Multimodal profiling reveals distinct endothelial activation pathways regulated by flow and heparan sulfate
Data files
Oct 29, 2025 version files 31.20 MB
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Harding_OHare_Ebong_2025_Flow_HS_DataQuantification.xlsx
37.54 KB
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Harding_OHare_Ebong_2025_RNAseq_ExpressionData.xlsx
31.15 MB
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README.md
12.91 KB
Abstract
This dataset contains the raw and processed quantitative outputs supporting the manuscript “Multimodal Profiling Reveals Distinct Endothelial Activation Pathways Regulated by Flow and Heparan Sulfate” (Harding, O’Hare, et al., 2025). The data quantify endothelial inflammatory and oxidative stress responses under four experimental conditions: (1) static, (2) uniform flow (12 dyn/cm²), (3) static + heparinase III, and (4) flow + heparinase III. The spreadsheets compile results from confocal immunofluorescence imaging, Western blot densitometry, reactive oxygen species (ROS) assays, and RNA sequencing. Quantified variables include mean fluorescence intensity (MFI) for protein markers (KLF2, ICAM-1, E-selectin, Nrf2, and Nf-κB p65), nuclear localization coefficients (Pearson’s coefficient for Nrf2 and DAPI), normalized band intensity ratios for Nox4, ICAM-1, and β-actin, and fluorescence-based ROS signals (H₂DCFDA and DHE assays). The RNA-seq dataset provides normalized counts, fold changes, and gene-set enrichment scores for oxidant, antioxidant, pro-inflammatory, and anti-inflammatory gene groups. Each data table corresponds to averaged biological replicates (n = 3–7 per condition) with normalization to static controls. Together, these data provide a quantitative framework linking glycocalyx integrity and flow-dependent mechanotransduction to endothelial inflammation and redox regulation. The files can be reused for comparative analyses of mechanosensitive gene programs, image quantification benchmarking, or integration with other omics datasets examining vascular dysfunction.
Dataset DOI: 10.5061/dryad.63xsj3vfg
Description of the data and file structure
Files and variables
File: Harding_OHare_Ebong_2025_Flow_HS_DataQuantification.xlsx
| Sheet Name | Description |
| Cell Information | Contains sample identifiers, condition labels (Static, Flow, Static + Heparinase III, Flow + Heparinase III), and experiment-level metadata linking replicates to imaging or assay datasets. Serves as the reference sheet for all quantification tabs. |
| KLF2_MFI_Quantification | Mean fluorescence intensity (MFI) of KLF2 measured by confocal immunocytochemistry under static and flow conditions ± Heparinase III. Reflects endothelial transcriptional activation by shear stress and suppression after glycocalyx degradation. |
| ICAM1_MFI_Quantification | MFI of ICAM-1 quantified from confocal images; marker of endothelial inflammatory activation. Normalized to static mean = 1.0 for fold-change comparison. |
| ESelectin_MFI_Quantification | Fluorescence quantification of E-selectin expression (adhesion molecule induced by inflammation). Data normalized to static control mean = 1.0. |
| ICAM1_WB_NormalizedRatios | Western blot densitometry of ICAM-1 normalized to β-actin and scaled to static control = 1.0. Each value represents one biological replicate. |
| NOX4_WB_NormalizedRatios | Western blot densitometry of NOX4 (NADPH oxidase 4), normalized to β-actin. Reflects oxidative enzyme induction under flow and glycocalyx degradation. |
| NRF2_NuclearLocalization | Pearson’s correlation coefficient between Nrf2 and DAPI fluorescence channels, quantifying nuclear translocation (activation of antioxidant response). |
| TotalROS_FluorescenceAssay | Mean fluorescence intensity from live-cell H₂DCFDA and DHE ROS assays. Represents intracellular ROS levels under each condition (Static, Flow, Static + Heparinase III, Flow + Heparinase III). |
Description: This Excel file contains all quantitative imaging, ROS, and Western blot data used to assess endothelial inflammatory and oxidative responses under four experimental conditions:
Static, Flow (12 dyn/cm²), **Static + Heparinase **, and Flow + Heparinase
All data were normalized to the mean value of the static control group.
Units are fluorescence arbitrary units (A.U.) or normalized protein ratios unless otherwise stated.
Blank cells indicate unavailable or uncollected data; no “NA” placeholders were used.
Variables
- MFI – Mean Fluorescence Intensity
- WB – Western Blot
- Hep III – Heparinase III (enzyme degrading heparan sulfate)
- β-actin – Housekeeping protein used for normalization
- Nrf2 – Nuclear factor erythroid 2–related factor 2 (antioxidant transcription factor)
- Pearson’s correlation coefficient: Degree of overlap** **between Nrf2 and DAPI fluorescence channels, indicating nuclear localization (activation).
- ICAM-1 – Intercellular Adhesion Molecule 1
- KLF2 – Krüppel-Like Factor 2
- ROS – Reactive Oxygen Species
File: Harding_OHare_Ebong_2025_RNAseq_ExpressionData.xlsx
| Sheet Name | Description |
| RNAseq_Master_DESeq2_Output | Full DESeq2 differential-expression results for all genes detected in human endothelial RNA-seq samples. Includes gene symbols, GO annotations, baseMean expression, log₂ fold-change (LFC), Wald statistics, raw and adjusted p-values. This serves as the comprehensive source table from which all functional subsets were derived. |
| ProInflammatory_Geneset | Curated subset of genes upregulated during endothelial inflammatory activation. Includes adhesion molecules (e.g., ICAM1, VCAM1, SELE), cytokines (IL6, CXCL8), and NF-κB-dependent targets. Values correspond to normalized DESeq2 counts and LFCs relative to static controls. |
| AntiInflammatory_Geneset | Curated subset of genes associated with endothelial quiescence and anti-inflammatory signaling (e.g., KLF2, NFKBIA, TGFB1, NOS3). Contains normalized DESeq2 results used in figure heat maps and summary plots. |
| Oxidant_Geneset | Genes encoding enzymes that produce reactive oxygen species (ROS) or propagate redox signaling, including NADPH oxidases (NOX4, CYBA), monoamine oxidases (MAOA), and other pro-oxidant effectors. Reflects oxidative stress induction under flow and glycocalyx degradation. |
| Antioxidant_Geneset | Genes mediating antioxidant defense and redox homeostasis, including glutathione- and thioredoxin-related enzymes (GSR, GCLC, TXNRD1), classical antioxidants (SOD1, CAT, GPX4), and transcriptional regulators (NFE2L2, HMOX1). These represent the endothelial adaptive response to oxidative stress. |
Description: This file includes RNA-sequencing results comparing transcriptomic changes across the same four experimental conditions.
All numeric values derive from DESeq2 normalized counts.
Log₂ fold-changes (LFC) are provided relative to static controls unless otherwise stated.
Missing values are indicated by blank cells or “NA” (if absent from DESeq2 output).
Each column header in the RNA-seq spreadsheet represents a single RNA-seq library corresponding to one biological replicate. Sample IDs (e.g., L1, L5, L6) align with the sequencing library identifiers used during Illumina demultiplexing and map to the four experimental groups defined below.
| Sample Name | Condition | Treatment | Replicate ID |
| Static Control L1 | Static (no flow) | Control | L1 |
| Static Control L5 | Static (no flow) | Control | L5 |
| Static Control L6 | Static (no flow) | Control | L6 |
| Static Enzyme L13 | Static (no flow) | Heparan sulfate degradation | L13 |
| Static Enzyme L18 | Static (no flow) | Heparan sulfate degradation | L18 |
| Static Enzyme L19 | Static (no flow) | Heparan sulfate degradation | L19 |
| Flow Control L4 | Flow (shear stress) | Control | L4 |
| Flow Control L8 | Flow (shear stress) | Control | L8 |
| Flow Control L9 | Flow (shear stress) | Control | L9 |
| Flow Enzyme L16 | Flow (shear stress) | Heparan sulfate degradation | L16 |
| Flow Enzyme L21 | Flow (shear stress) | Heparan sulfate degradation | L21 |
| Flow Enzyme L23 | Flow (shear stress) | Heparan sulfate degradation | L23 |
Variables
- log₂FC (log2FoldChange) – Log₂-transformed fold change
- Wald_Statistic – DESeq2 Wald test statistic
- NES – Normalized Enrichment Score (from GSEA)
- ES – Enrichment Score
- FDR q-value – False Discovery Rate–adjusted p-value
- BaseMean – Average normalized read count per gene
- padj – Adjusted p-value (Benjamini–Hochberg)
- Oxidant / Antioxidant / Pro-Inflammatory / Anti-Inflammatory – Functional gene-set categories analyzed via GSEA
Code/software
All data files can be viewed or reanalyzed using freely available, open-source or commonly accessible software. No proprietary programs are required.
General Viewing
- Microsoft Excel or Google Sheets can open all
.xlsxspreadsheets. - PDFs can be viewed using Adobe Acrobat Reader, Preview (macOS), or any open PDF viewer.
- No specialized software or code is required to view or interpret the numeric data.
Data Analysis and Quantification Workflow
Image and Fluorescence Data
- Software: ImageJ/Fiji (v2.9.0; National Institutes of Health, open source)
- Packages/Plug-ins Used:
- Coloc2 (for Pearson’s correlation between Nrf2 and DAPI)
- Measure and ROI Manager tools (for mean fluorescence intensity, MFI)
- Gel Analyzer (for Western blot densitometry)
- Workflow:
- Acquire images (Zeiss LSM 710/800/880 or AxioObserver).
- Open czi files in ImageJ/Fiji.
- Subtract background and measure MFI or nuclear colocalization.
- Export results to
.csvor.xlsxfor normalization and graphing.
RNA-Seq Data
- Software: R Statistical Environment (v3.6.0 or later; open source, https://www.r-project.org)
- Packages Used:
DESeq2(v1.26.0) for differential expressionGSEA(v2.2.1, Broad Institute) for gene-set enrichment- Base R packages (
tidyverse,ggplot2) for data organization and visualization
- Workflow:
- Import raw read counts (Illumina NextSeq500) into R.
- Perform normalization and Wald tests using
DESeq2. - Adjust p-values using Benjamini–Hochberg FDR correction.
- Conduct gene-set enrichment using
GSEA. - Export normalized counts, log₂ fold-changes, and enrichment results to Excel for sharing.
Statistical Analysis
- GraphPad Prism (v9) or open-source equivalents such as R (v3.6.0) or JASP (v0.17) can reproduce one-way ANOVAs, Tukey post-hoc tests, and Shapiro-Wilk tests
Access information
Other publicly accessible locations of the data:
- None
Data was derived from the following sources:
- None
Data Integrity and Reuse
All numeric values are provided as plain text within Excel (.xlsx) files formatted for accessibility. No macros, merged cells, or embedded figures are included. Each sheet represents a single flat table suitable for reanalysis in R, Python, or spreadsheet environments. Users may reuse these data for meta-analyses, benchmarking of endothelial transcriptomic responses, or integration with other vascular datasets.
Overview
All numeric values included in the spreadsheets derive from image-based fluorescence analysis, Western blot densitometry, and RNA-seq differential-expression outputs. Each sample was classified by condition (Static, Flow, Static + Hep III, Flow + Hep III) and replicate number. No data were averaged or filtered beyond standard normalization steps, allowing full reuse for independent statistical analysis.
1. Image Quantification (Immunocytochemistry and ROS Assays)
- Microscopy acquisition: Confocal (Zeiss LSM 710/800/880) images were collected using identical laser power, gain, and exposure settings within each experiment.
- Mean Fluorescence Intensity (MFI): For each marker (KLF2, ICAM-1, E-selectin, Nrf2, Nf-κB), ImageJ/Fiji was used to select cell-level regions of interest. Background fluorescence was subtracted, and the integrated density was divided by area to yield MFI values.
- Colocalization (Nrf2): Pearson’s correlation coefficient between Nrf2 and DAPI channels was calculated using the Coloc2 plug-in (ImageJ/Fiji) for ≥ 3 images per slide; averaged per sample to represent nuclear activation.
- ROS quantification: For H₂DCFDA (total ROS), live cell imaging was obtained using a Zeiss AxioObserver widefield microscope equipped with an incubator set at 37°C. Fluorescence images were converted to grayscale and measured for mean intensity. Nine images per sample were averaged to produce one replicate data point.
- Normalization: MFI values were normalized to the static mean (set = 1.0) to compare fold changes across conditions.
2. Western Blot Densitometry
- Blots were imaged using Bio-Rad ChemiDoc. Band intensities for target proteins (ICAM-1 and Nox4) and β-actin were measured with ImageJ/Fiji.
- Background intensity was subtracted for each lane.
- Ratios of target / β-actin were calculated and normalized to the static control lane (set = 1.0).
- Each value represents one biological replicate; group averages and standard deviations are listed in the spreadsheet.
3. RNA-seq Quantification and Gene-Set Scoring
- Normalized read counts were generated using DESeq2 (v1.26.0).
- Columns in the spreadsheet include: raw counts, baseMean, log₂ fold-change (Flow vs Static, Flow + Hep III vs Static + Hep III, etc.), Wald p-values, and adjusted p-values (Benjamini–Hochberg FDR).
- Gene-set enrichment scores (normalized enrichment score = NES) for oxidant, antioxidant, pro-inflammatory, and anti-inflammatory categories were computed via GSEA v2.2.1.
- Each gene was annotated by its directional contribution (up/down-regulated) based on the Wald statistic ≥ 1 or ≤ –1.