Single cell omics has revolutionised the way and the level of resolution by which life science research is conducted, not only impacting our understanding of fundamental cell biology but also providing novel solutions in cutting-edge medical research [1,2]. More recently, several approaches have been developed to profile spatial single cell gene expression within the tissue context, providing maps of spatial locations and interactions of cell types . Spatially resolved transcriptomics has been highlighted as an important technology for life science research through being awarded Nature Method of the Year 2020 . Despite being in the limelight for 2 years, guidelines and workflows for best practices for the analysis of spatially resolved transcriptomics data are somewhat lacking.
This project aims to combine the expertise of both biologically and technically focused researches to establish benchmarking datasets, identify highly performant tools and pipelines, and implement reproducible and portable workflows single-molecule/imaging -based spatially resolved transcriptomics (e.g. Xenium/ISS, Vizgen/MERFISH, Resolve/MolecularCartography, seqFISH, etc). There is also potential to focus on in-situ spatial transcriptomics methods where guidelines for data analysis are lacking (e.g. high resolution Stereo-seq ). Potential focus areas for such a workshop include:
- Identification and collation of datasets for benchmarking. Some key datasets will be collated and ready to use for the start of the hackathon, but we hope to expand this to include other relevant technologies and biological systems, especially with a focus on evaluating the quality of cell segmentation.
- Identification of key quality control metrics. We hope to establish clear definitions of key parameters for defining the quality and comparability of experiments such as number of UMI/molecules detected, off-target rate, consistency with prior knowledge from scRNAseq, noisy signals, sensitivity, etc.
- Defining and implementing keyprocessing guidelines for single molecule spatial transcriptomics:
- a)While in-situ capture methods (e.g. Visium) have clearer pre-processing steps, there is no clear standard for single molecule spatial transcriptomics data. We will look into (i) interfacing with output formats from the starfish pipeline which is establishing itself as the uniform pre-processing package, as well as standard outputs from companies; (ii) removing artifactsignals arising from overlapping tiles after stitching; (iii) removing off target signals.
- b)(Semi-)automated cell type assignment. Annotation of cell types in spatial data is still in it’s infancy, but there are some interesting paradigms emerging [e.g. 7].
- c)Interfacing with downstream analysis frameworks Seurat and Squidpy .
- Documentation(e.g. RTDs) and dissemination (e.g. workflow hubs, Galaxy)
An important consideration for a successful hackathon would be community engagement. We plan to further enhance outreach to relevant communities including the ELIXIR single cell omics community, SCOG, the CZI SpaceTX consortia, and other key stakeholders in the field of spatial transcriptomics within Germany and internationally. We also plan to reach out to companies (e.g. Resolve Biosciences, 10x Genomics, Vizgen, Rebus, etc) to learn from their experiences and identify key areas that require attention. In line with Open Science principles, we would aim to share our findings with the wider scientific community – by providing good data and access to robust and easy to use tools in an accessible framework, would make life a lot easier for people new to the field. We also hope to wrap up our major findings in white papers and/or contributions to ongoing studies.
 Tang et al (2009). “mRNA-Seq whole-transcriptome analysis of a single cell”. Nature Methods. https://doi.org/10.1038/nmeth.1315
 Aldridge & Teichmann (2021). “Single cell transcriptomics comes of age”. Nature Methods. https://doi.org/10.1038/s41467-020-18158-5
 Moses & Pachter (2022). “Museum of spatial transcriptomics”. Nature Methods. https://doi.org/10.1038/s41592-022-01409-2
 Marx (2021). “Method of the Year 2020: spatially resolved transcriptomics”. Nature Methods. https://doi.org/10.1038/s41592-020-01033-y
 Chen et al (2022). “Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays”. Cell. https://doi.org/10.1016/j.cell.2022.04.003
 Axelrod et al (2018). “starfish: scalable pipelines for image-based transcriptomics”. GitHub. http://github.com/spacetx/starfish.
 Zhang et al (2022). “Reference-based cell type matching of spatial transcriptomics data”. bioRxiv. https://doi.org/10.1101/2022.03.28.486139
 Satija et al (2015). “Spatial reconstruction of single-cell gene expression data”. Nature Biotechnology. https://doi.org/10.1038/nbt.3192
 Palla et al (2021). “Squidpy: a scalable framework for spatial omics analysis”. Nature Methods. https://doi.org/10.1038/s41592-021-01358-2