Uncovering the spatial landscape of molecular interactions within the tumor microenvironment through latent spaces

Abstract

Recent advances in spatial transcriptomics (ST) enable gene expression measurements from a tissue sample while retaining its spatial context. This technology enables unprecedented in situ resolution of the regulatory pathways that underlie the heterogeneity in the tumor and its microenvironment (TME). The direct characterization of cellular co-localization with spatial technologies facilities quantification of the molecular changes resulting from direct cell-cell interaction, as occurs in tumor-immune interactions. We present SpaceMarkers, a novel bioinformatics algorithm to infer molecular changes from cell-cell interaction from latent space analysis of ST data. We apply this approach to infer molecular changes from tumor-immune interactions in Visium spatial transcriptomics data of metastasis, invasive and precursor lesions, and immunotherapy treatment. Further transfer learning in matched scRNA-seq data enabled further quantification of the specific cell types in which SpaceMarkers are enriched. Altogether, SpaceMarkers can identify the location and context-specific molecular interactions within the TME from ST data.

Inflammatory Signaling in Pancreatic Cancer Transfers Between a Single-cell RNA Sequencing Atlas and Co-Culture

“…While additional computational tools enable more direct inference of molecular changes from cellular interactions 45 , the unique application of transfer learning between human scRNA-seq data and organoid co-culture enables direct bidirectional investigation of cellular state transitions and intercellular signaling between in silico discovery and experimental validation. Currently, this analysis relies on inferences resulting from a pipeline combining NMF-based pattern detection with CoGAPS 36 , transfer learning with ProjectR 33 , and finally ligand-receptor networks from Domino 34 .…”

Magnetic Resonance Imaging–Based Assessment of Pancreatic Fat Strongly Correlates With Histology-Based Assessment of Pancreas Composition

“…Prior reports correlate pancreatic fat content measured by MRI and computed tomography with histology determined by visual inspection of a limited number of tissue sections, 5,14,18,19 which may limit the accuracy of the comparison. Recent deep learning–based tissue segmentation algorithms are capable of rapidly deconvolving histologic slides into their various microanatomical components 20–24 . These algorithms allow rapid, consistent calculation of tissue composition that can be quantitatively validated.…”

1316 Enabling single-cell resolution in spatial transcriptomics for comprehensive cellular profiling with variational autoencoders and optimal transport

“…To determine the optimal latent dimensions, we employed various intrinsic dimensionality (ID) detection methods on singlenuclei images and pre-processed transcriptomic data. [10][11][12][13] We developed a Sequencing-VAE with an auxiliary classification task to extract spot identity features and an Imaging-VAE with a nuclei painting proxy task to distill meaningful nuclei morphological features. Through Monge mapping, we translated single-nuclei images into coupling points in transcriptomic latent spaces, which could be decoded by the Sequencing-VAE to generate RNA profiling correspondence.…”