Blebs promote cell survival by assembling oncogenic signalling hubs.

Most human cells require anchorage for survival. Cell-substrate adhesion activates diverse signalling pathways, without which cells undergo anoikis-a form of programmed cell death1. Acquisition of anoikis resistance is a pivotal step in cancer disease progression, as metastasizing cells often lose firm attachment to surrounding tissue2,3. In these poorly attached states, cells adopt rounded morphologies and form small hemispherical plasma membrane protrusions called blebs4-11. Bleb function has been thoroughly investigated in the context of amoeboid migration, but it has been examined far less in other scenarios12. Here we show by three-dimensional imaging and manipulation of cell morphological states that blebbing triggers the formation of plasma membrane-proximal signalling hubs that confer anoikis resistance. Specifically, in melanoma cells, blebbing generates plasma membrane contours that recruit curvature-sensing septin proteins as scaffolds for constitutively active mutant NRAS and effectors. These signalling hubs activate ERK and PI3K-well-established promoters of pro-survival pathways. Inhibition of blebs or septins has little effect on the survival of well-adhered cells, but in detached cells it causes NRAS mislocalization, reduced MAPK and PI3K activity, and ultimately, death. This unveils a morphological requirement for mutant NRAS to operate as an effective oncoprotein. Furthermore, whereas some BRAF-mutated melanoma cells do not rely on this survival pathway in a basal state, inhibition of BRAF and MEK strongly sensitizes them to both bleb and septin inhibition. Moreover, fibroblasts engineered to sustain blebbing acquire the same anoikis resistance as cancer cells even without harbouring oncogenic mutations. Thus, blebs are potent signalling organelles capable of integrating myriad cellular information flows into concerted cellular responses, in this case granting robust anoikis resistance.

Acquired Cross-Resistance in Small Cell Lung Cancer due to Extrachromosomal DNA Amplification of MYC Paralogs.

Small cell lung cancer (SCLC) presents as a highly chemosensitive malignancy but acquires cross-resistance after relapse. This transformation is nearly inevitable in patients but has been difficult to capture in laboratory models. Here, we present a preclinical system that recapitulates acquired cross-resistance, developed from 51 patient-derived xenograft (PDX) models. Each model was tested in vivo against three clinical regimens: cisplatin plus etoposide, olaparib plus temozolomide, and topotecan. These drug-response profiles captured hallmark clinical features of SCLC, such as the emergence of treatment-refractory disease after early relapse. For one patient, serial PDX models revealed that cross-resistance was acquired through MYC amplification on extrachromosomal DNA (ecDNA). Genomic and transcriptional profiles of the full PDX panel revealed that MYC paralog amplifications on ecDNAs were recurrent in relapsed cross-resistant SCLC, and this was corroborated in tumor biopsies from relapsed patients. We conclude that ecDNAs with MYC paralogs are recurrent drivers of cross-resistance in SCLC. SIGNIFICANCE: SCLC is initially chemosensitive, but acquired cross-resistance renders this disease refractory to further treatment and ultimately fatal. The genomic drivers of this transformation are unknown. We use a population of PDX models to discover that amplifications of MYC paralogs on ecDNA are recurrent drivers of acquired cross-resistance in SCLC. This article is featured in Selected Articles from This Issue, p. 695.

Cellular harmonics for the morphology-invariant analysis of molecular organization at the cell surface.

The spatiotemporal organization of membrane-associated molecules is central to the regulation of cellular signals. Powerful new microscopy techniques enable the three-dimensional visualization of localization and activation of these molecules; however, the quantitative interpretation and comparison of molecular organization on the three-dimensional cell surface remains challenging because cells themselves vary greatly in morphology. Here we introduce u-signal3D, a framework to assess the spatial scales of molecular organization at the cell surface in a cell-morphology-invariant manner. We validated the framework by analyzing synthetic signaling patterns painted onto observed cell morphologies, as well as measured distributions of cytoskeletal and signaling molecules. To demonstrate the framework's versatility, we further compared the spatial organization of cell surface signals both within, and between, cell populations, and powered an upstream machine-learning-based analysis of signaling motifs.

In vivo 3D profiling of site-specific human cancer cell morphotypes in zebrafish.

Tissue microenvironments affect the functional states of cancer cells, but determining these influences in vivo has remained a challenge. We present a quantitative high-resolution imaging assay of single cancer cells in zebrafish xenografts to probe functional adaptation to variable cell-extrinsic cues and molecular interventions. Using cell morphology as a surrogate readout of cell functional states, we examine environmental influences on the morphotype distribution of Ewing Sarcoma, a pediatric cancer associated with the oncogene EWSR1-FLI1 and whose plasticity is thought to determine disease outcome through non-genomic mechanisms. Computer vision analysis reveals systematic shifts in the distribution of 3D morphotypes as a function of cell type and seeding site, as well as tissue-specific cellular organizations that recapitulate those observed in human tumors. Reduced expression of the EWSR1-FLI1 protein product causes a shift to more protrusive cells and decreased tissue specificity of the morphotype distribution. Overall, this work establishes a framework for a statistically robust study of cancer cell plasticity in diverse tissue microenvironments.

U-Hack Med Gap Year-A Virtual Undergraduate Internship Program in Computer-Assisted Healthcare and Biomedical Research.

The COVID-19 healthcare crisis dramatically changed educational opportunities for undergraduate students. To overcome the lack of exposure to lab research and provide an alternative to cancelled classes and online lectures, the Lyda Hill Department of Bioinformatics at UT Southwestern Medical Center established an innovative, fully remote and paid "U-Hack Med Gap Year" internship program. At the core of the internship program were dedicated biomedical research projects spanning nine months in fields as diverse as computational microscopy, bioimage analysis, genome sequence analysis and establishment of a surgical skill analysis platform. To complement the project work, a biweekly Gap Year lab meeting was devised with opportunities to develop important skills in presenting, data sharing and analysis of new research. Despite a challenging year, all selected students completed the full internship period and over 30% will continue their project remotely after the end of the program.

Comparing lifeact and phalloidin for super-resolution imaging of actin in fixed cells.

Visualizing actin filaments in fixed cells is of great interest for a variety of topics in cell biology such as cell division, cell movement, and cell signaling. We investigated the possibility of replacing phalloidin, the standard reagent for super-resolution imaging of F-actin in fixed cells, with the actin binding peptide 'lifeact'. We compared the labels for use in single molecule based super-resolution microscopy, where AlexaFluor 647 labeled phalloidin was used in a dSTORM modality and Atto 655 labeled lifeact was used in a single molecule imaging, reversible binding modality. We found that imaging with lifeact had a comparable resolution in reconstructed images and provided several advantages over phalloidin including lower costs, the ability to image multiple regions of interest on a coverslip without degradation, simplified sequential super-resolution imaging, and more continuous labeling of thin filaments.

Robust, fiducial-free drift correction for super-resolution imaging.

We describe a robust, fiducial-free method of drift correction for use in single molecule localization-based super-resolution methods. The method combines periodic 3D registration of the sample using brightfield images with a fast post-processing algorithm that corrects residual registration errors and drift between registration events. The method is robust to low numbers of collected localizations, requires no specialized hardware, and provides stability and drift correction for an indefinite time period.

High-speed single molecule imaging datasets of membrane proteins in rat basophilic leukemia cells.

A high-speed fluorescence microscope operating at a 490 Hz frame rate was used to image two different membrane proteins- the high-affinity IgE receptor FcɛRI, a transmembrane protein, and an outer-leaflet GPI-anchored protein. The IgE receptor was imaged via IgE labeled with Janelia Fluor 646 and the GPI-anchored protein was imaged using a GPI-GFP fusion protein and an ATTO 647 N labeled anti-GFP nanobody. Data was collected for both proteins in untreated cells and cells that had actin stabilized by phalloidin. This dataset can be used for development and testing of single-particle tracking methods on experimental data and to explore the hypothesis that the actin cytoskeleton may affect the movement of membrane proteins.

Bayesian Multiple Emitter Fitting using Reversible Jump Markov Chain Monte Carlo.

In single molecule localization-based super-resolution imaging, high labeling density or the desire for greater data collection speed can lead to clusters of overlapping emitter images in the raw super-resolution image data. We describe a Bayesian inference approach to multiple-emitter fitting that uses Reversible Jump Markov Chain Monte Carlo to identify and localize the emitters in dense regions of data. This formalism can take advantage of any prior information, such as emitter intensity and density. The output is both a posterior probability distribution of emitter locations that includes uncertainty in the number of emitters and the background structure, and a set of coordinates and uncertainties from the most probable model.