2022
DOI: 10.1101/2022.06.02.490672
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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 interact… Show more

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Cited by 5 publications
(3 citation statements)
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“…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 .…”
Section: Discussionmentioning
confidence: 99%
“…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 .…”
Section: Discussionmentioning
confidence: 99%
“…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.…”
Section: Key Pointsmentioning
confidence: 99%
“…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.…”
mentioning
confidence: 99%