Label-free Cell Tracking Enables Collective Motion Phenotyping in Epithelial Monolayers

Collective cell migration is an umbrella term for a rich variety of cell behaviors, whose distinct character is essential for biological function, notably for cancer metastasis. One essential feature of collective behavior is the motion of cells relative to their immediate neighbors. We introduce an AI-based pipeline to segment and track cell nuclei from phase contrast images. Nuclei segmentation is based on a U‐Net convolutional neural network trained on images with nucleus staining. Tracking, based on the Crocker-Grier algorithm, quantifies nuclei movement and allows for robust downstream analysis of collective motion. Since the AI algorithm required no new training data, our approach promises to be applicable to and yield new insights for vast libraries of existing collective motion images. In a systematic analysis of a cell line panel with oncogenic mutations, we find that the collective rearrangement metric, D2min, which reflects non-affine motion, shows promise as an indicator of metastatic potential.

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Distinct roles of tumor associated mutations in collective cell migration

Scientific Reports ◽

10.1038/s41598-021-89130-6 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Rachel M. Lee ◽

Michele I. Vitolo ◽

Wolfgang Losert ◽

Stuart S. Martin

Keyword(s):

Collective Behavior ◽

Metastatic Potential ◽

Breast Epithelial Cell ◽

Collective Cell Migration ◽

Label Free ◽

Collective Migration ◽

Contrast Imaging ◽

Pten Deletion

AbstractRecent evidence suggests that groups of cells are more likely to form clinically dangerous metastatic tumors, emphasizing the importance of understanding mechanisms underlying collective behavior. The emergent collective behavior of migrating cell sheets in vitro has been shown to be disrupted in tumorigenic cells but the connection between this behavior and in vivo tumorigenicity remains unclear. We use particle image velocimetry to measure a multidimensional migration phenotype for genetically defined human breast epithelial cell lines that range in their in vivo behavior from non-tumorigenic to aggressively metastatic. By using cells with controlled mutations, we show that PTEN deletion enhances collective migration, while Ras activation suppresses it, even when combined with PTEN deletion. These opposing effects on collective migration of two mutations that are frequently found in patient tumors could be exploited in the development of novel treatments for metastatic disease. Our methods are based on label-free phase contrast imaging, and thus could easily be applied to patient tumor cells. The short time scales of our approach do not require potentially selective growth, and thus in combination with label-free imaging would allow multidimensional collective migration phenotypes to be utilized in clinical assessments of metastatic potential.

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Correlating cell shape and cellular stress in motile confluent tissues

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1705921114 ◽

2017 ◽

Vol 114 (48) ◽

pp. 12663-12668 ◽

Cited By ~ 38

Author(s):

Xingbo Yang ◽

Dapeng Bi ◽

Michael Czajkowski ◽

Matthias Merkel ◽

M. Lisa Manning ◽

...

Keyword(s):

Cell Mechanics ◽

Cell Shape ◽

Cancer Metastasis ◽

Collective Motion ◽

Regulatory Mechanism ◽

Temporal Correlation ◽

Collective Cell Migration ◽

Cell Shapes ◽

Cellular Stresses ◽

Interaction Stress

Collective cell migration is a highly regulated process involved in wound healing, cancer metastasis, and morphogenesis. Mechanical interactions among cells provide an important regulatory mechanism to coordinate such collective motion. Using a self-propelled Voronoi (SPV) model that links cell mechanics to cell shape and cell motility, we formulate a generalized mechanical inference method to obtain the spatiotemporal distribution of cellular stresses from measured traction forces in motile tissues and show that such traction-based stresses match those calculated from instantaneous cell shapes. We additionally use stress information to characterize the rheological properties of the tissue. We identify a motility-induced swim stress that adds to the interaction stress to determine the global contractility or extensibility of epithelia. We further show that the temporal correlation of the interaction shear stress determines an effective viscosity of the tissue that diverges at the liquid–solid transition, suggesting the possibility of extracting rheological information directly from traction data.

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On Improving an Already Competitive Segmentation Algorithm for the Cell Tracking Challenge - Lessons Learned

10.1101/2021.06.26.450019 ◽

2021 ◽

Author(s):

Tim Scherr ◽

Katharina Loeffler ◽

Oliver Neumann ◽

Ralf Mikut

Keyword(s):

Cell Tracking ◽

Data Augmentation ◽

Signal To Noise Ratio ◽

Data Representation ◽

Lessons Learned ◽

Training Data ◽

Fine Tuning ◽

Data Sets ◽

Cell Nuclei ◽

Distance Map

The virtually error-free segmentation and tracking of densely packed cells and cell nuclei is still a challenging task. Especially in low-resolution and low signal-to-noise-ratio microscopy images erroneously merged and missing cells are common segmentation errors making the subsequent cell tracking even more difficult. In 2020, we successfully participated as team KIT-Sch-GE (1) in the 5th edition of the ISBI Cell Tracking Challenge. With our deep learning-based distance map regression segmentation and our graph-based cell tracking, we achieved multiple top 3 rankings on the diverse data sets. In this manuscript, we show how our approach can be further improved by using another optimizer and by fine-tuning training data augmentation parameters, learning rate schedules, and the training data representation. The fine-tuned segmentation in combination with an improved tracking enabled to further improve our performance in the 6th edition of the Cell Tracking Challenge 2021 as team KIT-Sch-GE (2).

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Entelon® (Vitis vinifera Seed Extract) Prevents Cancer Metastasis via the Downregulation of Interleukin-1 Alpha in Triple-Negative Breast Cancer Cells

Molecules ◽

10.3390/molecules26123644 ◽

2021 ◽

Vol 26 (12) ◽

pp. 3644

Author(s):

Daeun You ◽

Yisun Jeong ◽

Sun Young Yoon ◽

Sung A Kim ◽

Eunji Lo ◽

...

Keyword(s):

Breast Cancer ◽

Signaling Pathway ◽

Lung Metastasis ◽

Triple Negative Breast Cancer ◽

Cancer Metastasis ◽

Triple Negative ◽

Interleukin 1 ◽

Metastatic Potential ◽

Cancer Cell Proliferation ◽

Dependent Pathway

Interleukin-1 (IL1) is a proinflammatory cytokine and promotes cancer cell proliferation and invasiveness in a diversity of cancers, such as breast and colon cancer. Here, we focused on the pharmacological effect of Entelon® (ETL) on the tumorigenesis of triple-negative breast cancer (TNBC) cells by IL1-alpha (IL1A). IL1A enhanced the cell growth and invasiveness of TNBC cells. We observed that abnormal IL1A induction is related with the poor prognosis of TNBC patients. IL1A also increased a variety of chemokines such as CCL2 and IL8. Interestingly, IL1A expression was reduced by the ETL treatment. Here, we found that ETL significantly decreased the MEK/ERK signaling pathway in TNBC cells. IL1A expression was reduced by UO126. Lastly, we studied the effect of ETL on the metastatic potential of TNBC cells. Our results showed that ETL significantly reduced the lung metastasis of TNBC cells. Our results showed that IL1A expression was regulated by the MEK/ERK- and PI3K/AKT-dependent pathway. Taken together, ETL inhibited the MEK/ERK and PI3K/AKT signaling pathway and suppressing the lung metastasis of TNBC cells through downregulation of IL1A. Therefore, we propose the possibility of ETL as an effective adjuvant for treating TNBC.

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Label-Free Automated Cell Tracking: Analysis of the Role of E-cadherin Expression in Collective Electrotaxis

Cellular and Molecular Bioengineering ◽

10.1007/s12195-016-0471-6 ◽

2016 ◽

Vol 10 (1) ◽

pp. 89-101 ◽

Cited By ~ 3

Author(s):

Mark L. Lalli ◽

Brooke Wojeski ◽

Anand R. Asthagiri

Keyword(s):

Cell Tracking ◽

Label Free ◽

E Cadherin

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Coarse Raman and optical diffraction tomographic imaging enable label-free phenotyping of isogenic breast cancer cells of varying metastatic potential

10.1101/2020.09.23.309138 ◽

2020 ◽

Author(s):

Santosh Kumar Paidi ◽

Vaani Shah ◽

Piyush Raj ◽

Kristine Glunde ◽

Rishikesh Pandey ◽

...

Keyword(s):

Breast Cancer ◽

Raman Spectroscopy ◽

Molecular Imaging ◽

Single Cell ◽

Cancer Cells ◽

Breast Cancer Cells ◽

Metastatic Potential ◽

Optical Diffraction ◽

Multivariate Curve Resolution ◽

Label Free

AbstractIdentification of the metastatic potential represents one of the most important tasks for molecular imaging of cancer. While molecular imaging of metastases has witnessed substantial progress as an area of clinical inquiry, determining precisely what differentiates the metastatic phenotype has proven to be more elusive underscoring the need to marry emerging imaging techniques with tumor biology. In this study, we utilize both the morphological and molecular information provided by 3D optical diffraction tomography and Raman spectroscopy, respectively, to propose a label-free route for optical phenotyping of cancer cells at single-cell resolution. By using an isogenic panel of cell lines derived from MDA-MB-231 breast cancer cells that vary in their metastatic potential, we show that 3D refractive index tomograms can capture subtle morphological differences among the parental, circulating tumor cells, and lung metastatic cells. By leveraging the molecular specificity of Raman spectroscopy, we demonstrate that coarse Raman microscopy is capable of rapidly mapping a sufficient number of cells for training a random forest classifier that can accurately predict the metastatic potential of cells at a single-cell level. We also leverage multivariate curve resolution – alternating least squares decomposition of the spectral dataset to demarcate spectra from cytoplasm and nucleus, and test the feasibility of identifying metastatic phenotypes using the spectra only from the cytoplasmic and nuclear regions. Overall, our study provides a rationale for employing coarse Raman mapping to substantially reduce measurement time thereby enabling the acquisition of reasonably large training datasets that hold the key for label-free single-cell analysis and, consequently, for differentiation of indolent from aggressive phenotypes.

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AMPK-independent LKB1 activity is required for efficient epithelial ovarian cancer metastasis

10.1101/591073 ◽

2019 ◽

Author(s):

Adrian Buensuceso ◽

Yudith Ramos Valdes ◽

Gabriel E. DiMattia ◽

Trevor G. Shepherd

Keyword(s):

Ovarian Cancer ◽

Epithelial Ovarian Cancer ◽

Cell Survival ◽

Cell Lines ◽

Cancer Metastasis ◽

Metastatic Potential ◽

Metastatic Tumour ◽

Ampk Signaling ◽

Dual Specificity ◽

Liver Kinase B1

ABSTRACTEpithelial ovarian cancer (EOC) spreads by direct dissemination of malignant cells and multicellular clusters, known as spheroids, into the peritoneum followed by implantation and growth on abdominal surfaces. Using a spheroid model system of EOC metastasis, we discovered that Liver kinase B1 (LKB1), encoded by theSTK11gene, and its canonical substrate AMP-activated protein kinase (AMPK) are activated in EOC spheroids, yet only LKB1 is required for cell survival. We have now generatedSTK11-knockout cell lines using normal human FT190 cells and three EOC cell lines, OVCAR8, HeyA8, and iOvCa147.STK11KO did not affect growth and viability in adherent culture, but it decreased anchorage-independent growth of EOC cells. EOC spheroids lacking LKB1 had markedly impaired growth and viability, whereas there was no difference in normal FT190 spheroids. To test whether LKB1 loss affects EOC metastasis, we performed intraperitoneal injections of OVCAR8-, HeyA8-, and iOvCa147-STK11KO cells, and respective controls. LKB1 loss exhibited a dramatic reduction on tumour burden and metastatic potential; in particular, OVCAR8-STK11KO tumours had evidence of extensive necrosis, apoptosis and hypoxia. Interestingly, LKB1 loss did not affect AMPKα phosphorylation in EOC spheroids and tumour xenografts, indicating that LKB1 signaling to support EOC cell survival in spheroids and metastatic tumour growth occurs via other downstream mediators. We identified the dual-specificity phosphatase DUSP4 as a commonly upregulated protein due to LKB1 loss; indeed,DUSP4knockdown in HeyA8-STK11KO cells restored spheroid formation and viability. Our results strongly indicate that intact LKB1 activity independent of downstream AMPK signaling is required during EOC metastasis.

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miR-200 deficiency promotes lung cancer metastasis by activating cancer-associated fibroblasts

10.1101/2020.09.02.276550 ◽

2020 ◽

Author(s):

Bin Xue ◽

Chen-Hua Chuang ◽

Haydn M. Prosser ◽

Cesar Seigi Fuziwara ◽

Claudia Chan ◽

...

Keyword(s):

Lung Cancer ◽

Lung Adenocarcinoma ◽

Cancer Cells ◽

Molecular Mechanisms ◽

Cancer Metastasis ◽

Lung Cancer Mortality ◽

Tumor Stroma ◽

Metastatic Potential ◽

Human Lung Cancer ◽

Cancer Associated Fibroblasts

AbstractLung adenocarcinoma, the most prevalent lung cancer subtype, is characterized by its high propensity to metastasize. Despite the importance of metastasis in lung cancer mortality, its underlying cellular and molecular mechanisms remain largely elusive. Here, we identified miR-200 miRNAs as potent suppressors for lung adenocarcinoma metastasis. miR-200 expression is specifically repressed in mouse metastatic lung adenocarcinomas, and miR-200 decrease strongly correlates with poor patient survival. Consistently, deletion of mir-200c/141 in the KrasLSL-G12D/+; Trp53flox/flox lung adenocarcinoma mouse model significantly promoted metastasis, generating a desmoplastic tumor stroma highly reminiscent of metastatic human lung cancer. miR-200 deficiency in lung cancer cells promotes the proliferation and activation of adjacent cancer-associated fibroblasts (CAFs), which in turn elevates the metastatic potential of cancer cells. miR-200 regulates the functional interaction between cancer cells and CAFs, at least in part, by targeting Notch ligand Jagged1 and Jagged2 in cancer cells and inducing Notch activation in adjacent CAFs. Hence, the interaction between cancer cells and CAFs constitutes an essential mechanism to promote metastatic potential.

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Fibronectin-Expressing Mesenchymal Tumor Cells Promote Breast Cancer Metastasis

Cancers ◽

10.3390/cancers12092553 ◽

2020 ◽

Vol 12 (9) ◽

pp. 2553 ◽

Cited By ~ 3

Author(s):

Brian H. Jun ◽

Tianqi Guo ◽

Sarah Libring ◽

Monica K. Chanda ◽

Juan Sebastian Paez ◽

...

Keyword(s):

Tumor Cells ◽

Cell Tracking ◽

Cancer Metastasis ◽

Breast Cancer Metastasis ◽

Mesenchymal Tumor ◽

Minority Population ◽

Metastatic Niche ◽

Epithelial Tumor ◽

Epithelial Tumor Cells ◽

Cell Series

Tumor metastasis is connected to epithelial-mesenchymal heterogeneity (EMH) and the extracellular matrix (ECM) within the tumor microenvironment. Mesenchymal-like fibronectin (FN) expressing tumor cells enhance metastasis within tumors that have EMH. However, the secondary tumors are primarily composed of the FN null population. Interestingly, during tumor cell dissemination, the invasive front has more mesenchymal-like characteristics, although the outgrowths of metastatic colonies consist of a more epithelial-like population of cells. We hypothesize that soluble FN provided by mesenchymal-like tumor cells plays a role in supporting the survival of the more epithelial-like tumor cells within the metastatic niche in a paracrine manner. Furthermore, due to a lower rate of proliferation, the mesenchymal-like tumor cells become a minority population within the metastatic niche. In this study, we utilized a multi-parametric cell-tracking algorithm and immunoblotting to evaluate the effect of EMH on the growth and invasion of an isogenic cell series within a 3D collagen network using a microfluidic platform. Using the MCF10A progression series, we demonstrated that co-culture with FN-expressing MCF10CA1h cells significantly enhanced the survival of the more epithelial MCF10CA1a cells, with a two-fold increase in the population after 5 days in co-culture, whereas the population of the MCF10CA1a cells began to decrease after 2.5 days when cultured alone (p < 0.001). However, co-culture did not significantly alter the rate of proliferation for the more mesenchymal MCF10CA1h cells. Epithelial tumor cells not only showed prolonged survival, but migrated significantly longer distances (350 µm compared with 150 µm, respectively, p < 0.01) and with greater velocity magnitude (4.5 µm/h compared with 2.1 µm/h, respectively, p < 0.001) under co-culture conditions and in response to exogenously administered FN. Genetic depletion of FN from the MCF10CA1h cells resulted in a loss of survival and migration capacity of the epithelial and mesenchymal populations. These data suggest that mesenchymal tumor cells may function to support the survival and outgrowth of more epithelial tumor cells within the metastatic niche and that inhibition of FN production may provide a valuable target for treating metastatic disease.

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Addressing cellular heterogeneity in tumor and circulation for refined prognostication

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1907904116 ◽

2019 ◽

Vol 116 (36) ◽

pp. 17957-17962 ◽

Cited By ~ 8

Author(s):

Su Bin Lim ◽

Trifanny Yeo ◽

Wen Di Lee ◽

Ali Asgar S. Bhagat ◽

Swee Jin Tan ◽

...

Keyword(s):

Risk Model ◽

Expression Profiles ◽

Metastatic Potential ◽

Cellular Heterogeneity ◽

Intratumor Heterogeneity ◽

Specific Gene ◽

Label Free ◽

Tissue Samples ◽

Inertial Focusing ◽

Gene Signatures

Despite pronounced genomic and transcriptomic heterogeneity in non–small-cell lung cancer (NSCLC) not only between tumors, but also within a tumor, validation of clinically relevant gene signatures for prognostication has relied upon single-tissue samples, including 2 commercially available multigene tests (MGTs). Here we report an unanticipated impact of intratumor heterogeneity (ITH) on risk prediction of recurrence in NSCLC, underscoring the need for a better genomic strategy to refine prognostication. By leveraging label-free, inertial-focusing microfluidic approaches in retrieving circulating tumor cells (CTCs) at single-cell resolution, we further identified specific gene signatures with distinct expression profiles in CTCs from patients with differing metastatic potential. Notably, a refined prognostic risk model that reconciles the level of ITH and CTC-derived gene expression data outperformed the initial classifier in predicting recurrence-free survival (RFS). We propose tailored approaches to providing reliable risk estimates while accounting for ITH-driven variance in NSCLC.

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