Biology subjects

Hicks, S. C.

Publications and source records attributed to Hicks, S. C..

At least 19 recordsLinked to original sources

bioRxiv2026

Transcript quantification using context-aware MAPQ modeling

Long-read RNA-sequencing enables accurate transcript-level quantification, but prior static filtering of MAPping Quality (MAPQ) can ignore transcriptome-intrinsic ambiguity, and discarding reads on this basis distorts abundance estimates. We present CALMeR (Context-Aware Long-read MAPQ-based Rescuer), which conditions MAPQ filtering on transcript properties rather than a fixed cutoff. CALMeR fits a zero-one inflated beta regression on various transcript and read-level features to model the expected MAPQ distribution for each alignment. Reads whose MAPQ fits this context, such as low-MAPQ reads from multi-isoform genes and paralog families are retained; reads with anomalously low MAPQ are removed as likely artifacts. Retained reads can be summed per transcript or passed to any downstream quantifier. Compared with a MAPQ [≥] 1 filter and expectation-maximization-based transcript quantification methods, CALMeR recovers more spike-in transcripts and better matches known spike-in concentrations. CALMeR is available at https://github.com/sparthib/CALMeR.

bioinformatics↗

bioRxiv2026

Overlapping yet distinct spatiomolecular profiles across mood disorders and biological sex in the human prefrontal cortex

Major depressive disorder (MDD) and bipolar disorder (BD) exhibit both overlapping and distinct clinical features, including sex differences in prevalence and symptomatology, but how these similarities and differences are reflected at the molecular and cellular level in the human brain remains poorly understood. Here, we applied spatial transcriptomics to the human dorsolateral prefrontal cortex from individuals with MDD, BD, and neurotypical controls to resolve molecular programs associated with diagnosis and biological sex. Our analysis revealed sex- and diagnosis-specific differences across white matter, the neurovascular unit (NVU), and neuronal compartments. Within white matter, we identified female-specific survival-response programs shared in both MDD and BD, and divergent myelin-related gene expression patterns across disorders (CLDN11, CNDP1). Within the NVU, transcriptional evidence of impaired vascular integrity was accompanied by astrocytic response profiles in females with MDD and males with BD, with inflammatory responses further evident in females with MDD (IFITM3, CEBPD, IL1R1). In donors with BD of both sexes and in females with MDD, inhibitory neurons exhibited impaired neuropeptide expression (SST, CORT, CRH). Integration with donor genotype information highlighted that genetic risk for mood disorders influences MAPK3 expression, implicating altered ERK signaling, which was further supported by disorder-associated changes in Ras/MEK/ERK pathway components in neurons (MAPK3, DUSP6, RASD1, ELK1). Together, these findings reveal coordinated, cell type- and spatial domain-specific molecular programs that distinguish shared and disorder-specific transcriptomic changes across mood disorders in a sex-dependent manner.

neuroscience↗

bioRxiv2026

ALBIS: simulation of multi-resolution and multi-dimensional spatial transcriptomics data

The rapid adoption of spatial transcriptomics (ST) technologies has been accompanied by substantial growth in computational methods, creating an increasing need for systematic and reliable benchmarking, particularly as analyses increasingly extend from individual tissue sections to three-dimensional (3D) tissue reconstruction. Existing benchmarks often rely on a small and similar set of experimentally-derived datasets because they offer a reliable ground truth label, which is hard to find in practice. Simulated or synthetic data provide an alternative, yet most existing simulators either adapt single-cell RNA-sequencing models to spatial coordinates or generate individual sections in a two-dimensional (2D) space at predefined resolutions. More recent approaches can simulate 2D and 3D tissues, but generally do not derive multiple spatial resolutions from the same molecular realization. Here, we introduce ALBIS (A muLti-resolution Biological In-silico Simulator for spatial transcriptomics), a statistically grounded and highly customizable framework that generates synthetic ST data from an in silico 3D tissue. ALBIS defines spatially structured cell populations with corresponding markers for gene expression, then initializes individual mRNA molecules in 3D space to create cell-level information with a ground truth 3D coordinate for each molecule and cell. From this common realization, ALBIS cuts the 3D tissue into multiple 2D tissue sections with cell-, bin-, and spot-level measurements, enabling direct comparison across spatial resolutions. Properties of the simulated data, including spatial domains, cell type composition, gene-expression effects, batch effects, measurement resolution, and tissue scale, can be systematically varied, enabling rigorous stress-testing of computational methods under known and increasingly challenging conditions.

bioinformatics↗

bioRxiv2026

Three dimensional reconstruction of the human nucleus accumbens reveals topographic organization and molecular heterogeneity of D1-islands across the anterior posterior axis

Spatial transcriptomics has transformed molecular characterization of the human brain, but most studies profile individual two-dimensional tissue sections and therefore cannot capture molecular organization across 3D neuroanatomical axes. Here, we developed a spatial genomics framework to reconstruct the 3D cellular architecture of the human nucleus accumbens (NAc) by densely sampling and aligning serial sections across its anterior-posterior (AP) extent. Combining single-cell Xenium profiling with near-transcriptome-wide VisiumHD, we mapped cell type composition and topography across AP, mediolateral and dorsoventral axes. Neuronal populations varied in relative abundance across the AP axis while maintaining characteristic spatial topographies. These differences were particularly pronounced among D1-islands, a topographically organized structure within the NAc in which transcriptionally distinct subpopulations of DRD1-expressing medium spiny neurons (MSNs) aggregate into discrete cellular formations. D1-islands comprised two molecularly distinct spatial domains with conserved counterparts across species. Both domains varied in relative abundance across the AP axis, but demonstrated distinct 3D distribution; one was present throughout the NAc, whereas the other, corresponding to islands of Calleja (ICj), was more restricted to intermediate-to-posterior levels. Local spatial analyses revealed region- and AP-dependent cellular relationships, including segregation of the two island populations despite their anatomical proximity. Together, these findings establish a framework for three dimensional spatial genomics of human brain tissue and reveal molecular and cellular organization not captured by individual tissue sections.

neuroscience↗

bioRxiv2026

Interpretable spherical geometry of single-cell state transitions from dominant principal components

Single-cell RNA-seq atlases are commonly explored with nonlinear embeddings that preserve neighborhoods but provide limited coordinate-level interpretation. We asked whether projecting the dominant principal components (PCs) of single-cell gene expression onto a unit sphere would yield an interpretable coordinate system. SPHERE-PCA L2-normalizes the first three PC coordinates, aligns a biologically defined root to the north pole, and represents each cell by three coordinates: root-aligned geodesic distance ({theta}), angular position ({phi}), and pre-projection radial magnitude (r). Across developmental and disease-associated datasets, this representation reveals structured spherical geometry, ranging from near-great-circle trajectories to multi-arc manifolds. In developmental atlases, root-aligned geodesic distance increases as CytoTRACE-inferred stemness decreases, while gene-coordinate analyses separate programs associated with angular position from those associated with radial magnitude. Fixed-loading perturbations decompose each gene's effect on cell position into progression, branch- or state-position, and radial activity components. SPHERE-PCA therefore provides a deterministic, loading-preserving coordinate framework for interpreting dominant transcriptomic variance and establishing a transparent geometric coordinate framework for perturbation analysis and virtual-cell models.

systems biology↗

bioRxiv2026

Pretrained gene representations transfer mean expression more broadly than spatial patterns in virtual spatial transcriptomics

Models that combine tissue images with pretrained gene representations aim to predict spatial expression for genes not used to fit the downstream predictor. Yet success on held-out genes can reflect two capabilities: estimating a gene's mean expression across tissue locations and recovering its spatial variation. Across four cohorts spanning three human brain regions and HER2-positive breast cancer, we evaluated held-out genes in held-out individuals and separated these components. For spatial predictors using fixed gene representations from Decima or scGPT, reductions in gene-mean error accounted for more than 91% of the reduction in mean squared error relative to matched random vectors. Independently fitted mean-only models using the same representations but no tissue images retained 90-99% of the corresponding gain in full-matrix correlation. Spatial gains were smaller on average, increased with expression variation in training tissue and differed across cohorts and representations. Across these settings, pretrained gene representations broadly transferred mean expression but selectively improved spatial recovery, showing that cross-gene generalization in virtual spatial transcriptomics is not a single capability.

bioinformatics↗

bioRxiv2026

SCG: Spatially Co-Expressed Gene Identification through Spatially Varying Networks

Spatial transcriptomics has enabled the advancement of gene expression analysis, yet spatial co-expression remains understudied. We introduce spatial covariance regression (SCR), a scalable Bayesian factor-model-based framework for estimation of spatially-resolved gene co-expression networks across tissue domains. These networks provide the spatial map of gene-gene correlations and enable the identification of spatially co-expressed genes (SCGs), which serve as potential prognostic biomarkers and therapeutic targets.

genomics↗

bioRxiv2026

Multiscale spatial transcriptomics resolves the cellular and molecular architecture of the human amygdala

The amygdala is a central hub for emotional learning that is dysregulated across numerous psychiatric disorders, yet its molecular architecture in humans remains poorly defined. We here present a spatial transcriptomic atlas of the human amygdala from nine neurotypical donors. Integrating Visium, Xenium, and VisiumHD technologies, we profiled over 1.1 million cells across 13 spatial domains spanning the basolateral complex, central, medial, and cortical nuclei, as well as the intercalated islands, each defined by distinct marker genes and gene co-expression networks. By integrating snRNA-seq reference atlases, we found that each subdivision of the basolateral subnucleus contains distinct excitatory neuron classes. We additionally found three transcriptionally distinct populations of intercalated neuron types, two of which form spatially distinct islands neighboring the basolateral complex, and were able to refine cell type diversity across the central nucleus and related amygdalostriatal transition areas. Finally, we localized psychiatric genetic risk across the amygdala, revealing both broad neuronal enrichment and subnuclear specificity. Together, this atlas provides a foundational resource and framework for accelerating cross-species comparisons and human disease-focused investigations.

neuroscience↗

bioRxiv2026

Mind the Alignment Gap: A Spatial Transcriptomics Benchmark for Scientific Coding Agents

AO_SCPLOWBSTRACTC_SCPLOWScientific coding agents are difficult to benchmark because many research tasks require executable work yet produce ambiguous or hard-to-verify outputs. Because benchmark construction requires substantial time and resources, automation offers a path to accelerating methods evaluation. We introduce an interactive framework for constructing scientificagent benchmarks from peer-reviewed papers and diagnosing agent behavior through trace inspection. We apply it as a case study in spatial transcriptomics alignment, constructing 40 tasks from SABench in which agents submit coordinate tables aligning pairs of two-dimensional tissue slices. Across 120 runs and three configurations, we compare a basic prompt, a package-aware prompt, and a full prompt with a prebuilt virtual environment. In this setting, richer package and environment context increased tool exploration but reduced the mean alignment score relative to the basic prompt (0.36 vs. 0.43; 95% CI, [-0.11,-0.03]). Trace inspection showed that added scaffolding often induced unnecessary transformations, fragile package-first workflows, and infrastructure failures. These results illustrate how specialized tooling can alter agent behavior and why scientific-agent benchmarks should evaluate agent traces and the workflows that produce them in addition to the final outputs.

bioinformatics↗

bioRxiv2026

Perturbation of genes linked to common schizophrenia risk variants identifies cilia programs

Schizophrenia (SCZ) is a common psychiatric disorder characterized by psychosis, emotional withdrawal, and cognitive deficits. Most SCZ risk variants reside in non-coding regions of the genome and are thought to influence disease risk by modulating gene regulation. However, the target genes, biological pathways, and cell types through which these variants exert their effects remain poorly understood. To address this gap, we employed in vivo CRISPR droplet sequencing (CROP-seq) in the postnatal mouse neocortex. We perturbed 12 SCZ risk genes previously linked to functionally validated risk variants, followed by single-cell RNA sequencing. We identified 3,031 differentially expressed genes (DEGs) that recapitulate transcriptional alterations observed in postmortem SCZ brains. Integrative analysis using DEG clustering, factor analysis, and gene regulatory network inference uncovered convergent gene programs with distinct biological functions and cell type specificity. Notably, ciliary transcriptional programs consistently emerged across analytical frameworks. The primary cilium is a neurocircuit modulating signaling organelle in neurons and glia that remains understudied in SCZ. Perturbation of key contributors to the ciliary transcriptional programs led to significant alterations in ciliary structure, suggesting that SCZ genetic risk factors may influence how brain cells sense and transduce extracellular signals through synapse-independent mechanisms. Together, this study provides the first in vivo characterization of the functional consequence of common variant architecture in SCZ and implicates ciliary dysfunction as a convergent downstream mechanism.

neuroscience↗

bioRxiv2026

Spatially-resolved integration of microglia morphological diversity and gene expression using Visium with protein co-detection

Microglia exhibit a dynamic range of morphologies that actively shift in response to local environmental cues. There has been a concerted effort as a field to move away from dualistic characterization of all microglia as resting or activated, which is often described in terms of their morphological differences alone, towards more integrated definitions of microglial states based on multiple data types. Recent advances in spatially-resolved transcriptomics offer new ways to understand microglial gene expression in situ. Here, we related microglia morphology to gene expression within a published, spatially-resolved proteogenomics (Visium-SPG) dataset of the human dorsolateral prefrontal cortex as proof of principle to combine the contributions of microglia morphology to regional transcriptomics differences in the brain. The published dataset uniquely combined spatial transcriptomics with immunofluorescent staining for four major brain cell types, including microglia, which enabled us to link microglial morphologies to gene expression in a spatially-resolved manner. Using a computational toolset, MicrogliaMorphology and MicrogliaMorphologyR, we classified individual microglia into morphological subtypes, assigned microglia to spots, and integrated these data with transcriptomic profiles for each Visium-SPG spot. We applied non-negative matrix factorization to account for contributions from multiple cell types, morphological features, and regional context in downstream differential expression modeling. Our approach offers a methodological framework for investigating the relationship between microglial morphology and gene expression in larger cohorts and within disease contexts where changes in microglia morphology are anticipated. Author summaryRecent advances in spatially-resolved transcriptomics and the development of methods to deconvolve cell type signals has offered new ways to understand microglial gene expression in various tissue contexts. In this work, we related microglia morphologies to gene expression within a published spatial transcriptomics dataset of the human dorsolateral prefrontal cortex to assess whether morphologically different microglia are transcriptomically different in the healthy human brain. This dataset uniquely combines spatial transcriptomics with immunofluorescent staining for four major brain cell types, including microglia, enabling the linkage of microglial morphologies to gene expression in a spatially-resolved manner. Using a computational toolset for microglia morphology analysis, we classified individual microglia into morphological subtypes and integrated these data with spatial transcriptomic profiles while accounting for contributions from multiple cell types, morphological features, and regional context. This allowed for the identification of microglia morphology-related genes that are differentially expressed between microglial subtypes and relevant to human brain disorders. Findings together provide proof of principle for exploring the relationship between microglial morphology and gene expression in larger cohorts and diverse experimental contexts.

neuroscience↗

bioRxiv2026

SpatialArtifacts: a computational framework for tissue artifact detection in spatial transcriptomics data

Spatial transcriptomics data are frequently compromised by technical artifacts, such as dry patches, tissue lifting, and uneven reagent coverage, which manifests as regions with low UMI counts, in particular at tissue borders. It can often be challenging to identify these regions using existing quality control methods. Here, we present SpatialArtifacts, a framework that combines median absolute deviation (MAD)-based outlier detection with mathematical morphology operations to identify and classify spatially contiguous tissue artifacts. Focal operations including 3x3 fill, 5x5 outline, and star-pattern connectivity link low-quality spots while preserving true biological domains. We use a hierarchical classification system to distinguish edge versus interior artifacts and large versus small regions, enabling downstream removal or targeted manual review. We demonstrate the performance of our method in human hippocampus, dorsolateral prefrontal cortex, and colorectal cancer tissues using 10x Genomics Visium and VisiumHD platforms. Our SpatialArtifacts package is freely available on Bioconductor at https://bioconductor.org/packages/SpatialArtifacts and on PyPI at https://pypi.org/project/spatial-artifacts/.

bioinformatics↗

bioRxiv2026

Mapping spatially organized molecular and genetic signatures of schizophrenia across multiple scales in human prefrontal cortex

The dorsolateral prefrontal cortex (dlPFC) is central to cognitive dysfunction in schizophrenia (SCZ), yet how molecular changes are organized across cortex remains unclear. Here, we applied complementary spatial transcriptomic approaches spanning laminar domains, microenvironments, and cell types in postmortem human dlPFC. At the laminar level, SCZ-associated transcriptional changes were strongest in glia-enriched domains (layer 1/meninges and white matter), including down-regulation of microglia-associated genes, whereas genetic risk localized to neuronal-rich gray matter. We next analyzed SCZ-linked microenvironments including neuropil, neuronal, perineuronal net, and vascular compartments. Within these, neuronal and synaptic transcriptional changes were most prominent in neuropil, showing down-regulation of activity-dependent synaptic genes and inhibitory neuron markers. At the cellular level, these signals reflected intrinsic alterations localized to cell types. Across analyses, patterns converged on altered BDNF-TrkB signaling and inhibitory circuit dysfunction. Together, our findings highlight spatial scale as a key determinant in resolving neuronal and non-neuronal aspects of SCZ-associated biology.

neuroscience↗

bioRxiv2026

Deconvolved tumor adipocyte proportions and high grade serous ovarian carcinoma survival

BackgroundSingle-cell-based analyses of high-grade serous ovarian carcinoma (HGSOC) survival have largely ignored adipocytes, which are fragile and under-represented in single-cell references. Adipocytes are known active components of the tumor microenvironment in many cancers, and HGSOC tumors frequently metastasize to the omentum, a lining of adipose tissue. MethodsWe created a composite reference that combines single-nucleus adipose profiles with published HGSOC single-cell data to deconvolve 588 bulk RNA-seq tumours from the Schildkraut cohorts. We used stage-stratified Cox models to quantify the association between intratumoural adipocyte fractions and overall survival while adjusting for age, body mass index (BMI), race, and residual disease. We also evaluated associations with deconvolved immune, stromal, and epithelial cell groups. ResultsA 10% increase in estimated tumor adipocyte content was associated with a 41% increase in the hazard of death (HR = 1.41, 95% CI 1.18-1.70, p = 0.0002) after adjusting for age, BMI and race (n=566). A 10% increase in immune cell proportion was associated with favorable survival (HR = 0.82, 95% CI 0.69-0.97, p = 0.024). Stromal and epithelial macro-fractions were not associated with survival. Associations with adipocyte and immune cell type proportions were unchanged in models additionally controlling the other cell type proportions. Results were similar after additionally adjusting for residual disease after debulking surgery. ConclusionsAdipocytes may be a tumor-intrinsic factor associated with adverse outcomes in HGSOC. Quantifying adipocyte burden using bulk RNA-seq could enhance risk stratification and guide the development of adipocyte-targeted therapies.

genomics↗

bioRxiv2025

spaTransfer: transfer learning for single-cell and spatialtranscriptomics data using non-negative matrix factorization

Recent advances in spatially-resolved transcriptomics have enabled profiling of gene expression in a spatial context, which has led to the generation of large-scale single-cell and spatial atlases with computationally-derived cell type or spatial domain labels. An increasingly important task with these data has become the transfer of cell type or spatial domain annotations from a given reference (or source) atlas into a new target tissue or sample. The reference and target datasets could be at different resolutions or measured on different experimental platforms. Here, we present a method to perform cross-platform transfer learning that takes as input single-cell or spatial domain labels from a reference atlas or dataset and transfers the labels to a target dataset at a similar or different resolution. Specifically, we use non-negative matrix factorization (NMF) on the reference data to identify factors associated with labels of interest and project these factors into the target dataset to label each new observation. We use a multinomial model with the factors as covariates and labels as the response to predict labels in the target dataset. In contrast to existing approaches, the advantage of our approach is interpretability, without compromising on accuracy. We demonstrate the performance of the method in two human brain tissues and show that our model identifies spatially coherent domains in the target datasets with concordance of marker gene expression. We implement spaTransfer in open-source software as an R package (github.com/cindyfang70/spaTransfer).

bioinformatics↗

bioRxiv2025

BatchSVG: identifying batch-biased genes in the application of spatially variable gene detection

1A standard task in the analysis of spatially resolved transcriptomics data is to identify spatially variable genes (SVGs). This is most commonly done within one tissue section at a time because the spatial relationships between the tissue sections are typically unknown. However, large-scale spatial atlases are being generated, for example across hundreds of donors, where the goal is to identify a common set of SVGs to use for downstream analyses. One challenge is how to identify and remove SVGs that are associated with a known bias or technical artifact, such as the slide or capture area, which can lead to poor performance in downstream analyses, such as spatial domain detection. Here, we introduce BatchSVG, a tool to identify batch-biased genes in the application of SVG detection. Our approach compares the rank of per-gene deviance under a binomial model (i) with and (ii) without including a covariate in the model that is associated with the known bias or technical artifact. If the rank of a gene changes significantly between these, then we infer that this gene is likely associated with the bias or technical artifact and should be removed from the downstream analysis. We consider two SRT datasets and show how our model can improve the results of downstream analysis.

genomics↗

bioRxiv2025

Spotsweeper-py: spatially-aware quality control metrics for spatial omics data in the Python ecosystem

Spatially-resolved transcriptomics (SRT) generates large and heterogeneous datasets where global (tissue-wide) quality control (QC) metrics often over-aggressively remove biologically meaningful regions or miss localized artifacts. Recently, spatially-aware QC metrics have been introduced in SpotSweeper, but this is limited to the R programming language, which makes it challenging to use these metrics within the Python/scverse ecosystem. Here, we present SpotSweeper-py, a Python equivalent package of SpotSweeper that computes neighborhood-aware z-scores for standard QC metrics such as total counts, log total counts, number of detected genes, and percentage of mitochondrial counts. We demonstrate the performance and usability of SpotSweeper-py on two public datasets from the 10x Genomics Visium and VisiumHD platforms. This implementation of local spatially-aware QC metrics enables direct integration with Python/scverse ecosystem, reduces false positives from global quality control while preserving tissue-specific architecture. Plotting utilities are also included for quick visualizations of flagged outliers. By making robust local QC accessible in Python, SpotSweeper-py strengthens the reliability of pipelines for analyzing SRT data. The open-source software is available on PyPI (https://pypi.org/project/spotsweeper).

bioinformatics↗

bioRxiv2025

ClustSIGNAL identifies cell types and subtypes using an adaptive smoothing approach for scalable spatial clustering

Unsupervised clustering of spatial transcriptomics data is challenging due to sparsity and complex tissue structures. We introduce ClustSIGNAL spatial clustering method that employs neighbourhood complexity-based expression smoothing. ClustSIGNAL uses entropy to measure neighbourhood compositions and generate cell-specific weights that control the effective smoothing radius within neighbourhoods. Simulation analysis demonstrates its robustness to segmentation errors and sparsity. Benchmarking across diverse SRT datasets shows its ability to perform scalable, multi-sample clustering of atlas-level data. By stabilising expression in homogeneous regions and preserving distinct expression in heterogeneous regions, ClustSIGNAL avoids over-smoothing and identifies biologically meaningful spatially-informed cell states. ClustSIGNAL Bioconductor package: bioconductor.org/packages/clustSIGNAL.

bioinformatics↗