Biology subjects

Isserlin, R.

Publications and source records attributed to Isserlin, R..

7 recordsLinked to original sources

bioRxiv2026

Extra-lineage tissue programs define the transcription states of human pancreatic cancer

Cancers acquire alternate transcriptional states as they evolve, but the origins, timing and determinants of this plasticity are poorly understood in many tumours. We investigated the transcriptional states of pancreatic cancer by integrating [~]1000 tumour-enriched genomes and transcriptomes from 464 patients combined with scRNA-seq, multiome profiling, and spatial proteomics. Four epithelial states covering the spectrum of lineage plasticity were identified (Classical-1, Classical-2, Basal-1, Basal-2). Comparing these states to normal and pan-cancer human single cell atlases showed each state reflects distinct tissue programs found in other malignancies. Single cell analysis uncovered that the main transcription state of this disease (Classical-1) emerges before KRAS mutations. Spatial proteomics from patients and cancer-free donors showed that the Classical-1 program emerges during acinar-to-ductal metaplasia, and also unexpectedly, in normal ducts without disrupting their morphology. Overall, these findings link the extensive lineage plasticity potential of this organ to the origins of the transcriptional states.

cancer biology↗

bioRxiv2020

Dynamics of the cell-free DNA methylome of metastatic castration resistant prostate cancer during androgen-targeting treatment

AimWe examined methylation changes in cell-free DNA (cfDNA) in metastatic castration resistant prostate cancer (mCRPC) during treatment. Materials and MethodsGenome-wide methylation analysis of sequentially collected cfDNA samples derived from mCRPC patients undergoing androgen-targeting therapy was performed. ResultsAlterations in methylation states previously implicated in prostate cancer progression were observed and patients that maintained methylation changes throughout therapy tended to have a longer time to clinical progression (TTP). Importantly, we also report that markers associated with a highly aggressive form of the disease, Neuroendocrine-CRPC, were associated with a faster TTP. ConclusionOur findings highlight the potential of monitoring cfDNA methylome during therapy in mCRPC, which may serve as predictive markers of response to androgen-targeting agents.

cancer biology↗

bioRxiv2020

Control of tissue development by cell cycle dependent transcriptional filtering

Cell cycle duration changes dramatically during development, starting out fast to generate cells quickly and slowing down over time as the organism matures. The cell cycle can also act as a transcriptional filter to control the expression of long genes which are partially transcribed in short cycles. Using mathematical simulations of cell proliferation, we identify an emergent property, that this filter can act as a tuning knob to control cell fate, cell diversity and the number and proportion of different cell types in a tissue. Our predictions are supported by comparison to single-cell RNA-seq data captured over embryonic development. Evolutionary genome analysis shows that fast developing organisms have a narrow genomic distribution of gene lengths while slower developers have an expanded number of long genes. Our results support the idea that cell cycle dynamics may be important across multicellular animals for controlling gene expression and cell fate.

developmental biology↗

bioRxiv2019

RCy3: Network Biology using Cytoscape from within R

RCy3 is an R package in Bioconductor that communicates with Cytoscape via its REST API, providing access to the full feature set of Cytoscape from within the R programming environment. RCy3 has been redesigned to streamline its usage and future development as part of a broader Cytoscape Automation effort. Over 100 new functions have been added, including dozens of helper functions specifically for intuitive data overlay operations. Over 40 Cytoscape apps have implemented automation support so far, making hundreds of additional operations accessible via RCy3. Two-way conversion with networks from igraph and graph ensures interoperability with existing network biology workflows and dozens of other Bioconductor packages. These capabilities are demonstrated in a series of use cases involving public databases, enrichment analysis pipelines, shortest path algorithms and more. With RCy3, bioinformaticians will be able to quickly deliver reproducible network biology workflows as integrations of Cytoscape functions, complex custom analyses and other R packages.

bioinformatics↗

bioRxiv2018

Single-cell transcriptomics of the aged mouse brain reveals convergent, divergent and unique aging signatures

The mammalian brain is complex, with multiple cell types performing a variety of diverse functions, but exactly how the brain is affected with aging remains largely unknown. Here we performed a single-cell transcriptomic analysis of young and old mouse brains. We provide a comprehensive dataset of aging-related genes, pathways and ligand-receptor interactions in nearly all brain cell types. Our analysis identified gene signatures that vary in a coordinated manner across cell types and gene sets that are regulated in a cell type specific manner, even at times in opposite directions. Thus, our data reveals that aging, rather than inducing a universal program drives a distinct transcriptional course in each cell population. These data provide an important resource for the aging community and highlight key molecular processes, including ribosomal biogenesis, underlying aging. We believe that this large-scale dataset, which is publicly accessible online (aging-mouse-brain), will facilitate additional discoveries directed towards understanding and modifying the aging process.

neuroscience↗

bioRxiv2017

Pathway enrichment analysis of -omics data

Pathway enrichment analysis helps gain mechanistic insight into large gene lists typically resulting from genome scale (-omics) experiments. It identifies biological pathways that are enriched in the gene list more than expected by chance. We explain pathway enrichment analysis and present a practical step-by-step guide to help interpret gene lists resulting from RNA-seq and genome sequencing experiments. The protocol comprises three major steps: define a gene list from genome scale data, determine statistically enriched pathways, and visualize and interpret the results. We focus on differentially expressed genes and mutated cancer genes, however the described principles can be applied to diverse -omics data. The protocol is designed for biologists with no prior bioinformatics training and uses freely available software including g:Profiler, GSEA, Cytoscape and Enrichment Map.

bioinformatics↗

bioRxiv2016

netDx: Patient classification using integrated patient similarity networks

Patient classification has widespread biomedical and clinical applications, including diagnosis, prognosis and treatment response prediction. A clinically useful prediction algorithm should be accurate, generalizable, be able to integrate diverse data types, and handle sparse data. A clinical predictor based on genomic data needs to be easily interpretable to drive hypothesis-driven research into new treatments. We describe netDx, a novel supervised patient classification framework based on patient similarity networks. netDx meets the above criteria and particularly excels at data integration and model interpretability. As a machine learning method, netDx demonstrates consistently excellent performance in a cancer survival benchmark across four cancer types by integrating up to six genomic and clinical data types. In these tests, netDx has significantly higher average performance than most other machine-learning approaches across most cancer types and its best model outperforms all other methods for two cancer types. In comparison to traditional machine learning-based patient classifiers, netDx results are more interpretable, visualizing the decision boundary in the context of patient similarity space. When patient similarity is defined by pathway-level gene expression, netDx identifies biological pathways important for outcome prediction, as demonstrated in diverse data sets of breast cancer and asthma. Thus, netDx can serve both as a patient classifier and as a tool for discovery of biological features characteristic of disease. We provide a software complete implementation of netDx along with sample files and automation workflows in R.

bioinformatics↗