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.

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.

Transformer-based spatial-temporal detection of apoptotic cell death in live-cell imaging.

Intravital microscopy has revolutionized live-cell imaging by allowing the study of spatial-temporal cell dynamics in living animals. However, the complexity of the data generated by this technology has limited the development of effective computational tools to identify and quantify cell processes. Amongst them, apoptosis is a crucial form of regulated cell death involved in tissue homeostasis and host defense. Live-cell imaging enabled the study of apoptosis at the cellular level, enhancing our understanding of its spatial-temporal regulation. However, at present, no computational method can deliver robust detection of apoptosis in microscopy timelapses. To overcome this limitation, we developed ADeS, a deep learning-based apoptosis detection system that employs the principle of activity recognition. We trained ADeS on extensive datasets containing more than 10,000 apoptotic instances collected both in vitro and in vivo, achieving a classification accuracy above 98% and outperforming state-of-the-art solutions. ADeS is the first method capable of detecting the location and duration of multiple apoptotic events in full microscopy timelapses, surpassing human performance in the same task. We demonstrated the effectiveness and robustness of ADeS across various imaging modalities, cell types, and staining techniques. Finally, we employed ADeS to quantify cell survival in vitro and tissue damage in mice, demonstrating its potential application in toxicity assays, treatment evaluation, and inflammatory dynamics. Our findings suggest that ADeS is a valuable tool for the accurate detection and quantification of apoptosis in live-cell imaging and, in particular, intravital microscopy data, providing insights into the complex spatial-temporal regulation of this process.

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.

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.

Neutrophil subtypes shape HIV-specific CD8 T-cell responses after vaccinia virus infection.

Neutrophils are innate immune cells involved in the elimination of pathogens and can also induce adaptive immune responses. Nα and Nß neutrophils have been described with distinct in vitro capacity to generate antigen-specific CD8 T-cell responses. However, how these cell types exert their role in vivo and how manipulation of Nß/Nα ratio influences vaccine-mediated immune responses are not known. In this study, we find that these neutrophil subtypes show distinct migratory and motility patterns and different ability to interact with CD8 T cells in the spleen following vaccinia virus (VACV) infection. Moreover, after analysis of adhesion, inflammatory, and migration markers, we observe that Nß neutrophils overexpress the α4ß1 integrin compared to Nα. Finally, by inhibiting α4ß1 integrin, we increase the Nß/Nα ratio and enhance CD8 T-cell responses to HIV VACV-delivered antigens. These findings provide significant advancements in the comprehension of neutrophil-based control of adaptive immune system and their relevance in vaccine design.

A trainable clustering algorithm based on shortest paths from density peaks.

Clustering is a technique to analyze empirical data, with a major application for biomedical research. Essentially, clustering finds groups of related points in a dataset. However, results depend on both metrics for point-to-point similarity and rules for point-to-group association. Non-appropriate metrics and rules can lead to artifacts, especially in case of multiple groups with heterogeneous structure. In this work, we propose a clustering algorithm that evaluates the properties of paths between points (rather than point-to-point similarity) and solves a global optimization problem, finding solutions not obtainable by methods relying on local choices. Moreover, our algorithm is trainable. Hence, it can be adapted and adopted for specific datasets and applications by providing examples of valid and invalid paths to train a path classifier. We demonstrate its applicability to identify heterogeneous groups in challenging synthetic datasets, segment highly nonconvex immune cells in confocal microscopy images, and classify arrhythmic heartbeats in electrocardiographic signals.

Characterization of the Dynamic Behavior of Neutrophils Following Influenza Vaccination.

Neutrophils are amongst the first cells to respond to inflammation and infection. Although they play a key role in limiting the dissemination of pathogens, the study of their dynamic behavior in immune organs remains elusive. In this work, we characterized in vivo the dynamic behavior of neutrophils in the mouse popliteal lymph node (PLN) after influenza vaccination with UV-inactivated virus. To achieve this, we used an image-based systems biology approach to detect the motility patterns of neutrophils and to associate them to distinct actions. We described a prominent and rapid recruitment of neutrophils to the PLN following vaccination, which was dependent on the secretion of the chemokine CXCL1 and the alarmin molecule IL-1α. In addition, we observed that the initial recruitment occurred mainly via high endothelial venules located in the paracortical and interfollicular regions of the PLN. The analysis of the spatial-temporal patterns of neutrophil migration demonstrated that, in the initial stage, the majority of neutrophils displayed a patrolling behavior, followed by the formation of swarms in the subcapsular sinus of the PLN, which were associated with macrophages in this compartment. Finally, we observed using multiple imaging techniques, that neutrophils phagocytize and transport influenza virus particles. These processes might have important implications in the capacity of these cells to present viral antigens.

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.

Leukocyte Tracking Database, a collection of immune cell tracks from intravital 2-photon microscopy videos.

Recent advances in intravital video microscopy have allowed the visualization of leukocyte behavior in vivo, revealing unprecedented spatiotemporal dynamics of immune cell interaction. However, state-of-the-art software and methods for automatically measuring cell migration exhibit limitations in tracking the position of leukocytes over time. Challenges arise both from the complex migration patterns of these cells and from the experimental artifacts introduced during image acquisition. Additionally, the development of novel tracking tools is hampered by the lack of a sound ground truth for algorithm validation and benchmarking. Therefore, the objective of this work was to create a database, namely LTDB, with a significant number of manually tracked leukocytes. Broad experimental conditions, sites of imaging, types of immune cells and challenging case studies were included to foster the development of robust computer vision techniques for imaging-based immunological research. Lastly, LTDB represents a step towards the unravelling of biological mechanisms by video data mining in systems biology.

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-ß (IFN-ß) 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.