Spatial molecular profiling: platforms, applications and analysis tools

Abstract

Molecular profiling technologies, such as genome sequencing and proteomics, have transformed biomedical research, but most such technologies require tissue dissociation, which leads to loss of tissue morphology and spatial information. Recent developments in spatial molecular profiling technologies have enabled the comprehensive molecular characterization of cells while keeping their spatial and morphological contexts intact. Molecular profiling data generate deep characterizations of the genetic, transcriptional and proteomic events of cells, while tissue images capture the spatial locations, organizations and interactions of the cells together with their morphology features. These data, together with cell and tissue imaging data, provide unprecedented opportunities to study tissue heterogeneity and cell spatial organization. This review aims to provide an overview of these recent developments in spatial molecular profiling technologies and the corresponding computational methods developed for analyzing such data.

Engineered Tools to Study Intercellular Communication

“…As strategies for multiplexed feature detection improve, commensurate advances in computational power will be required to process and interpret the results. [ 179,180 ] Multiplexed molecular validation techniques must also be developed for sequencing techniques that search for cell–cell interactions across entire tissues or organisms. For example, while techniques like ligand–receptor mapping can identify putative cell–cell interactions by the proportionate expression of ligand–receptor pairs, inferred results must be validated with more stringent molecular techniques that demonstrate the physical colocalization of each pair.…”

Mixed Effects Machine Learning Models for Colon Cancer Metastasis Prediction using Spatially Localized Immuno-Oncology Markers

“…Within a slide, there is spatial variation in marker abundance, such that regions of interest (ROI) that are closer together may exhibit similar gene/protein expression 5,8,16 . Spatial autocorrelation is typically assessed using a Gaussian Process (GP), a mixed-effects model with a parameterized kernel.…”

“…Although multiplexed immunohistochemistry and laser capture microdissection approaches once served as popular methods for assessing spatially resolved omics information, these approaches are largely intractable for high throughput analysis because quantitative analysis is hindered by significant tissue distortion from chemical restaining processes, insufficient multiplexing, and prohibitive costs. New spatial omics technologies can multiplex far more markers through targeted cleavage of oligonucleotide tags [2][3][4][5][6][7][8] . For instance, the Nanostring GeoMX Digital Spatial Profiler (DSP) first utilizes multiple immunofluorescent (IF) antibody stains to highlight various cellular lineages, then uses UV-light to cleave attached oligos for quantification via the nCounter instrument or next-generation sequencing (NGS) 9,10 .…”

spatialGE: Quantification and visualization of the tumor microenvironment heterogeneity using spatial transcriptomics

“…Several ST technologies to study the TME have been developed (6)(7)(8)(9)(10)(11)(12). The most common approach involves mRNA probe hybridization in immobilized tissues on surfaces (7).…”