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

Deshpande, Atul; Loth, Melanie; Sidiropoulos, Dimitrios N.; Zhang, Shuming; Yuan, Long; Bell, Alexander T.F.; Zhu, Qingfeng; Ho, Won Jin; Santa-Maria, Cesar Augusto; Gilkes, Daniele M.; Williams, Stephen R.; Uytingco, Cedric; Chew, Jennifer; Hartnett, Andrej; Bent, Zachary; Favorov, Alexander V.; Popel, Aleksander S.; Yarchoan, Mark; Kiemen, Ashley; Wu, Pei‐Hsun; Fujikura, Kohei; Wirtz, Denis; Wood, Laura D.; Jaffee, Elizabeth M.; Anders, Robert A.; Danilova, Ludmila; Stein-O’Brien, Genevieve; Kagohara, Luciane T.; Fertig, Elana J.

doi:10.1016/j.cels.2023.03.004

Cited by 9 publications

(16 citation statements)

References 43 publications

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“…Researchers often aim to detect differences in gene expression between cells or tissue niches, with many methods available for non-spatially informed assays such as single-cell or "bulk" RNAseq 49,50,53,54 . Although spatial statistics methods have existed in the literature for several decades 51 , only recently have spatial statistics been applied to the detection of spatially variable genes in biological tissues assayed with ST 35,[38][39][40][41][42][43][44] . In this study, we have shown that the detection of differentially expressed genes in ST data benefits from statistical models that consider spatial autocorrelation.…”

Section: Discussionmentioning

confidence: 99%

Differential gene expression analysis of spatial transcriptomic experiments using spatial mixed models

Ospina,

Soupir,

Manjarres-Betancur

et al. 2024

Preprint

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Spatial transcriptomics (ST) assays represent a revolution in the way the architecture of tissues is studied, by allowing for the exploration of cells in their spatial context. A common element in the analysis is the delineation of tissue domains or "niches" followed by the detection of differentially expressed genes to infer the biological identity of the tissue domains or cell types. However, many studies approach differential expression analysis by using statistical approaches often applied in the analysis of non-spatial scRNA data (e.g., two-sample t-tests, Wilcoxon rank sum test), hence neglecting the spatial dependency observed in ST data. In this study, we show that applying linear mixed models with spatial correlation structures using spatial random effects effectively accounts for the spatial autocorrelation and reduces inflation of type-I error rate that is observed in non-spatial based differential expression testing. We also show that spatial linear models with an exponential correlation structure provide a better fit to the ST data as compared to non-spatial models, particularly for spatially resolved technologies that quantify expression at finer scales (i.e., single-cell resolution).

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Section: Discussionmentioning

confidence: 99%

Differential gene expression analysis of spatial transcriptomic experiments using spatial mixed models

Ospina,

Soupir,

Manjarres-Betancur

et al. 2024

Preprint

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“…Similarly, it would also be useful to understand how local microenvironments or cellular interactions, e.g. as learned by [150, 112, 30], vary along the GASTON spatial gradients. Finally, several recent papers have studied how genetic variants affect single-cell gene expression measurements, i.e.…”

Section: Discussionmentioning

confidence: 99%

Mapping the topography of spatial gene expression with interpretable deep learning

Chitra,

Arnold,

Sarkar

et al. 2023

Preprint

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Spatially resolved transcriptomics technologies provide high-throughput measurements of gene expression in a tissue slice, but the sparsity of this data complicates the analysis of spatial gene expression patterns such as gene expression gradients. We address these issues by deriving atopographic mapof a tissue slice—analogous to a map of elevation in a landscape—using a novel quantity called theisodepth. Contours of constant isodepth enclose spatial domains with distinct cell type composition, while gradients of the isodepth indicate spatial directions of maximum change in gene expression. We develop GASTON, an unsupervised and interpretable deep learning algorithm that simultaneously learns the isodepth, spatial gene expression gradients, and piecewise linear functions of the isodepth that model both continuous gradients and discontinuous spatial variation in the expression of individual genes. We validate GASTON by showing that it accurately identifies spatial domains and marker genes across several biological systems. In SRT data from the brain, GASTON reveals gradients of neuronal differentiation and firing, and in SRT data from a tumor sample, GASTON infers gradients of metabolic activity and epithelial-mesenchymal transition (EMT)-related gene expression in the tumor microenvironment.

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“…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%

“…Recent deep learning-based tissue segmentation algorithms are capable of rapidly deconvolving histologic slides into their various microanatomical components. [20][21][22][23][24] These algorithms allow rapid, consistent calculation of tissue composition that can be quantitatively validated. In addition, deep learning algorithms can discern diverse pancreatic structures, allowing comparisons of fat content to a large number of other pancreatic tissue components.…”

mentioning

confidence: 99%

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

Kiemen,

Dbouk,

Diwan

et al. 2024

Pancreas

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Objective The aim of the study is to assess the relationship between magnetic resonance imaging (MRI)-based estimation of pancreatic fat and histology-based measurement of pancreatic composition. Materials and Methods In this retrospective study, MRI was used to noninvasively estimate pancreatic fat content in preoperative images from high-risk individuals and disease controls having normal pancreata. A deep learning algorithm was used to label 11 tissue components at micron resolution in subsequent pancreatectomy histology. A linear model was used to determine correlation between histologic tissue composition and MRI fat estimation. Results Twenty-seven patients (mean age 64.0 ± 12.0 years [standard deviation], 15 women) were evaluated. The fat content measured by MRI ranged from 0% to 36.9%. Intrapancreatic histologic tissue fat content ranged from 0.8% to 38.3%. MRI pancreatic fat estimation positively correlated with microanatomical composition of fat (r = 0.90, 0.83 to 0.95], P < 0.001); as well as with pancreatic cancer precursor (r = 0.65, P < 0.001); and collagen (r = 0.46, P < 0.001) content, and negatively correlated with pancreatic acinar (r = −0.85, P < 0.001) content. Conclusions Pancreatic fat content, measurable by MRI, correlates to acinar content, stromal content (fibrosis), and presence of neoplastic precursors of cancer.

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

Cited by 9 publications

References 43 publications

Differential gene expression analysis of spatial transcriptomic experiments using spatial mixed models

Differential gene expression analysis of spatial transcriptomic experiments using spatial mixed models

Mapping the topography of spatial gene expression with interpretable deep learning

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

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