Biology subjects

Sohn, L. L.

Publications and source records attributed to Sohn, L. L..

5 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↗

bioRxiv2024

CandyCollect: An Open-Microfluidic Device for the Direct Capture and Enumeration of Salivary-Extracellular Vesicles

Extracellular vesicles (EVs) are promising biomarkers for disease detection using a liquid biopsy approach, in which they are enriched and analyzed directly from biofluids. However, implementing EV biomarker technologies in the clinic remains limited by the need for practical and patient-centric biofluid collection methods that are compatible with downstream EV processing and analysis. While saliva offers a non-invasive source of EVs, its complexity and heterogeneity--cells, debris, and other non-EV proteins--can present hurdles when using traditional analytical platforms. Here, we present the CandyCollect, a lollipop-inspired sampling device with open microfluidic channels, as a patient-friendly approach for rapid salivary EV capture. CandyCollect simplifies sample preparation by effectively pre-concentrating EVs in oxygen-plasma treated open microfluidic channels. In this proof-of-principle study, we show that following a 3-5 minute-oral sampling period, EVs collected by the CandyCollect can be released with high purity within minutes and subsequently quantified and analyzed for cargo content. We observed consistent EV capture across repeated collections within individuals and expected variability across healthy participants. Additionally, single and pooled collections of EVs from a healthy participant resulted in a concordant protein profile. Overall, the CandyCollect is a new platform for rapid, non-invasive salivary EV collection and analysis for clinical diagnostics.

bioengineering↗

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

Mechanical phenotyping of acute promyelocytic leukemia reveals unique biomechanical responses in retinoic acid-resistant populations

While all-trans retinoic acid (ATRA) is an essential therapy in the treatment of acute promyelocytic leukemia (APL), an aggressive subtype of acute myeloid leukemia, nearly 20% of APL patients are resistant to ATRA. As no biomarkers for ATRA resistance yet exist, we investigated whether cell mechanics could be associated with this pathological phenotype. Using mechano-node-pore sensing, a single-cell mechanical phenotyping platform, and patient-derived APL cell lines, NB4 (ATRA-sensitive) and AP-1060 (ATRA-resistant), we discovered that ATRA-resistant APL cells are less mechanically pliable. By investigating how different subcellular components of APL cells contribute to whole-cell mechanical phenotype, we determined that nuclear mechanics strongly influence an APL cells mechanical response. By arresting APL cells in S-phase or M-phase in the cell cycle, we found cell pliability to be inversely related to DNA content. In addition to DNA content affecting cell pliability, we observed that chromatin condensation also affects nuclear mechanics: decondensing chromatin with trichostatin A is especially effective in softening ATRA-resistant APL cells. RNA-Seq allowed us to compare the transcriptomic differences between ATRA-resistant and ATRA-responsive APL cells and highlighted gene expression changes that could be associated with mechanical changes. Overall, we demonstrate the potential of "physical" biomarkers in identifying APL resistance.

bioengineering↗

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↗