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The objective of this work is to showcase the ortho-positronium lifetime as a probe for soft-tissue characterization. We employed positron annihilation lifetime spectroscopy to experimentally measure the three components of the positron annihilation lifetime—para-positronium (p-Ps), positron, and ortho-positronium (o-Ps)—for three types of porcine, non-fixated soft tissues ex vivo: adipose, hepatic, and muscle. Then, we benchmarked our measurements with X-ray phase-contrast imaging, which is the current state-of-the-art for soft-tissue analysis. We found that the o-Ps lifetime in adipose tissues (2.54 ± 0.12 ns) was approximately 20% longer than in hepatic (2.04 ± 0.09 ns) and muscle (2.03 ± 0.12 ns) tissues. In addition, the separation between the measurements for adipose tissue and the other tissues was better from o-Ps lifetime measurement than from X-ray phase-contrast imaging. This experimental study proved that the o-Ps lifetime is a viable non-invasive probe for characterizing and classifying the different soft tissues. Specifically, o-Ps lifetime as a soft-tissue characterization probe had a strong sensitivity to the lipid content that can be potentially implemented in commercial positron emission tomography scanners that feature list-mode data acquisition.

Cellular microarrays are powerful experimental tools for high-throughput screening of large numbers of test samples. Miniaturization increases assay throughput while reducing reagent consumption and the number of cells required, making these systems attractive for a wide range of assays in drug discovery, toxicology, stem cell research and potentially therapy. Here, we provide an overview of the emerging technologies that can be used to generate cellular microarrays, and we highlight recent significant advances in the field. This emerging and multidisciplinary approach offers new opportunities for the design and control of stem cells in tissue engineering and cellular therapies and promises to expedite drug discovery in the biotechnology and pharmaceutical industries.

The histology topography cytometry analysis toolbox (histoCAT) enables quantitative analysis and exploration of highly multiplexed imaging data for better understanding of individual cells in the context of tissue architecture. Single-cell, spatially resolved omics analysis of tissues is poised to transform biomedical research and clinical practice. We have developed an open-source, computational histology topography cytometry analysis toolbox (histoCAT) to enable interactive, quantitative, and comprehensive exploration of individual cell phenotypes, cell–cell interactions, microenvironments, and morphological structures within intact tissues. We highlight the unique abilities of histoCAT through analysis of highly multiplexed mass cytometry images of human breast cancer tissues.

A recently developed matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS) method to spatially profile the location and distribution of multiple N-linked glycan species in frozen tissues has been extended and improved for the direct analysis of glycans in clinically derived formalin-fixed paraffin-embedded (FFPE) tissues. Formalin-fixed tissues from normal mouse kidney, human pancreatic and prostate cancers, and a human hepatocellular carcinoma tissue microarray were processed by antigen retrieval followed by on-tissue digestion with peptide N-glycosidase F. The released N-glycans were detected by MALDI-IMS analysis, and the structural composition of a subset of glycans could be verified directly by on-tissue collision-induced fragmentation. Other structural assignments were confirmed by off-tissue permethylation analysis combined with multiple database comparisons. Imaging of mouse kidney tissue sections demonstrates specific tissue distributions of major cellular N-linked glycoforms in the cortex and medulla. Differential tissue distribution of N-linked glycoforms was also observed in the other tissue types. The efficacy of using MALDI-IMS glycan profiling to distinguish tumor from non-tumor tissues in a tumor microarray format is also demonstrated. This MALDI-IMS workflow has the potential to be applied to any FFPE tissue block or tissue microarray to enable higher throughput analysis of the global changes in N-glycosylation associated with cancers.

Background The improvement of feed efficiency is a key economic goal within the pig production industry. The objective of this study was to examine transcriptomic differences in both the liver and muscle of pigs divergent for feed efficiency, thus improving our understanding of the molecular mechanisms influencing feed efficiency and enabling the identification of candidate biomarkers. Residual feed intake (RFI) was calculated for two populations of pigs from two different farms of origin/genotype. The 6 most efficient (LRFI) and 6 least efficient (HRFI) animals from each population were selected for further analysis of Longissimus Dorsi muscle ( n  = 22) and liver ( n  = 23). Transcriptomic data were generated from liver and muscle collected post-slaughter. Results The transcriptomic data segregated based on the RFI value of the pig rather than genotype/farm of origin. A total of 6463 genes were identified as being differentially expressed (DE) in muscle, while 964 genes were identified as being DE in liver. Genes that were commonly DE between muscle and liver ( n  = 526) were used for the multi-tissue analysis. These 526 genes were associated with protein targeting to membrane , extracellular matrix organisation and immune function . In the muscle-only analysis, genes associated with RNA processing , protein synthesis and energy metabolism were down regulated in the LRFI animals while in the liver-only analysis, genes associated with cell signalling and lipid homeostasis were up regulated in the LRFI animals. Conclusions Differences in the transcriptome segregated on pig RFI value rather than the genotype/farm of origin. Multi-tissue analysis identified that genes associated with GO terms protein targeting to membrane , extracellular matrix organisation and a range of terms relating to immune function were over represented in the differentially expressed genes of both liver and muscle.

Gene expression profiling (GEP) has stratified diffuse large B-cell lymphoma (DLBCL) into molecular subgroups that correspond to different stages of lymphocyte development–namely germinal center B-cell like and activated B-cell like. This classification has prognostic significance, but GEP is expensive and not readily applicable into daily practice, which has lead to immunohistochemical algorithms proposed as a surrogate for GEP analysis. We assembled tissue microarrays from 475 de novo DLBCL patients who were treated with rituximab-CHOP chemotherapy. All cases were successfully profiled by GEP on formalin-fixed, paraffin-embedded tissue samples. Sections were stained with antibodies reactive with CD10, GCET1, FOXP1, MUM1 and BCL6 and cases were classified following a rationale of sequential steps of differentiation of B cells. Cutoffs for each marker were obtained using receiver-operating characteristic curves, obviating the need for any arbitrary method. An algorithm based on the expression of CD10, FOXP1 and BCL6 was developed that had a simpler structure than other recently proposed algorithms and 92.6% concordance with GEP. In multivariate analysis, both the International Prognostic Index and our proposed algorithm were significant independent predictors of progression-free and overall survival. In conclusion, this algorithm effectively predicts prognosis of DLBCL patients matching GEP subgroups in the era of rituximab therapy.

Ovarian cancer is a leading cause of deaths, yet many aspects of the biology of the disease and a routine means of its detection are lacking. We have used protein microarrays and autoantibodies from cancer patients to identify proteins that are aberrantly expressed in ovarian tissue. Sera from 30 cancer patients and 30 healthy individuals were used to probe microarrays containing 5,005 human proteins. Ninety-four antigens were identified that exhibited enhanced reactivity from sera in cancer patients relative to control sera. The differential reactivity of four antigens was tested by using immunoblot analysis and tissue microarrays. Lamin A/C, SSRP1, and RALBP1 were found to exhibit increased expression in the cancer tissue relative to controls. The combined signals from multiple antigens proved to be a robust test to identify cancerous ovarian tissue. These antigens were also reactive with tissue from other types of cancer and thus are not specific to ovarian cancer. Overall our studies identified candidate tissue marker proteins for ovarian cancer and demonstrate that protein microarrays provide a powerful approach to identify proteins aberrantly expressed in disease states.

Many drugs have progressed through preclinical and clinical trials and have been available – for years in some cases – before being recalled by the FDA for unanticipated toxicity in humans. One reason for such poor translation from drug candidate to successful use is a lack of model systems that accurately recapitulate normal tissue function of human organs and their response to drug compounds. Moreover, tissues in the body do not exist in isolation, but reside in a highly integrated and dynamically interactive environment, in which actions in one tissue can affect other downstream tissues. Few engineered model systems, including the growing variety of organoid and organ-on-a-chip platforms, have so far reflected the interactive nature of the human body. To address this challenge, we have developed an assortment of bioengineered tissue organoids and tissue constructs that are integrated in a closed circulatory perfusion system, facilitating inter-organ responses. We describe a three-tissue organ-on-a-chip system, comprised of liver, heart, and lung, and highlight examples of inter-organ responses to drug administration. We observe drug responses that depend on inter-tissue interaction, illustrating the value of multiple tissue integration for in vitro study of both the efficacy of and side effects associated with candidate drugs.

CD90 (Thy-1) plays important roles in oncogenesis and shows potential as a candidate marker for cancer stem cells (CSCs) in various malignancies. Herein, we investigated the expression of CD90 in pancreatic adenocarcinoma (PDAC), with a comparison to normal pancreas and non-malignant pancreatic disease, by immunohistochemical (IHC) analysis of tissue microarrays containing 183 clinical tissue specimens. Statistical analysis was performed to evaluate the correlation between CD90 expression and the major clinicopathological factors after adjustment of age and gender. The IHC data showed that CD90 was significantly overexpressed in PDAC and its metastatic cancers as compared to chronic pancreatitis and benign islet tumors, while it was negative in normal pancreas and 82.7% of adjacent normal pancreas tissues. The abundant CD90 expression was predominantly present in PDAC stroma, such as fibroblasts and vascular endothelial cells, which could serve as a promising marker to distinguish pancreatic adenocarcinoma from normal pancreas and non-malignant pancreatic diseases. Double immunostaining of CD90 with CD24, a CSC marker for PDAC, showed that there was little overlap between these two markers. However, CD90+ fibroblast cells were clustered around CD24+ malignant ducts, suggesting that CD90 may be involved in the tumor-stroma interactions and promote pancreatic cancer development. Furthermore, CD90 mostly overlapped with α-smooth muscle actin (αSMA, a marker of activated pancreatic stellate cells (PSCs)) in PDAC stroma, which demonstrated that CD90+ stromal cells consist largely of activated PSCs. Double immunostaining of CD90 and a vascular endothelial cell marker CD31 demonstrated that CD90 expression on vascular endothelial cells was significantly increased in PDACs as compared to normal pancreas and non-malignant pancreatic diseases. Our findings suggest that CD90 could serve as a promising marker for pancreatic adenocarcinoma where desmoplastic stroma plays an important role in tumor growth and angiogenesis.

Metastasis is the ultimate cause of breast cancer related mortality. Epithelial-mesenchymal transition (EMT) is thought to play a crucial role in the metastatic potential of breast cancer. Growing evidence has implicated the SUMO E3 ligase PIAS1 in the regulation of EMT in mammary epithelial cells and breast cancer metastasis. However, the relevance of PIAS1 in human cancer and mechanisms by which PIAS1 might regulate breast cancer metastasis remain to be elucidated. Using tissue-microarray analysis (TMA), we report that the protein abundance and subcellular localization of PIAS1 correlate with disease specific overall survival of a cohort of breast cancer patients. In mechanistic studies, we find that PIAS1 acts via sumoylation of the transcriptional regulator SnoN to suppress invasive growth of MDA-MB-231 human breast cancer cell-derived organoids. Our studies thus identify the SUMO E3 ligase PIAS1 as a prognostic biomarker in breast cancer, and suggest a potential role for the PIAS1-SnoN sumoylation pathway in controlling breast cancer metastasis.