Conversion of anterograde into retrograde trains is an intrinsic property of intraflagellar transport.

Cilia or eukaryotic flagella are microtubule-based organelles found across the eukaryotic tree of life. Their very high aspect ratio and crowded interior are unfavorable to diffusive transport of most components required for their assembly and maintenance. Instead, a system of intraflagellar transport (IFT) trains moves cargo rapidly up and down the cilium (Figure 1A).1-3 Anterograde IFT, from the cell body to the ciliary tip, is driven by kinesin-II motors, whereas retrograde IFT is powered by cytoplasmic dynein-1b motors.4 Both motors are associated with long chains of IFT protein complexes, known as IFT trains, and their cargoes.5-8 The conversion from anterograde to retrograde motility at the ciliary tip involves (1) the dissociation of kinesin motors from trains,9 (2) a fundamental restructuring of the train from the anterograde to the retrograde architecture,8,10,11 (3) the unloading and reloading of cargo,2 and (4) the activation of the dynein motors.8,12 A prominent hypothesis is that there is dedicated calcium-dependent protein-based machinery at the ciliary tip to mediate these processes.4,13 However, the mechanisms of IFT turnaround have remained elusive. In this study, we use mechanical and chemical methods to block IFT at intermediate positions along the cilia of the green algae Chlamydomonas reinhardtii, in normal and calcium-depleted conditions. We show that IFT turnaround, kinesin dissociation, and dynein-1b activation can consistently be induced at arbitrary distances from the ciliary tip, with no stationary tip machinery being required. Instead, we demonstrate that the anterograde-to-retrograde conversion is a calcium-independent intrinsic ability of IFT.

Democratising deep learning for microscopy with ZeroCostDL4Mic.

Deep Learning (DL) methods are powerful analytical tools for microscopy and can outperform conventional image processing pipelines. Despite the enthusiasm and innovations fuelled by DL technology, the need to access powerful and compatible resources to train DL networks leads to an accessibility barrier that novice users often find difficult to overcome. Here, we present ZeroCostDL4Mic, an entry-level platform simplifying DL access by leveraging the free, cloud-based computational resources of Google Colab. ZeroCostDL4Mic allows researchers with no coding expertise to train and apply key DL networks to perform tasks including segmentation (using U-Net and StarDist), object detection (using YOLOv2), denoising (using CARE and Noise2Void), super-resolution microscopy (using Deep-STORM), and image-to-image translation (using Label-free prediction - fnet, pix2pix and CycleGAN). Importantly, we provide suitable quantitative tools for each network to evaluate model performance, allowing model optimisation. We demonstrate the application of the platform to study multiple biological processes.

Content-aware image restoration for electron microscopy.

Multiple approaches to use deep neural networks for image restoration have recently been proposed. Training such networks requires well registered pairs of high and low-quality images. While this is easily achievable for many imaging modalities, e.g., fluorescence light microscopy, for others it is not. Here we summarize on a number of recent developments in the fast-paced field of Content-Aware Image Restoration (CARE), in particular, and the associated area of neural network training, more in general. We then give specific examples how electron microscopy data can benefit from these new technologies.