Biology subjects

Phipson, B.

Publications and source records attributed to Phipson, B..

16 recordsLinked to original sources

bioRxiv2026

jazzPanda: A hybrid approach to find spatial markergenes in imaging-based spatial transcriptomics data

MotivationSpatial transcriptomics resolves the organisation of tissues and their cellular neigh-bourhoods, where cell type identification depends on reliable marker gene detection. Existing marker methods were developed for single-cell RNA sequencing and ignore the spatial coordinates of cells and transcripts, a particular problem for imaging-based platforms where transcript counts per gene per cell are extremely sparse. ResultsWe present jazzPanda, a method for detecting spatially informative marker genes in imaging-based spatial transcriptomics. Transcript and cell coordinates are aggregated into one-dimensional vectors by spatial binning, and gene vectors can be built directly from transcript coordinates without cell segmentation. Markers are identified either by permutation-based rank correlation for single-sample data, or by a lasso-regularised generalised linear model that accommo-dates multiple samples and platform-specific background signal from negative-control probes. To our knowledge, jazzPanda is the only marker detection method to account jointly for spatial distri-bution, replication across samples, and platform background. Benchmarked against the Wilcoxon rank sum test and t-tests on public Xenium and CosMx data, jazzPanda recovers markers with stronger spatial concordance and greater specificity, yielding smaller, more interpretable marker sets. The same vector framework also extends to cluster- and gene-level co-location analysis. Availability and implementationjazzPanda is implemented as an open-source R/Bioconductor package, freely available at https://bioconductor.org/packages/jazzPanda. Analysis code for this article is at https://github.com/phipsonlab/jazzPanda_paper, with an accompanying analysis website at https://phipsonlab.github.io/jazzPanda_workflowr/. Datasets and scripts are deposited at https://zenodo.org/records/18149456. Contactphipson.b@wehi.edu.au

bioinformatics↗

bioRxiv2024

Myeloid-instructed CD14+CD4+ T cells within the tumor microenvironment are associated with TNFα signaling and prolonged survival in non-small cell lung cancer

The tumor microenvironment (TME) harbors a diverse array of innate and adaptive immune cells that can either support anti-tumor immunity or facilitate tumor progression. Among these, myeloid cells are key drivers of immunosuppression, yet therapeutic strategies targeting their function have achieved limited clinical success. A deeper understanding of the mechanisms by which immunosuppressive myeloid cells promote tumor progression is needed to guide the development of more effective treatments. Using spatial multi-omics analyses, we identified a population of myeloid-instructed CD14+ T cells present in both the tumor core and adjacent non-malignant lung tissue of patients with non-small cell lung cancer (NSCLC). CD4+ T cells acquired CD14 from myeloid cells by trogocytosis, resulting in an atypical T cell phenotype. High infiltration of CD14+CD4+ T cells in the tumor core was associated with poor patient survival. Spatial transcriptomics profiling revealed that tumors enriched in CD14+CD4+ T cells exhibited increased TNF signaling. Functional assays demonstrated that TNF enhanced trogocytosis, promoting the transfer of myeloid membrane components to CD4{square} T cells and driving the accumulation of CD14{square}CD4{square} T cells. These findings uncover a novel TNF-mediated mechanism of immunosuppression in the TME, whereby TNF promotes aberrant myeloid-T cell interactions that may contribute to tumor progression. This work highlights a previously unrecognized axis of innate-adaptive immune crosstalk in NSCLC and suggests that targeting TNF could disrupt this pathway, restore effective T cell function, and improve therapeutic outcomes.

cancer biology↗

bioRxiv2024

GLMsim: a GLM-based single cell RNA-seq simulator incorporating batch and biological effects

With development of the single cell RNA-seq technologies, large numbers of cells can now be routinely sequenced by different platforms. This requires us to choose an efficient integration tool to merge those cells, and computational simulators to help benchmark and assess the performance of these tools. Although existing single cell RNA-seq simulators can simulate library size, biological and batch effects separately, they currently do not capture associations among these three factors. Here we present GLMsim, the first single cell RNA-seq simulator to simultaneously capture the library size, biology and unwanted variation and their associations via a generalized linear model, and to simulate data resembling the original experimental data in these respects. GLMsim is capable of quantitatively benchmarking different single cell integration methods, and assessing their abilities to retain biology and remove library size and batch effects.

bioinformatics↗

bioRxiv2024

Defining focal neuroendocrine differentiation as a transcriptionally distinct form of prostate cancer pathology characterized by the expression of androgen receptors

BackgroundMen with neuroendocrine prostate cancer (NEPC) have a poor prognosis. NEPC is commonly diagnosed by immunohistochemical markers (CHGA, SYP and NCAM1) and genomic features (mutations in RB1, PTEN, TP53). But by pathology, NEPC tumours are variable, leading to a classification of NE subtypes such as small cell and large cell neuroendocrine carcinomas, focal neuroendocrine differentiation (Focal NED), and Amphicrine. We postulated the diversity observed in NEPC pathologies might arise from differences in transcriptional profiles and the aim of this study is to utilize single-cell RNA sequencing to define the transcriptional differences between NEPC subtype pathologies. MethodsGene expression profiles were obtained for 18,632 individual tumour cells from 9 patient-derived xenograft (PDX) models representing five distinct neuroendocrine pathologies of prostate cancer. Integration and clustering of cell-level data demarked transcriptionally distinct sub-populations of cells. Differential gene expression, gene set enrichment and transcriptional factor regulon analysis identified expression signatures unique to specific neuroendocrine pathologies. Copy-number estimated from expression data revealed the clonal structure of PDXs with mixed adenocarcinoma and neuroendocrine pathologies. ResultsSignificant differences were observed in the transcriptional profiles of NEPC pathology subtypes. Focal NED cells maintain AR signaling, similar to the amphicrine subtype but different from small and large cell carcinomas. Cellular sub-populations enriched for expression of KRAS, IL2-STAT5 and TNF-signaling genes were found in focal NED and amphicrine pathologies, but not in small or large cell carcinomas. In contrast, sub-populations enriched for the YAP, Myc and E2F pathways were detected in small cell, large cell and amphicrine tumours, but not in focal NED cells. Each pathology showed unique patterns of master regulator activity as well, further implicating focal NED as a transcriptionally distinct entity. Based on copy number alterations within PDXs of mixed pathology, focal NED cells showed little clonal divergence from neighboring adenocarcinoma cells, whereas cells with small cell neuroendocrine pathology were clonally distinct. ConclusionsNeuroendocrine prostate cancer subtypes can be identified by pathology and our study shows that transcriptional features identified by single-cell RNA-sequencing also distinguish neuroendocrine subtypes pathologies from each other. In particular, our data redefine focal neuroendocrine differentiation as a pathology expressing androgen receptors (AR), exhibiting its distinctive composition of transcriptionally unique sub-populations. These findings advocate for differences in the treatment of NEPC tumors, particularly those displaying focal NED.

cancer biology↗

bioRxiv2023

Extraction and quantification of lineage-tracing barcodes with NextClone and CloneDetective

SummaryThe study of clonal dynamics has significantly advanced our understanding of cellular heterogeneity and lineage trajectories. With recent developments in lineage-tracing protocols such as ClonMapper or SPLINTR, which combine DNA barcoding with single-cell RNA sequencing (scRNA-seq), biologists can trace the lineage and evolutionary paths of individual clones while simultaneously observing their transcriptomic changes over time. Here, we present NextClone and CloneDetective, an integrated highly scalable Nextflow pipeline and R package for efficient extraction and quantification of clonal barcodes from scRNA-seq data and DNA sequencing data tagged with lineage-tracing barcodes. We applied both NextClone and CloneDetective to data from a barcoded MCF7 cell line and demonstrate their utility for advancing clonal analysis in the era of high-throughput sequencing. Availability and implementationNextClone and CloneDetective are freely available and open-source on github (https://github.com/phipsonlab/NextClone and https://github.com/phipsonlab/CloneDetective). Documentations and tutorials for NextClone and CloneDetective can be found at https://phipsonlab.github.io/NextClone/ and https://phipsonlab.github.io/CloneDetective/respectively.

bioinformatics↗

bioRxiv2023

SuperCellCyto: enabling efficient analysis of large scale cytometry datasets

Advancements in cytometry technologies have enabled quantification of up to 50 proteins across millions of cells at single cell resolution. Analysis of cytometry data routinely involves tasks such as data integration, clustering, and dimensionality reduction. While numerous tools exist, many require extensive run times when processing large cytometry data containing millions of cells. Existing solutions, such as random subsampling, are inadequate as they risk excluding rare cell subsets. To address this, we propose SuperCellCyto, an R package that builds on the SuperCell tool which groups highly similar cells into supercells. SuperCellCyto is available on GitHub (https://github.com/phipsonlab/SuperCellCyto).

bioinformatics↗

bioRxiv2023

Library size confounds biology in spatial transcriptomics data

Spatial molecular technologies have revolutionised the study of disease microenvironments by providing spatial context to tissue heterogeneity. Recent spatial technologies are increasing the throughput and spatial resolution of measurements, resulting in larger datasets. The added spatial dimension and volume of measurements poses an analytics challenge that has, in the short-term, been addressed by adopting methods designed for the analysis of single-cell RNA-seq data. Though these methods work well in some cases, not all necessarily translate appropriately to spatial technologies. A common assumption is that total sequencing depth, also known as library size, represents technical variation in single-cell RNA-seq technologies, and this is often normalised out during analysis. Through analysis of several different spatial datasets, we noted that this assumption does not necessarily hold in spatial molecular data. To formally assess this, we explore the relationship between library size and independently annotated spatial regions, across 23 samples from 4 different spatial technologies with varying throughput and spatial resolution. We found that library size confounded biology across all technologies, regardless of the tissue being investigated. Statistical modelling of binned total transcripts shows that tissue region is strongly associated with library size across all technologies, even after accounting for cell density of the bins. Through a benchmarking experiment, we show that normalising out library size leads to sub-optimal spatial domain identification using common graph-based clustering algorithms. On average, better clustering was achieved when library size effects were not normalised out explicitly, especially with data from the newer sub-cellular localised technologies. Taking these results into consideration, we recommend that spatial data should not be specifically corrected for library size prior to analysis unless strongly motivated. We also emphasise that spatial data are different to single-cell RNA-seq and care should be taken when adopting algorithms designed for single cell data.

bioinformatics↗

bioRxiv2022

Reduced specification of lateral plate mesoderm progenitors underpins heterochronic development of the emu wing

The tetrapod limb has evolved a broad diversity of form and function, enabling vertebrates to adapt to a wide range of environments. One of the most striking examples of this is the evolution of powered flight in birds. However, during the avian radiation, flightless ratites (Paleognathae) secondarily evolved multiple instances of wing reductions, utilizing distinct molecular mechanisms, and displaying heterochrony with flighted birds (Neoaves). Among ratites, the emu displays the most marked reduction of wing anatomy, developing a small wing with a single clawed digit. This major divergence from the typical avian pattern provides a robust model to determine the mechanisms underpinning developmental plasticity in the tetrapod limb. To interrogate the ontogenetic origins of vestigial wing development, we applied comparative single cell transcriptomics of the embryonic emu forelimb field with the chicken, as it possess a typical wing. Stunted wing development in the emu occurs through a heterotopic reduction of lateral plate mesoderm (LPM)-derived limb progenitor cells, and accompanying downregulation of genes associated with LPM development and early limb initiation. Here, forelimb bud initiation and outgrowth is altered by changes to the specification and allocation of limb progenitor cells, acting as an underlying template for pre- and post-patterning mechanisms. These findings provide novel insights into the evolution of limb development in tetrapods, and emphasizes an important, yet underappreciated role for heterotopy as major driver of limb disparity. Significance statementAnimals exhibit a striking variety of morphologies, yet our understanding of how these forms evolve and arise during development are lagging. The emu is a large, flightless bird which has evolved a vestigial wing. This model allows detailed interrogations into the mechanisms which underlie evolution and development of a distinct morphological structure. Through application of comparative single cell transcriptomics between the emu and chicken, we identify an early embryological mechanism, heterotopy, underlying the evolution and vestigial development of the emu wing. This mechanism features reduced allocation of limb bud progenitor cells in the lateral plate mesoderm (LPM). This data provides new information on the mechanisms that underpin evolutionary diversity in morphological features.

evolutionary biology↗

bioRxiv2021

propeller: testing for differences in cell type proportions in single cell data

MotivationSingle cell RNA Sequencing (scRNA-seq) has rapidly gained popularity over the last few years for profiling the transcriptomes of thousands to millions of single cells. This technology is now being used to analyse experiments with complex designs including biological replication. One question that can be asked from single cell experiments, which has been difficult to directly address with bulk RNA-seq data, is whether the cell type proportions are different between two or more experimental conditions. As well as gene expression changes, the relative depletion or enrichment of a particular cell type can be the functional consequence of disease or treatment. However, cell type proportions estimates from scRNA-seq data are variable and statistical methods that can correctly account for different sources of variability are needed to confidently identify statistically significant shifts in cell type composition between experimental conditions. ResultsWe have developed propeller, a robust and flexible method that leverages biological replication to find statistically significant differences in cell type proportions between groups. Using simulated cell type proportions data we show that propeller performs well under a variety of scenarios. We applied propeller to test for significant changes in proportions of cell types related to human heart development, ageing and COVID-19 disease severity. Availability and implementationThe propeller method is publicly available in the open source speckle R package (https://github.com/phipsonlab/speckle). All the analysis code for the paper is available at https://github.com/phipsonlab/propeller-paper-analysis/, and the associated analysis website is available at https://phipsonlab.github.io/propeller-paper-analysis/. ContactAlicia Oshlack: Alicia.Oshlack@petermac.org Belinda Phipson: phipson.b@wehi.edu.au Supplementary informationYes.

bioinformatics↗

bioRxiv2020

Gene set enrichment analysis for genome-wide DNA methylation data

DNA methylation is one of the most commonly studied epigenetic marks, due to its role in disease and development. Illumina methylation arrays have been extensively used to measure methylation across the human genome. Methylation array analysis has primarily focused on preprocessing, normalisation and identification of differentially methylated CpGs and regions. GOmeth and GOregion are new methods for performing unbiased gene set testing following differential methylation analysis. Benchmarking analyses demonstrate GOmeth outperforms other approaches and GOregion is the first method for gene set testing of differentially methylated regions. Both methods are publicly available in the missMethyl Bioconductor R package.

bioinformatics↗

bioRxiv2017

Transcriptional evaluation of the developmental accuracy, reproducibility and robustness of kidney organoids derived from human pluripotent stem cells

We have previously reported a protocol for the directed differentiation of human induced pluripotent stem cells to kidney organoids comprised of nephrons, proximal and distal epithelium, vasculature and surrounding interstitial elements. The utility of this protocol for applications such as disease modelling will rely implicitly on the developmental accuracy of the model, technical robustness of the protocol and transferability between iPSC lines. Here we report extensive transcriptional analyses of the sources of variation across the timecourse of differentiation from pluripotency to complete kidney organoid, focussing on repeated differentiations to day 18 organoid. Individual organoids generated within the same differentiation experiment show Spearmans correlation coefficients of >0.99. The greatest source of variation was seen between experimental batch, with the enrichment for genes that also varied temporally between day 10 and day 25 organoids implicating nephron maturation as contributing to transcriptional variance between individual differentiation experiments. A morphological analysis revealed a transition from renal vesicle to capillary loop stage nephrons across the same time period. Distinct iPSC clones were also shown to display congruent transcriptional programs with inter-experimental and inter-clonal variation most strongly associated with nephron patterning. Even epithelial cells isolated from organoids showed transcriptional alignment with total organoids of the same day of differentiation. This data provides a framework for managing experimental variation, thereby increasing the utility of this approach for personalised medicine and functional genomics.

bioinformatics↗

bioRxiv2017

High throughput single cell RNA-seq of developing mouse kidney and human kidney organoids reveals a roadmap for recreating the kidney

Recent advances in our capacity to differentiate human pluripotent stem cells to human kidney tissue are moving the field closer to novel approaches for renal replacement. Such protocols have relied upon our current understanding of the molecular basis of mammalian kidney morphogenesis. To date this has depended upon population based-profiling of non-homogenous cellular compartments. In order to improve our resolution of individual cell transcriptional profiles during kidney morphogenesis, we have performed 10x Chromium single cell RNA-seq on over 6000 cells from the E18.5 developing mouse kidney, as well as more than 7000 cells from human iPSC-derived kidney organoids. We identified 16 clusters of cells representing all major cell lineages in the E18.5 mouse kidney. The differentially expressed genes from individual murine clusters were then used to guide the classification of 16 cell clusters within human kidney organoids, revealing the presence of distinguishable stromal, endothelial, nephron, podocyte and nephron progenitor populations. Despite the congruence between developing mouse and human organoid, our analysis suggested limited nephron maturation and the presence of off target populations in human kidney organoids, including unidentified stromal populations and evidence of neural clusters. This may reflect unique human kidney populations, mixed cultures or aberrant differentiation in vitro. Analysis of clusters within the mouse data revealed novel insights into progenitor maintenance and cellular maturation in the major renal lineages and will serve as a roadmap to refine directed differentiation approaches in human iPSC-derived kidney organoids.

developmental biology↗

bioRxiv2017

Exploring the single-cell RNA-seq analysis landscape with the scRNA-tools database

As single-cell RNA-sequencing (scRNA-seq) datasets have become more widespread the number of tools designed to analyse these data has dramatically increased. Navigating the vast sea of tools now available is becoming increasingly challenging for researchers. In order to better facilitate selection of appropriate analysis tools we have created the scRNA-tools database (www.scRNA-tools.org) to catalogue and curate analysis tools as they become available. Our database collects a range of information on each scRNA-seq analysis tool and categorises them according to the analysis tasks they perform. Exploration of this database gives insights into the areas of rapid development of analysis methods for scRNA-seq data. We see that many tools perform tasks specific to scRNA-seq analysis, particularly clustering and ordering of cells. We also find that the scRNA-seq community embraces an open-source approach, with most tools available under open-source licenses and preprints being extensively used as a means to describe methods. The scRNA-tools database provides a valuable resource for researchers embarking on scRNA-seq analysis and records of the growth of the field over time.\n\nAuthor summaryIn recent years single-cell RNA-sequeing technologies have emerged that allow scientists to measure the activity of genes in thousands of individual cells simultaneously. This means we can start to look at what each cell in a sample is doing instead of considering an average across all cells in a sample, as was the case with older technologies. However, while access to this kind of data presents a wealth of opportunities it comes with a new set of challenges. Researchers across the world have developed new methods and software tools to make the most of these datasets but the field is moving at such a rapid pace it is difficult to keep up with what is currently available. To make this easier we have developed the scRNA-tools database and website (www.scRNA-tools.org). Our database catalogues analysis tools, recording the tasks they can be used for, where they can be downloaded from and the publications that describe how they work. By looking at this database we can see that developers have focued on methods specific to single-cell data and that they embrace an open-source approach with permissive licensing, sharing of code and preprint publications.

bioinformatics↗

bioRxiv2017

STRetch: detecting and discovering pathogenic short tandem repeats expansions

Short tandem repeat (STR) expansions have been identified as the causal DNA mutation in dozens of Mendelian diseases. Historically, pathogenic STR expansions could only be detected by single locus techniques, such as PCR and electrophoresis. The ability to use short read sequencing data to screen for STR expansions has the potential to reduce both the time and cost to reaching diagnosis and enable the discovery of new causal STR loci. Most existing tools detect STR variation within the read length, and so are unable to detect the majority of pathogenic expansions. Those tools that can detect large expansions are limited to a set of known disease loci and as yet no new disease causing STR expansions have been identified with high-throughput sequencing technologies.\n\nHere we address this by presenting STRetch, a new genome-wide method to detect STR expansions at all loci across the human genome. We demonstrate the use of STRetch for detecting pathogenic STR expansions in short-read whole genome sequencing data with a very low false discovery rate. We further demonstrate the application of STRetch to solve cases of patients with undiagnosed disease and apply STRetch to the analysis of 97 whole genomes to reveal variation at STR loci. STRetch assesses expansions at all STR loci in the genome and allows screening for novel disease-causing STRs.\n\nSTRetch is open source software, available from github.com/Oshlack/STRetch.

bioinformatics↗

bioRxiv2017

Splatter: Simulation Of Single-Cell RNA Sequencing Data

As single-cell RNA sequencing technologies have rapidly developed, so have analysis methods. Many methods have been tested, developed and validated using simulated datasets. Unfortunately, current simulations are often poorly documented, their similarity to real data is not demonstrated, or reproducible code is not available.\n\nHere we present the Splatter Bioconductor package for simple, reproducible and well-documented simulation of single-cell RNA-seq data. Splatter provides an interface to multiple simulation methods including Splat, our own simulation, based on a gamma-Poisson distribution. Splat can simulate single populations of cells, populations with multiple cell types or differentiation paths.

bioinformatics↗

bioRxiv2017

Gene Length And Detection Bias In Single Cell RNA Sequencing Protocols

Single cell RNA sequencing (scRNA-seq) has rapidly gained popularity for profiling transcriptomes of hundreds to thousands of single cells. This technology has led to the discovery of novel cell types and revealed insights into the development of complex tissues. However, many technical challenges need to be overcome during data generation. Due to minute amounts of starting material, samples undergo extensive amplification, increasing technical variability. A solution for mitigating amplification biases is to include Unique Molecular Identifiers (UMIs), which tag individual molecules. Transcript abundances are then estimated from the number of unique UMIs aligning to a specific gene and PCR duplicates resulting in copies of the UMI are not included in expression estimates. Here we investigate the effect of gene length bias in scRNA-Seq across a variety of datasets differing in terms of capture technology, library preparation, cell types and species. We find that scRNA-seq datasets that have been sequenced using a full-length transcript protocol exhibit gene length bias akin to bulk RNA-seq data. Specifically, shorter genes tend to have lower counts and a higher rate of dropout. In contrast, protocols that include UMIs do not exhibit gene length bias, and have a mostly uniform rate of dropout across genes of varying length. Across four different scRNA-Seq datasets profiling mouse embryonic stem cells (mESCs), we found the subset of genes that are only detected in the UMI datasets tended to be shorter, while the subset of genes detected only in the full-length datasets tended to be longer. We briefly discuss the role of these genes in the context of differential expression testing and GO analysis. In addition, despite clear differences between UMI and full-length transcript data, we illustrate that full-length and UMI data can be combined to reveal underlying biology influencing expression of mESCs.

bioinformatics↗