Multi-modal image cytometry approach - From dynamic to whole organ imaging.

Optical imaging is a valuable tool to visualise biological processes in the context of the tissue. Each imaging modality provides the biologist with different types of information - cell dynamics and migration over time can be tracked with time-lapse imaging (e.g. intra-vital imaging); an overview of whole tissues can be acquired using optical clearing in conjunction with light sheet microscopy; finer details such as cellular morphology and fine nerve tortuosity can be imaged at higher resolution using the confocal microscope. Multi-modal imaging combined with image cytometry - a form of quantitative analysis of image datasets - provides an objective basis for comparing between sample groups. Here, we provide an overview of technical aspects to look out for in an image cytometry workflow, and discuss issues related to sample preparation, image post-processing and analysis for intra-vital and whole organ imaging.

Deep Learning in Image Cytometry: A Review.

Artificial intelligence, deep convolutional neural networks, and deep learning are all niche terms that are increasingly appearing in scientific presentations as well as in the general media. In this review, we focus on deep learning and how it is applied to microscopy image data of cells and tissue samples. Starting with an analogy to neuroscience, we aim to give the reader an overview of the key concepts of neural networks, and an understanding of how deep learning differs from more classical approaches for extracting information from image data. We aim to increase the understanding of these methods, while highlighting considerations regarding input data requirements, computational resources, challenges, and limitations. We do not provide a full manual for applying these methods to your own data, but rather review previously published articles on deep learning in image cytometry, and guide the readers toward further reading on specific networks and methods, including new methods not yet applied to cytometry data. © 2018 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

Multiplexed Single-Cell Imaging: Past, Present, and Future.

Jia-Ren Lin from the Laboratory of Systems Pharmacology at Harvard Medical School was awarded best poster at the annual Society of Biomolecular Imaging and Informatics meeting held in Boston, September 2016. His work focuses on single-cell imaging, especially on developing new methods for simultaneously detecting many antigens, named cyclic immunofluorescence (CycIF). This method could be applied in different stages of drug development, from discovery phase, preclinical research to clinical research. The current works and future directions of CycIF method are summarized in the following overview.

Lipid, protein and poly(NIPAM) coated mesoporous silica nanoparticles for biomedical applications.

In the past decade, mesoporous silica nanoparticles (MSNs) as nanocarriers have showed much potential in advanced nanomaterials due to their large surface area and pore volume. Especially, more and more MSNs based nanodevices have been designed as efficient drug delivery systems (DDSs) or biosensors. In this paper, lipid, protein and poly(NIPAM) coated MSNs are reviewed from the preparation, properties and their potential application. We also introduce the preparative methods including physical adsorption, covalent binding and self-assembly on the MSNs' surfaces. Furthermore, the interaction between the aimed cells and these molecular modified MSNs is discussed. We also demonstrate their typical applications, such as photodynamic therapy, bioimaging, controlled release and selective recognition in biomedical field.

The evolution of anatomic pathology: new strategies and emerging trends.

Science advances both by conceptual leaps and by improved observational and analytic tools. Mechanism and function in biological systems can best be understood in the context of the complex microenvironments in which they occur, and for this purpose morphologic analysis can be critical. The technological advances in cell and tissue imaging described in this book are currently finding application in a wide variety of basic, translational, and clinical biomedical studies. We have chosen some specific approaches that illustrate the various categories of imaging methodologies available. Many other ways of applying modern morphology-based interrogation of cells and tissues have already been described and are continuously evolving. This chapter provides examples of some of these. On the clinical front, radiologists have embraced new imaging technique to a greater extent than have pathologists. This chapter discusses some of the factors responsible for this, and suggests that pathology and radiology are converging towards a more holistic approach to diagnostic imaging.

Automated sputum cytometry for detection of intraepithelial neoplasias in the lung.

BACKGROUND: Despite the benefits of early lung cancer detection, no effective strategy for early screening and treatment exists, partly due to a lack of effective surrogate biomarkers. Our novel sputum biomarker, the Combined Score (CS), uses automated image cytometric analysis of ploidy and nuclear morphology to detect subtle intraepithelial changes that often precede lung tumours. METHODS: 2249 sputum samples from 1795 high-risk patients enrolled in ongoing chemoprevention trials were subjected to automated quantitative image cytometry after Feulgen-thionin staining. Samples from normal histopathology patients were compared against samples from carcinoma in situ (CIS) and cancer patients to train the CS. RESULTS: CS correlates with several lung cancer risk factors, including histopathological grade, age, smoking status, and p53 and Ki67 immunostaining. At 50% specificity, CS detected 78% of all highest-risk subjects-those with CIS or worse plus those with moderate or severe dysplasia and abnormal nuclear morphology. CONCLUSION: CS is a powerful yet minimally invasive tool for rapid and inexpensive risk assessment for the presence of precancerous lung lesions, enabling enrichment of chemoprevention trials with highest-risk dysplasias. CS correlates with other biomarkers, so CS may find use as a surrogate biomarker for patient assessment and as an endpoint in chemoprevention clinical trials.

Automated reconstruction of neuronal morphology: an overview.

Digital reconstruction of neuronal morphology is a powerful technique for investigating the nervous system. This process consists of tracing the axonal and dendritic arbors of neurons imaged by optical microscopy into a geometrical format suitable for quantitative analysis and computational modeling. Algorithmic automation of neuronal tracing promises to increase the speed, accuracy, and reproducibility of morphological reconstructions. Together with recent breakthroughs in cellular imaging and accelerating progress in optical microscopy, automated reconstruction of neuronal morphology will play a central role in the development of high throughput screening and the acquisition of connectomic data. Yet, despite continuous advances in image processing algorithms, to date manual tracing remains the overwhelming choice for digitizing neuronal morphology. We summarize the issues involved in automated reconstruction, overview the available techniques, and provide a realistic assessment of future perspectives.

Semi-automated reconstruction of neural circuits using electron microscopy.

Reconstructing neuronal circuits at the level of synapses is a central problem in neuroscience, and the focus of the nascent field of connectomics. Previously used to reconstruct the C. elegans wiring diagram, serial-section transmission electron microscopy (ssTEM) is a proven technique for the task. However, to reconstruct more complex circuits, ssTEM will require the automation of image processing. We review progress in the processing of electron microscopy images and, in particular, a semi-automated reconstruction pipeline deployed at Janelia Farm. Drosophila circuits underlying identified behaviors are being reconstructed in the pipeline with the goal of generating a complete Drosophila connectome.

Advances in light microscopy for neuroscience.

Since the work of Golgi and Cajal, light microscopy has remained a key tool for neuroscientists to observe cellular properties. Ongoing advances have enabled new experimental capabilities using light to inspect the nervous system across multiple spatial scales, including ultrastructural scales finer than the optical diffraction limit. Other progress permits functional imaging at faster speeds, at greater depths in brain tissue, and over larger tissue volumes than previously possible. Portable, miniaturized fluorescence microscopes now allow brain imaging in freely behaving mice. Complementary progress on animal preparations has enabled imaging in head-restrained behaving animals, as well as time-lapse microscopy studies in the brains of live subjects. Mouse genetic approaches permit mosaic and inducible fluorescence-labeling strategies, whereas intrinsic contrast mechanisms allow in vivo imaging of animals and humans without use of exogenous markers. This review surveys such advances and highlights emerging capabilities of particular interest to neuroscientists.