SuperCUT, an unsupervised multimodal image registration with deep learning for biomedical microscopy.

Numerous imaging techniques are available for observing and interrogating biological samples, and several of them can be used consecutively to enable correlative analysis of different image modalities with varying resolutions and the inclusion of structural or molecular information. Achieving accurate registration of multimodal images is essential for the correlative analysis process, but it remains a challenging computer vision task with no widely accepted solution. Moreover, supervised registration methods require annotated data produced by experts, which is limited.

Segmentation metric misinterpretations in bioimage analysis.

Quantitative evaluation of image segmentation algorithms is crucial in the field of bioimage analysis. The most common assessment scores, however, are often misinterpreted and multiple definitions coexist with the same name. Here we present the ambiguities of evaluation metrics for segmentation algorithms and show how these misinterpretations can alter leaderboards of influential competitions.

Correlative Fluorescence and Raman Microscopy to Define Mitotic Stages at the Single-Cell Level: Opportunities and Limitations in the AI Era.

Nowadays, morphology and molecular analyses at the single-cell level have a fundamental role in understanding biology better. These methods are utilized for cell phenotyping and in-depth studies of cellular processes, such as mitosis. Fluorescence microscopy and optical spectroscopy techniques, including Raman micro-spectroscopy, allow researchers to examine biological samples at the single-cell level in a non-destructive manner.

Cell Type-Specific Arousal-Dependent Modulation of Thalamic Activity in the Lateral Geniculate Nucleus.

State-dependent thalamocortical activity is important for sensory coding, oscillations, and cognition. The lateral geniculate nucleus (LGN) relays visual information to the cortex, but the state-dependent spontaneous activity of LGN neurons in awake behaving animals remains controversial. Using a combination of pupillometry, extracellular, and intracellular recordings from identified LGN neurons in behaving mice, we show that thalamocortical (TC) neurons and interneurons are distinctly correlated to arousal forming two complementary coalitions.

SpheroidPicker for automated 3D cell culture manipulation using deep learning.

Recent statistics report that more than 3.7 million new cases of cancer occur in Europe yearly, and the disease accounts for approximately 20% of all deaths. High-throughput screening of cancer cell cultures has dominated the search for novel, effective anticancer therapies in the past decades.

Cell lines and clearing approaches: a single-cell level 3D light-sheet fluorescence microscopy dataset of multicellular spheroids.

Nowadays, three dimensional (3D) cell cultures are widely used in the biological laboratories and several optical clearing approaches have been proposed to visualize individual cells in the deepest layers of cancer multicellular spheroids. However, defining the most appropriate clearing approach for the different cell lines is an open issue due to the lack of a gold standard quantitative metric. In this article, we describe and share a single-cell resolution 3D image dataset of human carcinoma spheroids imaged using a light-sheet fluorescence microscope.

Raman Spectral Signatures of Serum-Derived Extracellular Vesicle-Enriched Isolates May Support the Diagnosis of CNS Tumors.

Investigating the molecular composition of small extracellular vesicles (sEVs) for tumor diagnostic purposes is becoming increasingly popular, especially for diseases for which diagnosis is challenging, such as central nervous system (CNS) malignancies. Thorough examination of the molecular content of sEVs by Raman spectroscopy is a promising but hitherto barely explored approach for these tumor types. We attempt to reveal the potential role of serum-derived sEVs in diagnosing CNS tumors through Raman spectroscopic analyses using a relevant number of clinical samples.

A quantitative metric for the comparative evaluation of optical clearing protocols for 3D multicellular spheroids.

3D multicellular quickly emerged as models because they represent the tumor environment better than standard 2D cell cultures. However, with current microscopy technologies, it is difficult to visualize individual cells in the deeper layers of 3D samples mainly because of limited light penetration and scattering. To overcome this problem several optical clearing methods have been proposed but defining the most appropriate clearing approach is an open issue due to the lack of a gold standard metric.

Automatic deep learning-driven label-free image-guided patch clamp system.

Patch clamp recording of neurons is a labor-intensive and time-consuming procedure. Here, we demonstrate a tool that fully automatically performs electrophysiological recordings in label-free tissue slices. The automation covers the detection of cells in label-free images, calibration of the micropipette movement, approach to the cell with the pipette, formation of the whole-cell configuration, and recording.

nucleAIzer: A Parameter-free Deep Learning Framework for Nucleus Segmentation Using Image Style Transfer.

Single-cell segmentation is typically a crucial task of image-based cellular analysis. We present nucleAIzer, a deep-learning approach aiming toward a truly general method for localizing 2D cell nuclei across a diverse range of assays and light microscopy modalities. We outperform the 739 methods submitted to the 2018 Data Science Bowl on images representing a variety of realistic conditions, some of which were not represented in the training data.

Phenotypic Image Analysis Software Tools for Exploring and Understanding Big Image Data from Cell-Based Assays.

Phenotypic image analysis is the task of recognizing variations in cell properties using microscopic image data. These variations, produced through a complex web of interactions between genes and the environment, may hold the key to uncover important biological phenomena or to understand the response to a drug candidate. Today, phenotypic analysis is rarely performed completely by hand.

DIC image reconstruction using an energy minimization framework to visualize optical path length distribution.

Label-free microscopy techniques have numerous advantages such as low phototoxicity, simple setup and no need for fluorophores or other contrast materials. Despite their advantages, most label-free techniques cannot visualize specific cellular compartments or the location of proteins and the image formation limits quantitative evaluation. Differential interference contrast (DIC) is a qualitative microscopy technique that shows the optical path length differences within a specimen.

The NF45/NF90 Heterodimer Contributes to the Biogenesis of 60S Ribosomal Subunits and Influences Nucleolar Morphology.

The interleukin enhancer binding factors ILF2 (NF45) and ILF3 (NF90/NF110) have been implicated in various cellular pathways, such as transcription, microRNA (miRNA) processing, DNA repair, and translation, in mammalian cells. Using tandem affinity purification, we identified human NF45 and NF90 as components of precursors to 60S (pre-60S) ribosomal subunits. NF45 and NF90 are enriched in nucleoli and cosediment with pre-60S ribosomal particles in density gradient analysis.