Biology subjects

Pattison, A. D.

Publications and source records attributed to Pattison, A. D..

3 recordsLinked to original sources

bioRxiv2025

A spatial atlas of colorectal cancer reveals the influence of stromal niches on tumour differentiation

Colorectal cancer is the third most common cancer worldwide and the second leading cause of cancer-related mortality. Tumour architecture is spatially heterogeneous, ranging from the necrotic core to the invasive front, accompanied by diverse stromal and immune responses that influence tumour progression and treatment outcomes. To explore the spatial organisation of the tumour microenvironment, we profiled 1000 genes in 846,469 cells in 23 normal and late-stage colorectal tumour samples. We identified nine distinct spatial niches based on their cellular composition. We show that lymphoid aggregates enriched for CCR7/SELL+ CD4 T cells displayed heightened interferon signalling, more proliferating B cells and gene expression changes indicative of an adaptive immune response. We defined granulocyte-rich regions are concomitant with inflammation-mediated stromal reprogramming and increased tumour stemness. Finally, we uncovered the impact of the tumour microenvironment by distinguishing gene expression programs that were intrinsic to cancerous epithelial cells from those mediated by niche-specific changes. Key pointsO_LIWe present a spatial atlas of colorectal cancer and normal colon alongside an R/shiny interface to interactively explore this data: https://abud-apps.abud-lab-spatial.cloud.edu.au/shiny/cosmxos/. We analysed nine CosMx slides and provide QC metrics, identify strengths and weaknesses and provide suggestions for analysis of imaging-based spatial data. C_LIO_LIWe identify and characterise nine distinct spatial niches, each with their unique gene expression profile. C_LIO_LIWe observe granulocyte infiltration associated with spatially controlled TNF and IL1 signalling from granulocytes and myeloid cells. This signalling associates with a reduction in pro-differentiation CXCL14 fibroblasts and an increase in MMP tissue-remodelling fibroblasts, ultimately reprogramming tumours toward a more foetal/stem-like (progenitor) state. We suggest IL24 as a potential regulatory factor of this response. C_LIO_LIGranulocyte chemoattractants were consistently expressed at higher levels by both cancerous epithelial cells and the tumour microenvironment (TME) relative to normal colon. C_LIO_LIWe identify colonic LAs associated with T cell infiltration, including a population of CCR7/SELL+ CD4 T cells linked to significant reprogramming of B cells. Furthermore, we show that granulocyte-driven fibroblast and myeloid reprogramming is reversed within LAs. C_LIO_LIWe show that mutated epithelial cells within tumours are uniquely marked by an FXYD5+ PIGRlow expression profile, allowing for tumour vs normal epithelial cell differential gene expression analysis. This distinction clarifies cancerous tumour-intrinsic expression changes versus those promoted by the TME. C_LI

cancer biology↗

bioRxiv2018

Widespread cytoplasmic polyadenylation programs asymmetry in the germline and early embryo

BACKGROUNDThe program of embryonic development is launched by selective activation of a silent maternal transcriptome. In Caenorhabditis elegans, nuclei of the adult germline are responsible for the synthesis of at least two distinct mRNA populations; those required for housekeeping functions, and those that program the oocyte-to-embryo transition. We mapped this separation by changes to the length-distribution of poly(A)-tails that depend on GLD-2 mediated cytoplasmic polyadenylation and its regulators genome-wide.\n\nRESULTSMore than 1000 targets of cytoplasmic polyadenylation were identified by differential polyadenylation. Amongst mRNA with the greatest dependence on GLD-2 were those encoding RNA binding proteins with known roles in spatiotemporal patterning such as mex-5 and pos-1. In General, the 3 UTR of GLD-2 targets were longer, contained cytosine-patches, and were enriched for non-standard polyadenylation-motifs. To identify the deadenylase that initiated transcript silencing, we depleted the known deadenylases in the gld-2(0) mutant background. Only the loss of CCF-1 suppressed the short-tailed phenotype of GLD-2 targets suggesting that in addition to its general role in RNA turnover, this is the major deadenylase for regulatory silencing of maternal mRNA. Analysis of poly(A)-tail length-change in the embryo lacking specific RNA-binding proteins revealed new candidates for asymmetric expression in the first embryonic divisions.\n\nCONCLUSIONThe concerted action of RNA binding proteins exquisitely regulates GLD-2 activity in space and time. We present our data as interactive web resources for a model where GLD-2 mediated cytoplasmic polyadenylation regulates target mRNA at each stage of worm germline and early embryonic development.

developmental biology↗

bioRxiv2018

Topconfects: a package for confident effect sizes in differential expression analysis provides improved usability ranking genes of interest

BackgroundA differential gene expression analysis may produce a set of significantly differentially expressed genes that is too large to easily investigate, so that a means of ranking genes by their biological interest level is desirable. The life-sciences have grappled with the abuse of p-values to rank genes for this purpose. As an alternative, a lower confidence bound on the magnitude of Log Fold Change (LFC) could be used to rank genes, but it has been unclear how to reconcile this with the need to perform False Discovery Rate (FDR) correction. The TREAT test of McCarthy and Smyth is a step in this direction, finding genes significantly exceeding a specified LFC threshold. Here we describe the use of test inversion on TREAT to present genes ranked by a confidence bound on the LFC, while still controlling FDR.\n\nResultsTesting the Topconfects R package with simulated gene expression data shows the method outperforming current statistical approaches across a wide range of experiment sizes in the identification of genes with largest LFCs. Applying the method to a TCGA breast cancer data-set shows the method ranks some genes with large LFC higher than would traditional ranking by p-value. Importantly these two ranking methods lead to a different biological emphasis, in terms both of specific highly ranked genes and gene-set enrichment.\n\nConclusionsThe choice of ranking method in differential expression analysis can affect the biological interpretation. The common default of ranking by p-value is implicitly by an effect size in which each gene is standardized to its own variability, rather than comparing genes on a common scale, which may not be appropriate. The Topconfects approach of presenting genes ranked by confident LFC effect size is a variation on the TREAT method with improved usability, removing the need to fine-tune a threshold parameter and removing the temptation to abuse p-values as a de-facto effect size.

bioinformatics↗