A spatially resolved timeline of the human maternal-fetal interface

Data files provideddenoised.zip,denoised1.zip: denoised MIBI images for the entire cohort. The images are divided into two archive files due to uploading file size limitations - we apologize for the inconvenience. The files are organized by field of view (FOV), with each FOV subfolder containing TIF images for each channel that have undergone low-level processing as described in the methods section “Low-level image processing”.mask_labeled.zip: labeled feature annotations for the entire cohort. These are organized by FOV, with each FOV subfolder containing individual TIFs for each annotated feature (arteries, vessels, glands) in the image (see Methods section “Feature annotation”). Features in each TIF have been assigned a unique label for downstream analyses.lineage_CPMs_for_cohort.zip: contains cell phenotype map (CPM) pseudo color overlays for the entire cohort. These are labeled by FOV, and each FOV shows the cell segmentation mask colored by cell lineage assignment (see Methods).cell_cell_and_cell_artery_spatial_enrichment_per_image.zip - cell-cell and cell-artery enrichment z-scores per image (see Methods)Single_cells_MIBI.csv.zip - a table enumerating all single cells in this study and provides their location, morphological characteristics (such as size and shape), marker expression, FlowSOM cluster assignment and cell type assignmentSegmentation.zip: segmentation results per FOV, pixels value in each cell correspond to the cell label in the single cell table. CodeThe code in the “Code” folder is divided into three subfolders as follows:Digitized artery morphology - MATLAB code for digitized measurement of artery morphological features from MIBI images and corresponding artery masks (see Methods section “Automated digitization of artery morphological features”). The code requires user input - marking the center of each analyzed artery via GUI. For running this code, download data folders mask_labeled.zip then download and recombine into a single folder the content of the archive files denoised.zip, denoised1.zip. Required additional software: MAUI - freely available at https://github.com/angelolab/MAUI. Start by opening the code file “MAIN_script_start_here.m”, further running instructions are at the head of this file. For expected output see Supplemental Table 3 in the manuscript.Remodeling score delta calculation - MATLAB and R code for calculating the remodeling score per artery (see Methods section “Calculation of continuous SAR remodeling score δ”). Start by opening the code file “MAIN_script_Prepare_LDA_table_calc_delta.m”, further running instructions are at the head of this file. The input table for this code is inside it's subfolder (artery properties table generated by code in #1 above- artery_staging_input.mat) and none of the data folders are required to run. For expected output see Supplemental Table 3 in the manuscript.Code for generating LDA of EVT by compartment (see Methods) - R code for calculating ld1 per EVT, see Methods section “LDA of EVTs by compartment”). The input table for this code - z-scored marker expression in all EVT (EVT_markers_for_LDA,csv), is inside it's subfolder and none of the data folders are required to run. For expected output see Supplemental Tables 19,20 in the manuscript.MATLAB code was written and tested on MATLAB 2020b and MAC OS Catalina Version 10.15.7. R code was tested on R version 4.0.3 MAC OS Catalina Version 10.15.7, code was written using the following versions:R 3.5.1RStudio 1.1.463MASS_7.3-51.5dplyr_0.8.3data.table_1.12.8Estimated runtime for code in above enumerated folders:Depends on manual user input, estimated at a few minutes per arteryMATLAB parts run instantaneously while R script for LDA can take approximately 30 minutes.R script for LDA can take approximately 30 minutes.

提供的数据文件包括denoised.zip和denoised1.zip：这些文件包含整个队列的去噪MIBI图像。由于上传文件大小的限制，图像被分为两个归档文件，对此带来的不便我们表示歉意。文件按照视场（FOV）组织，每个FOV子文件夹包含经过所述方法部分“低级图像处理”描述的低级处理后的每个通道的TIF图像.mask_labeled.zip：包含整个队列的特征标注。这些标注按照FOV组织，每个FOV子文件夹包含图像中每个标注特征（如动脉、血管、腺体）的单独TIF图像（参见方法部分“特征标注”）。每个TIF中的特征已被分配一个唯一的标签，以便进行下游分析.lineage_CPMs_for_cohort.zip：包含整个队列的细胞表型图（CPM）伪彩色叠加。这些叠加按照FOV标注，每个FOV展示了通过细胞谱系分配着色的细胞分割掩码（参见方法）。cell_cell_and_cell_artery_spatial_enrichment_per_image.zip：包含每张图像的细胞-细胞和细胞-动脉富集z分数（参见方法）。Single_cells_MIBI.csv.zip：一个表格，列出了本研究中的所有单个细胞，并提供了它们的位置、形态学特征（如大小和形状）、标记表达、FlowSOM聚类分配和细胞类型分配.Segmentation.zip：每个FOV的分割结果，每个细胞中的像素值对应于单细胞表中的细胞标签。代码“代码”文件夹中的代码分为三个子文件夹，具体如下：

- 数字化动脉形态学 - 用于从MIBI图像及其对应的动脉掩码中数字化测量动脉形态学特征的MATLAB代码（参见方法部分“动脉形态学特征的自动化数字化”）。该代码需要用户输入，通过GUI标记每个分析的动脉中心。运行此代码，请先下载数据文件夹mask_labeled.zip，然后下载并重新组合denoised.zip和denoised1.zip的内容到单个文件夹中。所需的附加软件：MAUI - 可在https://github.com/angelolab/MAUI免费获取。首先打开代码文件“MAIN_script_start_here.m”，进一步的运行说明位于此文件的头部。预期输出请参见手稿的补充表3。

- 重塑分数变化计算 - 用于计算每个动脉重塑分数的MATLAB和R代码（参见方法部分“连续SAR重塑分数δ的计算”）。首先打开代码文件“MAIN_script_Prepare_LDA_table_calc_delta.m”，进一步的运行说明位于此文件的头部。此代码的输入表位于其子文件夹中（由上述代码生成的动脉属性表 - artery_staging_input.mat），运行此代码不需要任何数据文件夹。预期输出请参见手稿的补充表3。

- 按隔室生成EVT的LDA代码 - 用于计算每个EVT的ld1的R代码（参见方法部分“按隔室EVT的LDA”）。此代码的输入表位于其子文件夹中（所有EVT的z分数标记表达 - EVT_markers_for_LDA.csv），运行此代码不需要任何数据文件夹。预期输出请参见手稿的补充表19、20。

MATLAB代码在MATLAB 2020b和MAC OS Catalina版本10.15.7上编写和测试。R代码在R版本4.0.3和MAC OS Catalina版本10.15.7上测试，代码编写使用了以下版本：R 3.5.1、RStudio 1.1.463、MASS_7.3-51.5、dplyr_0.8.3、data.table_1.12.8。上述列举的文件夹中代码的估计运行时间：

- 取决于手动用户输入，每条动脉估计需要几分钟。

MATLAB部分运行瞬时，而R脚本中的LDA可能需要大约30分钟。