Spatial omics technologies at multimodal and single cell/subcellular level

Abstract

Spatial omics technologies enable a deeper understanding of cellular organizations and interactions within a tissue of interest. These assays can identify specific compartments or regions in a tissue with differential transcript or protein abundance, delineate their interactions, and complement other methods in defining cellular phenotypes. A variety of spatial methodologies are being developed and commercialized; however, these techniques differ in spatial resolution, multiplexing capability, scale/throughput, and coverage. Here, we review the current and prospective landscape of single cell to subcellular resolution spatial omics technologies and analysis tools to provide a comprehensive picture for both research and clinical applications.

An efficient induction method for human spinal lower motor neurons and high-throughput image analysis at the single cell level

“…In our study, we expanded the application of single cell tracking (Shin et al, 2018) to cell survival analysis to accurately evaluate the cellular vulnerability of ALS patients using long-term live imaging and single cell tracking of iPSC-derived spinal LMNs. It would be interesting to further associate the morphological data of each cell with omics expression data (Watson et al, 2022, Park et al, 2022. For ALS studies using iPSC-derived LMNs, it would be highly beneficial to associate phenotypic data, such as cell morphology and ICC images, with the omics data.…”

High-throughput microfluidic systems accelerated by artificial intelligence for biomedical applications

“…Moreover, multimodal data analysis may offer more comprehensive solutions to challenges. 52,53 For example, several multimodal data processing algorithms have already been applied in the biomedical field like the correlationbased method canonical correlation analysis (CCA), 54 the matrix factorization-based method multi-omics factor analysis v2 (MOFA+), 55,56 and a multi-view autoencoder. 57…”

Using Transcriptomics and Cell Morphology Data in Drug Discovery: The Long Road to Practice

“…More recent technologies include single-cell as well as spatially and time-resolved transcriptomics. − Those technologies have led to pipelines for, e.g., drug repurposing based on single-cell data, a better understanding of the tumor microenvironment in lung cancer, and insights into early development of human embryos as well as other organisms. However, in addition to high cost and low throughput, there is often no clear path to practical deployment of many of those readouts in a decision-making context, since data generation, processing, and decision making have not yet converged on standardized pipelines to this end.…”