Biology subjects

LaBarge, M. A.

Publications and source records attributed to LaBarge, M. A..

9 recordsLinked to original sources

bioRxiv2025

MechanoAge, a machine learning platform to identify individuals susceptible to breast cancer based on mechanical properties of single cells

BackgroundExisting breast cancer risk models inadequately identify individuals at latent risk, particularly among women without known genetic mutations or family history. Risk is often underestimated or overestimated due to reliance on population-level data and neglect of cellular aging and mechanobiological alterations. MethodsWe profiled primary human mammary epithelial cells (HMECs) from women of varying ages and risk backgrounds using mechano-node-pore sensing (mechano-NPS), a high-throughput microfluidic platform that captures single-cell mechanical properties. Using machine learning, we developed a classifier, MechanoAge, to predict age-related mechanical phenotypes and introduce a novel index, mechano-RISQ, to quantify deviations linked to breast cancer risk. We further assessed the cytoskeletal protein keratin 14 (KRT14) as a molecular mediator of these mechanical states through overexpression and knockdown experiments. FindingsCells from younger women carrying BRCA1/2 mutations or with a family history of breast cancer exhibited accelerated mechanical aging compared to age-matched controls. Elevated mechano-RISQ scores reflected an increased proportion of cells with "older" mechanical profiles. KRT14 overexpression induced an aged mechanical phenotype in younger cells, while knockdown partially reversed this state in older cells. CyTOF profiling and modeling showed KRT14 modulation impacted protein expression signatures associated with aging and risk, particularly in luminal cells. InterpretationMechanical properties of breast epithelial cells reflect biologic aging and cancer susceptibility. Mechano-RISQ offers a new approach for identifying individuals at elevated risk, especially among average-risk populations, and may complement existing risk models by incorporating biophysical measures of epithelial aging.

cancer biology↗

bioRxiv2023

Comprehensive single cell aging atlas of mammary tissues reveals shared epigenomic and transcriptomic signatures of aging and cancer

Aging is the greatest risk factor for breast cancer; however, how age-related cellular and molecular events impact cancer initiation is unknown. We investigate how aging rewires transcriptomic and epigenomic programs of mouse mammary glands at single cell resolution, yielding a comprehensive resource for aging and cancer biology. Aged epithelial cells exhibit epigenetic and transcriptional changes in metabolic, pro-inflammatory, or cancer-associated genes. Aged stromal cells downregulate fibroblast marker genes and upregulate markers of senescence and cancer-associated fibroblasts. Among immune cells, distinct T cell subsets (Gzmk+, memory CD4+, {gamma}{delta}) and M2-like macrophages expand with age. Spatial transcriptomics reveal co-localization of aged immune and epithelial cells in situ. Lastly, transcriptional signatures of aging mammary cells are found in human breast tumors, suggesting mechanistic links between aging and cancer. Together, these data uncover that epithelial, immune, and stromal cells shift in proportions and cell identity, potentially impacting cell plasticity, aged microenvironment, and neoplasia risk.

genomics↗

bioRxiv2023

Configurational entropy is an intrinsic driver of tissue structural heterogeneity

Tissues comprise ordered arrangements of cells that can be surprisingly disordered in their details. How the properties of single cells and their microenvironment contribute to the balance between order and disorder at the tissue-scale remains poorly understood. Here, we address this question using the self-organization of human mammary organoids as a model. We find that organoids behave like a dynamic structural ensemble at the steady state. We apply a maximum entropy formalism to derive the ensemble distribution from three measurable parameters - the degeneracy of structural states, interfacial energy, and tissue activity (the energy associated with positional fluctuations). We link these parameters with the molecular and microenvironmental factors that control them to precisely engineer the ensemble across multiple conditions. Our analysis reveals that the entropy associated with structural degeneracy sets a theoretical limit to tissue order and provides new insight for tissue engineering, development, and our understanding of disease progression.

bioengineering↗

bioRxiv2022

Luminal epithelial cells integrate variable responses to aging into stereotypical changes that underlie breast cancer susceptibility

Effects from aging in single cells are unpredictable, whereas aging phenotypes at the organ- and tissue-levels tend to appear as stereotypical changes. The mammary epithelium is a bilayer of two major phenotypically and functionally distinct cell lineages, the luminal epithelial and myoepithelial cells. Mammary epithelia exhibit substantial stereotypical changes with age that merits attention because they are putative breast cancer-cells-of-origin. We hypothesize that effects from aging that impinge upon maintenance of lineage fidelity increases susceptibility to cancer initiation. We identified two models of age-dependent changes in gene expression, directional changes and increased variance, which contributed to genome-wide loss of lineage fidelity. Age-dependent variant responses were common to both lineages, whereas directional changes were almost exclusively detected in luminal epithelia and implicated downregulation of chromatin and genome organizers such as SATB1. Epithelial expression of gap junction protein GJB6 increased with age, and modulation of GJB6 expression in heterochronous co-cultures revealed that it provided a communication conduit from myoepithelial cells that drove directional change in luminal cells. Age-dependent luminal transcriptomes comprised a prominent signal detectable in bulk tissue during aging and transition into cancers. A machine learning classifier based on luminal-specific aging distinguished normal from cancer tissue and was predictive of breast cancer subtype. We speculate that luminal epithelia are the ultimate site of integration of the variant responses to aging in their surrounding tissue and that their emergent aging phenotype both endows cells with the ability to become cancer-cells-of-origin and embodies a biosensor that presages cancer susceptibility.

cancer biology↗

bioRxiv2021

Pre-neoplastic stromal cells drive BRCA1-mediated breast tumorigenesis

Women with germline mutations in BRCA1 (BRCA1+/mut) have increased risk for developing hereditary breast cancer1, 2. Cancer initiation in BRCA1+/mut is associated with pre-malignant changes in the breast epithelium including altered differentiation3-5, proliferative stress6 and genomic instability7. However, the role of the epithelium- associated stromal niche during BRCA1-driven tumor initiation remains unclear. Here, we show that the pre-malignant stromal niche promotes epithelial proliferation and BRCA1- driven cancer initiation in trans. Using single-cell RNAseq (scRNAseq) analysis of human pre-neoplastic BRCA1+/mut and control breast tissues, we show that stromal cells provide numerous pro-proliferative paracrine signals inducing epithelial proliferation. We identify a subpopulation of pre-cancer associated fibroblasts (pre-CAFs) that produces copious amounts of pro-tumorigenic factors including matrix metalloproteinase 3 (MMP3)8, 9, and promotes BRCA1-driven tumorigenesis in vivo. Our gene-signature analysis and mathematical modeling of epithelial differentiation reveals that stromal-induced proliferation leads to the accumulation of luminal progenitor cells with altered differentiation, and thus contributes to increased breast cancer risk in BRCA1+/mut. Our results demonstrate how alterations in cell-cell communication can induce imbalances in epithelial homeostasis ultimately leading to cancer initiation. We anticipate our results to form the foundation for novel disease monitoring and therapeutic strategies to improve patient management in hereditary breast cancer. For example, pre-CAF specific proteins may serve as biomarkers for pre-cancerous disease initiation to inform whether radical bilateral mastectomy is needed. In addition, MMP inhibitors could be re-indicated for primary cancer prevention treatment in women with high-risk BRCA1 mutations.

cancer biology↗

bioRxiv2021

Evaluating sources of technical variability in the mechano-node-pore sensing pipeline and their effect on the reproducibility of single-cell mechanical phenotyping

Cellular mechanical properties can reveal physiologically relevant characteristics in many cell types, and several groups have developed microfluidics-based platforms to perform single-cell mechanical testing with high throughput. However, prior work has performed only limited characterization of these platforms technical variability and reproducibility. Here, we evaluate the repeatability performance of mechano-node-pore sensing, which is a single-cell mechanical phenotyping platform developed by our research group. We measured the degree to which device-to-device variability and semi-manual data processing affected this platforms measurements of single-cell mechanical properties, and we demonstrated high repeatability across the entire technology pipeline even for novice users. We then compared results from identical mechano-node-pore sensing experiments performed by researchers in two different labs with different analytical instruments, demonstrating that the mechanical testing results from these two locations are in agreement. Our findings quantify the expectation of technical variability in mechano-node-pore sensing even in minimally experienced hands. Most importantly, we find that the repeatability performance we measured is fully sufficient for interpreting biologically relevant single-cell mechanical measurements with high confidence.

bioengineering↗

bioRxiv2021

Epigenetic changes with age primes mammary luminal epithelia for cancer initiation

Aging causes molecular changes that manifest as stereotypical phenotypes yet aging-associated diseases progress only in certain individuals. At lineage-specific resolution, we show how stereotyped and variant responses are integrated in mammary epithelia. Age-dependent directional changes in gene expression and DNA methylation (DNAm) occurred almost exclusively in luminal cells and implicated genome organizers SATB1 and CTCF. DNAm changes were robust indicators of aging luminal cells, and were either directly (anti-)correlated with expression changes or served as priming events for subsequent dysregulation, such as demethylation of ESR1-binding regions in DNAm-regulatory CXXC5 in older luminal cells and luminal-subtype cancers. Variance-driven changes in the transcriptome of both luminal and myoepithelial lineages further contributed to age-dependent loss of lineage fidelity. The pathways affected by transcriptomic and DNAm changes during aging are commonly linked with breast cancer, and together with the differential variability found across individuals, influence aging-associated cancer susceptibility in a subtype-specific manner.

cancer biology↗

bioRxiv2021

Deep Proteome Profiling of Human Mammary Epithelia at Lineage and Age Resolution

Age is the major risk factor in most carcinomas, yet little is known about how proteomes change with age in any human epithelium. We present comprehensive proteomes comprised of >9,000 total proteins, and >15,000 phosphopeptides, from normal primary human mammary epithelia at lineage resolution from ten women ranging in age from 19 to 68. Data were quality controlled, and results were biologically validated with cell-based assays. Age-dependent protein signatures were identified using differential expression analyses and weighted protein co-expression network analyses. Up-regulation of basal markers in luminal cells, including KRT14 and AXL, were a prominent consequence of aging. PEAK1 was identified as an age-dependent signaling kinase in luminal cells, which revealed a potential age-dependent vulnerability for targeted ablation. Correlation analyses between transcriptome and proteome revealed age-associated loss of proteostasis regulation. Protein expression and phosphorylation changes in the aging breast epithelium identify potential therapeutic targets for reducing breast cancer susceptibility.

cell biology↗

bioRxiv2020

Aging leads to stochastic loss of silencing at transposons in mammary luminal epithelial cells

Luminal epithelial cells (LEps) are a key cell lineage implicated in age-related luminal breast cancers. Alterations to the epigenome are a hallmark of aging cells. However, the extent of age-associated DNA methylation alterations in LEps, and the corresponding functional consequences of these alterations, have remained unclear. We report here that aging leads to distinct methylation changes in LEps. Luminal lineage-specific genes gain promoter methylation, whereas myoepithelial-specific genes lose promoter methylation. Regulatory elements display methylation changes at lineage-specific TF binding sites consistent with the loss of lineage fidelity. CpG islands (CGIs) and transposable elements (TEs) have stochastic methylation gain and loss, respectively. PRC2 target genes that are hypermethylated in luminal breast cancer exhibit stochastic methylation increase with age. TEs with stochastic methylation loss are activated in breast cancer and potentially function as regulatory elements contributing to the loss of lineage fidelity with age. Each of these classes of methylation changes impact the regulation of genes associated with luminal breast cancer. Altogether, our results indicate that aging leads to DNA methylation changes that could determine breast cancer susceptibility. SignificanceMammary luminal epithelial cells lose lineage-specific expression with age and accumulate stochastic methylation changes that lead to loss of silencing at transposable elements. These age-dependent events potentially promote breast cancer susceptibility.

genomics↗