Improving regulatory T cell production through mechanosensing.

Induced Tregs (iTregs) have great promise in adoptive immunotherapy for treatment of autoimmune diseases. This report investigates the impacts of substrate stiffness on human Treg induction, providing a powerful yet simple approach to improving production of these cells. Conventional CD4+ human T cells were activated on materials of different elastic modulus and cultured under suppressive conditions. Enhanced Treg induction was observed on softer materials as early as 3 days following activation and persisted for multiple weeks. Substrate stiffness also affected epigenetic modification of Treg specific genes and Treg suppressive capacity. Tregs induced on substrates of an optimal stiffness balance quantity and suppressive quality.

A heterogeneous pharmaco-transcriptomic landscape induced by targeting a single oncogenic kinase.

Over-activation of the epidermal growth factor receptor (EGFR) is a hallmark of glioblastoma. However, EGFR-targeted therapies have led to minimal clinical response. While delivery of EGFR inhibitors (EGFRis) to the brain constitutes a major challenge, how additional drug-specific features alter efficacy remains poorly understood. We apply highly multiplex single-cell chemical genomics to define the molecular response of glioblastoma to EGFRis. Using a deep generative framework, we identify shared and drug-specific transcriptional programs that group EGFRis into distinct molecular classes. We identify programs that differ by the chemical properties of EGFRis, including induction of adaptive transcription and modulation of immunogenic gene expression. Finally, we demonstrate that pro-immunogenic expression changes associated with a subset of tyrphostin family EGFRis increase the ability of T-cells to target glioblastoma cells.

Starfysh integrates spatial transcriptomic and histologic data to reveal heterogeneous tumor-immune hubs.

Spatially resolved gene expression profiling provides insight into tissue organization and cell-cell crosstalk; however, sequencing-based spatial transcriptomics (ST) lacks single-cell resolution. Current ST analysis methods require single-cell RNA sequencing data as a reference for rigorous interpretation of cell states, mostly do not use associated histology images and are not capable of inferring shared neighborhoods across multiple tissues. Here we present Starfysh, a computational toolbox using a deep generative model that incorporates archetypal analysis and any known cell type markers to characterize known or new tissue-specific cell states without a single-cell reference. Starfysh improves the characterization of spatial dynamics in complex tissues using histology images and enables the comparison of niches as spatial hubs across tissues. Integrative analysis of primary estrogen receptor (ER)-positive breast cancer, triple-negative breast cancer (TNBC) and metaplastic breast cancer (MBC) tissues led to the identification of spatial hubs with patient- and disease-specific cell type compositions and revealed metabolic reprogramming shaping immunosuppressive hubs in aggressive MBC.

Substrate stiffness enhances human regulatory T cell induction and metabolism.

Regulatory T cells (Tregs) provide an essential tolerance mechanism to suppress the immune response. Induced Tregs hold the potential to treat autoimmune diseases in adoptive therapy and can be produced with stimulating signals to CD3 and CD28 in presence of the cytokine TGF-ß and IL-2. This report examines the modulation of human Treg induction by leveraging the ability of T cells to sense the mechanical stiffness of an activating substrate. Treg induction on polyacrylamide gels (PA-gels) was sensitive to the substrate's elastic modulus, increasing with greater material stiffness. Single-cell RNA-Seq analysis revealed that Treg induction on stiffer substrates involved greater use of oxidative phosphorylation (OXPHOS). Inhibition of ATP synthase significantly reduced the rate of Treg induction and abrogated the difference among gels while activation of AMPK (AMP-activated protein kinase) increased Treg induction on the softer sample but not on the harder sample. Treg induction is thus mechanosensitive and OXPHOS-dependent, providing new strategies for improving the production of these cells for cellular immunotherapy.

Deep generative model deciphers derailed trajectories in acute myeloid leukemia.

Single-cell genomics has the potential to map cell states and their dynamics in an unbiased way in response to perturbations like disease. However, elucidating the cell-state transitions from healthy to disease requires analyzing data from perturbed samples jointly with unperturbed reference samples. Existing methods for integrating and jointly visualizing single-cell datasets from distinct contexts tend to remove key biological differences or do not correctly harmonize shared mechanisms. We present Decipher, a model that combines variational autoencoders with deep exponential families to reconstruct derailed trajectories (https://github.com/azizilab/decipher). Decipher jointly represents normal and perturbed single-cell RNA-seq datasets, revealing shared and disrupted dynamics. It further introduces a novel approach to visualize data, without the need for methods such as UMAP or TSNE. We demonstrate Decipher on data from acute myeloid leukemia patient bone marrow specimens, showing that it successfully characterizes the divergence from normal hematopoiesis and identifies transcriptional programs that become disrupted in each patient when they acquire NPM1 driver mutations.

Biphasic response of T cell activation to substrate stiffness.

T cell activation is sensitive to the mechanical properties of an activating substrate. However, there are also contrasting results on how substrate stiffness affects T cell activation, including differences between T cells of mouse and human origin. Towards reconciling these differences, this report examines the response of primary human T cells to polyacrylamide gels with stiffness between 5 and 110 kPa presenting activating antibodies to CD3 and CD28. T cell proliferation and IL-2 secretion exhibited a biphasic functional response to substrate stiffness, which can be shifted by changing density of activating antibodies and abrogated by inhibition of cellular contractility. T cell morphology was modulated by stiffness at early time points. RNA-seq indicates that T cells show differing monotonic trends in upregulated genes and pathways towards both ends of the stiffness spectrum. These studies provide a framework of T cell mechanosensing and suggest an effect of ligand density that may reconcile different, contrasting patterns of stiffness sensing seen in previous studies.

Tri-culture system for pro-hapten sensitizer identification and potency classification.

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-1ß, 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.