Biology subjects

Georgaka, S.

Publications and source records attributed to Georgaka, S..

5 recordsLinked to original sources

bioRxiv2026

Proteomics-constrained deconvolution reveals spatial cell-type programs in tumours

Accurately resolving cell-type mixtures in spatial transcriptomics remains challenging, particularly in heterogeneous tumours where cell populations are intermixed and matched single-cell references may be unavailable or poorly aligned. Current deconvolution approaches either require high-quality scRNA-seq references, suffer from scalability limitations, or lack interpretability. We introduce PISTACHIO, a proteomics-informed spatial transcriptomics deconvolution framework based on constrained non-negative matrix factorization with a negative-binomial likelihood. Rather than using probabilistic priors, PISTACHIO incorporates spatial cell-type constraints derived from paired Imaging Mass Cytometry, enforcing biologically grounded sparsity and explicit spatial feasibility of cell-type presence. PISTACHIO improved recovery of spatial cell-type distributions compared with Cell2location and STdeconvolve across synthetic and real tumour datasets. Our approach remains robust under cell-type assignment errors, maintaining high correlation with ground-truth under moderate noise, and achieves fast runtime on standard hardware, enabling practical large-scale deployment.

bioinformatics↗

bioRxiv2023

CellPie: a fast spatial transcriptomics topic discovery method via joint factorization of gene expression and imaging data

Spatially resolved transcriptomics has enabled the study of expression of genes within tissues while retaining their spatial identity. Most spatial transcriptomics technologies generate a matched histopathological image as part of the standard pipeline, providing morphological information that can complement the transcriptomics data. Here we present CellPie, a fast, unsupervised factor discovery method, based on joint non-negative matrix factorisation of spatial RNA transcripts and histological image features.CellPie employs the accelerated hierarchical least squares method to significantly reduce the computational time, enabling efficient application to high-dimensional spatial transcriptomics datasets. We assessed CellPie on two different human cancer types and spatial resolutions, showing an improved performance against published factorisation methods. Additionally, we applied CellPie to a highly resolved Visium HD dataset, demonstrating its high computational efficiency compared to standard non-negative matrix factorisation and other existing methods. Availabilityhttps://github.com/ManchesterBioinference/CellPie

bioinformatics↗

bioRxiv2023

A spatial map of human liver cirrhosis reveals the patho-architecture and gene signatures associated with cell state transitions during liver disease

Liver fibrosis is a major cause of death worldwide. As a progressive step in chronic liver disease, fibrosis is almost always diagnosed too late with limited treatment options. Here, we uncover the spatial transcriptional landscape driving human liver fibrosis using single nuclei RNA and Assay for Transposase-Accessible Chromatin (ATAC) sequencing to deconvolute multi-cell spatial transcriptomic profiling in human liver cirrhosis. Through multi-modal data integration, we define molecular signatures driving cell state transitions in liver disease and define an impaired cellular response and directional trajectory from hepatocytes to cholangiocytes associated with disease remodelling. We identify pro-fibrogenic signatures in non-parenchymal cell subpopulations co-localised within the fibrotic niche and localise transitional cell states at the scar interface. This combined approach provides a spatial atlas of gene regulation and defines molecular signatures associated liver disease for targeted therapeutics or as early diagnostic markers of progressive liver disease.

genomics↗

bioRxiv2023

Hypoxia coordinates the spatial landscape of myeloid cells within glioblastoma to affect outcome

Myeloid cells are highly prevalent in glioblastoma (GBM), existing in a spectrum of phenotypic and activation states. We currently have limited knowledge of the tumour microenvironment (TME) determinants that influence the localisation and the functions of the diverse myeloid cell populations in GBM. Here we have utilised orthogonal imaging mass cytometry with single cell and spatial transcriptomics approaches to identify and map the various myeloid populations in the human GBM tumour microenvironment (TME). Our results show that different myeloid populations have distinct and reproducible compartmentalisation patterns in the GBM TME that is driven by tissue hypoxia, regional chemokine signalling, and varied homotypic and heterotypic cellular interactions. We subsequently identified specific tumour sub-regions in GBM, based upon composition of identified myeloid cell populations, that were linked to patient survival. Our results provide new insight into the spatial organisation of myeloid cell sub populations in GBM, and how this is predictive of clinical outcome. TeaserMulti-modal mapping reveals that the spatial organisation of myeloid cells in glioblastoma impacts disease outcome.

cancer biology↗

bioRxiv2020

Non-parametric modelling of temporal and spatial counts data from RNA-seq experiments

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSMotivationC_ST_ABSThe negative binomial distribution has been shown to be a good model for counts data from both bulk and single-cell RNA-sequencing (RNA-seq). Gaussian process (GP) regression provides a useful non-parametric approach for modeling temporal or spatial changes in gene expression. However, currently available GP regression methods that implement negative binomial likelihood models do not scale to the increasingly large datasets being produced by single-cell and spatial transcriptomics. ResultsThe GPcounts package implements GP regression methods for modelling counts data using a negative binomial likelihood function. Computational efficiency is achieved through the use of variational Bayesian inference. The GP function models changes in the mean of the negative binomial likelihood through a logarithmic link function and the dispersion parameter is fitted by maximum likelihood. We validate the method on simulated time course data, showing that it is better able to identify changes in over-dispersed counts data than methods based on Gaussian or Poisson likelihoods. To demonstrate temporal inference, we apply GPcounts to single-cell RNA-seq datasets after pseudotime and branching inference. To demonstrate spatial inference, we apply GPcounts to data from the mouse olfactory bulb to identify spatially variable genes and compare to two published GP methods. We also provide the option of modelling additional dropout using a zero-inflated negative binomial. Our results show that GPcounts can be used to model temporal and spatial counts data in cases where simpler Gaussian and Poisson likelihoods are unrealistic. AvailabilityGPcounts is implemented using the GPflow library in Python and is available at https://github.com/ManchesterBioinference/GPcounts along with the data, code and notebooks required to reproduce the results presented here. Contactnuha.bintayyash@manchester.ac.uk or magnus.rattray@manchester.ac.uk

bioinformatics↗