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Publications (10 of 122) Show all publications
Kukanja, P., Mattsson Langseth, C., Rodríguez-Kirby, L. A. R., Agirre, E., Zheng, C., Raman, A., . . . Castelo-Branco, G. (2024). Cellular architecture of evolving neuroinflammatory lesions and multiple sclerosis pathology. Cell, 187(8), 1990-2009
Open this publication in new window or tab >>Cellular architecture of evolving neuroinflammatory lesions and multiple sclerosis pathology
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2024 (English)In: Cell, ISSN 0092-8674, E-ISSN 1097-4172, Vol. 187, no 8, p. 1990-2009Article in journal (Refereed) Published
Abstract [en]

Multiple sclerosis (MS) is a neurological disease characterized by multifocal lesions and smoldering pathology. Although single-cell analyses provided insights into cytopathology, evolving cellular processes underlying MS remain poorly understood. We investigated the cellular dynamics of MS by modeling temporal and regional rates of disease progression in mouse experimental autoimmune encephalomyelitis (EAE). By performing single-cell spatial expression profiling using in situ sequencing (ISS), we annotated disease neighborhoods and found centrifugal evolution of active lesions. We demonstrated that disease-associated (DA)-glia arise independently of lesions and are dynamically induced and resolved over the disease course. Single-cell spatial mapping of human archival MS spinal cords confirmed the differential distribution of homeostatic and DA-glia, enabled deconvolution of active and inactive lesions into sub-compartments, and identified new lesion areas. By establishing a spatial resource of mouse and human MS neuropathology at a single-cell resolution, our study unveils the intricate cellular dynamics underlying MS.

National Category
Cell Biology Cell and Molecular Biology
Identifiers
urn:nbn:se:su:diva-231552 (URN)10.1016/j.cell.2024.02.030 (DOI)001229191700001 ()38513664 (PubMedID)2-s2.0-85189500592 (Scopus ID)
Available from: 2024-06-25 Created: 2024-06-25 Last updated: 2024-06-25Bibliographically approved
Wernersson, E., Gelali, E., Girelli, G., Wang, S., Castillo, D., Mattsson Langseth, C., . . . Bienko, M. (2024). Deconwolf enables high-performance deconvolution of widefield fluorescence microscopy images. Nature Methods
Open this publication in new window or tab >>Deconwolf enables high-performance deconvolution of widefield fluorescence microscopy images
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2024 (English)In: Nature Methods, ISSN 1548-7091, E-ISSN 1548-7105Article in journal (Refereed) Epub ahead of print
Abstract [en]

Microscopy-based spatially resolved omic methods are transforming the life sciences. However, these methods rely on high numerical aperture objectives and cannot resolve crowded molecular targets, limiting the amount of extractable biological information. To overcome these limitations, here we develop Deconwolf, an open-source, user-friendly software for high-performance deconvolution of widefield fluorescence microscopy images, which efficiently runs on laptop computers. Deconwolf enables accurate quantification of crowded diffraction limited fluorescence dots in DNA and RNA fluorescence in situ hybridization images and allows robust detection of individual transcripts in tissue sections imaged with x20 air objectives. Deconvolution of in situ spatial transcriptomics images with Deconwolf increased the number of transcripts identified more than threefold, while the application of Deconwolf to images obtained by fluorescence in situ sequencing of barcoded Oligopaint probes drastically improved chromosome tracing. Deconwolf greatly facilitates the use of deconvolution in many bioimaging applications. Deconwolf is a computationally efficient and user-friendly software tool for fluorescence microscopy image deconvolution that improves the analysis of diverse fluorescence in situ hybridization methods and can handle large datasets.

National Category
Biochemistry and Molecular Biology
Identifiers
urn:nbn:se:su:diva-231208 (URN)10.1038/s41592-024-02294-7 (DOI)001242316400003 ()38844629 (PubMedID)2-s2.0-85195378441 (Scopus ID)
Available from: 2024-06-18 Created: 2024-06-18 Last updated: 2024-06-18
Sountoulidis, A., Marco Salas, S., Braun, E., Avenel, C., Bergenstråhle, J., Theelke, J., . . . Samakovlis, C. (2023). A topographic atlas defines developmental origins of cell heterogeneity in the human embryonic lung. Nature Cell Biology, 25(2), 351-365
Open this publication in new window or tab >>A topographic atlas defines developmental origins of cell heterogeneity in the human embryonic lung
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2023 (English)In: Nature Cell Biology, ISSN 1465-7392, E-ISSN 1476-4679, Vol. 25, no 2, p. 351-365Article in journal (Refereed) Published
Abstract [en]

The lung contains numerous specialized cell types with distinct roles in tissue function and integrity. To clarify the origins and mechanisms generating cell heterogeneity, we created a comprehensive topographic atlas of early human lung development. Here we report 83 cell states and several spatially resolved developmental trajectories and predict cell interactions within defined tissue niches. We integrated single-cell RNA sequencing and spatially resolved transcriptomics into a web-based, open platform for interactive exploration. We show distinct gene expression programmes, accompanying sequential events of cell differentiation and maturation of the secretory and neuroendocrine cell types in proximal epithelium. We define the origin of airway fibroblasts associated with airway smooth muscle in bronchovascular bundles and describe a trajectory of Schwann cell progenitors to intrinsic parasympathetic neurons controlling bronchoconstriction. Our atlas provides a rich resource for further research and a reference for defining deviations from homeostatic and repair mechanisms leading to pulmonary diseases.

National Category
Biological Sciences
Identifiers
urn:nbn:se:su:diva-215134 (URN)10.1038/s41556-022-01064-x (DOI)000916842700001 ()36646791 (PubMedID)2-s2.0-85146289982 (Scopus ID)
Available from: 2023-03-03 Created: 2023-03-03 Last updated: 2024-02-28Bibliographically approved
Hernández-Neuta, I., Magoulopoulou, A., Pineiro, F., Lisby, J. G., Gulberg, M. & Nilsson, M. (2023). Highly multiplexed targeted sequencing strategy for infectious disease surveillance. BMC Biotechnology, 23, Article ID 31.
Open this publication in new window or tab >>Highly multiplexed targeted sequencing strategy for infectious disease surveillance
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2023 (English)In: BMC Biotechnology, E-ISSN 1472-6750, Vol. 23, article id 31Article in journal (Refereed) Published
Abstract [en]

Background Global efforts to characterize diseases of poverty are hampered by lack of affordable and comprehensive detection platforms, resulting in suboptimal allocation of health care resources and inefficient disease control. Next generation sequencing (NGS) can provide accurate data and high throughput. However, shotgun and metagenome-based NGS approaches are limited by low concentrations of microbial DNA in clinical samples, requirements for tailored sample and library preparations plus extensive bioinformatics analysis. Here, we adapted molecular inversion probes (MIPs) as a cost-effective target enrichment approach to characterize microbial infections from blood samples using short-read sequencing. We designed a probe panel targeting 2 bacterial genera, 21 bacterial and 6 fungi species and 7 antimicrobial resistance markers (AMRs).

Results Our approach proved to be highly specific to detect down to 1 in a 1000 pathogen DNA targets contained in host DNA. Additionally, we were able to accurately survey pathogens and AMRs in 20 out of 24 samples previously profiled with routine blood culture for sepsis.

Conclusions Overall, our targeted assay identifies microbial pathogens and AMRs with high specificity at high throughput, without the need for extensive sample preparation or bioinformatics analysis, simplifying its application for characterization and surveillance of infectious diseases in medium- to low- resource settings.

Keywords
Molecular inversion probes (MIPs), Next generation sequencing (NGS), Infectious diseases, Diagnostics, Disease surveillance, Pathogen detection
National Category
Medical Biotechnology (with a focus on Cell Biology (including Stem Cell Biology), Molecular Biology, Microbiology, Biochemistry or Biopharmacy)
Identifiers
urn:nbn:se:su:diva-221221 (URN)10.1186/s12896-023-00804-7 (DOI)001053777000001 ()37612665 (PubMedID)2-s2.0-85168591487 (Scopus ID)
Available from: 2023-09-20 Created: 2023-09-20 Last updated: 2024-01-10Bibliographically approved
Magoulopoulou, A., Marco Salas, S., Tiklova, K., Samuelsson, E. R., Hilscher, M. M. & Nilsson, M. (2023). Padlock Probe-Based Targeted In Situ Sequencing: Overview of Methods and Applications. Annual review of genomics and human genetics (Print), 24, 133-150
Open this publication in new window or tab >>Padlock Probe-Based Targeted In Situ Sequencing: Overview of Methods and Applications
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2023 (English)In: Annual review of genomics and human genetics (Print), ISSN 1527-8204, E-ISSN 1545-293X, Vol. 24, p. 133-150Article, review/survey (Refereed) Published
Abstract [en]

Elucidating spatiotemporal changes in gene expression has been an essential goal in studies of health, development, and disease. In the emerging field of spatially resolved transcriptomics, gene expression profiles are acquired with the tissue architecture maintained, sometimes at cellular resolution. This has allowed for the development of spatial cell atlases, studies of cell-cell interactions, and in situ cell typing. In this review, we focus on padlock probe-based in situ sequencing, which is a targeted spatially resolved transcriptomic method. We summarize recent methodological and computational tool developments and discuss key applications. We also discuss compatibility with other methods and integration with multiomic platforms for future applications.

Keywords
in situ sequencing, ISS, spatially resolved transcriptomics, SRT, rolling circle amplification, RCA, padlock probes, spatial cell atlas, in situ cell typing
National Category
Bioinformatics and Systems Biology
Identifiers
urn:nbn:se:su:diva-221720 (URN)10.1146/annurev-genom-102722-092013 (DOI)001055515700006 ()37018847 (PubMedID)2-s2.0-85168805790 (Scopus ID)
Available from: 2023-09-28 Created: 2023-09-28 Last updated: 2023-09-28Bibliographically approved
Li, X., Andrusivova, Z., Czarnewski, P., Mattsson Langseth, C., Andersson, A., Liu, Y., . . . Sundström, E. (2023). Profiling spatiotemporal gene expression of the developing human spinal cord and implications for ependymoma origin. Nature Neuroscience, 26(5), 891-901
Open this publication in new window or tab >>Profiling spatiotemporal gene expression of the developing human spinal cord and implications for ependymoma origin
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2023 (English)In: Nature Neuroscience, ISSN 1097-6256, E-ISSN 1546-1726, Vol. 26, no 5, p. 891-901Article in journal (Refereed) Published
Abstract [en]

The spatiotemporal regulation of cell fate specification in the human developing spinal cord remains largely unknown. In this study, by performing integrated analysis of single-cell and spatial multi-omics data, we used 16 prenatal human samples to create a comprehensive developmental cell atlas of the spinal cord during post-conceptional weeks 5–12. This revealed how the cell fate commitment of neural progenitor cells and their spatial positioning are spatiotemporally regulated by specific gene sets. We identified unique events in human spinal cord development relative to rodents, including earlier quiescence of active neural stem cells, differential regulation of cell differentiation and distinct spatiotemporal genetic regulation of cell fate choices. In addition, by integrating our atlas with pediatric ependymomas data, we identified specific molecular signatures and lineage-specific genes of cancer stem cells during progression. Thus, we delineate spatiotemporal genetic regulation of human spinal cord development and leverage these data to gain disease insight.

National Category
Neurosciences
Identifiers
urn:nbn:se:su:diva-228932 (URN)10.1038/s41593-023-01312-9 (DOI)000975560000004 ()37095395 (PubMedID)2-s2.0-85153355240 (Scopus ID)
Available from: 2024-05-06 Created: 2024-05-06 Last updated: 2024-06-10Bibliographically approved
Vicari, M., Mirzazadeh, R., Nilsson, A., Shariatgorji, R., Bjärterot, P., Larsson, L., . . . Lundeberg, J. (2023). Spatial multimodal analysis of transcriptomes and metabolomes in tissues. Nature Biotechnology
Open this publication in new window or tab >>Spatial multimodal analysis of transcriptomes and metabolomes in tissues
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2023 (English)In: Nature Biotechnology, ISSN 1087-0156, E-ISSN 1546-1696Article in journal (Refereed) Epub ahead of print
Abstract [en]

We present a spatial omics approach that combines histology, mass spectrometry imaging and spatial transcriptomics to facilitate precise measurements of mRNA transcripts and low-molecular-weight metabolites across tissue regions. The workflow is compatible with commercially available Visium glass slides. We demonstrate the potential of our method using mouse and human brain samples in the context of dopamine and Parkinson’s disease.

National Category
Medical Biotechnology (with a focus on Cell Biology (including Stem Cell Biology), Molecular Biology, Microbiology, Biochemistry or Biopharmacy)
Identifiers
urn:nbn:se:su:diva-225397 (URN)10.1038/s41587-023-01937-y (DOI)001118956800001 ()37667091 (PubMedID)2-s2.0-85169825438 (Scopus ID)
Available from: 2024-01-18 Created: 2024-01-18 Last updated: 2024-01-18
Sallinger, K., Gruber, M., Mueller, C.-T., Bonstingl, L., Pritz, E., Pankratz, K., . . . El-Heliebi, A. (2023). Spatial tumour gene signature discriminates neoplastic from non-neoplastic compartments in colon cancer: unravelling predictive biomarkers for relapse. Journal of Translational Medicine, 21(1), Article ID 528.
Open this publication in new window or tab >>Spatial tumour gene signature discriminates neoplastic from non-neoplastic compartments in colon cancer: unravelling predictive biomarkers for relapse
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2023 (English)In: Journal of Translational Medicine, E-ISSN 1479-5876, Vol. 21, no 1, article id 528Article in journal (Refereed) Published
Abstract [en]

Background: Opting for or against the administration of adjuvant chemotherapy in therapeutic management of stage II colon cancer remains challenging. Several studies report few survival benefits for patients treated with adjuvant therapy and additionally revealing potential side effects of overtreatment, including unnecessary exposure to chemotherapy-induced toxicities and reduced quality of life. Predictive biomarkers are urgently needed. We, therefore, hypothesise that the spatial tissue composition of relapsed and non-relapsed colon cancer stage II patients reveals relevant biomarkers.

Methods: The spatial tissue composition of stage II colon cancer patients was examined by a novel spatial transcriptomics technology with sub-cellular resolution, namely in situ sequencing. A panel of 176 genes investigating specific cancer-associated processes such as apoptosis, proliferation, angiogenesis, stemness, oxidative stress, hypoxia, invasion and components of the tumour microenvironment was designed to examine differentially expressed genes in tissue of relapsed versus non-relapsed patients. Therefore, FFPE slides of 10 colon cancer stage II patients either classified as relapsed (5 patients) or non-relapsed (5 patients) were in situ sequenced and computationally analysed.

Results: We identified a tumour gene signature that enables the subclassification of tissue into neoplastic and non-neoplastic compartments based on spatial expression patterns obtained through in situ sequencing. We developed a computational tool called Genes-To-Count (GTC), which automates the quantification of in situ signals, accurately mapping their position onto the spatial tissue map and automatically identifies neoplastic and non-neoplastic tissue compartments. The GTC tool was used to quantify gene expression of biological processes upregulated within the neoplastic tissue in comparison to non-neoplastic tissue and within relapsed versus non-relapsed stage II colon patients. Three differentially expressed genes (FGFR2, MMP11 and OTOP2) in the neoplastic tissue compartments of relapsed patients in comparison to non-relapsed patients were identified predicting recurrence in stage II colon cancer.

Conclusions: In depth spatial in situ sequencing showed potential to provide a deeper understanding of the underlying mechanisms involved in the recurrence of disease and revealed novel potential predictive biomarkers for disease relapse in colon cancer stage II patients. Our open-access GTC-tool allowed us to accurately capture the tumour compartment and quantify spatial gene expression in colon cancer tissue.

Keywords
In situ sequencing, Spatial transcriptomics, colon cancer, Predictive biomarker, Tumour compartment, Tumour gene signature
National Category
Cancer and Oncology
Identifiers
urn:nbn:se:su:diva-220913 (URN)10.1186/s12967-023-04384-0 (DOI)001043338700002 ()37543577 (PubMedID)2-s2.0-85166597313 (Scopus ID)
Available from: 2023-09-18 Created: 2023-09-18 Last updated: 2024-07-04Bibliographically approved
Marco Salas, S., Yuan, X., Sylven, C., Nilsson, M., Wählby, C. & Partel, G. (2022). De novo spatiotemporal modelling of cell-type signatures in the developmental human heart using graph convolutional neural networks. PloS Computational Biology, 18(8), Article ID e1010366.
Open this publication in new window or tab >>De novo spatiotemporal modelling of cell-type signatures in the developmental human heart using graph convolutional neural networks
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2022 (English)In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 18, no 8, article id e1010366Article in journal (Refereed) Published
Abstract [en]

With the emergence of high throughput single cell techniques, the understanding of the molecular and cellular diversity of mammalian organs have rapidly increased. In order to understand the spatial organization of this diversity, single cell data is often integrated with spatial data to create probabilistic cell maps. However, targeted cell typing approaches relying on existing single cell data achieve incomplete and biased maps that could mask the true diversity present in a tissue slide. Here we applied a de novo technique to spatially resolve and characterize cellular diversity of in situ sequencing data during human heart development. We obtained and made accessible well defined spatial cell-type maps of fetal hearts from 4.5 to 9 post conception weeks, not biased by probabilistic cell typing approaches. With our analysis, we could characterize previously unreported molecular diversity within cardiomyocytes and epicardial cells and identified their characteristic expression signatures, comparing them with specific subpopulations found in single cell RNA sequencing datasets. We further characterized the differentiation trajectories of epicardial cells, identifying a clear spatial component on it. All in all, our study provides a novel technique for conducting de novo spatial-temporal analyses in developmental tissue samples and a useful resource for online exploration of cell-type differentiation during heart development at sub-cellular image resolution.

Keywords
Convolutional neural networks, Cytology, Image resolution, Mammals, Tissue, Cell data, Cell types, Cell typing, Cellular diversity, Heart development, Human heart, Molecular diversity, Probabilistics, Single cells, Spatio-temporal models, Cells, article, cardiac muscle cell, cell differentiation, conception, convolutional neural network, fetus heart, human, human tissue, single cell RNA seq, spatiotemporal analysis, animal, genetics, mammal, metabolism, Animals, Humans, Myocytes, Cardiac, Neural Networks, Computer
National Category
Cell and Molecular Biology
Identifiers
urn:nbn:se:su:diva-212073 (URN)10.1371/journal.pcbi.1010366 (DOI)2-s2.0-85137127312 (Scopus ID)
Available from: 2022-12-01 Created: 2022-12-01 Last updated: 2022-12-01Bibliographically approved
van Bruggen, D., Pohl, F., Mattsson Langseth, C., Kukanja, P., Lee, H., Albiach, A. M., . . . Castelo-Branco, G. (2022). Developmental landscape of human forebrain at a single-cell level identifies early waves of oligodendrogenesis. Developmental Cell, 57(11), 1421-1436, 1421-1436.e1-e5
Open this publication in new window or tab >>Developmental landscape of human forebrain at a single-cell level identifies early waves of oligodendrogenesis
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2022 (English)In: Developmental Cell, ISSN 1534-5807, E-ISSN 1878-1551, Vol. 57, no 11, p. 1421-1436, 1421-1436.e1-e5Article in journal (Refereed) Published
Abstract [en]

Oligodendrogenesis in the human central nervous system has been observed mainly at the second trimester of gestation, a much later developmental stage compared to oligodendrogenesis in mice. Here, we characterize the transcriptomic neural diversity in the human forebrain at post-conception weeks (PCW) 8–10. Using single-cell RNA sequencing, we find evidence of the emergence of a first wave of oligodendrocyte lineage cells as early as PCW 8, which we also confirm at the epigenomic level through the use of single-cell ATAC-seq. Using regulatory network inference, we predict key transcriptional events leading to the specification of oligodendrocyte precursor cells (OPCs). Moreover, by profiling the spatial expression of 50 key genes through the use of in situ sequencing (ISS), we identify regions in the human ventral fetal forebrain where oligodendrogenesis first occurs. Our results indicate evolutionary conservation of the first wave of oligodendrogenesis between mice and humans and describe regulatory mechanisms involved in human OPC specification.

National Category
Biological Sciences
Identifiers
urn:nbn:se:su:diva-208413 (URN)10.1016/j.devcel.2022.04.016 (DOI)000822525500003 ()35523173 (PubMedID)2-s2.0-85131771145 (Scopus ID)
Available from: 2022-08-29 Created: 2022-08-29 Last updated: 2022-08-29Bibliographically approved
Projects
Companion Diagnostics Initiative [2009-00215_VINNOVA]; Uppsala University
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-9985-0387

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