Biology subjects

Coleman, K.

Publications and source records attributed to Coleman, K..

14 recordsLinked to original sources

bioRxiv2026

Pan-pediatric brain tumor microRNA networks define immune microenvironment states and responses to CAR T-cell therapy

Pediatric central nervous system (CNS) tumors remain a leading cause of cancer-related mortality in children, yet molecular programs that shape their immune microenvironments and response to immunotherapy remain poorly defined. Because microRNAs (miRNAs) coordinately regulate gene expression across multiple pathways, we hypothesized that miRNA programs may capture broader immune states than individual immune-regulatory molecules. We integrated small RNA and bulk transcriptomic profiles from diffuse midline glioma, medulloblastoma, ependymoma, and low-grade glioma to identify miRNA programs associated with tumor immune states. Across tumor histologies, dysregulated miRNAs were associated with T-cell, natural killer cell, antigen-presentation, and myeloid programs. Diffuse midline glioma exhibited a distinct miRNA program associated with reduced lymphocyte signatures and increased suppressive myeloid features. Longitudinal profiling of immunocompetent glioma models during B7-H3 CAR T-cell therapy revealed dynamic remodeling of miRNA and immune transcriptional programs. Human tumor-associated miRNAs were significantly enriched among miRNAs associated with CAR T response in vivo. Functional interrogation of miR-149 demonstrated enhanced CAR T-cell expansion and sustained antitumor activity during repeated antigen stimulation. Together, these data define miRNA programs associated with immune states across pediatric CNS tumors and identify miR-149 as a candidate regulator of CAR T-cell function, providing a genomic framework for developing miRNA-directed strategies to improve CAR T-cell therapy.

cancer biology↗

bioRxiv2026

Tumor-specific Kinase Motif Enrichment Analysis Identifies Personalized Therapeutic Cancer Targets

Gastroenteropancreatic neuroendocrine tumors (GEP-NETs) are an uncommon and poorly understood malignancy with low mutational burden, lacking well-defined oncogenic drivers. GEP-NET mortality frequently results from extensive hepatic metastases. Accordingly, we interrogated phosphoproteomic data from GEP-NET liver metastases and patient-matched uninvolved liver to identify tumor-specific signaling and targetable tumor vulnerabilities using Kinase Motif Enrichment Analysis (KMEA), a new tool leveraging the recent Kinase Library compendium of the substrate motif specificity for nearly the entire human kinome. KMEA identified patient tumor-specific upregulation of mTOR or casein kinase 2 (CK2) activity that would be undiscoverable by standard personalized genomic and transcriptomic approaches. Striking concordance was observed between KMEA predictions for specific tumors, and their sensitivity to inhibitors of mTOR or CK2 using patient tumor-derived organoids. These findings reveal potential clinically-actionable protein kinases hyperactivated in GEP-NETs, and more broadly indicate a general method for personalized cancer treatment using phosphoproteomics and KMEA-derived kinase activity signatures.

cancer biology↗

bioRxiv2026

SpoIVA contributes to efficient engulfment through a cytoskeletal-like mechanism during Bacillus subtilis sporulation

During endospore (spore) development in bacteria, polar cell division generates two transcriptionally distinct cellular compartments, the mother cell and future spore (forespore). Signalling between these cells leads to sequential and compartmentalized transcription, along with key morphogenetics events, including the phagocytic-like process of engulfment and the recruitment of coat proteins to the engulfing membrane. The SpoIVA ATPase is an essential sporulation protein that assembles into static filaments at the forespore surface during engulfment, where it functions as the basement layer for coat assembly. Here, using Bacillus subtilis, we reveal an additional role for SpoIVA during engulfment. Cytological analysis of a spoIVA null mutant ({Delta}spoIVA) revealed engulfment defects such as septal membrane bulges and asymmetric membrane migration, similar to those typically associated with impaired peptidoglycan remodelling during engulfment. Engulfment defects were exacerbated when {Delta}spoIVA was combined mutants known to impact engulfment progression and efficiency. Importantly, a spoIVA mutant (K30A) impaired for ATP hydrolysis and filament formation in vitro but partially functional for coat assembly in vivo, closely phenocopies the spoIVA null mutant engulfment defects. Based on these data, we propose a model whereby SpoIVA polymerisation at the spore surface, independently of coat assembly, plays a mechanical and structural role during engulfment, akin to the cytoskeletal proteins that drive phagocytosis in eukaryotic cells. IMPORTANCEEndospore formation relies on membrane remodeling to generate highly resistant dormant spores in human, animal and insect pathogens. During engulfment, the mother-cell membrane migrates around the forespore in a phagocytic-like process while the spore coat is simultaneously assembled. SpoIVA is widely recognized as the ATPase that polymerizes at the forespore surface to nucleate assembly of the multilayered spore coat. Here, we demonstrate that SpoIVA also performs a distinct function during engulfment that is independent of its role in coat formation. Cells lacking SpoIVA, or carrying a polymerization-defective SpoIVA variant, exhibit membrane deformations characteristic of impaired engulfment. We propose that SpoIVA polymers provide structural and mechanical support for membrane migration, analogous to the role of cytoskeletal systems during eukaryotic phagocytosis. These findings reveal an unexpected function for SpoIVA and demonstrate that bacterial protein polymers can provide mechanical support for membrane remodeling during complex developmental processes, analogous to cytoskeletal systems in eukaryotic cells

microbiology↗

bioRxiv2026

erm-1 mRNA and ERM-1 protein co-translationally localize to the plasma membrane through a microtubule- and BMK-1-dependent pathway

The Ezrin, Radixin, and Moesin (ERM) family of proteins anchors the actin cytoskeleton to the plasma membrane for the purpose of either stabilizing or altering cell shape. In Caenorhabditis elegans, ERM-1, is essential for cell polarity, signaling, intestine development, and larval viability. Interestingly, ERM-1 proteins are produced by erm-1 mRNA transcripts that concentrate at the plasma membrane in embryos. The localization of erm-1 mRNA to the plasma membrane occurs in a 3UTR-independent, translation-dependent manner, directed by the PH-subdomain within ERM-1s N-terminal FERM domain. This has led to the model that erm-1 mRNA, its associated ribosome, and its emerging nascent peptide are all transported together to the plasma membrane as a complex. Here, we characterize the transport mechanism. Using a microscopy approach, we observed that the localizations of erm-1 mRNA and ERM-1 protein to the plasma membrane were disrupted by nocodazole treatment, illustrating a microtubule role. Furthermore, erm-1 mRNA and ERM-1 protein localized to the plasma membrane independently of myosin and dynein motors, but dependent on the kinesin bmk-1 (bmk-1), a plus-end-directed, Kinesin-5 family motor protein. Loss of bmk-1 did not reduce the total number of erm-1 mRNA molecules in the cell, arguing against a diffusion- and protection-based mechanism of mRNA localization. Together, these findings suggest that erm-1 mRNA is localized via an active transport pathway mediated by a plus-end-directed kinesin adapter. Interestingly, loss of bmk-1 led to diffuse localization of ERM-1 protein along the plasma membrane and reduced ERM-1 protein levels at the site of abscission, the midbody, and the midbody remnant. This suggests that ERM-1 local translation at the plasma membrane is critical for its proteins ultimate spatial patterning in the cell.

cell biology↗

bioRxiv2026

Reconstructing multi-scale tissue spatial architecture from single-cell RNA-seq with REMAP

Understanding spatial organization of cells is critical for deciphering tissue function and disease. Single-cell RNA-sequencing (scRNA-seq) profiles transcriptomes at scale but loses spatial context, while spatial transcriptomics (ST) preserves spatial information but is constrained by cost and gene coverage. Here, we present REMAP, a deep learning framework that integrates gene expression with neighborhood-level gene-gene covariance to reconstruct multi-scale spatial organization of scRNA-seq data using one or multiple ST references. Across 2D and 3D mouse brain, human fetal cortex, and seven human cancer types, REMAP consistently outperformed existing approaches. Applied to a human multiple sclerosis atlas, REMAP resolved microglial neighborhood heterogeneity, and identified a rare pro-inflammatory microglia-astrocyte subpopulation. Across diverse cancers, REMAP recovered conserved spatially-defined cancer-associated fibroblast subtypes with known prognostic significance. By transforming cost-efficient single-cell datasets into spatially interpretable tissue maps, REMAP enables spatial hypothesis generation, microenvironment discovery, and population-scale inference of conserved and perturbed architectural principles in human disease.

genomics↗

bioRxiv2025

The SPN-4 Rbfox RNA-binding protein selects maternal mRNAs for CCR4-NOT-dependent clearance in early Caenorhabditis elegans embryos

The elimination of maternal mRNAs is an essential feature of the maternal-to-zygotic transition. We report an essential pathway that clears many maternal transcripts from early C. elegans embryos using the Rbfox-related SPN-4 RNA-binding protein as a specificity factor and the CCR4-NOT deadenylase complex as an effector. We biochemically identified SPN-4-associated mRNAs from late-stage oocytes and found that the set of SPN-4-associated transcripts is enriched for maternal mRNAs that undergo early decay. Single-molecule fluorescence in situ hybridization experiments established that many SPN-4-associated mRNAs fail to be eliminated in the absence of SPN-4. In the 3UTRs of two target mRNAs, we identified Rbfox motifs that are required for SPN-4-dependent clearance in vivo and bind SPN-4 in vitro. In a genetic screen to identify factors that work with SPN-4, we isolated mutant alleles of CCR4-NOT components. Auxin-induced degradation of the LET-711/NOT1 scaffold and the CCF-1 deadenylase disrupted clearance of two SPN-4-associated transcripts. Our results support a model in which SPN-4 initiates expression in late-stage oocytes, associates with maternal mRNA targets through RNA sequences in their 3UTRs and promotes CCR4-NOT-mediated decay during early embryogenesis.

developmental biology↗

bioRxiv2024

Comparative lifespan and healthspan of nonhuman primate species common to biomedical research

There is a critical need to generate age- and sex-specific survival curves to characterize chronological aging consistently across nonhuman primates (NHP) used in biomedical research. Sex-specific Kaplan-Meier survival curves were computed in 12 translational aging models: baboon, bonnet macaque, chimpanzee, common marmoset, coppery titi monkey, cotton-top tamarin, cynomolgus macaque, Japanese macaque, pigtail macaque, rhesus macaque, squirrel monkey, and vervet/African green. After employing strict inclusion criteria, primary results are based on 12,269 NHP that survived to adulthood and died of natural/health-related causes. A secondary analysis was completed for 32,616 NHP that died of any cause. Results show a pattern of reduced male survival among catarrhines (African and Asian primates), especially macaques, but not platyrrhines (Central and South American primates). For many species, median lifespans were lower than previously reported. An important consideration is that these analyses may offer a better reflection of healthspan than lifespan since research NHP are typically euthanized for humane welfare reasons before their natural end of life. This resource represents the most comprehensive characterization of sex-specific lifespan and age-at-death distributions for 12 biomedically relevant species, to date. These results clarify relationships among NHP ages and provide a valuable resource for the aging research community, improving human-NHP age equivalencies, informing investigators of expected survival rates, providing a metric for comparisons in future studies, and contributing to understanding of factors driving lifespan differences within and among species.

physiology↗

bioRxiv2024

Spatial Single-cell Analysis Decodes Cortical Layer and Area Specification

The human cerebral cortex, pivotal for advanced cognitive functions, is composed of six distinct layers and dozens of functionally specialized areas1,2. The layers and areas are distinguished both molecularly, by diverse neuronal and glial cell subtypes, and structurally, through intricate spatial organization3,4. While single-cell transcriptomics studies have advanced molecular characterization of human cortical development, a critical gap exists due to the loss of spatial context during cell dissociation5,6,7,8. Here, we utilized multiplexed error-robust fluorescence in situ hybridization (MERFISH)9, augmented with deep-learning-based cell segmentation, to examine the molecular, cellular, and cytoarchitectural development of human fetal cortex with spatially resolved single-cell resolution. Our extensive spatial atlas, encompassing 16 million single cells, spans eight cortical areas across four time points in the second and third trimesters. We uncovered an early establishment of the six-layer structure, identifiable in the laminar distribution of excitatory neuronal subtypes by mid-gestation, long before the emergence of cytoarchitectural layers. Notably, while anterior-posterior gradients of neuronal subtypes were generally observed in most cortical areas, a striking exception was the sharp molecular border between primary (V1) and secondary visual cortices (V2) at gestational week 20. Here we discovered an abrupt binary shift in neuronal subtype specification at the earliest stages, challenging the notion that continuous morphogen gradients dictate mid-gestation cortical arealization6,10. Moreover, integrating single-nuclei RNA-sequencing and in situ whole transcriptomics revealed an early upregulation of synaptogenesis in V1-specific Layer 4 neurons, suggesting a role of synaptogenesis in this discrete border formation. Collectively, our findings underscore the crucial role of spatial relationships in determining the molecular specification of cortical layers and areas. This work not only provides a valuable resource for the field, but also establishes a spatially resolved single-cell analysis paradigm that paves the way for a comprehensive developmental atlas of the human brain.

neuroscience↗

bioRxiv2023

DeepComBat: A Statistically Motivated, Hyperparameter-Robust, Deep Learning Approach to Harmonization of Neuroimaging Data

Neuroimaging data from multiple batches (i.e. acquisition sites, scanner manufacturer, datasets, etc.) are increasingly necessary to gain new insights into the human brain. However, multi-batch data, as well as extracted radiomic features, exhibit pronounced technical artifacts across batches. These batch effects introduce confounding into the data and can obscure biological effects of interest, decreasing the generalizability and reproducibility of findings. This is especially true when multi-batch data is used alongside complex downstream analysis models, such as machine learning methods. Image harmonization methods seeking to remove these batch effects are important for mitigating these issues; however, significant multivariate batch effects remain in the data following harmonization by current state-of-the-art statistical and deep learning methods. We present DeepCombat, a deep learning harmonization method based on a conditional variational autoencoder architecture and the ComBat harmonization model. DeepCombat learns and removes subject-level batch effects by accounting for the multivariate relationships between features. Additionally, DeepComBat relaxes a number of strong assumptions commonly made by previous deep learning harmonization methods and is empirically robust across a wide range of hyperparameter choices. We apply this method to neuroimaging data from a large cognitive-aging cohort and find that DeepCombat outperforms existing methods, as assessed by a battery of machine learning methods, in removing scanner effects from cortical thickness measurements while preserving biological heterogeneity. Additionally, DeepComBat provides a new perspective for statistically-motivated deep learning harmonization methods.

neuroscience↗

bioRxiv2023

SpaDecon: cell-type deconvolution in spatial transcriptomics with semi-supervised learning

Spatially resolved transcriptomics (SRT) has advanced our understanding of the spatial patterns of gene expression, but the lack of single-cell resolution in spatial barcoding-based SRT hinders the inference of specific locations of individual cells. To determine the spatial distribution of cell types in SRT, we present SpaDecon, a semi-supervised learning approach that incorporates gene expression, spatial location, and histology information for cell-type deconvolution. SpaDecon was evaluated through analyses of four real SRT datasets using knowledge of the expected distributions of cell types. Quantitative evaluations were performed for four pseudo-SRT datasets constructed according to benchmark proportions. Using mean squared error and Jensen-Shannon divergence with the benchmark proportions as evaluation criteria, we show that SpaDecon performance surpasses that of published cell-type deconvolution methods. Given the accuracy and computational speed of SpaDecon, we anticipate it will be valuable for SRT data analysis and will facilitate the integration of genomics and digital pathology.

genomics↗

bioRxiv2022

Deciphering tumor ecosystems at super-resolution from spatial transcriptomics with TESLA

Recent advances in spatial transcriptomics (ST) have enabled the comprehensive characterization of gene expression in tumor microenvironment. However, ST only measures expression in discrete spots, which limits their usefulness in studying the detailed structure of TME. Here we present TESLA, a machine learning framework for multi-level tissue annotation in ST. TESLA integrates histological information with gene expression to annotate heterogeneous immune and tumor cells directly on the histology image, and further detects tertiary lymphoid structures and differential transcriptome programs between the edge and core of a tumor. TESLA provides a powerful tool for understanding the spatial architecture of the TME.

bioinformatics↗

bioRxiv2022

Spatially resolved human kidney multi-omics single cell atlas highlights the key role of the fibrotic microenvironment in kidney disease progression

Kidneys possess one of the most intricate three-dimensional cellular structures in the body, yet the spatial and molecular principles of kidney health and disease remain inadequately understood. Here, we have generated high-quality datasets for 81 samples, including single cell (sc), single nuclear (sn), spot level (Visium) and single cell resolution (CosMx) spatial (sp)-RNA expression, and sn open chromatin, capturing cells from healthy, diabetic, and hypertensive diseased human kidneys. By combining the snRNA, snATAC and scRNA sequencing we identify cell types and map these cell types to their locations within the tissue. Unbiased deconvolution of the spatial data identifies 4 distinct spatial microenvironments: glomerular, immune, tubule and fibrotic. We describe the complex, heterogenous cellular and spatial organization of human microenvironments in health and disease. Further, we find that the fibrotic microenvironment spatial gene signature is not only able to molecularly classify human kidneys, but it also offers an improved prognosis prediction compared to traditional histopathological analysis. We provide a comprehensive spatially resolved molecular roadmap of the human kidney and the fibrotic process, demonstrating the clinical utility of spatial transcriptomics.

genetics↗

bioRxiv2021

OpenMonkeyChallenge: Dataset and Benchmark Challenges for Pose Tracking of Non-human Primates

The ability to automatically track non-human primates as they move through the world is important for several subfields in biology and biomedicine. Inspired by the recent success of computer vision models enabled by benchmark challenges (e.g., object detection), we propose a new benchmark challenge called OpenMonkeyChallenge that facilitates collective community efforts through an annual competition to build generalizable non- human primate pose tracking models. To host the benchmark challenge, we provide a new public dataset consisting of 111,529 annotated (17 body landmarks) photographs of non-human primates in naturalistic contexts obtained from various sources including the internet, three National Primate Research Centers, and the Minnesota Zoo. Such annotated datasets will be used for the training and testing datasets to develop generalizable models with standardized evaluation metrics. We demonstrate the effectiveness of our dataset quantitatively by comparing it with existing datasets based on seven state-of-the-art pose tracking models.

animal behavior and cognition↗

bioRxiv2020

Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network

Recent advances in spatial transcriptomics technologies have enabled comprehensive characterization of gene expression patterns in the context of tissue microenvironment. To elucidate spatial gene expression variation, we present SpaGCN, a graph convolutional network approach that integrates gene expression, spatial location and histology in spatial transcriptomics data analysis. Through graph convolution, SpaGCN aggregates gene expression of each spot from its neighboring spots, which enables the identification of spatial domains with coherent expression and histology. The subsequent domain guided differential expression analysis then detects genes with enriched expression patterns in the identified domains. Analyzing five spatially resolved transcriptomics datasets using SpaGCN, we show it can detect genes with much more enriched spatial expression patterns than existing methods. Furthermore, genes detected by SpaGCN are transferrable and can be utilized to study spatial variation of gene expression in other datasets. SpaGCN is computationally fast, making it a desirable tool for spatial transcriptomics studies.

bioinformatics↗