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127 protocols using Illustrator 2021

All figures are original and were generated by using GraphPad Prism software version 9.2.0 and Microsoft Excel version 16.54, and finished in Adobe Illustrator 2021 (version 25.4.1; Adobe Inc., San Jose, CA). Alignment plots were developed by using the package ggmsa (version 1.1.0) on R (GitHub, https://github.com/YuLab-SMU/ggmsa, last accessed November 10, 2021) and finished in Adobe Illustrator version 26.0.3.
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Images were processed using Fiji (Schindelin et al., 2012 (link)) and assembled in Adobe Illustrator 2021 (Adobe Systems Incorporated, San Jose, USA). Heatmaps were generated using Heatmapper (Babicki et al., 2016 (link)).
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All data in this study are described as the mean ± standard deviation (SED) or median and interquartile range (IQR). Statistical significance was defined as a P value (2-tailed) < 0.05. Student's t test was used to compare data from two cohorts that followed a normal distribution, while the Mann–Whitney U test was used to compare nonnormally distributed data. The Wilcoxon signed ranks test was used to test paired samples for which data failed to follow a normal distribution. Correlations between serum ANXA3 levels and various laboratory parameters were assessed via Spearman rank correlation analysis.
SPSS 23.0 was used to analyze all statistical results. GraphPad Prism 8.0 and Adobe Illustrator 2021 were used to create the figures.
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All experiments were replicated in triplicate. The statistical analysis software SPSS 22.0 (IBM, Armonk, NY, USA) was used for original data analysis, and expressed as mean value ± standard deviation. All charts were drawn by Adobe Illustrator 2021 (Adobe Systems Incorporated, San Jose, CA, USA) and Origin 2019 (Origin Lab., Northampton, MA, USA).
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Microscopic images were processed using the public domain software ImageJ 1.53k70 (link) in combination with plugin Fil-Tracer (this study). Cell outlines were traced and morphometric measurements recorded. Fluorescent intensities were measured along the septal plane and plotted as fraction of the normalized cell length. Automatic cell recognition was double-checked manually. For representative images, the background subtraction function of ImageJ 1.53k was used and brightness and contrast were adjusted for better visibility. Data analysis was performed using Excel 2021 (Microsoft Corporation, USA), plots were created with ggplot2 in R (http://www.R-project.org/). Septa length (Fig. 7 and Supplementary Fig. 11) of BADA and TADA labeled cells were analyzed using the public domain software Fiji71 (link). Cell and septa lengths were measured manually. Notably, only cells that showed a BADA and TADA signal were considered for the septa length measurements. Two-tailed unpaired T tests were performed using GraphPad Prism version 9.3.0 for Mac (La Jolla California USA, www.graphpad.com). Figures were compiled using Adobe Photoshop 2021 and Adobe Illustrator 2021 (Adobe Systems, USA).
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The frequency of codon usage in the SH-01 genome was analyzed using the CUSP program (https://www.bioinformatics.nl/cgi-bin/emboss/cusp accessed on 17 June 2022). The genome map of SH-01 was drawn with Adobe Illustrator 2021 (Adobe, San Jose, CA, USA). A graph of the sequence lengths of the amino acids of proteins encoded by open reading frames (ORFs) on the X-axis and the number of proteins per length on the Y-axis was calculated by Geneious Prime v2022.2.1 (Biomatters, Auckland, New Zealand).
The signal peptide (SP) sequences and transmembrane domains (TMDs) of proteins encoded by SH-01 were predicted using signalP-5.0 (https://services.healthtech.dtu.dk/service.php?SignalP-5.0 accessed on 21 June 2022) and TMHMM 2.0 (https://services.healthtech.dtu.dk/service.php?TMHMM-2.0 accessed on 21 June 2022), respectively. Then, the function features of all the proteins encoded by SH-01 were also predicted and analyzed using the conserved domains database (CDD; https://www.ncbi.nlm.nih.gov/cdd accessed on 21 June 2022) with an E-value < e−5 as a cut-off [32 (link)].
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Flow cytometry data were analysed using FlowJo software v10 (BD). qPCR data were analysed using Bio-Rad CFX Manager 3.1 (Bio-Rad). Confocal images were processed with Zen 2.3 software (Zeiss). Statistical comparisons were performed by Student’s t-test or one-way analysis of variance (ANOVA) and computed with SPSS version 13.0 (SPSS Inc.). A p-value < 0.05 was considered statistically significant. Data graphs were constructed using GraphPad Prism 8 (GraphPad). Figures were created using Adobe Illustrator 2021 (Adobe Systems Inc.) or PowerPoint 2013 (Microsoft).
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All the PCR primers relevant to this study were designed by SnapGene v4.1.9. The sequences of the Bacillus species were downloaded from the NCBI database (with a similarity criterion of >95%). The multiple sequence alignment was performed by ClustalW. The concentration and purity of the RNA were determined by measuring the absorbance at 260/280 nm (NanoDrop 1000, Thermo Scientific, Wilmington, DE, USA) and then statistically analyzed by one-way ANOVA and Duncan’s test in SPSS Statistics version v17.0 (SPSS Inc., Chicago, IL, USA) and Excel 2019 (Microsoft Corp., Redmond, WA, USA). Significance was assessed at p ≤ 0.05. The graphs were edited by GraphPad Prism 8.0.1, Adobe Photoshop 2021, and Adobe Illustrator 2021.
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Data analysis was performed with R [21 ] and GraphPad Prism 9 (GraphPad Software, USA). If not stated otherwise, statistical tests and analyses were performed as indicated in the respective figure legends. Figures were generated using GraphPad Prism 9 and Adobe Illustrator 2021 (Adobe Inc., USA).
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10

Comprehensive Multi-Omics Analysis of ENPP1 Expression

Publicly available sequencing data were analyzed using the cBioPortal for Cancer Genomics and the UALCAN analysis tool [30 (link),31 (link),32 (link)]. mRNA and protein expression were determined using TCGA (The Cancer Genome Atlas) and CPTAC (Clinical Proteomic Tumor Analysis Consortium) datasets, respectively. For analyzing the expression levels across cancer types, TCGA Pan-Cancer dataset was selected in UALCAN and a bar graph was plotted, showing the gene expression levels of ENPP1 across cancers. ENPP1 protein and gene expression correlation was conducted using cBioPortal. The CCLE datasets were considered for the analysis. A correlation matrix was plotted for all the cell lines, where MDA-MB-231 and A549 cells were highlighted for better representation. All the figures and generated plots were edited using Adobe Illustrator 2021 (vCS5.1; Adobe Systems, San Jose, CA, USA).
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