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The flagellar movement of the mammalian sperm plays a crucial role in fertilization. In the female reproductive tract, human spermatozoa undergo a process called capacitation which promotes changes in their motility. Only capacitated spermatozoa may be hyperactivated and only those that transition to hyperactivated motility are capable of fertilizing the egg. Hyperactivated motility is characterized by asymmetric flagellar bends of greater amplitude and lower frequency. Historically, clinical fertilization studies have used two-dimensional analysis to classify sperm motility, despite the inherently three-dimensional (3D) nature of sperm motion. Recent research has described several 3D beating features of sperm flagella. However, the 3D motility pattern of hyperactivated spermatozoa has not yet been characterized. One of the main challenges in classifying these patterns in 3D is the lack of a ground-truth reference, as it can be difficult to visually assess differences in flagellar beat patterns. Additionally, it is worth noting that only a relatively small proportion, approximately 10-20% of sperm incubated under capacitating conditions exhibit hyperactivated motility. In this work, we used a multifocal image acquisition system that can acquire, segment, and track sperm flagella in 3D+t. We developed a feature-based vector that describes the spatio-temporal flagellar sperm motility patterns by an envelope of ellipses. The classification results obtained using our 3D feature-based descriptors can serve as potential label for future work involving deep neural networks. By using the classification results as labels, it will be possible to train a deep neural network to automatically classify spermatozoa based on their 3D flagellar beating patterns. We demonstrated the effectiveness of the descriptors by applying them to a dataset of human sperm cells and showing that they can accurately differentiate between non-hyperactivated and hyperactivated 3D motility patterns of the sperm cells. This work contributes to the understanding of 3D flagellar hyperactive motility patterns and provides a framework for research in the fields of human and animal fertility.

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Melanoma is the deadliest form of skin cancer. Due to its high mutation rates, melanoma is a convenient model to study antitumor immune responses. Dendritic cells (DCs) play a key role in activating cytotoxic CD8+ T lymphocytes and directing them to kill tumor cells. Although there is evidence that DCs infiltrate melanomas, information about the profile of these cells, their activity states, and potential antitumor function remains unclear, particularly for conventional DCs type 1 (cDC1). Approaches to profiling tumor-infiltrating DCs are hindered by their diversity and the high number of signals that can affect their state of activation. Multiplexed immunofluorescence (mIF) allows the simultaneous analysis of multiple markers, but image-based analysis is time-consuming and often inconsistent among analysts. In this work, we evaluated several machine learning (ML) algorithms and established a workflow of nine-parameter image analysis that allowed us to study cDC1s in a reproducible and accessible manner. Using this workflow, we compared melanoma samples between disease-free and metastatic patients at diagnosis. We observed that cDC1s are more abundant in the tumor infiltrate of the former. Furthermore, cDC1s in disease-free patients exhibit an expression profile more congruent with an activator function: CD40highPD-L1low CD86+IL-12+. Although disease-free patients were also enriched with CD40-PD-L1+ cDC1s, these cells were also more compatible with an activator phenotype. The opposite was true for metastatic patients at diagnosis who were enriched for cDC1s with a more tolerogenic phenotype (CD40lowPD-L1highCD86-IL-12-IDO+). ML-based workflows like the one developed here can be used to analyze complex phenotypes of other immune cells and can be brought to laboratories with standard expertise and computer capacity.

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Due to the wave nature of light, optical microscopy has a lower-bound lateral resolution limit of approximately half of the wavelength of visible light, that is, within the range of 200 to 350 nm. Fluorescence fluctuation-based super-resolution microscopy (FF-SRM) is a term used to encompass a collection of image analysis techniques that rely on the statistical processing of temporal variations of the fluorescence signal. FF-SRM aims to reduce the uncertainty of the location of fluorophores within an image, often improving spatial resolution by several tens of nanometers. FF-SRM is suitable for live-cell imaging due to its compatibility with most fluorescent probes and relatively simple instrumental and experimental requirements, which are mostly camera-based epifluorescence instruments. Each FF-SRM approach has strengths and weaknesses, which depend directly on the underlying statistical principles through which enhanced spatial resolution is achieved. In this review, the basic concepts and principles behind a range of FF-SRM methods published to date are described. Their operational parameters are explained and guidance for their selection is provided.

Due to light's wave nature, an optical microscope's resolution range is 200 to 350 nanometers. Several techniques enhance resolution; this work encompasses several fluorescence fluctuation super-resolution (FF-SMR) methods capable of achieving nanoscopic scales. FF-SRM is known to be suitable for fixed or live-cell imaging and compatible with most conventional microscope setups found in a laboratory. However, each FF-SRM approach has its strengths and weaknesses, which depend directly on the underlying principles through which enhanced spatial resolution is achieved. Therefore, the basic concepts and principles behind diverse FF-SRM methods are revisited in this review. In addition, their operational parameters are explained, and guidance for their selection is provided for microscopists interested in FF-SRM.

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Arabidopsis (Arabidopsis thaliana) primary and lateral roots (LRs) are well suited for 3D and 4D microscopy, and their development provides an ideal system for studying morphogenesis and cell proliferation dynamics. With fast-advancing microscopy techniques used for live-imaging, whole tissue data are increasingly available, yet present the great challenge of analyzing complex interactions within cell populations. We developed a plugin "Live Plant Cell Tracking" (LiPlaCeT) coupled to the publicly available ImageJ image analysis program and generated a pipeline that allows, with the aid of LiPlaCeT, 4D cell tracking and lineage analysis of populations of dividing and growing cells. The LiPlaCeT plugin contains ad hoc ergonomic curating tools, making it very simple to use for manual cell tracking, especially when the signal-to-noise ratio of images is low or variable in time or 3D space and when automated methods may fail. Performing time-lapse experiments and using cell-tracking data extracted with the assistance of LiPlaCeT, we accomplished deep analyses of cell proliferation and clonal relations in the whole developing LR primordia and constructed genealogical trees. We also used cell-tracking data for endodermis cells of the root apical meristem (RAM) and performed automated analyses of cell population dynamics using ParaView software (also publicly available). Using the RAM as an example, we also showed how LiPlaCeT can be used to generate information at the whole-tissue level regarding cell length, cell position, cell growth rate, cell displacement rate, and proliferation activity. The pipeline will be useful in live-imaging studies of roots and other plant organs to understand complex interactions within proliferating and growing cell populations. The plugin includes a step-by-step user manual and a dataset example that are available at https://www.ibt.unam.mx/documentos/diversos/LiPlaCeT.zip.

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The reiterative process of lateral root (LR) formation is widespread and underlies root system formation. However, early LR primordium (LRP) morphogenesis is not fully understood. In this study, we conducted both a clonal analysis and time-lapse experiments to decipher the pattern and sequence of pericycle founder cell (FC) participation in LR formation. Most commonly, LRP initiation starts with the specification of just one FC longitudinally. Clonal and anatomical analyses suggested that a single FC gradually recruits neighboring pericycle cells to become FCs. This conclusion was validated by long-term time-lapse live-imaging experiments. Once the first FC starts to divide, its immediate neighbors, both lengthwise and laterally, are recruited within the hour, after which they recruit their neighboring cells within a few hours. Therefore, LRP initiation is a gradual, multistep process. FC recruitment is auxin-dependent and is abolished by treatment with a polar auxin transport inhibitor. Furthermore, FC recruitment establishes a morphogenetic field where laterally peripheral cells have a lower auxin response, which is associated with a lower proliferation potential, compared to centrally located FCs. The lateral boundaries of the morphogenetic field are determined by phloem-adjacent pericycle cells, which are the last cells to be recruited as FCs. The proliferation potential of these cells is limited, but their recruitment is essential for root system formation, resulting in the formation of a new vascular connection between the nascent and parent root, which is crucial for establishing a continuous and efficient vascular system.

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