Detecting cells in intravital video microscopy using a deep convolutional neural network

Motion blur microscopy: in vitro imaging of cell adhesion dynamics in whole blood flow

Imaging and characterizing the dynamics of cellular adhesion in blood samples is of fundamental importance in understanding biological function. In vitro microscopy methods are widely used for this task but typically require diluting the blood with a buffer to allow for transmission of light. However, whole blood provides crucial signaling cues that influence adhesion dynamics, which means that conventional approaches lack the full physiological complexity of living microvasculature. We can reliably image cell interactions in microfluidic channels during whole blood flow by motion blur microscopy (MBM) in vitro and automate image analysis using machine learning. MBM provides a low cost, easy to implement alternative to intravital microscopy, for rapid data generation where understanding cell interactions, adhesion, and motility is crucial. MBM is generalizable to studies of various diseases, including cancer, blood disorders, thrombosis, inflammatory and autoimmune diseases, as well as providing rich datasets for theoretical modeling of adhesion dynamics.

Motion Blur Microscopy

Imaging and characterizing the dynamics of cellular adhesion in blood samples is of fundamental importance in understanding biological function. In vitro microscopy methods are widely used for this task, but typically require diluting the blood with a buffer to allow for transmission of light. However whole blood provides crucial mechanical and chemical signaling cues that influence adhesion dynamics, which means that conventional approaches lack the full physiological complexity of living microvasculature. We propose to overcome this challenge by a new in vitro imaging method which we call motion blur microscopy (MBM). By decreasing the source light intensity and increasing the integration time during imaging, flowing cells are blurred, allowing us to identify adhered cells. Combined with an automated analysis using machine learning, we can for the first time reliably image cell interactions in microfluidic channels during whole blood flow. MBM provides a low cost, easy to implement alternative to intravital microscopy, the in vivo approach for studying how the whole blood environment shapes adhesion dynamics. We demonstrate the method's reproducibility and accuracy in two example systems where understanding cell interactions, adhesion, and motility is crucial-sickle red blood cells adhering to laminin, and CAR-T cells adhering to E-selectin. We illustrate the wide range of data types that can be extracted from this approach, including distributions of cell size and eccentricity, adhesion durations, trajectories and velocities of adhered cells moving on a functionalized surface, as well as correlations among these different features at the single cell level. In all cases MBM allows for rapid collection and processing of large data sets, ranging from thousands to hundreds of thousands of individual adhesion events. The method is generalizable to study adhesion mechanisms in a variety of diseases, including cancer, blood disorders, thrombosis, inflammatory and autoimmune diseases, as well as providing rich datasets for theoretical modeling of adhesion dynamics.

Intravital microscopy visualizes innate immune crosstalk and function in tissue microenvironment

Significant advances have been made in the field of intravital microscopy (IVM) on myeloid cells due to the growing number of validated fluorescent probes and reporter mice. IVM provides a visualization platform to directly observe cell behavior and deepen our understanding of cellular dynamics, heterogeneity, plasticity, and cell-cell communication in native tissue environments. This review outlines the current studies on the dynamic interaction and function of innate immune cells with a focus on those that are studied with IVM and covers the advances in data analysis with emerging artificial intelligence-based algorithms. Finally, the prospects of IVM on innate immune cells are discussed.

Fourier ptychographic and deep learning using breast cancer histopathological image classification

Automated, as well as accurate classification with breast cancer histological images, was crucial for medical applications because of detecting malignant tumors via histopathological images. In this work create a Fourier ptychographic (FP) and deep learning using breast cancer histopathological image classification. Here the FP method used in the process begins with such a random guess that builds a high-resolution complex hologram, subsequently uses iterative retrieval using FP constraints to stitch around each other low-resolution multi-view means of production owned from either the hologram's high-resolution hologram's elemental images captured via integral imaging. Next, the feature extraction process includes entropy, geometrical features, and textural features. The entropy-based normalization is used to optimize the features. Finally, it attains the classification process of the proposed ENDNN classifies the breast cancer images into normal or abnormal. The experimental outcomes demonstrate that our presented technique overtakes the traditional techniques.

Intravital microscopy for real-time monitoring of drug delivery and nanobiological processes

Intravital microscopy (IVM) expands our understanding of cellular and molecular processes, with applications ranging from fundamental biology to (patho)physiology and immunology, as well as from drug delivery to drug processing and drug efficacy testing. In this review, we highlight modalities, methods and model organisms that make up today's IVM landscape, and we present how IVM - via its high spatiotemporal resolution - enables analysis of metabolites, small molecules, nanoparticles, immune cells, and the (tumor) tissue microenvironment. We furthermore present examples of how IVM facilitates the elucidation of nanomedicine kinetics and targeting mechanisms, as well as of biological processes such as immune cell death, host-pathogen interactions, metabolic states, and disease progression. We conclude by discussing the prospects of IVM clinical translation and examining the integration of machine learning in future IVM practice.

Brain Immunoinformatics: A Symmetrical Link between Informatics, Wet Lab and the Clinic

Breakthrough advances in informatics over the last decade have thoroughly influenced the field of immunology. The intermingling of machine learning with wet lab applications and clinical results has hatched the newly defined immunoinformatics society. Immunoinformatics of the central neural system, referred to as neuroimmunoinformatics (NII), investigates symmetrical and asymmetrical interactions of the brain-immune interface. This interdisciplinary overview on NII is addressed to bioscientists and computer scientists. We delineate the dominating trajectories and field-shaping achievements and elaborate on future directions using bridging language and terminology. Computation, varying from linear modeling to complex deep learning approaches, fuels neuroimmunology through three core directions. Firstly, by providing big-data analysis software for high-throughput methods such as next-generation sequencing and genome-wide association studies. Secondly, by designing models for the prediction of protein morphology, functions, and symmetrical and asymmetrical protein–protein interactions. Finally, NII boosts the output of quantitative pathology by enabling the automatization of tedious processes such as cell counting, tracing, and arbor analysis. The new classification of microglia, the brain’s innate immune cells, was an NII achievement. Deep sequencing classifies microglia in “sensotypes” to accurately describe the versatility of immune responses to physiological and pathological challenges, as well as to experimental conditions such as xenografting and organoids. NII approaches complex tasks in the brain-immune interface, recognizes patterns and allows for hypothesis-free predictions with ultimate targeted individualized treatment strategies, and personalizes disease prognosis and treatment response.