BEHAV3D: a 3D live imaging platform for comprehensive analysis of engineered T cell behavior and tumor response.

Modeling immuno-oncology by using patient-derived material and immune cell co-cultures can advance our understanding of immune cell tumor targeting in a patient-specific manner, offering leads to improve cellular immunotherapy. However, fully exploiting these living cultures requires analysis of the dynamic cellular features modeled, for which protocols are currently limited. Here, we describe the application of BEHAV3D, a platform that implements multi-color live 3D imaging and computational tools for: (i) analyzing tumor death dynamics at both single-organoid or cell and population levels, (ii) classifying T cell behavior and (iii) producing data-informed 3D images and videos for visual inspection and further insight into obtained results. Together, this enables a refined assessment of how solid and liquid tumors respond to cellular immunotherapy, critically capturing both inter- and intratumoral heterogeneity in treatment response. In addition, BEHAV3D uncovers T cell behavior involved in tumor targeting, offering insight into their mode of action. Our pipeline thereby has strong implications for comparing, prioritizing and improving immunotherapy products by highlighting the behavioral differences between individual tumor donors, distinct T cell therapy concepts or subpopulations. The protocol describes critical wet lab steps, including co-culture preparations and fast 3D imaging with live cell dyes, a segmentation-based image processing tool to track individual organoids, tumor and immune cells and an analytical pipeline for behavioral profiling. This 1-week protocol, accessible to users with basic cell culture, imaging and programming expertise, can easily be adapted to any type of co-culture to visualize and exploit cell behavior, having far-reaching implications for the immuno-oncology field and beyond.

A multispectral 3D live organoid imaging platform to screen probes for fluorescence guided surgery.

Achieving complete tumor resection is challenging and can be improved by real-time fluorescence-guided surgery with molecular-targeted probes. However, pre-clinical identification and validation of probes presents a lengthy process that is traditionally performed in animal models and further hampered by inter- and intra-tumoral heterogeneity in target expression. To screen multiple probes at patient scale, we developed a multispectral real-time 3D imaging platform that implements organoid technology to effectively model patient tumor heterogeneity and, importantly, healthy human tissue binding.

Uncovering the mode of action of engineered T cells in patient cancer organoids.

Extending the success of cellular immunotherapies against blood cancers to the realm of solid tumors will require improved in vitro models that reveal therapeutic modes of action at the molecular level. Here we describe a system, called BEHAV3D, developed to study the dynamic interactions of immune cells and patient cancer organoids by means of imaging and transcriptomics. We apply BEHAV3D to live-track >150,000 engineered T cells cultured with patient-derived, solid-tumor organoids, identifying a 'super engager' behavioral cluster comprising T cells with potent serial killing capacity. Among other T cell concepts we also study cancer metabolome-sensing engineered T cells (TEGs) and detect behavior-specific gene signatures that include a group of 27 genes with no previously described T cell function that are expressed by super engager killer TEGs. We further show that type I interferon can prime resistant organoids for TEG-mediated killing. BEHAV3D is a promising tool for the characterization of behavioral-phenotypic heterogeneity of cellular immunotherapies and may support the optimization of personalized solid-tumor-targeting cell therapies.

Revealing the spatio-phenotypic patterning of cells in healthy and tumor tissues with mLSR-3D and STAPL-3D.

Despite advances in three-dimensional (3D) imaging, it remains challenging to profile all the cells within a large 3D tissue, including the morphology and organization of the many cell types present. Here, we introduce eight-color, multispectral, large-scale single-cell resolution 3D (mLSR-3D) imaging and image analysis software for the parallelized, deep learning-based segmentation of large numbers of single cells in tissues, called segmentation analysis by parallelization of 3D datasets (STAPL-3D). Applying the method to pediatric Wilms tumor, we extract molecular, spatial and morphological features of millions of cells and reconstruct the tumor's spatio-phenotypic patterning. In situ population profiling and pseudotime ordering reveals a highly disorganized spatial pattern in Wilms tumor compared to healthy fetal kidney, yet cellular profiles closely resembling human fetal kidney cells could be observed. In addition, we identify previously unreported tumor-specific populations, uniquely characterized by their spatial embedding or morphological attributes. Our results demonstrate the use of combining mLSR-3D and STAPL-3D to generate a comprehensive cellular map of human tumors.