Biology subjects

Flowers, S.

Publications and source records attributed to Flowers, S..

5 recordsLinked to original sources

bioRxiv2026

Beyond Segmentation Accuracy: Evaluating Biological Fidelity in Spatial Pathology

Multiplex immunofluorescence (MxIF) resolves cellular identity, state, and spatial organization within tumor tissue, enabling higher-order representations of cancer ecosystems including immune infiltration, multicellular neighborhoods, and tissue niches. Yet these analyses depend on a deceptively simple first step: accurately identifying individual cells. Segmentation errors can alter marker quantification, cellular phenotypes, cell-cell relationships, and ultimately the spatial organization inferred from an image. Consequently, improvements in conventional segmentation benchmarks matter biologically only if they propagate to more faithful representations of the tumor microenvironment. We test this relationship directly using an expert-curated benchmark of 31,924 expert-reviewed cells on three slides under a leave-slide-out protocol. We compare a domain-specific self-supervised encoder, adapted to multiplex tissue imaging, with supervised, microscopy-specific, prompted generalist, and general-purpose vision models, and pass every method's predicted cells through an identical downstream pipeline. We evaluate fidelity across four levels: cell segmentation, phenotype assignment, spatial neighborhood structure, and tissue organization, comprising niches and tumor-immune interactions. The resulting hierarchy reveals that these levels do not improve in lockstep. Cellular phenotype assignment is comparatively robust to segmentation differences, whereas missed cells increasingly disrupt neighborhood structure and tumor-immune interactions. As a stringent spatial-interaction test, tumor-CD8+ contact recovery is poorly predicted by conventional segmentation quality (Panoptic Quality: {rho} = 0.18), while errors specifically at tumor-immune interfaces are strongly associated with contact fidelity ({rho} = -0.80). Methods with nearly identical segmentation accuracy can therefore produce substantially different biological representations. These results argue that cell segmentation for spatial pathology should be evaluated not only by boundary and instance accuracy, but also by preservation of the biological relationships that downstream analyses are intended to measure.

bioinformatics↗

bioRxiv2026

Discovering Latent Scientific Concepts through Discrete Representation Learning

Scientific imaging is undergoing a fundamental transformation. Advances in multiplex immunofluorescence, computational pathology, and electron microscopy now enable observation of biological and physical systems at unprecedented spatial resolution. Rather than simply classifying known objects, these data offer the opportunity to discover previously unknown organizational principles. However, scientific image datasets are typically highly curated, information-rich, and limited in size, making reliable representation learning particularly challenging. We show that standard continuous self-supervised adaptation can become unstable in this regime, allowing teacher representations to drift and degrading downstream transfer. We introduce Kodiak, a discrete representation learning architecture that discovers latent scientific concepts through Sinkhorn-balanced codebook assignments. Instead of learning unconstrained continuous embeddings, Kodiak constructs a reusable vocabulary of discrete concepts that provides stable supervision for both masked-patch prediction and cross-view representation learning within a single-stage framework. Across five scientific imaging domains, including multiplex immunofluorescence for pancreatic cancer pathology and scanning electron microscopy, Kodiak consistently improves representation quality under both continued foundation-model adaptation and training from scratch. Beyond improved classification and transfer,the learned concepts are interpretable, reusable, and produce spatially coherent concept maps that support applications such as cell phenotyping, segmentation-free patch phenotyping, spatial niche discovery, tissue organization, and materials characterization. We view Kodiak as the representation-learning engine of our foundation model for spatial biology. More broadly, our results suggest that discrete representation learning provides a principled foundation for discovering reusable latent scientific concepts that can accelerate scientific interpretation across imaging domains.

cancer biology↗

bioRxiv2026

A Paracrine Dietary Lipid Axis Constrains Antitumor Immunity in Liver Cancer

Overnutrition-related liver dysfunction and cancer are increasingly prevalent and highly resistant to immunotherapy. While metabolic dysregulation is a hallmark of hepatocellular carcinoma (HCC), how nutrient overload impairs antitumor immunity remains unclear. Here, we show that short-term Western diet (WD) exposure drives near-complete loss of CD8 T cell infiltration and antitumor function in HCC. We identify dietary linoleic acid (LA), the most abundant {omega}-6 fatty acid, as the dominant immunosuppressive driver. Cancer cell-restricted FADS2-mediated desaturation of LA to longer-chain {omega}-6 PUFAs drives their accumulation in the tumor interstitial fluid, suppressing infiltrating CD8 T cells via lipid peroxidation. FADS2 inhibition restores CD8 T cell function and sensitizes WD-driven HCC to PD-1-based immunotherapy. Further, the Parkinsons disease-associated deglycase DJ-1 protects LA-handling proteins from methylglyoxal-mediated glycation, sustaining tumoral immunosuppressive PUFA production. Across multiple independent human MASLD-HCC cohorts, LA metabolic activity correlates with CD8 T cell impairment, immune exclusion, and immunotherapy resistance. Overall, these studies identify a dietary lipid axis as a therapeutically actionable vulnerability in WD-associated HCC.

Cancer Biology↗

bioRxiv2026

Senescence-directed nanotherapy ameliorates fibrosis and overcomes immune exclusion in cancer

Fibrotic remodeling of tissues and tumors establishes immunosuppressive microenvironments that drive organ dysfunction and, in cancer, limit response to immunotherapy. Senescent-like cells are conserved drivers of fibrosis and therapeutic targets, yet their functional heterogeneity complicates therapeutic intervention. Here, we show that P-selectin is expressed by a subset of senescent-like cells in fibrotic tissues and tumors. Leveraging fucoidan-based nanoparticles that bind P-selectin, we developed senescence-modulating nanoparticles (SMNPs) to selectively target these disease-associated states. SMNPs exerted potent antifibrotic and immunomodulatory effects while improving therapeutic index. Mechanistically, we identified a pathogenic, immunosuppressive macrophage population as a functional target in vivo. In fibrotic tumors, niche remodeling restored immune infiltration and sensitized tumors to immune-checkpoint-based therapies. These findings establish SMNPs as a generalizable strategy to target pathogenic senescent cell subsets across fibrosis and cancer.

cancer biology↗

bioRxiv2021

The Atypical Antipsychotic Quetiapine Induces Multiple Antibiotic Resistance in Escherichia coli

Atypical antipsychotic (AAP) medication is a critical tool for treating symptoms of psychiatric disorders. While AAPs primarily target dopamine (D2) and serotonin (5HT2A and 5HT1A) receptors, they also exhibit intrinsic antimicrobial activity as an off-target effect. Because AAPs are often prescribed to patients for many years, a potential risk associated with long-term AAP use is the unintended emergence of bacteria with antimicrobial resistance (AMR). Here, we show that exposure to the AAP quetiapine at estimated gut concentrations promotes AMR in Escherichia coli after six weeks. Quetiapine-exposed isolates exhibited an increase in minimal inhibitory concentrations (MICs) for ampicillin, tetracycline, ceftriaxone, and levofloxacin. By whole genome sequencing analysis, we identified mutations in genes that confer AMR, including the repressor for the multiple antibiotic resistance mar operon (marR), and real-time RT-qPCR analysis showed increased levels of marA, acrA, and tolC mRNAs and a reduced level of ompF mRNA in the isolates carrying marR mutations. To determine the contribution of each marR mutation to AMR, we constructed isogenic strains carrying individual mutant marR alleles in the parent background and re-evaluated their resistant phenotypes using MIC and RT-qPCR assays. While marR mutations induced a robust activity of the mar operon, they resulted in only a modest increase in MICs. Interestingly, although these marR mutations did not fully recapitulate the AMR phenotype of the quetiapine-exposed isolates, we show that marR mutations promote growth fitness in the presence of quetiapine. Our findings revealed an important link between the use of AAPs and AMR development in E. coli.

microbiology↗