Dr John George Lock


Fields of Research (FoR)

Cancer Cell Biology, Artificial Intelligence and Image Processing, Biostatistics, Cellular Interactions (incl. Adhesion, Matrix, Cell Wall)

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Dr John Lock is Head of the Cancer Systems Microscopy lab within the School of Medical Sciences 


Dr Lock completed his doctorate within the Institute for Molecular Bioscience at the University of Queensland. He then began postdoctoral research at the Karolinska Institute in Stockholm with 5 years of back to back fellowships from the Wenner-Gren Foundation and the Swedish Cancer Foundation....view more

Dr John Lock is Head of the Cancer Systems Microscopy lab within the School of Medical Sciences 


Dr Lock completed his doctorate within the Institute for Molecular Bioscience at the University of Queensland. He then began postdoctoral research at the Karolinska Institute in Stockholm with 5 years of back to back fellowships from the Wenner-Gren Foundation and the Swedish Cancer Foundation. He received accelerated promotion to Assistant Professor in recognition of his role in pioneering Systems Microscopy and leading a 9-person multidisciplinary team in its application to fundamental cancer research.

Dr Lock returned to Australia with a senior position in the lab of Professor Katharina Gaus at UNSW, within the EMBL Node for Single Molecule Science. He helped conceive and fund formation of Australia’s first dedicated Systems Microscopy facility with $2.5M funding support from the Australian Research Council and the prestigious Ramaciotti Biomedical Research Award (co-CI, now academic lead of facility). This success enabled his initiation of the Cancer Systems Microscopy (CSM) lab in 2018.

The CSM lab has since formed collaborations with an array of fundamental and translational cancer researchers, cancer clinicians, commercial microscopy and automation technology developers, as well as data science, machine / deep learning and visual analytics experts. This forms a truly multidisciplinary ecosystem of interactions. Reflecting these, Dr Lock is an invited member of the UNSW Steering Committee for the >$10M Expanded Perception & Interaction Centre (EPICentre) for data visualisation and AI research, the UNSW Data Science Hub (uDASH) and the UNSW AI & Data Science Network. More recently, Dr Lock has led Artificial Intelligence research to augment and integrate image data for both fundamental and precision medicine applications, res

Dr Lock co-founded (with Professor Sarah Russell, PeterMacCallum Cancer Centre, Melbourne) the Systems Microscopy Australia Network to build and extend multidisciplinary capacity and collaborations across Australia and beyond, and has become a co-organiser of the Functional High Throughput Technologies Australia meeting, which focuses on screening-related technologies and their applications to drug discovery, precision medicine and functional genomics.

Current Appointments & Positions Held

  • Senior Lecturer, Head of Cancer Systems Microscopy lab, Department of Pharmacology, School of Biomedical Sciences
  • Associate Director of Research for the School of Biomedical Sciences
  • Deputy Chair of the School of Biomedical Sciences Research Strategy Committee
  • Chair of HDR review panel and member of School of Biomedical Sciences HDR Committee
  • Affiliate Researcher, Ingham Institute for Applied Medical Research
  • UNSW Artificial Intelligence Institute member
  • UNSW Data Science Hub member
  • UNSW Artificial Intelligence Working Group member & Faculty of Medicine & Health Artificial Intelligence Working Group member
  • Garvan Institute of Medical Research Imaging Platform Strategic Advisory Committee member
  • Co-organiser, Functional High Throughput Technologies Australia meeting

Membership in Societies

  • Australian and New Zealand Society for Cell and Developmental Biology
  • American Society for Cell Biology
  • Australian Microscopy & Microanalysis Society

Research keywords

Cell Biology, Cancer, Liquid Biopsy, Circulating Tumour Cells, Imaging, Microscopy, Artificial Intelligence, Signalling, Statistics, Data Visualisation, Machine Learning, Deep Learning


My Grants

  • 2024: NHMRC Ideas - GNT2028506 Liquid Biopsy for personalised PSMA-lutetium
  • 2024: NHMRC Equipment Grant - GNT2034287 Integrated liquid-handling & imaging platform
  • 2023: UNSW Scientia Fellow (2023-2027)
  • 2023: Tour de Cure Pioneering Grant - RSP547FY2023 Prototyping a Precision Diagnostic for Advanced Prostate Cancer
  • 2023: NHMRC Ideas - GNT2019473 Targeting phenotypic plasticity (funded AI)
  • 2022: UNSW Commercialisation Seed Fund - CellaSense drug lead discovery platform development
  • 2022: ARC Discovery Project - DP220104036 Mapping networks governing cell state plasticity: how, where and when?
  • 2022: NHMRC Ideas – GNT2012848 for Revolutionising immunotherapy response prediction in non-small cell lung cancer
  • 2021: UNSW RIS – Spatial Light Interference Microscopy (SLIM) system
  • 2020 UNSW Research Fellowship
  • 2020 ARC LIEF LE210100011 for Multiplexed Imaging
  • 2019: NHMRC Ideas - GNT1184009 Revolutionising circulating tumour cell (CTC) analysis in castrate resistant prostate cancer
  • 2019: UNSW ResTech - BioDive integrated image and numerical data visualisation platform
  • 2018: UNSW RIS - Multi-Modal Collaborative High-End Visualisation System
  • 2018: UNSW RIS - Automated biological sample labelling system
  • 2017: Ramaciotti Biomedical Research Award for Systems Microscopy Facility – 1 awarded nationally every 2 years
  • 2017: ARC LIEF LE180100157 for Systems Microscopy
  • 2017: ARC DP170103599 - Statistical analyses for spatial organisation in T cell signalling
  • 2016: UNSW RIS - High-dimensional super-resolution live cell imaging

My Qualifications

  • 2001-2006 PhD, Institute for Molecular Bioscience, University of Queensland.
  • 2000-2001 Honours (1st Class) Biochemistry, Institute for Molecular Bioscience, University of Queensland. 
  • 1997-2000 BSc (Biochemistry), University of Queensland.

My Awards

2008: Eva & Alex Wallestroms Foundation Award

2010: Karolinska Institute Young Researcher Award

2017: Ramaciotti Biomedical Research Award

2021: Finalist in Shenzhen Innovation & Entrepreneurship International Competition (Australasia), 3rd place

2023: UNSW Scientia Fellow

My Research Activities

Research Interests

The Cancer Systems Microscopy Lab (CSM) aims to contribute to improved cancer treatment outcomes by advancing:

  •  Precision Diagnostics; using a unique suite of imaging-based methods for diagnosis of cancer-drivers in individual patients, enabling precision use of existing targeted therapies
  •  Targeted Therapies; via a novel high-content strategy for discovery and diversification of cancer therapy leads, supporting accelerated development of new targeted therapies
  •  Fundamental Insights; deploying multidisciplinary methods to analyse core mechanisms underpinning cancer progression, finding new vulnerabilities in human cancer
    • Including development of statistical, machine learning and data visualisation tools (together with UNSW EPICentre) to interrogate, interpret and communicate high-dimensional data derived from quantitative single cell imaging

The core research technologies of the CSM lab revolve around the concept of imaging-based systems biology, otherwise known as Systems Microscopy. As a result, the lab incorporates a multidisciplinary approach spanning novel aspects of experimental design, experimental automation, automated imaging, quantitative image analysis, statistical analysis, machine learning and data visualisation / visual analytics.

This builds on pioneering efforts in the initial conception of Systems Microscopy as a research strategy designed to replicate the scalability, reproducibility and quantitative rigour of existing single cell systems biology (‘Omics’) techniques, whilst also incorporating the critical dimensions of space and time into molecular analyses of cellular regulation and function. Not only a powerful approach for fundamental cell biology research, Systems Microscopy leverages the high signal-to-noise characteristics of (immuno)-fluorescence imaging to provide a powerful alternative / complement to current gold-standard strategies for precision medicine and also drug discovery. 

Below we outline a number of core projects developing within the lab.


Circulating tumour cell analysis for precision diagnostics

CTC project5.png

We aim to enable precision medicine in cancer by revolutionising analysis of signals driving disease progression and therapy-resistance. With longitudinally sampled circulating tumour cells (CTCs) analysed using Proteomic Microscopy, we image up to 50 markers per CTC to quantify activity across multiple resistance-linked signalling pathways. Using artificial intelligence (AI) to analyse this data, we classify resistant cancers by their signalling-drivers and train models predicting resistance mechanisms. This defines biomarker signatures with potential to guide patient stratification for targeted therapies. Beginning with a focus on prostate cancer, this precision diagnostic strategy is generalisable to a range of cancers.


Unbiased drug discovery for targeted therapy development


Together with UNSW Medicine collaborators Professors Peter Gunning and Edna Hardeman (CGMU), we have developed and tested a new strategy for drug lead-discovery that incorporates phenotypic screening of large-scale drug libraries (> 100, 000 compounds to-date) via high-throughput imaging, quantitative image analysis and multivariate statistical data analysis / machine learning to identify structurally and mechanistically diverse compounds with desirable biological effects. Already employed to explore the phenotypic plasticity of the actin cytoskeleton and at the same time identify new actin-regulating compounds, we are now working to generalise this approach in order to accelerate the discovery of lead compounds as part of the drug development pipeline.


Proteomic Microscopy analysis of subcellular signalling during Epithelial to Mesenchymal transition (EMT)


Through sequential immunofluorescence multiplexing of up to 60 subcellular signalling and regulatory protein markers per cell, we aim to reconstruct signalling systems biology in situ within individual cells as they undergo EMT, a process that drives cancer metastasis and therapy-resistance. Bridging the gap between the computational systems biology modelling approaches and the noisy complexity of signalling systems spatially distributed in heterogeneous cells, this approach has the potential to dramatically advance our understanding of EMT regulation in the four dimensions of space and time, thereby defining new therapeutic strategies to control this process in the context of cancer progression. 


Recent Selected Publications

An introduction to representation learning for single-cell data analysis

Ihuan Gunawan, Fatemeh Vafaee, Erik Meijering, John G Lock

Cell Reports Methods 


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.

Mapping Phenotypic Plasticity upon the Cancer Cell State Landscape Using Manifold Learning

Daniel B. Burkhardt, Beatriz P. San Juan, John G. Lock*, Smita Krishnaswamy*, Christine Chaffer*

Cancer Discovery

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.


Deep Representation Learning for Image-Based Cell Profiling

Wenzhao Wei, Sacha Haidinger, John Lock*, Erik Meijering*

Machine Learning in Medical Imaging. MLMI 2021. Lecture Notes in Computer Science, vol 12966, pp. 487 - 497. Springer International Publishing


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?

Tanzila Khan, Kieran F Scott, Therese M Becker, John Lock, Mohammed Nimir, Yafeng Ma, Paul de Souza

Prostate Cancer, volume 2020, Article ID 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

DOI: 10.1083/jcb.201811160

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.

DOI: 10.1145/3284398.3284412

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.

DOI: 10.1083/jcb.201707075

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).

DOI: 10.15252/embj.201898994

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.


Additional publications can be found here.

My Research Supervision

Areas of supervision

I am available to supervise HDR and honours students. I am currently seeking 2 HDR (PhD) candidates for projects with existing funding.

Currently supervising

- Primary supervisor of 4 HDR students, Joint Supervisor of 1 HDR student


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