The CRISPR/Cas9 ribonucleoprotein (RNP)-mediated technology represents a fascinating tool for modifying gene expression or mutagenesis as this system allows for obtaining transgene-free plants, avoiding exogenous DNA integration. Holm oak (Quercus ilex) has an important social, economic, and ecological role in the Mediterranean climate zones of Western Europe and North Africa and is severely affected by oak decline syndrome. Here we report the first example of the application of the CRISPR/Cas9-RNP technology in holm oak. Firstly, we evaluated the protoplast isolation from both in vitro leaves and proembryogenic masses. Proembryogenic masses represented the best material to get high protoplast yield (11 x 106 protoplasts/ml) and viability. Secondly, the protoplast transfection ability was evaluated through a vector expressing green fluorescence protein as marker gene of transfection, reaching a transfection percentage of 62% after 24 hours. CRISPR/Cas9 RNPs were successfully delivered into protoplasts resulting in 5.6% ± 0.5% editing efficiency at phytoene desaturase (pds) target genomic region. Protoplasts were then cultured in semisolid media and, after 45 days in culture, developed embryogenic calli were observed in a Murashige and Skoog media with half concentration of NH4NO3 and KNO3 supplemented with 0.1 mg/L benzylaminopurine and 0.1 mg/L 2,4-dichlorophenoxyacetic acid.

Absolute quantification of biological samples provides precise numerical expression levels, enhancing accuracy, and performance for rare templates. Current methodologies, however, face challenges-flow cytometers are costly and complex, whereas fluorescence imaging, relying on software or manual counting, is time-consuming and error-prone. It is presented that Deep-qGFP, a deep learning-aided pipeline for the automated detection and classification of green fluorescent protein (GFP) labeled microreactors, enables real-time absolute quantification. This approach achieves an accuracy of 96.23% and accurately measures the sizes and occupancy status of microreactors using standard laboratory fluorescence microscopes, providing precise template concentrations. Deep-qGFP demonstrates remarkable speed, quantifying over 2000 microreactors across ten images in just 2.5 seconds, with a dynamic range of 56.52-1569.43 copies µL-1 . The method demonstrates impressive generalization capabilities, successfully applied to various GFP-labeling scenarios, including droplet-based, microwell-based, and agarose-based applications. Notably, Deep-qGFP is the first all-in-one image analysis algorithm successfully implemented in droplet digital polymerase chain reaction (PCR), microwell digital PCR, droplet single-cell sequencing, agarose digital PCR, and bacterial quantification, without requiring transfer learning, modifications, or retraining. This makes Deep-qGFP readily applicable in biomedical laboratories and holds potential for broader clinical applications.

Here, we provide experimental verification supporting the use of short-section imaging bundles for two-photon microscopy imaging of the mouse brain. The 8 mm long bundle is made of a pair of heavy-metal oxide glasses with a refractive index contrast of 0.38 to ensure a high numerical aperture NA = 1.15. The bundle is composed of 825 multimode cores, ordered in a hexagonal lattice with a pixel size of 14 μm and a total diameter of 914 μm. We demonstrate successful imaging through custom-made bundles with 14 μm resolution. As the input, we used a 910 nm Ti-sapphire laser with 140 fs pulse and a peak power of 9 × 104 W. The excitation beam and fluorescent image were transferred through the fiber imaging bundle. As test samples, we used 1 μm green fluorescent latex beads, ex vivo hippocampal neurons expressing green fluorescent protein and cortical neurons in vivo expressing the fluorescent reporter GCaMP6s or immediate early gene Fos fluorescent reporter. This system can be used for minimal-invasive in vivo imaging of the cerebral cortex, hippocampus, or deep brain areas as a part of a tabletop system or an implantable setup. It is a low-cost solution, easy to integrate and operate for high-throughput experiments.

Osteoarthritis (OA) is a whole joint disorder that is characterized by cartilage damage and abnormal remodeling of subchondral bone. Injecting adipose-derived stem cells (ASCs) into the knee joint cavity can assist in repairing osteoarthritic joints, but their ability to migrate to the damaged site is limited. Our previous studies have shown that knee loading can improve the symptoms of OA, but the effect and mechanism of knee loading on the migration of ASCs in OA remain unclear. We employed a mouse model of OA in the knee and applied knee loading (1 N at 5 Hz for 6 min/day for 2 weeks) after the intra-articular injection of ASCs. The cartilage and subchondral bone repair were assessed by histopathological analysis. Immunofluorescence assays were also used to analyze the migration of ASCs. Using cell cultures, we evaluated the migration of ASCs using the transwell migration and wound healing assays. In vivo experiments showed that knee loading promoted the migration of ASCs, increased the local SDF-1 level, and accelerated the repair of the OA-damaged sites. Mechanistically, the observed effects were blocked by the SDF-1/CXCR4 inhibitor. The in vitro results further revealed that knee loading promoted the migration of ASCs and the inhibition of SDF-1/CXCR4 significantly suppressed the beneficial loading effect. The results herein suggested that the migration of ASCs was enhanced by knee loading through the SDF-1/CXCR4 regulatory axis, and mechanical loading promoted the joint-protective effect of ASCs.

Difficulty in preventing crops from plant viruses urges to discover novel efficient antiviral chemicals, which is sped up by precise screening methods. Fluorescence-based methods have recently been applied as innovative and rapid tools for visually monitoring the replication of viruses and screening of antivirals, whereas the quantification of fluorescence signals mainly depends on manually calculating the fluorescent spots, which is time-consuming and imprecise. In the present work, the fluorescence spots were automatically identified, and the fluorescence area was directly quantified by a program developed in our group, which avoided subjective errors from the operators. We further employed this digital and visual screening assay to identify antivirals using the tobacco mosaic virus-green fluorescence protein (TMV-GFP) construct, in which the expression of GFP intuitively reflected the efficacy of antivirals. The accuracy of this assay was validated by quantifying the activities of the commercial antiviral inhibitors ribavirin and ningnanmycin and then was applied to evaluate the subtle activity differences of a series of newly synthesized carbazole and β-carboline alkaloid derivatives. Among them, compounds 5 (76%) and 11 (63%) exhibited anti-TMV activities comparable to that of ningnanmycin (65%) at 50 μM, and they delayed the multiplication of TMV in the early stage of infection without phytotoxicity. Taken together, these findings demonstrated that the digital and visual TMV-GFP screening method was competent to test the antiviral activities of compounds with subtle modifications and facilitated the discovery of novel antivirals.

Cotton encounters long-term drought stress problems resulting in major yield losses. Transcription factors (TFs) plays an important role in response to biotic and abiotic stresses. The coexpression patterns of gene networks associated with drought stress tolerance were investigated using transcriptome profiles. Applying a weighted gene coexpression network analysis, we discovered a salmon module with 144 genes strongly linked to drought stress tolerance. Based on coexpression and RT-qPCR analysis GH_D01G0514 was selected as the candidate gene, as it was also identified as a hub gene in both roots and leaves with a consistent expression in response to drought stress in both tissues. For validation of GH_D01G0514, Virus Induced Gene Silencing was performed and VIGS plants showed significantly higher excised leaf water loss and ion leakage, while lower relative water and chlorophyll contents as compared to WT (Wild type) and positive control plants. Furthermore, the WT and positive control seedlings showed higher CAT and SOD activities, and lower activities of hydrogen peroxide and MDA enzymes as compared to the VIGS plants. Gh_D01G0514 (GhNAC072) was localized in the nucleus and cytoplasm. Y2H assay demonstrates that Gh_D01G0514 has a potential of auto activation. It was observed that the Gh_D01G0514 was highly upregulated in both tissues based on RNA Seq and RT-qPCR analysis. Thus, we inferred that, this candidate gene might be responsible for drought stress tolerance in cotton. This finding adds significantly to the existing knowledge of drought stress tolerance in cotton and deep molecular analysis are required to understand the molecular mechanisms underlying drought stress tolerance in cotton.

A major obstacle to studying membrane proteins by biophysical techniques is the difficulty in producing sufficient amounts of materials for functional and structural studies. To overexpress the target membrane protein heterologously, especially an eukaryotic protein, a key step is to find the optimal host expression system and perform subsequent expression optimization. In this chapter, we describe protocols for screening membrane protein production using bacterial and insect cells, solubilization screening, large-scale production, and commonly used affinity chromatography purification methods. We discuss general optimization conditions, such as promoters and tags, and describe current techniques that can be used in any laboratory without specialized expensive equipment. Especially for insect cells, GFP fusions are particularly useful for localization and in-gel fluorescence detection of the proteins on SDS-PAGE. We give detailed protocols that can be used to screen the best expression and purification conditions for membrane protein study.

X-linked adrenoleukodystrophy (X-ALD) is a severe inherited metabolic disease with cerebral inflammatory demyelination and abnormal accumulation of very long chain fatty acid (VLCFA) in tissues, especially the brain. At present, bone marrow transplantation (BMT) at an early stage of the disease is the only effective treatment for halting disease progression, but the underlying mechanism of the treatment has remained unclear. Here, we transplanted GFP-expressing wild-type (WT) or Abcd1-deficient (KO) bone marrow cells into recipient KO mice, which enabled tracking of the donor GFP+ cells in the recipient mice. Both the WT and KO donor cells were equally distributed throughout the brain parenchyma, and displayed an Iba1-positive, GFAP- and Olig2-negative phenotype, indicating that most of the donor cells were engrafted as microglia-like cells. They constituted approximately 40% of the Iba1-positive cells. Unexpectedly, no decrease of VLCFA in the cerebrum was observed when WT bone marrow cells were transplanted into KO mice. Taken together, murine study suggests that bone marrow-derived microglia-like cells engrafted in the cerebrum of X-ALD patients suppress disease progression without evidently reducing the amount of VLCFA in the cerebrum.

Mounting evidence suggests that the ATP-gated P2X7 receptor contributes to increased hyperexcitability in the brain. While increased expression of P2X7 in the hippocampus and cortex following status epilepticus and during epilepsy has been repeatedly demonstrated, the cell type-specific expression of P2X7 and its expression in extra-hippocampal brain structures remains incompletely explored. In this study, P2X7 expression was visualized by using a transgenic mouse model overexpressing P2X7 fused to the fluorescent protein EGFP. The results showed increased P2X7-EGFP expression after status epilepticus induced by intra-amygdala kainic acid and during epilepsy in different brain regions including the hippocampus, cortex, striatum, thalamus and cerebellum, and this was most evident in microglia and oligodendrocytes. Co-localization of P2X7-EGFP with cell type-specific markers was not detected in neurons or astrocytes. These data suggest that P2X7 activation is a common pathological hallmark across different brain structures, possibly contributing to brain inflammation and neurodegeneration following acute seizures and during epilepsy.

Deep-learning workflows of microscopic image analysis are sufficient for handling the contextual variations because they employ biological samples and have numerous tasks. The use of well-defined annotated images is important for the workflow. Cancer stem cells (CSCs) are identified by specific cell markers. These CSCs were extensively characterized by the stem cell (SC)-like gene expression and proliferation mechanisms for the development of tumors. In contrast, the morphological characterization remains elusive. This study aims to investigate the segmentation of CSCs in phase contrast imaging using conditional generative adversarial networks (CGAN). Artificial intelligence (AI) was trained using fluorescence images of the Nanog-Green fluorescence protein, the expression of which was maintained in CSCs, and the phase contrast images. The AI model segmented the CSC region in the phase contrast image of the CSC cultures and tumor model. By selecting images for training, several values for measuring segmentation quality increased. Moreover, nucleus fluorescence overlaid-phase contrast was effective for increasing the values. We show the possibility of mapping CSC morphology to the condition of undifferentiation using deep-learning CGAN workflows.