Biology subjects

Moses, L.

Publications and source records attributed to Moses, L..

11 recordsLinked to original sources

bioRxiv2026

Wayfarer: A multiscale framework for spatial analysis of tumor progression

Spatial biology spans multiple length scales, from intracellular organization to tissue-level architecture. Spatial transcriptomics captures this structure, yet most analyses operate at a single spatial resolution, implicitly assuming that biological organization is scale-consistent. In practice, spatial autocorrelation and co-localization are functions of scale, and conclusions can depend on arbitrary aggregation choices. Here we present Wayfarer, a multiscale framework for spatial -omics that tracks how spatial association metrics evolve across nested spatial aggregations, enabling statistical comparison of multiscale structure across biological conditions. Using Xenium data from lung adenocarcinoma (LUAD), we show that spatial patterns often co-exist at fine and coarse scales and that progression is accompanied by reproducible shifts in scale-response profiles. These include increased fine-scale coherence of ERBB2-high tumor regions and coarse-scale clustering of immune-associated markers that are not apparent at a single resolution. Wayfarer converts spatial aggregation from a confounder into a diagnostic signal and is implemented as an R package to be released through Bioconductor.

bioinformatics↗

bioRxiv2025

Online HD-tRNS over the right temporoparietal junction modulates social inference but not motor coordination

Social interactions are fundamental to human cognition, with the right temporoparietal junction (rTPJ) playing a key role in integrating motor coordination and social inference. Transcranial random noise stimulation (tRNS) is a promising technique for modulating cortical excitability, yet its effect on dynamic social processes remains unexplored. This study aimed to establish a causal link between rTPJ and real-time social interaction by applying high-definition transcranial random noise stimulation (HD-tRNS) during the Human Dynamic Clamp (HDC) paradigm. Specifically, we investigated whether online HD-tRNS modulates motor coordination and social inference when interacting with an adaptive virtual partner (VP). Eighty right-handed participants were assigned to one of two experiments: (Exp1) a block design with active HD-tRNS and sham blocks or (Exp2) a trial-by-trial design with intermixed active and sham conditions. Participants engaged in an interactive coordination task with a covert VP that could behave cooperatively or competitively. Kinematic data and self-reported measures of perceived cooperativeness and humanness were analyzed. HD-tRNS over the rTPJ did not influence motor coordination or task performance. However, in Exp1, participants receiving active stimulation progressively attributed lower cooperativeness and humanness to the competitive VP, suggesting improved detection of competitive intent. This effect was absent in Exp2, indicating that repeated stimulation was necessary for cumulative neuromodulatory effects. Online HD-tRNS over the rTPJ does not acutely impact motor coordination but progressively modulates social inference, particularly in competitive interactions. The findings highlight the rTPJs role in self-other distinction and suggest that repeated short neuromodulation sessions can shape social perception. Significant statementSocial interactions rely on distinguishing between cooperative and competitive behaviors, a process involving the right temporoparietal junction (rTPJ). Using high-definition transcranial random noise stimulation (HD-tRNS), we tested the rTPJs causal role in real-time social inference. While HD-tRNS did not affect motor coordination, repeated stimulation progressively reduced perceived cooperativeness and humanness of a competitive virtual partner, suggesting enhanced sensitivity to competitive intent. Our findings provide insights into social perception mechanisms and suggest HD-tRNS as a potential tool for studying and modulating social cognition, with implications for conditions affecting social inference, such as autism and schizophrenia.

neuroscience↗

bioRxiv2025

Geospatially informed representation of spatial genomics data with SpatialFeatureExperiment

SpatialFeatureExperiment is a Bioconductor package that leverages the versatility of Simple Features for spatial data analysis and SpatialExperiment for single-cell -omics to provide an expansive and convenient S4 class for working with spatial -omics data. SpatialFeatureExperiment can be used to store and analyze a variety of spatial -omics data types, including data from the Visium, Xenium, MERFISH, SeqFish, and Slide-seq platforms, bringing spatial operations to the SingleCellExperiment ecosystem.

bioinformatics↗

bioRxiv2024

Oxygen-releasing nanodroplets relieve intratumoral hypoxia in head and neck cancer spheroids.

AO_SCPLOWBSTRACTC_SCPLOWHypoxia in solid tumors, including head and neck cancer (HNC), contributes to treatment resistance, aggressive phenotypes, and poor clinical outcomes. Perfluorocarbon nanodroplets have emerged as promising oxygen carriers to alleviate tumor hypoxia. However, a thorough characterization of the hypoxia alleviation effects in terms of sustenance of oxygenated environments have not been thoroughly studied. In this study, we developed and characterized perfluoropentane nanodroplets (PFP NDs) for co-delivery of oxygen and the photoactivatable drug or photosensitizer benzoporphyrin derivative (BPD) to hypoxic HNC spheroids. The PFP NDs exhibited excellent stability, efficient oxygen loading/release, and biocompatibility. Using 3D multicellular tumor spheroids of FaDu and SCC9 HNC cells, we demonstrated the ability of oxygenated PFP NDs to penetrate the hypoxic core and alleviate hypoxia, as evidenced by reduced fluorescence of a hypoxia-sensing reagent and downregulation of hypoxia-inducible factors HIF-1 and HIF-2. BPD-loaded PFP NDs successfully delivered the photosensitizer into the spheroid core in a time-dependent manner. These findings highlight the potential of PFP NDs as a co-delivery platform to overcome hypoxia-mediated treatment resistance and improve therapy outcomes in HNC.

bioengineering↗

bioRxiv2024

The impact of package selection and versioning on single-cell RNA-seq analysis

Standard single-cell RNA-sequencing analysis (scRNA-seq) workflows consist of converting raw read data into cell-gene count matrices through sequence alignment, followed by analyses including filtering, highly variable gene selection, dimensionality reduction, clustering, and differential expression analysis. Seurat and Scanpy are the most widely-used packages implementing such workflows, and are generally thought to implement individual steps similarly. We investigate in detail the algorithms and methods underlying Seurat and Scanpy and find that there are, in fact, considerable differences in the outputs of Seurat and Scanpy. The extent of differences between the programs is approximately equivalent to the variability that would be introduced in benchmarking scRNA-seq datasets by sequencing less than 5% of the reads or analyzing less than 20% of the cell population. Additionally, distinct versions of Seurat and Scanpy can produce very different results, especially during parts of differential expression analysis. Our analysis highlights the need for users of scRNA-seq to carefully assess the tools on which they rely, and the importance of developers of scientific software to prioritize transparency, consistency, and reproducibility for their tools.

bioinformatics↗

bioRxiv2023

kallisto, bustools, and kb-python for quantifying bulk, single-cell, and single-nucleus RNA-seq

The term "RNA-seq" refers to a collection of assays based on sequencing experiments that involve quantifying RNA species from bulk tissue, from single cells, or from single nuclei. The kallisto, bustools, and kb-python programs are free, open-source software tools for performing this analysis that together can produce gene expression quantification from raw sequencing reads. The quantifications can be individualized for multiple cells, multiple samples, or both. Additionally, these tools allow gene expression values to be classified as originating from nascent RNA species or mature RNA species, making this workflow amenable to both cell-based and nucleus-based assays. This protocol describes in detail how to use kallisto and bustools in conjunction with a wrapper, kb-python, to preprocess RNA-seq data.

bioinformatics↗

bioRxiv2023

Voyager: exploratory single-cell genomics data analysis with geospatial statistics

Exploratory spatial data analysis (ESDA) can be a powerful approach to understanding single-cell genomics datasets, but it is not yet part of standard data analysis workflows. In particular, geospatial analyses, which have been developed and refined for decades, have yet to be fully adapted and applied to spatial single-cell analysis. We introduce the Voyager platform, which systematically brings the geospatial ESDA tradition to (spatial) -omics, with local, bivariate, and multivariate spatial methods not yet commonly applied to spatial -omics, united by a uniform user interface. Using Voyager, we showcase biological insights that can be derived with its methods, such as biologically relevant negative spatial autocorrelation. Underlying Voyager is the SpatialFeatureExperiment data structure, which combines Simple Feature with SingleCellExperiment and AnnData to represent and operate on geometries bundled with gene expression data. Voyager has comprehensive tutorials demonstrating ESDA built on GitHub Actions to ensure reproducibility and scalability, using data from popular commercial technologies. Voyager is implemented in both R/Bioconductor and Python/PyPI, and features compatibility tests to ensure that both implementations return consistent results.

bioinformatics↗

bioRxiv2023

Quantitative assessment of single-cell RNA-seq clustering with CONCORDEX

The rapid advancement of spatially resolved transcriptomics (SRT) technologies has facilitated exploration of how gene expression varies across tissues. However, identifying spatially variable genes remains challenging due to confounding variation introduced by the spatial distribution of cell types. We introduce a new approach to identifying spatial domains that are homogeneous with respect to cell-type composition that facilitates the decomposition of gene expression patterns by cell-type and spatial variation. Our method, called concordex, is efficient and effective across technological platforms and tissue types, and using several biological datasets we show that it can be used to identify genes with subtle variation patterns that are missed when considering only cell-type variation, or spatial variation, alone. The con-cordex tool is freely available at https://github.com/pachterlab/concordexR.

bioinformatics↗

bioRxiv2023

The Genetic Architecture of Dietary Iron Overload and Associated Pathology in Mice

Tissue iron overload is a frequent pathologic finding in multiple disease states including non-alcoholic fatty liver disease (NAFLD), neurodegenerative disorders, cardiomyopathy, diabetes, and some forms of cancer. The role of iron, as a cause or consequence of disease progression and observed phenotypic manifestations, remains controversial. In addition, the impact of genetic variation on iron overload related phenotypes is unclear, and the identification of genetic modifiers is incomplete. Here, we used the Hybrid Mouse Diversity Panel (HMDP), consisting of over 100 genetically distinct mouse strains optimized for genome-wide association studies and systems genetics, to characterize the genetic architecture of dietary iron overload and pathology. Dietary iron overload was induced by feeding male mice (114 strains, 6-7 mice per strain on average) a high iron diet for six weeks, and then tissues were collected at 10-11 weeks of age. Liver metal levels and gene expression were measured by ICP-MS/ICP-AES and RNASeq, and lipids were measured by colorimetric assays. FaST-LMM was used for genetic mapping, and Metascape, WGCNA, and Mergeomics were used for pathway, module, and key driver bioinformatics analyses. Mice on the high iron diet accumulated iron in the liver, with a 6.5 fold difference across strain means. The iron loaded diet also led to a spectrum of copper deficiency and anemia, with liver copper levels highly positively correlated with red blood cell count, hemoglobin, and hematocrit. Hepatic steatosis of various severity was observed histologically, with 52.5 fold variation in triglyceride levels across the strains. Liver triglyceride and iron mapped most significantly to an overlapping locus on chromosome 7 that has not been previously associated with either trait. Based on network modeling, significant key drivers for both iron and triglyceride accumulation are involved in cholesterol biosynthesis and oxidative stress management. To make the full data set accessible and useable by others, we have made our data and analyses available on a resource website. Author summaryThe response to a high iron diet is determined in part by genetic factors. We now report the responses to such a diet in a diverse set of inbred strains of mice, known as the Hybrid Mouse Diversity Panel, that enables high resolution genetic mapping and systems genetics analyses. The levels of iron in the liver varied about >5 fold across the strains, with genetic variation explaining up to 74% of the variation in liver iron. Pathologies included copper deficiency, anemia, and fatty liver, with liver triglycerides varying over 50 fold among the strains. Genetic mapping and network modeling identified significant genetic loci and pathways underlying the response to diet.

genetics↗

bioRxiv2021

Museum of Spatial Transcriptomics

The function of many biological systems, such as embryos, liver lobules, intestinal villi, and tumors depends on the spatial organization of their cells. In the past decade high-throughput technologies have been developed to quantify gene expression in space, and computational methods have been developed that leverage spatial gene expression data to identify genes with spatial patterns and to delineate neighborhoods within tissues. To assess the ability and potential of spatial gene expression technologies to drive biological discovery, we present a curated database of literature on spatial transcriptomics dating back to 1987, along with a thorough analysis of trends in the field such as usage of experimental techniques, species, tissues studied and computational approaches used. Our analysis places current methods in historical context, and we derive insights about the field that can guide current research strategies. A companion supplement offers a more detailed look at the technologies and methods analyzed: https://pachterlab.github.io/LP_2021/.

bioinformatics↗

bioRxiv2017

Impact of global change on future Ebola emergence and epidemic potential in Africa

Animal-borne or zoonotic human diseases (e.g., SARS, Rabies) represent major health and economic burdens throughout the world, disproportionately impacting poor communities. In 2013-2016, an outbreak of the Ebola virus disease (EVD), a zoonotic disease spread from animal reservoirs caused by the Zaire Ebola virus (EBOV), infected approximately 30,000 people, causing considerable negative social and economic impacts in an unexpected geographical location(Sierra Leone, Guinea, and Liberia). It is not known whether the spatial distribution of this outbreak and unprecedented severity was precipitated by environmental changes and, if so, which areas might be at risk in the future. To better address the major health and economic impacts of zoonotic diseases we develop a system-dynamics approach to capture the impact of future climate, land use and human population change on Ebola (EVD). We create future risk maps for affected areas and predict between a 1.75-3.2 fold increase in EVD outbreaks per year by 2070. While the best case future scenarios we test saw a reduction in the likelihood of epidemics, other future scenarios with high human population growth and low rates of socioeconomic development saw a fourfold increase in the risk of epidemics occurring and almost 50% increase in the risk of catastrophic epidemics. As well as helping to target where health infrastructure might be further developed or vaccines best deployed, our modelling framework can be used to target global interventions and forecast risk for many other zoonotic diseases.\n\nSignificance StatementDespite the severe health and economic impacts of outbreaks of diseases like SARS or Zika, there has been surprisingly little progress in predicting where and when human infectious disease outbreaks will occur next. By modelling the impacts of future climate, land use and human population change on one particular disease Ebola, we develop future risk maps for the affected areas and predict 1.7-3.2 times as many human Ebola outbreaks per year by 2070, and a 50% increase in the chance that these outbreaks will become epidemics. As well as helping to target where health infrastructure might be further developed or vaccines deployed, our approach can also be used to target actions and predict risk hotspots for many other infectious diseases.

epidemiology↗