Brugiolo, M. | Search bioRxiv

bioRxiv2023

Cellos: High-throughput deconvolution of 3D organoid dynamics at cellular resolution for cancer pharmacology

Three-dimensional (3D) culture models, such as organoids, are flexible systems to interrogate cellular growth and morphology, multicellular spatial architecture, and cell interactions in response to drug treatment. However, new computational methods to segment and analyze 3D models at cellular resolution with sufficiently high throughput are needed to realize these possibilities. Here we report Cellos (Cell and Organoid Segmentation), an accurate, high throughput image analysis pipeline for 3D organoid and nuclear segmentation analysis. Cellos segments organoids in 3D using classical algorithms and segments nuclei using a Stardist-3D convolutional neural network which we trained on a manually annotated dataset of 3,862 cells from 36 organoids confocally imaged at 5 m z-resolution. To evaluate the capabilities of Cellos we then analyzed 74,450 organoids with 1.65 million cells, from multiple experiments on triple negative breast cancer organoids containing clonal mixtures with complex cisplatin sensitivities. Cellos was able to accurately distinguish ratios of distinct fluorescently labelled cell populations in organoids, with <3% deviation from the seeding ratios in each well and was effective for both fluorescently labelled nuclei and independent DAPI stained datasets. Cellos was able to recapitulate traditional luminescence-based drug response quantifications by analyzing 3D images, including parallel analysis of multiple cancer clones in the same well. Moreover, Cellos was able to identify organoid and nuclear morphology feature changes associated with treatment. Finally, Cellos enables 3D analysis of cell spatial relationships, which we used to detect ecological affinity between cancer cells beyond what arises from local cell division or organoid composition. Cellos provides powerful tools to perform high throughput analysis for pharmacological testing and biological investigation of organoids based on 3D imaging.

bioinformatics↗

bioRxiv2021

MYC regulates a pan-cancer network of co-expressed oncogenic splicing factors

MYC is dysregulated in >50% of cancers, but direct targeting of MYC has been clinically unsuccessful. Targeting downstream MYC effector pathways represents an attractive alternative. MYC regulates alternative mRNA splicing, a hallmark of cancer, but the mechanistic links between MYC and the splicing machinery remain underexplored. Here, we identify a network of splicing factors (SFs) co-expressed as SF-modules in MYC-active breast tumors. Of these, one is a pan-cancer SF-module, correlating with MYC-activity across 33 tumor types. In mammary cell models, MYC activation leads to co-upregulation of pan-cancer module SFs and to changes in >4,000 splicing events. In breast cancer organoids, co-overexpression of the pan-cancer SF-module is sufficient to induce splicing events that are also MYC-regulated in patient tumors and to increase organoid size and invasiveness, while its knockdown decreases organoid size. Finally, we uncover a pan-cancer splicing signature of MYC activity which correlates with survival in multiple tumor types. Our findings provide insight into the mechanisms and function of MYC-regulated splicing and for the development of therapeutics for MYC-driven tumors.

cancer biology↗