DOI: 10.5061/dryad.63xsj3vfg
title: Multimodal profiling reveals distinct endothelial activation pathways regulated by flow and heparan sulfate

description: 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.
description: <strong>Overview</strong> 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.
 <strong>1. Image Quantification
 (Immunocytochemistry and ROS Assays)</strong> 
 <strong>Microscopy acquisition:</strong> Confocal
 (Zeiss LSM 710/800/880) images were collected using identical laser power,
 gain, and exposure settings within each experiment. 
 <strong>Mean Fluorescence Intensity (MFI):</strong> 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. <strong>Colocalization
 (Nrf2):</strong> 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. <strong>ROS
 quantification:</strong> 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. 
 <strong>Normalization:</strong> MFI values were normalized to
 the static mean (set = 1.0) to compare fold changes across
 conditions. <strong>2.
 Western Blot Densitometry</strong> 
 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.
 <strong>3. RNA-seq Quantification
 and Gene-Set Scoring</strong> 
 Normalized read counts were generated using <strong>DESeq2
 (v1.26.0)</strong>. 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
 <strong>GSEA v2.2.1</strong>. Each gene
 was annotated by its directional contribution (up/down-regulated) based on
 the Wald statistic ≥ 1 or ≤ –1.
description: # Multimodal profiling reveals distinct endothelial activation pathways
 regulated by flow and heparan sulfate Dataset DOI:
 [10.5061/dryad.63xsj3vfg](https://doi.org/10.5061/dryad.63xsj3vfg) ##
 Description of the data and file structure ### Files and variables ####
 File: Harding_OHare_Ebong_2025_Flow_HS_DataQuantification.xlsx | 
 
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 | | :--------------------------------- |
 :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | **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 | 
 
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 | | :--------------------------------- |
 :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **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. 
 
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 | | :---------------- | :------------------ | :--------------------------
 | :--------------- | | **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 `.xlsx`
 spreadsheets. * **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:** 1. Acquire images (Zeiss LSM 710/800/880 or
 AxioObserver). 2. Open czi files in ImageJ/Fiji. 3. Subtract background
 and measure MFI or nuclear colocalization. 4. Export results to `.csv` or
 `.xlsx` for normalization and graphing. --- #### **RNA-Seq Data** *
 **Software:** *R Statistical Environment* (v3.6.0 or later; open source,
 [https://www.r-project.org](https://www.r-project.org)) * **Packages
 Used:** * `DESeq2` (v1.26.0) for differential expression * `GSEA` (v2.2.1,
 Broad Institute) for gene-set enrichment * Base R packages (`tidyverse`,
 `ggplot2`) for data organization and visualization * **Workflow:** 1.
 Import raw read counts (Illumina NextSeq500) into R. 2. Perform
 normalization and Wald tests using `DESeq2`. 3. Adjust p-values using
 Benjamini–Hochberg FDR correction. 4. Conduct gene-set enrichment using
 `GSEA`. 5. 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.