Abstract 177: Increasing throughput of high-quality spatial biology data via low magnification, high NA scanning on the CellScape platform
The etiology of solid tumor formation is diverse and complex, and studies that better incorporate diversity in sample selection will result in more robust patient stratification and treatment. This reasoning applies to spatial biology studies as well, but increasing sample diversity can quickly lead to added technical challenges, more cost, and more time to data. There is then an obvious benefit to any innovation that increases the throughput of sample processing without unacceptable degradation in data quality. The CellScape™ Precise Spatial Proteomics platform offers spatial resolution at 182 nm/pixel while sampling 0.815 mm2 per field of view (FOV). This provides a balance of speed and resolution optimal for many applications. However, there are use cases where fast collection is more important than high resolution. To serve these, we explored fitting the CellScape with a primary 10x 0.45 NA objective. The instrument can then collect 3.18 mm2 of sample area per FOV, quickly scanning through large specimens such as whole embryo mounts, resection biopsies, and tissue micro arrays (TMAs). Combining this large scanning area with the CellScape whole-slide imaging chamber (WSIC) tissue window of 44 mm x 16 mm provides a very efficient workflow for gathering multiplexed immunofluorescence (mIF) data on large area samples. Here we provide data describing the efficiency and data quality of such a system. We found that a complete tonsil resection measuring >57 mm2 can be captured in less than 20 FOVs. And that a single FOV can capture an entire 1.6 mm diameter core without cropping. Further maximization of scanning efficiency was provided by a modification to the typical spatial biology imaging workflow. During TMA scanning, replacing grid-based scanning, we place each FOV at the center of each core to avoid any unnecessary scanning of blank area or double-scanning of single cores. We deployed a 40-plex assay on a 96-core TMA grid using a combination of large-FOV, high-numerical-aperture scanning and on-center FOV placement. We assessed the time required to process the samples from archived FFPE TMA block to image collection and found that using the WSIC and the CellScape platform optimized the mIF assay for rapid data collection. Importantly, we also illustrated the preservation of high-quality imaging data provided by a digital sampling rate of 364 nm per pixel, maintaining compatibility with downstream data analysis pipelines including image segmentation, cell phenotyping, and cellular neighborhood assignment. When speed outweighs resolution, the increased efficiency demonstrated here applies to most mIF spatial biology applications. Large area samples require less FOVs to capture. Small area samples can be batched on the same slide. And small, single samples can be captured without image stitching. All these examples benefit from efficient placement of large FOVs. Citation Format: J Spencer Schwarz, Charles E. Jackson, Kayla E. Cashion, Mathew H. Ingalls, Brian J. Lane, Joseph Lownik, Oliver Braubach. Increasing throughput of high-quality spatial biology data via low magnification, high NA scanning on the CellScape platform [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 177.
Authors
- Oliver Braubach (ORCID: https://orcid.org/0000-0003-2038-7954)
- Joseph Lownik (ORCID: https://orcid.org/0000-0002-9025-8210)
- Brian J. Lane (ORCID: https://orcid.org/0000-0003-3416-2377)
- Kayla Cashion
- J Spencer Schwarz
- Charles E. Jackson
- Mathew H. Ingalls
Institutions
- Cedars-Sinai Medical Center (US)
- Bruker (United States) (US)
Publication Details
- Journal
- Cancer Research
- Published
- 2025-04-21
- DOI
- https://doi.org/10.1158/1538-7445.am2025-177
- Primary Topic
- Single-cell and spatial transcriptomics
- Type
- article
- Field-Weighted Citation Impact
- 0.00