Biology subjects

Canete, N. P.

Publications and source records attributed to Canete, N. P..

4 recordsLinked to original sources

bioRxiv2022

An in situ quantitative map of initial human colorectal HIV transmission

The initial immune response to HIV is critical in determining transmission. However, due to technical limitations we still do not have a comparative map of early mucosal transmission events. We combined RNAscope, cyclic-immunofluorescence and novel image analysis tools to quantify HIV transmission dynamics in intact human colorectal tissue. We mapped HIV enrichment to mucosal dendritic cells (DC) and submucosal macrophages, but not CD4+ T-cells, the primary targets of downstream infection. DCs appeared to funnel virus to lymphoid aggregates which acted as early sanctuaries of high viral titres whilst facilitating HIV passage to the submucosa. Finally, HIV entry induced rapid recruitment and clustering of target cells, facilitating DC and macrophage mediated HIV transfer and enhanced infection of CD4+ T-cells. These data demonstrate a rapid response to HIV structured to maximise the likelihood of mucosal infection, and provide a framework for in situ studies of host pathogen interactions and immune mediated pathologies. Highlights- in situ quantification of host cellular microenvironment response to pathogen invasion in human colorectal tissue. - HIV first localises to mucosal DCs and submucosal macrophages, but not CD4+ T cells. - Viral enrichment first occurs in lymphoid aggregates which is associated with passage into the submucosa. - Early localisation of HIV to CD4+ T cells is associated with interactions with DCs and macrophages. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/490175v1_ufig1.gif" ALT="Figure 1"> View larger version (67K): org.highwire.dtl.DTLVardef@ef8000org.highwire.dtl.DTLVardef@1bc8ed7org.highwire.dtl.DTLVardef@45ddbdorg.highwire.dtl.DTLVardef@e0a57c_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2021

Spatial analysis for highly multiplexed imaging data to identify tissue microenvironments

Highly multiplexed in situ imaging cytometry assays have made it possible to study the spatial organisation of numerous cell types simultaneously. We have addressed the challenge of quantifying complex multi-cellular relationships by proposing a statistical method which clusters local indicators of spatial association. Our approach successfully identifies distinct tissue architectures in datasets generated from three state-of-the-art high-parameter assays demonstrating its value in summarising the information-rich data generated from these technologies.

bioinformatics↗

bioRxiv2021

spicyR: Spatial analysis of in situ cytometry data in R

MotivationHigh parameter histological techniques have allowed for the identification of a variety of distinct cell types within an image, providing a comprehensive overview of the tissue environment. This allows the complex cellular architecture and environment of diseased tissue to be explored. While spatial analysis techniques have revealed how cell-cell interactions are important within the disease pathology, there remains a gap in exploring changes in these interactions within the disease process. Specifically, there are currently no established methods for performing inference on cell localisation changes across images, hindering an understanding of how cellular environments change with a disease pathology. ResultsWe have developed the spicyR R package to perform inference on changes in the spatial localisation of cell types across groups of images. Application to simulated data demonstrates a high sensitivity and specificity. We demonstrate the utility of spicyR by applying it to a type 1 diabetes imaging mass cytometry dataset, revealing changes in cellular associations that were relevant to the disease progression. Ultimately, spicyR allows changes in cellular environments to be explored under different pathologies or disease states. Availability and ImplementationR package freely available at http://bioconductor.org/packages/release/bioc/html/spicyR.html and shiny app implementation at http://shiny.maths.usyd.edu.au/spicyR/ Contactellis.patrick@sydney.edu.au Supplementary informationCode for reproducing key figures available at https://github.com/nickcee/spicyRPaper.

bioinformatics↗

bioRxiv2019

Digital removal of autofluorescence from microscopy images

Autofluorescence is a long-standing problem that has hindered fluorescence microscopy image analysis. To address this, we have developed a method that identifies and removes autofluorescent signals from multi-channel images post acquisition. We demonstrate the broad utility of this algorithm in accurately assessing protein expression in situ through the removal of interfering autofluorescent signals. Availability and implementationhttps://ellispatrick.github.io/AFremover Contactellis.patrick@sydney.edu.au Supplementary informationSupplementary Figs. 1-13

bioinformatics↗