Selected Publications
AAnet resolves a continuum of spatially-localized cell states to unveil intratumoral heterogeneity
Venkat A, Youlten SE, Juan BPS, Purcell CA, Gupta S, Amodio M, Neumann DP, Lock JG, Westacott AE, McCool CS, Burkhardt DB, Benz A, Mollbrink A, Lundeberg J, Dijk D van, Holst J, Goldstein LD, Kummerfeld S, Krishnaswamy S, Chaffer CL
Cancer Discovery 2025
https://doi.org/10.1158/2159-8290.cd-24-0684
Identifying functionally important cell states and structure within heterogeneous tumors remains a significant biological and computational challenge. Current clustering or trajectory-based models are ill-equipped to address the notion that cancer cells reside along a phenotypic continuum. We present Archetypal Analysis network (AAnet), a neural network that learns archetypal states within a phenotypic continuum in single-cell data. Unlike traditional archetypal analysis, AAnet learns archetypes in simplex-shaped neural network latent space. Using pre-clinical models and clinical breast cancers, AAnet resolves distinct cell states and processes, including cell proliferation, hypoxia, metabolism and immune interactions. Primary tumor archetypes are recapitulated in matched liver, lung and lymph node metastases. Spatial transcriptomics reveal archetypal organization within the tumor, and, intra-archetypal mirroring between cancer and adjacent stromal cells. AAnet identifies GLUT3 within the hypoxic archetype that proves critical for tumor growth and metastasis. AAnet is a powerful tool, capturing complex, functional cell states from multimodal data.
Extensible Immunofluorescence (ExIF) accessibly generates high-plexity datasets by integrating standard 4-plex imaging data.
Gunawan, I; Kohane, FV; Dey, M; Nguyen, K; Zheng, Y; Neumann, DP; Vafaee, F; Meijering, E; Lock, JG
Nature Communications. 2025;16(1):4606
DOI: 10.1038/s41467-025-59592-7
Standard immunofluorescence imaging captures just ~4 molecular markers (4-plex) per cell, limiting dissection of complex biology. Inspired by multimodal omics-based data integration approaches, we propose an Extensible Immunofluorescence (ExIF) framework that transforms carefully designed but easily produced panels of 4-plex immunofluorescence into a unified dataset with theoretically unlimited marker plexity, using generative deep learning-based virtual labelling. ExIF enables integrated analyses of complex cell biology, exemplified here through interrogation of the epithelial-mesenchymal transition (EMT), driving significant improvements in downstream quantitative analyses usually reserved for omics data, including: classification of cell phenotypes; manifold learning of cell phenotype heterogeneity; and pseudotemporal inference of molecular marker dynamics. Introducing data integration concepts from omics to microscopy, ExIF empowers life scientists to use routine 4-plex fluorescence microscopy to quantitatively interrogate complex, multimolecular single-cell processes in a manner that approaches the performance of multiplexed labelling methods whose uptake remains limited.
Abstract A031: Harnessing the potential of circulating tumour cells for precision diagnostics via multiplexed imaging of the levels and subcellular localizations of over 50 cell identity, state and signaling markers per cell.
Lock, JG; Mann, TJ; Zheng, Y; Khan, T; Neumann, D; James, A; Becker, T; Roberts, T
Clinical Cancer Research. 2024;30(21_Supplement):A031-A031
DOI: 10.1158/1557-3265.liqbiop24-a031
Abstract Due to their longitudinal accessibility through liquid biopsy, circulating tumour cells (CTCs) hold huge potential as sources of molecular information capable of underpinning precision diagnostics in cancer. Yet key methodological limitations have thus far constrained the practical utility of CTCs as diagnostic analytes, including: a) reliance on positive CTC selection strategies that can bias against distinctive CTC subpopulations (e.g. low EpCAM, low size, low density), and; b) standard fluorescence imaging methods that capture only 4-6 molecular markers per cell. Since 3-4 markers are required to identify CTCs with even moderate confidence, such low marker plexity acutely restricts derivation of additional molecular insights about the cellular states and molecular signals that may be driving disease. Yet these insights are essential to guide selection of optimal therapies, to adapt therapeutic strategies as cycles of resistance arise, and even to stratify patients to the trials needed to expand the therapeutic arsenal. To overcome these challenges and harness the potential of CTCs for precision diagnostics, we have developed a comprehensive pipeline for CTC isolation, storage and batch processing via multiplexed immunofluorescence imaging of 50+ molecular markers per cell. These markers illuminate CTC identity, state (epithelial vs mesenchymal vs stemness), fate (proliferation, death, senescence) and signalling (spanning ∼10 alternate 'driver' signalling pathways), providing truly unprecedented molecular insights per CTC, across heterogeneous CTC populations, and regarding potential therapeutic targets. Downstream single-cell quantitative image analyses enable robust but adaptable computational parsing of CTC identity, removing false-positive and false-negative instances otherwise resulting from standard 4-plex CTC definitions (e.g. DAPI, CD45, EpCAM, pan-Cytokeratin) and complementing our upstream use of negative selection methods that capture the full, unbiased diversity of CTCs. The remaining 40+ markers facilitate integrated analyses of relationships between CTC state, fate and signalling pathways, capturing not only protein and phospho-protein expression levels per cell, but also variations in subcellular localisation that constitute additional layers of functional regulation not captured by most omics methods. Excitingly, when combined with machine learning, our approach can discern therapy- resistant cell subpopulations with over 98% accuracy in models of prostate cancer therapy resistance. We are now assessing CTC diversity across patients ranging from treatment naïve to advanced castrate-resistance prostate cancers. Overall, our unique high-plexity analysis now enables interrogation of detailed states, signals and dependencies in each patient CTC, providing the foundations for next-generation precision diagnostics to continuously match individual patients to optimal therapies at each stage of their disease journey. We believe this marks a significant step towards harnessing the true potential of CTCs for precision diagnostics. Citation Format: John G Lock, Tim J Mann, Ye Zheng, Tanzila Khan, Daniel Neumann, Alexander James, Therese Becker, Tara Roberts. Harnessing the potential of circulating tumour cells for precision diagnostics via multiplexed imaging of the levels and subcellular localizations of over 50 cell identity, state and signaling markers per cell [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr A031.
An introduction to representation learning for single-cell data analysis.
Gunawan, I; Vafaee, F; Meijering, E; Lock, JG
Cell Reports Methods. 2023;3(8):100547
DOI: 10.1016/j.crmeth.2023.100547
Single-cell-resolved systems biology methods, including omics- and imaging-based measurement modalities, generate a wealth of high-dimensional data characterizing the heterogeneity of cell populations. Representation learning methods are routinely used to analyze these complex, high-dimensional data by projecting them into lower-dimensional embeddings. This facilitates the interpretation and interrogation of the structures, dynamics, and regulation of cell heterogeneity. Reflecting their central role in analyzing diverse single-cell data types, a myriad of representation learning methods exist, with new approaches continually emerging. Here, we contrast general features of representation learning methods spanning statistical, manifold learning, and neural network approaches. We consider key steps involved in representation learning with single-cell data, including data pre-processing, hyperparameter optimization, downstream analysis, and biological validation. Interdependencies and contingencies linking these steps are also highlighted. This overview is intended to guide researchers in the selection, application, and optimization of representation learning strategies for current and future single-cell research applications.
Copper chelation suppresses epithelial-mesenchymal transition by inhibition of canonical and non-canonical TGF-β signaling pathways in cancer.
Poursani, EM; Mercatelli, D; Raninga, P; Bell, JL; Saletta, F; Kohane, FV; Neumann, DP; Zheng, Y; Rouaen, JRC; Jue, TR; Michniewicz, FT; Schadel, P; Kasiou, E; Tsoli, M; Cirillo, G; Waters, S; Shai-Hee, T; Cazzoli, R; Brettle, M; Slapetova, I; Kasherman, M; Whan, R; Souza-Fonseca-Guimaraes, F; Vahdat, L; Ziegler, D; Lock, JG; Giorgi, FM; Khanna, K; Vittorio, O
Cell & Bioscience. 2023;13(1):132
DOI: 10.1186/s13578-023-01083-7
Metastatic cancer cells exploit Epithelial-mesenchymal-transition (EMT) to enhance their migration, invasion, and resistance to treatments. Recent studies highlight that elevated levels of copper are implicated in cancer progression and metastasis. Clinical trials using copper chelators are associated with improved patient survival; however, the molecular mechanisms by which copper depletion inhibits tumor progression and metastasis are poorly understood. This remains a major hurdle to the clinical translation of copper chelators. Here, we propose that copper chelation inhibits metastasis by reducing TGF-β levels and EMT signaling. Given that many drugs targeting TGF-β have failed in clinical trials, partly because of severe side effects arising in patients, we hypothesized that copper chelation therapy might be a less toxic alternative to target the TGF-β/EMT axis. Our cytokine array and RNA-seq data suggested a link between copper homeostasis, TGF-β and EMT process. To validate this hypothesis, we performed single-cell imaging, protein assays, and in vivo studies. Here, we used the copper chelating agent TEPA to block copper trafficking. Our in vivo study showed a reduction of TGF-β levels and metastasis to the lung in the TNBC mouse model. Mechanistically, TEPA significantly downregulated canonical (TGF-β/SMAD2&3) and non-canonical (TGF-β/PI3K/AKT, TGF-β/RAS/RAF/MEK/ERK, and TGF-β/WNT/β-catenin) TGF-β signaling pathways. Additionally, EMT markers of MMP-9, MMP-14, Vimentin, β-catenin, ZEB1, and p-SMAD2 were downregulated, and EMT transcription factors of SNAI1, ZEB1, and p-SMAD2 accumulated in the cytoplasm after treatment. Our study suggests that copper chelation therapy represents a potentially effective therapeutic approach for targeting TGF-β and inhibiting EMT in a diverse range of cancers.
Genomic and Phenotypic Biomarkers for Precision Medicine Guidance in Advanced Prostate Cancer.
Davoudi, F; Moradi, A; Becker, TM; Lock, JG; Abbey, B; Fontanarosa, D; Haworth, A; Clements, J; Ecker, RC; Batra, J
Current Treatment Options in Oncology. 2023:1-21
DOI: 10.1007/s11864-023-01121-z
Prostate cancer (PCa) is the second most diagnosed malignant neoplasm and is one of the leading causes of cancer-related death in men worldwide. Despite significant advances in screening and treatment of PCa, given the heterogeneity of this disease, optimal personalized therapeutic strategies remain limited. However, emerging predictive and prognostic biomarkers based on individual patient profiles in combination with computer-assisted diagnostics have the potential to guide precision medicine, where patients may benefit from therapeutic approaches optimally suited to their disease. Also, the integration of genotypic and phenotypic diagnostic methods is supporting better informed treatment decisions. Focusing on advanced PCa, this review discusses polygenic risk scores for screening of PCa and common genomic aberrations in androgen receptor (AR), PTEN-PI3K-AKT, and DNA damage response (DDR) pathways, considering clinical implications for diagnosis, prognosis, and treatment prediction. Furthermore, we evaluate liquid biopsy, protein biomarkers such as serum testosterone levels, SLFN11 expression, total alkaline phosphatase (tALP), neutrophil-to-lymphocyte ratio (NLR), tissue biopsy, and advanced imaging tools, summarizing current phenotypic biomarkers and envisaging more effective utilization of diagnostic and prognostic biomarkers in advanced PCa. We conclude that prognostic and treatment predictive biomarker discovery can improve the management of patients, especially in metastatic stages of advanced PCa. This will result in decreased mortality and enhanced quality of life and help design a personalized treatment regimen.
Mapping Phenotypic Plasticity upon the Cancer Cell State Landscape Using Manifold Learning.
Burkhardt, DB; Juan, BPS; Lock, JG; Krishnaswamy, S; Chaffer, CL
Cancer Discovery. 2022:OF1-OF13
DOI: 10.1158/2159-8290.cd-21-0282
Abstract Phenotypic plasticity describes the ability of cancer cells to undergo dynamic, nongenetic cell state changes that amplify cancer heterogeneity to promote metastasis and therapy evasion. Thus, cancer cells occupy a continuous spectrum of phenotypic states connected by trajectories defining dynamic transitions upon a cancer cell state landscape. With technologies proliferating to systematically record molecular mechanisms at single-cell resolution, we illuminate manifold learning techniques as emerging computational tools to effectively model cell state dynamics in a way that mimics our understanding of the cell state landscape. We anticipate that "state-gating" therapies targeting phenotypic plasticity will limit cancer heterogeneity, metastasis, and therapy resistance. Significance: Nongenetic mechanisms underlying phenotypic plasticity have emerged as significant drivers of tumor heterogeneity, metastasis, and therapy resistance. Herein, we discuss new experimental and computational techniques to define phenotypic plasticity as a scaffold to guide accelerated progress in uncovering new vulnerabilities for therapeutic exploitation.
Choice of antibody is critical for specific and sensitive detection of androgen receptor splice variant-7 in circulating tumor cells.
Khan, T; Lock, JG; Ma, Y; Harman, DG; de Souza, P; Chua, W; Balakrishnar, B; Scott, KF; Becker, TM
Scientific Reports. 2022;12(1):16159
DOI: 10.1038/s41598-022-20079-w
Androgen receptor variant 7 (AR-V7) is an important biomarker to guide treatment options for castration-resistant prostate cancer (CRPC) patients. Its detectability in circulating tumour cells (CTCs) opens non-invasive diagnostic avenues. While detectable at the transcript level, AR-V7 protein detection in CTCs may add additional information and clinical relevance. The aim of this study was to compare commercially available anti-AR-V7 antibodies and establish reliable AR-V7 immunocytostaining applicable to CTCs from prostate cancer (PCa) patients. We compared seven AR-V7 antibodies by western blotting and immmunocytostaining using a set of PCa cell lines with known AR/AR-V7 status. The emerging best antibody was validated for detection of CRPC patient CTCs enriched by negative depletion of leucocytes. The anti-AR-V7 antibody, clone E308L emerged as the best antibody in regard to signal to noise ratio with a specific nuclear signal. Moreover, this antibody detects CRPC CTCs more efficiently compared to an antibody previously shown to detect AR-V7 CTCs. We have determined the best antibody for AR-V7 detection of CTCs, which will open future studies to correlate AR-V7 subcellular localization and potential co-localization with other proteins and cellular structures to patient outcomes.
Deep Representation Learning for Image-Based Cell Profiling
Wei, W; Haidinger, S; Lock, JG; Meijering, E
Lecture Notes in Computer Science. 2021:487-497
DOI: 10.1007/978-3-030-87589-3_50
High-content, microscopic image-based screening data are widely used in cell profiling to characterize cell phenotype diversity and extract explanatory biomarkers differentiating cell phenotypes induced by experimental perturbations or disease states. In recent years, high-throughput manifold embedding techniques such as t-distributed stochastic neighbor embedding (t-SNE), uniform manifold approximation and projection (UMAP), and generative networks have been increasingly applied to interpret cell profiling data. However, the resulting representations may not exploit the full image information, as these techniques are typically applied to quantitative image features defined by human experts. Here we propose a novel framework to analyze cell profiling data, based on two-stage deep representation learning using variational autoencoders (VAEs). We present quantitative and qualitative evaluations of the learned cell representations on two datasets. The results show that our framework can yield better representations than the currently popular methods. Also, our framework provides researchers with a more flexible tool to analyze underlying cell phenotypes and interpret the automatically defined cell features effectively.
The Prospect of Identifying Resistance Mechanisms for Castrate-Resistant Prostate Cancer Using Circulating Tumor Cells: Is Epithelial-to-Mesenchymal Transition a Key Player?
Khan, T; Scott, KF; Becker, TM; Lock, J; Nimir, M; Ma, Y; de Souza, P
Prostate cancer. 2020;2020:7938280
DOI: 10.1155/2020/7938280
Prostate cancer (PCa) is initially driven by excessive androgen receptor (AR) signaling with androgen deprivation therapy (ADT) being a major therapeutic approach to its treatment. However, the development of drug resistance is a significant limitation on the effectiveness of both first-line and more recently developed second-line ADTs. There is a need then to study AR signaling within the context of other oncogenic signaling pathways that likely mediate this resistance. This review focuses on interactions between AR signaling, the well-known phosphatidylinositol-3-kinase/AKT pathway, and an emerging mediator of these pathways, the Hippo/YAP1 axis in metastatic castrate-resistant PCa, and their involvement in the regulation of epithelial-mesenchymal transition (EMT), a feature of disease progression and ADT resistance. Analysis of these pathways in circulating tumor cells (CTCs) may provide an opportunity to evaluate their utility as biomarkers and address their importance in the development of resistance to current ADT with potential to guide future therapies.
High-Content Imaging of Unbiased Chemical Perturbations Reveals that the Phenotypic Plasticity of the Actin Cytoskeleton is Constrained.
Bryce NS, Failes TW, Stehn JR, Baker K, Zahler S, Arzhaeva Y, Bischof L, Lyons C, Dedova I, Arndt GM, Gaus K, Goult BT, Hardeman EC, Gunning PW, Lock JG.
Cell Systems. 2019-09
DOI: 10.1016/j.cels.2019.09.002
Although F-actin has a large number of binding partners and regulators, the number of phenotypic states available to the actin cytoskeleton is unknown. Here, we quantified 74 features defining filamentous actin (F-actin) and cellular morphology in >25 million cells after treatment with a library of 114,400 structurally diverse compounds. After reducing the dimensionality of these data, only ∼25 recurrent F-actin phenotypes emerged, each defined by distinct quantitative features that could be machine learned. We identified 2,003 unknown compounds as inducers of actin-related phenotypes, including two that directly bind the focal adhesion protein, talin. Moreover, we observed that compounds with distinct molecular mechanisms could induce equivalent phenotypes and that initially divergent cellular responses could converge over time. These findings suggest a conceptual parallel between the actin cytoskeleton and gene regulatory networks, where the theoretical plasticity of interactions is nearly infinite, yet phenotypes in vivo are constrained into a limited subset of practicable configurations.
Clathrin-containing adhesion complexes.
Lock JG, Baschieri F, Jones MC, Humphries JD, Montagnac G, Stromblad S, Humphries MJ.
J Cell Biol. 2019;218(7):2086-2095
An understanding of the mechanisms whereby cell adhesion complexes (ACs) relay signals bidirectionally across the plasma membrane is necessary to interpret the role of adhesion in regulating migration, differentiation, and growth. A range of AC types has been defined, but to date all have similar compositions and are dependent on a connection to the actin cytoskeleton. Recently, a new class of AC has been reported that normally lacks association with both the cytoskeleton and integrin-associated adhesome components, but is rich in components of the clathrin-mediated endocytosis machinery. The characterization of this new type of adhesion structure, which is emphasized by mitotic cells and cells in long-term culture, identifies a hitherto underappreciated link between the adhesion machinery and clathrin structures at the plasma membrane. While this discovery has implications for how ACs are assembled and disassembled, it raises many other issues. Consequently, to increase awareness within the field, and stimulate research, we explore a number of the most significant questions below.
Chemical biology approaches targeting the actin cytoskeleton through phenotypic screening.
Bryce NS, Hardeman EC, Gunning PW, Lock JG.
Curr Op Chem Biol. 2019;51:40-47
DOI: 10.1016/j.cbpa.2019.02.013
The actin cytoskeleton is dysregulated in cancer, yet this critical cellular machinery has not translated as a druggable clinical target due to cardio-toxic side-effects. Many actin regulators are also considered undruggable, being structural proteins lacking clear functional sites suitable for targeted drug design. In this review, we discuss opportunities and challenges associated with drugging the actin cytoskeleton through its structural regulators, taking tropomyosins as a target example. In particular, we highlight emerging data acquisition and analysis trends driving phenotypic, imaging-based compound screening. Finally, we consider how the confluence of these trends is now bringing functionally integral machineries such as the actin cytoskeleton, and associated structural regulatory proteins, into an expanded repertoire of druggable targets with previously unexploited clinical potential.
Reticular adhesions are a distinct class of cell-matrix adhesions that mediate attachment during mitosis.
Lock JG, Jones MC, Askari JA, Gong X, Oddone A, Olofsson H, Goransson S, Lakadamyali M, Humphries MJ, Stromblad S.
Nat Cell Biol. 2018;20(11):1290-1302.
DOI: 10.1038/s41556-018-0220-2
Adhesion to the extracellular matrix persists during mitosis in most cell types. However, while classical adhesion complexes, such as focal adhesions, do and must disassemble to enable mitotic rounding, the mechanisms of residual mitotic cell–extracellular matrix adhesion remain undefined. Here, we identify ‘reticular adhesions’, a class of adhesion complex that is mediated by integrin αvβ5, formed during interphase, and preserved at cell–extracellular matrix attachment sites throughout cell division. Consistent with this role, integrin β5 depletion perturbs mitosis and disrupts spatial memory transmission between cell generations. Reticular adhesions are morphologically and dynamically distinct from classical focal adhesions. Mass spectrometry defines their unique composition, enriched in phosphatidylinositol-4,5-bisphosphate (PtdIns(4,5)P2)-binding proteins but lacking virtually all consensus adhesome components. Indeed, reticular adhesions are promoted by PtdIns(4,5)P2, and form independently of talin and F-actin. The distinct characteristics of reticular adhesions provide a solution to the problem of maintaining cell–extracellular matrix attachment during mitotic rounding and division. Lock et al. identify reticular adhesion complexes that maintain cell–extracellular-matrix attachments during cell division. Reticular adhesions transmit spatial memory between cell generations, mediated by αvβ5 integrin and PtdIns(4,5)P2.
Visual Analytics of Single Cell Microscopy Data Using a Collaborative Immersive Environment.
Lock JG, Filonik D, Lawther R, Pather N, Gaus K, Kenderdine S, Bednarz T.
Proceedings of the 16th Acm Siggraph International Conference on Virtual-Reality Continuum and Its Applications in Industry (Vrcai 2018). 2018.
Understanding complex physiological processes demands the in- tegration of diverse insights derived from visual and quantitative analysis of bio-image data, such as microscopy images. This pro- cess is currently constrained by disconnects between methods for interpreting data, as well as by language barriers that hamper the necessary cross-disciplinary collaborations. Using immersive ana- lytics, we leveraged bespoke immersive visualizations to integrate bio-images and derived quantitative data, enabling deeper compre- hension and seamless interaction with multi-dimensional cellular information. We designed and developed a visualization platform that combines time-lapse confocal microscopy recordings of can- cer cell motility with image-derived quantitative data spanning 52 parameters. The integrated data representations enable rapid, in- tuitive interpretation, bridging the divide between bio-images and quantitative information. Moreover, the immersive visualization environment promotes collaborative data interrogation, supporting vital cross-disciplinary collaborations capable of deriving transfor- mative insights from rapidly emerging bio-image big data.
Active and inactive beta1 integrins segregate into distinct nanoclusters in focal adhesions.
Spiess M, Hernandez-Varas P, Oddone A, Olofsson H, Blom H, Waithe D, Lock JG, Lakadamyali M, Stromblad S.
J Cell Biol. 2018;217(6):1929-1940.
Integrins are the core constituents of cell–matrix adhesion complexes such as focal adhesions (FAs) and play key roles in physiology and disease. Integrins fluctuate between active and inactive conformations, yet whether the activity state influences the spatial organization of integrins within FAs has remained unclear. In this study, we address this question and also ask whether integrin activity may be regulated either independently for each integrin molecule or through locally coordinated mechanisms. We used two distinct superresolution microscopy techniques, stochastic optical reconstruction microscopy (STORM) and stimulated emission depletion microscopy (STED), to visualize active versus inactive β1 integrins. We first reveal a spatial hierarchy of integrin organization with integrin molecules arranged in nanoclusters, which align to form linear substructures that in turn build FAs. Remarkably, within FAs, active and inactive β1 integrins segregate into distinct nanoclusters, with active integrin nanoclusters being more organized. This unexpected segregation indicates synchronization of integrin activities within nanoclusters, implying the existence of a coordinate mechanism of integrin activity regulation.
Using Systems Microscopy to Understand the Emergence of Cell Migration from Cell Organization.
Stromblad S, Lock JG.
Methods Mol Biol. 2018;1749:119-134.
DOI: 10.1007/978-1-4939-7701-7_10
Cell migration is a dynamic process that emerges from fine-tuned networks coordinated in three-dimensional space, spanning molecular, subcellular, and cellular scales, and over multiple temporal scales, from milliseconds to days. Understanding how cell migration arises from this complexity requires data collection and analyses that quantitatively integrate these spatial and temporal scales. To meet this need, we have combined quantitative live and fixed cell fluorescence microscopy, customized image analysis tools, multivariate statistical methods, and mathematical modeling. Collectively, this constitutes the systems microscopy strategy that we have applied to dissect how cells organize themselves to migrate. In this overview, we highlight key principles, concepts, and components of our systems microscopy methodology, and exemplify what we have learnt so far and where this approach may lead.
KIF13A-regulated RhoB plasma membrane localization governs membrane blebbing and blebby amoeboid cell migration.
Gong X, Didan Y, Lock JG, Stromblad S.
EMBO J. 2018;37(17).
Membrane blebbing‐dependent (blebby) amoeboid migration can be employed by lymphoid and cancer cells to invade 3D‐environments. Here, we reveal a mechanism by which the small GTPase RhoB controls membrane blebbing and blebby amoeboid migration. Interestingly, while all three Rho isoforms (RhoA, RhoB and RhoC) regulated amoeboid migration, each controlled motility in a distinct manner. In particular, RhoB depletion blocked membrane blebbing in ALL (acute lymphoblastic leukaemia), melanoma and lung cancer cells as well as ALL cell amoeboid migration in 3D‐collagen, while RhoB overexpression enhanced blebbing and 3D‐collagen migration in a manner dependent on its plasma membrane localization and down‐stream effectors ROCK and Myosin II. RhoB localization was controlled by endosomal trafficking, being internalized via Rab5 vesicles and then trafficked either to late endosomes/lysosomes or to Rab11‐positive recycling endosomes, as regulated by KIF13A. Importantly, KIF13A depletion not only inhibited RhoB plasma membrane localization, but also cell membrane blebbing and 3D‐migration of ALL cells. In conclusion, KIF13A‐mediated endosomal trafficking modulates RhoB plasma membrane localization to control membrane blebbing and blebby amoeboid migration. KIF13A kinesin regulates plasma membrane localization of the small GTPase RhoB, thereby controlling membrane blebbing and blebby amoeboid migration employed by lymphoid and cancer cells to invade 3D‐environments. Rho isoforms RhoA, RhoB and RhoC all regulate amoeboid migration but control motility in distinct manners. Membrane blebbing control by RhoB depends on its plasma membrane localization and down‐stream effectors ROCK and Myosin II. RhoB localization is controlled by internalization from the plasma membrane and different endosomal trafficking routes. KIF13A regulates cell membrane blebbing and 3D‐migration by controlling recycling of RhoB to the plasma membrane. Depletion of the kinesin KIF13A causes improper endosomal trafficking of RhoB to the plasma membrane, which in turn inhibits cancer cell 3D‐migration.
Spheroids-on-a-chip: Recent advances and design considerations in microfluidic platforms for spheroid formation and culture.
Moshksayan K, Kashaninejad N, Warkiani ME, Lock JG, Moghadas H, Firoozabadi B, Saidi MS, Nguyen NT.
Sensor Actuat B-Chem. 2018;263:151-176.
DOI: 10.1016/j.snb.2018.01.223
A cell spheroid is a three-dimensional (3D) aggregation of cells. Synthetic, in-vitro spheroids provide similar metabolism, proliferation, and species concentration gradients to those found in-vivo. For instance, cancer cell spheroids have been demonstrated to mimic in-vivo tumor microenvironments, and are thus suitable for in-vitro drug screening. The first part of this paper discusses the latest microfluidic designs for spheroid formation and culture, comparing their strategies and efficacy. The most recent microfluidic techniques for spheroid formation utilize emulsion, microwells, U-shaped microstructures, or digital microfluidics. The engineering aspects underpinning spheroid formation in these microfluidic devices are therefore considered. In the second part of this paper, design considerations for microfluidic spheroid formation chips and microfluidic spheroid culture chips (μSFCs and μSCCs) are evaluated with regard to key parameters affecting spheroid formation, including shear stress, spheroid diameter, culture medium delivery and flow rate. This review is intended to benefit the microfluidics community by contributing to improved design and engineering of microfluidic chips capable of forming and/or culturing three-dimensional cell spheroids.
The Limits of Phenotypic Plasticity in the Actin Cytoskeleton Revealed by Unbiased Chemical Perturbation.
Bryce NS, Failes TW, Stehn JR, Baker K, Zahler S, Arzhaeva Y, Bischof L, Lyons C, Dedova I, Arndt GM, Gaus K, Goult BT, Hardeman EC, Gunning PW, Lock JG.
SSRN Electronic Journal. 2018.
DOI: 10.2139/ssrn.3299445
Numerous proteins and pathways regulate F-actin organisation, meaning that, in combinatorial terms, an almost unlimited number of regulatory states are conceivable. Consequently, the potential for plasticity in F-actin phenotypes appears virtually unbounded. To estimate the actual limits of F-actin phenotype plasticity, we used a library of 114,400 structurally diverse compounds to induce unbiased chemical perturbations. Remarkably, just 25 distinct, recurrent F-actin phenotypes emerged. Correspondingly, select compounds with distinct molecular mechanisms inducede quivalent phenotypes, suggesting that these recurring phenotypes reflect a low number of equilibrium or attractorstates inactin organisation. This was supported by dynamic analyses comparing phenotype trajectories over time, showing how initially divergent phenotypes ultimately convergedinto equivalent end-states. We propose that infrequent attractor states in the actin phenotypic landscape reflect a channelling of high perturbative diversity into low phenotypic variety and consider how this may suppress chaotic outcomes during the evolution of this complex, functionally integral system.
Reticular adhesions: A new class of adhesion complex that mediates cell-matrix attachment during mitosis.
Lock JG, Jones MC, Askari JA, Gong X, Oddone A, Olofsson H, Goransson S, Lakadamyali M, Humphries MJ, Stromblad S.
BioRxiv. 2017.
DOI: 10.1101/234237
Adhesion to the extracellular matrix (ECM) persists during mitosis in most cell types. Yet, classical adhesion complexes (ACs), such as focal adhesions and focal complexes, do and must disassemble to enable cytoskeletal rearrangements associated with mitotic rounding. Given this paradox, mechanisms of mitotic cell-ECM adhesion remain undefined. Here, we identify ‘reticular adhesions’, a new class of AC that is mediated by integrin αvβ5, formed during interphase and preserved at cell-ECM attachment sites throughout cell division. Consistent with this role, integrin β5 depletion perturbs mitosis and disrupts spatial memory transmission between cell generations. Quantitative imaging reveals reticular adhesions to be both morphologically and dynamically distinct from classic focal adhesions, while mass spectrometry defines their unique composition; lacking virtually all consensus adhesome components. Indeed, remarkably, reticular adhesions are functionally independent of both talin and F-actin, yet are promoted by phosphatidylinositol-4,5-bisphosphate (PI-4,5-P2). Overall, the distinct characteristics of reticular adhesions provide a unique solution to the problem of maintaining cell-ECM attachment during mitotic rounding and division.
An analysis toolbox to explore mesenchymal migration heterogeneity reveals adaptive switching between distinct modes.
Shafqat-Abbasi H, Kowalewski JM, Kiss A, Gong X, Hernandez-Varas P, Berge U, Jafari-Mamaghani M, Lock JG#, Stromblad S#.
Elife. 2016;5:e11384.
DOI: 10.7554/elife.11384
Mesenchymal (lamellipodial) migration is heterogeneous, although whether this reflects progressive variability or discrete, 'switchable' migration modalities, remains unclear. We present an analytical toolbox, based on quantitative single-cell imaging data, to interrogate this heterogeneity. Integrating supervised behavioral classification with multivariate analyses of cell motion, membrane dynamics, cell-matrix adhesion status and F-actin organization, this toolbox here enables the detection and characterization of two quantitatively distinct mesenchymal migration modes, termed 'Continuous' and 'Discontinuous'. Quantitative mode comparisons reveal differences in cell motion, spatiotemporal coordination of membrane protrusion/retraction, and how cells within each mode reorganize with changed cell speed. These modes thus represent distinctive migratory strategies. Additional analyses illuminate the macromolecular- and cellular-scale effects of molecular targeting (fibronectin, talin, ROCK), including 'adaptive switching' between Continuous (favored at high adhesion/full contraction) and Discontinuous (low adhesion/inhibited contraction) modes. Overall, this analytical toolbox now facilitates the exploration of both spontaneous and adaptive heterogeneity in mesenchymal migration.
Disentangling Membrane Dynamics and Cell Migration; Differential Influences of F-actin and Cell-Matrix Adhesions.
Kowalewski JM, Shafqat-Abbasi H, Jafari-Mamaghani M, Endrias Ganebo B, Gong X, Stromblad S, Lock JG.
PLoS One. 2015;10(8):e0135204.
DOI: 10.1371/journal.pone.0135204
Cell migration is heavily interconnected with plasma membrane protrusion and retraction (collectively termed "membrane dynamics"). This makes it difficult to distinguish regulatory mechanisms that differentially influence migration and membrane dynamics. Yet such distinctions may be valuable given evidence that cancer cell invasion in 3D may be better predicted by 2D membrane dynamics than by 2D cell migration, implying a degree of functional independence between these processes. Here, we applied multi-scale single cell imaging and a systematic statistical approach to disentangle regulatory associations underlying either migration or membrane dynamics. This revealed preferential correlations between membrane dynamics and F-actin features, contrasting with an enrichment of links between cell migration and adhesion complex properties. These correlative linkages were often non-linear and therefore context-dependent, strengthening or weakening with spontaneous heterogeneity in cell behavior. More broadly, we observed that slow moving cells tend to increase in area, while fast moving cells tend to shrink, and that the size of dynamic membrane domains is independent of cell area. Overall, we define macromolecular features preferentially associated with either cell migration or membrane dynamics, enabling more specific interrogation and targeting of these processes in future.
Non-monotonic cellular responses to heterogeneity in talin protein expression-level.
Kiss A, Gong X, Kowalewski JM, Shafqat-Abbasi H, Stromblad S, Lock JG.
Integr Biol (Camb). 2015;7(10):1171-1185.
DOI: 10.1039/c4ib00291a
Talin is a key cell-matrix adhesion component with a central role in regulating adhesion complex maturation, and thereby various cellular properties including adhesion and migration. However, knockdown studies have produced inconsistent findings regarding the functional influence of talin in these processes. Such discrepancies may reflect non-monotonic responses to talin expression-level variation that are not detectable via canonical "binary" comparisons of aggregated control versus knockdown cell populations. Here, we deployed an "analogue" approach to map talin influence across a continuous expression-level spectrum, which we extended with sub-maximal RNAi-mediated talin depletion. Applying correlative imaging to link live cell and fixed immunofluorescence data on a single cell basis, we related per cell talin levels to per cell measures quantitatively defining an array of cellular properties. This revealed both linear and non-linear correspondences between talin expression and cellular properties, including non-monotonic influences over cell shape, adhesion complex-F-actin association and adhesion localization. Furthermore, we demonstrate talin level-dependent changes in networks of correlations among adhesion/migration properties, particularly in relation to cell migration speed. Importantly, these correlation networks were strongly affected by talin expression heterogeneity within the natural range, implying that this endogenous variation has a broad, quantitatively detectable influence. Overall, we present an accessible analogue method that reveals complex dependencies on talin expression-level, thereby establishing a framework for considering non-linear and non-monotonic effects of protein expression-level heterogeneity in cellular systems.
A plastic relationship between vinculin-mediated tension and adhesion complex area defines adhesion size and lifetime.
Hernandez-Varas P, Berge U, Lock JG#, Stromblad S#.
Nat Commun. 2015;6:7524.
DOI: 10.1038/ncomms8524
Cell-matrix adhesions are central mediators of mechanotransduction, yet the interplay between force and adhesion regulation remains unclear. Here we use live cell imaging to map time-dependent cross-correlations between vinculin-mediated tension and adhesion complex area, revealing a plastic, context-dependent relationship. Interestingly, while an expected positive cross-correlation dominated in mid-sized adhesions, small and large adhesions display negative cross-correlation. Furthermore, although large changes in adhesion complex area follow vinculin-mediated tension alterations, small increases in area precede vinculin-mediated tension dynamics. Modelling based on this mapping of the vinculin-mediated tension-adhesion complex area relationship confirms its biological validity, and indicates that this relationship explains adhesion size and lifetime limits, keeping adhesions focal and transient. We also identify a subpopulation of steady-state adhesions whose size and vinculin-mediated tension become stabilized, and whose disassembly may be selectively microtubule-mediated. In conclusion, we define a plastic relationship between vinculin-mediated tension and adhesion complex area that controls fundamental cell-matrix adhesion properties.