| 1. |
|
|
| 2. |
- Bergenstråhle, Joseph, et al.
(author)
-
SpatialCPie : An R/Bioconductor package for spatial transcriptomics cluster evaluation
- 2020
-
In: BMC Bioinformatics. - : Springer Nature. - 1471-2105. ; 21:1
-
Journal article (peer-reviewed)abstract
- Background: Technological developments in the emerging field of spatial transcriptomics have opened up an unexplored landscape where transcript information is put in a spatial context. Clustering commonly constitutes a central component in analyzing this type of data. However, deciding on the number of clusters to use and interpreting their relationships can be difficult. Results: We introduce SpatialCPie, an R package designed to facilitate cluster evaluation for spatial transcriptomics data. SpatialCPie clusters the data at multiple resolutions. The results are visualized with pie charts that indicate the similarity between spatial regions and clusters and a cluster graph that shows the relationships between clusters at different resolutions. We demonstrate SpatialCPie on several publicly available datasets. Conclusions: SpatialCPie provides intuitive visualizations of cluster relationships when dealing with Spatial Transcriptomics data.
|
|
| 3. |
- Bergenstråhle, Ludvig, et al.
(author)
-
Super-resolved spatial transcriptomics by deep data fusion
- 2022
-
In: Nature Biotechnology. - : Nature Research. - 1087-0156 .- 1546-1696. ; 40:4, s. 476-479
-
Journal article (peer-reviewed)abstract
- Current methods for spatial transcriptomics are limited by low spatial resolution. Here we introduce a method that integrates spatial gene expression data with histological image data from the same tissue section to infer higher-resolution expression maps. Using a deep generative model, our method characterizes the transcriptome of micrometer-scale anatomical features and can predict spatial gene expression from histology images alone.
|
|
| 4. |
- Berglund, Emelie, et al.
(author)
-
Automation of Spatial Transcriptomics library preparation to enable rapid and robust insights into spatial organization of tissues
- 2020
-
In: BMC Genomics. - : Springer Nature. - 1471-2164. ; 21:1
-
Journal article (peer-reviewed)abstract
- Background: Interest in studying the spatial distribution of gene expression in tissues is rapidly increasing. Spatial Transcriptomics is a novel sequencing-based technology that generates high-throughput information on the distribution, heterogeneity and co-expression of cells in tissues. Unfortunately, manual preparation of high-quality sequencing libraries is time-consuming and subject to technical variability due to human error during manual pipetting, which results in sample swapping and the accidental introduction of batch effects. All these factors complicate the production and interpretation of biological datasets.Results: We have integrated an Agilent Bravo Automated Liquid Handling Platform into the Spatial Transcriptomics workflow. Compared to the previously reported Magnatrix 8000+ automated protocol, this approach increases the number of samples processed per run, reduces sample preparation time by 35%, and minimizes batch effects between samples. The new approach is also shown to be highly accurate and almost completely free from technical variability between prepared samples.Conclusions: The new automated Spatial Transcriptomics protocol using the Agilent Bravo Automated Liquid Handling Platform rapidly generates high-quality Spatial Transcriptomics libraries. Given the wide use of the Agilent Bravo Automated Liquid Handling Platform in research laboratories and facilities, this will allow many researchers to quickly create robust Spatial Transcriptomics libraries.
|
|
| 5. |
- Erickson, A, et al.
(author)
-
Spatially resolved clonal copy number alterations in benign and malignant tissue
- 2022
-
In: Nature. - : Springer Science and Business Media LLC. - 1476-4687 .- 0028-0836. ; 608:7922, s. 360-
-
Journal article (peer-reviewed)abstract
- Defining the transition from benign to malignant tissue is fundamental to improving early diagnosis of cancer1. Here we use a systematic approach to study spatial genome integrity in situ and describe previously unidentified clonal relationships. We used spatially resolved transcriptomics2 to infer spatial copy number variations in >120,000 regions across multiple organs, in benign and malignant tissues. We demonstrate that genome-wide copy number variation reveals distinct clonal patterns within tumours and in nearby benign tissue using an organ-wide approach focused on the prostate. Our results suggest a model for how genomic instability arises in histologically benign tissue that may represent early events in cancer evolution. We highlight the power of capturing the molecular and spatial continuums in a tissue context and challenge the rationale for treatment paradigms, including focal therapy.
|
|
| 6. |
- Larsson, Ludvig, et al.
(author)
-
Spatial transcriptomics
- 2022
-
In: Cell. - : Elsevier BV. - 0092-8674 .- 1097-4172. ; 185:15, s. 2840-2840.e1
-
Journal article (other academic/artistic)abstract
- Spatially resolved transcriptomics methodologies using RNA sequencing principles have and will continue to contribute to decode the molecular landscape of tissues. Linking quantitative sequencing data with tissue morphology empowers profiling of cellular morphology and transcription over time and space in health and disease. To view this SnapShot, open or download the PDF.
|
|
| 7. |
- Muus, Christoph, et al.
(author)
-
Single-cell meta-analysis of SARS-CoV-2 entry genes across tissues and demographics
- 2021
-
In: Nature Medicine. - : Springer Science and Business Media LLC. - 1078-8956 .- 1546-170X. ; 27:3, s. 546-559
-
Journal article (peer-reviewed)abstract
- Angiotensin-converting enzyme 2 (ACE2) and accessory proteases (TMPRSS2 and CTSL) are needed for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cellular entry, and their expression may shed light on viral tropism and impact across the body. We assessed the cell-type-specific expression of ACE2, TMPRSS2 and CTSL across 107 single-cell RNA-sequencing studies from different tissues. ACE2, TMPRSS2 and CTSL are coexpressed in specific subsets of respiratory epithelial cells in the nasal passages, airways and alveoli, and in cells from other organs associated with coronavirus disease 2019 (COVID-19) transmission or pathology. We performed a meta-analysis of 31 lung single-cell RNA-sequencing studies with 1,320,896 cells from 377 nasal, airway and lung parenchyma samples from 228 individuals. This revealed cell-type-specific associations of age, sex and smoking with expression levels of ACE2, TMPRSS2 and CTSL. Expression of entry factors increased with age and in males, including in airway secretory cells and alveolar type 2 cells. Expression programs shared by ACE2(+)TMPRSS2(+) cells in nasal, lung and gut tissues included genes that may mediate viral entry, key immune functions and epithelial-macrophage cross-talk, such as genes involved in the interleukin-6, interleukin-1, tumor necrosis factor and complement pathways. Cell-type-specific expression patterns may contribute to the pathogenesis of COVID-19, and our work highlights putative molecular pathways for therapeutic intervention. An integrated analysis of over 100 single-cell and single-nucleus transcriptomics studies illustrates severe acute respiratory syndrome coronavirus 2 viral entry gene coexpression patterns across different human tissues, and shows association of age, smoking status and sex with viral entry gene expression in respiratory cell populations.
|
|
| 8. |
- Andersson, Alma, et al.
(author)
-
Single-cell and spatial transcriptomics enables probabilistic inference of cell type topography
- 2020
-
In: Communications Biology. - : Nature Research. - 2399-3642. ; 3:1
-
Journal article (peer-reviewed)abstract
- The field of spatial transcriptomics is rapidly expanding, and with it the repertoire of available technologies. However, several of the transcriptome-wide spatial assays do not operate on a single cell level, but rather produce data comprised of contributions from a – potentially heterogeneous – mixture of cells. Still, these techniques are attractive to use when examining complex tissue specimens with diverse cell populations, where complete expression profiles are required to properly capture their richness. Motivated by an interest to put gene expression into context and delineate the spatial arrangement of cell types within a tissue, we here present a model-based probabilistic method that uses single cell data to deconvolve the cell mixtures in spatial data. To illustrate the capacity of our method, we use data from different experimental platforms and spatially map cell types from the mouse brain and developmental heart, which arrange as expected.
|
|
| 9. |
- Bergenstråhle, Ludvig
(author)
-
Computational Models of Spatial Transcriptomes
- 2024
-
Doctoral thesis (other academic/artistic)abstract
- Spatial biology is a rapidly growing field that has seen tremendous progress over the last decade. We are now able to measure how the morphology, genome, transcriptome, and proteome of a tissue vary across space. Datasets generated by spatial technologies reflect the complexity of the systems they measure: They are multi-modal, high-dimensional, and layer an intricate web of dependencies between biological compartments at different length scales. To add to this complexity, measurements are often sparse and noisy, obfuscating the underlying biological signal and making the data difficult to interpret. In this thesis, we describe how data from spatial biology experiments can be analyzed with methods from deep learning and generative modeling to accelerate biological discovery. The thesis is divided into two parts. The first part provides an introduction to the fields of deep learning and spatial biology, and how the two can be combined to model spatial biology data. The second part consists of four papers describing methods that we have developed for this purpose. Paper I presents a method for inferring spatial gene expression from hematoxylin and eosin stains. The proposed method offers a data-driven approach to analyzing histopathology images without relying on expert annotations and could be a valuable tool for cancer screening and diagnosis in the clinics. Paper II introduces a method for jointly modeling spatial gene expression with histology images. We show that the method can predict super-resolved gene expression and transcriptionally characterize small-scale anatomical structures. Paper III proposes a method for learning flexible Markov kernels to model continuous and discrete data distributions. We demonstrate the method on various image synthesis tasks, including unconditional image generation and inpainting. Paper IV leverages the techniques introduced in Paper III to integrate data from different spatial biology experiments. The proposed method can be used for data imputation, super resolution, and cross-modality data transfer.
|
|
| 10. |
- Bergenstråhle, Ludvig, et al.
(author)
-
Learning Stationary Markov Processes with Contrastive Adjustment
-
Other publication (other academic/artistic)abstract
- We introduce a new optimization algorithm, termed contrastive adjustment, for learning Markov transition kernels whose stationary distribution matches the data distribution. Contrastive adjustment is not restricted to a particular family of transition distributions and can be used to model data in both continuous and discrete state spaces. Inspired by recent work on noise-annealed sampling, we propose a particular transition operator, the noise kernel, that can trade mixing speed for sample fidelity. We show that contrastive adjustment is highly valuable in human-computer design processes, as the stationarity of the learned Markov chain enables local exploration of the data manifold and makes it possible to iteratively refine outputs by human feedback. We compare the performance of noise kernels trained with contrastive adjustment to current state-of-the-art generative models and demonstrate promising results on a variety of image synthesis tasks.
|
|
| 11. |
- Bergenstråhle, Ludvig, et al.
(author)
-
Multi-Modal Modeling of Spatial Biology Data
-
Other publication (other academic/artistic)abstract
- Spatial biology technologies provide complementary information about tissue anatomy but are often challenging or costly to combine experimentally. Here, we propose a method for multi-modal modeling of spatial biology data that integrates diverse data types and can be used for cross-modality data transfer. We demonstrate the method on histology-guided gene expression imputation and super resolution in sequencing-based spatial transcriptomics, and on feature imputation in high-resolution in situ data.
|
|
| 12. |
|
|
| 13. |
- He, B., et al.
(author)
-
Integrating spatial gene expression and breast tumour morphology via deep learning
- 2020
-
In: Nature Biomedical Engineering. - : Nature Research. - 2157-846X. ; 4:8, s. 827-834
-
Journal article (peer-reviewed)abstract
- Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.
|
|
| 14. |
- Ji, Andrew L., et al.
(author)
-
Multimodal Analysis of Composition and Spatial Architecture in Human Squamous Cell Carcinoma
- 2020
-
In: Cell. - : Cell Press. - 0092-8674 .- 1097-4172. ; 182:2, s. 497-
-
Journal article (peer-reviewed)abstract
- To define the cellular composition and architecture of cutaneous squamous cell carcinoma (cSCC), we combined single-cell RNA sequencing with spatial transcriptomics and multiplexed ion beam imaging from a series of human cSCCs and matched normal skin. cSCC exhibited four tumor subpopulations, three recapitulating normal epidermal states, and a tumor-specific keratinocyte (TSK) population unique to cancer, which localized to a fibrovascular niche. Integration of single-cell and spatial data mapped ligand-receptor networks to specific cell types, revealing TSK cells as a hub for intercellular communication. Multiple features of potential immunosuppression were observed, including T regulatory cell (Treg) co-localization with CD8 T cells in compartmentalized tumor stroma. Finally, single-cell characterization of human tumor xenografts and in vivo CRISPR screens identified essential roles for specific tumor subpopulation-enriched gene networks in tumorigenesis. These data define cSCC tumor and stromal cell subpopulations, the spatial niches where they interact, and the communicating gene networks that they engage in cancer.
|
|
| 15. |
- Marklund, Maja, et al.
(author)
-
Spatio-temporal analysis of prostate tumors in situ suggests pre-existence of treatment-resistant clones
- 2022
-
In: Nature Communications. - : Springer Nature. - 2041-1723. ; 13:1
-
Journal article (peer-reviewed)abstract
- The molecular mechanisms underlying lethal castration-resistant prostate cancer remain poorly understood, with intratumoral heterogeneity a likely contributing factor. To examine the temporal aspects of resistance, we analyze tumor heterogeneity in needle biopsies collected before and after treatment with androgen deprivation therapy. By doing so, we are able to couple clinical responsiveness and morphological information such as Gleason score to transcriptome-wide data. Our data-driven analysis of transcriptomes identifies several distinct intratumoral cell populations, characterized by their unique gene expression profiles. Certain cell populations present before treatment exhibit gene expression profiles that match those of resistant tumor cell clusters, present after treatment. We confirm that these clusters are resistant by the localization of active androgen receptors to the nuclei in cancer cells post-treatment. Our data also demonstrates that most stromal cells adjacent to resistant clusters do not express the androgen receptor, and we identify differentially expressed genes for these cells. Altogether, this study shows the potential to increase the power in predicting resistant tumors. Spatial heterogeneity in prostate cancer can contribute to its resistance to androgen deprivation therapy (ADT). Here, the authors analyse prostate cancer samples before and after ADT using Spatial Transcriptomics, and find heterogeneous pre-treatment tumour cell populations and stromal cells that are associated with resistance.
|
|
| 16. |
- Vickovic, Sanja, et al.
(author)
-
High-definition spatial transcriptomics for in situ tissue profiling
- 2019
-
In: Nature Methods. - : NATURE PUBLISHING GROUP. - 1548-7091 .- 1548-7105. ; 16:10, s. 987-
-
Journal article (peer-reviewed)abstract
- Spatial and molecular characteristics determine tissue function, yet high-resolution methods to capture both concurrently are lacking. Here, we developed high-definition spatial transcriptomics, which captures RNA from histological tissue sections on a dense, spatially barcoded bead array. Each experiment recovers several hundred thousand transcriptcoupled spatial barcodes at 2-mu m resolution, as demonstrated in mouse brain and primary breast cancer. This opens the way to high-resolution spatial analysis of cells and tissues.
|
|
