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Garcia-Fossa F, Moraes-Lacerda T, Rodrigues-da-Silva M, Diaz-Rohrer B, Singh S, Carpenter AE, Cimini BA, de Jesus MB. Live Cell Painting: image-based profiling in live cells using Acridine Orange. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.28.610144. [PMID: 39257795 PMCID: PMC11383656 DOI: 10.1101/2024.08.28.610144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2024]
Abstract
Image-based profiling has been used to analyze cell health, drug mechanism of action, CRISPR-edited cells, and overall cytotoxicity. Cell Painting is a broadly used image-based assay that uses morphological features to capture how cells respond to treatments. However, this method requires cell fixation for staining, which prevents examining live cells. To address this limitation, here we present Live Cell Painting (LCP), a high-content method based on Acridine orange, a metachromatic dye that labels different organelles and cellular structures. We began by showing that LCP can be applied to follow acidic vesicle redistribution of cells exposed to acidic vesicles inhibitors. Next, we show that LCP can identify subtle changes in cells exposed to silver nanoparticles that are not detected by techniques such as MTT assay. In drug treatments, LCP was helpful in assessing the dose-response relationship and creating profiles that allow clustering of drugs that cause liver injury. Here, we present an affordable and easy-to-use image-based assay capable of assessing overall cell health and showing promise for use in various applications such as assessing drugs and nanoparticles. We envisage the use of Live Cell Painting as an initial screening of overall cell health while providing insights into new biological questions.
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Moser LM, Gogoberidze N, Papaleo A, Lucas A, Dao D, Friedrich CA, Paavolainen L, Molnar C, Stirling DR, Hung J, Wang R, Tromans-Coia C, Li B, Evans EL, Eliceiri KW, Horvath P, Carpenter AE, Cimini BA. Piximi - An Images to Discovery web tool for bioimages and beyond. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.03.597232. [PMID: 38895349 PMCID: PMC11185650 DOI: 10.1101/2024.06.03.597232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Deep learning has greatly accelerated research in biological image analysis yet it often requires programming skills and specialized tool installation. Here we present Piximi, a modern, no-programming image analysis tool leveraging deep learning. Implemented as a web application at Piximi.app, Piximi requires no installation and can be accessed by any modern web browser. Its client-only architecture preserves the security of researcher data by running all computation locally. Piximi offers four core modules: a deep learning classifier, an image annotator, measurement modules, and pre-trained deep learning segmentation modules. Piximi is interoperable with existing tools and workflows by supporting import and export of common data and model formats. The intuitive researcher interface and easy access to Piximi allows biological researchers to obtain insights into images within just a few minutes. Piximi aims to bring deep learning-powered image analysis to a broader community by eliminating barriers to entry.
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Affiliation(s)
- Levin M Moser
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge MA, USA
| | - Nodar Gogoberidze
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge MA, USA
| | - Andréa Papaleo
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge MA, USA
| | - Alice Lucas
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge MA, USA
| | | | | | - Lassi Paavolainen
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Csaba Molnar
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge MA, USA
- Synthetic and Systems Biology Unit, HUN-REN Biological Research Centre (BRC), Szeged, Hungary
| | - David R Stirling
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge MA, USA
| | - Jane Hung
- Department of Chemical Engineering, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
| | - Rex Wang
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge MA, USA
| | | | - Bin Li
- Center for Quantitative Cell Imaging, University of Wisconsin-Madison, Madison, WI, USA
| | - Edward L Evans
- Center for Quantitative Cell Imaging, University of Wisconsin-Madison, Madison, WI, USA
| | - Kevin W Eliceiri
- Center for Quantitative Cell Imaging, University of Wisconsin-Madison, Madison, WI, USA
| | - Peter Horvath
- Synthetic and Systems Biology Unit, HUN-REN Biological Research Centre (BRC), Szeged, Hungary
- Synthetic and Systems Biology Unit, HUN-REN Biological Research Centre (BRC), Szeged, Hungary; Institute of AI for Health, Helmholtz Zentrum München, Neuherberg, Germany
| | - Anne E Carpenter
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge MA, USA
| | - Beth A Cimini
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge MA, USA
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Han Z, Zhang Z, Yang X, Li Z, Sang S, Islam MT, Guo AA, Li Z, Wang X, Wang J, Zhang T, Sun Z, Yu L, Wang W, Xiong W, Li G, Jiang Y. Development and interpretation of a pathomics-driven ensemble model for predicting the response to immunotherapy in gastric cancer. J Immunother Cancer 2024; 12:e008927. [PMID: 38749538 PMCID: PMC11097892 DOI: 10.1136/jitc-2024-008927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/24/2024] [Indexed: 05/18/2024] Open
Abstract
BACKGROUND Only a subset of patients with gastric cancer experience long-term benefits from immune checkpoint inhibitors (ICIs). Currently, there is a deficiency in precise predictive biomarkers for ICI efficacy. The aim of this study was to develop and validate a pathomics-driven ensemble model for predicting the response to ICIs in gastric cancer, using H&E-stained whole slide images (WSI). METHODS This multicenter study retrospectively collected and analyzed H&E-stained WSIs and clinical data from 584 patients with gastric cancer. An ensemble model, integrating four classifiers: least absolute shrinkage and selection operator, k-nearest neighbors, decision trees, and random forests, was developed and validated using pathomics features, with the objective of predicting the therapeutic efficacy of immune checkpoint inhibition. Model performance was evaluated using metrics including the area under the curve (AUC), sensitivity, and specificity. Additionally, SHAP (SHapley Additive exPlanations) analysis was used to explain the model's predicted values as the sum of the attribution values for each input feature. Pathogenomics analysis was employed to explain the molecular mechanisms underlying the model's predictions. RESULTS Our pathomics-driven ensemble model effectively stratified the response to ICIs in training cohort (AUC 0.985 (95% CI 0.971 to 0.999)), which was further validated in internal validation cohort (AUC 0.921 (95% CI 0.839 to 0.999)), as well as in external validation cohort 1 (AUC 0.914 (95% CI 0.837 to 0.990)), and external validation cohort 2 (0.927 (95% CI 0.802 to 0.999)). The univariate Cox regression analysis revealed that the prediction signature of pathomics-driven ensemble model was a prognostic factor for progression-free survival in patients with gastric cancer who underwent immunotherapy (p<0.001, HR 0.35 (95% CI 0.24 to 0.50)), and remained an independent predictor after multivariable Cox regression adjusted for clinicopathological variables, (including sex, age, carcinoembryonic antigen, carbohydrate antigen 19-9, therapy regime, line of therapy, differentiation, location and programmed death ligand 1 (PD-L1) expression in all patients (p<0.001, HR 0.34 (95% CI 0.24 to 0.50)). Pathogenomics analysis suggested that the ensemble model is driven by molecular-level immune, cancer, metabolism-related pathways, and was correlated with the immune-related characteristics, including immune score, Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data score, and tumor purity. CONCLUSIONS Our pathomics-driven ensemble model exhibited high accuracy and robustness in predicting the response to ICIs using WSIs. Therefore, it could serve as a novel and valuable tool to facilitate precision immunotherapy.
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Affiliation(s)
- Zhen Han
- Department of General Surgery & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine,Southern Medical University, Guangzhou, Guangdong, China
| | - Zhicheng Zhang
- Department of Gastroenterology, The First Hospital of Jilin University, Changchun, Jilin, China
- JancsiLab, JancsiTech, Hongkong, China
| | - Xianqi Yang
- Department of Gastric Surgery, and State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China
| | - Zhe Li
- School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an, Shaanxi, China
| | - Shengtian Sang
- Department of Radiology, Stanford University School of Medicine, Stanford, California, USA
| | - Md Tauhidul Islam
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California, USA
| | - Alyssa A Guo
- Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA
| | - Zihan Li
- Department of Bioengineering, University of Washington, Seattle, Washington, USA
| | - Xiaoyan Wang
- Department of Pathology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, Guangdong, China
| | - Jing Wang
- Department of Pathology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, Guangdong, China
| | - Taojun Zhang
- Department of General Surgery & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine,Southern Medical University, Guangzhou, Guangdong, China
| | - Zepang Sun
- Department of General Surgery & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine,Southern Medical University, Guangzhou, Guangdong, China
| | - Lequan Yu
- Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong, Hong Kong
| | - Wei Wang
- Department of Gastric Surgery, and State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China
| | - Wenjun Xiong
- Department of Gastrointestinal Surgery, Guangdong Provincial Hospital of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Guoxin Li
- Department of General Surgery & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine,Southern Medical University, Guangzhou, Guangdong, China
- School of Clinical Medicine, Tsinghua University, Beijing Tsinghua Changgung Hospital, Beijing, China
| | - Yuming Jiang
- Department of Radiation Oncology, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA
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Goes CP, Botezelli VS, De La Cruz SM, Cruz MC, Azambuja AP, Simoes-Costa M, Yan CYI. ASCL1 promotes Scrt2 expression in the neural tube. Front Cell Dev Biol 2024; 12:1324584. [PMID: 38655067 PMCID: PMC11036302 DOI: 10.3389/fcell.2024.1324584] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 03/05/2024] [Indexed: 04/26/2024] Open
Abstract
ASCL1 is a transcription factor that directs neural progenitors towards lineage differentiation. Although many of the molecular mechanisms underlying its action have been described, several of its targets remain unidentified. We identified in the chick genome a putative enhancer (cE1) upstream of the transcription factor Scratch2 (Scrt2) locus with a predicted heterodimerization motif for ASCL1 and POU3F2. In this study, we investigated the role of ASCL1 and this enhancer in regulating the expression of the Scrt2 in the embryonic spinal cord. We confirmed that cE1 region interacted with the Scrt2 promoter. cE1 was sufficient to mediate ASCL1-driven expression in the neural tube through the heterodimerization sites. Moreover, Scrt2 expression was inhibited when we removed cE1 from the genome. These findings strongly indicate that ASCL1 regulates Scrt2 transcription in the neural tube through cE1.
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Affiliation(s)
- Carolina Purcell Goes
- Department of Cell and Developmental Biology, Institute of Biomedical Sciences, University of São Paulo (USP), São Paulo, Brazil
| | - Vitória Samartin Botezelli
- Department of Cell and Developmental Biology, Institute of Biomedical Sciences, University of São Paulo (USP), São Paulo, Brazil
| | - Shirley Mirna De La Cruz
- Department of Cell and Developmental Biology, Institute of Biomedical Sciences, University of São Paulo (USP), São Paulo, Brazil
- Facultad de Ciencias de la Salud, Universidad Científica del Sur, Lima, Peru
| | - Mário Costa Cruz
- Core Research Facilities (CEFAP), Institute of Biomedical Sciences, University of São Paulo (USP), São Paulo, Brazil
| | - Ana Paula Azambuja
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY, United States
- Department of Systems Biology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Marcos Simoes-Costa
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY, United States
- Department of Systems Biology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Chao Yun Irene Yan
- Department of Cell and Developmental Biology, Institute of Biomedical Sciences, University of São Paulo (USP), São Paulo, Brazil
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Holme B, Bjørnerud B, Pedersen NM, de la Ballina LR, Wesche J, Haugsten EM. Automated tracking of cell migration in phase contrast images with CellTraxx. Sci Rep 2023; 13:22982. [PMID: 38151514 PMCID: PMC10752880 DOI: 10.1038/s41598-023-50227-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 12/17/2023] [Indexed: 12/29/2023] Open
Abstract
The ability of cells to move and migrate is required during development, but also in the adult in processes such as wound healing and immune responses. In addition, cancer cells exploit the cells' ability to migrate and invade to spread into nearby tissue and eventually metastasize. The majority of cancer deaths are caused by metastasis and the process of cell migration is therefore intensively studied. A common way to study cell migration is to observe cells through an optical microscope and record their movements over time. However, segmenting and tracking moving cells in phase contrast time-lapse video sequences is a challenging task. Several tools to track the velocity of migrating cells have been developed. Unfortunately, most of the automated tools are made for fluorescence images even though unlabelled cells are often preferred to avoid phototoxicity. Consequently, researchers are constrained with laborious manual tracking tools using ImageJ or similar software. We have therefore developed a freely available, user-friendly, automated tracking tool called CellTraxx. This software makes it easy to measure the velocity and directness of migrating cells in phase contrast images. Here, we demonstrate that our tool efficiently recognizes and tracks unlabelled cells of different morphologies and sizes (HeLa, RPE1, MDA-MB-231, HT1080, U2OS, PC-3) in several types of cell migration assays (random migration, wound healing and cells embedded in collagen). We also provide a detailed protocol and download instructions for CellTraxx.
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Affiliation(s)
- Børge Holme
- SINTEF Industry, Forskningsveien 1, 0373, Oslo, Norway
| | - Birgitte Bjørnerud
- Department of Tumor Biology, Institute for Cancer Research, The Norwegian Radium Hospital, Oslo University Hospital, Montebello, 0379, Oslo, Norway
- Centre for Cancer Cell Reprogramming, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Montebello, 0379, Oslo, Norway
| | - Nina Marie Pedersen
- Centre for Cancer Cell Reprogramming, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Montebello, 0379, Oslo, Norway
- Department of Molecular Cell Biology, Institute for Cancer Research, The Norwegian Radium Hospital, Oslo University Hospital, Montebello, 0379, Oslo, Norway
- Department of Nursing, Health and Laboratory Science, Faculty of Health, Welfare and Organisation, Østfold University College, PB 700, NO-1757, Halden, Norway
| | - Laura Rodriguez de la Ballina
- Centre for Cancer Cell Reprogramming, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Montebello, 0379, Oslo, Norway
- Department of Molecular Cell Biology, Institute for Cancer Research, The Norwegian Radium Hospital, Oslo University Hospital, Montebello, 0379, Oslo, Norway
| | - Jørgen Wesche
- Department of Tumor Biology, Institute for Cancer Research, The Norwegian Radium Hospital, Oslo University Hospital, Montebello, 0379, Oslo, Norway
- Centre for Cancer Cell Reprogramming, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Montebello, 0379, Oslo, Norway
- Department of Molecular Medicine, Institute of Basic Medical Sciences, University of Oslo, 0372, Oslo, Norway
| | - Ellen Margrethe Haugsten
- Department of Tumor Biology, Institute for Cancer Research, The Norwegian Radium Hospital, Oslo University Hospital, Montebello, 0379, Oslo, Norway.
- Centre for Cancer Cell Reprogramming, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Montebello, 0379, Oslo, Norway.
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