DOI: 10.5061/dryad.vdncjsz87
title: Data from: Dual-mode microfluidic immunostaining device for diagnostic biomarkers detection and tumor microenvironment evaluation

description: The limited availability of tissue samples from rare tumors poses a major
 barrier to advances in precise diagnosis, prognostic evaluation, and
 therapeutic research—challenges exemplified by primary central nervous
 system diffuse large B‑cell lymphoma (PCNS‑DLBCL). Here, we developed a
 dual-mode microfluidic immunostaining (Dumi) device, which integrates
 diagnostic and research workflows into a single, automated platform,
 reducing tissue section consumption by over 90 %. Using just 1–2 slides,
 it enables both regional detection of biomarkers for subtyping diagnostic
 (≤ 16) and construction of multiplex tumor microenvironment (TME) maps.
 Joint analysis of multi-region diagnostic biomarkers in the TME map
 indicates that tumor cell subpopulations defined by specific diagnostic
 biomarkers can actively shape their in situ microenvironmental niche. Dumi
 offers an efficient, cost-effective, and multifunctional immunostaining
 method, overcoming the limitations of scarce tissue resources and
 providing a clinically accessible solution to the diagnostic and
 therapeutic challenges of rare tumors.
description: # Dual-Mode Microfluidic Immunostaining Device for Diagnostic Biomarkers
 Detection and Tumor Microenvironment Evaluation ## Data Components 1.
 **tonsil-fig3.zip:** For the paired immunohistochemistry of conventional
 IHC (manual) and whole-chamber IHC (Dumi) in Fig. 3 of the associated
 article, the positive intensity values, mean background, and SBR of 23 ×
 48 matrices. 2. **tonsil-fig4.xlsx:** Single-cell data from multiplex
 immunofluorescence images derived from three tonsil sections. Each row
 represents a single segmented cell. 3. **PCNSL-fig6.xlsx:** Single-cell
 data from multiplex immunofluorescence images derived from one PCNSL
 section. Each row represents a single segmented cell. 4.
 **PCNSL-fig7.zip:** Single-cell data of multiplex immunofluorescence
 generated after a sequential multi-channel to whole-chamber IF staining
 strategy was performed on a single PCNSL slide. Each row represents a
 single segmented cell. 5. **registration.zip:** Paired raw images after
 multi-channel and whole-chamber staining derived from three tonsil
 sections. The single-cell data provided in tonsil-fig4, PCNSL-fig6, and
 PCNSL-fig7 contain the following variables: * sheet T1-T3: ROIs from each
 tonsil section. * Classifications: Contains the positive labels for
 different biomarkers. * Centroid X px: X-coordinate in the ROI (unit:
 pixels). * Centroid Y px: Y-coordinate in the ROI (unit: pixels). *
 Cell/Nucleus/Cytoplasm CD20/Ki67/CD68/CD8a/CD4/CD34 mean: The measured
 fluorescence intensity value for a representative area selected for
 different biomarkers on each cell. * multi-channel: Single-cell data
 measured within the microchannels for different diagnostic biomarkers in
 the PCNSL sample. ## Code Description This repository contains all Python
 (Data Analysis) and MATLAB (Image Registration) code used in this study.
 Each code package (DataAnalysis.zip, ImageRegistration.zip) contains its
 own detailed README.md and requirements.txt file. ### 1. Data Analysis
 (Python) This section includes Python scripts used to generate the
 analyses for Figures 4, 6, and 7. #### 1.1 Figure 4: Tonsil
 Reproducibility Analysis This pipeline (scripts 1\_ to 6_) assesses the
 reproducibility of the 6-plex mIF experiment across three serial tonsil
 sections. * **Workflow:** 1. **Preprocessing
 (1_Data_preprocessing_QC.py):** Loads 3tonsils.xlsx, merges samples, and
 applies per-sample Z-score normalization. 2. **Clustering
 (2_phenograph_3sections.py):** Runs Phenograph clustering and t-SNE on the
 combined, normalized data. 3. **Data Prep (4_spatial_analysis.py):**
 Creates a master file merging coordinates, cluster IDs (from Step 2), and
 threshold-based 'Phenotype' labels. 4. **Spatial Analysis
 (5_cluster_based.py, 6_phenotype_based.py):** Performs a dual-approach
 spatial consistency analysis using both 'Cluster' and
 'Phenotype' labels. It calculates neighborhood enrichment and
 quantifies similarity (Cosine, Jaccard) between samples. #### 1.2 Figure
 6: PCNSL Clustering & Spatial Statistics This pipeline analyzes
 clustering and spatial statistics for the PCNSL sample. * **Workflow:** 1.
 **Clustering (1_PhenographPCNSL.py):** Loads and normalizes PCNSL data,
 then runs Phenograph clustering (k = 30) and Barnes-Hut t-SNE. 2.
 **Visualization (2_cell_populations.py, 3_marker_intensity.py):**
 Generates t-SNE plots colored by annotated cell type and by individual
 marker intensity. 3. **Spatial Statistics (4_Ripley's_K...py,
 5_Monte_Carlo.py):** Calculates various spatial statistics, including
 Cross Ripley's K (with Monte Carlo simulation) and Moran's I, to
 analyze cell-cell spatial relationships (e.g., CD8+ vs. CD20+/CD34+). 4.
 **Annotation (6_clustermap_annotation.py):** Creates an annotated heatmap
 of mean marker expression per cluster, visualized alongside cell counts.
 #### 1.3 Figure 7: PCNSL 16-Plex Diagnostic Strip Analysis This is a
 complex pipeline that aligns data from 16 diagnostic "strips"
 onto a single "whole image" coordinate scaffold for integrated
 spatial analysis. * **Workflow:** 1. **Preprocessing
 (1_data_preprocessing.py):** The core script. Loads the Whole image.xlsx
 as a coordinate scaffold. It then loads all 16 strip{Marker}.xlsx files
 and uses a k-d tree (cKDTree) to spatially match and transfer annotations
 (cell classifications) to the master scaffold. 2. **Global Analysis
 (2_global_analysis.py, 3_global_anlaysis_advanced.py):** Performs global
 spatial analyses (KDE density maps, Correlograms, NND, PCF) on the aligned
 master dataset. 3. **Core Statistics (4_spatial_analysis_full.py):** The
 main statistical script. It iterates through each of the 16 diagnostic
 markers, performing parallelized permutation tests to quantify spatial
 neighborhood enrichment/avoidance against key environmental markers (e.g.,
 CD8a+, CD68+). 4. **Visualization (5_visualization.py,
 6_doulble_positive...py):** Generates the final summary figures from the
 statistical output, including the primary Bubble Heatmap, volcano plots,
 and dumbbell plots comparing double-positive (e.g., CD20+/Bcl-6+) vs.
 single-positive cells. #### 1.4 Python Dependencies All Python scripts
 require Python 3.x. The specific libraries for each analysis (e.g.,
 pandas, numpy, seaborn, scikit-learn, phenograph, libpysal) are listed in
 the requirements.txt file within each respective folder. ### 2. Image
 Registration (MATLAB) This package contains MATLAB scripts for
 feature-based geometric transformation and image registration. * **System
 Requirements:** * MATLAB R2020b or newer (tested on R2020b). * Image
 Processing Toolbox. * **Workflow:** 1. **Entry Point (main.m):** This is
 the main script to run the registration pipeline. 2. **Parameter Setup
 (SetParameter.m):** A dialog box prompts the user to define parameters,
 such as block size, strip numbers (StripNum), and Gaussian sigma. 3.
 **Preprocessing (function/OnlySaveStripInfo.m,
 function/MakeStripAndOverallInSameSize.m):** These functions isolate the
 relevant "strip" regions from the full image, handle rough
 alignment using phase correlation, and ensure the fixed and moving images
 are the same size. 4. **Registration (function/MakeRegister.m,
 function/MakeRegister1.m):** Implements the core block-based registration.
 It matches features (e.g., KAZE, SIFT, BRISK, corner) within local blocks.
 MakeRegister1.m includes an alternative path using Mutual Information (MI)
 to guide the matching strategy. 5. **Finalization:** The script maps all
 successful local feature matches to the global coordinate space and
 estimates a final geometric transformation (T_global), which is then
 applied to the moving image to create the registered TIFF file. *
 **Detailed instructions** and descriptions of all helper functions are
 available in the README.md file within the RegistrationCode directory. ##
 Authors *Bing Shi Hainan University
 [shibing@hainanu.edu](mailto:shibing@hainanu.edu) *Zhen-li Huang Hainan
 University [huang2020@hainanu.edu](mailto:huang2020@hainanu.edu) ## Human
 subjects data The use of human samples in this study was approved by by
 the Biomedical Ethics Committee of Haikou People's Hospital (Approval
 No. 2025-(Ethics)-073). The Biomedical Ethics Committee waived the
 requirement for informed consent due to the de-identified retrospective
 nature of Data were de-identified.