snPATHO-seq: unlocking the pathology archives

Wang, Taopeng; Harvey, Kate; Morlanes, Javier Escudero; Kiedik, Beata; Al-Eryani, Ghamdan; Greenwald, Alissa; Kalavros, Nikolaos; Dezem, Felipe Segato; Ma, Yuling; Pita-Juarez, Yered H.; Wise, Kellie; Degletagne, Cyril; Elz, Anna; Hadadianpour, Azi; Johanneson, Jack; Pakiam, Fiona; Ryu, Heeju; Newell, Evan W.; Tonon, Laurie; Kohlway, Andrew; Drennon, Tingsheng; Abousoud, Jawad; Stott, Ryan; Lund, Paul; Durruthy, Jens; Vallejo, Andres F; Kaczorowski, Dominik; Warren, Joanna; Butler, Lisa M.; O’Toole, Sandra; Plummer, Jasmine; Vlachos, Ioannis S; Lundeberg, Joakim; Swarbrick, Alexander; Martelotto, Luciano

doi:10.1101/2023.12.07.570700

Cited by 3 publications

(3 citation statements)

References 22 publications

(24 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A single section from in-between was stained with Hematoxylin and Eosin (H&E) for histological analysis. Furthermore, to establish a matched reference for the transcriptional profiles of cell types within the tumor, four adjacent sections underwent fixed RNA profiling from single nuclei (snPATHO-seq) 9 .…”

Section: Processing Matched Prostate Cancer Samples With Xenium Cosmx...mentioning

confidence: 99%

“…Comparisons such as this are crucial, as these technologies represent the forefront of digital pathology tools and will become instrumental in advancing our understanding of complex biological systems. To establish a robust benchmark for transcript quantifications, we also generated single-nucleus RNA sequencing data from the same tissue block using an established method for processing formalin-fixed paraffin-embedded (FFPE) samples 9 —hereafter snPATHO-seq . Our findings indicate that despite their obvious similarities, the two platforms differ significantly across all metrics.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Comparative Analysis of Imaging-Based Spatial Transcriptomics Platforms

Cook,

Jensen,

Wise

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

Spatial transcriptomics is a rapidly evolving field, overwhelmed by a multitude of technologies. This study aims to offer a comparative analysis of datasets generated from leadingin situimaging platforms. We have generated spatial transcriptomics data from serial sections of prostate adenocarcinoma using the 10x Genomics Xenium and NanoString CosMx SMI platforms. Additionally, orthogonal single-nucleus RNA sequencing (snRNA-seq) was performed on the same FFPE tissue to establish a reference for the tumor’s transcriptional profiles. We assessed various technical aspects, such as reproducibility, sensitivity, dynamic range, cell segmentation, cell type annotation, and congruence with single-cell profiling. The practicality of assessing cellular organization and biomarker localization was evaluated. Although fewer genes are measured (CosMx: 960, Xenium: 377, with an overlap of 125), Xenium consistently demonstrates higher sensitivity, a broader dynamic range, and better alignment with single-cell reference profiles. Conversely, CosMx’s out-of-the-box segmentation outperformed Xenium’s, resulting in noticeable transcript misassignment in Xenium within certain tissue areas. However, the impact of this on the cells’ transcriptional profile was minimal. Together, this comprehensive comparison of two leading commercial platforms for spatial transcriptomics provides essential metrics for assessing their performance, offering invaluable insights for future research and technological advancements in this dynamic field.

show abstract

Section: Processing Matched Prostate Cancer Samples With Xenium Cosmx...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Comparative Analysis of Imaging-Based Spatial Transcriptomics Platforms

Cook,

Jensen,

Wise

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Single-cell methods covering FFPE, FF and snap frozen tissues represented an unmet need for the field until very recently. The 10x Gene Expression Flex kit is one solution to obtain single-cell transcriptomic data from these types of samples 13 .…”

Section: Introductionmentioning

confidence: 99%

10x Genomics Gene Expression Flex is a powerful tool for single-cell transcriptomics of xenograft models

Llora-Batlle,

Farcas,

Fransen

et al. 2024

Preprint

View full text Add to dashboard Cite

The 10x Genomics Gene Expression Flex protocol allows profiling of fixed or frozen material, greatly simplifying the logistics of sample collection, storage and transfer prior to single -cell sequencing. The method makes single-cell transcriptomics possible for existing fresh-frozen or FFPE tissue samples, but also facilitates the logistics of the sampling process, allowing instant preservation of samples. The technology relies on species-specific probes available for human and mouse. Nevertheless, processing of patient-derived (PDX) or cell line (CDX) xenografts, which contain mixed human and mouse cells, is currently not supported by this protocol due to the high degree of homology between the probe sets. Here we show that it is feasible to simultaneously profile populations containing both human and mouse cells by mixing the transcriptome probe sets of both species. Cellranger outputs a count table for each of the species allowing evaluation of the performance of the different probe sets. Cross-reactive probes are greatly outperformed by the specific probe hybridizations leading to a clear difference in the recovery of UMIs and unique genes per cell. Furthermore, we developed a pipeline that removes cross-reactive signal from the data and provides species-specific count tables for further downstream analysis. Hence, the 10x Genomics Gene Expression Flex protocol can be used to process xenograft samples without the need for separation of human and mouse cells by flow sorting and allows analysis of the human and mouse single-cell transcriptome from each sample. We anticipate it will be increasingly used for single-cell sequencing of cancer cell line and patient-derived xenografts, facilitating the preservation of the samples and allowing the interrogation of both the (human) xenograft and the (mouse) tumor microenvironment at single-cell resolution.

show abstract

Comparison of Fixed Single Cell RNA-seq Methods to Enable Transcriptome Profiling of Neutrophils in Clinical Samples

Hatje,

Schneider,

Danilin

et al. 2024

Preprint

View full text Add to dashboard Cite

Monitoring neutrophil gene expression is a powerful tool for understanding disease mechanisms, developing new diagnostics, therapies and optimizing clinical trials. Neutrophils are sensitive to the processing, storage and transportation steps that are involved in clinical sample analysis. This study is the first to evaluate the capabilities of technologies from 10X Genomics, PARSE Biosciences, and HIVE (Honeycomb Biotechnologies) to generate high-quality RNA data from human blood-derived neutrophils. Our comparative analysis shows that all methods produced high quality data, importantly capturing the transcriptomes of neutrophils. 10X FLEX cell populations in particular showed a close concordance with the flow cytometry data. Here, we establish a reliable single-cell RNA sequencing workflow for neutrophils in clinical trials: we offer guidelines on sample collection to preserve RNA quality and demonstrate how each method performs in capturing sensitive cell populations in clinical practice.

show abstract

snPATHO-seq: unlocking the pathology archives

Cited by 3 publications

References 22 publications

A Comparative Analysis of Imaging-Based Spatial Transcriptomics Platforms

A Comparative Analysis of Imaging-Based Spatial Transcriptomics Platforms

10x Genomics Gene Expression Flex is a powerful tool for single-cell transcriptomics of xenograft models

Comparison of Fixed Single Cell RNA-seq Methods to Enable Transcriptome Profiling of Neutrophils in Clinical Samples

Contact Info

Product

Resources

About