About us

Background


TissUUmaps Hackathon, 2021-09-29
TissUUmaps is a tool, but started out as a research project within the Wählby lab at the Dept. of IT, Uppsala University, Sweden, in 2015. Or to be completely true, it started in 2011, when Carolina Wählby was working at the Broad Institute, and was contacted by former collaborator Mats Nilsson at Stockholm University, who had invented a method to sequence mRNA directly in tissue sections, and needed computational tools to decode the image data. This led to a joint publication on in situ sequencing in Nature Methods in 2013, and a collaboration that is still ongoing.
Originally, the project was called TissueMaps, but to avoid confusion with other tools, we re-named it TissUUmaps, emphasizing out loyalty to ‘UU’, Uppsala University.
Apart from the TissUUmaps tool, the project as such has also generated several methods for pre- and post-processing of data from in situ sequencing experiments. We also work with learning-based methods to explore tissue morphology, with application in digital pathology. This research is presented in our publications, listed below.

Publications


  • N. Pielawski, A. Andersson, C. Avenel, A. Behanova, E. Chelebian, A. Klemm, F. Nysjö, L. Solorzano, C. Wählby.

    TissUUmaps 3: Interactive visualization and quality assessment of large-scale spatial omics data.

    bioRxiv, doi: 10.1101/2022.01.28.478131, January 28. 2022.

  • A. Behanova, A. Klemm, C. Wählby.

    Spatial Statistics for Understanding Tissue Organization.

    Frontiers in Physiology, doi: 10.3389/fphys.2022.832417, January 28. 2022.

  • A. Sountoulidis, S.M. Salas, E. Braun, C. Avenel, J. Bergenstrahle, M. Vicari, P. Czarnewski, J. Theelke, A. Liontos, X. Abalo, Z. Andrusivova, M. Asp, X. Li, L. Hu, S. Sariyar, A.M. Casals, B. Ayoglu, A. Firsova, J. Michaëlsson, E. Lundberg, C. Wählby, E. Sundström, S. Linnarsson, J. Lundeberg, M. Nilsson, C. Samakovlis.

    Developmental origins of cell heterogeneity in the human lung.

    bioRxiv, doi: 10.1101/2022.01.11.475631, January 12. 2022.

  • Y. Wang, B. Acs, S. Robertson, B. Liu, L. Solorzano, C. Wählby, J. Hartman, M. Rantalainen.

    Improved breast cancer histological grading using deep learning.

    Annals of Oncology, doi: 10.1016/j.annonc.2021.09.007, Sept 29. 2021.

  • E. Chelebian, C. Avenel, K. Kartasalo, M. Marklund, A. Tanoglidi, T. Mirtti, R. Colling, A. Erickson, A.D. Lamb, J. Lundeberg, and C. Wählby.

    Morphological Features Extracted by AI Associated with Spatial Transcriptomics in Prostate Cancer.

    Cancers, doi: 10.3390/cancers13194837, Sept 21. 2021.

  • K. Kartasalo, W. Bulten, B. Delahunt, P.C. Chen, H. Pinckaers, H. Olsson, X. Ji, N. Mulliqi, H. Samaratunga, T. Tsuzuki, J. Lindberg, M. Rantalainen, C. Wählby, G. Litjens, P. Ruusuvuori, L. Egevad, and M. Eklund.

    Artificial Intelligence for Diagnosis and Gleason Grading of Prostate Cancer in Biopsies - Current Status and Next Steps.

    European Urology Focus 7(4) p687-691, doi: 10.1016/j.euf.2021.07.002, July 2021.

  • L. Solorzano, L. Wik, T. Olsson Bontell, Y. Wang, A.H. Klemm, J. Öfverstedt, A.S. Jakola, A. Östman, and C. Wählby.

    Machine learning for cell classification and neighborhood analysis in glioma tissue.

    Cytometry, doi: 10.1002/cyto.a.24467, June 4. 2021.

  • A. Andersson, F. Diego, F.A. Hamprecht, and C. Wählby.

    ISTDECO: In Situ Transcriptomics Decoding by Deconvolution.

    doi: https://doi.org/10.1101/2021.03.01.433040, March 02, 2021.

  • N. Pielawski, E. Wetzer, J. Öfverstedt, J. Lu, C. Wählby, J. Lindblad, and N. Sladoje.

    CoMIR: Contrastive Multimodal Image Representation for Registration.

    In proceedings of Neural Information Processing Systems 2020 (NeurIPS 2020), Nov. 2020.

  • G. Partel, M.M. Hilscher, G. Milli, L. Solorzano, A.H. Klemm, M. Nilsson, and C. Wählby.

    Automated identification of the mouse brain’s spatial compartments from in situ sequencing data.

    BMC Biology, doi.org/10.1186/s12915-020-00874-5, Oct 2020.

  • G. Partel and C. Wählby.

    Spage2vec: Unsupervised representation of localized spatial gene expression signatures

    FEBS Journal, doi: 10.1111/febs.15572, Sept 2020.

  • G. Edfeldt, J. Lajoie, M. Röhl, J. Oyugi, A. Åhlberg, B. Khalilzadeh-Binicy, F. Bradley, M. Mack, J. Kimani, K. Omollo, C. Wählby, K. R. Fowke, K. Broliden, A. Tjernlund

    Regular use of depot medroxyprogesterone acetate causes thinning of the superficial lining and apical distribution of HIV target cells in the human ectocervix.

    The Journal of Infectious Diseases, doi: 10.1093/infdis/jiaa514 Aug 2020.

  • G. Partel and C. Wählby.

    Graph-based image decoding for multiplexed in situ RNA detection.

    To appear in the proceedings of the International Conference on Pattern Recognition (ICPR), 2020. A pre-publication with similar content can be found here; Permanent arXiv identifier: 1802.08894.

  • L. Solorzano, C. Pereira, D.Martins, R. Almeida, F. Carneiro, G. Almeida, C. Oliveira and C. Wählby.

    Towards automatic protein co-expression quantification in immunohistochemical TMA slides.

    IEEE Journal of Biomedical and Health Informatics, doi:10.1109/JBHI.2020.3008821, July 2020.

  • H. Wieslander, P. J Harrison, G. Skogberg, S. Jackson, M. Friden, J. Karlsson, O. Spjuth, and C. Wählby.

    Deep learning and conformal prediction for hierarchical analysis of large-scale whole-slide tissue images

    IEEE Journal of Biomedical and Health Informatics, doi:10.1109/JBHI.2020.2996300, June 2020.

  • L. Solorzano, G. Partel, and C. Wählby.

    TissUUmaps: Interactive visualization of large-scale spatial gene expression and tissue morphology data.

    Bioinformatics, doi:10.1093/bioinformatics/btaa541, May 2020.

  • N. Pielawski and C. Wählby.

    Introducing Hann windows for reducing edge-effects in patch-based image segmentation.

    PLoS One 15(3):e0229839. Mar 12, 2020, doi:10.1371/journal.pone.0229839.

  • A. Andersson, G. Partel, L. Solorzano, and C. Wählby.

    Transcriptome-supervised classification of tissue morphology using deep learning

    Proc IEEE Int Symp Biomed Imaging 2020, April 2020, doi:10.1109/ISBI45749.2020.9098361

  • P. Ström, K. Kartasalo, H. Olsson, L. Solorzano, B. Delahunt, D.M. Berney, D.G. Bostwick, A.J. Evans, D.J. Grignon, P.A. Humphrey, K.A. Iczkowski, J.G. Kench, G. Kristiansen, T.H. van der Kwast, K.R.M. Leite, J.K. McKenney, J. Oxley, C-C. Pan, H. Samaratunga, J.R. Srigley, H. Takahashi, T. Tsuzuki, M. Varma, M. Zhou, J. Lindberg, C. Lindskog, P. Ruusuvuori, C. Wählby, H. Grönberg, M. Rantalainen, L. Egevad, and M. Eklund.

    Artificial intelligence for diagnosis and grading of prostate cancer in biopsies: a population-based, diagnostic study.

    Lancet Oncology, doi: 10.1016/S1470-2045(19)30738-7, Jan 8, 2020. The paper is mentioned in Läkartidningen.

  • G. Günaydın, G. Edfeldt, D. A. Garber, M. Asghar, L. Noël-Romas, A. Burgener, C. Wählby, L. Wang, L. C. Rohan, P.Guenthner, J. Mitchell, N. Matoba, J. M. McNicholl, K. E. Palmer, A. Tjernlund, and K. Broliden

    Impact of Q-Griffithsin anti-HIV microbicide gel in non-human primates: In situ analyses of epithelial and immune cell markers in rectal mucosa.

    Scientific Reports, doi: 10.1038/s41598-019-54493-4 Dec 2, 2019.

  • A. Gupta, P.J. Harrison, H. Wieslander, N. Pielawski, K. Kartasalo, G. Partel, L. Solorzano, A. Suveer, A. H. Klemm, O. Spjuth, I-M. Sintorn, and C. Wählby.

    Deep Learning in Image Cytometry: A Review.

    Cytometry A. doi: 10.1002/cyto.a.23701, Dec 19 2018.

  • L. Solorzano, G. M. Almeida, B. Mesquita, D. Martins, C. Oliveira, and C. Wählby.

    Whole Slide Image Registration for the Study of Tumor Heterogeneity.

    Presented at COMPAY workshop of MICCAI 2018 21st International Conference on Medical Image Computing and Computer Assisted Intervention, September 16-20 2018, Granada, Spain, published in Lecture Notes in Computer Science, Computational Pathology and Ophthalmic Medical Image Analysis; LNCS 11039, pp 95-102, Springer, Cham. doi: 10.1007/978-3-030-00949-6_12

  • M. Bombrun, P. Ranefall, J. Lindblad, A. Allalou, G. Partel, L. Solorzano, X. Qian, M. Nilsson, and C. Wählby.

    Decoding Gene Expression in 2D and 3D.

    Presented at SCIA17 (Scandinavian Conference on Image Analysis), Tromsö, Norway, June 12-14, 2017, published in Lecture Notes in Computer Science; LNCS 10270: 257-268. Springer, Cham.

  • M. Mignardi, O. Ishaq, X. Qian, and C. Wählby.

    Bridging Histology and Bioinformatics - Computational Analysis of Spatially Resolved Transcriptomics.

    Proceedings of the IEEE 99, doi: 10.1109/JPROC.2016.2538562, April 2016.

  • A. Pacureanu, R. Ke, M. Mignardi, M. Nilsson, and C. Wählby.

    Image based in situ sequencing for RNA analysis in tissue.

    Proc IEEE ISBI 2014, International Society of Biomedical Imaging, 29 April - 2 May, 2014, Beijing, China.

  • R. Ke, M. Mignardi, A. Pacureanu, J. Svedlund, J. Botling, C. Wählby, and M. Nilsson.

    In situ sequencing for RNA analysis in preserved tissue and cells.

    Nature Methods, 2013 (10), 857-860. doi: 10.1038/nmeth.2563. PMID: 23852452

Funding


The project is funded by the European Research Council, through an ERC consolidator grant to Carolina Wählby (ERC CoG 682810).

Some of the activities are also funded by SSF Big Data grant on ‘Hierarchical Analysis of Temporal and Spatial Image Data - From intelligent data acquisition via smart data-management to confident predictions’, where Carolina Wählby acts as PI.

TissUUmaps is also supported as a technology development project which is part of the SciLifeLab BioImageInformatics Facility of SciLifeLab, a national infrastructure for life science research in Sweden.

Contact