A shortest-path based clustering algorithm for joint human-machine analysis of complex datasets

Clustering is a technique for the analysis of datasets obtained by empirical studies in several disciplines with a major application for biomedical research. Essentially, clustering algorithms are executed by machines aiming at finding groups of related points in a dataset. However, the result of grouping depends on both metrics for point-to-point similarity and rules for point-to-group association. Indeed, non-appropriate metrics and rules can lead to undesirable clustering artifacts. This is especially relevant for datasets, where groups with heterogeneous structures co-exist. In this work, we propose an algorithm that achieves clustering by exploring the paths between points. This allows both, to evaluate the properties of the path (such as gaps, density variations, etc.), and expressing the preference for certain paths. Moreover, our algorithm supports the integration of existing knowledge about admissible and non-admissible clusters by training a path classifier. We demonstrate the accuracy of the proposed method on challenging datasets including points from synthetic shapes in publicly available benchmarks and microscopy data

AnyDijkstra, an algorithm to compute shortest paths on images with anytime properties

Images conveniently capture the result of physical processes, representing rich source of information for data driven medicine, engineering, and science. The modeling of an image as a graph allows the application of graph-based algorithms for content analysis. Amongst these, one of the most used is the Dijkstra Single Source Shortest Path algorithm (DSSSP), which computes the path with minimal cost from one starting node to all the other nodes of the graph. However, the results of DSSSP remains unknown for nodes until they are explored. Moreover, DSSSP execution is associated to frequent jumps between distant locations in the graph, which results in non-optimal memory access, reduced parallelization, and finally increased execution time. Therefore, we propose AnyDijkstra, an iterative implementation of the Dijkstra SSSP algorithm optimized for images, that retains anytime properties while accessing memory following a cache-friendly scheme and maximizing parallelization.Comment: 7 pages, 4 figure

Tracking unlabeled cancer cells imaged with low resolution in wide migration chambers via U-NET class-1 probability (pseudofluorescence).

Cell migration is a pivotal biological process, whose dysregulation is found in many diseases including inflammation and cancer. Advances in microscopy technologies allow now to study cell migration in vitro, within engineered microenvironments that resemble in vivo conditions. However, to capture an entire 3D migration chamber for extended periods of time and with high temporal resolution, images are generally acquired with low resolution, which poses a challenge for data analysis. Indeed, cell detection and tracking are hampered due to the large pixel size (i.e., cell diameter down to 2 pixels), the possible low signal-to-noise ratio, and distortions in the cell shape due to changes in the z-axis position. Although fluorescent staining can be used to facilitate cell detection, it may alter cell behavior and it may suffer from fluorescence loss over time (photobleaching).Here we describe a protocol that employs an established deep learning method (U-NET), to specifically convert transmitted light (TL) signal from unlabeled cells imaged with low resolution to a fluorescent-like signal (class 1 probability). We demonstrate its application to study cancer cell migration, obtaining a significant improvement in tracking accuracy, while not suffering from photobleaching. This is reflected in the possibility of tracking cells for three-fold longer periods of time. To facilitate the application of the protocol we provide WID-U, an open-source plugin for FIJI and Imaris imaging software, the training dataset used in this paper, and the code to train the network for custom experimental settings

Influenza vaccination induces NK-cell-mediated type-II IFN response that regulates humoral immunity in an IL-6-dependent manner

The role of natural killer (NK) cells in the immune response against vaccines is not fully understood. Here, we examine the function of infiltrated NK cells in the initiation of the inflammatory response triggered by inactivated influenza virus vaccine in the draining lymph node (LN). We observed that, following vaccination, NK cells are recruited to the interfollicular and medullary areas of the LN and become activated by type I interferons (IFNs) produced by LN macrophages. The activation of NK cells leads to their early production of IFNγ, which in turn regulates the recruitment of IL-6+ CD11b+ dendritic cells. Finally, we demonstrate that the interleukin-6 (IL-6)-mediated inflammation is important for the development of an effective humoral response against influenza virus in the draining LN

Macrophage death following influenza vaccination initiates the inflammatory response that promotes dendritic cell function in the draining lymph node

The mechanism by which inflammation influences the adaptive response to vaccines is not fully understood. Here, we examine the role of lymph node macrophages (LNMs) in the induction of the cytokine storm triggered by inactivated influenza virus vaccine. Following vaccination, LNMs undergo inflammasome-independent necrosis-like death that is reliant on MyD88 and Toll-like receptor 7 (TLR7) expression and releases pre-stored interleukin-1α (IL-1α). Furthermore, activated medullary macrophages produce interferon-β (IFN-β) that induces the autocrine secretion of IL-1α. We also found that macrophage depletion promotes lymph node-resident dendritic cell (LNDC) relocation and affects the capacity of CD11b+ LNDCs to capture virus and express co-stimulatory molecules. Inhibition of the IL-1α-induced inflammatory cascade reduced B cell responses, while co-administration of recombinant IL-1α increased the humoral response. Stimulation of the IL-1α inflammatory pathway might therefore represent a strategy to enhance antigen presentation by LNDCs and improve the humoral response against influenza vaccines

A hierarchy of graph-based methods to study the behavior of immune cells in vivo

The immune system has a critical role in diseases of primary importance such as infections and cancer. Hence, it represents a target for novel therapeutic strategies. However, the immune system relies on a complex network of cell-to-cell interactions which remains largely unknown, or difficult to be interpreted. The combination of experimental data with computational methods is of paramount importance to analyze these interactions. Indeed, recently established 2-photon intravital microscopes (2P-IVM), can capture videos of cells while interacting in organs of living animals. These interactions are often associated with specific movement patterns. Hence, computer vision methods have the potential to extract knowledge from these videos by analyzing the movement of cells. Unfortunately, common analysis methods poorly apply to 2P-IVM videos capturing the cells of the immune system. This is mainly due to the complex appearance and biomechanical properties of these cells, as well as challenges introduced by in vivo imaging. Additionally, a lack of publicly available 2P-IVM datasets hampers the development of novel analysis methods along with data-driven studies of the immune system. Finally, common measures of cell motility, poorly describe the dynamic behavior of immune cells. In this thesis, we address these limitations by • Making available the first database of 2P-IVM videos and tracks of immune cells. • Modeling as graph the content of 2P-IVM videos, from pixels to biological processes. • Developing, refining, and applying a variety of computational methods to extract knowledge from this graph. • Shifting the analysis of cell motility towards the recognition of cell actions, which does not necessarily require cell tracking. This combination of microscopy data, graph-based methods, and action-based models allowed us to quantify the complex movement patterns of neutrophils, revealing different phases of the immune response to influenza vaccination

In Vivo Motility Patterns Displayed by Immune Cells Under Inflammatory Conditions

The migration of immune cells plays a key role in inflammation. This is evident in the fact that inflammatory stimuli elicit a broad range of migration patterns in immune cells. Since these patterns are pivotal for initiating the immune response, their dysregulation is associated with life-threatening conditions including organ failure, chronic inflammation, autoimmunity, and cancer, amongst others. Over the last two decades, thanks to advancements in the intravital microscopy technology, it has become possible to visualize cell migration in living organisms with unprecedented resolution, helping to deconstruct hitherto unexplored aspects of the immune response associated with the dynamism of cells. However, a comprehensive classification of the main motility patterns of immune cells observed in vivo, along with their relevance to the inflammatory process, is still lacking. In this review we defined cell actions as motility patterns displayed by immune cells, which are associated with a specific role during the immune response. In this regard, we summarize the main actions performed by immune cells during intravital microscopy studies. For each of these actions, we provide a consensus name, a definition based on morphodynamic properties, and the biological contexts in which it was reported. Moreover, we provide an overview of the computational methods that were employed for the quantification, fostering an interdisciplinary approach to study the immune system from imaging data.ISSN:1664-322

CANCOL, a Computer-Assisted Annotation Tool to Facilitate Colocalization and Tracking of Immune Cells in Intravital Microscopy.

Two-photon intravital microscopy (2P-IVM) has become a widely used technique to study cell-to-cell interactions in living organisms. Four-dimensional imaging data obtained via 2P-IVM are classically analyzed by performing automated cell tracking, a procedure that computes the trajectories followed by each cell. However, technical artifacts, such as brightness shifts, the presence of autofluorescent objects, and channel crosstalking, affect the specificity of imaging channels for the cells of interest, thus hampering cell detection. Recently, machine learning has been applied to overcome a variety of obstacles in biomedical imaging. However, existing methods are not tailored for the specific problems of intravital imaging of immune cells. Moreover, results are highly dependent on the quality of the annotations provided by the user. In this study, we developed CANCOL, a tool that facilitates the application of machine learning for automated tracking of immune cells in 2P-IVM. CANCOL guides the user during the annotation of specific objects that are problematic for cell tracking when not properly annotated. Then, it computes a virtual colocalization channel that is specific for the cells of interest. We validated the use of CANCOL on challenging 2P-IVM videos from murine organs, obtaining a significant improvement in the accuracy of automated tracking while reducing the time required for manual track curation

Fast deep learning reconstruction techniques for preclinical magnetic resonance fingerprinting

We propose a deep learning (DL) model and a hyperparameter optimization strategy to reconstruct T1 and T2 maps acquired with the magnetic resonance fingerprinting (MRF) methodology. We applied two different MRF sequence routines to acquire images of ex vivo rat brain phantoms using a 7-T preclinical scanner. Subsequently, the DL model was trained using experimental data, completely excluding the use of any theoretical MRI signal simulator. The best combination of the DL parameters was implemented by an automatic hyperparameter optimization strategy, whose key aspect is to include all the parameters to the fit, allowing the simultaneous optimization of the neural network architecture, the structure of the DL model, and the supervised learning algorithm. By comparing the reconstruction performances of the DL technique with those achieved from the traditional dictionary-based method on an independent dataset, the DL approach was shown to reduce the mean percentage relative error by a factor of 3 for T1 and by a factor of 2 for T2, and to improve the computational time by at least a factor of 37. Furthermore, the proposed DL method enables maintaining comparable reconstruction performance, even with a lower number of MRF images and a reduced k-space sampling percentage, with respect to the dictionary-based method. Our results suggest that the proposed DL methodology may offer an improvement in reconstruction accuracy, as well as speeding up MRF for preclinical, and in prospective clinical, investigations.We proposed a deep learning (DL) method and an optimization strategy for the reconstruction of T1 and T2 maps acquired with preclinical magnetic resonance fingerprinting (MRF) sequences. Compared with the traditional dictionary-based method, the DL approach improved the estimation of the maps, and reduced the computational time required for estimation. Moreover, our DL method allowed us to maintain comparable reconstruction performance, even with compressed MRF acquisition sequences.imag