Biology subjects

Ashton, J. M.

Publications and source records attributed to Ashton, J. M..

6 recordsLinked to original sources

bioRxiv2026

Reconstruction of septin higher-order nano-size structures in ovarian cancer cells uncover susceptibility to the septin-targeting small molecule UR214-9

In cancer cells, septins assemble into enigmatic higher-order structures of 300-700 nanometers, including long needle-like filaments, thick perinuclear rings, and cytoplasmic bundles or aggregates. The absence of genetic or pharmacological tools to recapitulate these architectures in-vitro has impeded mechanistic studies of their formation, function, and therapeutic targeting. Here, first, determining the overexpression of septin-2 in epithelial ovarian cancer (EOC) and its association with increased mortalities and dependencies, we select SKOV-3 ovarian cancer cells as a tractable model in which septin supramolecular assemblies can be recreated in-vitro and interrogated. This system shows that the forchlorfenuron (FCF) analog UR214-9 remodels septin architecture, converting co-expressed human septin octamers (SEPT2-SEPT6-SEPT7-SEPT9-SEPT9-SEPT7-SEPT6-SEPT2) into large cytoplasmic aggregates. In parallel, transiently expressed SEPT2 is reorganized into septin-rich noodle-like filaments, perinuclear rings, and web-like networks encircling the nucleus upon UR214-9 treatment. Mechanistically, UR214-9 disrupts the incorporation of SEPT2, SEPT7, and SEPT9 into canonical septin hetero-octamers, resulting in assembly-defective or imperfect oligomers that preferentially reorganize into these aberrant higher-order structures. This aggregation likely prevents septin-2 migration during interphase-to-cleavage furrow transition in NRK-49F-SEPT2-EGFP homozygous cells and impacts SKOV-3 cytokinesis, cell proliferation, adhesion and invasion and migration while sparing ceramide transport to the Golgi, preserving ER and cis-Golgi structure. These effects manifested in reduced growth of ovarian, endometrial and breast cancer xenografts without attracting significant off-target engagements per the global transcriptomic analysis of JIMT1 breast cancer and PANC-1 pancreatic cells. UR214-9 treated animals showed observable safety in animals. Thus, a tool to recreate aberrant septin structures and identification of septins as a druggable cytoskeletal target for ovarian, endometrial, breast and pancreatic cancer by perturbing their hetero-octamerization assembly is presented. SignificanceWe provide a method to reconstruct the higher-order septin architecture observed in cancer cells, to study their assembly and functions. Intriguingly, cancer cells tolerate hetero-oligomeric septins lacking specific subunits, suggesting that compositionally deficient oligomers are not efficiently targeted for degradation, unlike unincorporated septin monomers in normal cells. This tolerance may enable accumulation of structurally aberrant septin complexes acquiring long-needles, rings or thick-aggregates in disease cells. We further show that septin oligomerization can be pharmacologically perturbed. By integrating structural, cellular, and energetic readouts using in-silico techniques, we establish a quantitative framework for septin-targeted modulation, generating UR214-9 as a new chemotype that disrupts septin oligomeric assembly via preventing incorporation of SEPT2/7/9, into canonical hetero-octamers, causes defects in cytokinesis, altered cell migration, viability, and remodels septin-actin architectures, ultimately impairing tumor cell growth. Thus, pharmacological targeting of septin assembly represents a tractable strategy to perturb septin-dependent cellular processes in cancer and neurodegenerative diseases with reported septin dysregulation.

cancer biology↗

bioRxiv2026

An assessment of normalization and differential expression methods for miRNA-seq analysis using a realistic benchmark dataset

MicroRNAs are short noncoding RNAs that regulate gene expression and are commonly profiled by small RNA sequencing (miRNA-seq). Despite the widespread use of miRNA-seq, datasets are often analyzed with RNA-seq method such as DESeq2 or edgeR, which do not take into account the specific characteristics of miRNA-seq data. Here, we present a benchmark study of normalization and differential expression approaches using a realistic ground-truth dataset. By mixing mouse RNA of two organs, we generated expression trends while capturing biological and technical variability. Using monotonicity across the dataset and expected fold changes from the mixture design, we assessed normalization and differential expression methods. Normalization benchmarking showed that within-sample scaling, particularly Read Per Million (RPM), best preserved the expected monotonic trends, outperforming cross-sample methods such as TMM, rlog, and VST. These approaches sometimes recovered apparent monotonicity among abundant miRNAs, but inspection of individual profiles suggested likely over-correction. Regarding differential expression, edgeR consistently ranked among the best-performing methods across several metrics, including log2 fold-change estimation, with performance comparable to miRNA-seq-specific tools such as miRglmm and NBSR. DESeq2, edgeR-v4, and limma-based approaches tended to systematically underestimate log2 fold changes. Applying a common RPM-based normalization substantially improved the performance of cross-sample methods, highlighting the strong influence of normalization on differential expression analysis. Overall, our findings support within-sample scaling methods such as RPM for normalization, and edgeR, miRglmm, or NBSR for differential expression. The dataset has been made publicly available, providing a valuable resource for objective method comparison and future miRNA-seq software development.

bioinformatics↗

bioRxiv2024

Temporal Single Cell Analysis of Leukemia Microenvironment Identifies Taurine-Taurine Transporter Axis as a Key Regulator of Myeloid Leukemia

Signals from the microenvironment are known to be critical for development, sustaining adult stem cells, and for oncogenic progression. While candidate niche-driven signals that can promote cancer progression have been identified1-6, concerted efforts to comprehensively map microenvironmental ligands for cancer stem cell specific surface receptors have been lacking. Here, we use temporal single cell RNA-sequencing to identify molecular cues from the bone marrow stromal niche that engage leukemia stem cells (LSC) during oncogenic progression. We integrate these data with our RNA-seq analysis of human LSCs from distinct aggressive myeloid cancer subtypes and our CRISPR based in vivo LSC dependency map7 to develop a temporal receptor-ligand interactome essential for disease progression. These analyses identify the taurine transporter (TauT)-taurine axis as a critical dependency of myeloid malignancies. We show that taurine production is restricted to the osteolineage population during cancer initiation and expansion. Inhibiting taurine synthesis in osteolineage cells impairs LSC growth and survival. Our experiments with the TauT genetic loss of function murine model indicate that its loss significantly impairs the progression of aggressive myeloid leukemias in vivo by downregulating glycolysis. Further, TauT inhibition using a small molecule strongly impairs the growth and survival of patient derived myeloid leukemia cells. Finally, we show that TauT inhibition can synergize with the clinically approved oxidative phosphorylation inhibitor venetoclax8, 9 to block the growth of primary human leukemia cells. Given that aggressive myeloid leukemias continue to be refractory to current therapies and have poor prognosis, our work indicates targeting the taurine transporter may be of therapeutic significance. Collectively, our data establishes a temporal landscape of stromal signals during cancer progression and identifies taurine-taurine transporter signaling as an important new regulator of myeloid malignancies.

cancer biology↗

bioRxiv2023

The impact of PCR duplication on RNA-seq data generated using NovaSeq 6000, NovaSeq X, AVITI and G4 sequencers.

RNA sequencing (RNA-seq) is a powerful technology for gene expression and functional genomics profiling. Expression profiles generated using this approach can be impacted by the methods utilised for cDNA library generation. Selection of the optimal parameters for each step during the protocol are crucial for acquisition of high-quality data. Polymerase chain reaction (PCR) amplification of transcripts is a common step in many RNA-seq protocols and, if not optimised, high PCR duplicate proportions can be generated, resulting in the inflation of transcript counts and introduction of bias. In this study, we investigate the impact of input amount and PCR cycle number on the PCR duplication rate and on the RNA-seq data quality using a broad range of inputs (1 ng -1,000 ng) for RNA-seq library preparation with unique molecular identifiers (UMIs) and sequencing the data on four different short-read sequencing platforms: Illumina NovaSeq 6000, Illumina NovaSeq X, Element Biosciences AVITI, and Singular Genomics G4. Across all platforms, samples of input amounts greater than 125 ng had a negligible PCR duplication rate and the number of PCR cycles did not have a significant effect on data quality. However, for input amounts lower than 125ng we observed a strong negative correlation between input amount and the proportion of PCR duplicates; between 34% and 96% of reads were discarded via deduplication. Fortunately, UMIs were effective for removing in silico PCR duplicates without removing valuable biological information. Removal of PCR duplicates resulted in more comparable gene expression obtained from the different PCR cycles. Data generated with each of the four sequencing platforms presented similar associations between starting material amount and the number of PCR cycles on PCR duplicates, a similar number of genes detected, and comparable gene expression profiles. However, the sequencers using conversion kits for Illumina libraries (AVITI, G4) exhibited lower adapter dimer abundance across all input amounts, but also a higher PCR duplication rate in very low input amounts (<15ng). Overall, this study showed that the choice of input amount and number of PCR cycles are important parameters for obtaining high-quality RNA-seq data across all sequencing platforms. UMI deduplication is an effective way to remove PCR duplicates, improving the data quality and removing any variation caused by the conversion kits.

genomics↗

bioRxiv2023

Myelodysplastic syndromes disable human CD271+VCAM1+CD146+ niches supporting normal hematopoietic stem/progenitor cells

Mesenchymal stem/stromal cells (MSCs) within the bone marrow microenvironment (BMME) support normal hematopoietic stem and progenitor cells (HSPCs). However, the heterogeneity of human MSCs has limited the understanding of their contribution to clonal dynamics and evolution to myelodysplastic syndromes (MDS). We combined three MSC cell surface markers, CD271, VCAM-1 (Vascular Cell Adhesion Molecule-1) and CD146, to isolate distinct subsets of human MSCs from bone marrow aspirates of healthy controls (Control BM). Based on transcriptional and functional analysis, CD271+CD106+CD146+ (NGFR+/VCAM1+/MCAM+/Lin-; NVML) cells display stem cell characteristics, are compatible with murine BM- derived Leptin receptor positive MSCs and provide superior support for normal HSPCs. MSC subsets from 17 patients with MDS demonstrated shared transcriptional changes in spite of mutational heterogeneity in the MDS clones, with loss of preferential support of normal HSPCs by MDS-derived NVML cells. Our data provide a new approach to dissect microenvironment-dependent mechanisms regulating clonal dynamics and progression to MDS.

cancer biology↗

bioRxiv2020

Comparative analysis of single-cell RNA Sequencing Platforms and Methods

Single-cell RNA sequencing (scRNA-seq) offers great new opportunities for increasing our understanding of complex biological processes. In particular, development of an accurate Human Cell Atlas is largely dependent on the rapidly advancing technologies and molecular chemistries employed in scRNA-seq. These advances have already allowed an increase in throughput for scRNA-seq from 96 to 80,000 cells on a single instrument run by capturing cells within nano-liter droplets. While this increase in throughput is critical for many experimental questions, a thorough comparison between microfluidic-, plate-, and droplet-based technologies or between multiple available platforms utilizing these technologies is largely lacking. Here, we report scRNA-seq data from SUM149PT cells treated with the histone deacetylase inhibitor TSA vs. untreated controls across several scRNA-seq platforms (Fluidigm C1, WaferGen iCell8, 10X Genomics Chromium Controller, and Illumina/BioRad ddSEQ). The primary goal of this project was to demonstrate RNA sequencing (RNA-seq) methods for profiling the ultra-low amounts of RNA present in individual cells, and this report discusses the results of the study as well as technical challenges/lesson learned and present general guidelines for best practices in sample preparation and analysis.

genomics↗