Biology subjects

Boyd, J. D.

Publications and source records attributed to Boyd, J. D..

6 recordsLinked to original sources

bioRxiv2023

JUMP Cell Painting dataset: morphological impact of 136,000 chemical and genetic perturbations

Image-based profiling has emerged as a powerful technology for various steps in basic biological and pharmaceutical discovery, but the community has lacked a large, public reference set of data from chemical and genetic perturbations. Here we present data generated by the Joint Undertaking for Morphological Profiling (JUMP)-Cell Painting Consortium, a collaboration between 10 pharmaceutical companies, six supporting technology companies, and two non-profit partners. When completed, the dataset will contain images and profiles from the Cell Painting assay for over 116,750 unique compounds, over-expression of 12,602 genes, and knockout of 7,975 genes using CRISPR-Cas9, all in human osteosarcoma cells (U2OS). The dataset is estimated to be 115 TB in size and capturing 1.6 billion cells and their single-cell profiles. File quality control and upload is underway and will be completed over the coming months at the Cell Painting Gallery: https://registry.opendata.aws/cellpainting-gallery. A portal to visualize a subset of the data is available at https://phenaid.ardigen.com/jumpcpexplorer/.

bioinformatics↗

bioRxiv2022

Sucrose-mediated formation and adhesion strength of Streptococcus mutans biofilms on titanium

Biofilms consist of bacterial cells surrounded by a matrix of extracellular polymeric substance (EPS), which protects the colony from many countermeasures, including antibiotic treatments. Biofilm EPS composition is affected by environmental factors. In the oral cavity, the presence of sucrose affects the growth of Streptococcus mutans that produce acids, eroding enamel and forming dental caries. Biofilm formation on dental implants commonly leads to severe infections and failure of the implant. This work determines the effect of sucrose concentration on biofilm EPS formation and adhesion of Streptococcus mutans, a common oral colonizer. Bacterial biofilms are grown with varying concentrations of sucrose on titanium substrates simulating dental implant material. Strategies for measuring adhesion for films such as peel tests are inadequate for biofilms, which have low cohesive strength and will fall apart when tensile loading is applied directly. The laser spallation technique is used to apply stress wave loading to the biofilm, causing the biofilm to delaminate at a critical tensile stress threshold. Biofilm formation and EPS structures are visualized at high magnification with scanning electron microscopy (SEM). Biofilm substrate coverage and adhesion strength of biofilms initially increase with increasing sucrose concentration, but then decrease as sucrose concentration continues to increase. For biofilms grown with non-zero concentrations of sucrose, S. mutans adhesion to the substrate is higher than the adhesion of osteoblast-like cells to the same substrates. These results suggest sucrose-mediated adhesion and formation on titanium of S. mutans biofilms may outcompete osteoblasts during osseointegration, which could explain higher rates of peri-implant disease associated with high sugar diets.

bioengineering↗

bioRxiv2022

Optimizing the Cell Painting assay for image-based profiling

In image-based profiling, software extracts thousands of morphological features of cells from multi-channel fluorescence microscopy images, yielding single-cell profiles that can be used for basic research and drug discovery. Powerful applications have been proven, including clustering chemical and genetic perturbations based on their similar morphological impact, identifying disease phenotypes by observing differences in profiles between healthy and diseased cells, and predicting assay outcomes using machine learning, among many others. Here we provide an updated protocol for the most popular assay for image-based profiling, Cell Painting. Introduced in 2013, it uses six stains imaged in five channels and labels eight diverse components of the cell: DNA, cytoplasmic RNA, nucleoli, actin, Golgi apparatus, plasma membrane, endoplasmic reticulum, and mitochondria. The original protocol was updated in 2016 based on several years experience running it at two sites, after optimizing it by visual stain quality. Here we describe the work of the Joint Undertaking for Morphological Profiling (JUMP) Cell Painting Consortium, aiming to improve upon the assay via quantitative optimization, based on the measured ability of the assay to detect morphological phenotypes and group similar perturbations together. We find that the assay gives very robust outputs despite a variety of changes to the protocol and that two vendors dyes work equivalently well. We present Cell Painting version 3, in which some steps are simplified and several stain concentrations can be reduced, saving costs. Cell culture and image acquisition take 1-2 weeks for a typically sized batch of 20 or fewer plates; feature extraction and data analysis take an additional 1-2 weeks. Key references using this protocolVirtual screening for small-molecule pathway regulators by image-profile matching (https://doi.org/10.1016/j.cels.2022.08.003) - recent work examining the ability to use collected Cell Painting profiles to screen for regulators of a number of diverse biological pathways. JUMP Cell Painting dataset: images and profiles from two billion cells perturbed by 140,000 chemical and genetic perturbations (DOI) - the description of the main JUMP master public data set, using this protocol in the production of >200 TB of image data and >200 TB of measured profiles. Key data used in this protocolCell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes (https://doi.org/10.1038/nprot.2016.105) - this paper provides the first step-by-step Cell Painting protocol ever released.

bioinformatics↗

bioRxiv2019

Biofilm and Cell Adhesion Strength on Dental Implant Surfaces via the Laser Spallation Technique

ObjectivesThe aim of this study is to quantify the adhesion strength differential between an oral bacterial biofilm and an osteoblast-like cell monolayer to a dental implant-simulant surface and develop a metric that quantifies the biocompatible efficacy of implant surfaces. MethodsHigh-amplitude short-duration stress waves generated by laser pulse absorption are used to spall bacteria and cells from titanium substrates. By carefully controlling laser fluence and calibration of laser fluence with applied stress, the adhesion difference between dental carry Streptococcus mutans biofilms and MG 63 osteoblast-like cell monolayers on smooth and rough titanium substrates is obtained. The Adhesion Index consists of a ratio of cell adhesion strength to biofilm adhesion strength obtaining a nondimensionalized parameter for biocompatibility assessments. ResultsAdhesion strength of 145{+/-}42 MPa is measured for MG 63 on smooth titanium, which increases to 288{+/-}24 MPa on roughened titanium. Adhesion strength for S. mutans on smooth titanium is 315{+/-}9 MPa and remained relatively constant at 332{+/-}9 MPa on roughened titanium. The Adhesion Index for smooth titanium is 0.46{+/-}0.12 which increased to 0.87{+/-}0.05 on roughened titanium. SignificanceThe laser spallation technique provides a platform to examine the tradeoffs of adhesion modulators on both biofilm and cell adhesion. This tradeoff is characterized by the Adhesion Index, which is proposed to aid biocompatibility screening and could result in improved implantation outcomes. The Adhesion Index is implemented to determine surface factors that promote favorable adhesion of cells greater than biofilms. Here, an Adhesion Index >> 1 suggests favorable biocompatibility. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=76 SRC="FIGDIR/small/873240v1_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@c661ecorg.highwire.dtl.DTLVardef@46f768org.highwire.dtl.DTLVardef@11cfd33org.highwire.dtl.DTLVardef@3321e4_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIBiofilm and cell monolayer adhesion are measured via the laser spallation technique C_LIO_LISmooth and roughened dental implant-mimicking titanium surfaces are investigated C_LIO_LISurface roughness increases cell adhesion but does not alter the adhesion of biofilms C_LIO_LIAn Adhesion Index is developed to directly quantify the adhesive competition between bacteria and cells on an implant surface C_LI

bioengineering↗

bioRxiv2019

Biofilm rupture by laser-induced stress waves increases with loading amplitude, independent of location

Integral to the production of safe and biocompatible medical devices is to determine the interfacial properties that affect or control strong biofilm adhesion. The laser spallation technique has recently emerged as an advantageous method to quantify biofilm adhesion across candidate biomedical surfaces. However, there is a possibility that membrane tension is a factor that contributes to the stress required to separate biofilm and substrate. In that case, the stress amplitude, controlled by laser fluence, that initiates biofilm rupture would vary systematically with location on the biofilm. Film rupture, also known as spallation, occurs when film material is ejected during stress wave loading. In order to determine effects of membrane tension, we present a protocol that measures spall size with increasing laser fluence (variable fluence) and with respect to distance from the biofilm centroid (iso-fluence). Streptococcus mutans biofilms on titanium substrates serves as our model system. A total of 185 biofilm loading locations are analyzed in this study. We demonstrate that biofilm spall size increases monotonically with laser fluence and apply our procedure to failure of non-biological films. In iso-fluence experiments, no correlation is found between biofilm spall size and loading location, thus providing evidence that membrane tension does not play a dominant role in biofilm adhesion measurements. We recommend our procedure as a straightforward method to determine membrane effects in the measurement of adhesion of biological films on substrate surfaces via the laser spallation technique.\n\nGraphical Abstract\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=73 SRC=\"FIGDIR/small/827964v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (26K):\norg.highwire.dtl.DTLVardef@f32f98org.highwire.dtl.DTLVardef@1b9d3d4org.highwire.dtl.DTLVardef@102356borg.highwire.dtl.DTLVardef@13cbc_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2018

Real-time markerless video tracking of body parts in mice using deep neural networks.

Markerless and accurate tracking of mouse movement is of interest to many biomedical, pharmaceutical, and behavioral science applications. The additional capability of tracking body parts in real-time with minimal latency opens up the possibility of manipulating motor feedback, allowing detailed explorations of the neural basis for behavioral control. Here we describe a system capable of tracking specific movements in mice at a frame rate of 30.3 Hz. To achieve these results, we adapt DeepLabCut - a robust movement-tracking deep neural network framework - for real-time tracking of body movements in mice. We estimate paw movements of mice in real time and demonstrate the concept of movement-triggered optogenetic stimulation by flashing a USB-CGPIO controlled LED that is triggered when real time analysis of movement exceeds a pre-set threshold. The mean time delay between movement initiation and LED flash was 93.44 ms, a latency sufficient for applying behaviorally-triggered feedback. This manuscript presents the rationale and details of the algorithms employed and shows implementation of the system using behaving mice. This system lays the groundwork for a behavior-triggered closed loop brain-machine interface with optogenetic stimulation of specific brain regions for feedback.

neuroscience↗