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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 reportCellos(Cell and Organoid Segmentation), an accurate, high throughput image analysis pipeline for 3D organoid and nuclear segmentation analysis.Cellossegments 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 μm z-resolution. To evaluate the capabilities ofCelloswe 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.Celloswas able to accurately distinguish ratios of distinct fluorescently labelled cell populations in organoids, with Celloswas 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,Celloswas able to identify organoid and nuclear morphology feature changes associated with treatment. Finally,Cellosenables 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.Cellosprovides powerful tools to perform high throughput analysis for pharmacological testing and biological investigation of organoids based on 3D imaging.

Triple negative breast cancer (TNBC) has long been characterized by having a high degree of intratumoral heterogeneity. While significant contributions have been made in understanding the components of TNBC tumors, examining the dynamics of interaction between subclones in TNBC is relatively unexplored. By using single-cell RNA sequencing (scRNA-seq) technology, this study aims to focus on understanding changes in gene expression of two TNBC subclones: the cisplatin-resistant subclone (A50) and cisplatin-sensitive subclone (B), when cocultured in 3D organoids in the presence and absence of cisplatin. We hypothesized that there will be significant differentially expressed genes (DEGs) due to various concentrations of cisplatin and mixing effect. We found DEGs (ACTB, FOS, EIF5, BRD2, MAFG, SF3B4, ID1, MGMT, DDIT4 and LPP) due to treatment with cisplatin in both subclones, with the largest clear effect present at a high concentration of cisplatin. Additionally, the network analysis showed that cisplatin was an upstream regulator for 11 DEGs. Even though, gene expression changes due to mixing are much more subtle than effects due to treatment, we saw some DEGs caused by the mixing effect like MT1X which is related to cisplatin resistance. Taken together, the findings of this project will help in future studies when deducing which genes to target in TNBC chemotherapy treatments based on predictive molecular biomarkers. Citation Format: Shadae Nicholas, Patience Mukashyaka, Pooja Kumar, Jeffrey Chuang, Edison Liu. Uncovering transcriptional changes related to cisplatin treatment and mixing of TNBC subclones using single-cell RNA sequencing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1157.