Whole genome amplification is an increasingly common technique through which minute amounts of DNA can be multiplied to generate quantities suitable for genetic testing and analysis. Questions of ...amplification-induced error and template bias generated by these methods have previously been addressed through either small scale (SNPs) or large scale (CGH array, FISH) methodologies. Here we utilized whole genome sequencing to assess amplification-induced bias in both coding and non-coding regions of two bacterial genomes. Halobacterium species NRC-1 DNA and Campylobacter jejuni were amplified by several common, commercially available protocols: multiple displacement amplification, primer extension pre-amplification and degenerate oligonucleotide primed PCR. The amplification-induced bias of each method was assessed by sequencing both genomes in their entirety using the 454 Sequencing System technology and comparing the results with those obtained from unamplified controls.
All amplification methodologies induced statistically significant bias relative to the unamplified control. For the Halobacterium species NRC-1 genome, assessed at 100 base resolution, the D-statistics from GenomiPhi-amplified material were 119 times greater than those from unamplified material, 164.0 times greater for Repli-G, 165.0 times greater for PEP-PCR and 252.0 times greater than the unamplified controls for DOP-PCR. For Campylobacter jejuni, also analyzed at 100 base resolution, the D-statistics from GenomiPhi-amplified material were 15 times greater than those from unamplified material, 19.8 times greater for Repli-G, 61.8 times greater for PEP-PCR and 220.5 times greater than the unamplified controls for DOP-PCR.
Of the amplification methodologies examined in this paper, the multiple displacement amplification products generated the least bias, and produced significantly higher yields of amplified DNA.
Identification of common molecular pathways affected by genetic variation in autism is important for understanding disease pathogenesis and devising effective therapies. Here, we test the hypothesis ...that rare genetic variation in the metabotropic glutamate-receptor (mGluR) signaling pathway contributes to autism susceptibility. Single-nucleotide variants in genes encoding components of the mGluR signaling pathway were identified by high-throughput multiplex sequencing of pooled samples from 290 non-syndromic autism cases and 300 ethnically matched controls on two independent next-generation platforms. This analysis revealed significant enrichment of rare functional variants in the mGluR pathway in autism cases. Higher burdens of rare, potentially deleterious variants were identified in autism cases for three pathway genes previously implicated in syndromic autism spectrum disorder, TSC1, TSC2, and SHANK3, suggesting that genetic variation in these genes also contributes to risk for non-syndromic autism. In addition, our analysis identified HOMER1, which encodes a postsynaptic density-localized scaffolding protein that interacts with Shank3 to regulate mGluR activity, as a novel autism-risk gene. Rare, potentially deleterious HOMER1 variants identified uniquely in the autism population affected functionally important protein regions or regulatory sequences and co-segregated closely with autism among children of affected families. We also identified rare ASD-associated coding variants predicted to have damaging effects on components of the Ras/MAPK cascade. Collectively, these findings suggest that altered signaling downstream of mGluRs contributes to the pathogenesis of non-syndromic autism.
There is critical need for standardization of HER2 immunohistochemistry testing in the clinical laboratory setting. Recently, the American Society of Clinical Oncology and the College of American ...Pathologists have submitted guidelines recommending that laboratories achieve 95% concordance between assays and observers for HER2 testing.
As a potential aid to pathologists for achieving these new guidelines, we have conducted an examination using automated quantitative analysis (AQUA analysis) to provide a standardized HER2 immunohistochemistry expression score across instruments (sites), operators, and staining runs.
We analyzed HER2 expression by immunohistochemistry in a cohort (n = 669) of invasive breast cancers in tissue microarray format across different instruments (n = 3), operators (n = 3), and staining runs (n = 3). Using light source, instrument calibration techniques, and a new generation of image analysis software, we produced normalized AQUA scores for each parameter and examined their reproducibility.
The average percent coefficients of variation across instruments, operators, and staining runs were 1.8%, 2.0%, and 5.1%, respectively. For positive/negative classification between parameters, concordance rates ranged from 94.5% to 99.3% for all cases. Differentially classified cases only occurred around the determined cut point, not over the entire distribution.
These data demonstrate that AQUA analysis can provide a standardized HER2 immunohistochemistry test that can meet current guidelines by the American Society of Clinical Oncology/College of American Pathologists. The use of AQUA analysis could allow for standardized and objective HER2 testing in clinical laboratories.
Inherent to most tissue image analysis routines are user-defined steps whereby specific pixel intensity thresholds must be set manually to differentiate background from signal-specific pixels within ...multiple images. To reduce operator time, remove operator-to-operator variability, and to obtain objective and optimal pixel separation for each image, we have developed an unsupervised pixel-based clustering algorithm allowing for the objective and unsupervised differentiation of signal from background, and differentiation of compartment-specific pixels on an image-by-image basis. We used the Automated QUantitative Analysis (AQUA) platform, a well-established automated fluorescence-based immunohistochemistry image analysis platform used for quantification of protein expression in specific cellular compartments to demonstrate utility of this methodology. As a metric for cellular compartmentalization, we examined correlation of percentage nuclear volume with histologic grade in 3 serial sections of a large cohort (n=669) of invasive breast cancer samples. We observed a significant (P=0.002, 0.006, and 0.08) difference in mean percentage nuclear volume between low and high-grade tumors. Reproducibility of percentage nuclear volume was also significant (P<0.001) across 3 serial sections. We then quantified compartment-specific expression of 5 biomarkers in 3 cancer types for association with outcome: estrogen receptor (nuclear), progesterone receptor (nuclear), HER2 (membrane/cytoplasm), ERCC1 (nuclear), and PTEN (cytoplasm). All 5 markers showed an expected and significant (P<0.05) association with survival. This new clustering algorithm thus produces accurate and precise compartmentalization for assessment of target gene expression, and will enhance the efficiency and objectivity of the current Automated QUantitative Analysis and other image analysis platform.
Abstract
Recent technological breakthroughs in single cell sequencing have revealed that malignant cells and nonmalignant cells are both highly dynamic and in the case of tumor cells, that the ...intratumoral heterogeneity is quite remarkable. However, several questions remain, especially in a spatial context, and a rapidly growing need has been observed for imaging-based approaches with single-cell resolution to visualize and characterize the tumor microenvironment. In the current study, we used Miltenyi Biotec newly developed spatial gene expression technology and automated imaging platform, to acquire spatial microscopy data for multiple biological analytes and multiple cancer tissues. The approach is based on fluorescence microscopy and the MICS technology principle of cyclic staining (MACSima™ Imaging Cyclic Staining) along with validated fluorochrome-conjugated antibodies and specifically amplified oligonucleotide probes combined with a proprietary signal removal system. FFPE tissue blocks were analyzed and each gene, which display a unique emission spectrum during a single cycle, was decoded. Due to the non-destructive nature of the workflow, H&E staining, immunofluorescence and RNA data can be collected together into a single image and on a single section. Using our antibody and targeted RNA immune-oncology panel coupled with our smart segmentation and flexible gating software suite, we have generated a topographical map of the expression profile level for several cancer tissues i.e.: tonsil, pancreas, kidney and ovary. This technology allows for the analysis of hundreds of protein markers and RNA transcripts on a single tissue, the characterization of the spatial architecture and the expression topography of tumor tissue sections. A better understanding of tissue microenvironment and implementation of spatial biology approaches will help advance the identification of more suitable biomarkers and for cancer therapy.
Citation Format: Emily Neil, Dongju Park, Erica Lloyd, Michael Dibuono, Seiyu Hosono, Reto Muller, Hanna Lafayette, Hsinyi Smith, Robert Pinard. High-spatial-resolution multi-omics analysis of cancer tissues. abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5643.
Abstract
With the advent of immunotherapies such as immune checkpoint inhibitors and CAR T cells, the interest in the tumor microenvironment and tumor-infiltrating lymphocytes (TILs) cells has ...increased in recent years as these play a critical role in predicting response to therapy and clinical outcomes. We have reported on the identification of potential targets for CAR T cell therapies by analyzing a multitude of tumor samples with hundreds of antibodies by flow cytometry and a new spatial proteomics platform, the MACSima Imaging Platform (Schäfer et al. Nat. Commun., 2021; Kinkhabwala et.al. Sci. Rep., 2022). Here we report on the analysis of tumor microenvironment and tumor infiltrating immune cells using a novel combined spatial transcriptomics and proteomics approach. For protein expression analysis we generated a panel of 60 fluorochrome conjugated antibodies (REAscreen Immuno-oncology, human, FFPE, version 01) selected to identify immune cells, cancer associated fibroblasts, matrix, blood vessels, lymphatics and malignant epithelial cell populations. RNA analysis was based on a novel in situ hybridization and amplification method followed by a cyclic spatial decoding of transcripts (RNAsky). To standardize and further ease the process of protein and RNA detection we generated the antigen and RNA detection probes in a ready-to-use format, i.e., dried and sealed in 96 well plates ready to be inserted into the MACSima. FFPE specimens of different tumor entities reviewed by a pathologist were first hybridized to probes, which were amplified, and spatially decoded by fluorochrome conjugated oligonucleotides. Subsequently the same specimen was exposed to fluorescently labeled antibodies by cycles of antibody reaction, image acquisition and erasure of signal. Finally, specimens were stained with H&E to report on tissue integrity and correlate spatial orientation. The 2D image stacks were analyzed for transcript and antigen quantification and pattern recognition using both, pixel and segmented single-cell data (MACS iQ View Analysis Software). Across the specimens we identified more than 20 cell types including 12 immune cell subsets. The comparison of the tumor microenvironment, tumor-infiltrating immune cells (TILs) and their spatial organization in relation to the tumor cells allowed to identify differences and similarities across the tumor types. In summary we report here on a novel standardized and automated combined spatial RNA and protein expression analysis of the tumor microenvironment and tumor infiltrating immune cells across tumor types. The characterization dataset demonstrates the ability of our platform to perform standardized in-depth phenotyping of sample cohorts. This will enable discovery and further development of predictive and prognostic biomarkers critical to patient stratification for immunotherapy.
Citation Format: Julia Femel, Emily Neil, Dongju Park, Fabio El Yassouri, Anijutta Appelshoffer, Erica Lloyd, Michael DiBuono, Henry Sauer, Hanna Lafayette, Hsinyi Smith, Jinling Wang, Dominic Mangiardi, Alex Makrigiorgos, Paurush Praveen, Silvia Rüberg, Werner Müller, Tanya Wantenaar, Robert Pinard, Andreas Bosio. Analysis of the immune microenvironment and tumor-infiltrating immune cells across different solid tumors by combined spatial transcriptomics and proteomics. abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5633.
Abstract The recent increase in image-based, spatially-resolved technologies enables researchers to profile the tumor microenvironment (TME) by capturing gene expression profiles within tissue ...sections. However, a significant limitation of these technologies is the lack of ability to resolve protein and RNA information in the same section, as well as conveniently analyze multimodal data sets. Here, we report a spatial RNA detection method, RNAsky, using Miltenyi Biotec’s MACSima™ Platform as an automated, multiomic approach. Our method integrates spatial proteomics and transcriptomics data to provide in-depth profiling with single-cell resolution on the same tissue section. We demonstrate these capabilities by characterizing key immune-oncology markers across normal and diseased tissues using our specialized MACS® iQ View analysis software. MACS iQ View provides fast segmentation and intuitive gating and clustering strategies to simultaneously assess protein and RNA data. We investigated the impact of clustering using protein, RNA, or the combination of both to evaluate the contribution of different information modalities on TME spatial dynamics. This cutting-edge approach will enable the identification of valuable parameters and new cell types, furthering the discovery and development of predictive and prognostic biomarkers. Citation Format: Dongju Park, Emily Neil, Rebecca C. Hennessey, Michael DiBuono, Hanna Lafayette, Erica Lloyd, Hsinyi Lo, Julia Femel, Alex Makrigiorgos, Shaina Lu, John Lee, Sameh Soliman, Dominic Mangiardi, Paurush Praveen, Fabian Staubach, Ryan Hindman, Thomas Rothmann, Telma Santos, Stefan Borbe, Hansueli Meyer, Tanya Wantenaar, Jinling Wang, Werner Müller, Robert Pinard, Andreas Bosio. Same-section spatial multiomic analyses using MICS technology for investigating the dynamics of the tumor microenvironment abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4308.
Abstract The HubMap consortium has developed a new standard to report on normal histological samples using multicolor immunofluorescence imaging. An Organ Mapping Antibody Panel (OMAP) currently ...describes in a table the antibodies used, the cycle number assigned to a given reagent, and provides the rationale for using a particular antibody to better understand features of the tissue. This table is accompanied by antibody validation templates that show example images from the tissue described for the OMAP table. In addition, links to existing databases, gene symbols, the antibody features and colors are included. For each tissue, a further table called "Anatomical Structures, Cell Types, plus Biomarkers" (ASCT+B), which contains structures of the tissue, cell types in a given tissue structure, key RNA transcripts for a given cell type, and antibody stains as used in the OMAP table, is created. So far, this process has not been established for spatial multiomic datasets including protein and RNA detected on the same tissue section. We have extended spatial biology methodology to combine cyclic RNA transcript detection with cyclic antibody-based protein detection using the MACSima Imaging Cyclic Staining (MICS) technology as well as H&E-staining on the same tissue section. For such datasets, the current OMAP data table needs to be extended by adding information on the RNA detection, cycle IDs and RNA validation templates, demonstrating the correct performance of a given RNA probe detection. This poster will demonstrate a proposal to extend the OMAP table structure on an extended version of the “tonsil OMAP (OMAP-10)”, which was published on the Zenodo data repository page (https://zenodo.org/records/7875938). In addition, we will apply this extended OMAP to a colorectal cancer sample, to highlight the applicability and relevance of the OMAP structure for tumor tissue. Citation Format: Werner Müller, Julia Femel, Emily Neil, Dongju Park, Rebecca C. Hennessey, Eric C. DiBiasio, Michael DiBuono, Hanna Lafayette, Erica Lloyd, Hsinyi Lo, Alex Makrigiorgos, Sameh Soliman, Dominic Mangiardi, Paurush Praveen, Silvia Rüberg, Fabian Staubach, Ryan Hindman, Thomas Rothmann, Hansueli Meyer, Tanya Wantenaar, Jinling Wang, Robert Pinard, Andreas Bosio. A proposal to extend standardized organ mapping antibody panels (OMAPs) to integrate protein and RNA analysis in spatial biology abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4953.
Abstract In solid tumors, the tumor microenvironment (TME) is composed of diverse cell types including cancer cells, immune cells, stromal cells, and other tissue specific cell types. The complex ...intercellular interactions that occur between these various cell populations determine cancer development and progression. Cancer-associated fibroblasts (CAFs) have been identified as key players in the TME, capable of promoting tumor cell growth and invasion, as well as manipulating immune responses. To better resolve potential subpopulations, spatial relationships, and signaling occurring between cell types within the TME, we performed same-section multiomic profiling of colorectal cancer (CRC) using the MACSima™ Imaging Cyclic Staining (MICS) technology. We combined a custom 40plex RNA panel with a panel of antibodies to characterize the drivers of tumorigenesis and the activation state of immune cells. FFPE CRC specimens were reviewed by a pathologist for clinical assessment and region of interest selection. Then, gene expression profiles were generated using RNAsky™ technology with each gene being detected via cyclic rounds of detection probe hybridization, image acquisition, and signal erasure. Subsequently, fluorescently labeled antibodies were applied to the same section following an equivalent acquisition process. Finally, multiomic clustering and cell population analyses were performed using MACS® iQ View software. The analysis revealed spatially separated subpopulations of CAFs. Functional characterization of cellular neighborhoods and the cell-to-cell interactions occurring within the TME showed that one of the CAF populations potentially promoted cancer cell growth while another subpopulation, resembling antigen-presenting CAFs (apCAFs), closely interacted with T cells in the TME. These findings will deepen our understanding of tumor progression in colorectal cancer and potentially other solid tumors. Citation Format: Emily Neil, Rebecca C. Hennessey, David Agorku, Dongju Park, Julia Femel, Michael DiBuono, Hanna Lafayette, Erica Lloyd, Hsinyi Lo, Alex Makrigiorgos, Shaina Lu, John Lee, Sameh Soliman, Dominic Mangiardi, Paurush Praveen, Philip Ströbel, Silvia Rüberg, Fabian Staubach, Ryan Hindman, Thomas Rothmann, Olaf Hardt, Hansueli Meyer, Tanya Wantenaar, Jinling Wang, Werner Müller, Robert Pinard, Andreas Bosio. Multiomic characterization of colorectal cancer using MICS technology reveals interaction of antigen presenting cancer associated fibroblasts and T cells abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5565.
HOXA10 encodes a transcription factor required for endometrial receptivity and embryo implantation. The objective of this study was to identify and to characterize those molecular markers regulated ...by HOXA10 expression. The authors have identified putative HOXA10 target genes identified by microarray analysis employing a murine model of transient HOXA10 expression during the anticipated implantation window. Microarray analysis identified 40 statistically significant genes regulated by HOXA10 overexpression of which 31 genes were downregulated greater than 2-fold over control and 9 genes were upregulated. Cellular ontogenies of differentially expressed genes include cell adhesion molecules, signal transduction factors, and metabolic regulators. Semiquantitative real-time reverse transcriptase polymerase chain reaction confirmed regulation of selected candidate genes. Examples included clusterin (Clu), phoshoglycerate 3-dehydrogenase (3-Pgdh), and tumor-associated calcium signal transducer 2 (Tacstd2). Elucidation of these pathways will allow further characterization of the molecular mechanisms governing endometrial development, which also may function to enhance uterine receptivity.
PDF
