Biology subjects

Macnair, W.

Publications and source records attributed to Macnair, W..

7 recordsLinked to original sources

bioRxiv2026

scprocess: a pipeline for processing, integrating and visualising atlas-scale single cell data

MotivationThe transition toward "atlas-scale" single cell research has resulted in datasets comprising millions of cells across hundreds of samples, creating significant challenges for data management, computational efficiency, and reproducibility. While numerous methods are available for individual steps in single cell data processing, the highly complex nature of the analysis makes it challenging to maintain a clear record of every tool and parameter used. This makes final results difficult to reproduce, highlighting the need for a unified workflow that integrates multiple steps into a cohesive framework. Resultsscprocess is a Snakemake pipeline designed to streamline and automate the complex steps involved in processing single cell RNA sequencing data. Specifically optimized for data generated using the 10x Genomics technology, it provides a comprehensive solution that transforms raw sequencing files into standardized outputs suitable for a variety of downstream tasks. The pipeline is built to support the analysis of datasets comprising multiple (e.g. 100+) samples via a simple CLI, allowing researchers to efficiently explore their datasets while ensuring reproducibility and scalability in their workflows. Availability and implementationscprocess can be installed via GitHub (https://github.com/marusakod/scprocess) under the MIT license. Documentation, including setup instructions and tutorials on example datasets is available at https://marusakod.github.io/scprocess/.

bioinformatics↗

bioRxiv2025

Unique transcriptional profiles of adult human immature neurons in healthy aging, Alzheimer's disease, and cognitive resilience

The existence and functional significance of immature neurons in the adult human brain, particularly in the context of neurodegenerative disorders, remain controversial. While rodent studies have highlighted active roles for adult-born immature neurons in the hippocampus under both healthy conditions and in Alzheimers disease (AD), evidence from the human brain is limited and lacks detailed molecular characterization. To address this gap, we performed single-nucleus RNA sequencing in aged healthy, AD and dementia-resilient human hippocampus to probe immature neuronal signatures and gene expression alterations associated with AD pathology and resilience. Employing a novel experimental and computational pipeline, we identified persistent populations of immature neurons across all donor groups, with transcriptional profiles distinct from both fetal counterparts and adult mature hippocampal neurons. These profiles were associated with juvenile cellular functions, suggesting that the presence of these immature neuronal populations per se may actively contribute to maintaining homeostasis within the aged human hippocampus, a role that may be disrupted in AD. In the resilient brain, immature neurons were involved in transcriptional programs and intercellular interactions associated with anti-inflammatory, neurotrophic, neuroprotective, myelinating, anti-apoptotic and anti-amyloidogenic signaling pathways, suggesting active roles for the immature cells in enhancing cognitive resilience in the presence of AD pathology. Our findings reveal novel, putative physiological roles for immature neurons in the healthy and resilient adult human brain, and offer a resource for probing new strategies with potential functional relevance in AD.

neuroscience↗

bioRxiv2024

Foamy microglia link oxylipins to disease progression in multiple sclerosis

Multiple sclerosis (MS) is a neuroinflammatory disease characterized by expanding demyelinating lesions, leading to severe and irreversible disability. The mechanisms driving lesion expansion, however, remain poorly understood. Here, using a multi-omics approach, we identified foamy microglia as primary contributors to the molecular profile of lesions and disease progression in secondary progressive MS. Lesions with foamy microglia are marked by the accumulation of cholesterol esters, bismonoacylglycerolphosphates (BMP), and oxylipins, along with high B-cell infiltration, increased levels of immunoglobulin G1, and elevated expression of Fc{gamma}- and complement receptors. Lesions with foamy GPNMB+-microglia display markers of enhanced phagocytosis, lipid metabolism, lysosomal dysfunction, and antigen presentation, but lack classical pro-inflammatory markers. Our data suggest that sustained phagocytosis of myelin overwhelms microglial endo-lysosomal capacity, leading to lipid droplet and oxylipin formation. This microglial phenotype may induce further recruitment of adaptive immune cells, axonal damage, drive lesion expansion and prevent remyelination. Monoacylglycerol lipase, involved in producing oxylipin precursors, was identified as a potential therapeutic target to disrupt this cycle and prevent chronic lesion expansion.

neuroscience↗

bioRxiv2022

Single nuclei RNAseq stratifies multiple sclerosis patients into three distinct white matter glia responses

The lack of understanding of the cellular and molecular basis of clinical and genetic heterogeneity in progressive multiple sclerosis (MS) has hindered the search for new effective therapies. Here, to address this gap, we analysed 632,000 single nuclei RNAseq profiles of 156 brain tissue samples, comprising white matter (WM) lesions, normal appearing WM, grey matter (GM) lesions and normal appearing GM from 54 MS patients and 26 controls. We observed the expected changes in overall neuronal and glial numbers previously described within the classical lesion subtypes. We found highly cell type-specific gene expression changes in MS tissue, with distinct differences between GM and WM areas, confirming different pathologies. However, surprisingly, we did not observe distinct gene expression signatures for the classical different WM lesion types, rather a continuum of change. This indicates that classical lesion characterization better reflects changes in cell abundance than changes in cell type gene expression, and indicates a global disease effect. Furthermore, the major biological determinants of variability in gene expression in MS WM samples relate to individual patient effects, rather than to lesion types or other metadata. We identify four subgroups of MS patients with distinct WM glial gene expression signatures and patterns of oligodendrocyte stress and/or maturation, suggestive of engagement of different pathological processes, with an additional more variable regenerative astrocyte signature. The discovery of these patterns, which were also found in an independent MS patient cohort, provides a framework to use molecular biomarkers to stratify patients for optimal therapeutic approaches for progressive MS, significantly advances our mechanistic understanding of progressive MS, and highlights the need for precision-medicine approaches to address heterogeneity among MS patients.

neuroscience↗

bioRxiv2021

SampleQC: robust multivariate, multi-celltype, multi-sample quality control for single cell data

Quality control (QC) is a critical component of single-cell RNA-seq (scRNA-seq) processing pipelines. Current approaches to QC implicitly assume that datasets are comprised of one celltype, potentially resulting in biased exclusion of rare celltypes. We introduce SampleQC, which robustly fits a Gaussian mixture model across multiple samples, and improves sensitivity and reduces bias compared to current approaches. We show via simulations that SampleQC is less susceptible to exclusion of rarer celltypes. We also demonstrate SampleQC on a complex real dataset (867k cells over 172 samples). SampleQC is general, is implemented in R, and could be applied to other data types.

bioinformatics↗

bioRxiv2019

psupertime: supervised pseudotime inference for single cell RNA-seq data with sequential labels

Single cell RNA-seq has been successfully combined with pseudotime inference methods to investigate biological processes which have sequential labels, such as time series studies of development and differentiation. Pseudotime methods developed to date ignore the labels, and where there is substantial variation in the data not associated with the labels (such as cell cycle variation or batch effects), they can fail to find relevant genes. We introduce psupertime, a supervised pseudotime approach which outperforms benchmark pseudotime methods by explicitly using the sequential labels as input. psupertime uses a simple, regression-based model, which by acknowledging the labels assures that genes relevant to the process, rather than to major drivers of variation, are found. psupertime is applicable to the wide range of single cell RNA-seq datasets with sequential labels, derived from either experimental design or user-selected cell cluster sequences, and provides a tool for targeted identification of genes regulated along biological processes.

genomics↗

bioRxiv2017

Tree-ensemble analysis tests for presence of multifurcations in single cell data

We introduce TreeTop, an algorithm for single-cell data analysis to identify and assess statistical significance of branch points in biological processes with possibly multi-level branching hierarchies. We demonstrate branch point identification for processes with varying topologies, including T cell maturation, B cell differentiation and hematopoiesis. Our analyses are consistent with recent experimental studies suggesting a shallow hierarchy of differentiation events in hematopoiesis, rather than the classical multi-level hierarchy.

systems biology↗