GEN Biotechnology

There are thousands of rare genetic diseases lacking an approved treatment, many of which are life limiting to children. Those caused by a missing protein may represent a target for protein replacement either by enzyme replacement therapy or by gene therapy. One of the many challenges working on these types of genetic diseases is the availability of funding, as these diseases typically affect very small number of patients. Here we offer a novel case study of our approach to developing a treatment for one such rare disease, which has not required venture capital, angel investment, or funding by foundations to date. We have instead pursued NIH small business grants to fund the early preclinical work performed by our academic collaborators and ourselves. Our approach to developing a treatment for a rare disease on a shoestring budget is unlike any of the alternative approaches to funding.

Spatial omics technologies, including highly multiplexed histologic protein assays, nucleic acid abundance and/or sequence mapping, and spatial epigenetics assays, offer powerful tools for interrogating the complex biology of human tissues. These technologies have been broadly applied in basic and translational research, which presages deployment in clinical settings as well. In this article, we discuss spatial omics technologies with an emphasis on retrieval of disease-related information in single samples, with potential clinical applications in specialties such as oncology and immunology, and in the development of personalized treatment. Capable of localizing detailed molecular information within histologic structures, spatial omics technologies provide both cell-intrinsic information and microenvironmental interaction context. This will allow more precise diagnostic and prognostic classifications and more accurate predictions about treatment responses to be made. While technical and financial challenges to widespread deployment in clinical laboratories remain, spatial omics technologies are expected to dramatically expand actionable information obtained by human tissue sampling for pathologic analysis.

Recently, organoids, or three-dimensional (3D) cellular assemblies, have demonstrated promise as viable models for organ development and disease study. In contrast to challenging preclinical models, organoids are advantageous due to rapid fabrication times and greater patient specificity. The advent of spatial transcriptomics and single cell technologies has also enhanced the characterization of intraorganoid heterogeneity, thus highlighting 3D cell signaling and organ development at micro scales. In this study, we describe ongoing and future directions in spatial omics integrated with various imaging technologies for two-dimensional/3D organoid characterization. Utilizing both retinal organoids and native retinal tissues, we undertook an analysis to deconstruct the cellular compositions and structural attributes of their respective cell layers. Our findings indicate that the spatial organization of cell phenotypes is similar between organoids and native retinal tissue. However, it is noteworthy that native retinal tissue possesses thinner yet distinctly separated cell layers compared with the organoids.

Transcriptomics is one of the largest areas of research in biological sciences. Aside from RNA expression levels, the significance of RNA spatial context has also been unveiled in the recent decade, playing a critical role in diverse biological processes, from subcellular kinetic regulation to cell communication, from tissue architecture to tumor microenvironment, and more. To systematically unravel the positional patterns of RNA molecules across subcellular, cellular, and tissue levels, spatial transcriptomics techniques have emerged and rapidly became an irreplaceable tool set. Herein, we review and compare current spatial transcriptomics techniques on their methods, advantages, and limitations, as well as applications across a wide range of biological investigations. This review serves as a comprehensive guide to spatial transcriptomics for researchers interested in adopting this powerful suite of technologies.

Neuropathological lesions in the brains of individuals affected with neurodegenerative disorders are hypothesized to trigger molecular and cellular processes that disturb the homeostasis of local microenvironments. Here, we applied the 10x Genomics Visium Spatial Proteogenomics (Visium-SPG) platform, which couples spatial gene expression with immunofluorescence (IF) protein co-detection, to evaluate its ability to quantify changes in spatial gene expression with respect to amyloid-beta (Aβ) and hyperphosphorylated tau (pTau) pathology in post-mortem human brain tissue from individuals with Alzheimer's disease (AD). We identified transcriptomic signatures associated with proximity to Aβ in the human inferior temporal cortex during late-stage AD, which we further investigated at cellular resolution with combined IF and single-molecule fluorescent in situ hybridization (smFISH). The study provides a data analysis workflow for Visium-SPG, and the data represent a proof-of-principle for the power of multi-omic profiling in identifying changes in molecular dynamics that are spatially associated with pathology in the human brain. We provide the scientific community with web-based, interactive resources to access the datasets of the spatially resolved AD-related transcriptomes.

Head and neck squamous cell carcinomas (HNSCCs) are the seventh most common cancer and represent a global health burden. Immune checkpoint inhibitors (ICIs) have shown promise in treating recurrent/metastatic disease with durable benefit in ∼30% of patients. Current biomarkers for HNSCC are limited in their dynamic ability to capture tumor microenvironment (TME) features with an increasing need for deeper tissue characterization. Therefore, new biomarkers are needed to accurately stratify patients and predict responses to therapy. Here, we have optimized and applied an ultra-high plex, single-cell spatial protein analysis in HNSCC. Tissues were analyzed with a panel of 101 antibodies that targeted biomarkers related to tumor immune, metabolic and stress microenvironments. Our data uncovered a high degree of intra-tumoral heterogeneity intrinsic to HNSCC and provided unique insights into the biology of the disease. In particular, a cellular neighborhood analysis revealed the presence of six unique spatial neighborhoods enriched in functionally specialized immune subsets. In addition, functional phenotyping based on key metabolic and stress markers identified four distinct tumor regions with differential protein signatures. One region was marked by infiltration of CD8+ cytotoxic T cells and overexpression of BAK, a proapoptotic regulator, suggesting strong immune activation and stress. Another adjacent region within the same tumor had high expression of G6PD and MMP9, known drivers of tumor resistance and invasion, respectively. This dichotomy of immune activation-induced death and tumor progression in the same sample demonstrates the heterogenous niches and competing microenvironments that may underpin variable clinical responses. Our data integrate single-cell ultra-high plex spatial information with the functional state of the TME to provide insights into HNSCC biology and differential responses to ICI therapy. We believe that the approach outlined in this study will pave the way toward a new understanding of TME features associated with response and sensitivity to ICI therapies.

Hyperspectral imaging has emerged as a valuable technique for analyzing biological tissue compositions by probing intrinsic or exogenous biomolecules. However, conventional hyperspectral imaging methods predominantly rely on fluorescent signatures, limiting their application to nonfluorescent samples. To overcome this limitation, a label-free reflection-mode hyperspectral photoacoustic microscopy (RHS-PAM) system has been developed. RHS-PAM enables the imaging of thick biological samples with a wide range of intrinsic contrasts using excitation wavelengths ranging from ultraviolet to near infrared. RHS-PAM eliminates the need for tissue staining, and has achieved cellular-level spatial resolution and automatic image coregistrations at all wavelengths. Proof-of-concept applications of RHS-PAM have been demonstrated on various model organisms, including Caenorhabditis elegans, frog tadpole, zebrafish, and mouse. The technique has successfully imaged a wealth of structural and molecular features in these organisms, utilizing the optical absorption contrast of nucleic acids, proteins, hemoglobin, melanin, and lipids. The results highlight the capability of RHS-PAM to provide rich optical contrast, high spatial resolution, and an extended spectral range for label-free imaging. We believe that RHS-PAM represents a highly promising tool for single-cell biochemical mapping of diverse biological tissues.