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"Wilson, David L"
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Reimagining high-throughput profiling of reactive cysteines for cell-based screening of large electrophile libraries
Current methods used for measuring amino acid side-chain reactivity lack the throughput needed to screen large chemical libraries for interactions across the proteome. Here we redesigned the workflow for activity-based protein profiling of reactive cysteine residues by using a smaller desthiobiotin-based probe, sample multiplexing, reduced protein starting amounts and software to boost data acquisition in real time on the mass spectrometer. Our method, streamlined cysteine activity-based protein profiling (SLC-ABPP), achieved a 42-fold improvement in sample throughput, corresponding to profiling library members at a depth of >8,000 reactive cysteine sites at 18 min per compound. We applied it to identify proteome-wide targets of covalent inhibitors to mutant Kirsten rat sarcoma (KRAS)
G12C
and Bruton’s tyrosine kinase (BTK). In addition, we created a resource of cysteine reactivity to 285 electrophiles in three human cell lines, which includes >20,000 cysteines from >6,000 proteins per line. The goal of proteome-wide profiling of cysteine reactivity across thousand-member libraries under several cellular contexts is now within reach.
An improved workflow enables a 42-fold higher throughput of activity-based protein profiling.
Journal Article
Deep learning segmentation and quantification method for assessing epicardial adipose tissue in CT calcium score scans
Epicardial adipose tissue volume (EAT) has been linked to coronary artery disease and the risk of major adverse cardiac events. As manual quantification of EAT is time-consuming, requires specialized training, and is prone to human error, we developed a deep learning method (DeepFat) for the automatic assessment of EAT on non-contrast low-dose CT calcium score images. Our DeepFat intuitively segmented the tissue enclosed by the pericardial sac on axial slices, using two preprocessing steps. First, we applied a
HU-attention-window
with a window/level 350/40-HU to draw attention to the sac and reduce numerical errors. Second, we applied a novel
look ahead slab-of-slices with bisection
(“
bisect
”) in which we split the heart into halves and sequenced the lower half from bottom-to-middle and the upper half from top-to-middle, thereby presenting an always increasing curvature of the sac to the network. EAT volume was obtained by thresholding voxels within the sac in the fat window (− 190/− 30-HU). Compared to manual segmentation, our algorithm gave excellent results with volume Dice = 88.52% ± 3.3, slice Dice = 87.70% ± 7.5, EAT error = 0.5% ± 8.1, and R = 98.52% (p < 0.001). HU-attention-window and bisect improved Dice volume scores by 0.49% and 3.2% absolute, respectively. Variability between analysts was comparable to variability with DeepFat. Results compared favorably to those of previous publications.
Journal Article
Assessment of intraocular foreign body using high resolution 3D ultrasound imaging
Ocular trauma often involves intraocular foreign bodies (IOFBs) that pose challenges in accurate diagnosis due to their size, shape, and material composition. In this study, we proposed a novel whole-eye 3D ophthalmic ultrasound B-scan (3D-UBS) system for automating image acquisition and improved 3D visualization, thereby improving sensitivity for detecting IOFBs. 3D-UBS utilizes 14 MHz Clarius L20 probe, a motorized translation stage, and a surgical microscope for precise placement and movement. The system’s 3D point spread function (PSF) is 0.377 × 0.550 × 0.894 mm
3
characterized by the full-width at half-maximum intensity values in the axial, lateral and elevation directions. Digital phantom and ex vivo ocular models were prepared using four types of IOFBs (i.e., plastic, wood, metal, and glass). Ex vivo models were imaged with both 3D-UBS and clinical computed tomography (CT). Image preprocessing was performed on 3D-UBS images to remove uneven illumination and speckle noise. Multiplanar reformatting in 3D-UBS provides optimal plane selection after acquisition, reducing the need for a trained ultrasonographer. 3D-UBS outperforms CT in contrast for wood and plastic, with mean contrast improvement of 2.43 and 1.84 times, respectively. 3D-UBS was able to identify wood and plastic IOFBs larger than 250 µm and 300 in diameter, respectively. CT, with its wider PSF, was only able to detect wood and plastic IOFBs larger than 600 and 550 µm, respectively. Although contrast was higher in CT for metal and glass IOFBs, 3D-UBS provided sufficient contrast to identify those. 3D-UBS provides an easy-to-use, non-expert imaging approach for identifying small IOFBs of different materials and related ocular injuries at the point of care.
Journal Article
Fully automated plaque characterization in intravascular OCT images using hybrid convolutional and lumen morphology features
For intravascular OCT (IVOCT) images, we developed an automated atherosclerotic plaque characterization method that used a hybrid learning approach, which combined deep-learning convolutional and hand-crafted, lumen morphological features. Processing was done on innate A-line units with labels fibrolipidic (fibrous tissue followed by lipidous tissue), fibrocalcific (fibrous tissue followed by calcification), or other. We trained/tested on an expansive data set (6,556 images), and performed an active learning, relabeling step to improve noisy ground truth labels. Conditional random field was an important post-processing step to reduce classification errors. Sensitivities/specificities were 84.8%/97.8% and 91.4%/95.7% for fibrolipidic and fibrocalcific plaques, respectively. Over lesions, en face classification maps showed automated results that agreed favorably to manually labeled counterparts. Adding lumen morphological features gave statistically significant improvement (p < 0.05), as compared to classification with convolutional features alone. Automated assessments of clinically relevant plaque attributes (arc angle and length), compared favorably to those from manual labels. Our hybrid approach gave statistically improved results as compared to previous A-line classification methods using deep learning or hand-crafted features alone. This plaque characterization approach is fully automated, robust, and promising for live-time treatment planning and research applications.
Journal Article
Efficient DNA fluorescence labeling via base excision trapping
Fluorescence labeling of DNAs is broadly useful, but methods for labeling are expensive and labor-intensive. Here we describe a general method for fluorescence labeling of oligonucleotides readily and cost-efficiently via base excision trapping (BETr), employing deaminated DNA bases to mark label positions, which are excised by base excision repair enzymes generating AP sites. Specially designed aminooxy-substituted rotor dyes trap the AP sites, yielding high emission intensities. BETr is orthogonal to DNA synthesis by polymerases, enabling multi-uracil incorporation into an amplicon and in situ BETr labeling without washing. BETr also enables labeling of dsDNA such as genomic DNA at a high labeling density in a single tube by use of nick translation. Use of two different deaminated bases facilitates two-color site-specific labeling. Use of a multi-labeled DNA construct as a bright fluorescence tag is demonstrated through the conjugation to an antibody for imaging proteins. Finally, double-strand selectivity of a repair enzyme is harnessed in sensitive reporting on the presence of a target DNA or RNA in a mixture with isothermal turnover and single nucleotide specificity. Overall, the results document a convenient and versatile method for general fluorescence labeling of DNAs.
Methods for fluorescently labelling DNAs are expensive and labour-intensive. Here the authors report an in situ DNA labelling strategy for oligonucleotides as well as dsDNA that makes use of aldehyde-reactive rotor dyes to trap AP sites resulting from excision of deaminated DNA bases.
Journal Article
Enhancing cardiovascular risk prediction through AI-enabled calcium-omics
Whole-heart coronary calcium Agatston score is a well-established predictor of major adverse cardiovascular events (MACE), but it does not account for individual calcification features related to the pathophysiology of the disease (e.g., multiple-vessel disease, spread of the disease along the vessel, stable calcifications, numbers of lesions, and density). We used novel, hand-crafted calcification features (calcium-omics); Cox time-to-event modeling; elastic net; and up and down synthetic sampling methods for imbalanced data, to assess MACE risk. We used 2457 CT calcium score (CTCS) images enriched for MACE events from our large no-cost CLARIFY program (ClinicalTrials.gov Identifier: NCT04075162). Among calcium-omics features, numbers of calcifications, LAD mass, and diffusivity (a measure of spatial distribution) were especially important determinants of increased risk, with dense calcification (> 1000HU, stable calcifications) associated with reduced risk Our calcium-omics model with (training/testing, 80/20) gave C-index (80.5%/71.6%) and 2-year AUC (82.4%/74.8%). Although the C-index is notoriously impervious to model improvements, calcium-omics compared favorably to Agatston and gave a significant difference (
P
< 0.001). The calcium-omics model identified 73.5% of MACE cases in the high-risk group, a 13.2% improvement as compared to Agatston, suggesting that calcium-omics could be used to better identity candidates for intensive follow-up and therapies. The categorical net-reclassification index was NRI = 0.153. Our findings from this exploratory study suggest the utility of calcium-omics in improved risk prediction. These promising results will pave the way for more extensive, multi-institutional studies of calcium-omics.
Journal Article
Optimal slice thickness for improved accuracy of quantitative analysis of fluorescent cell and microsphere distribution in cryo-images
Cryo-imaging has been effectively used to study the biodistribution of fluorescent cells or microspheres in animal models. Sequential slice-by-slice fluorescent imaging enables detection of fluorescent cells or microspheres for corresponding quantification of their distribution in tissue. However, if slices are too thin, there will be data overload and excessive scan times. If slices are too thick, then cells can be missed. In this study, we developed a model for detection of fluorescent cells or microspheres to aid optimal slice thickness determination. Key factors include: section thickness (
X
), fluorescent cell intensity (
I
fluo
), effective tissue attenuation coefficient (
μ
T
), and a detection threshold (
T
). The model suggests an optimal slice thickness value that provides near-ideal sensitivity while minimizing scan time. The model also suggests a correction method to compensate for missed cells in the case that image data were acquired with overly large slice thickness. This approach allows cryo-imaging operators to use larger slice thickness to expedite the scan time without significant loss of cell count. We validated the model using real data from two independent studies: fluorescent microspheres in a pig heart and fluorescently labeled stem cells in a mouse model. Results show that slice thickness and detection sensitivity relationships from simulations and real data were well-matched with 99% correlation and 2% root-mean-square (RMS) error. We also discussed the detection characteristics in situations where key assumptions of the model were not met such as fluorescence intensity variation and spatial distribution. Finally, we show that with proper settings, cryo-imaging can provide accurate quantification of the fluorescent cell biodistribution with remarkably high recovery ratios (number of detections/delivery). As cryo-imaging technology has been used in many biological applications, our optimal slice thickness determination and data correction methods can play a crucial role in further advancing its usability and reliability.
Journal Article
Assessment of the therapeutic role of mesenchymal stromal cells in a mouse model of graft-versus-host disease using cryo-imaging
Insights regarding the biodistribution and homing of mesenchymal stromal cells (MSCs), as well as their interaction with alloreactive T-cells are critical for understanding how MSCs can regulate graft-versus-host disease (GVHD) following allogeneic (allo) bone marrow transplantation (BMT). We developed novel assays based on 3D, microscopic, cryo-imaging of whole-mouse-sized volumes to assess the therapeutic potential of human MSCs using an established mouse GVHD model. Following infusion, we quantitatively tracked fluorescently labeled, donor-derived, T-cells and third party MSCs in BMT recipients using multispectral cryo-imaging. Specific MSC homing sites were identified in the marginal zones in the spleen and the lymph nodes, where we believe MSC immunomodulation takes place. The number of MSCs found in spleen of the allo BMT recipients was about 200% more than that observed in the syngeneic group. To more carefully define the effects MSCs had on T cell activation and expansion, we developed novel T-cell proliferation assays including secondary lymphoid organ (SLO) enlargement and Carboxyfluoescein succinimidyl ester (CFSE) dilution. As anticipated, significant SLO volume enlargement and CFSE dilution was observed in allo but not syn BMT recipients due to rapid proliferation and expansion of labeled T-cells. MSC treatment markedly attenuated CFSE dilution and volume enlargement of SLO. These assays confirm evidence of potent, in vivo, immunomodulatory properties of MSC following allo BMT. Our innovative platform includes novel methods for tracking cells of interest as well as assessing therapeutic function of MSCs during GVHD induction. Our results support the use of MSCs treatment or prevention of GVHD and illuminate the wider adoption of MSCs as a standard medicinal cell therapy.
Journal Article