Most platforms used for the molecular reconstruction of the tumor–immune microenvironment (TIME) of a solid tumor fail to explore the spatial context of the three-dimensional (3D) space of the tumor at a single-cell resolution, and thus lack information about cell–cell or cell–extracellular matrix (ECM) interactions. To address this issue, a pipeline which integrated multiplex spatially resolved multi-omics platforms was developed to identify crosstalk signaling networks among various cell types and the ECM in the 3D TIME of two FFPE (formalin-fixed paraffin embedded) gynecologic tumor samples. These platforms include non-targeted mass spectrometry imaging (glycans, metabolites, and peptides) and Stereo-seq (spatial transcriptomics) and targeted seqIF (IHC proteomics). The spatially resolved imaging data in a two- and three-dimensional space demonstrated various cellular neighborhoods in both samples. The collection of spatially resolved analytes in a voxel (3D pixel) across serial sections of the tissue was also demonstrated. Data collected from this analytical pipeline were used to construct spatial 3D maps with single-cell resolution, which revealed cell identity, activation, and energized status. These maps will provide not only insights into the molecular basis of spatial cell heterogeneity in the TIME, but also novel predictive biomarkers and therapeutic targets, which can improve patient survival rates.

This study presents a multilayer in vitro human skin platform to quantitatively relate predicted spatial time–temperature history with measured tissue injury response. This information is needed to elucidate high-temperature, short-duration burn injury kinetics and enables determination of relevant input parameters for computational models to facilitate treatment planning. Multilayer in vitro skin platforms were constructed using human dermal keratinocytes and fibroblasts embedded in collagen I hydrogels. After three seconds of contact with a 50–100 °C burn tip, ablation, cell death, apoptosis, and HSP70 expression were spatially measured using immunofluorescence confocal microscopy. Finite element modeling was performed using the measured thermal characteristics of skin platforms to determine the temperature distribution within platforms over time. The process coefficients for the Arrhenius thermal injury model describing tissue ablation and cell death were determined such that the predictions calculated from the time–temperature histories fit the experimental burn results. The activation energy for thermal collagen ablation and cell death was found to be significantly lower for short-duration, high-temperature burns than those found for long-duration, low-temperature burns. Analysis of results suggests that different injury mechanisms dominate at higher temperatures, necessitating burn research in the temperature ranges of interest and demonstrating the practicality of the proposed skin platform for this purpose.

Tissues are complex cellular environments, made up of a vast array of cell types and biomolecules all interacting with each other to carry out the functions of the organ. Traditionally, techniques for the analysis of biomolecules such as metabolites, glycans, and proteins involved the homogenization of tissue, destroying all spatial information. These traditional methods inhibited the complete understanding of complex intra- and extracellular molecular interactions. Mass Spectrometry Imaging (MSI), on the other hand, not only preserves the spatial information of biomolecules in tissue but also allows for multiple classes of analytes to be detected from the same tissue section through sequential analysis at a near-single-cell resolution. This enables us to derive a more complete picture of molecular interactions across different classes of biomolecules. The protocol presented here outlines the steps for performing mass spectrometry imaging of metabolites, N-linked glycans, and tryptic peptides sequentially from the same tissue section at a 20 µm resolution. Conscientious consideration of the order in which the classes of analytes are imaged, along with careful handling of the sections to ensure integrity, allows for multiple high-quality images to be collected from the same section. These data can subsequently be integrated with other spatial omics data (transcriptomics, immunohistochemistry, etc.) collected from serial sections, where the same cell can be analyzed in these adjacent sections.

The growth rate of in situ-induced hepatic lesions in an Oncopig large animal model is quantitatively assessed. Oncopigs (n = 9) received baseline triple-phase CT scans prior to lesion induction. Lesions were subsequently induced by delivering the Ad-Cre vector to four locations in the liver. Triple-phase CT scans were obtained weekly to track the growth of the lesions. Animals were sacrificed at 14, 21, or 28 days (n = 3 in each group). The overall success rate of lesion generation was ~78%. Histopathology sections consistently revealed lesions that were highly inflammatory and consisted of a large leukocyte population without clear evidence of carcinomas. Lesions presented within imaging as hypovascular, low attenuating masses with slight contrast enhancement around the margins but little to no enhancement within the lesions themselves. The observed lesions were manually segmented on the venous phase image. Segmentation volumes were fitted to a logistic growth and decay model. Several lesions observed at earlier time points in the 28-day group had fully regressed by the time of the necropsy. The overall trend of rapid growth for the first 21 days, with spontaneous regression of the lesions being observed from day 21 to 28, suggests that the optimal window for experimental studies may be from days 14 to 21. The data and mathematical models generated from this study may be used for future computational models; however, the current model presented has moderate clinical relevance because many induced tumors resolved spontaneously within a few weeks. Awareness and careful consideration of the modest relevance and limitations of the model are advisable for each specific use case.

Background/Objectives: Uveal melanoma is the most common primary ocular cancer in adults. Patients with metastatic uveal melanoma (mUM) have limited treatment options and poor prognosis. mUM is characterized by high oxidative phosphorylation (OXPHOS), which may be a therapeutic vulnerability for this disease. ONC206 is an imipridone compound that can inhibit OXPHOS indirectly and is currently being evaluated in clinical trials. Thus, we tested the effects of ONC206 on human uveal melanoma cell lines and patient-derived xenografts (PDXs) in vitro and in vivo. Methods: The effects of ONC206 on cell survival, apoptosis, autophagy, oncogenic signaling pathways, and metabolic networks were assessed in vitro using human melanoma cell lines. ONC206 was then tested for safety and anti-tumor activity in vivo using two mUM PDX models. Results: ONC206 treatment produced dose-dependent inhibition of mUM cell growth in vitro, with induction of varying levels of apoptosis and autophagy. ONC206 treatment also downregulated OXPHOS effector proteins and metabolites, thereby impairing mitochondrial OXPHOS. Treatment with ONC206 significantly reduced tumor burden and improved survival in two UM PDX mouse models in vivo. Conclusions: Our findings position ONC206 as a mechanistically distinct agent to target mitochondrial metabolism and to inhibit mUM. As ONC206 is currently being evaluated in multiple clinical studies, our data support further evaluation as a potential new therapeutic strategy for mUM.

The functionality of cells within a host does not depend on isolated signals. Instead, all components, from the smallest RNA molecules to the largest proteins, must operate in harmony and coordination within the human body. As such, the importance of approaches that integrate cellular-level spatial investigations across multiple omics is paramount to understanding cell-cell interactions and the progression of disease. Dissecting the proteome and transcriptome in the same spatial assay helps us understand how not only what a cell is being instructed to carry out (RNA), but also how it is executing those instructions in the context of the microenvironmental niche it finds itself in (protein). This manuscript is focused on integrating sequential immunofluorescence (SeqIF) with RNA in situ hybridization (ISH) with an on-tissue microfluidics driven system for high-throughput protein and RNA investigation in a spatial context (seqRNA-ISH+seqIF) that will allow up to 12 RNA and 24 protein targets in a single run with additional protein targets possible to be added via sequential runs. This method provides a sequential targeted multiomics platform that does not require consideration of fluorophore compatibility and extensive optimization for higher plexing. This allows one to understand what messages the cell is priming or is sending into its microenvironment. This targeted approach helps to validate whole transcriptome methods while examining the interactions between the RNA and proteins in a more precise manner.