Points2Regions: Fast, interactive clustering of imaging-based spatial transcriptomics data in TissUUmaps

Introducing Points2Regions, a versatile Python tool integrated into TissUUmaps and Napari for clustering spatial biomarkers in omics data. Its flexible and efficient approach allows for pattern discovery across scales, surpassing the limitations of traditional clustering methods. With demonstrated performance across various datasets, it identifies biologically important regions and cell types, enhancing spatial omics data exploration. Try it out with our provided datasets to experience its powerful capabilities firsthand.

Test Points2Regions on Human Heart data:
Human Heart tissue

Test Points2Regions on Mouse Brain data:
Mouse Brain tissue

Test Points2Regions on your own data!:
Load your own data

DEPICTER: Deep representation clustering for histology annotation

DEPICTER (Deep rEPresentatIon ClusTERing) is an interactive segmentation tool for histopathology annotation that produces a patch-wise dense segmentation map at WSI level. The interactive nature of DEPICTER leverages self- and semi-supervised learning approaches to allow the user to participate in the segmentation producing reliable results while reducing the workload. DEPICTER consists of three steps: first, a pretrained model is used to compute embeddings from image patches. Next, the user selects a number of benign and cancerous patches from the multi-resolution image. Finally, guided by the deep representations, label propagation is achieved using our novel seeded iterative clustering method or by directly interacting with the embedding space via feature space gating.

More information is available in this publication:
Chelebian, E., Avenel, C., Ciompi, F., & Wählby, C. (2024). DEPICTER: Deep representation clustering for histology annotation. Computers in Biology and Medicine, 108026. doi:

A demo of DEPICTER with the CAMELYON17 dataset can be found here: DEPICTER on CAMELYON

Modelling of cell-type signatures in the developmental human heart

With the emergence of high throughput single cell techniques, the understanding of cellular diversity in biologically complex processes has rapidly increased. The next step towards comprehension of e.g. key organs in the mammal development is to obtain spatiotemporal atlases of the cellular diversity. However, targeted cell typing approaches relying on existing single cell data achieve incomplete and biased maps that could mask the molecular and cellular heterogeneity present in a tissue slide. Here we applied spage2vec, a de novo approach to spatially resolve and characterize cellular diversity during human heart development. Data from the original in situ sequencing experiment as well as identified cell types can be viewed in TissUUmaps.

More information is available in the original publication:
RS. Marco Salas, X. Yuan, C. Sylven, M. Nilsson, C. Wählby and G.Partel. De novo spatiotemporal modelling of cell-type signatures identifies novel cell populations in the developmental human heart. PLOS Computational Biology doi:

TissUUmaps interactive viewer: Human heart

Human Developmental Lung Cell Atlas (pcw 5- pcw 14)

The human lung is a highly complex tubular organ, whose main function is the gas exchange between blood and breathed air. In contains a large number of specialized cell-types of epithelial, endothelial, neuronal, stromal and immune cells that are necessary for normal organ function and structural integrity. To understand how this cell heterogeneity develops to create a healthy mature lung, we focused on the 1st trimester of gestation and applied state of art technologies to capture the gene expression profiles of all the cells in the developing organ, in time and space.

More information is available in the original publication:
A. Sountoulidis, S.M. Salas, E. Braun, C. Avenel, J. Bergenstråhle, M. Vicari, P. Czarnewski, J. Theelke, A. Liontos, X. Abalo, Ž. Andrusivová, 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:

TissUUmaps interactive viewer:
Single-cell RNA-sequencing
UMAP representation of single-cell clusters and sub-clusters, gene expression and metadata.

In situ sequencing data (ISS) - TissUUmaps interactive viewer:
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In situ sequencing data. Spot location + identity, per bin pie chart view of cell type probabilities and imputed genes.

SCRINSHOT data - TissUUmaps interactive viewer:
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SCRINSHOT data. Spot location + identity.

Spatial Transcriptomics data - TissUUmaps interactive viewer:
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Per gene or pie chart view of gene expression.

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

Neuroanatomical compartments of the mouse brain are identified and outlined mainly based on manual annotations of samples using features related to tissue and cellular morphology, taking advantage of publicly available reference atlases. However, this task is challenging since sliced tissue sections are rarely perfectly parallel or angled with respect to sections in the reference atlas and organs from different individuals may vary in size and shape and requires manual annotation. Here, we show how in situ sequencing data combined with dimensionality reduction and unsupervised clustering can be used to identify spatial compartments that correspond to known anatomical compartments of the brain. Here we show results on four different sections of mouse brains.

More information is available in this publication:
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,, Oct 2020.

TissUUmaps interactive viewer: Mouse brain

The original raw ISS data was published in Qian, X., Harris, K. D., Hauling, T., Nicoloutsopoulos, D., Muñoz-Manchado, A. B., Skene, N., … & Nilsson, M. (2020). Probabilistic cell typing enables fine mapping of closely related cell types in situ. Nature methods, 17(1), 101-106.

Data and code availability: All software was developed in Python 3 using open source libraries, and data processing of pipeline workflows was carried out using Anduril2 analysis framework. The processing pipelines, data, and the software version used to generate the analysis results and figures presented in this paper are available at or from our github repository

Spage2vec: Unsupervised representation of localized spatial gene expression signatures

Spage2vec is an unsupervised segmentation free approach for decrypting the spatial transcriptomic heterogeneity of complex tissues at subcellular resolution. Spage2vec represents the spatial transcriptomic landscape of tissue samples as a graph and leverage powerful machine learning graph representation technique to create a lower dimensional representation of local spatial gene expression. Here we visualize spage2vec localized gene expression signatures of different spatial transcriptomic datasets.
We thank Mats Nilsson, Sten Linnarsson and Xiaowei Zhuang for making their datasets publicly available.

TissUUmaps interactive viewer 1:
In situ sequencing mouse brain hippocampal area CA1
Qian, X., Harris, K. D., Hauling, T., Nicoloutsopoulos, D., Muñoz-Manchado, A. B., Skene, N., … & Nilsson, M. (2020). Probabilistic cell typing enables fine mapping of closely related cell types in situ. Nature methods, 17(1), 101-106.

TissUUmaps interactive viewer 2:
osmFISH mouse brain somatosensory cortex
Codeluppi, S., Borm, L. E., Zeisel, A., La Manno, G., van Lunteren, J. A., Svensson, C. I., & Linnarsson, S. (2018). Spatial organization of the somatosensory cortex revealed by osmFISH. Nature methods, 15(11), 932-935.

TissUUmaps interactive viewer 3:
MERFISH mouse brain hypothalamic preoptic area
Moffitt, J. R., Bambah-Mukku, D., Eichhorn, S. W., Vaughn, E., Shekhar, K., Perez, J. D., … & Zhuang, X. (2018). Molecular, spatial, and functional single-cell profiling of the hypothalamic preoptic region. Science, 362(6416), eaau5324.

TissUUmaps interactive viewer 4:
MERFISH human fibroblast cells (IMR90)
Chen, K. H., Boettiger, A. N., Moffitt, J. R., Wang, S., & Zhuang, X. (2015). Spatially resolved, highly multiplexed RNA profiling in single cells. Science, 348(6233), aaa6090.

Data and code availability: Spatial gene expression data are available in Zenodo database at Source code for reproducing analysis results and figures is available in Zenodo database at

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

An increasing volume of prostate biopsies and a worldwide shortage of urological pathologists puts a strain on pathology departments. Additionally, the high intra-observer and inter-observer variability in grading can result in overtreatment and undertreatment of prostate cancer. To alleviate these problems, we aimed to develop an artificial intelligence (AI) system with clinically acceptable accuracy for prostate cancer detection, localisation, and Gleason grading. Here we show examples of full-resolution digitized biopsies and corresponding AI-based grading.

More information is available in this publication:
P. Ström, K. Kartasalo, H. Olsson, L. Solorzano et al. Artificial intelligence for diagnosis and grading of prostate cancer in biopsies: a population-based, diagnostic study. The Lancet Oncology, Volume 21, Issue 2, 2020, Pages 222-232, ISSN 1470-2045, doi: 10.1016/S1470-2045(19)30738-7, url:

An overview of all sample datasets can be found here: Prostate cancer in biopsies