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Spatially-resolved gene expression profiling provides valuable insight into tissue organization and cell-cell crosstalk; however, spatial transcriptomics (ST) lacks single-cell resolution. Current ST analysis methods require single-cell RNA sequencing data as a reference for a rigorous interpretation of cell states and do not utilize associated histology images. Significant sample variation further complicates the integration of ST datasets, which is essential for identifying commonalities across tissues or altered cellular wiring in disease. Here, we present Starfysh, the first comprehensive computational toolbox for joint modeling of ST and histology data, dissection of refined cell states, and systematic integration of multiple ST datasets from complex tissues. Starfysh uses an auxiliary deep generative model that incorporates archetypal analysis and any known cell state markers to avoid the need for a single-cell-resolution reference in characterizing known or novel tissue-specific cell states. Additionally, Starfysh improves the characterization of spatial dynamics in complex tissues by leveraging histology images and enables the comparison of niches as spatial "hubs" across tissues. Integrative analysis of primary estrogen receptor-positive (ER+) breast cancer, triple-negative breast cancer (TNBC), and metaplastic breast cancer (MBC) tumors using Starfysh led to the identification of heterogeneous patient- and disease-specific hubs as well as a shared stromal hub with varying spatial orientation. Our results show the ability to delineate the spatial co-evolution of tumor and immune cell states and their crosstalk underlying intratumoral heterogeneity in TNBC and revealed metabolic reprogramming shaping immunosuppressive hubs in aggressive MBC. Starfysh is publicly available (https://github.com/azizilab/starfysh).

Allergic contact dermatitis (ACD) is an inflammatory disease that impacts 15-20% of the general population and accurate screening methods for chemical risk assessment are needed. However, most approaches poorly predict pre- and pro-hapten sensitizers, which require abiotic or metabolic conversion prior to inducing sensitization. We developed a tri-culture system comprised of MUTZ-3-derived Langerhans cells, HaCaT keratinocytes, and primary dermal fibroblasts to mimic the cellular and metabolic environments of skin sensitization. A panel of non-sensitizers and sensitizers was tested and the secretome was evaluated. A support vector machine (SVM) was used to identify the most predictive sensitization signature and classification trees identified statistical thresholds to predict sensitizer potency. The SVM computed 91% tri-culture prediction accuracy using the top 3 ranking biomarkers (IL-8, MIP-1β, and GM-CSF) and improved the detection of pre- and pro-haptens. This in vitro assay combined with in silico data analysis presents a promising approach and offers the possibility of multi-metric analysis for enhanced ACD sensitizer screening.

Immunotherapy using regulatory T cells (Tregs) has shown recent successes in the treatment of autoimmune and inflammatory diseases such as type 1 diabetes. While natural Tregs are unstable and dysfunctional in the inflammatory milieu, induced Tregs are more potent and durable. Tregs can be induced from CD4+CD25− T cells in vitro with TGF-β and IL-2 during activation. Chemical pathways in Treg induction have been heavily investigated, but the impact of mechanical cues on Treg induction has not been thoroughly explored. As T cell activation has been shown to be sensitive to the rigidity of the activating substrate, Treg induction may also be modulated by substrate rigidity. To test this hypothesis, Tregs were induced with 10 ng/ml TGF-β and IL-2 on different rigidities of polyacrylamide gels (5 to 110 kPa) coated with anti-CD3 and anti-CD28. The hydrogel stiffness was controlled by adjusting the acrylamide monomer and crosslinker content. The density of activating antibodies was varied by altering the concentration of streptavidin acrylamide that allows the binding of biotinylated antibodies to the gels. High ligand density on the stiffer substrate significantly upregulated the rate of Treg induction, measured by percent Foxp3 high T cells. Decreasing the ligand density shifted the optimal rigidity to softer substrates. This preliminary data has shown that Treg induction is mechanosensitive and dependent on ligand density, which can contribute to the understanding of mechanosensing in Treg induction and the improvement of biomaterial design for generating functional and stable Tregs to advance Treg adoptive therapy.