Recruitment of HIV-1 envelope occurs subsequent to lipid mixing: a fluorescence microscopic evidence

Entry of the human immunodeficiency virus (HIV) into the target cell is initiated by fusion with the cell membrane, mediated through the envelope glycoproteins gp120 and gp41, following engagement to CD4 and the co-receptor. Previous fusion kinetics studies on the HXB2 envelope protein (Env) revealed that Env recruitment occurred at about 13 min concurrent with the lipid mixing. To resolve the temporal sequence of lipid mixing and recruitment, we employed an inhibitory assay monitored by fluorescence microscopy using a gp41 ectodomain (gp41e) fragment, which blocked Env recruitment in stark contrast to the lack of gp41e effect on the lipid mixing. In addition, to demonstrate the mode of action for the inhibition of gp41e, our results strongly suggested that lipid mixing precedes the Env recruitment because lipid mixing can proceed with Env recruitment inhibited by exogeneous gp41e molecules. Importantly, it was found that the random clustering of Env molecules on the membrane surface occurred at ~1 minute whereas the Env recruitment was observed at 13 minutes after the attachment of Env-expressing cell to the target cell. This > 10-fold temporal discrepancy highlights that the productive assembly of Env molecules leading to fusion requires spatio-temporal coordination of several adjacent Env trimers aggregated via directed movement

Protocol for profiling in vitro intratumor heterogeneity using spatially annotated single-cell sequencing

Here, we present a protocol for spatially annotated single-cell sequencing, a technique for spatially profiling intratumor heterogeneity with deep single-cell RNA sequencing and single-cell resolution. By combining live-cell imaging and photopatterned illumination, we describe steps to identify regions of interest in an in vitro tumor model, label the selected cells with photoactivatable dyes, and isolate and subject them to scRNAseq. This protocol can be applied to a range of cell lines and could be expanded to tissue sections. For complete details on the use and execution of this protocol, please refer to Smit et al. (2022).1</p

Instant processing of large-scale image data with FACT, a real-time cell segmentation and tracking algorithm

Quantifying cellular characteristics from a large heterogeneous population is essential to identify rare, disease-driving cells. A recent development in the combination of high-throughput screening microscopy with single-cell profiling provides an unprecedented opportunity to decipher disease-driving phenotypes. Accurately and instantly processing large amounts of image data, however, remains a technical challenge when an analysis output is required minutes after data acquisition. Here, we present fast and accurate real-time cell tracking (FACT). FACT can segment ∼20,000 cells in an average of 2.5 s (1.9–93.5 times faster than the state of the art). It can export quantifiable features minutes after data acquisition (independent of the number of acquired image frames) with an average of 90%–96% precision. We apply FACT to identify directionally migrating glioblastoma cells with 96% precision and irregular cell lineages from a 24 h movie with an average F1 score of 0.91.</p

Instant processing of large-scale image data with FACT, a real-time cell segmentation and tracking algorithm

Quantifying cellular characteristics from a large heterogeneous population is essential to identify rare, disease-driving cells. A recent development in the combination of high-throughput screening microscopy with single-cell profiling provides an unprecedented opportunity to decipher disease-driving phenotypes. Accurately and instantly processing large amounts of image data, however, remains a technical challenge when an analysis output is required minutes after data acquisition. Here, we present fast and accurate real-time cell tracking (FACT). FACT can segment ∼20,000 cells in an average of 2.5 s (1.9–93.5 times faster than the state of the art). It can export quantifiable features minutes after data acquisition (independent of the number of acquired image frames) with an average of 90%–96% precision. We apply FACT to identify directionally migrating glioblastoma cells with 96% precision and irregular cell lineages from a 24 h movie with an average F1 score of 0.91.</p

Photostick: a method for selective isolation of target cells from culture

Sorting of target cells from a heterogeneous pool is technically difficult when the selection criterion is complex, e.g. a dynamic response, a morphological feature, or a combination of multiple parameters. At present, mammalian cell selections are typically performed either via static fluorescence (e.g. fluorescence activated cell sorter), via survival (e.g. antibiotic resistance), or via serial operations (flow cytometry, laser capture microdissection). Here we present a simple protocol for selecting cells based on any static or dynamic property that can be identified by video microscopy and image processing. The “photostick” technique uses a cell-impermeant photochemical crosslinker and digital micromirror array-based patterned illumination to immobilize selected cells on the culture dish. Other cells are washed away with mild protease treatment. The crosslinker also labels the selected cells with a fluorescent dye and a biotin for later identification. The photostick protocol preserves cell viability, permits genetic profiling of selected cells, and can be performed with complex functional selection criteria such as neuronal firing patterns.Chemistry and Chemical Biolog

Genomic Exploration of Distinct Molecular Phenotypes Steering Temozolomide Resistance Development in Patient-Derived Glioblastoma Cells

Chemotherapy using temozolomide is the standard treatment for patients with glioblastoma. Despite treatment, prognosis is still poor largely due to the emergence of temozolomide resistance. This resistance is closely linked to the widely recognized inter- and intra-tumoral heterogeneity in glioblastoma, although the underlying mechanisms are not yet fully understood. To induce temozolomide resistance, we subjected 21 patient-derived glioblastoma cell cultures to Temozolomide treatment for a period of up to 90 days. Prior to treatment, the cells’ molecular characteristics were analyzed using bulk RNA sequencing. Additionally, we performed single-cell RNA sequencing on four of the cell cultures to track the evolution of temozolomide resistance. The induced temozolomide resistance was associated with two distinct phenotypic behaviors, classified as “adaptive” (ADA) or “non-adaptive” (N-ADA) to temozolomide. The ADA phenotype displayed neurodevelopmental and metabolic gene signatures, whereas the N-ADA phenotype expressed genes related to cell cycle regulation, DNA repair, and protein synthesis. Single-cell RNA sequencing revealed that in ADA cell cultures, one or more subpopulations emerged as dominant in the resistant samples, whereas N-ADA cell cultures remained relatively stable. The adaptability and heterogeneity of glioblastoma cells play pivotal roles in temozolomide treatment and contribute to the tumor’s ability to survive. Depending on the tumor’s adaptability potential, subpopulations with acquired resistance mechanisms may arise.</p

Phylogeny and Historical Biogeography of Asian Pterourus Butterflies (Lepidoptera: Papilionidae): A Case of Intercontinental Dispersal from North America to East Asia

The phylogenetic status of the well-known Asian butterflies often known as Agehana (a species group, often treated as a genus or a subgenus, within Papilio sensu lato) has long remained unresolved. Only two species are included, and one of them especially, Papilio maraho, is not only rare but near-threatened, being monophagous on its vulnerable hostplant, Sassafras randaiense (Lauraceae). Although the natural history and population conservation of “Agehana” has received much attention, the biogeographic origin of this group still remains enigmatic. To clarify these two questions, a total of 86 species representatives within Papilionidae were sampled, and four genes (concatenated length 3842 bp) were used to reconstruct their phylogenetic relationships and historical scenarios. Surprisingly, “Agehana” fell within the American Papilio subgenus Pterourus and not as previously suggested, phylogenetically close to the Asian Papilio subgenus Chilasa. We therefore formally synonymize Agehana with Pterourus. Dating and biogeographic analysis allow us to infer an intercontinental dispersal of an American ancestor of Asian Pterourus in the early Miocene, which was coincident with historical paleo-land bridge connections, resulting in the present “East Asia-America” disjunction distribution. We emphasize that species exchange between East Asia and America seems to be a quite frequent occurrence in butterflies during the Oligocene to Miocene climatic optima.© 2015 Wu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

A comprehensive enhancer screen identifies TRAM2 as a key and novel mediator of YAP oncogenesis

Background: Frequent activation of the co-transcriptional factor YAP is observed in a large number of solid tumors. Activated YAP associates with enhancer loci via TEAD4-DNA-binding protein and stimulates cancer aggressiveness. Although thousands of YAP/TEAD4 binding-sites are annotated, their functional importance is unknown. Here, we aim at further identification of enhancer elements that are required for YAP functions. Results: We first apply genome-wide ChIP profiling of YAP to systematically identify enhancers that are bound by YAP/TEAD4. Next, we implement a genetic approach to uncover functions of YAP/TEAD4-associated enhancers, demonstrate its robustness, and use it to reveal a network of enhancers required for YAP-mediated proliferation. We focus on EnhancerTRAM2, as its target gene TRAM2 shows the strongest expression-correlation with YAP activity in nearly all tumor types. Interestingly, TRAM2 phenocopi

CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting

Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome. To drive innovation in this area, we setup a community-wide challenge using the largest available dataset of its kind to assess nuclear segmentation and cellular composition. Our challenge, named CoNIC, stimulated the development of reproducible algorithms for cellular recognition with real-time result inspection on public leaderboards. We conducted an extensive post-challenge analysis based on the top-performing models using 1,658 whole-slide images of colon tissue. With around 700 million detected nuclei per model, associated features were used for dysplasia grading and survival analysis, where we demonstrated that the challenge's improvement over the previous state-of-the-art led to significant boosts in downstream performance. Our findings also suggest that eosinophils and neutrophils play an important role in the tumour microevironment. We release challenge models and WSI-level results to foster the development of further methods for biomarker discovery