Probing the Heterogeneity of Protein Kinase Activation in Cells by Super-Resolution Microscopy

[Image: see text] Heterogeneity of mitogen-activated protein kinase (MAPK) activation in genetically identical cells, which occurs in response to epidermal growth factor receptor (EGFR) signaling, remains poorly understood. MAPK cascades integrate signals emanating from different EGFR spatial locations, including the plasma membrane and endocytic compartment. We previously hypothesized that in EGF-stimulated cells the MAPK phosphorylation (pMAPK) level and activity are largely determined by the spatial organization of the EGFR clusters within the cell. For experimental testing of this hypothesis, we used super-resolution microscopy to define EGFR clusters by receptor numbers (N) and average intracluster distances (d). From these data, we predicted the extent of pMAPK with 85% accuracy on a cell-to-cell basis with control data returning 54% accuracy (P < 0.001). For comparison, the prediction accuracy was only 61% (P = 0.382) when the diffraction-limited averaged fluorescence intensity/cluster was used. Large clusters (N ≥ 3) with d > 50 nm were most predictive for pMAPK level in cells. Electron microscopy revealed that these large clusters were primarily localized to the limiting membrane of multivesicular bodies (MVB). Many tighter packed dimers/multimers (d < 50 nm) were found on intraluminal vesicles within MVBs, where they were unlikely to activate MAPK because of the physical separation. Our results suggest that cell-to-cell differences in N and d contain crucial information to predict EGFR-activated cellular pMAPK levels and explain pMAPK heterogeneity in isogenic cells

High resolution optical DNA mapping

Many types of diseases including cancer and autism are associated with copy-number variations in the genome. Most of these variations could not be identified with existing sequencing and optical DNA mapping methods. We have developed Multi-color Super-resolution technique, with potential for high throughput and low cost, which can allow us to recognize more of these variations. Our technique has made 10–fold improvement in the resolution of optical DNA mapping. Using a 180 kb BAC clone as a model system, we resolved dense patterns from 108 fluorescent labels of two different colors representing two different sequence-motifs. Overall, a detailed DNA map with 100 bp resolution was achieved, which has the potential to reveal detailed information about genetic variance and to facilitate medical diagnosis of genetic disease

Biomimetic Hydrogels in the Study of Cancer Mechanobiology: Overview, Biomedical Applications, and Future Perspectives

Hydrogels are biocompatible polymers that are tunable to the system under study, allowing them to be widely used in medicine, bioprinting, tissue engineering, and biomechanics. Hydrogels are used to mimic the three-dimensional microenvironment of tissues, which is essential to understanding cell–cell interactions and intracellular signaling pathways (e.g., proliferation, apoptosis, growth, and survival). Emerging evidence suggests that the malignant properties of cancer cells depend on mechanical cues that arise from changes in their microenvironment. These mechanobiological cues include stiffness, shear stress, and pressure, and have an impact on cancer proliferation and invasion. The hydrogels can be tuned to simulate these mechanobiological tissue properties. Although interest in and research on the biomedical applications of hydrogels has increased in the past 25 years, there is still much to learn about the development of biomimetic hydrogels and their potential applications in biomedical and clinical settings. This review highlights the application of hydrogels in developing pre-clinical cancer models and their potential for translation to human disease with a focus on reviewing the utility of such models in studying glioblastoma progression

Multicolor Super-Resolution DNA Imaging for Genetic Analysis

Many types of cancer and neurodegenerative diseases are caused by abnormalities and variations in the genome. We have designed a high-resolution imaging technique with high throughput and low cost for determining structural variations of genes related to genetic diseases. We initially mapped all seven nicking sites of Nb.BbvCI endonuclease enzyme on lambda DNA. Then we resolved densely labeled patterns of 107 nicking sites on human BAC DNA that is digested by Nb.BsmI and Nb.BbvCI endonuclease enzymes. This high density resulted in several dyes being closer together than the diffraction limit. Overall, detailed DNA nicking sites mapping with 100 bp resolution was achieved, which has the potential to reveal information about genetic variance and to facilitate medical diagnosis of several genetic diseases

TNTdetect.AI: A Deep Learning Model for Automated Detection and Counting of Tunneling Nanotubes in Microscopy Images

Background: Tunneling nanotubes (TNTs) are cellular structures connecting cell membranes and mediating intercellular communication. TNTs are manually identified and counted by a trained investigator; however, this process is time-intensive. We therefore sought to develop an automated approach for quantitative analysis of TNTs. Methods: We used a convolutional neural network (U-Net) deep learning model to segment phase contrast microscopy images of both cancer and non-cancer cells. Our method was composed of preprocessing and model development. We developed a new preprocessing method to label TNTs on a pixel-wise basis. Two sequential models were employed to detect TNTs. First, we identified the regions of images with TNTs by implementing a classification algorithm. Second, we fed parts of the image classified as TNT-containing into a modified U-Net model to estimate TNTs on a pixel-wise basis. Results: The algorithm detected 49.9% of human expert-identified TNTs, counted TNTs, and calculated the number of TNTs per cell, or TNT-to-cell ratio (TCR); it detected TNTs that were not originally detected by the experts. The model had 0.41 precision, 0.26 recall, and 0.32 f-1 score on a test dataset. The predicted and true TCRs were not significantly different across the training and test datasets (p = 0.78). Conclusions: Our automated approach labeled and detected TNTs and cells imaged in culture, resulting in comparable TCRs to those determined by human experts. Future studies will aim to improve on the accuracy, precision, and recall of the algorithm

Multicolor Super-Resolution DNA Imaging for Genetic Analysis

Many types of cancer and neurodegenerative diseases are caused by abnormalities and variations in the genome. We have designed a high-resolution imaging technique with high throughput and low cost for determining structural variations of genes related to genetic diseases. We initially mapped all seven nicking sites of Nb.BbvCI endonuclease enzyme on lambda DNA. Then we resolved densely labeled patterns of 107 nicking sites on human BAC DNA that is digested by Nb.BsmI and Nb.BbvCI endonuclease enzymes. This high density resulted in several dyes being closer together than the diffraction limit. Overall, detailed DNA nicking sites mapping with 100bp resolution was achieved, which has the potential to reveal information about genetic variance and to facilitate medical diagnosis of several genetic diseases