HIV RGB: Automated Single-Cell Analysis of HIV-1 Rev-Dependent RNA Nuclear Export and Translation Using Image Processing in KNIME

Studying Retroviral Life Cycles Using Visible Viruses and Live Cell Imaging

Viruses exploit key host cell factors to accomplish each individual stage of the viral replication cycle. To understand viral pathogenesis and speed the development of new antiviral strategies, high-resolution visualization of virus-host interactions is needed to define where and when these events occur within cells. Here, we review state-of-the-art live cell imaging techniques for tracking individual stages of viral life cycles, focusing predominantly on retroviruses and especially human immunodeficiency virus type 1, which is most extensively studied. We describe how visible viruses can be engineered for live cell imaging and how nonmodified viruses can, in some instances, be tracked and studied indirectly using cell biosensor systems. We summarize the ways in which live cell imaging has been used to dissect the retroviral life cycle. Finally, we discuss select challenges for the future including the need for better labeling strategies, increased resolution, and multivariate systems that will allow for the study of full viral replication cycles.

PostFocus: automated selective post-acquisition high-throughput focus restoration using diffusion model for label-free time-lapse microscopy

MOTIVATION

High-throughput time-lapse imaging is a fundamental tool for efficient living cell profiling at single-cell resolution. Label-free phase-contrast video microscopy enables noninvasive, nontoxic, and long-term imaging. The tradeoff between speed and throughput, however, implies that despite the state-of-the-art autofocusing algorithms, out-of-focus cells are unavoidable due to the migratory nature of immune cells (velocities >10 μm/min). Here, we propose PostFocus to (i) identify out-of-focus images within time-lapse sequences with a classifier, and (ii) deploy a de-noising diffusion probabilistic model to yield reliable in-focus images.

RESULTS

De-noising diffusion probabilistic model outperformed deep discriminative models with a superior performance on the whole image and around cell boundaries. In addition, PostFocus improves the accuracy of image analysis (cell and contact detection) and the yield of usable videos.

AVAILABILITY AND IMPLEMENTATION

Open-source code and sample data are available at: https://github.com/kwu14victor/PostFocus.

Retroviral RNA Processing

This review is an accompaniment to a Special Issue on “Retroviral RNA Processing”. It discusses post-transcriptional regulation of retroviruses, ranging from the ancient foamy viruses to more modern viruses, such as HIV-1, HTLV-1, Rous sarcoma virus, murine leukemia virus, mouse mammary tumor virus, and Mason-Pfizer monkey virus. This review is not comprehensive. However, it tries to address some of the major questions in the field with examples of how different retroviruses express their genes. It is amazing that a single primary RNA transcript can have so many possible fates: genomic RNA, unspliced mRNA, and up to 50 different alternatively spliced mRNAs. This review will discuss the sorting of RNAs for packaging or translation, RNA nuclear export mechanisms, splicing, translation, RNA modifications, and avoidance of nonsense-mediated RNA decay.