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Angeline Lim, PhD | Applications Scientist | Molecular Devices
Christopher Nishioka | Field Applications Scientist | Molecular Devices
Misha Bashkurov | Product Owner | Molecular Devices

Introduction

High-content phenotypic profiling is increasingly popular in research areas that span from gene function studies, drug discovery, and toxicology. The strength in this approach lies in the unbiased, multidimensional information captured at single cell resolution which enables individual cell states to be analyzed, profiled, and compared to population-level data. This profiling approach is in contrast to the more common phenotypic screens where only a few parameters are selected a priori, typically due to significance in a pathway of interest¹. As a result, most of the biologically relevant changes caused by experimental treatments are often disregarded in conventional phenotypic screens.

The phenotypic profile of a cell is made up of hundreds to thousands of quantifiable features that are derived from the cell state. These features can include information extracted from biomarker expression, texture, and distribution. Often, these markers are specific to organelles and give information about their structure, spatial organization, and relationships to other sub-cellular structures. The assortment of information from all cells allows for an unbiased approach in studying effects of chemicals, small molecules, or genetic perturbations. Compounds with similar mechanisms of action (MOA) often lead to similar changes in cell morphology. Thus, comparisons between phenotypic profiles can provide insights into MOA for novel compounds3. Similarly, genetic perturbations in the same pathway often lead to similar phenotypic profiles, which suggests that phenotypic profiling can be used in high-throughput functional genomics studies⁷.

The Cell Painting assay, often used in phenotypic profiling, uses up to six fluorescent dyes to label and visualize a variety of subcellular structures at the single cell level. The aim of this assay is to visualize as much of the cell as possible in order to construct a representative image of cell state. The standard set of dyes for the Cell Painting assay labels the nuclei, endoplasmic reticulum, actin, Golgi apparatus, RNA (and nucleoli), as well as mitochondria. High-content cellular imaging systems equipped with suitable filter sets are used to rapidly acquire images of the fluorescently-labeled cells. This is followed by automated image analysis to identify, extract, and measure specific cellular features. The resulting set of measurements comprise the phenotypic profile, which can be further analyzed and used for hit picking or cluster analysis (Figure 1).

***Figure 1.*Typical Cell Painting assay workflow. The Cell Painting assay is a morphological profiling tool that generates multiparametric profiles at single cell resolution. It is highly versatile and can be adapted to different cell lines. A standard workflow will include seeding cells at suitable density followed by the desired treatment or perturbation. Next, various cellular structures are stained with the appropriate dyes and imaged using a high-content imaging system. Image analysis provides hundreds to thousands of measurements which form the cells’ phenotypic/morphological profile.

Here, we show a high-content phenotypic profiling workflow based on the Cell Painting protocol by Gustafsdottir et al³. This workflow utilizes easy to use tools without sacrificing quality – image segmentation and measurements are performed within IN Carta^™Image Analysis Software and data analysis in HC StratoMiner^™. Within IN Carta software, the image analysis routine can be adjusted to achieve robust detection of cells and organelles. The deep learning semantic segmentation module (SINAP) can be used to improve detection of challenging features. Pre-trained deep learning models are available for the detection of nuclei or cells. In addition, users can train their own models based on their specific object of interest with their own dataset. HC StratoMineR is a web-based tool developed for processing large multi-dimensional datasets. The platform features a guided step-by-step workflow for high-content data analysis. Because HC StratoMiner functions as a webbased tool accessible through a web browser, users do not need additional computational resources that is often required to process large datasets. Using this workflow, we find that cells treated with the same compound show similar phenotypic profiles. Hierarchical clustering analysis grouped highly toxic compounds such as paclitaxel and rotenone together. Chloroquine and tetrandrine, both of which affect autophagy,^2,4were also found in the same cluster. These results demonstrate that the proposed workflow is a user-friendly and robust approach for performing high-content phenotypic profiling.

Methods

Cell culture

U2OS cell line (ATCC) was passaged and maintained according to the manufacturer’s recommendations. The Cell Painting assay was performed according to Bray et al1. Briefly, U2OS cells were seeded in Greiner 384-well μClear plates at 2000 cells per well in a total of 40 μL of McCoy media (supplemented with 10% FBS). Cells were incubated at 37°C for 24 hours before compound treatment.

The culture medium was replaced with 2% (vol/vol) FBS in McCoy 24 hours after seeding and before compounds were added. The following 11 compounds were used here: Ca-074-Me, CCCP, chloroquine (Enzo), cytochalasin D, etoposide (Calbiochem), latrunculin B, rapamycin (Sigma), rotenone (Enzo), staurosporine, paclitaxel, and tetrandrine (unless indicated, all compounds were purchased from SeleckChem). Compounds were tested in quadruplicate wells in a seven point, 1:3 dilution series. DMSO controls, negative, and positive controls were included in the same plate. Cells were incubated with the compounds for 24 hours.

Staining

Live cells were stained with MitoTracker DeepRed (500 nM) for 30 min in the dark at 37°C, then fixed with PFA (3.2% vol/vol) for 20 min. Cells were washed and then permeabilized with triton-100 (0.1%) at room temperature for 20 min. Staining solution was prepared at the following concentrations: 5 μL/mL phalloidin, 100 μg/mL concanavalin A, 5 μg/mL Hoechst, 1.5 μg/mL WGA and 3 μM SYTO 14 dye in blocking solution (1X HBSS and 1% wt/vol BSA). Cells were washed and incubated with the staining solution for 30 min at room temperature. Staining solution was removed and cells were washed three times and then sealed with adhesive foil. All wash steps were performed with 1X HBSS.

Image acquisition

Images were acquired using the ImageXpress® Micro Confocal High-Content Imaging System (Molecular Devices) using the 20X Plan Apo objective, confocal pinhole size at 60 μm. The following filters were used (ex/em): DAPI 377/447, FITC 475/536, TRITC 543/593, TexasRed 560/624, Cy5 631/692. Four field of views were imaged per well. A small Z-stack of three images were acquired with best focus projection option used to account for plate flatness issues which may compromise image focus.

Feature extraction

Image analysis was carried out using IN Carta software. The image segmentation protocol was set up as follows: Hoechst-stained nuclei were segmented as a primary target using the custom segmentation (pre-trained Nuclei model). Objects touching edges were excluded. Cells were segmented within TRITC channel using the Robust option. Three additional organelle classes were segmented with the indicated option: Mitochondria (networks), actin (fibers) and endoplasmic reticulum (networks). A total of two hundred eighty measurements per cell were selected as an output.

Data analysis

After feature extraction was completed, cell level data was exported into csv format and uploaded into HC StratomineR (CoreLife Analytics) along with a text file that contains metadata with compound information. The plate map was defined within the StratoMineR interface. Using the Quality Control tab, outlier wells were removed from analysis (wells with less than 50 cells were removed). Data transformation was performed as recommended followed by feature scaling. Principal component analysis (PCA) was used for data reduction. The resulting 15 components were used to calculate the distance score which is defined as a measure of phenotypic effect of the treatment on the cells in those wells6. The distance score can then be used in hit selection and hierarchical clustering.

Results

Cells were treated with 11 compounds, some of which have been used in previous Cell Painting studies as reference compounds⁵. Following compound treatment, cells were stained for mitochondria, actin, the Golgi apparatus, nucleoli (RNA particles), endoplasmic reticulum (ER), and the nuclei (Figure 2).

***Figure 2.*Cell Painting assay. Cells were compound treated, stained, then imaged using the ImageXpress Micro Confocal system. Example images of each acquired channel from a control well is shown. The last panel shows a composite image consisting of actin, ER (endoplasmic reticulum), and nuclei staining.

IN Carta software was used to extract all the various cellular features from each detected cell. The software has A. an intuitive user interface B. provides measurements that are important for phenotypic profiling including intensity, texture, shape, spatial coordination (such as spatial relationships between organelles), and co-localization C. robust feature identification with the deep learning semantic segmentation module (SINAP) that allows for unbiased feature segmentation. These models can be further trained on images based on user specific objects of interest.

The SINAP module was used in the analysis protocol to improve nuclei identification (Figure 3). The built-in nuclei model was trained on over 1000 images. Training set included nuclei acquired using imaging modalities (fluorescence and brightfield) at different magnifications and stained with different dyes (DAPI, Hoechst, Hematoxylin/Eosin), therefore making it suitable for use on a large variety of images. During image analysis set up, we observe that the nuclei model gives improved nuclei detection and was able to accurately split touching nuclei. The SYTO14 stain was used to define the cytoplasm using the ‘Robust’ segmentation method. The ER, mitochondria, and actin were also segmented and analyzed. In addition, overall intensity measurements were taken from all channels in both the whole cell, nuclei and cytoplasm.

***Figure 3.*Feature extraction in IN Carta software The analysis protocol was created in the IN Carta software to segment the various cellular structures. Here, we used the built-in nuclei model to achieve robust segmentation of nuclei across all treatments. Other cellular features such as the cytoplasmic compartment, actin filament network, ER, and mitochondria were also segmented. Measurements related to intensity, texture, colocalization and shape are selected during the analysis setup. We selected a total of 280 measurements for cells and subcellular structures in the protocol. A) A screen capture of the analysis protocol used. Output of the analysis is selected under “Measures”. Shown here are some of the measurements available under the “Spatial” category. B) Example images with overlay of the feature mask in the IN Carta software.

The analyzed features can be easily reviewed within IN Carta software. Options such as heatmap display and histograms are available for general data exploration. Cell-by-cell or summary data can be exported as a csv file for downstream data mining and analysis (Figure 4).

***Figure 4.*Data view in IN Carta The software has various visualization tools for data exploration. A) Displayed measurements are linked to the object in the image and can be selected by clicking on a line in the measurement table or on the object/cell in the image view panel. The selected object here is shown in white overlay mask. B) Options such as heatmap display and histograms are available. The selected measurement can be viewed as a heat map across the entire plate. The heatmap scale can be adjusted so that only a selected range of values are displayed. Histogram display is also available for easy view of the distribution of the selected measurement. Measurements can be exported into csv format for further data analysis.

Data analysis

Measurements obtained from IN Carta software were exported and then uploaded into HC StratoMiner for further data analysis. Here, we used HC StratoMineR (a web-based high-content dataset tool), due to its ease-of-use, where the guided platform allows non datascience experts to navigate the data analysis workflow. Additionally, because HC StratoMineR is a web-based tool, there is no need for the end user to configure and set up additional computational resources required for analysis of large and complex datasets.

Principal component analysis (generalized weighted least squares) was used to reduce the 280 measurements into 15 components. Hierarchical clustering was then carried out, and the relationship between each well represented in a dendogram (Figure 5). Cells treated with the same compounds tend to be clustered together. For example, cluster 9 is made up of only staurosporine treated cells. Compounds known to have similar cellular effects were also clustered together. Cytochalasin D and latrunculin B, both actin polymerization inhibitors were found in cluster 6. Tetrandrine and chloroquine, both known to affect the autophagy pathway, were also clustered together (cluster 5). These results demonstrate that the proposed workflow is a feasible, and easy to implement approach for performing high-content phenotypic profiling.

***Figure 5.*Cluster analysis. A) A dendrogram that represents the hierarchical relationships is shown. Wells belonging to the same cluster (numbered) are represented by colored bars. P-values based on the distance score are shown for each well. B) Examples of compound treated cells belonging to the same clusters are shown. Cluster 5 consists of tetrandrine and chloroquine treated cells. Note the increased number of ER punctae in both wells. Cluster 4 consists of rotenone and paclitaxel treated cells. Note the presence of blebbing in some of the cells belonging in these wells, suggesting cytotoxic effects.

Conclusion

• The ImageXpress Micro Confocal system is suitable for imaging high-content, Cell Painting assays.
• The IN Carta software combines ease-of-use with advanced feature segmentation options (SINAP) to facilitate robust and unbiased identification of cellular features.
• The StratoMineR platform allows non-expert users to rapidly carry out phenotypic data analysis with its intuitive guided workflow.
• Overall, our results show the feasibility of using ImageXpress Micro Confocal system, IN Carta software, and StratoMineR for image-based profiling assays.

References

1. Bray MA, Singh S, Han H, Davis CT, Borgeson B, Hartland C, Kost-Alimova M, Gustafsdottir SM, Gibson CC, Carpenter AE. Cell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes. Nat Protoc. 2016 Sep;11(9):1757-74. doi: 10.1038/ nprot.2016.105. Epub 2016 Aug 25. PMID: 27560178; PMCID: PMC5223290.
2. Gong K, Chen C, Zhan Y, Chen Y, Huang Z, Li W. Autophagy-related gene 7 (ATG7) and reactive oxygen species/extracellular signal-regulated kinase regulate tetrandrine-induced autophagy in human hepatocellular carcinoma. J Biol Chem. 2012 Oct 12;287(42):35576-88. doi: 10.1074/jbc.M112.370585. Epub 2012 Aug 27. PMID: 22927446; PMCID: PMC3471698.
3. Gustafsdottir SM, Ljosa V, Sokolnicki KL, Anthony Wilson J, Walpita D, Kemp MM, Petri Seiler K, Carrel HA, Golub TR, Schreiber SL, Clemons PA, Carpenter AE, Shamji AF. Multiplex cytological profiling assay to measure diverse cellular states. PLoS One. 2013 Dec 2;8(12):e80999. doi: 10.1371/journal. pone.0080999. PMID: 24312513; PMCID: PMC3847047.
4. Mauthe M, Orhon I, Rocchi C, Zhou X, Luhr M, Hijlkema KJ, Coppes RP, Engedal N, Mari M, Reggiori F. Chloroquine inhibits autophagic flux by decreasing autophagosomelysosome fusion. Autophagy. 2018;14(8):1435-1455. doi: 10.1080/15548627.2018.1474314. Epub 2018 Jul 20. PMID: 29940786; PMCID: PMC6103682.
5. Nyffeler J, Willis C, Lougee R, Richard A, Paul-Friedman K, Harrill JA. Bioactivity screening of environmental chemicals using imaging-based high-throughput phenotypic profiling. *Toxicol Appl Pharmacol.*2020 Jan 15;389:114876. doi: 10.1016/j.taap.2019.114876. Epub 2019 Dec 30. PMID: 31899216.
6. Omta WA, van Heesbeen RG, Pagliero RJ, van der Velden LM, Lelieveld D, Nellen M, Kramer M, Yeong M, Saeidi AM, Medema RH, Spruit M, Brinkkemper S, Klumperman J, Egan DA. HC StratoMineR: A Web-Based Tool for the Rapid Analysis of High-Content Datasets. Assay Drug Dev Technol. 2016 Oct;14(8):439-452. doi: 10.1089/adt.2016.726. Epub 2016 Sep 16. PMID: 27636821.
7. Rohban MH, Singh S, Wu X, Berthet JB, Bray MA, Shrestha Y, Varelas X, Boehm JS, Carpenter AE. Systematic morphological profiling of human gene and allele function via Cell Painting. Elife. 2017 Mar 18;6:e24060. doi: 10.7554/ eLife.24060. PMID: 28315521; PMCID: PMC5386591.

Angeline Lim, PhD | Applications Scientist | Molecular Devices
Christopher Nishioka | Field Applications Scientist | Molecular Devices
Misha Bashkurov | Product Owner | Molecular Devices

背景介绍

高内涵表型分析越来越广泛应用于许多的研究领域，包 括基因功能研究、药物研发和毒理学。这种方法的优点 是非常客观的从单细水平获取多维的信息，能够对单个细 胞的状态进行分析和描述，并与总体分析数据进行比较。 而许多传统的表型分析则是在要研究的通路中，只主观 选取少数几个重要的指标来分析 1 。 因此，在传统的表型 分析中，大多数因实验处理导致的生物相关的变化容易 被忽略掉。

一个细胞的表型分析是由成千上万个表征细胞状态的可 量化指标组成的。这些指标包含从生物标识物表达、结 构和分布抽提出来的信息。通常，这些标记是细胞器特 异性识别的，并提供有关其结构、空间上与其他亚细胞 结构的关系的信息。这些细胞的分类信息能够在研究化 合物、小分子物质或基因扰动时提供客户有效的分析依 据。具有相似作用机制的化合物 (MOA) 常常导致相似的 细胞形态变化。因此，表型谱之间的比较可以为新化合 物的 MOA 提供依据 3 。同样地，同一途径中的遗传干扰 常常导致相似的表型谱，这说明表型谱可用于高通量功 能基因组学研究 7 。

细胞绘画分析，通常用于表型分析，使用多达六种荧光染 料来标记和显示单个细胞水平上的各种亚细胞结构。该 分析的目的是尽可能多地观察分析细胞，以构建表明细胞 状态的代表性图像。用于细胞绘画分析的标准染料组可 以标记细胞核、内质网、肌动蛋白、高尔基体、RNA( 和 核仁 ) 以及线粒体。高内涵细胞成像系统配备有合适的 滤片组，用于快速获取荧光标记的细胞图像。然后进行 自动图像分析，以识别、提取和测量特定的细胞特征。 这些包含表型谱的大量的参数能够被进一步分析并用于 针对性选择或聚类分析 (Figure 1)。

***Figure 1 典型细胞绘画分析工作流程。*细胞绘画分析是一种形态分析工具，它从单细胞水平生成多参数分析。它是高度通用的，可以适应不同的细胞 系。一个标准的工作流程是以适当的密度播种细胞，然后进行所需的处理或生物扰动。接下来用适当的染料染色各种细胞结构并使用高含量成像系统成 像。图像分析提供了成百上千的测量数据，形成了细胞的表型 / 形态轮廓的描述。

这 里，我 们 展 示 一 个 基 于 Gustafsdottir et al3 细胞绘 画方法的高内涵表型分析工作流程。该工作流程使用 IN CartaTM 图像分析软件和 HC StratoMinerTM 数据分析， 使整个分析易于且具有不牺牲质量的图像分割和测量工 具。在 IN Carta 软件中，图像分析程序可根据不同的成 像情况进行参数调整，以实现对细胞和细胞器的准确检 测。 其中深度学习分割模块 (SINAP) 可以用来提高对具 有挑战性特征的检测的分析准确性。该模块可用于检测 细胞核或其他的细胞参数。另外，用户可以使用自己某类 实验的数据集来训练深度学习分割模块 (SINAP)，使其成 为分析此类实验的专业分析模块。HC StratoMineR 是一 个基于网络的平台工具，用于处理大型多维数据集。该 平台具有引导式的工作流程用于分析高内涵的数据分析。 正因为 HC StratoMiner 是通过浏览器访问的网络工具， 因此用户不需要占用额外的计算机资源就可以处理大型 数据集。使用这个工作流程，同样的化合物处理的细胞 分析出的表型特征是相似的。 例如，层次聚类分析将紫 杉醇和鱼藤酮等剧毒化合物分组。同一分类中，氯喹和 汉防己甲素都影响自噬 2，4。这些结果证明，这个工作流 程是一个用户友好和准确强大的用于高内容表型分析的 方法。

实验方法

细胞培养

根据制造商提供的方法，对 U2OS 细胞系 (ATCC) 进行传 代和培养。根据 Bray et al1 提供的方案来实施细胞绘画 分析方法。简单地说，将 U2OS 细胞接种在 Greiner 384 孔 (384-well µClear® plates) 透明板中，每孔 2000 个细 胞，共 40 µL McCoy 培养基 ( 补充 10% FBS )。药物处 理前，细胞在 37℃ 培养 24 小时。

在细胞铺板后 24 小时并在添加化合物之前，使用 2% FBS ( 体 积 / 体 积 ) 含 量 的 McCoy 培 养 液 替 换 之 前 的 细 胞 培 养 基。以 下 列 举 11 种 需 要 用 到 的 化 合 物 : Ca074-Me, CCCP, chloroquine (Enzo), cytochalasin D, etoposide(Calbiochem), latrunculin B, rapamycin (Sigma), rotenone (Enzo), staurosporine, paclitaxel, and tetrandrine ( 除 非 另 有 说 明，所 有 化 合 物 均 购 自 SeleckChem )。化合物分别按照 1:3 的梯度稀释设置 7 个不同的浓度处理细胞，每个化合物浓度处理的样本设置 4 个检测复孔。在同一块检测板中同时设置 DMSO 对照 组，阴性对照组和阳性对照组。化合物处理的细胞孵育时 间为 24 小时。

细胞染色

活细胞使 用 MitoTracker DeepRed ( 终浓度 500nM) 在 暗 室 中 37 ℃ 孵 育 30 min， 然 后 使 用 PFA (3.2% vol/ vol) 细 胞 固 定 反 应 20 min。在 室 温下使 用 triton-100 (0.1%) 孵育细胞 20min。染色液配置成以下的浓度：5 µL/mL phalloidin, 100 µg/mL concanavalin A, 5 µg/ mL Hoechst, 1.5 µg/mL WGA and 3 µM SYTO 14 dye ， 溶于封闭液体系 (1X HBSS and 1% wt/vol BSA)。在室 温下，洗涤细胞并加入染色液孵育 30min。染色完毕后， 移除染色液并洗涤细胞 3 次，然后用锡箔纸包裹避光保 存。所有的细胞洗涤步骤均使用 1X HBSS 溶液。

细胞成像

使 用 ImageXpress® Micro Confocal 高 内 涵 成 像 系 统 (Molecular Devices) 进 行 细 胞 成 像。 成 像 使 用 20X Plan Apo 物镜 , 共聚焦针孔尺寸 60 µm； 所有荧光通 道 的 激 发 和 发 射 波 段 信 息 为 (ex/em): DAPI 377/447, FITC 475/536, TRITC 543/593, TexasRed 560/624, Cy5 631/692。 每孔设置 4 个成像视野。 利用最佳聚焦投影 方式获得了一个由三幅图像组成的小 Z 轴叠层图像， 以 解决可能影响图像聚焦的板形问题。

特征提取

利用 IN Carta 软件进行图像分析。 图像分割方法如下： 以 Hoechst 染色细胞核为主要目标，采用自定义分割算 法对图像中染色目标进行分割 ( 预染为细胞核的模型 )， 接触图像边缘的目标被排除在计算中。TRITC 通道使用 Robust 选项对细胞进行分割。三个额外的细胞器分类 方 法 使 用 指 定 选 项 : Mitochondria (networks), actin (fibers) and endoplasmic reticulum (networks)。每个 细胞总共可以选择 280 个测量值作为数据输出。

数据分析

在特征提取完毕后，单个细胞水平的数据被导出成 CSV 格式的文件并和包含有化合物信息的高内涵数据的 text 文件一起上传到 HC StratomineR (CoreLife Analytics)。 孔板预览导航图是使用 StratoMineR 界面来定义的。使 用质量控制工具，将状态异常的孔从分析中去除 ( 被移除 的异常孔少于 50 个 )。软件通过特征缩放的建议进行数 据转换。 主要成分分析 (PCA) 用于数据整理。由于产生的 15 个成分用以评估模型的距离评价，是用来衡量不同处 理下细胞的表型效应程度 6 。模型的距离评价可用于针对 性选择或聚类分析。

结果

实验共使用 11 种化合物处理细胞，其中一些已经在先 前的细胞绘画研究中用作参考化合物 5 。化合物孵育处理 后，对细胞中相应细胞器进行染色，包括线粒体、肌动 蛋白、高尔基体、核仁 (RNA 颗粒 )、内质网 (ER) 和细胞 核 (Figure 2)。

***Figure 2 细胞绘画分析。*细胞经过化合物处理、染色后，用 ImageXpress 共聚焦系统进行成像 . 示例图中展示了对照样本孔中每一个通道的图像。最 后一幅图显示了由肌动蛋白，ER（ 内质网 ）和细胞核染色组成的复合图像。

使用 IN Carta 软件从每个检测到的细胞中提取所有不同 类别细胞的细胞特征。这个软件拥有：A. 直观的用户界 面；B. 提供对表型分析很重要的测量数据，包括强度、纹 理、形状、空间协调 ( 如细胞器之间的空间关系 ) 和共定 位；C. 使用深度学习语义分割模块 (SINAP) 进行全面的特 征识别，该模块可以进行非常客观的特征分割。这些模 型能够通过图像进一步训练软件来自主识别用户感兴趣 的目标。

深 度学习分割 模 块 (SINAP) 加入 分析 流 程 用于改善 细 胞核的识别 (Figure 3)。内置的细胞核分析方法是在超 过 1000 多张细胞核图像的识别中建立起来的自主识别 方法。训 练 集包括 使 用不同放 大 倍 数的成像模 式 ( 荧 光 和 明 场 ) 获 得 的 细 胞 核，并用 不 同 的 染 料 ( DAPI， Hoechst，苏 木 精 / 曙红 ) 染色，因此使 其适用于各种 各样的图像。在这种方法的自学习建立过程中，图像集 包含了明场和荧光成像模式下不同放大倍率和各种染 色 (DAPI, Hoechst, Hematoxylin/Eosin) 的 情 况， 因 此 该方法能够适用于各种各样的图像分析。在图像分析设 置期间，我们能够给观察到该核模型分析方法提供了改 进的细胞核检测并且能够准确地分割相接触的细胞核 。 SYTO14 染色标识细胞质的样本使用 Robust 选项的细 胞分割方法来进行分析。实验同时还对内质网，线粒体 和肌动蛋白进行了识别和分析。另外，实验还从整个细 胞，细胞核和细胞质的所有通道进行总体强度的测量。

***Figure 3 IN Carta 软件中的特征提取。*在 IN Carta 软件中创建分析方法用于分割各种细胞结构。在这里，我们使用内置的细胞核模型来实现所有处 理中细胞核的准确分割。其他细胞特征，如细胞质室，肌动蛋白丝网络，内质网和线粒体也被指定模式分割。 在分析设置期间选择与强度，纹理，共定位 和形状相关的测量数据。我们总共选择了 280 个细胞和亚细胞结构的测量指标。A) 分析方法设置的屏幕截图。在“measurements”下选择要输出的数 据类别，图中展示的是在“Spatial”选项下可用分析项。B) 在 IN Carta 软件中样品图象的识别标识。

对特征的分析能够容易的在 IN Carta 软件中进行预览。 热图显示和直方图等选项可用于一般数据探索。单个 细胞的各项分析数据或整个样本孔的整体数据均可以以 CSV 格式的文件导出，并可用于进一步的数据挖掘和分 析 (Figure 4)。

***Figure 4 IN Carta 软件中的数据显示。*这个软件拥有多种可视化的工具用于对数据的探索。A) 显示的测量值链接到图像中的对象，可以通过单击测量 表中的一条线或图像视图面板中的对象 / 单元格来进行选择。 被选中的目标在途中使用白色的覆盖标识显示出来。 B) 热图和直方图等选项可用。被选 择的参数可在孔板示意图中使用热度显示出来。可以调整热图比例，以便仅显示选定的值范围。直方图显示也可用于轻松查看所选测量的分布情况。 测 量可以导出为 csv 格式以进行进一步的数据分析。

数据分析

从 IN Carta 软件获取的测量参数能够被导出并上传到 HC StratoMiner 软件中进行进一步的数据分析。这里， 我们使 用 HC StratoMiner 软件可以引导平台允许非数 据专家很容易的浏览整个数据分析工作流程。另外，HC StratoMiner 是通过浏览器访问的网络工具，因此终端 用户不需要占用额外的计算机资源就可以处理大型的复 杂的数据集。

主要成分分析方法 ( 广义加权最小二乘法 ) 用来将 280 个测量值减少到 15 个相关分量。然后进行层次聚类法， 并在树状图中表示每个孔之间的关系 (Figure 5)。用相 同化合物处理的细胞倾向于聚类在一起。例如，簇 9 仅 由星形孢菌素处理的细胞组成。已知具有相似细胞效应 的化合物也聚集在一起，如细胞松弛素 D 和 latrunculin B，两种肌动蛋白聚合抑制剂均在簇 6 中发现。已知影响 自噬途径的粉防己碱和氯喹也聚集在一起 ( 簇 5)。这些 结果表明，所提出的工作流程是执行高内涵表型分析的 一种可行且易于实现的方法。

***Figure 5 聚类分析。*A) 树状图显示了分层关系。属于同一簇（ 编号 ）的孔由彩色条表示。图中标识了每个孔的基于距离评价的 P 值。B) 属于相同簇的 化合物处理的细胞在图中展示出来。簇 5 由粉防己碱和氯喹处理的细胞组成。注意两个孔中 ER 斑点的数量增加。簇 4 由鱼藤酮和紫杉醇处理的细胞组 成。注意这些孔的一些细胞中存在泡状结构，表明细胞毒性的作用。

结论

• ImageXpress Micro Confocal 系统适用于高内涵成像和细胞绘画分析。
• IN-Carta 软件将易用性与高级特征分割选项 (SINAP) 相结合，以实现对细胞特征的准确和客观的识别。
• StratoMineR 平台允许非数据专家用户通过其直观的指导工作流程快速执行表型数据分析。
• 总体而言，我们的结果显示了使用 ImageXpress Micro Confocal 系统，IN-Carta 软件和 StratoMineR 进行基于图像 的分析测定的可行性方案。

参考文献

1. Bray MA, Singh S, Han H, Davis CT, Borgeson B, Hartland C, Kost-Alimova M, Gustafsdottir SM, Gibson CC, Carpenter AE. Cell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes. Nat Protoc. 2016 Sep;11(9):1757-74. doi: 10.1038/ nprot.2016.105. Epub 2016 Aug 25. PMID: 27560178; PMCID: PMC5223290.
2. Gong K, Chen C, Zhan Y, Chen Y, Huang Z, Li W. Autophagy-related gene 7 (ATG7) and reactive oxygen species/extracellular signal-regulated kinase regulate tetrandrine-induced autophagy in human hepatocellular carcinoma. J Biol Chem. 2012 Oct 12;287(42):35576-88. doi: 10.1074/jbc.M112.370585. Ep u b 2 01 2 A u g 27. PM I D : 2 2 9 274 4 6; PM C I D : PMC3471698.
3. Gustafsdottir SM, Ljosa V, Sokolnicki KL, Anthony Wilson J, Walpita D, Kemp MM, Petri Seiler K, Carrel HA, Golub TR, Schreiber SL, Clemons PA, Carpenter AE, Shamji AF. Multiplex cytological profiling assay to measure diverse cellular states. PLoS One. 2013 Dec 2;8(12):e80999. doi: 10.1371/journal. pone.0080999. PMID: 24312513; PMCID: PMC3847047.
4. Mauthe M, Orhon I, Rocchi C, Zhou X, Luhr M, Hijlkema KJ, Coppes RP, Engedal N, Mari M, Reg gior i F. Chloro quine inhibit s autophagic f lux by decreasing autophagosomelysosome fusion. Autophag y. 2018;14(8):1435 -1455. doi: 10.1080/15548627.2018.1474314. Epub 2018 Jul 20. PMID: 29940786; PMCID: PMC6103682.
5. Nyffeler J, Willis C, Lougee R, Richard A, PaulFriedman K, Harrill JA. Bioactivity screening of environmental chemicals using imaging-based high-throughput phenotypic profiling. Toxicol Appl Pharmacol. 2020 Jan 15;389:114876. doi: 10.1016/ j.taap.2019.114 876. Epub 2019 Dec 30. PMID: 31899216.
6. Omta WA, van Heesbeen RG, Pagliero RJ, van der Velden LM, Lelieveld D, Nellen M, Kramer M, Yeong M, Saeidi AM, Medema RH, Spruit M, Brinkkemper S, Klumperman J, Egan DA. HC StratoMineR: A Web-Based Tool for the Rapid Analysis of HighContent Datasets. Assay Drug Dev Technol. 2016 Oct;14(8):439-452. doi: 10.1089/adt.2016.726. Epub 2016 Sep 16. PMID: 27636821.
7. Rohban MH, Singh S, Wu X, Berthet JB, Bray MA, Shrestha Y, Varelas X, Boehm JS, Carpenter AE. Systematic morphological profiling of human gene and allele function via Cell Painting. Elife. 2017 Mar 18;6:e24060. doi: 10.7554/ eLife.24060. PMID: 28315521; PMCID: PMC5386591

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