Morphodynamics facilitate cancer cells to navigate 3D extracellular matrix

Systematic data analysis pipeline for quantitative morphological cell phenotyping

Quantitative morphological phenotyping (QMP) is an image-based method used to capture morphological features at both the cellular and population level. Its interdisciplinary nature, spanning from data collection to result analysis and interpretation, can lead to uncertainties, particularly among those new to this actively growing field. High analytical specificity for a typical QMP is achieved through sophisticated approaches that can leverage subtle cellular morphological changes. Here, we outline a systematic workflow to refine the QMP methodology. For a practical review, we describe the main steps of a typical QMP; in each step, we discuss the available methods, their applications, advantages, and disadvantages, along with the R functions and packages for easy implementation. This review does not cover theoretical backgrounds, but provides several references for interested researchers. It aims to broaden the horizons for future phenome studies and demonstrate how to exploit years of endeavors to achieve more with less.

Physical network regimes of 3D fibrillar collagen networks trigger invasive phenotypes of breast cancer cells

The mechanical characteristics of the extracellular environment are known to significantly influence cancer cell behavior in vivo and in vitro. The structural complexity and viscoelastic dynamics of the extracellular matrix (ECM) pose significant challenges in understanding its impact on cancer cells. Herein, we report distinct regulatory signatures in the invasion of different breast cancer cell lines into three-dimensional (3D) fibrillar collagen networks, caused by systematic modifications of the physical network properties. By reconstituting collagen networks of thin fibrils, we demonstrate that such networks can display network strand flexibility akin to that of synthetic polymer networks, known to exhibit entropic rubber elasticity. This finding contrasts with the predominant description of the mechanics of fibrillar collagen networks by an enthalpic bending elasticity of rod-like fibrils. Mean-squared displacement analysis of free-standing fibrils confirmed a flexible fiber regime in networks of thin fibrils. Furthermore, collagen fibrils in both networks were softened by the adsorption of highly negatively charged sulfonated polymers and colloidal probe force measurements of network elastic modulus again proofed the occurrence of the two different physical network regimes. Our cell assays revealed that the cellular behavior (morphology, clustering, invasiveness, matrix metalloproteinase (MMP) activity) of the 'weakly invasive' MCF-7 and 'highly invasive' MDA-MB-231 breast cancer cell lines is distinctively affected by the physical (enthalpic/entropic) network regime, and cannot be explained by changes of the network elastic modulus, alone. These results highlight an essential pathway, albeit frequently overlooked, how the physical characteristics of fibrillar ECMs affect cellular behavior. Considering the coexistence of diverse physical network regimes of the ECM in vivo, our findings underscore their critical role of ECM's physical network regimes in tumor progression and other cell functions, and moreover emphasize the significance of 3D in vitro collagen network models for quantifying cell responses in both healthy and pathological states.

Learning dynamical models of single and collective cell migration: a review

Single and collective cell migration are fundamental processes critical for physiological phenomena ranging from embryonic development and immune response to wound healing and cancer metastasis. To understand cell migration from a physical perspective, a broad variety of models for the underlying physical mechanisms that govern cell motility have been developed. A key challenge in the development of such models is how to connect them to experimental observations, which often exhibit complex stochastic behaviours. In this review, we discuss recent advances in data-driven theoretical approaches that directly connect with experimental data to infer dynamical models of stochastic cell migration. Leveraging advances in nanofabrication, image analysis, and tracking technology, experimental studies now provide unprecedented large datasets on cellular dynamics. In parallel, theoretical efforts have been directed towards integrating such datasets into physical models from the single cell to the tissue scale with the aim of conceptualising the emergent behaviour of cells. We first review how this inference problem has been addressed in both freely migrating and confined cells. Next, we discuss why these dynamics typically take the form of underdamped stochastic equations of motion, and how such equations can be inferred from data. We then review applications of data-driven inference and machine learning approaches to heterogeneity in cell behaviour, subcellular degrees of freedom, and to the collective dynamics of multicellular systems. Across these applications, we emphasise how data-driven methods can be integrated with physical active matter models of migrating cells, and help reveal how underlying molecular mechanisms control cell behaviour. Together, these data-driven approaches are a promising avenue for building physical models of cell migration directly from experimental data, and for providing conceptual links between different length-scales of description.

Morphological entropy encodes cellular migration strategies on multiple length scales

Cell migration is crucial for numerous physiological and pathological processes. A cell adapts its morphology, including the overall and nuclear morphology, in response to various cues in complex microenvironments, such as topotaxis and chemotaxis during migration. Thus, the dynamics of cellular morphology can encode migration strategies, from which diverse migration mechanisms can be inferred. However, deciphering the mechanisms behind cell migration encoded in morphology dynamics remains a challenging problem. Here, we present a powerful universal metric, the Cell Morphological Entropy (CME), developed by combining parametric morphological analysis with Shannon entropy. The utility of CME, which accurately quantifies the complex cellular morphology at multiple length scales through the deviation from a perfectly circular shape, is illustrated using a variety of normal and tumor cell lines in different in vitro microenvironments. Our results show how geometric constraints affect the MDA-MB-231 cell nucleus, the emerging interactions of MCF-10A cells migrating on collagen gel, and the critical transition from proliferation to invasion in tumor spheroids. The analysis demonstrates that the CME-based approach provides an effective and physically interpretable tool to measure morphology in real-time across multiple length scales. It provides deeper insight into cell migration and contributes to the understanding of different behavioral modes and collective cell motility in more complex microenvironments.

Imaging actin organisation and dynamics in 3D

The actin cytoskeleton plays a critical role in cell architecture and the control of fundamental processes including cell division, migration and survival. The dynamics and organisation of F-actin have been widely studied in a breadth of cell types on classical two-dimensional (2D) surfaces. Recent advances in optical microscopy have enabled interrogation of these cytoskeletal networks in cells within three-dimensional (3D) scaffolds, tissues and in vivo. Emerging studies indicate that the dimensionality experienced by cells has a profound impact on the structure and function of the cytoskeleton, with cells in 3D environments exhibiting cytoskeletal arrangements that differ to cells in 2D environments. However, the addition of a third (and fourth, with time) dimension leads to challenges in sample preparation, imaging and analysis, necessitating additional considerations to achieve the required signal-to-noise ratio and spatial and temporal resolution. Here, we summarise the current tools for imaging actin in a 3D context and highlight examples of the importance of this in understanding cytoskeletal biology and the challenges and opportunities in this domain.

Bridging live-cell imaging and next-generation cancer treatment

By providing spatial, molecular and morphological data over time, live-cell imaging can provide a deeper understanding of the cellular and signalling events that determine cancer response to treatment. Understanding this dynamic response has the potential to enhance clinical outcome by identifying biomarkers or actionable targets to improve therapeutic efficacy. Here, we review recent applications of live-cell imaging for uncovering both tumour heterogeneity in treatment response and the mode of action of cancer-targeting drugs. Given the increasing uses of T cell therapies, we discuss the unique opportunity of time-lapse imaging for capturing the interactivity and motility of immunotherapies. Although traditionally limited in the number of molecular features captured, novel developments in multidimensional imaging and multi-omics data integration offer strategies to connect single-cell dynamics to molecular phenotypes. We review the effect of these recent technological advances on our understanding of the cellular dynamics of tumour targeting and discuss their implication for next-generation precision medicine.

Facilitating cell segmentation with the projection-enhancement network

Contemporary approaches to instance segmentation in cell science use 2D or 3D convolutional networks depending on the experiment and data structures. However, limitations in microscopy systems or efforts to prevent phototoxicity commonly require recording sub-optimally sampled data that greatly reduces the utility of such 3D data, especially in crowded sample space with significant axial overlap between objects. In such regimes, 2D segmentations are both more reliable for cell morphology and easier to annotate. In this work, we propose the projection enhancement network (PEN), a novel convolutional module which processes the sub-sampled 3D data and produces a 2D RGB semantic compression, and is trained in conjunction with an instance segmentation network of choice to produce 2D segmentations. Our approach combines augmentation to increase cell density using a low-density cell image dataset to train PEN, and curated datasets to evaluate PEN. We show that with PEN, the learned semantic representation in CellPose encodes depth and greatly improves segmentation performance in comparison to maximum intensity projection images as input, but does not similarly aid segmentation in region-based networks like Mask-RCNN. Finally, we dissect the segmentation strength against cell density of PEN with CellPose on disseminated cells from side-by-side spheroids. We present PEN as a data-driven solution to form compressed representations of 3D data that improve 2D segmentations from instance segmentation networks.

Interaction of phenotypic sublines isolated from triple-negative breast cancer cell line MDA-MB-231 modulates their sensitivity to paclitaxel and doxorubicin in 2D and 3D assays

Breast cancer is a rapidly evolving, multifactorial disease that accumulates numerous genetic and epigenetic alterations. These result in molecular and phenotypic heterogeneity within the tumor, the complexity of which is further amplified through specific interactions between cancer cells. We aimed to analyze cell phenotypic sublines and the influence of their interaction on drug resistance, spheroid formation, and migration. Seven sublines were derived from the MDA-MB-231 breast cancer cell line using a multiple-cell suspension dilution. The growth rate, CD133 receptor expression, migration ability, and chemosensitivity of these sublines to anticancer drugs doxorubicin (DOX) and paclitaxel (PTX) were determined. Three sublines (F5, D8, H2) have been chosen to study their interaction in 2D and 3D assays. In the 2D model, the resistance of all sublines composition to DOX decreased, but in the 3D model, the resistance of all sublines except H2, increased to both PTX and DOX. In the 3D model, the combined sublines F5 and D8 had higher resistance to DOX and statistically significantly lower resistance for PTX compared to the control. The interaction between cancer stem-like cells (F5) and increased migration cells (D8) increased resistance to PTX in cell monolayer and increased resistance against both DOX and PTX in the spheroids. The interaction of DOX-resistant (H2) cells with other cell subpopulations (D8, F5, HF) decreased the resistance to DOX in cell monolayer and both DOX and PTX in spheroids.

Spatial topology of organelle is a new breast cancer cell classifier

Genomics and proteomics have been central to identify tumor cell populations, but more accurate approaches to classify cell subtypes are still lacking. We propose a new methodology to accurately classify cancer cells based on their organelle spatial topology. Herein, we developed an organelle topology-based cell classification pipeline (OTCCP), which integrates artificial intelligence (AI) and imaging quantification to analyze organelle spatial distribution and inter-organelle topology. OTCCP was used to classify a panel of human breast cancer cells, grown as 2D monolayer or 3D tumor spheroids using early endosomes, mitochondria, and their inter-organelle contacts. Organelle topology allows for a highly precise differentiation between cell lines of different subtypes and aggressiveness. These findings lay the groundwork for using organelle topological profiling as a fast and efficient method for phenotyping breast cancer function as well as a discovery tool to advance our understanding of cancer cell biology at the subcellular level.

Micromechanical remodeling of the extracellular matrix by invading tumors: anisotropy and heterogeneity

Altered tissue mechanics is an important signature of invasive solid tumors. While the phenomena have been extensively studied by measuring the bulk rheology of the extracellular matrix (ECM) surrounding tumors, micromechanical remodeling at the cellular scale remains poorly understood. By combining holographic optical tweezers and confocal microscopy on in vitro tumor models, we show that the micromechanics of collagen ECM surrounding an invading tumor demonstrate directional anisotropy, spatial heterogeneity and significant variations in time as tumors invade. To test the cellular mechanisms of ECM micromechanical remodeling, we construct a simple computational model and verify its predictions with experiments. We find that collective force generation of a tumor stiffens the ECM and leads to anisotropic local mechanics such that the extension direction is more rigid than the compression direction. ECM degradation by cell-secreted matrix metalloproteinase softens the ECM, and active traction forces from individual disseminated cells re-stiffen the matrix. Together, these results identify plausible biophysical mechanisms responsible for the remodeled ECM micromechanics surrounding an invading tumor.

Moving through a changing world: Single cell migration in 2D vs. 3D

Migration of single adherent cells is frequently observed in the developing and adult organism and has been the subject of many studies. Yet, while elegant work has elucidated molecular and mechanical cues affecting motion dynamics on a flat surface, it remains less clear how cells migrate in a 3D setting. In this review, we explore the changing parameters encountered by cells navigating through a 3D microenvironment compared to cells crawling on top of a 2D surface, and how these differences alter subcellular structures required for propulsion. We further discuss how such changes at the micro-scale impact motion pattern at the macro-scale.

Quantitative cell imaging approaches to metastatic state profiling

Genetic heterogeneity of metastatic dissemination has proven challenging to identify exploitable markers of metastasis; this bottom-up approach has caused a stalemate between advances in metastasis and the late stage of the disease. Advancements in quantitative cellular imaging have allowed the detection of morphological phenotype changes specific to metastasis, the morphological changes connected to the underlying complex signaling pathways, and a robust readout of metastatic cell state. This review focuses on the recent machine and deep learning developments to gain detailed information about the metastatic cell state using light microscopy. We describe the latest studies using quantitative cell imaging approaches to identify cell appearance-based metastatic patterns. We discuss how quantitative cancer biologists can use these frameworks to work backward toward exploitable hidden drivers in the metastatic cascade and pioneering new Frontier drug discoveries specific for metastasis.

Efficient deformation mechanisms enable invasive cancer cells to migrate faster in 3D collagen networks

Cancer cell migration is a widely studied topic but has been very often limited to two dimensional motion on various substrates. Indeed, less is known about cancer cell migration in 3D fibrous-extracellular matrix (ECM) including variations of the microenvironment. Here we used 3D time lapse imaging on a confocal microscope and a phase correlation method to follow fiber deformations, as well as cell morphology and live actin distribution during the migration of cancer cells. Different collagen concentrations together with three bladder cancer cell lines were used to investigate the role of the metastatic potential on 3D cell migration characteristics. We found that grade-3 cells (T24 and J82) are characterized by a great diversity of shapes in comparison with grade-2 cells (RT112). Moreover, grade-3 cells with the highest metastatic potential (J82) showed the highest values of migration speeds and diffusivities at low collagen concentration and the greatest sensitivity to collagen concentration. Our results also suggested that the small shape fluctuations of J82 cells are the signature of larger migration velocities. Moreover, the displacement fields generated by J82 cells showed significantly higher fiber displacements as compared to T24 and RT112 cells, regardless of collagen concentration. The analysis of cell movements enhanced the fact that bladder cancer cells were able to exhibit different phenotypes (mesenchymal, amoeboid). Furthermore, the analysis of spatio-temporal migration mechanisms showed that cancer cells are able to push or pull on collagen fibers, therefore producing efficient local collagen deformations in the vicinity of cells. Our results also revealed that dense actin regions are correlated with the largest displacement fields, and this correlation is enhanced for the most invasive J82 cancer cells. Therefore this work opens up new routes to understand cancer cell migration in soft biological networks.

Prolonged Cadmium Exposure Alters Migration Dynamics and Increases Heterogeneity of Human Uterine Fibroid Cells—Insights from Time Lapse Analysis

Cadmium (Cd) is one of the most prevalent environmental heavy metal contaminants and is considered an endocrine disruptor and carcinogen. In women with uterine fibroids, there is a correlation between blood Cd levels and fibroid tumor size. In this study, fibroid cells were exposed to 10 µM CdCl2 for 6 months and a fast-growing Cd-Resistant Leiomyoma culture, termed CR-LM6, was recovered. To characterize the morphological and mechanodynamic features of uterine fibroid cells associated with prolonged Cd exposure, we conducted time lapse imaging using a Zeiss confocal microscope and analyzed data by Imaris and RStudio. Our experiments recorded more than 64,000 trackable nuclear surface objects, with each having multiple parameters such as nuclear size and shape, speed, location, orientation, track length, and track straightness. Quantitative analysis revealed that prolonged Cd exposure significantly altered cell migration behavior, such as increased track length and reduced track straightness. Cd exposure also significantly increased the heterogeneity in nuclear size. Additionally, Cd significantly increased the median and variance of instantaneous speed, indicating that Cd exposure results in higher speed and greater variation in motility. Profiling of mRNA by NanoString analysis and Ingenuity Pathway Analysis (IPA) strongly suggested that the direction of gene expression changes due to Cd exposure enhanced cell movement and invasion. The altered expression of extracellular matrix (ECM) genes such as collagens, matrix metallopeptidases (MMPs), secreted phosphoprotein 1 (SPP1), which are important for migration contact guidance, may be responsible for the greater heterogeneity. The significantly increased heterogeneity of nuclear size, speed, and altered migration patterns may be a prerequisite for fibroid cells to attain characteristics favorable for cancer progression, invasion, and metastasis.

Three-dimensional cancer cell migration directed by dual mechanochemical guidance

Directed cell migration guided by external cues plays a central role in many physiological and pathophysiological processes. The microenvironment of cells often simultaneously contains various cues and the motility response of cells to multiplexed guidance is poorly understood. Here we combine experiments and mathematical models to study the three-dimensional migration of breast cancer cells in the presence of both contact guidance and a chemoattractant gradient. We find that the chemotaxis of cells is complicated by the presence of contact guidance as the microstructure of extracellular matrix (ECM) vary spatially. In the presence of dual guidance, the impact of ECM alignment is determined externally by the coherence of ECM fibers and internally by cell mechanosensing Rho/Rock pathways. When contact guidance is parallel to the chemical gradient, coherent ECM fibers significantly increase the efficiency of chemotaxis. When contact guidance is perpendicular to the chemical gradient, cells exploit the ECM disorder to locate paths for chemotaxis. Our results underscore the importance of fully characterizing the cancer cell microenvironment in order to better understand invasion and metastasis.