Human organoids with an autologous tissue-resident immune compartment.

The intimate relationship between the epithelium and immune system is crucial for maintaining tissue homeostasis, with perturbations therein linked to autoimmune disease and cancer1-3. Whereas stem cell-derived organoids are powerful models of epithelial function4, they lack tissue-resident immune cells that are essential for capturing organ-level processes. We describe human intestinal immuno-organoids (IIOs), formed through self-organization of epithelial organoids and autologous tissue-resident memory T (TRM) cells, a portion of which integrate within the epithelium and continuously survey the barrier. TRM cell migration and interaction with epithelial cells was orchestrated by TRM cell-enriched transcriptomic programs governing cell motility and adhesion. We combined IIOs and single-cell transcriptomics to investigate intestinal inflammation triggered by cancer-targeting biologics in patients. Inflammation was associated with the emergence of an activated population of CD8+ T cells that progressively acquired intraepithelial and cytotoxic features. The appearance of this effector population was preceded and potentiated by a T helper-1-like CD4+ population, which initially produced cytokines and subsequently became cytotoxic itself. As a system amenable to direct perturbation, IIOs allowed us to identify the Rho pathway as a new target for mitigation of immunotherapy-associated intestinal inflammation. Given that they recapitulate both the phenotypic outcomes and underlying interlineage immune interactions, IIOs can be used to study tissue-resident immune responses in the context of tumorigenesis and infectious and autoimmune diseases.

Statistical integration of multi-omics and drug screening data from cell lines.

Data integration methods are used to obtain a unified summary of multiple datasets. For multi-modal data, we propose a computational workflow to jointly analyze datasets from cell lines. The workflow comprises a novel probabilistic data integration method, named POPLS-DA, for multi-omics data. The workflow is motivated by a study on synucleinopathies where transcriptomics, proteomics, and drug screening data are measured in affected LUHMES cell lines and controls. The aim is to highlight potentially druggable pathways and genes involved in synucleinopathies. First, POPLS-DA is used to prioritize genes and proteins that best distinguish cases and controls. For these genes, an integrated interaction network is constructed where the drug screen data is incorporated to highlight druggable genes and pathways in the network. Finally, functional enrichment analyses are performed to identify clusters of synaptic and lysosome-related genes and proteins targeted by the protective drugs. POPLS-DA is compared to other single- and multi-omics approaches. We found that HSPA5, a member of the heat shock protein 70 family, was one of the most targeted genes by the validated drugs, in particular by AT1-blockers. HSPA5 and AT1-blockers have been previously linked to α-synuclein pathology and Parkinson's disease, showing the relevance of our findings. Our computational workflow identified new directions for therapeutic targets for synucleinopathies. POPLS-DA provided a larger interpretable gene set than other single- and multi-omic approaches. An implementation based on R and markdown is freely available online.

Modeling early pathophysiological phenotypes of diabetic retinopathy in a human inner blood-retinal barrier-on-a-chip.

Diabetic retinopathy (DR) is a microvascular disorder characterized by inner blood-retinal barrier (iBRB) breakdown and irreversible vision loss. While the symptoms of DR are known, disease mechanisms including basement membrane thickening, pericyte dropout and capillary damage remain poorly understood and interventions to repair diseased iBRB microvascular networks have not been developed. In addition, current approaches using animal models and in vitro systems lack translatability and predictivity to finding new target pathways. Here, we develop a diabetic iBRB-on-a-chip that produces pathophysiological phenotypes and disease pathways in vitro that are representative of clinical diagnoses. We show that diabetic stimulation of the iBRB-on-a-chip mirrors DR features, including pericyte loss, vascular regression, ghost vessels, and production of pro-inflammatory factors. We also report transcriptomic data from diabetic iBRB microvascular networks that may reveal drug targets, and examine pericyte-endothelial cell stabilizing strategies. In summary, our model recapitulates key features of disease, and may inform future therapies for DR.

Axonal Lysosomal Assays for Characterizing the Effects of LRRK2 G2019S.

The degeneration of axon terminals before the soma, referred to as "dying back", is a feature of Parkinson's disease (PD). Axonal assays are needed to model early PD pathogenesis as well as identify protective therapeutics. We hypothesized that defects in axon lysosomal trafficking as well as injury repair might be important contributing factors to "dying back" pathology in PD. Since primary human PD neurons are inaccessible, we developed assays to quantify axonal trafficking and injury repair using induced pluripotent stem cell (iPSC)-derived neurons with LRRK2 G2019S, which is one of the most common known PD mutations, and isogenic controls. We observed a subtle axonal trafficking phenotype that was partially rescued by a LRRK2 inhibitor. Mutant LRRK2 neurons showed increased phosphorylated Rab10-positive lysosomes, and lysosomal membrane damage increased LRRK2-dependent Rab10 phosphorylation. Neurons with mutant LRRK2 showed a transient increase in lysosomes at axotomy injury sites. This was a pilot study that used two patient-derived lines to develop its methodology; we observed subtle phenotypes that might correlate with heterogeneity in LRRK2-PD patients. Further analysis using additional iPSC lines is needed. Therefore, our axonal lysosomal assays can potentially be used to characterize early PD pathogenesis and test possible therapeutics.