Abstract 5237: High-throughput slide preparation for spatially-resolved, multiplexed quantification of protein or mRNA in tumor tissues with automation of GeoMx™ DSP Assays on Leica Biosystems BOND RX

The investigation of many stoichiometric variations involving large numbers of combinations of elements offers a means to discover a hydride with optimal properties. We introduce the use of spatially resolved infrared imaging as a high throughput hydrogen storage candidate screening technique. Analysis is presented of a sample that consists of 16 separate Mg-Ni-Fe ternary pads and 32 Mg-Ni or Mg-Fe binary pads. Hydrogen sorption related emissivity changes observed indicate a substantial decrease in hydriding temperatures, which sensitively depends on composition.

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SM-Omics: An automated platform for high-throughput spatial multi-omics

10.1101/2020.10.14.338418 ◽

2020 ◽

Author(s):

Sanja Vickovic ◽

Britta Lötstedt ◽

Johanna Klughammer ◽

Åsa Segerstolpe ◽

Orit Rozenblatt-Rosen ◽

...

Keyword(s):

High Throughput ◽

Spatial Organization ◽

Spatially Resolved ◽

Automated Platform

AbstractThe spatial organization of cells and molecules plays a key role in tissue function in homeostasis and disease. Spatial Transcriptomics (ST) has recently emerged as a key technique to capture and positionally barcode RNAs directly in tissues. Here, we advance the application of ST at scale, by presenting Spatial Multiomics (SM-Omics) as a fully automated high-throughput platform for combined and spatially resolved transcriptomics and antibody-based proteomics.

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Obtaining Spatially Resolved Tumor Purity Maps Using Deep Multiple Instance Learning In A Pan-cancer Study

10.1101/2021.07.08.451443 ◽

2021 ◽

Author(s):

Mustafa Umit Oner ◽

Jianbin Chen ◽

Egor Revkov ◽

Anne James ◽

Seow Ye Heng ◽

...

Keyword(s):

High Throughput ◽

Sample Selection ◽

Genomic Analysis ◽

Multiple Instance Learning ◽

Tumor Formation ◽

Spatially Resolved ◽

Pathologic Evaluation ◽

Tumor Purity ◽

Golden Standard ◽

Fresh Frozen

Tumor purity is the proportion of cancer cells in the tumor tissue. An accurate tumor purity estimation is crucial for accurate pathologic evaluation and for sample selection to minimize normal cell contamination in high throughput genomic analysis. We developed a novel deep multiple instance learning model predicting tumor purity from H&E stained digital histopathology slides. Our model successfully predicted tumor purity from slides of fresh-frozen sections in eight different TCGA cohorts and formalin-fixed paraffin-embedded sections in a local Singapore cohort. The predictions were highly consistent with genomic tumor purity values, which were inferred from genomic data and accepted as the golden standard. Besides, we obtained spatially resolved tumor purity maps and showed that tumor purity varies spatially within a sample. Our analyses on tumor purity maps also suggested that pathologists might have chosen high tumor content regions inside the slides during tumor purity estimation in the TCGA cohorts, which resulted in higher values than genomic tumor purity values. In short, our model can be utilized for high throughput sample selection for genomic analysis, which will help reduce pathologists' workload and decrease inter-observer variability. Moreover, spatial tumor purity maps can help better understand the tumor microenvironment as a key determinant in tumor formation and therapeutic response.

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Abstract 2162: Spatially resolved image-based transcriptomics using high-throughput single-molecule fluorescence in situ hybridization (HITSFISH)

10.1158/1538-7445.am2021-2162 ◽

2021 ◽

Author(s):

Andrej Coleski ◽

Sethu Pitchiaya ◽

Jeremy D'Silva ◽

Marcin Cieslik ◽

Arul Chinnaiyan

Keyword(s):

In Situ Hybridization ◽

Fluorescence In Situ Hybridization ◽

High Throughput ◽

Single Molecule ◽

Single Molecule Fluorescence ◽

Spatially Resolved

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P01.05 Development of signal amplification for spatially-resolved, highly multiplexed biomarker analysis of human tumor tissues

Journal for ImmunoTherapy of Cancer ◽

10.1136/jitc-2020-itoc7.18 ◽

2020 ◽

Vol 8 (Suppl 2) ◽

pp. A10.1-A10

Author(s):

O Braubach ◽

S Mistry ◽

G Dakshinamoorthy ◽

J Yuan ◽

P Noordam ◽

...

Keyword(s):

Tumor Microenvironment ◽

Signal Amplification ◽

Human Tumor ◽

Cost Effective ◽

Spatially Resolved ◽

Tumor Tissues ◽

Complementary Oligonucleotide ◽

Biomarker Analysis ◽

Part Time ◽

Automated Platform

BackgroundCharacterizing the complexities of the tumor microenvironment is fundamental to understanding cancer. Spatial relationships between infiltrating immune cells and the existing cellular matrix are now recognized as key determinants of tumor heterogeneity. Nevertheless, most available technologies for studying cells within the context of their tissue microenvironment, like traditional immunofluorescence (IF) and immunohistochemistry (IHC), are limited—allowing the visualization of only a few markers at a time.Materials and MethodsCO-Detection by indEXing (CODEX®) technology has overcome this limitation through a DNA-based labeling strategy, involving the sequential addition and removal of dye-labeled oligonucleotide reporters to antibodies equipped with complementary oligonucleotide tags. In this manner, it is possible to visualize tens of antibodies in the same tissue, in situ and at cellular resolution. Additionally, CODEX® interfaces with existing inverted microscopes and provides a cost-effective, fully automated platform for ultra-high plex immunofluorescence imaging. We have expanded the CODEX® platform to include Tyramide Signal Amplification of weak fluorescent signals, i.e. from low-expression biomarkers. This approach was tested with key biomarkers used in routine analyses of the tumor microenvironment, including PD-L1, PD-1 and FOXP3.ResultsWe demonstrate >50X amplification of PD-L1, PD-1 and FOXP3 signals when compared to control tissues. Moreover, we successfully included our amplification step in the CODEX® labeling/imaging workflow, so that it was possible to analyze amplified PD-L1, PD-1 and FOXP3 signals concurrently with a panel of 20+ additional antibodies. Analysis of our data also generated unique biological insights, including increased PD-L1 expression in Treg cells and other tumor and stromal regions.ConclusionsOur findings demonstrate the feasibility of amplifying weak biomarker signals in the CODEX® workflow. Furthermore, our experiments were conducted on human formalin fixed paraffin embedded tumor tissues, thereby demonstrating the applicability of CODEX® analyses for clinical and translational research agendas.Disclosure InformationO. Braubach: A. Employment (full or part-time); Significant; Akoya Biosciences. S. Mistry: A. Employment (full or part-time); Significant; Akoya Biosciences. G. Dakshinamoorthy: A. Employment (full or part-time); Significant; Akoya Biosciences. J. Yuan: A. Employment (full or part-time); Significant; Akoya Biosciences. P. Noordam: A. Employment (full or part-time); Significant; Akoya Biosciences. J. Kim: A. Employment (full or part-time); Significant; Akoya Biosciences. W. Lee: A. Employment (full or part-time); Significant; Akoya Biosciences. J. Kennedy-Darling: A. Employment (full or part-time); Significant; Akoya Biosciences.

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