Explore the vast range of titles available.

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.

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.

Purpose To evaluate the estrogen receptor coactivator amplified in breast cancer 1 (AIB1) as a prognostic marker, as well as a predictive marker for response to adjuvant tamoxifen and/or aromatase inhibitors, in early estrogen receptor-positive breast cancer. Method AIB1 was analyzed with immunohistochemistry in tissue microarrays of the Danish subcohort ( N  = 1396) of the International Breast Cancer Study Group’s trial BIG 1-98 (randomization between adjuvant tamoxifen versus letrozole versus the sequence of the two drugs). Results Forty-six percent of the tumors had a high AIB1 expression. In line with previous studies, AIB1 correlated to a more aggressive tumor-phenotype ( HER2 amplification and a high malignancy grade). High AIB1 also correlated to higher estrogen receptor expression (80–100 vs. 1–79%), and ductal histological type. High AIB1 expression was associated with a poor disease-free survival (univariable: hazard ratio 1.35, 95% confidence interval 1.12–1.63. Multivariable: hazard ratio 1.29, 95% confidence interval 1.06–1.58) and overall survival (univariable: hazard ratio 1.34, 95% confidence interval 1.07–1.68. Multivariable: hazard ratio 1.25, 95% confidence interval 0.99–1.60). HER2 did not seem to modify the prognostic effect of AIB1. No difference in treatment effect between tamoxifen and letrozole in relation to AIB1 was found. Conclusions In a subset of the large international randomized trial BIG 1-98, we confirm AIB1 to be a strong prognostic factor in early breast cancer. Hence, although tumor AIB1 expression does not seem to be useful for the choice of tamoxifen versus an aromatase inhibitor in postmenopausal endocrine-responsive breast cancer, AIB1 is an interesting target for new anti-cancer therapies and further investigations of this biomarker is warranted.

Tissue microarray (TMA) is a high throughput analysis tool to identify new diagnostic and prognostic markers in human cancers. However, standard automated method in tumour detection on both routine histochemical and immunohistochemistry (IHC) images is under developed. This paper presents a robust automated tumour cell segmentation model which can be applied to both routine histochemical tissue slides and IHC slides and deal with finer pixel-based segmentation in comparison with blob or area based segmentation by existing approaches. The presented technique greatly improves the process of TMA construction and plays an important role in automated IHC quantification in biomarker analysis where excluding stroma areas is critical. With the finest pixel-based evaluation (instead of area-based or object-based), the experimental results show that the proposed method is able to achieve 80% accuracy and 78% accuracy in two different types of pathological virtual slides, i.e., routine histochemical H&E and IHC images, respectively. The presented technique greatly reduces labor-intensive workloads for pathologists and highly speeds up the process of TMA construction and provides a possibility for fully automated IHC quantification.

Although an important biomarker in breast cancer, Ki67 lacks scoring standardization, which has limited its clinical use. Our previous study found variability when laboratories used their own scoring methods on centrally stained tissue microarray slides. In this current study, 16 laboratories from eight countries calibrated to a specific Ki67 scoring method and then scored 50 centrally MIB-1 stained tissue microarray cases. Simple instructions prescribed scoring pattern and staining thresholds for determination of the percentage of stained tumor cells. To calibrate, laboratories scored 18 ‘training’ and ‘test’ web-based images. Software tracked object selection and scoring. Success for the calibration was prespecified as Root Mean Square Error of scores compared with reference <0.6 and Maximum Absolute Deviation from reference <1.0 (log2-transformed data). Prespecified success criteria for tissue microarray scoring required intraclass correlation significantly >0.70 but aiming for observed intraclass correlation ≥0.90. Laboratory performance showed non-significant but promising trends of improvement through the calibration exercise (mean Root Mean Square Error decreased from 0.6 to 0.4, Maximum Absolute Deviation from 1.6 to 0.9; paired t -test: P =0.07 for Root Mean Square Error, 0.06 for Maximum Absolute Deviation). For tissue microarray scoring, the intraclass correlation estimate was 0.94 (95% credible interval: 0.90–0.97), markedly and significantly >0.70, the prespecified minimum target for success. Some discrepancies persisted, including around clinically relevant cutoffs. After calibrating to a common scoring method via a web-based tool, laboratories can achieve high inter-laboratory reproducibility in Ki67 scoring on centrally stained tissue microarray slides. Although these data are potentially encouraging, suggesting that it may be possible to standardize scoring of Ki67 among pathology laboratories, clinically important discrepancies persist. Before this biomarker could be recommended for clinical use, future research will need to extend this approach to biopsies and whole sections, account for staining variability, and link to outcomes.

Most genome-wide assays provide averages across large numbers of cells, but recent technological advances promise to overcome this limitation. Pioneering single-cell assays are now available for genome, epigenome, transcriptome, proteome, and metabolome profiling. Here, we describe how these different dimensions can be combined into multi-omics assays that provide comprehensive profiles of the same cell.

The assessment of programmed death 1 ligand 1 (PD-L1) expression by Immunohistochemistry (IHC) is the US Food and Drug Administration (FDA)-approved predictive marker to select responders to checkpoint blockade anti-PD-1/PD-L1 axis immunotherapies. Different PD-L1 immunohistochemistry (IHC) assays use different antibodies and different scoring methods in tumor cells and immune cells. Multiple studies have compared the performance of these assays with variable results. Here, we investigate an alternative method for assessment of PD-L1 using a new technology known as digital spatial profiling. We use a previously described standardization tissue microarray (TMA) to assess the accuracy of the method and compare digital spatial profiler (DSP) to each FDA-approved PD-L1 assays, one LDT assay and three quantitative fluorescence assays. The standardized cell line Index tissue microarray contains 10 isogenic cells lines in triplicates expressing various ranges of PD-L1. The dynamic range of PD-L1 digital counts was measured in the ten cell lines on the Index TMA using the GeoMx DSP assay and read on the nCounter platform. The digital method shows very high correlation with immunohistochemistry scored with quantitative software and with quantitative fluorescence. High correlation of PD-L1 digital DSP counts were seen between rows on the same Index TMA. Finally, experiments from two Index TMAs showed reproducibility of DSP counts were independent of variable slide storage time over a three-week period after antibody labeling but before collection of cleaved tags. In summary, DSP appears to have quantitative potential comparable to quantitative immunohistochemistry. It is possible that this technology could be used as a PD-L1 protein measurement system for companion diagnostic testing for immune therapy. Digital spatial profiling is a new high-plex technology with potential to multiplex hundreds of proteins on a single slide. Here the authors validate the digital aspect of the technology on a control tissue microarray with known amounts of PD-L1 expression to show it has quantitative capacity comparable to quantitative immunofluorescence.

Despite recent advances in diagnosis and treatment of lung cancers, the 5-year survival rate remains unsatisfactory, which necessitates the identification of novel factors that associates with disease progression and malignant degree for improving diagnostic and therapeutic strategies. Recent progress in cancer immunology research has unveiled critical roles for colony stimulating factor 1 receptor (CSF1R) in multiple aspects of the tumor microenvironment. CSF1R is expressed on tumor-associated macrophages (TAMs), and mediates important pro-tumorigenic functions. CSF1R also provides critical autocrine signals that promote cancer cell survival and proliferation. Activation of CSF1R can be achieved by two independent ligands; macrophage colony-stimulating factor (M-CSF) and interleukin 34 (IL-34). Accordingly, the expression of these ligands in cancer is expected to result in poor prognosis. In this study, we show that IL-34 and M-CSF expression correlates with poor survival in a cohort of lung cancer patients. Importantly, high co-expression of IL-34 and M-CSF associates with the poorest survival compared to cancers that show weak or absent expression of the two ligands. Furthermore, high expression of IL-34 and M-CSF associates with advanced stages of lung cancers. Together, these results indicate a correlation between IL-34/M-CSF expression with poor survival and disease progression in lung cancer patients.

The Immunological Genome Project aims to build a comprehensive database of gene-expression and gene-regulatory networks in the mouse immune system. Here Turley and colleagues analyze the transcriptomes of lymph-node stromal cells under steady-state and inflammatory conditions. Lymph node stromal cells (LNSCs) closely regulate immunity and self-tolerance, yet key aspects of their biology remain poorly elucidated. Here, comparative transcriptomic analyses of mouse LNSC subsets demonstrated the expression of important immune mediators, growth factors and previously unknown structural components. Pairwise analyses of ligands and cognate receptors across hematopoietic and stromal subsets suggested a complex web of crosstalk. Fibroblastic reticular cells (FRCs) showed enrichment for higher expression of genes relevant to cytokine signaling, relative to their expression in skin and thymic fibroblasts. LNSCs from inflamed lymph nodes upregulated expression of genes encoding chemokines and molecules involved in the acute-phase response and the antigen-processing and antigen-presentation machinery. Poorly studied podoplanin (gp38)-negative CD31 − LNSCs showed similarities to FRCs but lacked expression of interleukin 7 (IL-7) and were identified as myofibroblastic pericytes that expressed integrin α 7 . Together our data comprehensively describe the transcriptional characteristics of LNSC subsets.

The dissemination of circulating tumor cells (CTCs) that cause metastases in distant organs accounts for the majority of cancer-related deaths. CTCs have been established as a cancer biomarker of known prognostic value. The enrichment of viable CTCs for ex vivo analysis could further improve cancer diagnosis and guide treatment selection. We designed a new flexible micro spring array (FMSA) device for the enrichment of viable CTCs independent of antigen expression. Unlike previous microfiltration devices, flexible structures at the micro scale minimize cell damage to preserve viability, while maximizing throughput to allow rapid enrichment directly from whole blood with no need for sample preprocessing. Device performance with respect to capture efficiency, enrichment against leukocytes, viability, and proliferability was characterized. CTCs and CTC microclusters were enriched from clinical samples obtained from breast, lung, and colorectal cancer patients. The FMSA device enriched tumor cells with 90% capture efficiency, higher than 10(4) enrichment, and better than 80% viability from 7.5-mL whole blood samples in <10 min on a 0.5-cm(2) device. The FMSA detected at least 1 CTC in 16 out of 21 clinical samples (approximately 76%) compared to 4 out of 18 (approximately 22%) detected with the commercial CellSearch® system. There was no incidence of clogging in over 100 tested fresh whole blood samples. The FMSA device provides a versatile platform capable of viable enrichment and analysis of CTCs from clinically relevant volumes of whole blood.