DOI: 10.5061/dryad.hdr7sqvqr
title: Data from: Effect of pancreatic cancer-derived extracellular vesicles on bone marrow-derived macrophages

description: Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive
 cancers, with a majority of patients presenting with metastatic disease at
 diagnosis. Extracellular vesicles (EVs) released by cancer cells have
 emerged as key mediators of intracellular
 signaling, communication, and immune modulation within the tumor
 microenvironment (TME).  Tumor-associated macrophages
 (TAMs) are crucial components of TME that influence tumor growth and
 metastasis. The present study is focused on determining
 the role of PDAC-derived EVs in altering macrophage phenotype, function,
 and metabolism, and their impact on tumor cell proliferation and invasion.
 EVs were isolated from the conditioned medium that was used to culture one
 established pancreatic cancer cell (PANC-1), one PDX cell line (PPCL68),
 and a normal (non-tumorigenic) established pancreatic epithelial cell line
 (hTERT-HPNE), using size exclusion chromatography. The EVs were
 extensively characterized using nanoparticle tracking (NTA),
 cryo-electron microscopy, and immunoblot analysis as per
 ISEV guidelines. Macrophages were generated from the bone marrow cells of
 C57/BL6 mice cultured in vitro using macrophage colony-stimulating factor
 (M-CSF). Macrophages were co-cultured with isolated PDAC or normal
 cell-derived EVs. Flow cytometry and multi-omic analyses were performed to
 determine the effect of EVs on macrophage phenotype, function, and
 behavior. Our results show that PDACcell-derived EVs were taken up by the
 macrophage in a time-dependent manner that resulted in an
 immunosuppressive phenotype characterized by higher expression of CD206
 and PD-L1 and higher secretion of immunosuppressive cytokines, including
 TGF-beta, IL-10, and GM-CSF.  Functionally, PDAC-EV-treated
 macrophages were able to suppress the proliferation of CD8 T cells under
 in vitro and in vivo conditions. Metabolically, cancer EV-treated
 macrophages showed accumulation of immunosuppressive metabolites.
description: <strong>Targeted Metabolomics for cell
 samples</strong> <strong>1.       Sample
 preparation for cells</strong>: To cell samples
 in a 24-well plate, 300 µL of methanol/water (1:1) containing internal
 standards (200 ng/mL of debrisoquine for positive mode and 200 ng/mL of
 4-nitrobenzoic acid for negative mode) was added, followed by gentle
 scraping for 20 sec. The above samples were transferred to 2mL Eppendorf.
 To the 24-well plate, 200 µL of PBS was added and mixed well, followed by
 transferring the PBS wash to the previously collected cell lysate.  The
 sample collected above was freeze-dried (also known as lyophilization).
 The lyophilized powdered samples were suspended in 25 μL of PBS and
 vortexed gently for 30 sec. Samples were plunged into dry ice for 30 s and
 heat shocked by plunging into a 37◦C water bath for 90 s. This was
 repeated a total of three times. Samples were then sonicated for 30 s.
 Next, 100 μL of methanol/water (1:1) was added to the samples. Tubes were
 vortexed for 30 s, incubated on ice for 20 min, followed by the addition
 of 100 μL of chilled ACN. The samples were incubated at −20◦C for 20 min.
 Finally, samples were centrifuged at 13,000 x g for 20 min at 4◦C.
 Supernatant was transferred to MS vials for LC-MS analysis.
 <strong>2.       Sample preparation for
 media</strong>: 500 µL of media collected from
 each sample was freeze-dried (lyophilized) and suspended in 50 µL of PBS,
 followed by vortexing for 30 sec. To the above mixture, 100 μL of
 extraction buffer (methanol/water 50/50) containing 200 ng/mL of
 debrisoquine (DBQ) as an internal standard for positive mode and 200 ng/mL
 of 4-nitrobenzoic acid as an internal standard for negative mode, was
 added. The sample was vortexed for 30 seconds and incubated on ice for 20
 min, followed by the addition of 100 μL of acetonitrile and incubation at
 -20 ℃ for 20 min. Samples were centrifuged at 13,000 rpm for 20 min at 4
 ℃. The supernatant was transferred to an MS vial for LC-MS
 analysis. <strong>3.       Sample preparation for
 NIST Plasma:</strong> 25 µL of NIST plasma sample
 was dissolved in 100 μL of extraction buffer (methanol/water 50/50)
 containing 200 ng/mL of debrisoquine (DBQ) as internal standard for
 positive mode and 200 ng/mL of 4-nitrobenzoic acid as internal standard
 for negative mode. The sample was vortexed for 30 seconds and incubated on
 ice for 20 min, followed by the addition of 100 μL of acetonitrile and
 incubation at -20 ℃ for 20 min. Samples were centrifuged at 13,000 rpm for
 20 min at 4 ℃. The supernatant was transferred to an MS vial for LC-MS
 analysis. <strong>4.       Data
 acquisition</strong> Five microliters of the
 prepared sample was injected onto a Kinetex F5, 2.6 μm 100 Å 150 × 2.1 mm
 (Phenomenex, CA, USA) using SIL-30 AC auto sampler (Shimadzu) connected
 with a high flow LC-30AD solvent delivery unit (Shimadzu) and Exion 30AD
 communication bus module (Shimadzu) online with QTRAP 7500 (Sciex, MA,
 USA) operating in positive and negative ion mode. A binary solvent
 comprising water with 0.1% formic acid (solvent A) and acetonitrile with
 0.1% formic acid (solvent B) was used. The extracted metabolites were
 resolved at a 0.2 mL/min flow rate. The LC gradient conditions were as
 follows: Initial – 100% A, 0% B for 2.1 minutes; 14 minutes – 5% A, 95% B
 till 15 minutes; 15.1 minutes – 100% A, 0% B till 20 minutes. The auto
 sampler and oven were kept at 15 °C and 30 °C, respectively. Source and
 gas settings for the method were as follows: curtain gas = 45, CAD
 gas = 10, ion spray voltage = 2000 V in positive mode and ion spray
 voltage = 4500 V in negative mode, temperature = 500 °C, ion source gas
 1 = 4,5, and ion source gas 2 = 70. The data were normalized to internal
 standard area and processed using Sciex OS software.
 <strong>5.       Data Processing</strong>
 The data were normalized to the respective internal standard area
 and processed using Sciex OS. The quality and reproducibility of LC-MS
 data were ensured using several measures. The column was conditioned using
 the pooled QC samples initially and was also injected periodically to
 monitor shifts in signal intensities and retention time as measures of
 reproducibility and data quality of the LC-MS data. We also ran NIST
 plasma, periodically, prepared in the same manner to check the
 instrumental variance. We also have blank solvent runs between sets of
 samples to minimize carry-over effects.
 <strong>6.       Data analysis</strong>
 The abundance measurement for metabolites was expressed as
 intensity units that were initially normalized to internal standards and
 processed using MultiQuant 3.0.3 (Sciex). The data were pre-processed
 using a signal/noise ratio &gt;20:1 and retention time (RT) tolerance
 of 5 seconds, after manual checking of metabolite peaks by experts to find
 the reliable features. We also use 20% of missing values in each feature
 as a filter-out criterion. Missing values were imputed by half of the
 minimum positive value in the original data. Thereafter, we used 20% of
 the coefficient of variation (CV) as our filter criterion to remove any
 possible noise before data normalization. Analytical drifts (if any) were
 corrected by quality control-based robust LOESS signal correction
 (QC-RLSC) (Dieterle et al., 2006; Li et al., 2016). All the analyses were
 performed in MetaboAnalyst Online (V5.0). The normalized LC-MS data were
 Log10 transformed and Pareto scaled in MetaboAnalyst. For binary
 comparisons, the data in each of the normalized csv files were subset by
 groups for comparisons of interest before input into the online
 tool.
description: # Data from: Effect of pancreatic cancer-derived extracellular vesicles on
 bone marrow-derived macrophages #### Description of the data and file
 structure Cells, media, and NIST plasma samples were prepared using
 methanol/water extraction containing internal standards, followed by
 protein precipitation with acetonitrile, cold incubation, and
 centrifugation to obtain clear supernatants for analysis. Cell samples
 additionally underwent scraping, lyophilization, reconstitution,
 freeze–thaw cycles, and sonication to ensure efficient metabolite
 extraction, while media samples were lyophilized prior to extraction.
 Metabolites were analyzed by LC-MS/MS using a Kinetex F5 column under
 positive and negative ion modes with a water–acetonitrile gradient
 containing formic acid. Data were normalized to internal standards and
 processed using Sciex OS, with quality control ensured through pooled QC
 samples, NIST plasma, and blanks. The final dataset was filtered,
 corrected for analytical drift, log-transformed, Pareto-scaled, and
 statistically analyzed using MetaboAnalyst. **Normalized_Data_Cells.csv**
 * Intracellular metabolite levels are measured in cells. - Each column
 after the sample metadata is a metabolite. Metabolite naming Examples: *
 Urea - Alanine, Arginine, Aspartate * Hexose 6-Phosphate
 **Normalized_Data_Media.csv** * Metabolites are measured in the culture
 media (extracellular). - Reflects: 1. Nutrient consumption 2. Metabolite
 secretion by cells Metabolite naming * Similar metabolites but often fewer
 intracellular intermediates. - Many columns include explicit ion mode
 labels: 1. Lipoamide_pos 2. Glycerophosphocholine_pos 3.
 Alpha-Tocopherol_neg First columns (both files) * Sample: sample ID (e.g.,
 Sample 022). - Sample detail: experimental condition. Examples include:
 1\. Untreated control 2\. hTERT-HPNE EVs (Media file shows 4 distinct
 conditions total) Remaining columns (both files) * Metabolite features
 (hundreds of them). - Values are normalized intensities (unitless,
 relative quantities).