Biology subjects

Jones, D. C.

Publications and source records attributed to Jones, D. C..

7 recordsLinked to original sources

bioRxiv2024

Cell Simulation as Cell Segmentation

Single-cell spatial transcriptomics promises a highly detailed view of a cells transcriptional state and microenvironment, yet inaccurate cell segmentation can render this data murky by misattributing large numbers of transcripts to nearby cells or conjuring nonexistent cells. We adopt methods from ab initio cell simulation to rapidly infer morphologically plausible cell boundaries that preserve cell type heterogeneity. Benchmarking applied to datasets generated by three commercial platforms show superior performance and computational efficiency of this approach compared with existing methods. We show that improved accuracy in cell segmentation aids greatly in detection of difficult to accurately segment tumor infiltrating immune cells such as neutrophils and T cells. Lastly, through improvements in our ability to delineate subsets of tumor infiltrating T cells, we show that CXCL13-expressing CD8+ T cells tend to be more closely associated with tumor cells than their CXCL13-negative counterparts in data generated from renal cell carcinoma patient samples.

bioinformatics↗

bioRxiv2024

First report and characterization of a plasmid-encoded blaSFO-1 in a multi-drug resistant Aeromonas hydrophila clinical isolate

Antibiotic resistance remains one of the most pressing public health issues facing the world today. At the forefront of this battle lies the ever-increasing identification of extended spectrum beta-lactamases and carbapenemases within human pathogens, conferring resistance towards broad-spectrum and last-resort anti-microbials. This study was prompted due to the identification of a pathogenic Aeromonas hydrophila isolate (strain MAH-4) collected from abdominal fluid, which presented a robust resistance pattern against second, third, and fourth generation cephalosporins, ertapenem, ciprofloxacin, gentamicin, levofloxacin and moxifloxacin, and beta lactam/beta-lactamase inhibitor combinations. Whole genome sequencing was performed and identified a 328 kb plasmid (pMAH4) encoding 10 antibiotic resistance genes, including blaSFO-1, blaTEM-1, and blaOXA-1 of A. hydrophia MAH-4. This is the first report of beta-lactamase SFO-1 within a clinical strain of Aeromonas. Due to the remarkable sequence identity of pMAH4 to plasmids associated with Enterobacterales genera like Klebsiella, and the extensive capabilities of Aeromonas for horizontal gene transfer, our identification of a clinical isolate encoding SFO-1 on a plasmid suggests ARG mobility between Enterobacterales and non-Enterobacterales species.

microbiology↗

bioRxiv2022

An information theoretic approach to detecting spatially varying genes

Identifying genes with spatially coherent expression patterns is a key task in spatial transcriptomics. We adopt an information theoretic perspective on this problem by equating the degree of spatial coherence with the mutual information between nearby expression measurements. To avoid the notoriously difficult problem of computing mutual information, we use modern methods of approximation, in a method we call maximization of spatial information (Maxspin). As well as being highly scalable, we demonstrate improved accuracy across several spatial transcriptomics platforms and a variety of simulations when compared to both existing specialized methods and traditional spatial statistics methods. We use the method to analyze a renal cell carcinoma sample profiled using CosMx Spatial Molecular Imaging, revealing previously undescribed gene expression patterns.

bioinformatics↗

bioRxiv2020

Polee: RNA-Seq analysis using approximate likelihood

The analysis of mRNA transcript abundance with RNA-Seq is a central tool in molecular biology research, but often analyses fail to account for the uncertainty in these estimates, which can be significant, especially when trying to disentangle isoforms or duplicated genes. Preserving un-certainty necessitates a full probabilistic model of the all the sequencing reads, which quickly becomes intractable, as experiments can consist of billions of reads. To overcome these limitations, we propose a new method of approximating the likelihood function of a sparse mixture model, using a technique we call the Polya tree transformation. We demonstrate that substituting this approximation for the real thing achieves most of the benefits with a fraction of the computational costs, leading to more accurate detection of differential transcript expression. AvailabilityThe method is implemented in a Julia package available from https://github.com/dcjones/polee Contactdcjones@cs.washington.edu

bioinformatics↗

bioRxiv2020

Activation of the myeloid checkpoint inhibitor LILRB3 (ILT5) elicits profound immununomodulation

Despite advances in identifying the key immunoregulatory roles of many of the human leukocyte immunoglobulin (Ig)-like receptor (LILR) family members, the function of the inhibitory molecule LILRB3 (ILT5, CD85a, LIR3) remains unclear. Studies indicate a predominant myeloid expression; however, high homology within the LILR family and a relative paucity of reagents have hindered progress for this receptor. To investigate its function and potential immunomodulatory capacity, a panel of LILRB3-specific monoclonal antibodies (mAb) was generated. LILBR3-specific mAb bound to discrete epitopes in either Ig-like domain two or four. LILRB3 ligation on primary human monocytes by agonistic mAb resulted in phenotypic and functional changes, leading to potent inhibition of immune responses in vitro, including significant reduction in T cell proliferation. Importantly, agonizing LILRB3 in humanized mice induced tolerance and permitted efficient engraftment of allogeneic cells. Our findings reveal powerful immunosuppresive functions of LILRB3 and identify it as an important myeloid checkpoint receptor.

immunology↗

bioRxiv2017

Analysis of Copy Number Loss of the ErbB4 Receptor Tyrosine Kinase in Glioblastoma

Current treatments for glioblastoma multiforme (GBM)--an aggressive form of brain cancer--are minimally effective and yield a median survival of 14.6 months and a two-year survival rate of 30%. Given the severity of GBM and the limitations of its treatment, there is a need for the discovery of novel drug targets for GBM and more personalized treatment approaches based on the characteristics of an individuals tumor. Most receptor tyrosine kinases--such as EGFR--act as oncogenes, but publicly available data from the Cancer Cell Line Encyclopedia (CCLE) indicates copy number loss in the ERBB4 RTK gene across dozens of GBM cell lines, suggesting a potential tumor suppressor role. This loss is mutually exclusive with loss of its cognate ligand NRG1 in CCLE as well, more strongly suggesting a functional role. The availability of higher resolution copy number data from clinical GBM patients in The Cancer Genome Atlas (TCGA) revealed that a region in Intron 1 of the ERBB4 gene was deleted in 69.1% of tumor samples harboring ERBB4 copy number loss; however, it was also found to be deleted in the matched normal tissue samples from these GBM patients (n = 81). Using the DECIPHER Genome Browser, we also discovered that this mutation occurs at approximately the same frequency in the general population as it does in the disease population. We conclude from these results that this loss in Intron 1 of the ERBB4 gene is neither a de novo driver mutation nor a predisposing factor to GBM, despite the indications from CCLE. A biological role of this significantly occurring genetic alteration is still unknown. While this is a negative result, the broader conclusion is that while copy number data from large cell line-based data repositories may yield compelling hypotheses, careful follow up with higher resolution copy number assays, patient data, and general population analyses are essential to codify initial hypotheses.

systems biology↗

bioRxiv2016

Isolator: accurate and stable analysis of isoform-level expression in RNA-Seq experiments

While RNA-Seq has enabled great progress towards the goal of wide-scale isoform-level mRNA quantification, short reads have limitations when resolving complex or similar sets of isoforms. As a result, estimates of isoform abundance carry far more uncertainty than those made at the gene level. When confronted with this uncertainty, commonly used methods produce estimates that are often high-variance--small perturbations in the data often produce dramatically different results, confounding downstream analysis. We introduce a new method, Isolator, which analyzes all samples in an experiment in unison using a simple Bayesian hierarchical model. Combined with aggressive bias correction, it produces estimates that are simultaneously accurate and show high agreement between samples. In a comprehensive comparison of accuracy and variance, we show that this property is unique to Isolator. We further demonstrate that the approach of modeling an entire experiment enables new analyses, which we demonstrate by examining splicing monotonicity across several time points in the development of human cardiomyocyte cells.

bioinformatics↗