Spatial detection of fetal marker genes expressed at low level in adult human heart tissue

Asp, Michaela; Salmén, Fredrik; Ståhl, Patrik L.; Vicković, Sanja; Felldin, Ulrika; Löfling, Marie; Navarro, José Fernández; Maaskola, Jonas; Eriksson, Maria; Persson, Bengt; Corbascio, Matthias; Persson, Hans; Linde, Cecilia; Lundeberg, Joakim

doi:10.1038/s41598-017-13462-5

Cited by 69 publications

(50 citation statements)

References 28 publications

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“…In addition to prostate cancer, our modeling approach can be used to study spatial heterogeneity and coordination of metabolic activities in a wide range diseases where spatially resolved transcriptomic datasets are currently available, such as breast cancer 9 13 , and amyotrophic lateral sclerosis 82 . It can also be used to study spatial regulation of normal organ physiology in the liver 83 , heart 84,85 and kidney 86 .…”

Section: Discussionmentioning

confidence: 99%

Spatial modeling of prostate cancer metabolic gene expression reveals extensive heterogeneity and selective vulnerabilities

Wang

Ruzzo

2020

Sci Rep

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Spatial heterogeneity is a fundamental feature of the tumor microenvironment (TME), and tackling spatial heterogeneity in neoplastic metabolic aberrations is critical for tumor treatment. Genomescale metabolic network models have been used successfully to simulate cancer metabolic networks. However, most models use bulk gene expression data of entire tumor biopsies, ignoring spatial heterogeneity in the TME. To account for spatial heterogeneity, we performed spatially-resolved metabolic network modeling of the prostate cancer microenvironment. We discovered novel malignantcell-specific metabolic vulnerabilities targetable by small molecule compounds. We predicted that inhibiting the fatty acid desaturase SCD1 may selectively kill cancer cells based on our discovery of spatial separation of fatty acid synthesis and desaturation. We also uncovered higher prostaglandin metabolic gene expression in the tumor, relative to the surrounding tissue. Therefore, we predicted that inhibiting the prostaglandin transporter SLCO2A1 may selectively kill cancer cells. Importantly, SCD1 and SLCO2A1 have been previously shown to be potently and selectively inhibited by compounds such as CAY10566 and suramin, respectively. We also uncovered cancer-selective metabolic liabilities in central carbon, amino acid, and lipid metabolism. Our novel cancer-specific predictions provide new opportunities to develop selective drug targets for prostate cancer and other cancers where spatial transcriptomics datasets are available.Cancer cells reprogram their metabolism to fulfill the energetic and biosynthetic needs of proliferation, invasion and migration 1 . This is exemplified in prostate cancer, the second most common cancer in American men after melanoma 2 . Previous studies have uncovered profound metabolic dysregulation in multiple pathways, particularly in fatty acid and lipid metabolism 3,4 . Discovering novel cancer-specific metabolic aberrations has significant translational applications, because cancer-associated metabolic dysfunctions can be exploited to advance cancer detection (e.g., 18 F-FDG (Fludeoxyglucose) imaging based on elevated glycolysis in cancer 5 ) and treatment (e.g., L-asparaginase in treating acute lymphoblastic leukemia 6 ).Cancer metabolic reprograming is profoundly impacted by spatial heterogeneity, a fundamental feature of the tumor microenvironment (TME) 7 . Heterogeneous distributions of blood vessels and stromal tissues create uneven spatial gradients of nutrients and metabolic byproducts, which significantly shape the phenotypes of many cell types in the TME 8 . Recent technologies, such as spatial transcriptomics 9,10 and Slide-seq 11 have enabled transcriptomic profiling of hundreds of locations within tissue sections with high spatial resolution (2-100 μm), and have been used to study multiple types of malignancies, including prostate cancer, breast cancer, pancreatic cancer, and melanoma 9,10,12-14 . These spatially-resolved datasets provide novel opportunities to dissect spatial metabolic heterogeneity i...

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Section: Discussionmentioning

confidence: 99%

Spatial modeling of prostate cancer metabolic gene expression reveals extensive heterogeneity and selective vulnerabilities

Wang

Ruzzo

2020

Sci Rep

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“…ST data share many limitations of single cell RNA-seq, including low coverage and high dropout rate. So far, ST studies have relied on preprocessing pipelines inspired by single cell RNA-seq studies (Ståhl et al, 2016;Asp et al, 2017;Giacomello et al, 2017;Berglund et al, 2018;Lundmark et al, 2018;Salmen et al, 2018;Thrane et al, 2018). These include normalization of gene-wise read counts in a cell/spot by the total number of reads collected from that cell/spot.…”

Section: Dear Editormentioning

confidence: 99%

Transcriptional output, cell types densities and normalization in spatial transcriptomics

Saiselet

Rodrigues-Vitoria

Tourneur

et al. 2018

Preprint

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Spatial transcriptomics measures mRNA at hundreds of 100 micrometer-diameter spots evenly spread across 6.5×6.9 mm2 histological slices. Gene expression within each spot is commonly normalized by total read counts. However we show that the transcriptional output of individual spots reflects the number of cells they contain, hence total read counts per spot reflect relevant biology. Although per-spot read-count normalization reveals important enrichment trends, it may heavily distort cell-type-related absolute local expression and conceal important biological information.

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“…Then the ST-spots are aligned with the H&E image to visualise the transcriptome-wide gene expression within the spatial context of the intact tissue. Currently ST-seq has been used in embryonic, inflammatory and cancer tissue, but has yet to be extended to the mammalian kidney [14,[18][19][20][21][22][23][24][25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Spatially resolved transcriptome profiles of mammalian kidneys illustrate the molecular complexity of functional nephron segments, cell-to-cell interactions and genetic variants

Raghubar

Pham

Tan

et al. 2020

Preprint

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Understanding the molecular mechanisms underlying mammalian kidney function requires transcriptome profiling of the interplay between cells comprising nephron segments. Traditional transcriptomics requires cell dissociation, resulting in loss of the spatial context of gene expression within native tissue. To address this problem, we performed spatial transcriptomics (ST) to retain the spatial context of the transcriptome in human and mouse kidneys. The generated ST data allowed spatially resolved differential gene expression analysis, spatial identification of functional nephron segments, cell-to-cell interaction analysis, and chronic kidney disease-associated genetic variant calling. Novel ST thus provides an opportunity to enhance kidney diagnostics and knowledge, by retaining the spatial context of gene expression within intact tissue.

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Spatial detection of fetal marker genes expressed at low level in adult human heart tissue

Cited by 69 publications

References 28 publications

Spatial modeling of prostate cancer metabolic gene expression reveals extensive heterogeneity and selective vulnerabilities

Spatial modeling of prostate cancer metabolic gene expression reveals extensive heterogeneity and selective vulnerabilities

Transcriptional output, cell types densities and normalization in spatial transcriptomics

Spatially resolved transcriptome profiles of mammalian kidneys illustrate the molecular complexity of functional nephron segments, cell-to-cell interactions and genetic variants

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