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CellProfiler is a widely used software for creating reproducible, reusable image analysis workflows without needing to code. In addition to the >90 modules that make up the main CellProfiler program, CellProfiler has a plugins system that allows for creation of new modules which integrate with other Python tools or tools that are packaged in software containers. The CellProfiler-plugins repository contains a number of these CellProfiler modules, especially modules that are experimental and/or dependency-heavy. Here, we present an upgraded CellProfiler-plugins repository with examples of accessing containerized tools, improved documentation, and added citation/reference tools to facilitate the use and contribution of the community., 17 pages, 2 figures, 1 table

Image-based profiling quantitatively assesses the effects of perturbations on cells by capturing a breadth of changes via microscopy. Here we provide two complementary protocols to help explore and interpret data from image-based profiling experiments. In the first protocol, we examine the similarity among perturbed cell samples using data from compounds that cluster by their mechanism of action (MOAs). The protocol includes steps to examine feature-driving differences between samples and how to visualize correlations between features and treatments to create interpretable heatmaps using an open-source web tool, Morpheus. In the second protocol, we show how to interactively explore images together with the numerical data, while we provide scripts to create visualizations of representative single cells and image sites to understand how changes in features are reflected in the images. Together, these two tutorials help biologists and researchers interpret their image-based data to speed up research., Data are available at https://github.com/ciminilab/2022_Garcia-Fossa_submitted. Funding was provided by the National Institutes of Health (NIH COBA P41 GM135019 to BAC and AEC and MIRA R35 GM122547 to AEC). This project has been made possible partly by grant number 2020-225720 to BAC from the Chan Zuckerberg Initiative DAF, an advised fund of the Silicon Valley Community Foundation. São Paulo Research Foundation (FAPESP) was also provided funding #2022/01483-4, #2019/24033-1, and #2020/01218-3. SS and AEC serve as scientific advisors for companies that use image-based profiling and Cell Painting (AEC: Recursion, SS: Waypoint Bio, Dewpoint Therapeutics) and receive honoraria for occasional talks at pharmaceutical and biotechnology companies.

Image-based profiling quantitatively assesses the effects of perturbations on cells by capturing a breadth of changes via microscopy. Here we provide two complementary protocols to help explore and interpret data from image-based profiling experiments. In the first protocol, we examine the similarity among perturbed cell samples using data from compounds that cluster by their mechanism of action (MOAs). The protocol includes steps to examine feature-driving differences between samples using Morpheus software, and how to visualize correlations between features and treatments to create interpretable heatmaps. In the second protocol, we show how to interactively explore images together with the numerical data, while we provide scripts to create visualizations of representative single cells and image sites to understand how changes in features are reflected in the images. Together, these two tutorials help biologists and researchers interpret their image-based data to speed up research.

Orientador: Marcelo Bispo de Jesus Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Biologia Resumo: O estudo da nanotecnologia, e mais especificamente de nanopartículas têm crescido, principalmente de suas propriedades e efeitos colaterais. Dentre as nanopartículas, se destacam as nanopartículas lipídicas sólidas (SLN), produzidas com componentes biocompatíveis e biodegradáveis. Muito se estuda sobre os efeitos benéficos de nanopartículas na entrega específica de fármacos e genes às células, porém pouco se sabe sobre os efeitos em vias de sinalização intracelular. Nesse trabalho estudamos os efeitos de SLN catiônica em células não tumorais e tumorais do epitélio de próstata humano, relacionadas à via do TGF-beta e transição epitélio-mesenquimal (EMT). A via do TGF-beta está relacionada com o aumento da invasão e migração celular no câncer, estimulando a metástase através da ativação da EMT. Nossos resultados mostram que as SLN aumentam a migração de células de câncer de próstata PC-3, efeito não observado em células não tumorais de próstata PNT1A ¿ um resultado inesperado e inédito. Observamos que o aumento da migração depende da nanopartícula e não dos seus componentes, e é um efeito concentração-dependente. Ainda, proteínas relacionadas à EMT, como Zeb1, mostraram-se aumentadas após tratamento com a SLN, demonstrando a importância dessa via no efeito biológico. Além disso, o papel biológico, i.e., a transfecção, mediado por SLN é significativamente reduzido quando inibimos os receptores do TGF-beta ou a Smad3. Concluímos que a SLN é capaz de interferir na motilidade de células PC-3 através de sua ativação da via do TGF-beta e a EMT. Tal efeito, ao extrapolar para a clínica, poderia agravar o quadro de um paciente caso esses resultados reproduzam em ensaios in vivo. A compreensão dos mecanismos moleculares disparado por nanomateriais, bem como suas consequências para o funcionamento celular, é importante para o desenvolvimento de novos nanomateriais e para delimitar seu uso seguro e eficaz Abstract: The study of nanotechnology, and more specifically of nanoparticles, has grown, mainly due to its properties and side effects. Among the nanoparticles, we highlight the Solid Lipid Nanoparticles (SLN), produced with biocompatible and biodegradable components. Much has been researched about the beneficial effects of nanoparticles on the specific delivery of drugs and genes to cells, but little is known about the effects on intracellular signaling pathways. In this work we studied the effects of cationic SLN in non-tumor and tumor cells of the human prostate epithelium, related to the TGF-beta pathway and epithelial-mesenchymal transition (EMT). The TGF-beta pathway is related to increased cell invasion and migration in cancer, stimulating metastasis through activation of EMT. Our results show that SLNs increase the migration of PC-3 prostate cancer cells, an effect not seen in non-tumor prostate cells PNT1A - an unexpected and unprecedented result. We observed that the increase in migration depends on the nanoparticle and not on its components, and is a concentration-dependent effect. Also, EMT-related proteins, such as Zeb1, were shown to be increased after treatment with SLN, demonstrating the importance of this pathway in the biological effect. In addition, the biological effect, i.e., transfection, mediated by SLN is significantly reduced when we inhibit TGF-beta receptors or Smad3. We conclude that SLN is capable of interfering in the motility of PC-3 cells through its activation of the TGF-beta pathway and EMT. Such an effect, when extrapolating to the clinic, could worsen a patient's condition if these results reproduce in in vivo trials. Understanding the molecular mechanisms triggered by nanomaterials, as well as their consequences for cellular functioning, is important for the development of new nanomaterials and to delimit their safe and effective use Mestrado Bioquímica Mestra em Biologia Funcional e Molecular CAPES 88882.329744/2010-01

CellProfiler is a widely used software for creating reproducible, reusable image analysis workflows without needing to code. In addition to the >90 modules that make up the main CellProfiler program, CellProfiler has a plugins system that allows for the creation of new modules which integrate with other Python tools or tools that are packaged in software containers. The CellProfiler-plugins repository contains a number of these CellProfiler modules, especially modules that are experimental and/or dependency-heavy. Here, we present an upgraded CellProfiler-plugins repository, an example of accessing containerized tools, improved documentation, and added citation/reference tools to facilitate the use and contribution of the community.