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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.

The spatial distribution of lymphocyte clones within tissues is critical to their development, selection, and expansion. We have developed Spatial Transcriptomics of VDJ sequences (Spatial VDJ), which maps immunoglobulin and TR antigen receptors in human tissue sections. Spatial VDJ captures lymphocyte clones matching canonical T, B, and plasma cell distributions in tissues and amplifies clonal sequences confirmed by orthogonal methods. We confirm spatial congruency between paired receptor chains, develop a computational framework to predict receptor pairs, and link the expansion of distinct B cell clones to different tumor-associated gene expression programs. Spatial VDJ delineates B cell clonal diversity, class switch recombination, and lineage trajectories within their spatial context. Taken together, Spatial VDJ captures lymphocyte spatial clonal architecture across tissues, which could have important therapeutic implications.One-Sentence SummarySpatial transcriptomics-based technology co-captures T and B cell receptors within their anatomical niche in human tissue.

The human spinal cord contains diverse cell types, governed by a series of spatiotemporal events for tissue assembly and functions. However, the spatiotemporal regulation of cell fate specification in the human developing spinal cord remains largely unknown. Single-cell RNA sequencing and spatial transcriptomics techniques have advanced the understanding of human organ development considerably. By performing integrated analysis of single-cell and spatial multi-omics methods, we created a comprehensive developmental cell atlas of the first trimester human spinal cord. Our data revealed that the cell fate commitment of neural progenitor cells and their spatial positioning are spatiotemporally regulated by specific gene sets. Beyond this resource, we unexpectedly discovered unique events in human spinal cord development compared to rodents, including earlier quiescence of active neural stem cells, different regulation of stem cell differentiation, and distinct spatiotemporal genetic regulations of cell fate choices. In addition, using our atlas we identified specific gene expression in cancer stem cells in ependymomas. Thus, we demonstrate spatiotemporal genetic regulation of human spinal cord development as well as its potential to understand novel disease mechanisms and to inspire new therapies.

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.

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. ispartof: NATURE BIOTECHNOLOGY vol:40 issue:4 pages:476-+ ispartof: location:United States status: published

Defining the transition from benign to malignant tissue is fundamental to improve early diagnosis of cancer. Here, we provide an unsupervised approach to study spatial genome integrity in situ to describe previously unidentified clonal relationships. We employed spatially resolved transcriptomics 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.

Introduction: Defining the transition from benign to malignant tissue is fundamental to improve early diagnosis of cancer. In order to obtain spatial information of clonal genetic events, prior studies have used methods such as laser capture microdissection, which results in assessment of small regions or even single cells. These studies have an inherent bias as a limited number of regions per tissue section can be retrieved and examined. Furthermore, because investigators have selected such regions based on morphology, previous studies have limited their analyses to histologically defined tumour areas while excluding regions populated by benign cells. The possibility to perform unsupervised genome and tissue-wide analysis would therefore provide an important contribution to delineate clonal events. We sought study spatial genome integrity in situ to gain molecular insight into clonal relationships. Materials and Methods: We employed spatially resolved transcriptomics (Visium, 10x Genomics) to infer spatial copy number variations in >120 000 spatial regions across multiple organs, including three whole axial prostates and additional tissues from skin, breast and brain tumors. We used this information to deduce clonal relationships between regions harboring 5-20 cells. Results: We demonstrate that genome-wide copy number variation reveals distinct clonal patterns within tumours and in nearby benign tissue. We perform an in-depth spatial analysis of cancers that includes an unprecedented interrogation of up to 50,000 tissue domains in a single patient, and 120,000 tissue domains across 10 patients. In a prostate section, we observed that many CNVs occurred in histologically benign luminal epithelial cells, most notably in chromosomes 8 and 10. This clone constituted a region of exclusively benign acinar cells branching off a duct lined by largely copy neutral cells. The changes in these cells were shared with the nearby intermediate risk prostate cancer cells in the same tissue section. We observed similar findings in another patient’s cutaneous squamous cell carcinoma (cSCC), wherein benign squamous epithelial had alterations in chromosomes 1 and 12 that were shared with nearby cSCC. Our results suggest a model for how genomic instability arises in histo-pathologically benign tissue that may represent early events in cancer evolution. Furthermore the spatial information allowed us to identify small clonal units not evident from morphology and hence would be overlooked by pathologists. Conclusions: We present the first large-scale, comprehensive atlas of genomic evolution at high spatial resolution in prostate cancer. Our study adds an important new approach to the armamentarium of cancer molecular pathology. We highlight the power of an unsupervised approach to capture the molecular and spatial continuums in a tissue context and challenge the rationale for focal therapy in prostate cancer. Citation Format: Andrew Erickson, Emelie Berglund, Mengxiao He, Maja Marklund, Reza Mirzazadeh, Niklas Schultz, Linda Kvastad, Alma Andersson, Ludvig Bergenstråhle, Joseph Bergenstråhle, Ludvig Larsson, Alia Shamikh, Elisa Basmaci, Teresita Diaz De Ståhl, Timothy Rajakumar, Kim Thrane, Andrew L. Ji, Paul A. Khavari, Firaz Tarish, Anna Tanoglidi, Jonas Maaskola, Richard Colling, Tuomas Mirtti, Freddie Hamdy, Dan J. Woodcock, Thomas Helleday, Ian G. Mills, Alastair D. Lamb, Joakim Lundenberg. The spatial landscape of clonal somatic mutations in benign and malignant tissue [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2171.

Reconstruction of heterogeneity through single-cell transcriptional profiling has greatly advanced our understanding of the spatial liver transcriptome in recent years. However, global transcriptional differences across lobular units remain elusive in physical space. Here, we implement Spatial Transcriptomics to perform transcriptomic analysis across sectioned liver tissue. We confirm that the heterogeneity in this complex tissue is predominantly determined by lobular zonation. By introducing novel computational approaches, we enable transcriptional gradient measurements between tissue structures, including several lobules in a variety of orientations. Further, our data suggests the presence of previously transcriptionally uncharacterized structures within liver tissue, contributing to the overall spatial heterogeneity of the organ. This study demonstrates how comprehensive spatial transcriptomic technologies can be used to delineate extensive spatial gene expression patterns in the liver, indicating its future impact for studies of liver function, development and regeneration as well as its potential in pre-clinical and clinical pathology.

Summary The contribution of cellular heterogeneity and architecture to white adipose tissue (WAT) function is poorly understood. Herein, we combined spatially resolved transcriptional profiling with single-cell RNA sequencing and image analyses to map human WAT composition and structure. This identified 18 cell classes with unique propensities to form spatially organized homo- and heterotypic clusters. Of these, three constituted mature adipocytes that were similar in size, but distinct in their spatial arrangements and transcriptional profiles. Based on marker genes, we termed these AdipoLEP, AdipoPLIN, and AdipoSAA. We confirmed, in independent datasets, that their respective gene profiles associated differently with both adipocyte and whole-body insulin sensitivity. Corroborating our observations, insulin stimulation in vivo by hyperinsulinemic-euglycemic clamp showed that only AdipoPLIN displayed a transcriptional response to insulin. Altogether, by mining this multimodal resource we identify that human WAT is composed of three classes of mature adipocytes, only one of which is insulin responsive.

Reconstruction of spatial heterogeneity through single-cell transcriptional profiling has greatly advanced our understanding of the spatial liver transcriptome in recent years. However, global transcriptional differences across lobular units remain elusive in physical space. Here, we implement Spatial Transcriptomics to perform global transcriptomic analysis across sectioned liver tissue. We confirm that the heterogeneity in this complex tissue is predominantly determined by lobular zonation. By introducing novel computational approaches, we enable transcriptional gradient measurements between tissue structures, including several lobules in a variety of orientations. Further, our data suggests the presence of previously transcriptionally uncharacterized structures within liver tissue, contributing to the overall spatial heterogeneity of the organ. This study demonstrates how comprehensive spatial transcriptomic technologies can be used to delineate extensive spatial gene expression patterns in the liver, indicating its future impact for studies of liver function, development, and regeneration as well as being an asset in pre-clinical and clinical pathology. This repository contains original data such as count matrices, spot coordinate files, Hematoxylin &Eosin stained images, vein-masks, etc.Sample 1 to sample 3 refers to the individual ST experiments performed in this study. Sample 1 consists of 3 sections of the murine caudate lobe. Sample 2 consists of 4 sections of the right lobe, of which to were excluded for analysis. Sample 3 consists of 5 sections of the caudate lobe, of which two were excluded for analysis. These folders contain input data for the R markdown script, which can be found in this GitHub repository:https://github.com/almaan/ST-mLiver. The Hepaquery folder contains all data necessary to reproduce the results of the study and instructions on how to install the python package and reproduce the data of this study can also be found in the same GitHub repository (https://github.com/almaan/ST-mLiver). The folder termed "Immunofluorescence" contains all original images from the orthogonal validations of vein types (Supplementary Figure 22.1-22.2) in "Veins" and cell count estimations "Celltypes" by immunostaining. The Celltypes folder contains a subfolder "manual counting", with the randomly selected spots after overlaying 100 µm spots on the images (Supplementary figure 21). &nbsp

Reconstruction of heterogeneity through single cell transcriptional profiling has greatly advanced our understanding of the spatial liver transcriptome in recent years. However, global transcriptional differences across lobular units remain elusive in physical space. Here, we apply Spatial Transcriptomics to perform transcriptomic analysis across sectioned liver tissue. We confirm that the heterogeneity in this complex tissue is predominantly determined by lobular zonation. By introducing novel computational approaches, we enable transcriptional gradient measurements between tissue structures, including several lobules in a variety of orientations. Further, our data suggests the presence of previously transcriptionally uncharacterized structures within liver tissue, contributing to the overall spatial heterogeneity of the organ. This study demonstrates how comprehensive spatial transcriptomic technologies can be used to delineate extensive spatial gene expression patterns in the liver, indicating its future impact for studies of liver function, development and regeneration as well as its potential in pre-clinical and clinical pathology., Global transcriptional differences across lobular units in the liver remain unknown. Here the authors perform spatial transcriptomics of liver tissue to delineate transcriptional differences in physical space, confirm lobular zonation along transcriptional gradients and suggest the presence of previously uncharacterized structures within liver tissue.

Breast cancers are complex cellular ecosystems where heterotypic interactions play central roles in disease progression and response to therapy. However, our knowledge of their cellular composition and organization is limited. Here we present a single-cell and spatially resolved transcriptomics analysis of human breast cancers. We developed a single-cell method of intrinsic subtype classification (SCSubtype) to reveal recurrent neoplastic cell heterogeneity. Immunophenotyping using cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) provides high-resolution immune profiles, including new PD-L1/PD-L2+ macrophage populations associated with clinical outcome. Mesenchymal cells displayed diverse functions and cell-surface protein expression through differentiation within three major lineages. Stromal-immune niches were spatially organized in tumors, offering insights into antitumor immune regulation. Using single-cell signatures, we deconvoluted large breast cancer cohorts to stratify them into nine clusters, termed 'ecotypes', with unique cellular compositions and clinical outcomes. This study provides a comprehensive transcriptional atlas of the cellular architecture of breast cancer.

Formalin-fixed paraffin embedding (FFPE) is the most widespread long-term tissue preservation approach. Here we present a procedure to perform genome-wide spatial analysis of mRNA in FFPE tissue sections. The procedure takes advantage of well-established, commercially available methods for imaging and spatial barcoding using slides spotted with barcoded oligo(dT) probes to capture the 3’ end of mRNA molecules in tissue sections. First, we conducted expression profiling and cell type mapping in coronal sections from the mouse brain to demonstrate the method’s capability to delineate anatomical regions from a molecular perspective. Second, we explored the spatial composition of transcriptomic signatures in ovarian carcinosarcoma samples using data driven analysis methods, exemplifying the method’s potential to elucidate molecular mechanisms in heterogeneous clinical samples.

In the past decades, transcriptomic studies have revolutionized cancer treatment and diagnosis. However, tumor sequencing strategies typically result in loss of spatial information, critical to understand cell interactions and their functional relevance. To address this, we investigate spatial gene expression in HER2-positive breast tumors using Spatial Transcriptomics technology. We show that expression-based clustering enables data-driven tumor annotation and assessment of intra-and interpatient heterogeneity; from which we discover shared gene signatures for immune and tumor processes. We integrate and spatially map tumor-associated types from single cell data to find: segregated epithelial cells, interactions between B and T-cells and myeloid cells, co-localization of macrophage and T-cell subsets. A model is constructed to infer presence of tertiary lymphoid structures, applicable across tissue types and technical platforms. Taken together, we combine different data modalities to define novel interactions between tumor-infiltrating cells in breast cancer and provide tools generalizing across tissues and diseases.

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.

Introduction: Defining the transition from benign to malignant tissue is fundamental to improve early diagnosis of cancer. In order to obtain spatial information of clonal genetic events, prior studies have used methods such as laser capture microdissection, which results in assessment of small regions or even single cells. These studies have an inherent bias as a limited number of regions per tissue section can be retrieved and examined. Furthermore, because investigators have selected such regions based on morphology, previous studies have limited their analyses to histologically defined tumor areas while excluding regions populated by benign cells. The possibility to perform unsupervised genome and tissue-wide analysis would therefore provide an important contribution to delineate clonal events. We sought study spatial genome integrity in situ to gain molecular insight into clonal relationships. Materials and Methods: We employed spatially resolved transcriptomics (Visium, 10x Genomics) to infer spatial copy number variations in >120 000 spatial regions across multiple organs, including three whole axial prostates and additional tissues from skin, breast and brain tumors. We additionally performed in silico assessment of spatial copy number inference. We used this information to deduce clonal relationships between regions harboring 5-20 cells. Results: We demonstrate that genome-wide copy number variation reveals distinct clonal patterns within tumors and in nearby benign tissue. We perform an in-depth spatial analysis of cancers that includes an unprecedented interrogation of up to 50,000 tissue domains in a single patient, and 120,000 tissue domains across 10 patients. In a prostate section, we observed that many CNVs occurred in histologically benign luminal epithelial cells, most notably in chromosomes 8 and 10. This clone constituted a region of exclusively benign acinar cells branching off a duct lined by largely copy neutral cells. The changes in these cells were shared with the nearby intermediate risk prostate cancer cells in the same tissue section. We observed similar findings in another patient’s cutaneous squamous cell carcinoma (cSCC), wherein benign squamous epithelial had alterations in chromosomes 1 and 12 that were shared with nearby cSCC. Our results suggest a model for how genomic instability arises in histo-pathologically benign tissue that may represent early events in cancer evolution. Furthermore the spatial information allowed us to identify small clonal units not evident from morphology and hence would be overlooked by pathologists. Conclusions: We present the first large-scale, comprehensive atlas of genomic evolution at high spatial resolution in prostate cancer. Our study adds an important new approach to the armamentarium of cancer molecular pathology. We highlight the power of an unsupervised approach to capture the molecular and spatial continuums in a tissue context and challenge the rationale for focal therapy in prostate cancer Citation Format: Andrew Erickson, Emelie Berglund, Mengxiao He, Maja Marklund, Reza Mirzazadeh, Niklas Schultz, Ludvig Bergenstråhle, Linda Kvastad, Alma Andersson, Joseph Bergenstråhle, Ludvig Larsson, Alia Shamikh, Elisa Basmaci, Teresita Diaz De Ståhl, Timothy Rajakumar, Kim Thrane, Andrew L. Ji, Paul A. Khavari, Firaz Tarish, Anna Tanoglidi, Jonas Maaskola, Richard Colling, Tuomas Mirtti, Freddie C. Hamdy, Dan J. Woodcock, Thomas Helleday, Ian G. Mills, Alastair D. Lamb, Joakim Lundeberg. The spatial landscape of clonal somatic mutations in benign and malignant tissue [abstract]. In: Proceedings of the AACR Special Conference on the Evolutionary Dynamics in Carcinogenesis and Response to Therapy; 2022 Mar 14-17. Philadelphia (PA): AACR; Cancer Res 2022;82(10 Suppl):Abstract nr PR016.

Viral potassium channels (Kcv) are homologous to the pore module of complex $$\hbox {K}^+$$ -selective ion channels of cellular organisms. Due to their relative simplicity, they have attracted interest towards understanding the principles of $$\hbox {K}^+$$ conduction and channel gating. In this work, we construct a homology model of the $${\hbox {Kcv}}_{\text{PBCV-1}}$$ open state, which we validate by studying the binding of known blockers and by monitoring ion conduction through the channel. Molecular dynamics simulations of this model reveal that the re-orientation of selectivity filter carbonyl groups coincides with the transport of potassium ions, suggesting a possible mechanism for fast gating. In addition, we show that the voltage sensitivity of this mechanism can originate from the relocation of potassium ions inside the selectivity filter. We also explore the interaction of $${\hbox {Kcv}}_{\text{PBCV-1}}$$ with the surrounding bilayer and observe the binding of lipids in the area between two adjacent subunits. The model is available to the scientific community to further explore the structure/function relationship of Kcv channels.

In situ RNA capturing has made it possible to record histology and spatial gene expression from the same tissue section, but methods to jointly analyze both kinds of data are still missing. Here, we present XFuse, a scalable deep generative model for spatial data fusion. XFuse can infer high-resolution, full-transcriptome spatial gene expression from histological image data and be used to characterize transcriptional heterogeneity in detailed anatomical structures.

Spatial transcriptomics and single cell RNA-sequencing offer complementary insights into the transcriptional expression landscape. We here present a probabilistic method that integrates data from both techniques, leveraging their respective strengths in such a way that we are able to spatially map cell types to a tissue. The method is applied to several different types of tissue where the spatial cell type topographies are successfully delineated.

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.

Breast cancers are complex cellular ecosystems where heterotypic interactions play central roles in disease progression and response to therapy. However, our knowledge of the cellular composition and organization of breast cancer remains limited. We present a comprehensive single cell and spatially resolved transcriptomic atlas of human breast cancers. The 10X Genomics Chromium platform was used to generate single cell transcriptomic data (scRNA-Seq) from more than 120,000 cells sampled from 26 breast cancers. CITE-Seq was employed to simultaneously generate protein measurements using a panel of 157 antibodies against immune, stromal and epithelial cell surface markers and analysed using Seurat. Using single cell signatures, we estimated the cellular composition of more than 2000 breast cancers in the Metabric cohort using deconvolution methods. Spatial transcriptomics was conducted on 12 frozen tissues (Luminal, Her2+ and triple negative breast cancer (TNBC)) using the 10X genomics Visium solution. We also used a novel Spatial Whole Transcriptome Panel, targeting 18,000+ genes on the Nanostring GeoMX platform, to profile T cells and malignant cells across multiple tissue niches from 16 TNBC FFPE cases. Integrative scRNA-Seq analysis identifies recurrent gene modules driving neoplastic cell heterogeneity, including interferon signaling, estrogen receptor function and mutually exclusive patterns of proliferation versus EMT. We also develop a single cell classifier of intrinsic subtype (scSubtype) to reveal frequent intra-tumoral heterogeneity for breast cancer intrinsic subtypes. CITE-Seq revealed immune profiles at high resolution, leading to the identification of novel macrophage populations with high expression of PD-L1 and PD-L2 immune checkpoint ligands and associations with clinical outcome. We also observe enrichment of exhausted and proliferative CD8 T cells in TNBC, with unique patterns of cell-surface checkpoint protein expression when compared to other subtypes. Targeted analysis using the GeoMX revealed spatial segregation of T cell phenotypes, with exhausted and proliferative CD8 T cells forming small clusters adjacent to tumor cells with high interferon pathway activity. Analysis of scRNA-Seq data revealed that stromal cells generate diverse functions and cell surface protein expression through differentiation within 3 major lineages: fibroblast, endothelial and perivascular-like. Subsets of stromal cells had features associated with immune regulation and Visium data revealed that stromal-immune niches were spatially organized in tumors, offering insights into anti-tumor immune suppression by stromal cells. Finally, deconvolution stratified >2000 breast cancer cases in Metabric into nine clusters, termed ‘ecotypes', with distinct cellular compositions and clinical outcomes. This study provides a comprehensive atlas of the cellular architecture of breast cancer. Citation Format: Sunny Z. Wu, Daniel Roden, Ghamdan Al Eryani, Simon Junankar, Elgene Lim, Aatish Thennavan, Alma Andersson, Stephen Williams, Jingjing Gong, Robin Fropf, Kit Fuhrman, Joakim Lundeberg, Chuck Perou, Alexander Swarbrick. An integrated multi-omic cellular atlas of human breast cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 129.