the simulation and testing of various benchmarking scenarios and methods in parallel from a single pipeline execution. Different experimental configurations can be simulated, including % doublets and class size imbalance. The pipeline includes genotype-free methods, Vireo and souporcell, and additional existing or novel methods can be added modularly. Demux_bench is configurable to reproduce specific analyses and generalisable to address new research questions. Software dependencies are handled through containers via Apptainer, allowing portability to different compute environments and avoiding the need for manual installation of software. Demux_bench is available on WorkflowHub (https://workflowhub.eu/workflows/1769), and can also be run on Galaxy and other platforms through RO-Crates. Demux_bench facilitates gold standard benchmarking for genetic demultiplexing through reproducibility, reusability, scalability, and portability.

bioinformatics↗

bioRxiv2024

demuxSNP: supervised demultiplexing scRNAseq using cell hashing and SNPs

BackgroundMultiplexing single-cell RNA sequencing experiments reduces sequencing cost and facilitates larger scale studies. However, factors such as cell hashing quality and class size imbalance impact demultiplexing algorithm performance, reducing cost effectiveness FindingsWe propose a supervised algorithm, demuxSNP, leveraging both cell hashing and genetic variation between individuals (SNPs). The supervised algorithm addresses fundamental limitations in demultiplexing with only one data modality. The genetic variants (SNPs) of the subset of cells assigned with high confidence using a probabilistic hashing algorithm are used to train a KNN classifier that predicts the demultiplexing classes of unassigned or uncertain cells. We benchmark demuxSNP against hashing (HTODemux, cellhashR, GMM-demux, demuxmix) and genotype-free SNP (souporcell) methods on simulated and real data from renal cell cancer. Our results demonstrate that demuxSNP outperformed standalone hashing methods on low quality hashing data, improving overall classification accuracy and allowing more high RNA quality cells to be recovered. Through varying simulated doublet rates, we show genotype-free SNP methods are unable to identify biological samples with low cell counts at high doublet rates. When compared to unsupervised SNP demultiplexing methods, demuxSNPs supervised approach was more robust to doublet rate in experiments with class size imbalance. ConclusionsdemuxSNP is a performant demultiplexing approach that uses hashing and SNP data to demultiplex datasets with low hashing quality where biological samples are genetically distinct. Unassigned cells (negatives) with high RNA quality can be recovered, making more cells available for analysis, especially when applied to data with low hashing quality or suspected misassigned cells. Pipelines for simulated data and processed benchmarking data for 5-50% doublets are publicly available. demuxSNP is available as an R/Bioconductor package (https://doi.org/doi:10.18129/B9.bioc.demuxSNP).

bioinformatics↗