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Vital ex vivo tissue labeling and pathology-guided micropunching to characterize cellular heterogeneity in the tissue microenvironment.

ABSTRACT: Cellular heterogeneity within the tissue microenvironment may underlie chemotherapeutic resistance and response, enabling tumor evolution; however, this heterogeneity it is difficult to characterize. Here, we present a new approach-pathology-guided micropunching (PGM)-that enables identification and characterization of heterogeneous foci identified in viable human and animal model tissue slices. This technique consists of live-cell tissue labeling using fluorescent antibodies/small molecules to identify heterogeneous foci (e.g., immune infiltrates or cells with high levels of reactive oxygen species) in viable tissues, coupled with a micropunch step to isolate cells from these heterogeneous foci for downstream molecular or vital functional analysis. Micropunches obtained from epithelial or stromal fibroblast foci in human prostate tissue show 6- to 12-fold enrichment in transcripts specific for EpCam/cytokeratin 8 and vimentin/a-smooth muscle actin/integrin 1-?, respectively. Transcriptional enrichment efficiency agrees with epithelial and stromal laser capture microdissection samples isolated from human prostate. Micropunched foci show a loss of cellular viability in the periphery, but centrally localized cells retained viability before and after dissociation and grew out in culture.

SUBMITTER: Johnson BP

PROVIDER: S-EPMC5814138 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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Vital ex vivo tissue labeling and pathology-guided micropunching to characterize cellular heterogeneity in the tissue microenvironment.

Johnson Brian P BP Vitek Ross A RA Geiger Peter G PG Huang Wei W Jarrard David F DF Lang Joshua M JM Beebe David J DJ

BioTechniques 20180101 1

Cellular heterogeneity within the tissue microenvironment may underlie chemotherapeutic resistance and response, enabling tumor evolution; however, this heterogeneity it is difficult to characterize. Here, we present a new approach-pathology-guided micropunching (PGM)-that enables identification and characterization of heterogeneous foci identified in viable human and animal model tissue slices. This technique consists of live-cell tissue labeling using fluorescent antibodies/small molecules to ...[more]

PMID: 29384072

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Project description:Study questionIs it possible to establish an ex vivo endometriosis model using cryopreserved endometriotic tissue fragments?Summary answerCryopreserved endometriotic tissue fragments remain viable after thawing and during at least 3 days of culture and can therefore be used to establish an ex vivo endometriosis model to efficiently test potential therapeutic agents.What is known alreadyEndometriosis is the most prevalent benign gynecologic disease with an enormous societal burden; however, curative therapies are still lacking. To efficiently test potential new therapies, an ex vivo model based on previously cryopreserved endometriotic tissue that recapitulates the different endometriosis subtypes and their microenvironment is highly desirable.Study design, size, durationEndometriotic tissue fragments of three different subtypes were obtained from 28 patients by surgical resection. After cryopreservation and thawing, viability and metabolic activity of these tissue fragments were assessed. Viability was compared with fresh fragments from 11 patients directly after surgical removal. Experimental intervention studies were performed in cryopreserved and thawed tissue fragments from two patients to confirm the usability of these tissues for ex vivo intervention studies.Participants/materials, setting, methodsEndometriotic tissue fragments (n = 45) were cryopreserved according to three different protocols. After thawing, fragments were cultured for 24 h. A resazurin-based assay was performed to assess the metabolic activity of the tissue fragments. In addition, cell type-specific viability was analyzed by VivaFix, Hoechst 33342, and α-smooth muscle actin immunofluorescence staining and confocal microscopy. The presence of endometriosis was histologically confirmed based on hematoxylin-eosin staining. Cryopreserved and thawed tissue fragments were treated for 72 h with pirfenidone or metformin and COL1A1 and CEMIP gene expressions were assessed using RT-PCR and RT-qPCR, either in the whole tissue fragments or in myofibroblasts isolated by laser capture microdissection.Main results and the role of chanceMetabolic activity of endometriotic tissue fragments obtained from peritoneal (PER), ovarian (OMA), and deep (DE) endometriotic lesions was well preserved after cryopreservation in a dimethyl sulfoxide-based medium and was comparable with fresh tissue fragments. Relative metabolic activity compared to fresh tissue was 70% (CI: 92-47%) in PER, 43% (CI: 53-15%) in OMA and 94% (CI: 186-3%) in DE lesions. In fragments from PE lesions 92% (CI: 87-96%), from OMA lesions 95% (CI: 91-98%), and from DE lesions 88% (CI: 78-98%) of cells were viable after cryopreservation and thawing followed by a 24-h culture period. Differences in gene expression of fibrotic markers COL1A1 and CEMIP after 72-h treatment with pirfenidone or metformin could be detected in whole tissue fragments and in isolated myofibroblasts, indicating that cryopreserved and thawed endometriotic tissue fragments are suitable for testing anti-fibrotic interventions.Large scale dataN/A.Limitations, reasons for cautionViability and metabolic activity of the endometriotic tissue fragments may have been partially compromised by damage sustained during the surgical procedure, contributing to inter-sample variance.Wider implications of the findingsThe storage of viable endometriotic tissue fragments for later usage in an ex vivo model creates the possibility to efficiently test potential new therapeutic strategies and facilitates the exchange of viable endometriotic tissue between different research laboratories.Study funding/competing interest(s)This study was not financially supported by external funding. The authors declare no competing interest.Trial registration numberN/A.

Project description:Background:Cellular heterogeneity within the tumor microenvironment is essential to tumorigenesis and tumor development. A high-resolution global view of the tumor-infiltrating immune and stromal cells in breast tumors is needed. Methods:xCell was used to create a cellular heterogeneity map of 64 cell types in 1,092 breast tumor and adjacent normal tissues. xCell digitally dissects tissue cellular heterogeneity based on gene expression. Integrated statistical analyses were then performed. Results:There were noticeable differences between the cell fractions in tumor tissues and normal tissues. Tumors displayed higher proportions of immune cells, including CD4+ Tem, CD8+ naïve T cells, and CD8+ Tcm compared with normal tissues. Immune inhibitory receptors (PD1, CTLA4, LAG3 and TIM3) were co-expressed on certain subtypes of T cells in breast tumors, and PD1 and CTLA4 were both positively correlated with CD8+ Tcm and CD8+ T cells. 28 cell types were significantly associated with overall survival in univariate analysis. CD4+ Tem, CD8+ Tcm, CD8+ T-cells, CD8+ naive T-cells, and B cells were positive prognostic factors but CD4+ naive T-cells were negative prognostic factors for breast cancer patients. TDRD6 and TTK are promising T cell and B cell targets for tumor vaccines. Endothelial cells and fibroblasts were significantly less prevalent in tumor tissues; astrocytes and mesangial cells were negatively correlated with the T stage. Mesangial cells and keratinocytes were found to be favorable prognostic factors and myocytes were negative prognostic factors. Five cell types were found to be independent prognostic factors and we used these to create a reliable prognostic model for breast cancer patients. Cellular heterogeneity was discovered among different breast cancer subtypes by Her2, ER, and PR status. Tri-negative patients had the highest fraction of immune cells while luminal type patients had the lowest. The various cells may have diverse or opposing roles in the prognosis of breast cancer patients. Conclusions:We created a uniquecellular map for the diverse heterogeneity of immune and stromal phenotypes within the breast tumor microenvironment. This map may lead to potential therapeutic targets and biomarkers with prognostic utility.

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Project description:Synthetic biology efforts have largely focused on small engineered gene networks, yet understanding how to integrate multiple synthetic modules and interface them with endogenous pathways remains a challenge. Here we present the design, system integration, and analysis of several large scale synthetic gene circuits for artificial tissue homeostasis. Diabetes therapy represents a possible application for engineered homeostasis, where genetically programmed stem cells maintain a steady population of ?-cells despite continuous turnover. We develop a new iterative process that incorporates modular design principles with hierarchical performance optimization targeted for environments with uncertainty and incomplete information. We employ theoretical analysis and computational simulations of multicellular reaction/diffusion models to design and understand system behavior, and find that certain features often associated with robustness (e.g., multicellular synchronization and noise attenuation) are actually detrimental for tissue homeostasis. We overcome these problems by engineering a new class of genetic modules for 'synthetic cellular heterogeneity' that function to generate beneficial population diversity. We design two such modules (an asynchronous genetic oscillator and a signaling throttle mechanism), demonstrate their capacity for enhancing robust control, and provide guidance for experimental implementation with various computational techniques. We found that designing modules for synthetic heterogeneity can be complex, and in general requires a framework for non-linear and multifactorial analysis. Consequently, we adapt a 'phenotypic sensitivity analysis' method to determine how functional module behaviors combine to achieve optimal system performance. We ultimately combine this analysis with Bayesian network inference to extract critical, causal relationships between a module's biochemical rate-constants, its high level functional behavior in isolation, and its impact on overall system performance once integrated.

Project description:BackgroundOvarian cancer, as a highly malignant tumor, features the critical involvement of tumor-associated fibroblasts in the ovarian cancer tissue microenvironment. However, due to the apparent heterogeneity within fibroblast subpopulations, the specific functions of these subpopulations in the ovarian cancer tissue microenvironment remain insufficiently elucidated.MethodsIn this study, we integrated single-cell sequencing data from 32 ovarian cancer samples derived from four distinct cohorts and 3226 bulk RNA-seq data from GEO and TCGA-OV cohorts. Utilizing computational frameworks such as Seurat, Monocle 2, Cellchat, and others, we analyzed the characteristics of the ovarian cancer tissue microenvironment, focusing particularly on fibroblast subpopulations and their differentiation trajectories. Employing the CIBERSORTX computational framework, we assessed various cellular components within the ovarian cancer tissue microenvironment and evaluated their associations with ovarian cancer prognosis. Additionally, we conducted Mendelian randomization analysis based on cis-eQTL to investigate causal relationships between gene expression and ovarian cancer.ResultsThrough integrative analysis, we identified 13 major cell types present in ovarian cancer tissues, including CD8+ T cells, malignant cells, and fibroblasts. Analysis of the tumor microenvironment (TME) cell proportions revealed a significant increase in the proportion of CD8+ T cells and CD4+ T cells in tumor tissues compared to normal tissues, while fibroblasts predominated in normal tissues. Further subgroup analysis of fibroblasts identified seven subgroups, with the MMP11+Fib subgroup showing the highest activity in the TGFβ signaling pathway. Single-cell analysis suggested that oxidative phosphorylation could be a key pathway driving fibroblast differentiation, and the ATRNL1+KCN + Fib subgroup exhibited chromosomal copy number variations. Prognostic analysis using a large sample size indicated that high infiltration of MMP11+ fibroblasts was associated with poor prognosis in ovarian cancer. SMR analysis identified 132 fibroblast differentiation-related genes, which were linked to pathways such as platinum drug resistance.ConclusionsIn the context of ovarian cancer, fibroblasts expressing MMP11 emerge as the primary drivers of the TGF-beta signaling pathway. Their presence correlates with an increased risk of adverse ovarian prognoses. Additionally, the genetic regulation governing the differentiation of fibroblasts associated with ovarian cancer correlates with the emergence of drug resistance.

Dataset Information

Vital ex vivo tissue labeling and pathology-guided micropunching to characterize cellular heterogeneity in the tissue microenvironment.

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Vital ex vivo tissue labeling and pathology-guided micropunching to characterize cellular heterogeneity in the tissue microenvironment.

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