README

README--------------------------------------------------------------------------------------------------------

TITLE: Genetic variation in mouse islet Ca2+ oscillations reveals novel regulators of islet function
CITATION: Emfinger, Christopher and Clark, Lauren et al. (2022), Genetic variation in mouse islet Ca2+ oscillations reveals novel regulators of islet function, Dryad, Dataset
AUTHORS:
Christopher H. Emfinger1#, Lauren E. Clark1#, Brian Yandell2, Kathryn L. Schueler1, Shane P. Simonett1, Donnie S. Stapleton1, Kelly A. Mitok1, Matthew J. Merrins3,4, Mark P. Keller1, Alan D. Attie*1,2

1Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, 53706, USA
2Department of Statistics, University of Wisconsin-Madison, Madison, WI, 3706, USA
3Department of Medicine, Division of Endocrinology, University of Wisconsin Madison, Madison, WI, 53705, USA
4William S. Middleton Memorial Veterans Hospital, Madison, WI 53705 USA
#These authors contributed equally to this work
*corresponding author
For inquiries, please address correspondence to:
Alan D. Attie
543A HF DeLuca Biochemistry Laboratories, 433 Babcock Drive, Madison, WI 53706
(608) 262-1372
adattie@wisc.edu

KEYWORDS: Islet calcium, Insulin secretion, Genetic variation, Beta cell, Calcium imaging

DATA COLLECTED:
Date Range: 4/2021-04/2022
Location: University of Wisconsin-Madison, Madison, WI, 53706 United States
RELATED TO: BIORXIV preprint BIORXIV/2022/517741
FUNDING: This work was supported by NIH R01DK101573 to A. Attie, NIH R01DK113103, NIH R01DK127637, and VA I01B005113 to M. Merrins, and ADA 7-21-PDF-157 to C. Emfinger.

LICENSE: CC0

FILES:

Please note that there is a copy of this file in each of the component Files

File 1 (Stored on Dryad): raw_data

subfolder: NIS_output_files.
General summary: This folder contains the output files for the microscopy experiments of mouse islets showing the signal intensity across the different fluorescence channels for each islet.
see also the excel file raw_data_index.xlsx
These are excel files generated by NIS Elements software. There are tabs in each file that are as follows:
Fura_bound: the fluorescence channel containing the emission average for each ROI corresponding to the Ca2+-bound state of the Fura Red dye.
Fura_free: the fluorescence channel containing the emission average for each ROI for the Ca2+-free state of the Fura Red dye.
NIS_fura_ratio: A tab which is generated by the NIS Elements software. It calculates the ratio between Fura_bound and Fura_free emissions measurements. However, some as-yet undiscovered setting in the data skewed the values, so they were not used. We think it was calculating based on peak intensity rather than average but cannot say for certain.
Excel_fura_ratio: a tab which calculates the ratio in Excel for the average emission measures for Fura_bound/Fura_free.
There are a couple of exceptions to this general rule. For a few of the experiments there was a point at which acquisition was interrupted or had other corrections. These consequently have additional tabs. These files are:
Exp1_NZO_M1and2_M3and4_9.30.21.xlsx had a pressure issue which was fixed after briefly stopping the acquisition for 1 minute. The acquisitions for each individual movie are designated M1 for movie 1 and M2 for movie 2. The files tabs are:
Fura_bound_M1 Fura_free_M1 NIS_fura_ratio_M1 Fura_bound_M2 Fura_free_M2 NIS_fura_ratio_M2 Excel_fura_ratio_combined
The Excel_fura_ratio_combined tab is the calculated ratios for both movies in sequence
Exp1_NZOF1and2_11.10.21_002.xlsx had a computer artefact which increased the background until 155min into the recording. Its tabs include the standard ones (see above) and also:
Excel_fura_ratio_bkgrd_subtr Fura_free_bkgrd_subtr Fura_bound_bkgrd_subtr
The Fura_free_bkgrd_subtr and Fura_bound_bkgrd_subtr recalculated the Fura_free signal and Fura_bound signals for the period of high background using a ROI which contained no islets. The Excel_fura_ratio_bkgrd_subtr is the ratio of the Fura_bound_bkgrd_subtr to Fura_free_bkgrd_subtr calculated in excel.
Exp1_4.13.21_HFD_F_AJ.xlsx also had a pressure issue which required interrupting acquisition and restarting it. Its tabs are:
Fura_bound_M2 Fura_free_M2 NADH_M2 NIS_fura_ratio_M2 Excel_fura_ratio_M2
NIS_fura_ratio_M1 Fura_free_M1 Fura_bound_M1 NADH_M1 Excel_fura_ratio_M1 Excel_fura_ratio_combined
In this initial experiment, we also attempted to measure NADH in both movies but found that the results had very low signal to noise as a result of the confocal laser not being at optimal excitation wavelength and consequently did not measure it in the other experiments.
As with Exp1_NZO_M1and2_M3and4_9.30.21.xlsx, M1 designates movie 1 for each measurement, M2 designates movie 2 for each measurement, and the Excel_fura_ratio_combined contains the ratio of Fura_bound/Fura_free for both movies in sequence.
The file names are for each NIS output file are:
Date created and experiment performed on Bytes name Contains
6/9/2021 3,894,542 Ex3_PWK_DIRPM5_DiRNF6_6.9.21.xlsx Raw ROI data for the Fura Red calcium imaging
4/27/2021 2,125,327 Exp1_4.27.21_AJM_DiRPAJ4DiRNAJ5.xlsx Raw ROI data for the Fura Red calcium imaging
5/18/2021 3,324,541 Exp1_5.18.21_DiRP_B6F1_DiRN_B6F2_HFD.xlsx Raw ROI data for the Fura Red calcium imaging
5/21/2021 2,879,081 Exp1_5.21.21_129M_dirP1DirN2.xlsx Raw ROI data for the Fura Red calcium imaging
6/17/2021 2,735,621 Exp1_129M4f_DiRP_129M5_DirN_6.17.21.xlsx Raw ROI data for the Fura Red calcium imaging
4/23/2021 1,680,454 Exp1_AJF_HFD_4.23.21_DiRPAJF4_DIRNAJF5.xlsx Raw ROI data for the Fura Red calcium imaging
11/4/2021 2,081,173 Exp1_CASTF2DiRN_CASTF1_11.4.21.xlsx Raw ROI data for the Fura Red calcium imaging
11/5/2021 1,481,380 Exp1_CASTM1_CASTM2_11.5.21_001.nd2.xlsx Raw ROI data for the Fura Red calcium imaging
4/30/2021 1,524,352 Exp1_DiRP_AJF_DiRN_AJM_4.30.21.xlsx Raw ROI data for the Fura Red calcium imaging
7/29/2021 2,401,998 Exp1_DiRPB6M1_DiRNB6M2_7.29.21.xlsx Raw ROI data for the Fura Red calcium imaging
7/16/2021 3,129,011 Exp1_DiRPWSBF1_DirNWSBF2_7.16.21.xlsx Raw ROI data for the Fura Red calcium imaging
1/11/2022 1,446,358 Exp1_NODF1_NODF2_1.11.22.xlsx Raw ROI data for the Fura Red calcium imaging
1/12/2022 1,616,527 Exp1_NODF3_NODF4_1.12.22.xlsx Raw ROI data for the Fura Red calcium imaging
6/11/2021 2,150,207 Exp1_NODM_DiRP3_DirN4_6.11.21.xlsx Raw ROI data for the Fura Red calcium imaging
9/30/2021 934,620 Exp1_NZO_M1and2_M3and4_9.30.21.xlsx Raw ROI data for the Fura Red calcium imaging
11/10/2021 3,579,003 Exp1_NZOF1and2_11.10.21_002.xlsx Raw ROI data for the Fura Red calcium imaging
5/27/2021 2,043,676 Exp1_PWK_DiRF3_DiRNF4_5.27.21.xlsx Raw ROI data for the Fura Red calcium imaging
6/9/2021 2,837,803 Exp1_PWK_DIRPF6_DiRNF7_6.9.21.xlsx Raw ROI data for the Fura Red calcium imaging
5/25/2021 2,866,882 Exp1_PWK_M_HFD_5.25.21.xlsx Raw ROI data for the Fura Red calcium imaging
5/26/2021 2,822,841 Exp1_PWKF_HFD_5.26.21_DiRPF1DiRNF2.xlsx Raw ROI data for the Fura Red calcium imaging
6/8/2021 2,453,524 Exp1_PWKM4DiRP_129F5DiRN_6.8.21.xlsx Raw ROI data for the Fura Red calcium imaging
10/27/2021 2,600,884 Exp1_WSBF7DirP_WSBF8_10.27.21_001.xlsx Raw ROI data for the Fura Red calcium imaging
7/9/2021 3,050,655 Exp1_WSBM1DiRP_WSBM2DiRN_7.9.21.xlsx Raw ROI data for the Fura Red calcium imaging
10/6/2021 1,816,192 Exp1_WSBM4_WSBF6_10.6.21.xlsx Raw ROI data for the Fura Red calcium imaging
1/6/2022 2,272,289 Exp1_WSBM5WSBM6_1.6.22.xlsx Raw ROI data for the Fura Red calcium imaging
5/18/2021 2,304,600 Exp2_5.18.21_DiRP_B6F2_DiRN_B6F3_HFD.xlsx Raw ROI data for the Fura Red calcium imaging
7/7/2021 2,518,124 Exp2_7.7.21_PWKM6DirN_PWKM7DiRP.xlsx Raw ROI data for the Fura Red calcium imaging
6/8/2021 2,140,847 Exp2_129F4DiRP_129F6DiRN_6.8.21.xlsx Raw ROI data for the Fura Red calcium imaging
6/17/2021 2,525,163 Exp2_129M5_DiRP_129M6_DirN_6.17.21.xlsx Raw ROI data for the Fura Red calcium imaging
4/23/2021 1,928,007 Exp2_AJF_HFD_4.23.21_DiRPAJF3_DIRNAJF4.xlsx Raw ROI data for the Fura Red calcium imaging
7/27/2021 2,752,587 Exp2_B6F4_B6F5_7.27.21.xlsx Raw ROI data for the Fura Red calcium imaging
11/4/2021 1,998,895 Exp2_CASTF3DiRN_CASTF2_11.4.21.xlsx Raw ROI data for the Fura Red calcium imaging
11/5/2021 1,568,927 Exp2_CASTM2_CASTM3_11.5.21.xlsx Raw ROI data for the Fura Red calcium imaging
4/30/2021 2,431,428 Exp2_DiRP_AJM_DiRN_AJF_4.30.21.xlsx Raw ROI data for the Fura Red calcium imaging
7/29/2021 2,626,783 Exp2_DiRPB6M2_DiRNB6M3_7.29.21.xlsx Raw ROI data for the Fura Red calcium imaging
6/15/2021 2,247,339 Exp2_DiRPNODM6_DiRNNODM5_6.15.21.xlsx Raw ROI data for the Fura Red calcium imaging
1/11/2022 1,402,831 Exp2_NODF2_NODF1_1.11.22.xlsx Raw ROI data for the Fura Red calcium imaging
1/12/2022 1,855,180 Exp2_NODF4_NODF3_1.12.22.xlsx Raw ROI data for the Fura Red calcium imaging
6/11/2021 2,041,554 Exp2_NODM_DiRP4_DirN3_6.11.21.xlsx Raw ROI data for the Fura Red calcium imaging
9/30/2021 1,167,550 Exp2_NZO_M3andM4_M1andM2.xlsx Raw ROI data for the Fura Red calcium imaging
11/10/2021 1,594,199 Exp2_NZOF2_NZOF3_11.10.21.xlsx Raw ROI data for the Fura Red calcium imaging
11/11/2021 1,761,426 Exp2_NZOF5and6_11.11.21_002.xlsx Raw ROI data for the Fura Red calcium imaging
5/27/2021 1,870,262 Exp2_PWK_DiRF4_DiRNF3_5.27.21.xlsx Raw ROI data for the Fura Red calcium imaging
6/9/2021 2,589,142 Exp2_PWK_DIRPF5_DiRNM5_6.9.21.xlsx Raw ROI data for the Fura Red calcium imaging
5/25/2021 3,333,623 Exp2_PWK_M_HFD_5.25.21.xlsx Raw ROI data for the Fura Red calcium imaging
5/26/2021 4,594,490 Exp2_PWKF_HFD_5.26.21_DiRPF2DiRNF1.xlsx Raw ROI data for the Fura Red calcium imaging
7/16/2021 2,207,546 Exp2_WSBF1andF2_DiR_unclear_7.16.21.xlsx Raw ROI data for the Fura Red calcium imaging
10/6/2021 2,394,950 Exp2_WSBF6_WSBM4_10.6.21.xlsx Raw ROI data for the Fura Red calcium imaging
10/27/2021 2,444,709 Exp2_WSBF7DirN_WSBF8_10.27.21_003.xlsx Raw ROI data for the Fura Red calcium imaging
7/9/2021 2,344,808 Exp2_WSBM1DiRN_WSBM2DiRP_7.9.21.xlsx Raw ROI data for the Fura Red calcium imaging
1/6/2022 2,202,683 Exp2_WSBM5WSBM6_1.6.22.xlsx Raw ROI data for the Fura Red calcium imaging
5/18/2021 1,796,134 Exp3_5.18.21_DiRP_B6F3_DiRN_B6F1_HFD.xlsx Raw ROI data for the Fura Red calcium imaging
6/8/2021 2,448,024 Exp3_129F6DiRP_PWKM4DiRN_6.8.21.xlsx Raw ROI data for the Fura Red calcium imaging
7/27/2021 2,347,394 Exp3_B6F6_B6F7_7.27.21.xlsx Raw ROI data for the Fura Red calcium imaging
11/5/2021 2,382,385 Exp3_CASTM3_CASTM3_11.5.21.xlsx Raw ROI data for the Fura Red calcium imaging
4/30/2021 2,119,811 Exp3_DiRP_AJF_DiRN_AJM_4.30.21_runs_1and2.xlsx Raw ROI data for the Fura Red calcium imaging
7/29/2021 3,012,333 Exp3_DiRPB6M3_DiRNB6M1_7.29.21.xlsx Raw ROI data for the Fura Red calcium imaging
7/27/2021 2,410,933 Exp4_B6F6_B6F7_7.27.21.xlsx Raw ROI data for the Fura Red calcium imaging
6/9/2021 2,962,993 Exp4_PWK_DIRPF7_DiRNF5_6.9.21.xlsx Raw ROI data for the Fura Red calcium imaging
4/16/2021 358,288 AJ_M_Exp1_4.16.21.xlsx Raw ROI data for the Fura Red calcium imaging
4/19/2021 1,317,356 AJ_M_Exp1b_4.16.21.xlsx Raw ROI data for the Fura Red calcium imaging
11/13/2022 2,435,773 AJ_M_Exp2_4.16.21.xlsx Raw ROI data for the Fura Red calcium imaging
11/13/2022 2,291,674 Exp1_4.13.21_HFD_F_AJ.xlsx Raw ROI data for the Fura Red calcium imaging
5/11/2021 2,990,568 Exp1_5.11.21_129Fs_1and2_absentDIR.xlsx Raw ROI data for the Fura Red calcium imaging
7/7/2021 2,617,055 Exp1_7.7.21_PWKM6DirP_PWKM7DiRN.xlsx Raw ROI data for the Fura Red calcium imaging
7/27/2021 2,674,935 Exp1_B6F5_B6F4_7.27.21.xlsx Raw ROI data for the Fura Red calcium imaging
6/15/2021 2,574,970 Exp1_DiRPNODM5_DiRNNODM6_6.15.21.xlsx Raw ROI data for the Fura Red calcium imaging
7/20/2021 2,752,718 Exp1_DiRPWSBF3_DiRNWSBF4_7.20.21.xlsx Raw ROI data for the Fura Red calcium imaging
11/11/2021 1,816,842 Exp1_NZOF4and6_11.11.21_002.xlsx Raw ROI data for the Fura Red calcium imaging
4/13/2021 1,777,944 Exp2_4.13.21_HFD_F_AJ.xlsx Raw ROI data for the Fura Red calcium imaging
4/27/2021 2,256,642 Exp2_4.27.21_AJM_DiRPAJ5DiRNAJ4.xlsx Raw ROI data for the Fura Red calcium imaging
5/21/2021 3,080,396 Exp2_5.21.21_129M_dirP3DirN2.xlsx Raw ROI data for the Fura Red calcium imaging
5/14/2021 3,015,949 Exp2_129_F_5-11-21.xlsx Raw ROI data for the Fura Red calcium imaging
7/20/2021 2,227,050 Exp2_DiRNWSBF3_DiRPWSBF4_7.20.21.xlsx Raw ROI data for the Fura Red calcium imaging
2/18/2022 1,665,058 Exp3_CASTF1DiRN_CASTF3_11.4.21.xlsx Raw ROI data for the Fura Red calcium imaging

For each of the tabs in the files, there are standard column conventions. Each tab should have the following:
Name (the file name for the experiment in NIS) which should have the same value for every point from that experiment.
Time [s]: The timepoint in seconds that the data were collected.
ND.T : One of the standard NIS outputs but not used in our calculations. It appears to have the frame number for each point.
ND.Z : One of the standard NIS outputs the function of which is uncertain and which is not used in our calculations.
ND.M: our recalculation of the time in minutes.
The remaining columns designate the values for the individual ROIs. They are typically:
For Fura_bound and Fura_free, the ROI number (e.g. #1) and the wavelength of emission (e.g. 405 for the Fura_bound channel). So an example would be #1 (405) for a Fura_bound ROI or #1 (488) for a Fura_free ROI.
The NIS_fura_ratio ROI designations contain the ROI number (e.g. #1) and the ratios of the wavelengths for each measurement in parenthesis (e.g. Ratio 405/488). So the corresponding ROI NIS_fura_ratio measure for the previous examples would be #1 Ratio (405/488).

subfolder: Perifusion_data.

Summary: This contains the raw ELISA data (xlsx format) relevant to Figure 3. Files:
Date created size (bytes) ID

1/10/2022 91,330 Founder_ins_elisa_results_12.7.21done_1.7.22.xlsx
1/12/2022 84,422 Founder_ins_elisa_results_12.7.21done_1.12.22.xlsx
1/14/2022 83,702 Founder_ins_elisa_results_12.10.21done_1.13.22.xlsx
1/27/2022 17,870 Founder_Perifusion_Insulin_ELISA_1.27.22.xlsx
1/26/2022 95,437 ELISA_1.26.22_onFounder_1.14.22_run.xlsx
2/2/2022 137,880 Founder Perifusion Insulin ELISA 1.26.22.xlsx

2/2/2022 13,750 Founder_Perifusion_Insulin_ELISA_Rerun_2.1.22.xlsx
2/12/2022 113,709 ELISA_2.11.22ofperif_2.20.22.xlsx
2/12/2022 18,125 elisa_perif_cast_2.11.21.xlsx
2/2/2022 122,689 ELISA_1.27.22_onFounder_1.20.22_run_someglitches.xlsx
The files have the following architecture:
ELISA_1.26.22_onFounder_1.14.22_run.xlsx
Each 96-well plate has its own sheet designated Plate_# where the # is the plate
For the plate sheets, upper corner cells A1:N9 are the raw values read from the plate reader
Below this,
OD OD OD OD ng/mL ng/mL ng/mL ng/mL OD ng/mL
WSB9 NOD9 NOD10 129_10 WSB9 NOD9 NOD10 129_10 arranged interpolated

• column A indicates the fraction collected
• columns B-E represent the ODs for individual animals which are designated as (OD in row 11 followed by animal ID on row 12 (for example OD WSB9)
• columns F-I, the interpolated ng/mL insulin are indicated for the respective animals. The OD arranged is a concatenation of the raw OD values for determining their ng/mL using PRISM. The ng/mL interpolated is what PRISM returns by fitting these using its standard curve values from the plate (B9:H9) The processing tab is a concatenation of all of the fraction measurements from the respective plates.
• Following the columns for interpolated ng/mL (column I), the ng/mL is converted to ng/islet/fraction (columns J:M) then percent of total insulin secreted/fraction (% secreted, columns N:Q) and finally fold change of secretion vs basal secretion (FC, columns R:U). Above each column in row 1 for fold change (columns R:U) is the average percent insulin secreted during the basal period for the corresponding animal, which is used to calculate the fold change. Founder_ins_elisa_results_12.7.21done_1.7.22.xlsx Each 96 well-plate has its own sheet designated Plate_# where the # is the plate For the plate sheets, upper corner cells A1:N9 are the raw values read from the plate reader
• Below the raw data are columns identifying the fraction number (column A) on the plate and the corresponding animal values for the raw OD (columns B-E, with animal IDs in row 12) and the interpolated ng/mL values from PRISM (columns F-I, animal IDs in row 12) The summary sheet shows the concatenation of all fraction raw OD & interpolated ng/mL from all plates in columns B-I, with fraction numbers indicated in column A.
• Columns J-M are ng/mL * volume of each fraction.
• Columns N-Q divide the values in J-M by the number of islets in used for each animal.
• Columns R-U adjust for some fractions being diluted to be on the standard curve.
• In row 1 for columns V-Y are the corresponding total insulin values/islet. In these columns below the animal IDs (row 4), each of the values in R-U are divided by the total insulin value for that animal in row 1 and multiplied by 100 to give % of total insulin secreted/fraction. For row 1 in Z-AC, the values are the average % secretion for the basal period. Below the animal IDs (row 4) the values in columns V-Y are divided by the average basal values for each corresponding animal to give the fold change.

ELISA_2.11.22ofperif_2.20.22 Each 96-well plate has its own sheet designated Plate_# where the # is the plate For the plate sheets, upper corner cells A1:N9 are the raw values read from the plate reader. Below those, the structure resembles the layout of ELISA_1.26.22_onFounder_1.14.22_run.xlsx. The summary tab concatenates the values for OD and interpolated ng/mL similar to the summary tab of Founder_ins_elisa_results_12.7.21done_1.7.22.xlsx, with a few differences.

• Columns J-M include the number of islets in the calculation instead of just the volume of the fractions to give the ng/fraction/islet for each animal ID in row 4.
• For columns N-Q, the total insulin values for each animal per islet are listed in row 3, and all values below the IDs are the corresponding values from rows J-M divided by the total insulin value for that animal and multiplied by 100 to give % secreted.
• Columns R-U give the fold change over basal secretion period. The average % secreted for the basal secretion period for each animal is indicated in row 3, and the values for the fractions below the animal ids (row 4) are the values in N-Q divided by the average basal secretion in row 3 for each animal. elisa_perif_cast_2.11.21.xlsx contains raw data for a perifusion in 3 CAST male mice. Each 96 well plate has its own sheet designated Plate_# where the # is the plate For the plate sheets, upper corner cells A1:N9 are the raw values read from the plate reader The calculations for converting the raw OD data to secretion data are not in this file.

ELISA_1.27.22_onFounder_1.20.22_run_someglitches.xlsx For this set, the glitches refers to some of the values being under-diluted and slightly off the standard curve. This file shares a layout with ELISA_1.26.22_onFounder_1.14.22_run.xlsx for the sheets for each plate. It shares the structure of Founder_ins_elisa_results_12.7.21done_1.7.22.xlsx for the summary tab. Founder Perifusion Insulin ELISA 1.26.22.xlsx shares the structure of ELISA_1.26.22_onFounder_1.14.22_run.xlsx for its plate sheets. The summary tab is structured as the summary tab is for Founder_ins_elisa_results_12.7.21done_1.7.22.xlsx. There is a duplicate of the summary tab on Sheet1 Founder_Perifusion_Insulin_ELISA_Rerun_2.1.22.xlsx is a single plate with raw data in columns A1-N9. The values below row 9 are organized into fraction (column A), OD (column B), interpolated ng/mL (column C) and the adjusted values for dilution (column D) for each of the animals Identified in column E. The reason for this was to test buffer issues with a previous run that limited the color development in the last part of the assay. Founder_Perifusion_Insulin_ELISA_1.27.22.xlsx is just the raw data corresponding to the ELISA_1.27.22_onFounder_1.20.22_run_someglitches.xlsx Founder_ins_elisa_results_12.10.21done_1.13.22.xlsx has the same format as Founder_ins_elisa_results_12.7.21done_1.7.22.xlsx. Founder_ins_elisa_results_12.7.21done_1.12.22.xlsx has the same format as Founder_ins_elisa_results_12.7.21done_1.7.22.xlsx. It is a redo of those same samples.

File 2 (Stored on Zenodo): R_scripts

These were produced using the following R builds
platform x86_64-w64-mingw32

arch x86_64

OS mingw32

system x86_64, mingw32statusmajor 4

minor 0.2

year 2020 month 06 day 22
svn rev 78730 language R version.string R version 4.0.2 (2020-06-22) nickname Taking Off Again

Date created/last updated Size (bytes) File name

11/23/2022 17,251 for_graphing_matlab_imaging_data_summaries_v5_migrated.R
11/23/2022 3,690 histogram_data.R

11/23/2022 1,947 founder_islet_heatmaps.R
10/22/2022 17,033 for_finding_baselines3_founderpaper.R
3/5/2022 16,668 SD_summarizer_V2.R
10/22/2022 54,251 decay_correction_8.R
5/5/2022 9,571 HiC_protein_CE2.R
3/18/2022 1,037 for_Biomart_queries_v1.R

These files are for, in the following order above:
Graphing the imaging summaries from the matlab analysis scripts (seen in figure 4 and 7)
For graphing histograms (such as that seen in Figure 5)
For making heatmaps of the correlation coefficients
For finding baseline and peak values for the pulses
For creating summaries of the spectral density data generated by the decay_correction_8 script
For processing the NIS raw excel files. This function has several features including generating the detrended curves (figure 1, supplemental figures 1 and 2), performing spectral density derivation, and others.
For determining the HiC loops given the data from (1)
For identifying orthologues of the relevant proteins using Biomart
Note- the HiC queries were done in conjunction with scripts previously published: https://doi.org/10.5281/zenodo.6540721

File 3 (Stored on Zenodo): Supplemental_data

subfolder: PRISM_files.

Summary: This contains the prism file for the perifusion ELISA data relevant to Figure 3. The file is Perifusions.pzfx

Subfolder: initial_processing

This folder contains the matlab and spectral density processed data from the scripts as well as the summary by-group averages and Z-scores. Note- that if working with group data that includes the NZO males, one must use a different population average than when that group is not included. The insulin secretion data, the calcium data, and the clinical data include a few NZO males but the proteomic sets do not. Thus, there are sets for correlating traits including the male NZO and those without, as indicated by file name. These are Excel files. The file
Files:
date last modified size (bytes) File name
11/15/2022 124,176 Raw_data_matlab_noNZOm.xlsx
11/14/2022 126,144 Raw_data_matlab_with_NZOM.xlsx
11/23/2022 66,207 Spectral_density_data_withNZOM.xlsx
11/23/2022 98,333 Spectral_density_data_noNZOm.xlsx
11/15/2022 80,925 clinical_traits_all_mitok_etal.xlsx
11/14/2022 79,374 clinical_exvivo_traits_noNZOm_mitoketal.xlsx
11/15/2022 10,346,328 Islet_proteomic_data_noNZOM.xlsx

Subfolder: correlations

This folder contains the correlation data calculated from the Z-scores. For the Proteins_to_calcium.xlsx file, the lists of the most correlated/anticorrelated proteins to the specific calcium parameters are also included on separate tabs. Files:
date last modified size (bytes) name
11/23/2022 13,477,715 Proteins_to_calcium.xlsx
11/17/2022 6,831,406 Proteins_to_clinical_exvivo.xlsx
11/17/2022 1,603,415 calcium_to_clintraits.xlsx
11/18/2022 2,818,894 for_heatmaps_prot_calc.xlsx
11/17/2022 2,590,213 prot_to_clin_forheatmap.xlsx
11/19/2022 165 ~$Proteins_to_calcium.xlsx

Proteins_to_calcium shows the correlation coefficients between the calcium parameters and islet proteins' z-scores

Proteins_to_clinical_exvivo shows the correlation coefficients between the proteins and clinical traits/ex vivo secretion traits z-scores

Calcium_clintraits shows correlation coefficients between the calcium parameters' z-scores and the z-scores for the clinical and ex vivo islet secretion parameters.

Note- the raw proteomic data and raw clinical/ex vivo secretion data were previously published (2).

Subfolder: Supplemental_data
This folder contains the supplemental figures as well as both Table 3, which lists the proteins absolutely correlated to the 5 parameters of interest that also have SNPs for glycemic traits in the Type-2 Diabetes Knowledge portal (https://t2d.hugeamp.org/), and Supplemental Table 1, which provides the Enrichr links for the proteins correlated to the specified parameters.
Files:

Date last modified size (bytes) Name
11/23/2022 50,362 Table_3_Correlatedproteins_with_glycemic_SNPs.xlsx
11/23/2022 1,926,285 Emfinger_Clark_Attie_Supplemental.pdf
3/27/2023 692,377 Supplemental_table_1

Raw Movies

Refer to the table below for the folder breakdown.
Owing to size, these had to be uploaded in parts (tar files):

• raw_movies_part1 contains the folders 4.31.21 through 5.27.21
• raw_movies_part2 contains the folders 6.8.21 through 7.9.21
• raw_movies_part3 contains the folders 7.16.21 through 10.27.21
• raw_movies_part4 contains the folders 11.4.21 through 1.25.22
• the information in the table below can be found in the raw_movie_index.xlsx file

Within the tar files, these folders contain the raw calcium images in .nd2 file format. This is the output of NIS Elements but can be read by ImageJ

METHODS

Chemicals

All general chemicals, amino acids, BSA, DMSO, glucose, gastric inhibitory polypeptide (GIP,G2269), cOmplete Mini EDTA-free Protease Inhibitor Cocktail Tablets (11836170001), and heat-inactivated FBS (12306C) were purchased from Sigma Aldrich. RPMI 1640 base medium (11-875-093), antibioticantimycotic solutions (15240112), NP-40 Alternative (492016), Fura Red Ca2+ imaging dye (F3020), DiR (D12731), and agarose (BP1356-500) were purchased from ThermoFisher. Glass-bottomed culture dishes were ordered from Mattek (P35G-0-14-C). Fura Red stocks were prepared at 5 mM concentrations in DMSO, aliquoted into light-shielded tubes, and stored at 20C until day of use (5 M final concentration). DiR was prepared in DMSO at 2 mg/ mL, aliquoted to light-shielded tubes, and stored at 4C until use. All imaging solutions were prepared in a bicarbonate/HEPES-buffered imaging medium (formula in Table 1). Amino acids were prepared as 100 stock in the biocarbonate/HEPES-buffered imaging medium, aliquoted into 1.5 mL tubes, and frozen at 20C until day of use. Aliquots of GIP stock were prepared at 100 M in water and kept at -20C until day of use.

Animals

Animal care and experimental protocols were approved by the University of Wisconsin-Madison Animal Care and Use Committee. Most strains (B6, AJ, 129, NOD, PWK, and WSB) were bred in-house, although two strains (CAST and NZO) were purchased from Jackson Laboratory (Bar Harbor, ME). All mice were fed a high-fat, high-sucrose Western-style diet (WD, consisting of 44.6% kcal fat, 34% carbohydrate, and 17.3% protein) from Envigo Teklad (TD.08811) beginning at 4 weeks and continuing until sacrifice (aged ~19-20 weeks for all strains except the NZO males). The NZO males were sacrificed at 12 weeks of age owing to complications from severe diabetes. For each strain, 3-7 males and females from at least 2 litters were analyzed. Animals were sacrificed by cervical dislocation prior to islet isolation.

In vivo measurements

Fasting blood glucose and insulin levels were measured in mice at 19 weeks of age, except for the NZO males which were measured at 12 weeks of age. Glucose was analyzed by the glucose oxidase method using a commercially available kit (TR15221, Thermo Fisher Scientific), and insulin was measured by radioimmunoassay (RIA; SRI-13K, Millipore).

Islet imaging

Islets were isolated as previously described (3) and incubated in recovery medium (RPMI 1640, 11.1 mM glucose, 1% antibiotic/antimycotic, 10% FBS) overnight at 37C and 5% CO2. Islets were then incubated with Fura Red (5 M in recovery medium) at 37C for 45 minutes. Imaging dishes were created from glass-bottomed 10 cm2 dishes that had been filled with agarose. A channel with a central well was cut into the agarose with expanded ports on either side of the well for inflow and outflow lines. Prior to loading the chambers were perfused with the initial imaging solution (8 mM glucose in imaging medium). Islets were then loaded into these dishes. The imaging chamber was placed on a 37Cheated microscope stage (Tokai Hit TIZ) of a Nikon A1R-Si+ confocal microscope. All solution reservoirs were kept in a 37C water bath. Solutions were perfused through the chamber at 0.25 mL/min, with constant flow controlled by a Fluigent MCFS-EZ and M-switch valve assembly (Fluigent). The scope was integrated with a Nikon Eclipse-Ti Inverted scope and equipped with a Nikon CFI Apochromat Lambda D 10x/0.45 objective (Nikon Instruments), fluorescence spectral detector, and multiple laser lines (Nikon LU-NV laser unit; 405, 440, 488, 514, 561, 640nm). Bound dye was excited with the 405nm laser and the spectral detectors variable filter was set to 620-690nm. The free dye was excited with the 488nm laser and the variable filter collected from 640-690nm. Images were collected at 1 frame/sec at 6-second intervals. Each islet was considered a region of interest for further analysis. ROI intensity was collected by NIS Elements and exported for further analysis. All microscopy was performed at the University of Wisconsin-Madison Biochemistry Optical Core, which was established with support from the University of Wisconsin-Madison Department of Biochemistry Endowment.

Islet perifusion

Isolated islets were kept in RPMI-based medium (see above) overnight prior to perifusion, which was performed as previously described, with minor modifications (4, 5). Islets were equilibrated in 2 mM glucose for 55 minutes, after which 100 L fractions were collected every minute with the perifusion solutions set at a flow rate of 100 L/min. All solutions and islet chambers were kept at 37C. After the final fraction was collected, islet chambers were disconnected, inverted, and flushed with 2 mL of NP-40 Alternative lysis buffer containing protease inhibitors for islet insulin extraction.

Secreted insulin assay

Insulin in each perifusion fraction and islet insulin content were determined using a custom assay, as previously described (2).

Imaging data analysis

Trace segments for each solution condition were analyzed using Matlab and R. Traces were detrended using custom R scripts and Graphpad PRISM. Custom Matlab scripts (https://github.com/hrfoster/Merrins-Lab-Matlab-Scripts, also stored on Dryad https://doi.org/10.5281/zenodo.6540721) determined oscillation peak amplitude, pulse duration, active duration (the time when Ca2+ is above 50% peak amplitude), silent duration (the difference between period and active duration). , plateau fraction (the fraction of overall time per pulse spent in the active duration), pulse period and other parameters. Spectral density deconvolution for the trace segments to determine principal frequencies was done using R. Animal averages for the different parameters defined by Matlab and R were computed and graphed using custom R scripts. Figures were created using CorelDraw and Biorender.com.

Correlation and Z-score calculations

Correlation analysis was performed using the imaging data measurements and our published islet protein abundance data, ex vivo static insulin secretion measurements, and in vivo measurements made in a separate cohort of mice on the WD from the same strains and sexes used in these studies (2). For each imaging parameter or previously published measurement, the Z-score was calculated using the formula z = (x-) / where z is the Z-score, x is the animal average for that trait given the strain and sex, is the average of all animals values for that trait, and is the standard deviation for all animals values for that trait. Z-scores were computed in R and excel for the imaging parameters and the previously published (2) islet proteomic, ex vivo secretion, and in vivo measurements.

Correlation coefficients between the Z-score values of the imaging parameters and Z-scores of the previously published protein abundance, islet secretion, and in vivo traits were computed in Excel using the CORREL function. The equation used for this function is:
Correl(X,Y)=((x-)(y-))/((x-)^2*(y-)^2 )

Where X and Y are the Z-scores for the correlated traits/parameters, is the population average for trait X and is the population average for trait Y. Traits were considered highly correlated if absolute value for their Z-score correlation coefficients was 0.5.

Gene enrichment and human GWAS analysis

Proteins highly correlated or anticorrelated to imaging parameters were further analyzed using pathway enrichment and presence of human GWAS SNPs. Briefly, for a given parameter, pathway analysis for the highly correlated or anti-correlated proteins to that parameter was done using Enrichr (6, 7).

For GWAS analysis, human orthologues for genes encoding the previously measured islet proteins were identified using BioMart (8). For highly correlated proteins, the protein was deemed of human interest if its orthologue had SNPs for glycemia-related traits (see table 2) either along the gene body, within +/- 100 kbp of the gene start or end, or if any region in the gene body was connected to regions with SNPs by chromatin looping. SNPs were queried using Lunaris tool of the Common Metabolic Diseases Knowledge Portal (cmdkp.org). Chromatin loop anchor points for the relevant gene orthologues were identified using previously published human islet promoter-capture HiC data (1) and the alignment between these anchor loops and orthologues of interest was done using R scripts.

For those proteins having ortholgoues with SNPs via this analysis, we conducted further literature searches using Pubmed, Google Scholar, ChEMBL (9-11), canSAR (12), Uniprot (13), Tabula Muris (14), and the Human Protein Atlas (15, 16), and other resources (17-19) to determine tissue expression and identify any prior roles in islet biology. Figures for the relevant protein examples were created using Prism, CorelDraw, and the WashU Epigenome Browser (20).

Web resource

A web resource was created to explore the islet calcium and proteomic data and their relationships (https://rstudio.it.wisc.edu/FounderCalciumStudy). This resource sits on an RStudio/Connect server (see https://posit.co/). It enables the user to select traits from the calcium and protein datasets to plot by strain, sex, and calcium parameters. Distinct mice were assayed for calcium and protein. Individual strains can be selected on the main menu using the checkboxes, or all strains (default) can be viewed.
The different datasets available in the main menu are:

calcium: calcium parameters & spectral density data, with stimulatory secretion conditions
protein: islet proteomic measurements
basal: average calcium in 2mM glucose

The calcium data have three stimulatory conditions (8G, 8G/QLA, and 8G/QLA/GIP) that are displayed together for each calcium parameter. The proteomic data (protein) are displayed for each identified peptide. In rare cases of multiple peptides per gene, both gene symbol and peptide identifier (PP number) are included (e.g. Pkm_PP_1521 for the M1 isoform of the protein PKM). Desired proteins can be selected simultaneously with desired calcium parameters for correlation analysis and paired display by both datasets. The basal elements retained from the calcium data include the Average Calcium measurement for 2mM glucose. Proteomic data were log10-transformed. All traits were transformed into normal scores, keeping the sample mean and variance the same.

Scatter plots display data across sex and calcium conditions. When plotting calcium against protein or basal traits, means by strain and sex are used, as the two experiments used different mice. Correlation of selected traits with all other traits in the resource use Pearson correlation on pairwise-complete data. The user can order traits by their significance or by their correlation to other selected traits.
Statistical modeling terms include strain, sex, and the strain:sex interation, plus additional terms for comparing calcium condition with respect to strain and sex. Users can view volcano plots displaying deviation of term effects, measured as the standard deviation (SD = square root of mean square error) divided by the raw SD for that trait, against their significance (p-value after adjusting for all other model terms, presented on -log10 scale). In addition to the terms, a composite signal captures the combined effect of terms strain:condition + strain:condition:sex using a general F test computation.
All data handling and web app construction for the resource was performed using R scripts in publicly available GitHub repositories, with specifics for the calcium study at https://github.com/byandell/FounderCalciumStudy and the general purpose analysis and web deployment package at https://github.com/byandell/foundr.

Statistics:

For the islet perifusion insulin measurements, statistics were determined in GraphPad Prism. Fractional secretion area-under-the-curve (AUC) was determined using Prism and differences in AUCs analyzed using post-tests following 2-way ANOVA for the indicated trace segments. Islet total insulins between strains were compared using a two-tailed Students t-test with Welchs correction.

Study approval:

All protocols were approved by the University of Wisconsin-Madison IACUC (Protocol A005821-R01)

CITATIONS:

1. Miguel-Escalada I, Bonas-Guarch S, Cebola I, Ponsa-Cobas J, Mendieta-Esteban J, Atla G, et al. Human pancreatic islet three-dimensional chromatin architecture provides insights into the genetics of type 2 diabetes. Nature genetics. 2019.
2. Mitok KA, Freiberger EC, Schueler KL, Rabaglia ME, Stapleton DS, Kwiecien NW, et al. Islet proteomics reveals genetic variation in dopamine production resulting in altered insulin secretion. The Journal of biological chemistry. 2018;293(16):5860-77.
3. Rabaglia ME, Gray-Keller MP, Frey BL, Shortreed MR, Smith LM, and Attie AD. -Ketoisocaproate-induced hypersecretion of insulin by islets from diabetes-susceptible mice. American Journal of Physiology-Endocrinology and Metabolism. 2005;289(2):E218-E24.
4. Emfinger CH, de Klerk E, Schueler KL, Rabaglia ME, Stapleton DS, Simonett SP, et al. Cellspecific deletion of Zfp148 improves nutrient-stimulated cell Ca2+ responses. JCI Insight. 2022;7(10).
5. Bhatnagar S, Oler AT, Rabaglia ME, Stapleton DS, Schueler KL, Truchan NA, et al. Positional cloning of a type 2 diabetes quantitative trait locus; tomosyn-2, a negative regulator of insulin secretion. PLoS Genet. 2011;7(10):e1002323.
6. Chen EY, Tan CM, Kou Y, Duan Q, Wang Z, Meirelles GV, et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics. 2013;14:128.
7. Kuleshov MV, Jones MR, Rouillard AD, Fernandez NF, Duan Q, Wang Z, et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic acids research. 2016;44(W1):W90-7.
8. Smedley D, Haider S, Ballester B, Holland R, London D, Thorisson G, et al. BioMart--biological queries made easy. BMC Genomics. 2009;10:22.
9. Gaulton A, Hersey A, Nowotka M, Bento AP, Chambers J, Mendez D, et al. The ChEMBL database in 2017. Nucleic acids research. 2017;45(D1):D945-D54.
10. Davies M, Nowotka M, Papadatos G, Dedman N, Gaulton A, Atkinson F, et al. ChEMBL web services: streamlining access to drug discovery data and utilities. Nucleic acids research. 2015;43(W1):W612-W20.
11. Jupp S, Malone J, Bolleman J, Brandizi M, Davies M, Garcia L, et al. The EBI RDF platform: linked open data for the life sciences. Bioinformatics. 2014;30(9):1338-9.
12. Coker EA, Mitsopoulos C, Tym JE, Komianou A, Kannas C, Di Micco P, et al. canSAR: update to the cancer translational research and drug discovery knowledgebase. Nucleic acids research. 2019;47(D1):D917-D22.
13. The UniProt C. UniProt: the universal protein knowledgebase in 2021. Nucleic acids research. 2021;49(D1):D480-D9.
14. Schaum N, Karkanias J, Neff NF, May AP, Quake SR, Wyss-Coray T, et al. Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris. Nature. 2018;562(7727):367-72.
15. Uhln M, Fagerberg L, Hallstrm BM, Lindskog C, Oksvold P, Mardinoglu A, et al. Proteomics. Tissue-based map of the human proteome. Science (New York, NY). 2015;347(6220):1260419.
16. Thul PJ, kesson L, Wiking M, Mahdessian D, Geladaki A, Ait Blal H, et al. A subcellular map of the human proteome. Science (New York, NY). 2017;356(6340).
17. Varshney A, Scott LJ, Welch RP, Erdos MR, Chines PS, Narisu N, et al. Genetic regulatory signatures underlying islet gene expression and type 2 diabetes. Proceedings of the National Academy of Sciences of the United States of America. 2017;114(9):2301-6.
18. Lawlor N, George J, Bolisetty M, Kursawe R, Sun L, Sivakamasundari V, et al. Single-cell transcriptomes identify human islet cell signatures and reveal cell-type-specific expression changes in type 2 diabetes. Genome research. 2017;27(2):208-22.
19. Uhln M, Karlsson MJ, Hober A, Svensson AS, Scheffel J, Kotol D, et al. The human secretome. Science signaling. 2019;12(609).
20. Li D, Hsu S, Purushotham D, Sears RL, and Wang T. WashU Epigenome Browser update 2019. Nucleic acids research. 2019;47(W1):W158-W65.