ADD mouse summary figure

ENH paraphrase the nmr method section
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Nathan Dwarshuis 2021-07-31 16:22:15 -04:00
parent d0b852acc0
commit 0bf5b03827
1 changed files with 32 additions and 11 deletions

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@ -2084,7 +2084,7 @@ Flow cytometry was performed analogously to \cref{sec:flow_cytometry}.
Cytokines were quantified via Luminex as described in Cytokines were quantified via Luminex as described in
\cref{sec:luminex_analysis}. \cref{sec:luminex_analysis}.
% TODO paraphrase this entire section since I didn't do it % TODO add a footnote saying I didn't do this
\subsection{NMR metabolomics} \subsection{NMR metabolomics}
Prior to analysis, samples were centrifuged at \SI{2990}{\gforce} for Prior to analysis, samples were centrifuged at \SI{2990}{\gforce} for
@ -2108,8 +2108,8 @@ One-dimensional spectra were manually phased and baseline corrected in TopSpin.
Two-dimensional spectra were processed in NMRpipe37. One dimensional spectra Two-dimensional spectra were processed in NMRpipe37. One dimensional spectra
were referenced, water/end regions removed, and normalized with the PQN were referenced, water/end regions removed, and normalized with the PQN
algorithm38 using an in-house MATLAB (The MathWorks, Inc.) toolbox. algorithm38 using an in-house MATLAB (The MathWorks, Inc.) toolbox.
% (https://github.com/artedison/Edison_Lab_Shared_Metabolomics_UGA).
% TODO add the supplemental figure alluded to here?
To reduce the total number of spectral features from approximately 250 peaks and To reduce the total number of spectral features from approximately 250 peaks and
enrich for those that would be most useful for statistical modeling, a enrich for those that would be most useful for statistical modeling, a
variance-based feature selection was performed within MATLAB. For each digitized variance-based feature selection was performed within MATLAB. For each digitized
@ -2130,14 +2130,15 @@ spectral data supporting the match as previously described11. Annotated
metabolites were matched to previously selected features used for statistical metabolites were matched to previously selected features used for statistical
analysis. analysis.
Using the list of annotated metabolites obtained above, an approximation of a % I'm pretty sure this isn't relevant
representative experimental spectrum was generated using the GISSMO mixture % Using the list of annotated metabolites obtained above, an approximation of a
simulation tool.39,40 With the simulated mixture of compounds, generated at 600 % representative experimental spectrum was generated using the GISSMO mixture
MHz to match the experimental data, a new simulation was generated at 80 MHz to % simulation tool.39,40 With the simulated mixture of compounds, generated at 600
match the field strength of commercially available benchtop NMR spectrometers. % MHz to match the experimental data, a new simulation was generated at 80 MHz to
The GISSMO tool allows visualization of signals contributed from each individual % match the field strength of commercially available benchtop NMR spectrometers.
compound as well as the mixture, which allows annotation of features in the % The GISSMO tool allows visualization of signals contributed from each individual
mixture belonging to specific compounds. % compound as well as the mixture, which allows annotation of features in the
% mixture belonging to specific compounds.
Several low abundance features selected for analysis did not have database Several low abundance features selected for analysis did not have database
matches and were not annotated. Statistical total correlation spectroscopy41 matches and were not annotated. Statistical total correlation spectroscopy41
@ -3216,7 +3217,6 @@ advantage via \gls{il15} signaling.
% cell density in the DMS cultures would lead to more of these trans interactions, % cell density in the DMS cultures would lead to more of these trans interactions,
% and therefore upregulate the IL15 pathway and lead to more memory T cells. % and therefore upregulate the IL15 pathway and lead to more memory T cells.
\chapter{aim 3}\label{aim3} \chapter{aim 3}\label{aim3}
\section{introduction} \section{introduction}
@ -3488,6 +3488,27 @@ other groups in regard to the final tumor burden.
\label{fig:mouse_timecourse_ivis} \label{fig:mouse_timecourse_ivis}
\end{figure*} \end{figure*}
% RESULT this figure
% DISCUSSION this figure
\begin{figure*}[ht!]
\begingroup
\includegraphics{../figures/mouse_summary.png}
\phantomsubcaption\label{fig:mouse_summary_1}
\phantomsubcaption\label{fig:mouse_summary_2}
\endgroup
\caption[Mouse Summary]
{Summary of cells injected into mice during for
\subcap{fig:mouse_summary_1}{the first mouse experiment} and
\subcap{fig:mouse_summary_2}{the second mouse experiment}. The y axis
maximum is set to the maximum number of cells injected between both
experiments (\num{1.25e6}).
}
\label{fig:mouse_summary}
\end{figure*}
\section{discussion} \section{discussion}
% TABLE make a summary table showing the results from both experiments; this is % TABLE make a summary table showing the results from both experiments; this is