Biology subjects

Fuhrman, K.

Publications and source records attributed to Fuhrman, K..

3 recordsLinked to original sources

bioRxiv2025

g.nome, A Transparent Bioinformatics Pipeline that Enables Differential Expression and Alternative Splicing Analysis by Non-Computational Biologists

Reproducibility and accessibility are cardinal principles in the rapidly evolving field of bioinformatics. As the collection of biological data grows, proper use of pipelines to analyze datasets can become a bottleneck restricting efficient analysis. Biologists who collect data and test hypotheses may not have strong computational backgrounds and may not be able to fully understand the underlying strengths and weaknesses of computational approaches or fully exploit their data. Some data may be misunderstood and, perhaps more importantly, critical findings may remain unobserved. High throughput RNA sequencing (RNAseq) has advanced our understanding of transcriptomics across diverse applications. Here we introduce g.nome, a bioinformatics platform that integrates contemporary tools necessary for independent analysis. A user-friendly graphical interface simplifies running jobs and allows simplified analysis of different datasets by non-bioinformaticians. g.nome was used to analyze the consequences of localizing the critical RNAi factor argonaute (AGO) to nuclei of colorectal cancer cell line HCT116. Analysis using the pipeline facilitated the straightforward identification of splicing changes and the prioritization of these splicing changes for validation and further experimental analysis. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=114 SRC="FIGDIR/small/652286v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@f0fa7org.highwire.dtl.DTLVardef@ccac5borg.highwire.dtl.DTLVardef@147c331org.highwire.dtl.DTLVardef@5fe66c_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2021

Spatially resolved whole transcriptome profiling in human and mouse tissue using Digital Spatial Profiling

Emerging spatial profiling technology has enabled high-plex molecular profiling in biological tissues, preserving the spatial and morphological context of gene expression. Here we describe expanding the chemistry for the Digital Spatial Profiling platform to quantify whole transcriptomes in human and mouse tissues using a wide range of spatial profiling strategies and sample types. We designed multiplexed in situ hybridization probe pools targeting the protein-coding genes in the human and mouse transcriptomes, hereafter referred to as the human or mouse Whole Transcriptome Atlas (WTA). We validated the human and mouse WTA using cell lines to demonstrate concordance with orthogonal gene expression profiling methods in profiled region sizes ranging from ~10-500 cells. By benchmarking against bulk RNAseq and fluorescence in situ hybridization, we demonstrate robust transcript detection possible down to ~100 transcripts per region. To assess the performance of WTA across tissue and sample types, we applied WTA to biological questions in cancer, molecular pathology, and developmental biology. We show that spatial profiling with WTA can detect expected spatial gene expression differences between tumor and tumor microenvironment, identify spatial disease-specific heterogeneity in gene expression in histological structures of the human kidney, and comprehensively map transcriptional programs in anatomical substructures of nine organs in the developing mouse embryo. Digital Spatial Profiling technology with the WTA assays provides a flexible method for spatial whole transcriptome profiling applicable to diverse tissue types and biological contexts.

genomics↗

bioRxiv2020

Single-nucleus and spatial transcriptomics of archival pancreatic cancer reveals multi-compartment reprogramming after neoadjuvant treatment

Pancreatic ductal adenocarcinoma (PDAC) remains a treatment-refractory disease. Characterizing PDAC by mRNA profiling remains particularly challenging. Previously identified bulk expression subtypes were influenced by contaminating stroma and have not yet informed clinical management, whereas single cell RNA-seq (scRNA-seq) of fresh tumors under-represented key cell types. Here, we developed a robust single-nucleus RNA-seq (snRNA-seq) technique for frozen archival PDAC specimens and used it to study both untreated tumors and those that received neoadjuvant chemotherapy and radiotherapy (CRT). Gene expression programs learned across untreated malignant cell and fibroblast profiles uncovered a clinically relevant molecular taxonomy with improved prognostic stratification compared to prior classifications. Moreover, in the increasingly-adopted neoadjuvant treatment context, there was a depletion of classical-like phenotypes in malignant cells in favor of basal-like phenotypes associated with TNF-NFkB and interferon signaling as well as the presence of novel acinar and neuroendocrine classical-like states, which may be more resilient to cytotoxic treatment. Spatially-resolved transcriptomics revealed an association between malignant cells expressing these basal-like programs and higher immune infiltration with increased lymphocytic content, whereas those exhibiting classical-like programs were linked to sparser macrophage-predominant microniches, perhaps pointing to susceptibility to distinct therapeutic strategies. Our refined molecular taxonomy and spatial resolution can help advance precision oncology in PDAC through informative stratification in clinical trials and insights into differential therapeutic targeting leveraging the immune system.

genomics↗