Biology subjects

Chien, M.-P.

Publications and source records attributed to Chien, M.-P..

8 recordsLinked to original sources

bioRxiv2026

Behavior-coupled single-cell transcriptomics reveals trafficking protein-dependent regulation of tumor cell migration

Aggressive cellular migration underlies cancer invasion and metastatic dissemination, yet the molecular programs defining highly migratory tumor cell states remain poorly characterized. In head and neck squamous cell carcinoma (HNSCC), metastatic progression is associated with poor clinical outcome, motivating method developments that link migratory behavior to underlying mechanisms. Here, we present behavior-coupled single-cell sequencing (BC-seq), a live-cell imaging-guided transcriptomic platform that enables real-time identification, isolation, and profiling of individual cells based on migratory behavior. Applying BC-seq to 10 HPV-negative oral squamous cell carcinoma (OSCC), the most common HNSCC subtype, we distinguished fast-migrating cells (FMCs) from slow-migrating cells (SMCs) and identified transcriptional programs associated with aggressive migration. Mechanistically, BC-seq uncovered cholesterol-regulated signaling pathways and associated trafficking proteins involving SREBP2, STARD4, Cavin1/3, and VEGFR2 that promotes actin cytoskeletal remodeling and cellular motility. Disruption of STARD4-mediated cholesterol trafficking or Cavin1/3-dependent VEGFR2 nuclear translocation significantly impaired cell migration, linking cholesterol metabolism to migratory machinery. We next examined whether molecular programs associated with aggressive migration are reflected in clinical outcomes. A 15-gene expression signature derived from FMCs predicted distant metastasis with 83.6% accuracy in an independent OSCC cohort, outperforming conventional clinicopathological risk stratification. Together, our study establishes BC-seq as an approach for integrating dynamic cellular behavior with single-cell transcriptomics to uncover mechanisms of aggressive migration and clinically relevant molecular signatures.

cancer biology↗

bioRxiv2025

Unraveling the molecular mechanisms underlying spontaneous multipolar mitosis through CIN-seq

Multipolar mitosis, a hallmark of chromosomal instability (CIN), drives tumor heterogeneity and therapy resistance, yet remains difficult to study in live cells due to its rare and dynamic nature. To address this, we developed CIN-seq, a targeted single-cell multiomics method that enables large-scale profiling of rare CIN phenotypes and captures their temporally regulated gene expression. Applying CIN-seq, we investigated viable, spontaneous multipolar mitosis, an abnormal division process that cancer cells can survive. Genomic analysis revealed that this mitosis produces polyploid or chromosomally variable progeny, confirming its role in genomic instability. Aneuploidy of Chromosome 16 was linked to increased tripolar mitosis, a finding validated with CRISPR imaging. Transcriptomic analysis showed activation of the Rho GTPase cycle, which was associated with cytokinesis failure, while PTEN attenuation emerged as a key player of viable multipolar mitosis by promoting cell cycle progression and survival via BCL2L1. We also uncovered a novel link between this phenotype and degranulation-like stress responses, which may contribute to cancer cell adaptation to chromosomal instability. Overall, CIN-seq offers a powerful approach for studying rare, live CIN events at single-cell resolution and reveals new mechanisms by which cancer cells adapt to chromosomal instability.

cell biology↗

bioRxiv2024

In Vivo Plasmonic Enhancement of the Photocycle Dynamics and Fluorescence of Genetically Encoded Voltage Indicators

Plasmonic nanoparticles are key components in nanophotonics1-11 and have applications in molecular detection and diagnostic platforms. 12-14 Coupling of dipoles to plasmonic antennas has allowed engineering of qualitatively altered behavior in isolated quantum systems, 8,15 but this promise has not been fulfilled in living systems, where the use of plasmonics is limited to tissue level applications of plasmonic particles as contrast agents16 or heat sources.17 Here we show that coupling to designed plasmonic nanoparticles can control the electrophysiological function of proteins in living cells. We designed nanostar geometries and achieved robust near-field coupling of these optimized nanoparticles to plasma membrane-localized Archaerhodopsin proteins. We enhanced the fluorescence of the coupled rhodopsins and increased their response speed to membrane voltage. We incorporated this plasmonic enhancement into a Markov chain photocycle model of the Archaerhodopsin mutant QuasAr6a, showing an increased fluorescence emission rate and manipulation of the protein dynamics through a combination of photocycle transition rate enhancements. These results represent the first scalable near-field coupling between plasmonic particles and fluorescent proteins in living cells. They show enhancement of protein function beyond what has been achievable through genetic engineering. This opens up a range of possibilities for engineering biological functionality through plasmonics.

biophysics↗

bioRxiv2023

Identification of inter- and intra-tumoral molecular phenotypes steering temozolomide resistance in patient-derived glioblastoma cells.

BackgroundRadiation therapy and chemotherapy using Temozolomide are the standard adjuvant treatments for patients with glioblastoma. Despite maximal treatment prognosis is still poor largely due to the emergence of Temozolomide resistance. This resistance is closely linked to the widely recognized inter- and intra-tumoral heterogeneity in glioblastoma, although the underlying mechanisms are not yet fully understood. This study aims to investigate the diverse molecular mechanisms involved in temozolomide resistance. MethodsTo induce temozolomide resistance, we subjected 21 patient-derived glioblastoma cell cultures to Temozolomide treatment for a period of up to 90 days. Prior to treatment, the cells molecular characteristics were analyzed using bulk RNA sequencing. Additionally, we performed single-cell sequencing on four of the cell cultures to track the evolution of temozolomide resistance. ResultsThe induced temozolomide resistance was associated with two distinct phenotypic behaviors, classified as "adaptive" (ADA) or "non-adaptive" (N-ADA) to temozolomide. The ADA phenotype displayed neurodevelopmental and metabolic gene signatures, whereas the N-ADA phenotype expressed genes related to cell cycle regulation, DNA repair, and protein synthesis. Single-cell RNA sequencing revealed that in ADA cell cultures, one or more subpopulations emerged as dominant in the resistant samples, whereas N-ADA cell cultures remained relatively stable. ConclusionsThe adaptability and heterogeneity of glioblastoma cells play pivotal roles in temozolomide treatment and contribute to the tumors ability to survive. Depending on the tumors adaptability potential, subpopulations with acquired resistance mechanisms may arise. Further research is necessary to deepen our understanding of these mechanisms and develop strategies to overcome them.

cancer biology↗

bioRxiv2023

Fast and Accurate Cell Tracking: a real-time cell segmentation and tracking algorithm to instantly export quantifiable cellular characteristics from large scale image data

Quantitative characterizations of cellular dynamics and features of individual cells from a large heterogenous population is essential to identify rare, disease-driving cells, which often exhibit aberrant cellular behaviors like abnormal division, aggressive migration or irregular phylogenetic cell lineages. A recent development in the combination of high-throughput screening microscopy with single cell profiling provides an unprecedented opportunity to decipher the underlying mechanisms of disease-driving phenotypes observed under a microscope. However, accurately and instantly processing large amounts of image data like longitudinal time lapse movies remains a technical challenge when an immediate analysis output (in minutes) of quantitative characterizations is required after data acquisition. Here we present a Fast and Accurate real-time Cell Tracking (FACT) algorithm, which combines GPU-based, ground truth-assisted trainable Weka segmentation and real-time Gaussian mixture model-based cell linking. FACT also implements an automatic cell track correction function to improve the tracking accuracy. With FACT, we can segment [~]20,000 cells in 2 seconds ([~]4.5-27.5 times faster than state-of-the-art), and can export quantifiable features from the cell tracking results minutes after data acquisition (independent of the number of acquired image frames) with average 90-95% tracking precision. Such performance is not feasible with state-of-the-art cell tracking algorithms. We applied FACT to real-time identify directionally migrating glioblastoma cells with 96% precision and to identify rare, irregular cell lineages in a population of [~]10,000 cells from a 24hr-time lapse movie with an average 91% F1 score, results from both were exported instantly, mere minutes after image acquisition.

bioengineering↗

bioRxiv2021

Functional single cell proteomic profiling of cells with abnormal DNA damage response dynamics

Tumor heterogeneity is an important source of cancer therapy resistance. Single cell proteomics has the potential to decipher protein content leading to heterogeneous cellular phenotypes. Single-Cell ProtEomics by Mass Spectrometry (SCoPE-MS) is a recently developed, promising, unbiased proteomic profiling techniques, which allows profiling several tens of single cells for >1000 proteins per cell. However, a method to link single cell proteomes with cellular behaviors is needed to advance this type of profiling technique. Here, we developed a microscopy-based functional single cell proteomic profiling technology, called FUNpro, to link the proteome of individual cells with phenotypes of interest, even if the phenotypes are dynamic or the cells of interest are sparse. FUNpro enables one i) to screen thousands of cells with subcellular resolution and monitor (intra)cellular dynamics using a custom-built microscope, ii) to real-time analyze (intra)cellular dynamics of individual cells using an integrated cell tracking algorithm, iii) to promptly isolate the cells displaying phenotypes of interest, and iv) to single cell proteomically profile the isolated cells. We applied FUNpro to proteomically profile a newly identified small subpopulation of U2OS osteosarcoma cells displaying an abnormal, prolonged DNA damage response (DDR) after ionizing radiation (IR). With this, we identified PDS5A and PGAM5 proteins contributing to the abnormal DDR dynamics and helping the cells survive after IR.

bioengineering↗

bioRxiv2021

Functional single cell selection and annotated profiling of dynamically changing cancer cells

A method connecting single cell genomic or transcriptomic profiles to functional cellular characteristics, in particular time-varying phenotypic changes, would be transformative for single cell and cancer biology. Here, we present fSCS: functional single cell selection. This technology combines a custom-built ultrawide field-of-view optical screening microscope, fast automated image analysis and a new photolabeling method, phototagging, using a newly synthesized visible-light-photoactivatable dye. Using fSCS, we screen, selectively photolabel and isolate cells of interest from large heterogeneous populations based on functional dynamics like fast migration, morphological variation, small molecule uptake or cell division. We combined fSCS with single cell RNA sequencing for functionally annotated transcriptomic profiling of fast migrating and spindle-shaped MCF10A cells with or without TGF{beta} induction. We identified critical genes and pathways driving aggressive migration as well as mesenchymal-like morphology that could not be detected with state-of-the-art single cell transcriptomic analysis. fSCS provides a crucial upstream selection paradigm for single cell sequencing independent of biomarkers, allows enrichment of rare cells and can facilitate the identification and understanding of molecular mechanisms underlying functional phenotypes.

bioengineering↗

bioRxiv2017

Two-photon photoactivated voltage imaging in tissue with an Archaerhodopsin-derived reporter

Robust voltage imaging in tissue remains a technical challenge. Existing combinations of genetically encoded voltage indicators (GEVIs) and microscopy techniques cannot simultaneously achieve sufficiently high voltage sensitivity, background rejection, and time resolution for highresolution mapping of sub-cellular voltage dynamics in intact brain tissue. We developed a pooled high-throughput screening approach to identify Archaerhodopsin mutants with unusual photophysical properties. After screening ~105 cells, we identified a novel GEVI, NovArch, whose 1-photon near infrared fluorescence is reversibly enhanced by weak 2-photon excitation. Because the 2-photon excitation acts catalytically rather than stoichiometrically, high fluorescence signals, optical sectioning, and high time resolution are achieved simultaneously, at modest 2-photon laser power. We developed a microscopy system optimized for NovArch imaging in tissue. The combination of protein and optical engineering enhanced signal contrast sufficiently to enable optical mapping of back-propagating action potentials in dendrites in acute mouse brain slice.

neuroscience↗