Biology subjects

Flynn, W. F.

Publications and source records attributed to Flynn, W. F..

11 recordsLinked to original sources

bioRxiv2026

Spatial organization of voltage-gated ion channel expression across molecularly defined neuronal populations in the mouse mammillary bodies

The mammillary bodies (MB) are a hypothalamic component of the limbic Papez circuit that plays a critical role in spatial and episodic memory in mammals. Degeneration of the MB occurs in disorders associated with cognitive impairment, including Korsakoff's syndrome and Alzheimer's disease, yet the molecular organization and intrinsic properties of MB neurons remain poorly understood. Recent single-cell RNA sequencing identified multiple transcriptionally distinct neuronal populations within the MB and suggested that they differentially express voltage-gated ion channels that regulate neuronal excitability. Here, we used fluorescence in situ hybridization (FISH) to define the anatomical organization of cluster-enriched molecular markers and determine the spatial distribution of transcripts encoding voltage-gated sodium (NaV), potassium (KV), and hyperpolarization-activated cyclic nucleotide-gated (HCN) channels among defined subregions of the mouse MB. We found that marker transcripts occupy characteristic but partially overlapping spatial domains that broadly correspond to classical anatomical subdivisions. In addition, several ion channel transcripts, including Scn1a, Scn2a, Kcnq2, Kcnq3, and Hcn1, exhibited distinct patterns of enrichment across molecularly defined neuronal populations and MB subregions. Multiplex FISH further revealed unexpected co-expression of Scn1a and Scn2a within Pvalb-enriched neuronal populations, while whole-cell recordings demonstrated distinct intrinsic firing properties of neurons in the lateral and medial mammillary nuclei. Together, these findings establish a molecular framework linking neuronal population identity with voltage-gated ion channel expression in the MB and provide a foundation for future studies investigating how cell type-specific differences in intrinsic excitability contribute to memory function and neurological disease.

neuroscience↗

bioRxiv2025

High-Resolution Spatial Transcriptomics Reveals Fibroblast and Neuroimmune Microenvironments in Endometriosis Lesions

Endometriosis is a chronic, systemic, inflammatory disease characterized by the presence of endometrium-like tissue growing outside of the uterus. One of its main symptoms is chronic pain and inflammation leading to a decreased quality of life. This is a common disease, as at least one in ten female-born individuals have endometriosis. Yet the understanding of the mechanisms that drive pain symptoms and disease progression remain poorly defined. This study establishes the precise spatial transcriptomic cartography of human ovarian and peritoneal lesions, two of the most commonly found lesions. We identified shared spatial features across lesion types, including immune cell infiltration, fibroblast specific compartments surrounding epithelial glands, and distinct distributions of neuronal and macrophage subsets. We precisely defined sensory neuronal subtypes, and mapped their spatial location relative to immune cells. We further validate the epithelial-neuronal interactome, using an in vitro 3D model of peripheral sensory brain organoids co-cultured with human endometriosis epithelial and fibroblast cells. By mapping spatial cellular interactions and identifying conserved features across lesion types linked to pain, our study provides emerging insights into endometriosis pathophysiology, paving the way for the development of novel targeted therapeutic strategies.

cell biology↗

bioRxiv2025

A high-throughput, image-based assay to assess drug sensitivity of Acanthamoeba castellanii cysts.

Pathogenic free-living amoebae (FLA) such as Acanthamoeba castellanii are present in soil and water worldwide. A. castellanii causes systemic infections with very high mortality rates, yet drugs specifically targeting this pathogen are not available. Methods to reliably generate and assay cysts, which drive infection recurrence and drug resistance, are unavailable in a high-throughput format suitable for drug screening and testing. In this study, we developed a robust and reproducible protocol for encysting A. castellanii as well as a high-throughput, quantitative cyst viability assay using fluorescent live/dead staining coupled with microscopy and automated image analysis. These methods were used to screen the cysticidal activity of 17 clinically relevant drugs and disinfectants and identified five agents, including caspofungin, as active against cysts.

microbiology↗

bioRxiv2024

STAMP: Single-Cell Transcriptomics Analysis and Multimodal Profiling through Imaging

We introduce Single-Cell Transcriptomics Analysis and Multimodal Profiling (STAMP), a scalable profiling approach of individual cells. Leveraging transcriptomics and proteomics imaging platforms, STAMP eliminates sequencing costs, to enable single-cell genomics from hundreds to millions of cells at an unprecedented low cost. Stamping cells in suspension onto imaging slides, STAMP supports single-modal (RNA or protein) and multimodal (RNA and protein) profiling and flexible, ultra-high-throughput formats. STAMP allows the analysis of a single or multiple samples within the same experiment, enhancing experimental flexibility, throughput and scale. We tested STAMP with diverse sample types, including peripheral blood mononuclear cells (PBMCs), dissociated cancer cells and differentiated embryonic stem cell cultures, as well as whole cells and nuclei. Combining RNA and protein profiling, we applied immuno-phenotyping of millions of blood cells simultaneously. We also used STAMP to identify ultra-rare cell populations, simulating clinical applications to identify circulating tumor cells (CTCs). Performing in vitro differentiation studies, we further showed its potential for large-scale perturbation studies. Together, STAMP establishes a new standard for cost-effective, scalable single-cell analysis. Without the need for sequencing, STAMP makes high-resolution profiling more affordable and accessible. Designed to meet the needs of research labs, diagnostic cores and pharmaceutical companies, STAMP holds the promise to transform our capacity to map human biology, diagnose diseases and drug discovery.

genomics↗

bioRxiv2024

Mapping the genetic landscape establishing a tumor immune microenvironment favorable for anti-PD-1 response in mice and humans

Identifying host genetic factors modulating immune checkpoint inhibitor (ICI) efficacy has been experimentally challenging because of variations in both host and tumor genomes, differences in the microbiome, and patient life exposures. Utilizing the Collaborative Cross (CC) multi-parent mouse genetic resource population, we developed an approach that fixes the tumor genomic configuration while varying host genetics. With this approach, we discovered that response to anti-PD-1 (aPD1) immunotherapy was significantly heritable in four distinct murine tumor models (H2 between 0.18-0.40). For the MC38 colorectal carcinoma system (H2 = 0.40), we mapped four significant ICI response quantitative trait loci (QTL) localized to mouse chromosomes (mChr) 5, 9, 15 and 17, and identified significant epistatic interactions between specific QTL pairs. Differentially expressed genes within these QTL were highly enriched for immune genes and pathways mediating allograft rejection and graft vs host disease. Using a cross species analytical approach, we found a core network of 48 genes within the four QTLs that showed significant prognostic value for overall survival in aPD1 treated human cohorts that outperformed all other existing validated immunotherapy biomarkers, especially in human tumors of the previously defined immune subtype 4. Functional blockade of two top candidate immune targets within the 48 gene network, GM-CSF and high affinity IL-2/IL-15 signaling, completely abrogated the MC38 tumor transcriptional response to aPD1 therapy in vivo. Thus, we have established a powerful cross species in vivo platform capable of uncovering host genetic factors that establish the tumor immune microenvironment configuration propitious for ICI response.

cancer biology↗

bioRxiv2023

Immunosuppressive tumor microenvironment of osteosarcoma

Osteosarcoma is the most common malignant bone tumor in children, characterized by a high degree of genomic instability, resulting in copy-number alterations and genomic rearrangements without disease-defining recurrent mutations. Clinical trials based on molecular characterization have failed to find new effective therapies or improve outcomes over the last 40 years. To better understand the immune microenvironment of osteosarcoma, we performed single-cell RNA sequencing on six tumor biopsy samples, combined with a previously-published cohort of six samples. Additional osteosarcoma samples were profiled using spatial transcriptomics for validation of discovered subtypes and to add spatial context. Analysis revealed immunosuppressive cells, including myeloid-derived suppressor cells (MDSCs), regulatory and exhausted T-cells, and LAMP3+ dendritic cells. Using cell-cell communication modeling, we identified robust interactions between MDSCs and other cells, leading to NF-{kappa}B upregulation and an immunosuppressive microenvironment, as well as interactions involving regulatory T-cells and osteosarcoma cells that promoted tumor progression and a proangiogenic niche. Statement of SignificanceOsteosarcoma patient survival has remained stagnant for several decades due to lack of successful therapy for high-risk patients, including those with metastatic disease. Identifying novel therapeutics including immunotherapies is of great clinical importance. Our study highlights several important immunosuppressive mechanisms within osteosarcoma that should be considered when developing future immunotherapies.

cancer biology↗

bioRxiv2021

A Cellular Reference Resource for the Mouse Urinary Bladder

The urinary bladder functions as a reservoir to store and extrude liquid bodily waste. Significant debate exists as to this tissues cellular composition and genes associated with their functions. We use a repertoire of cell profiling tools to comprehensively define and spatial resolve cell types. We characterize spatially validated, basal-to-luminal gene expression dynamics within the urothelium, the cellular source of most bladder cancers. We define three distinct populations of fibroblasts that spatially organize from the sub-urothelial layer through to the detrusor muscle, clarifying knowledge around these controversial interstitial cells, and associate increased fibroblasts with aging. We overcome challenges of profiling the detrusor muscle, absence from earlier single cell studies, to report on its transcriptome with many novel and neuronal-like features presumably associated with neuromuscular junctions. Our approach provides a blueprint for tissue atlas construction and the data provides the foundation for future studies of bladder function in health and disease.

genomics↗

bioRxiv2021

Single cell analysis of endometriosis reveals a coordinated transcriptional program driving immunotolerance and angiogenesis across eutopic and ectopic tissues.

Endometriosis is characterized by growth of endometrial-like tissue outside of the uterus affecting many women in their reproductive age, causing years of pelvic pain and potential infertility. Its pathophysiology remains largely unknown, limiting diagnosis and treatment. We characterized peritoneal and ovarian lesions at single-cell transcriptome resolution and compared to matched eutopic endometrium, control endometrium, and organoids derived from these tissues, generating data on over 100,000 cells across 12 individuals. We spatially localized many of the cell types using imaging mass cytometry. We identify a perivascular mural cell unique to the peritoneal lesions with dual roles in angiogenesis promotion and immune cell trafficking. We define an immunotolerant peritoneal niche, fundamental differences in eutopic endometrium and between lesions microenvironments, and a novel progenitor-like epithelial cell subpopulation. Altogether, this study provides a holistic view of the endometriosis microenvironment representing the first comprehensive cell atlas of the disease, essential information for advancing therapeutics and diagnostics.

genomics↗

bioRxiv2020

Cellular taxonomy and spatial organization of the ventral posterior hypothalamus reveals neuroanatomical parcellation of the mammillary bodies

The ventral posterior hypothalamus (VPH) is an anatomically complex brain region implicated in arousal, reproduction, energy balance and memory processing. However, neuronal cell type diversity within the VPH is poorly understood, an impediment to deconstructing the roles of distinct VPH circuits in physiology and behavior. To address this question, we employed a droplet-based single cell RNA sequencing (scRNA-seq) approach to systematically classify molecularly distinct cell types in the mouse VPH. Analysis of >16,000 single cells revealed 20 neuronal and 18 non-neuronal cell populations, defined by suites of discriminatory markers. We validated differentially expressed genes in a selection of neuronal populations through fluorescence in situ hybridization (FISH). Focusing on the mammillary bodies (MB), we discovered transcriptionally-distinct clusters that exhibit a surprising degree of segregation within neuroanatomical subdivisions of the MB, while genetically-defined MB cell types project topographically to the anterior thalamus. This single cell transcriptomic atlas of cell types in the VPH provides a detailed resource for interrogating the circuit-level mechanisms underlying the diverse functions of VPH circuits in health and disease.

neuroscience↗

bioRxiv2018

Identification of slow-cycling germline stem cells and their regulation by PLZF

Long-term maintenance of spermatogenesis in mammals is supported by GDNF, an essential growth factor required for spermatogonial stem cell (SSC) self-renewal. Exploiting a transgenic GDNF overexpression model, which expands and normalizes the pool of undifferentiated spermatogonia between Plzf+/+ and Plzflu/lu mice, we used RNAseq to identify a rare subpopulation of cells that express EOMES, a T-box transcription factor. Lineage tracing and busulfan challenge show that these are long-term SSCs that contribute to steady state spermatogenesis as well as regeneration following chemical injury. EOMES+ SSCs have a lower proliferation index than EOMES- GFRA1+ spermatogonia in wild-type but not in Plzflu/lu mice. This comparison demonstrates that PLZF regulates their proliferative activity and suggests that EOMES+ SSCs are lost through proliferative exhaustion in Plzflu/lu mice. Single cell RNA sequencing of EOMES+ cells from Plzf+/+ and Plzflu/lu mice support a hierarchical model of both slow- and rapid-cycling SSCs.

developmental biology↗

bioRxiv2018

Pan-cancer machine learning predictors of tissue of origin and molecular subtype

BackgroundIt is estimated by the American Cancer Society that approximately 5% of all metastatic tumors have no defined primary site (tissue) of origin and are classified as cancers of unknown primary (CUPs). The current standard of care for CUP patients depends on immunohistochemistry (IHC) based approaches to identify the primary site. The addition of post-mortem evaluation to IHC based tests helps to reveal the identity of the primary site for only 25% of the CUPs, emphasizing the acute need for better methods of determination of the site of origin. CUP patients are therefore given generic chemotherapeutic agents resulting in poor prognosis. When the tissue of origin is known, patients can be given site specific therapy with significant improvement in clinical outcome. Similarly, identifying the primary site of origin of metastatic cancer is of great importance for designing treatment.\n\nIdentification of the primary site of origin is an import first step but may not be sufficient information for optimal treatment of the patient. Recent studies, primarily from The Cancer Genome Atlas (TCGA) project, and others, have revealed molecular subtypes in several cancer types with distinct clinical outcome. The molecular subtype captures the fundamental mechanisms driving the cancer and provides information that is essential for the optimal treatment of a cancer. Thus, along with primary site of origin, molecular subtype of a tumor is emerging as a criterion for personalized medicine and patient entry into clinical trials.\n\nHowever, there is no comprehensive toolset available for precise identification of tissue of origin or molecular subtype for precision medicine and translational research.\n\nMethods and FindingsWe posited that metastatic tumors will harbor the gene expression profiles of the primary site of origin of the cancer. Therefore, we decided to learn the molecular characteristics of the primary tumors using the large number of cancer genome profiles available from the TCGA project. Our predictors were trained for 33 cancer types and for the 11 cancers where there are established molecular subtypes. We estimated the accuracy of several machine learning models using cross-validation methods. The extensive testing using independent test sets revealed that the predictors had a median sensitivity and specificity of 97.2% and 99.9% respectively without losing classification of any tumor. Subtype classifiers achieved median sensitivity of 87.7% and specificity of 94.5% via cross validation and presented median sensitivity of 79.6% and specificity of 94.6% in two external datasets of 1,999 total samples. Importantly, these external data shows that our classifiers can robustly predict the primary site of origin from external microarray data, metastatic cancer data, and patient-derived xenograft (PDX) data.\n\nConclusionWe have demonstrated the utility of gene expression profiles to solve the important clinical challenge of identifying the primary site of origin and the molecular subtype of cancers based on machine learning algorithms. We show, for the first time to our knowledge, that our pan-cancer classifiers can predict multiple cancers primary site of origin from metastatic samples. The predictors will be made available as open source software, freely available for academic non-commercial use.

cancer biology↗