William B Dodd’s research while affiliated with Clemson University and other places

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Publications (5)


Figure 2. Simulation setup. (A) Simulating emission spectra. Process of condensing the original emission spectra at every nanometer according to the emission binning and noise of the simulated instrument. (B) Cases for the simulation experiment setup based on Cytek flow cytometers.
Figure 3. Workflow for probe removal. (A) Obtaining the list of good probes based on classification metrics. First, the emission spectra of a mixture of probes is simulated given a set of probes. Next, noise is added to the emission spectra, and the spectra is unmixed (using the reference matrix) to predict the mixture composition of probes. Binary classification is performed and finally, the predicted mixture composition is compared to the actual mixture composition. This process is repeated for each probe, and the worst performing probe is removed until the overall classification is perfect. (B) Graphical representation of probe removal results. Individual probes are removed until the overall MCC value (based on confusion matrices shown on the right-hand side) is perfect (i.e., equal to 1). (C) Workflow of sequential list trimming of good MuSIC probes. The final list of good MuSIC probes for single MuSIC probes (simulation 1) is used as the starting list for simulation 2. Then, the final list of good MuSIC probes for barcodes (simulation 2) is used as the starting list for simulation 3.
Predicted Number of gRNA That Could be Used for Genetic and Genetic Interaction Screens a # of good probes # of gRNA
Theory for High-Throughput Genetic Interaction Screening
  • Article
  • Full-text available

July 2023

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21 Reads

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3 Citations

ACS Synthetic Biology

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William B Dodd

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Xiaoming Lu

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Systematic, genome-scale genetic screens have been instrumental for elucidating genotype-phenotype relationships, but approaches for probing genetic interactions have been limited to at most ∼100 pre-selected gene combinations in mammalian cells. Here, we introduce a theory for high-throughput genetic interaction screens. The theory extends our recently developed Multiplexing using Spectral Imaging and Combinatorics (MuSIC) approach to propose ∼105 spectrally unique, genetically encoded MuSIC barcodes from 18 currently available fluorescent proteins. Simulation studies based on constraints imposed by spectral flow cytometry equipment suggest that genetic interaction screens at the human genome-scale may be possible if MuSIC barcodes can be paired to guide RNAs. While experimental testing of this theory awaits, it offers transformative potential for genetic perturbation technology and knowledge of genetic function. More broadly, the availability of a genome-scale spectral barcode library for non-destructive identification of single cells could find more widespread applications such as traditional genetic screening and high-dimensional lineage tracing.

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A Theory for High-Throughput Genetic Interaction Screening

October 2022

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18 Reads

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2 Citations

Systematic, genome-scale genetic screens have been instrumental for elucidating genotype-phenotype relationships, but approaches for probing genetic interactions have been limited to at most ~100 pre-selected gene combinations in mammalian cells. Here, we introduce a theory for high-throughput genetic interaction screens. The theory extends our recently developed Multiplexing using Spectral Imaging and Combinatorics (MuSIC) approach to propose ~105 spectrally unique, genetically-encoded MuSIC barcodes from 18 currently available fluorescent proteins. Simulation studies based on constraints imposed by spectral flow cytometry equipment suggest that genetic interaction screens at the human genome-scale may be possible if MuSIC barcodes can be paired to guide RNAs. While experimental testing of this theory awaits, it offers transformative potential for genetic perturbation technology and knowledge of genetic function. More broadly, the availability of a genome-scale spectral barcode library for non-destructive identification of single-cells could find more widespread applications such as traditional genetic screening and high-dimensional lineage tracing.


Mesowestern Blot: Simultaneous Analysis of Hundreds of Submicroliter Lysates

August 2022

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25 Reads

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1 Citation

ACS Omega

Western blotting is a widely used technique for molecular-weight-resolved analysis of proteins and their posttranslational modifications, but high-throughput implementations of the standard slab gel arrangement are scarce. The previously developed Microwestern requires a piezoelectric pipetting instrument, which is not available for many labs. Here, we report the Mesowestern blot, which uses a 3D-printable gel casting mold to enable high-throughput Western blotting without piezoelectric pipetting and is compatible with the standard sample preparation and small (∼1 μL) sample sizes. The main tradeoffs are reduced molecular weight resolution and higher sample-to-sample CV, making it suitable for qualitative screening applications. The casted polyacrylamide gel contains 336, ∼0.5 μL micropipette-loadable sample wells arranged within a standard microplate footprint. Polyacrylamide % can be altered to change molecular weight resolution profiles. Proof-of-concept experiments using both infrared-fluorescent molecular weight protein ladder and cell lysate (RIPA buffer) demonstrate that the protein loaded in Mesowestern gels is amenable to the standard Western blotting steps. The main difference between Mesowestern and traditional Western is that semidry horizontal instead of immersed vertical gel electrophoresis is used. The linear range of detection is at least 32-fold, and at least ∼500 attomols of β-actin can be detected (∼29 ng of total protein from mammalian cell lysates: ∼100−300 cells). Because the gel mold is 3D-printable, users with access to additive manufacturing cores have significant design freedom for custom layouts. We expect that the technique could be easily adopted by any typical cell and molecular biology laboratory already performing Western blots.


SPARCED is a structured, human interpretable, and easy to modify big mechanistic model
a The schematic of the underlying model for SPARCED. Image adapted from⁴⁰. b The pan-cancer mechanistic model Bouhaddou2018 is re-written in open-source and structured file format. The steps of model construction include input file creation and conversion into an SBML file. The optional initialization step calibrates model parameters for new cellular contexts and phenotypic behaviors. The annotated SBML model file and stochastic module are simulated together at single-cell level locally or by using cloud-computing. The benefits of the new SPARCED model include easy alteration and expansion capabilities through text file editing, human-readable annotated input files, and use of Jupyter notebooks for model creation and simulation. The modeling pipeline introduced here are inline with good practices of re-usable big mechanistic models⁵⁷. c The Bouhaddou2018 model file types are simplified and converted into open-source platforms.
SPARCED-jupyter enables single-cell response simulations using Jupyter notebooks
a The model creation notebook processes the input files and converts them into the model SMBL file, which is compiled for simulations using the AMICI python package. b When a model is generated for a new cellular context (using new omics input data), next is an initialization step to adjust protein translation rate constants and cell death/DNA damage related parameters. c The model simulation starts with specifying and importing the SPARCED model SBML. The user defines the model file name and the sets four additional parameters: (i) The flag (1 or 0) to specify if the model should run in deterministic or in hybrid mode (see d and e), respectively. (ii) The time duration in hours for which the model should run. (iii) The vector of ligand concentrations (in nM) to stimulate the cells. (iv) The output file name. Next, the species initial conditions are, by default, read-in from the “Species” input file. Then, the model file is imported and simulated according to the specified input. The model outputs three matrices: species concentrations over time, the activation states of genes over time, and the time points of simulation in seconds. d The model is simulated iteratively for each 30 s, where the current species concentrations are inputs for the gene expression module, which then outputs new mRNA levels to update the SBML model states. The model is then run for another 30 s, until the total simulation time reaches the user input (th) or until the cell dies. The cell is considered dead when [cleaved-PARP] > [PARP]. e In the gene expression module, in hybrid mode, the model randomly decides which genes become active or inactive, and which mRNAs are transcribed or degraded. This SGEmodule.py script is called every 30 s with updated species concentrations, simulated using the models SBML with AMICI package. f When the model is hybrid-simulated three times, the different cell responses are observed. Shown are serum-starved average cells stimulated with full growth media for 24 h. Plotted are free ppERK and ppAKT species concentrations (nM).
SPARCED model recapitulates experimental observations and deterministic/hybrid (deterministic + stochastic) simulation results of the Bouhaddou2018 model
a Summary of comparisons of SPARCED model deterministic simulations to Bouhaddou2018 model simulations. The area under the curve (AUC) values of each simulation (see Supplementary Fig. 12) are calculated and plotted for the two model results. b Simulation results from Bouhaddou2018 model (line) and SPARCED-nf model (triangle) run on Kubernetes cluster workflow are the same as SPARCED model (circle) results. Comparisons of selected panels from (a) are shown only. c Experimental and stochastic simulation results from Bouhaddou2018 model are reproduced by SPARCED model simulations. Each dot is a different condition, explained in Supplementary Fig. 13a. Error bars show experimental or simulation standard error of the mean. Simulations are of at least 100 cells, and three independent experimental observations where applicable. d Stochastic simulation of 100 cells recapture protein level trajectories (active p53, Cyclin A, and cPARP) from older model qualitatively. Panels with blue background are SPARCED simulations and white background panels are from Bouhaddou2018 model. 100 stochastic cells are stimulated with EGF + Insulin for 72 h before Etoposide treatment for another 72 h. Etoposide is stimulated also with EGF + Insulin. Results for Etoposide treatment without prior growth factor stimulations are shown in Supplementary Fig. 13b. e Quantification of results in (d) shows that SPARCED model simulations coincide with earlier observations in percentage of death induced by etoposide treatment. See Supplementary Fig. 13c for the effect of no growth factor stimulation before Etoposide treatment. Gray bars represent mean ± s.e.m. of three independent biological replicates (dots). Blue and orange bars represent the percentage of dead cells at specified time ± s.e.m. Source data are provided in Source Data.
SPARCED model is enlarged to include interferon-gamma (IFNγ) signaling pathway
a Experiments showed that IFNγ treatment significantly decreases cell proliferation induced by EGF alone (p-value = 1.86E-04). Fraction of EdU positive cells at 48 h after EGF alone or EGF + IFNγ treatment are calculated. Bars represent mean ± s.e.m. of three independent biological replicates. Significance tested using two-sided two-sample t-test, where ***p-value < 0.001. b Yamada2003 model schematic of IFNγ pathway added into SPARCED model. c Overview of the added IFNγ-IFNGR pathway in relation to the Bouhaddou2018 model pathways. The “SOCS1” link is the candidate mechanism tested in the next section. d Simulations of the Yamada2003 model, SPARCED-I model, and SPARCED-I model with MCF10A context show qualitative and quantitative agreements. The Yamada2003 model results (blue diamonds) are obtained by running the model file in COPASI⁶¹. e 100 stochastic cell (hybrid) simulations (area plots) and the deterministic (dashed-black lines) simulation of 10 nM IFNγ stimulation are shown. Colored dark lines represent median cell trajectories, dark and light-colored regions represent 70th and 95th quantiles, respectively. The right-most plot shows mRNA count of SOCS1 in each cell, colored differently. Source data are provided in Source Data.
SPARCED-I model suggests that a SOCS1-based sequestration mechanism can explain IFNγ-induced cell proliferation decreases
a Experimental setup and simulation workflow for SPARCED-I model variant analysis. See Methods. b Candidate mechanism schematic: SOCS1 sequesters activated ligand-receptor complexes. c The reactions show one set of examples added to capture the inhibitory mechanism. pSCD: activated ligand-receptor complexes. Kd: Disassociation constant. d Simulation of 100 stochastic cells for SPARCED-I-SOCS1 model at different Kd values (varied uniformly between −1 and 6 in log10 scale) for SOCS1 binding (blue bars) showed that SOCS1 sequestration of activated receptor complexes can explain the IFNγ effect on cell proliferation inhibition, compared to experiments (gray bar). The number of cells in S-phase at 48 h of simulation time are shown on the x-axis (first row: Kd values, second row: cells with EGF, third row: cells with EGF + IFNγ). The y-axis is the ratio of number of cells in S-phase with IFNγ to without IFNγ. The experimental data (Exp.) are from three independent replicates (dots) and represent mean ± s.e.m. The mean of each experimental condition is given in x-axis. The orange bar is Kd set at the estimated initial value (from GRB2-Receptor binding/unbinding reactions). e The Kd value used for these trajectories corresponds to the orange bar in (d). Normalized ppAKT and ppMAPK levels show a significant decrease after IFNγ treatment, when EGF + IFNγ SPARCED-I-SOCS1 model simulations are compared to EGF alone case. RPPA data (synapse.org/LINCS_MCF10A) are shown in black, from three independent replicates (error bars are s.e.m.). Colored dark lines represent median cell trajectories from simulations, dark and light-colored regions represent 70th and 95th quantiles, respectively. Source data are provided in Source Data.
A scalable, open-source implementation of a large-scale mechanistic model for single cell proliferation and death signaling

June 2022

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236 Reads

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17 Citations

Mechanistic models of how single cells respond to different perturbations can help integrate disparate big data sets or predict response to varied drug combinations. However, the construction and simulation of such models have proved challenging. Here, we developed a python-based model creation and simulation pipeline that converts a few structured text files into an SBML standard and is high-performance- and cloud-computing ready. We applied this pipeline to our large-scale, mechanistic pan-cancer signaling model (named SPARCED) and demonstrate it by adding an IFNγ pathway submodel. We then investigated whether a putative crosstalk mechanism could be consistent with experimental observations from the LINCS MCF10A Data Cube that IFNγ acts as an anti-proliferative factor. The analyses suggested this observation can be explained by IFNγ-induced SOCS1 sequestering activated EGF receptors. This work forms a foundational recipe for increased mechanistic model-based data integration on a single-cell level, an important building block for clinically-predictive mechanistic models.


Mesowestern Blot: Simultaneous Analysis of Hundreds of Sub-Microliter Lysates

November 2021

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24 Reads

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1 Citation

Western blotting is a widely-used technique for molecular-weight-resolved analysis of proteins and their post-translational modifications, but has been refractory to affordable scale-up. Here, we report the Mesowestern blot, which uses a 3D-printable gel-casting mold to enable affordable, high-throughput Western blotting with standard sample preparation and small (<1 uL) sample sizes. The casted polyacrylamide gel contains 336, 0.5 uL micropipette-loadable sample wells arranged within a standard microplate footprint. Polyacrylamide % can be altered to change molecular weight resolution range. Proof-of-concept experiments using both infrared-fluorescent molecular weight protein ladder as well as cell lysate (RIPA buffer) demonstrate protein loaded in Mesowestern gels is amenable to the standard Western blotting steps. The main difference between Mesowestern and traditional Western is that semi-dry horizontal instead of immersed vertical gel electrophoresis is used. The linear range of detection is approximately 2 orders of magnitude, with a limit of detection (for beta-actin) of around 30 ng of total protein from mammalian cell lysates (~30-3000 cells). Because the gel mold is 3D-printable, users have significant design freedom for custom layouts, and there are few barriers to adoption by the typical cell and molecular biology laboratory already performing Western blots.

Citations (5)


... We recently proposed theory for and simulations studies supporting a fluorescent protein-based, single-cell barcoding method that bridges fast, non-destructive fluorescence readouts with the large barcode diversity 38 . The barcoding approach is based on Multiplexing using Spectral Imaging and Combinatorics (MuSIC) 27,[38][39][40] , which generates unique emission spectra signatures from combinations of individual fluorescent proteins. ...

Reference:

Genetically-Encoded Fluorescence Barcodes for Single-Cell Analysis
Theory for High-Throughput Genetic Interaction Screening

ACS Synthetic Biology

... To maximize the potential of this new increased intensity probe design, the next step will be to select different combinations of fluorophores to assemble a palette of spectrally unique antibody-conjugated MuSIC probes. Approaches to do so can include stimulation studies for compatibility using a workflow similar to that described in our previous work 19 , and then testing the highest-ranked fluorophore combinations experimentally. For these simulations, the emission spectra of each possible MuSIC . ...

A Theory for High-Throughput Genetic Interaction Screening
  • Citing Preprint
  • October 2022

... Obstacles to microwestern adoption include the piezoelectric pipetting apparatus (expensive, difficult to use, sample loss in tubing, restricted to non-standard lysis buffers, flat gel with no wells), or the use of semi-dry electrophoresis versus the typical immersed tank (expense and difficulty of use). The mesowestern blot 13 , in part developed by us, has the same scale as the microwestern but eliminates the need for piezoelectric pipetting. Yet, it still uses semi-dry electrophoresis, and requires small (~1 μ L) sample sizes, which although can be a benefit, is also a detriment if a low abundance target is of interest. ...

Mesowestern Blot: Simultaneous Analysis of Hundreds of Submicroliter Lysates

ACS Omega

... Stochastic simulations suggested that ERK dynamics, but not AKT, may drive variability in cell proliferation [147]. Furthermore, a comprehensive mechanistic signaling model called SPARCED was developed to explore the crosstalk between EGF-induced pathways and the IFNγ pathway [148]. Using extensive experimental data (i.e., 200 sets) from both the literature and in-house experiments, a comprehensive model was developed that included 67 molecular species from various signaling pathways, such as the three MAPK modules, as well as the AKT, NF-κB, and JAK/STAT pathways [149]. ...

A scalable, open-source implementation of a large-scale mechanistic model for single cell proliferation and death signaling

... Forty-eight hours post-transfection, cells were lysed in 100µL of the fresh-made ice-cold RIPA buffer per well 73 . The plates were kept on ice for 15 minutes and the cells were agitated every 5 minutes to ensure thorough lysis. ...

Mesowestern Blot: Simultaneous Analysis of Hundreds of Sub-Microliter Lysates