Tracking motile cells in time-lapse series is challenging and is required in many biomedical applications. Cell tracks can be mathematically represented as acyclic oriented graphs. Their vertices ...describe the spatio-temporal locations of individual cells, whereas the edges represent temporal relationships between them. Such a representation maintains the knowledge of all important cellular events within a captured field of view, such as migration, division, death, and transit through the field of view. The increasing number of cell tracking algorithms calls for comparison of their performance. However, the lack of a standardized cell tracking accuracy measure makes the comparison impracticable. This paper defines and evaluates an accuracy measure for objective and systematic benchmarking of cell tracking algorithms. The measure assumes the existence of a ground-truth reference, and assesses how difficult it is to transform a computed graph into the reference one. The difficulty is measured as a weighted sum of the lowest number of graph operations, such as split, delete, and add a vertex and delete, add, and alter the semantics of an edge, needed to make the graphs identical. The measure behavior is extensively analyzed based on the tracking results provided by the participants of the first Cell Tracking Challenge hosted by the 2013 IEEE International Symposium on Biomedical Imaging. We demonstrate the robustness and stability of the measure against small changes in the choice of weights for diverse cell tracking algorithms and fluorescence microscopy datasets. As the measure penalizes all possible errors in the tracking results and is easy to compute, it may especially help developers and analysts to tune their algorithms according to their needs.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Multiple myeloma (MM) progression and myeloma-associated bone disease (MBD) are highly dependent on bone marrow mesenchymal stromal cells (MSCs). MM-MSCs exhibit abnormal transcriptomes, suggesting ...the involvement of epigenetic mechanisms governing their tumor-promoting functions and prolonged osteoblast suppression. Here, we identify widespread DNA methylation alterations of bone marrow-isolated MSCs from distinct MM stages, particularly in Homeobox genes involved in osteogenic differentiation that associate with their aberrant expression. Moreover, these DNA methylation changes are recapitulated in vitro by exposing MSCs from healthy individuals to MM cells. Pharmacological targeting of DNMTs and G9a with dual inhibitor CM-272 reverts the expression of hypermethylated osteogenic regulators and promotes osteoblast differentiation of myeloma MSCs. Most importantly, CM-272 treatment prevents tumor-associated bone loss and reduces tumor burden in a murine myeloma model. Our results demonstrate that epigenetic aberrancies mediate the impairment of bone formation in MM, and its targeting by CM-272 is able to reverse MBD.
Locoregional failure (LRF) in patients with breast cancer post-surgery and post-irradiation is linked to a dismal prognosis. In a refined new model, we identified ectonucleotide ...pyrophosphatase/phosphodiesterase 1/CD203a (ENPP1) to be closely associated with LRF. ENPP1hi circulating tumor cells (CTC) contribute to relapse by a self-seeding mechanism. This process requires the infiltration of polymorphonuclear myeloid-derived suppressor cells and neutrophil extracellular trap (NET) formation. Genetic and pharmacologic ENPP1 inhibition or NET blockade extends relapse-free survival. Furthermore, in combination with fractionated irradiation, ENPP1 abrogation obliterates LRF. Mechanistically, ENPP1-generated adenosinergic metabolites enhance haptoglobin (HP) expression. This inflammatory mediator elicits myeloid invasiveness and promotes NET formation. Accordingly, a significant increase in ENPP1 and NET formation is detected in relapsed human breast cancer tumors. Moreover, high ENPP1 or HP levels are associated with poor prognosis. These findings unveil the ENPP1/HP axis as an unanticipated mechanism exploited by tumor cells linking inflammation to immune remodeling favoring local relapse.
CTC exploit the ENPP1/HP axis to promote local recurrence post-surgery and post-irradiation by subduing myeloid suppressor cells in breast tumors. Blocking this axis impairs tumor engraftment, impedes immunosuppression, and obliterates NET formation, unveiling new opportunities for therapeutic intervention to eradicate local relapse and ameliorate patient survival. This article is highlighted in the In This Issue feature, p. 1171.
Locoregional failure (LRF) in breast cancer patients post-surgery and post-irradiation (IR) is linked to a dismal prognosis. In a refined new model, we identified Enpp1 (Ectonucleotide ...pyrophosphatase /phosphodiesterase 1/CD203a) to be closely associated with LRF. Enpp1high circulating tumor cells (CTC) contribute to relapse by a self-seeding mechanism. This process requires the infiltration of PMN-MDSC and neutrophil extracellular traps (NET) formation. Genetic and pharmacological Enpp1 inhibition or NET blockade extend relapse-free survival. Furthermore, in combination with fractionated irradiation (FD), Enpp1 abrogation obliterates LRF. Mechanistically, Enpp1-generated adenosinergic metabolites enhance Haptoglobin (Hp) expression. This inflammatory mediator elicits myeloid invasiveness and promotes NET formation. Accordingly, a significant increase in ENPP1 and NET formation is detected in relapsed human breast cancer tumors. Moreover, high ENPP1 or HP levels are associated with poor prognosis. These findings unveil the ENPP1/HP axis as an unanticipated mechanism exploited by tumor cells linking inflammation to immune remodeling favoring local relapse.
Particle tracking is of key importance for quantitative analysis of intracellular dynamic processes from time-lapse microscopy image data. Because manually detecting and following large numbers of ...individual particles is not feasible, automated computational methods have been developed for these tasks by many groups. Aiming to perform an objective comparison of methods, we gathered the community and organized an open competition in which participating teams applied their own methods independently to a commonly defined data set including diverse scenarios. Performance was assessed using commonly defined measures. Although no single method performed best across all scenarios, the results revealed clear differences between the various approaches, leading to notable practical conclusions for users and developers.
•First weakly-supervised DL framework that learns the tumor microenvironment from highly multiplexed images.•Learning the tumor microenvironment in three levels of spatial complexity.•Blindly ...associates tumor microenvironment elements with clinical information.•Demonstrated accuracy and interpretability on both real and synthetic datasets.
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Understanding the spatial interactions between the elements of the tumor microenvironment -i.e. tumor cells. fibroblasts, immune cells- and how these interactions relate to the diagnosis or prognosis of a tumor is one of the goals of computational pathology. We present NaroNet, a deep learning framework that models the multi-scale tumor microenvironment from multiplex-stained cancer tissue images and provides patient-level interpretable predictions using a seamless end-to-end learning pipeline. Trained only with multiplex-stained tissue images and their corresponding patient-level clinical labels, NaroNet unsupervisedly learns which cell phenotypes, cell neighborhoods, and neighborhood interactions have the highest influence to predict the correct label. To this end, NaroNet incorporates several novel and state-of-the-art deep learning techniques, such as patch-level contrastive learning, multi-level graph embeddings, a novel max-sum pooling operation, or a metric that quantifies the relevance that each microenvironment element has in the individual predictions. We validate NaroNet using synthetic data simulating multiplex-immunostained images where a patient label is artificially associated to the -adjustable- probabilistic incidence of different microenvironment elements. We then apply our model to two sets of images of human cancer tissues: 336 seven-color multiplex-immunostained images from 12 high-grade endometrial cancer patients; and 382 35-plex mass cytometry images from 215 breast cancer patients. In both synthetic and real datasets, NaroNet provides outstanding predictions of relevant clinical information while associating those predictions to the presence of specific microenvironment elements.
Collapsin response mediator protein 2 (CRMP2) is an adaptor protein that adds tubulin dimers to the growing tip of a microtubule. First described in neurons, it is now considered a ubiquitous protein ...that intervenes in processes such as cytoskeletal remodeling, synaptic connection and trafficking of voltage channels. Mounting evidence supports that CRMP2 plays an essential role in neuropathology and, more recently, in cancer. We have previously described a positive correlation between nuclear phosphorylation of CRMP2 and poor prognosis in lung adenocarcinoma patients. In this work, we studied whether this cytoskeleton molding protein is involved in cancer cell migration. To this aim, we evaluated CRMP2 phosphorylation and localization in the extending lamella of lung adenocarcinoma migrating cells using in vitro assays and in vivo confocal microscopy. We demonstrated that constitutive phosphorylation of CRMP2 impaired lamella formation, cell adhesion and oriented migration. In search of a mechanistic explanation of this phenomenon, we discovered that CRMP2 Ser522 phospho-mimetic mutants display unstable tubulin polymers, unable to bind EB1 plus-Tip protein and the cortical actin adaptor IQGAP1. In addition, integrin recycling is defective and invasive structures are less evident in these mutants. Significantly, mouse xenograft tumors of NSCLC expressing CRMP2 phosphorylation mimetic mutants grew significantly less than wild-type tumors. Given the recent development of small molecule inhibitors of CRMP2 phosphorylation to treat neurodegenerative diseases, our results open the door for their use in cancer treatment.
Cancer related deaths are primarily due to tumor metastasis. To facilitate their dissemination to distant sites, cancer cells develop invadopodia, actin-rich protrusions capable of degrading the ...surrounding extracellular matrix (ECM). We aimed to determine whether β3 integrin participates in invadopodia formed by lung carcinoma cells, based on our previous findings of specific TGF-β induction of β3 integrin dependent metastasis in animal models of lung carcinoma. In this study, we demonstrate that lung carcinoma cells form invadopodia in response to TGF-β exposure. Invadopodia formation and degradation activity is dependent on β3 integrin expression since β3 integrin deficient cells are not able to degrade gelatin-coated surfaces. Even more, transient over-expression of SRC did not restore invadopodia formation in β3 integrin deficient cells. Finally, we observed that blockade of PLC-dependent signaling leads to more intense labeling for β3 integrin in invadopodia. Our results suggest that β3 integrin function, and location, in lung cancer cells are essential for invadopodia formation, and this integrin regulates the activation of different signal pathways necessary for the invasive structure. β3 integrin has been associated with poor prognosis and increased metastasis in several carcinoma types, including lung cancer. Our findings provide new evidence to support the use of targeted therapies against this integrin to combat the onset of metastases.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Abstract
Motivation
Recent advances in multiplex immunostaining and multispectral cytometry have opened the door to simultaneously visualizing an unprecedented number of biomarkers both in liquid and ...solid samples. Properly unmixing fluorescent emissions is a challenging task, which normally requires the characterization of the individual fluorochromes from control samples. As the number of fluorochromes increases, the cost in time and use of reagents becomes prohibitively high. Here, we present a fully unsupervised blind spectral unmixing method for the separation of fluorescent emissions in highly mixed spectral data, without the need for control samples. To this end, we extend an existing method based on non-negative Matrix Factorization, and introduce several critical improvements: initialization based on the theoretical spectra, automated selection of ‘sparse’ data and use of a re-initialized multilayer optimizer.
Results
Our algorithm is exhaustively tested using synthetic data to study its robustness against different levels of colocalization, signal to noise ratio, spectral resolution and the effect of errors in the initialization of the algorithm. Then, we compare the performance of our method to that of traditional spectral unmixing algorithms using novel multispectral flow and image cytometry systems. In all cases, we show that our blind unmixing algorithm performs robust unmixing of highly spatially and spectrally mixed data with an unprecedently low computational cost. In summary, we present the first use of a blind unmixing method in multispectral flow and image cytometry, opening the door to the widespread use of our method to efficiently pre-process multiplex immunostaining samples without the need of experimental controls.
Availability and implementation
https://github.com/djimenezsanchez/Blind_Unmixing_NMF_RI/ contains the source code and all datasets used in this manuscript.
Supplementary information
Supplementary data are available at Bioinformatics online.
The Cell Tracking Challenge is an ongoing benchmarking initiative that has become a reference in cell segmentation and tracking algorithm development. Here, we present a significant number of ...improvements introduced in the challenge since our 2017 report. These include the creation of a new segmentation-only benchmark, the enrichment of the dataset repository with new datasets that increase its diversity and complexity, and the creation of a silver standard reference corpus based on the most competitive results, which will be of particular interest for data-hungry deep learning-based strategies. Furthermore, we present the up-to-date cell segmentation and tracking leaderboards, an in-depth analysis of the relationship between the performance of the state-of-the-art methods and the properties of the datasets and annotations, and two novel, insightful studies about the generalizability and the reusability of top-performing methods. These studies provide critical practical conclusions for both developers and users of traditional and machine learning-based cell segmentation and tracking algorithms.
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