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Deep sequencing and automated histochemistry of human tissue slice cultures improve their usability as preclinical model for cancer research.

ABSTRACT: Cancer research requires models closely resembling the tumor in the patient. Human tissue cultures can overcome interspecies limitations of animal models or the loss of tissue architecture in in vitro models. However, analysis of tissue slices is often limited to histology. Here, we demonstrate that slices are also suitable for whole transcriptome sequencing and present a method for automated histochemistry of whole slices. Tumor and peritumoral tissue from a patient with glioblastoma was processed to slice cultures, which were treated with standard therapy including temozolomide and X-irradiation. Then, RNA sequencing and automated histochemistry were performed. RNA sequencing was successfully accomplished with a sequencing depth of 243 to 368 x 106 reads per sample. Comparing tumor and peritumoral tissue, we identified 1888 genes significantly downregulated and 2382 genes upregulated in tumor. Treatment significantly downregulated 2017 genes, whereas 1399 genes were upregulated. Pathway analysis revealed changes in the expression profile of treated glioblastoma tissue pointing towards downregulated proliferation. This was confirmed by automated analysis of whole tissue slices stained for Ki67. In conclusion, we demonstrate that RNA sequencing of tissue slices is possible and that histochemical analysis of whole tissue slices can be automated which increases the usability of this preclinical model.

SUBMITTER: Haehnel S

PROVIDER: S-EPMC6934722 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Deep sequencing and automated histochemistry of human tissue slice cultures improve their usability as preclinical model for cancer research.

Haehnel Susann S Reiche Kristin K Loeffler Dennis D Horn Andreas A Blumert Conny C Puppel Sven-Holger SH Kaiser Nicole N Rapp Felicitas F Rade Michael M Horn Friedemann F Meixensberger Juergen J Bechmann Ingo I Gaunitz Frank F Winter Karsten K

Scientific reports 20191227 1

Cancer research requires models closely resembling the tumor in the patient. Human tissue cultures can overcome interspecies limitations of animal models or the loss of tissue architecture in in vitro models. However, analysis of tissue slices is often limited to histology. Here, we demonstrate that slices are also suitable for whole transcriptome sequencing and present a method for automated histochemistry of whole slices. Tumor and peritumoral tissue from a patient with glioblastoma was proces ...[more]

PMID: 31882946

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Project description:Determining the aggressiveness of renal cell carcinoma (RCC) noninvasively is a critical part of the diagnostic workup for treating this disease that kills more than 15,000 people annually in the USA. Recently, we have shown that not only the amount of lactate produced, as a consequence of the Warburg effect, but also its efflux out of the cell, is a critical marker of RCC aggressiveness and differentiating RCCs from benign renal tumors. Enzymatic conversions can now be measured in situ with hyperpolarized (HP) 13 C magnetic resonance (MR) on a sub-minute time scale. Using RCC models, we have shown that this technology can interrogate in real time both lactate production and compartmentalization, which are associated with tumor aggressiveness. The dynamic HP MR data have enabled us to robustly characterize parameters that have been elusive to measure directly in intact living cells and murine tumors thus far. Specifically, we were able to measure the same intracellular lactate longitudinal relaxation time in three RCC cell lines of 16.42 s, and lactate efflux rate ranging from 0.14 to 0.8 s-1 in the least to the most aggressive RCC cell lines and correlate it to monocarboxylate transporter isoform 4 expression. We also analyzed dynamic HP lactate and pyruvate data from orthotopic murine RCC tumors using a simplified one-compartment model, and showed comparable apparent pyruvate to lactate conversion rate (kPL ) values with those measured in vitro. This kinetic modeling was then extended to characterize the lactate dynamics in patient-derived living RCC tissue slices; and even without direct measurement of the extracellular lactate signal the efflux parameter was still assessed and was distinct between the benign renal tumors and RCCs. Across all these preclinical models, the rate parameters of kPL and lactate efflux correlated to cancer aggressiveness, demonstrating the validity of our modeling approach for noninvasive assessment of RCC aggressiveness.

Dataset Information

Deep sequencing and automated histochemistry of human tissue slice cultures improve their usability as preclinical model for cancer research.

Publications

Deep sequencing and automated histochemistry of human tissue slice cultures improve their usability as preclinical model for cancer research.

Similar Datasets

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