Mapping spatially resolved transcriptomes in human and mouse pulmonary fibrosis.

Idiopathic pulmonary fibrosis (IPF) is a progressive lung disease with poor prognosis and limited treatment options. Efforts to identify effective treatments are thwarted by limited understanding of IPF pathogenesis and poor translatability of available preclinical models. Here we generated spatially resolved transcriptome maps of human IPF (n = 4) and bleomycin-induced mouse pulmonary fibrosis (n = 6) to address these limitations. We uncovered distinct fibrotic niches in the IPF lung, characterized by aberrant alveolar epithelial cells in a microenvironment dominated by transforming growth factor beta signaling alongside predicted regulators, such as TP53 and APOE. We also identified a clear divergence between the arrested alveolar regeneration in the IPF fibrotic niches and the active tissue repair in the acutely fibrotic mouse lung. Our study offers in-depth insights into the IPF transcriptional landscape and proposes alveolar regeneration as a promising therapeutic strategy for IPF.

A versatile tissue-rolling technique for spatial-omics analyses of the entire murine gastrointestinal tract.

Tissues are dynamic and complex biological systems composed of specialized cell types that interact with each other for proper biological function. To comprehensively characterize and understand the cell circuitry underlying biological processes within tissues, it is crucial to preserve their spatial information. Here we report a simple mounting technique to maximize the area of the tissue to be analyzed, encompassing the whole length of the murine gastrointestinal (GI) tract, from mouth to rectum. Using this method, analysis of the whole murine GI tract can be performed in a single slide not only by means of histological staining, immunohistochemistry and in situ hybridization but also by multiplexed antibody staining and spatial transcriptomic approaches. We demonstrate the utility of our method in generating a comprehensive gene and protein expression profile of the whole GI tract by combining the versatile tissue-rolling technique with a cutting-edge transcriptomics method (Visium) and two cutting-edge proteomics methods (ChipCytometry and CODEX-PhenoCycler) in a systematic and easy-to-follow step-by-step procedure. The entire process, including tissue rolling, processing and sectioning, can be achieved within 2-3 d for all three methods. For Visium spatial transcriptomics, an additional 2 d are needed, whereas for spatial proteomics assays (ChipCytometry and CODEX-PhenoCycler), another 3-4 d might be considered. The whole process can be accomplished by researchers with skills in performing murine surgery, and standard histological and molecular biology methods.

Spatial landmark detection and tissue registration with deep learning.

Spatial landmarks are crucial in describing histological features between samples or sites, tracking regions of interest in microscopy, and registering tissue samples within a common coordinate framework. Although other studies have explored unsupervised landmark detection, existing methods are not well-suited for histological image data as they often require a large number of images to converge, are unable to handle nonlinear deformations between tissue sections and are ineffective for z-stack alignment, other modalities beyond image data or multimodal data. We address these challenges by introducing effortless landmark detection, a new unsupervised landmark detection and registration method using neural-network-guided thin-plate splines. Our proposed method is evaluated on a diverse range of datasets including histology and spatially resolved transcriptomics, demonstrating superior performance in both accuracy and stability compared to existing approaches.

Clonally heritable gene expression imparts a layer of diversity within cell types.

Cell types can be classified according to shared patterns of transcription. Non-genetic variability among individual cells of the same type has been ascribed to stochastic transcriptional bursting and transient cell states. Using high-coverage single-cell RNA profiling, we asked whether long-term, heritable differences in gene expression can impart diversity within cells of the same type. Studying clonal human lymphocytes and mouse brain cells, we uncovered a vast diversity of heritable gene expression patterns among different clones of cells of the same type in vivo. We combined chromatin accessibility and RNA profiling on different lymphocyte clones to reveal thousands of regulatory regions exhibiting interclonal variation, which could be directly linked to interclonal variation in gene expression. Our findings identify a source of cellular diversity, which may have important implications for how cellular populations are shaped by selective processes in development, aging, and disease. A record of this paper's transparent peer review process is included in the supplemental information.

Spatial transcriptomics of B cell and T cell receptors reveals lymphocyte clonal dynamics.

The spatial distribution of lymphocyte clones within tissues is critical to their development, selection, and expansion. We have developed spatial transcriptomics of variable, diversity, and joining (VDJ) sequences (Spatial VDJ), a method that maps B cell and T cell receptor sequences in human tissue sections. Spatial VDJ captures lymphocyte clones that match canonical B and T cell distributions and amplifies clonal sequences confirmed by orthogonal methods. We found spatial congruency between paired receptor chains, developed a computational framework to predict receptor pairs, and linked the expansion of distinct B cell clones to different tumor-associated gene expression programs. Spatial VDJ delineates B cell clonal diversity and lineage trajectories within their anatomical niche. Thus, Spatial VDJ captures lymphocyte spatial clonal architecture across tissues, providing a platform to harness clonal sequences for therapy.

Comprehensive cell atlas of the first-trimester developing human brain.

The adult human brain comprises more than a thousand distinct neuronal and glial cell types, a diversity that emerges during early brain development. To reveal the precise sequence of events during early brain development, we used single-cell RNA sequencing and spatial transcriptomics and uncovered cell states and trajectories in human brains at 5 to 14 postconceptional weeks (pcw). We identified 12 major classes that are organized as ~600 distinct cell states, which map to precise spatial anatomical domains at 5 pcw. We described detailed differentiation trajectories of the human forebrain and midbrain and found a large number of region-specific glioblasts that mature into distinct pre-astrocytes and pre-oligodendrocyte precursor cells. Our findings reveal the establishment of cell types during the first trimester of human brain development.

Semla: a versatile toolkit for spatially resolved transcriptomics analysis and visualization.

SUMMARY: Spatially resolved transcriptomics technologies generate gene expression data with retained positional information from a tissue section, often accompanied by a corresponding histological image. Computational tools should make it effortless to incorporate spatial information into data analyses and present analysis results in their histological context. Here, we present semla, an R package for processing, analysis, and visualization of spatially resolved transcriptomics data generated by the Visium platform, that includes interactive web applications for data exploration and tissue annotation. AVAILABILITY AND IMPLEMENTATION: The R package semla is available on GitHub (https://github.com/ludvigla/semla), under the MIT License, and deposited on Zenodo (https://doi.org/10.5281/zenodo.8321645). Documentation and tutorials with detailed descriptions of usage can be found at https://ludvigla.github.io/semla/.

Spatial transcriptomic analysis of virtual prostate biopsy reveals confounding effect of tissue heterogeneity on genomic signatures.

Genetic signatures have added a molecular dimension to prognostics and therapeutic decision-making. However, tumour heterogeneity in prostate cancer and current sampling methods could confound accurate assessment. Based on previously published spatial transcriptomic data from multifocal prostate cancer, we created virtual biopsy models that mimic conventional biopsy placement and core size. We then analysed the gene expression of different prognostic signatures (OncotypeDx®, Decipher®, Prostadiag®) using a step-wise approach with increasing resolution from pseudo-bulk analysis of the whole biopsy, to differentiation by tissue subtype (benign, stroma, tumour), followed by distinct tumour grade and finally clonal resolution. The gene expression profile of virtual tumour biopsies revealed clear differences between grade groups and tumour clones, compared to a benign control, which were not reflected in bulk analyses. This suggests that bulk analyses of whole biopsies or tumour-only areas, as used in clinical practice, may provide an inaccurate assessment of gene profiles. The type of tissue, the grade of the tumour and the clonal composition all influence the gene expression in a biopsy. Clinical decision making based on biopsy genomics should be made with caution while we await more precise targeting and cost-effective spatial analyses.

Spatial multimodal analysis of transcriptomes and metabolomes in tissues.

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.

Expansion spatial transcriptomics.

Capture array-based spatial transcriptomics methods have been widely used to resolve gene expression in tissues; however, their spatial resolution is limited by the density of the array. Here we present expansion spatial transcriptomics to overcome this limitation by clearing and expanding tissue prior to capturing the entire polyadenylated transcriptome with an enhanced protocol. This approach enables us to achieve higher spatial resolution while retaining high library quality, which we demonstrate using mouse brain samples.

Single-Cell and Spatial Transcriptomic Analysis of Human Skin Delineates Intercellular Communication and Pathogenic Cells.

Epidermal homeostasis is governed by a balance between keratinocyte proliferation and differentiation with contributions from cell-cell interactions, but conserved or divergent mechanisms governing this equilibrium across species and how an imbalance contributes to skin disease are largely undefined. To address these questions, human skin single-cell RNA sequencing and spatial transcriptomics data were integrated and compared with mouse skin data. Human skin cell-type annotation was improved using matched spatial transcriptomics data, highlighting the importance of spatial context in cell-type identity, and spatial transcriptomics refined cellular communication inference. In cross-species analyses, we identified a human spinous keratinocyte subpopulation that exhibited proliferative capacity and a heavy metal processing signature, which was absent in mouse and may account for species differences in epidermal thickness. This human subpopulation was expanded in psoriasis and zinc-deficiency dermatitis, attesting to disease relevance and suggesting a paradigm of subpopulation dysfunction as a hallmark of the disease. To assess additional potential subpopulation drivers of skin diseases, we performed cell-of-origin enrichment analysis within genodermatoses, nominating pathogenic cell subpopulations and their communication pathways, which highlighted multiple potential therapeutic targets. This integrated dataset is encompassed in a publicly available web resource to aid mechanistic and translational studies of normal and diseased skin.

Profiling spatiotemporal gene expression of the developing human spinal cord and implications for ependymoma origin.

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 landscape of gene expression isoforms in tissue sections.

In situ capturing technologies add tissue context to gene expression data, with the potential of providing a greater understanding of complex biological systems. However, splicing variants and full-length sequence heterogeneity cannot be characterized at spatial resolution with current transcriptome profiling methods. To that end, we introduce spatial isoform transcriptomics (SiT), an explorative method for characterizing spatial isoform variation and sequence heterogeneity using long-read sequencing. We show in mouse brain how SiT can be used to profile isoform expression and sequence heterogeneity in different areas of the tissue. SiT reveals regional isoform switching of Plp1 gene between different layers of the olfactory bulb, and the use of external single-cell data allows the nomination of cell types expressing each isoform. Furthermore, SiT identifies differential isoform usage for several major genes implicated in brain function (Snap25, Bin1, Gnas) that are independently validated by in situ sequencing. SiT also provides for the first time an in-depth A-to-I RNA editing map of the adult mouse brain. Data exploration can be performed through an online resource (https://www.isomics.eu), where isoform expression and RNA editing can be visualized in a spatial context.

A topographic atlas defines developmental origins of cell heterogeneity in the human embryonic lung.

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.

Spatially resolved transcriptomic profiling of degraded and challenging fresh frozen samples.

Spatially resolved transcriptomics has enabled precise genome-wide mRNA expression profiling within tissue sections. The performance of methods targeting the polyA tails of mRNA relies on the availability of specimens with high RNA quality. Moreover, the high cost of currently available spatial resolved transcriptomics assays requires a careful sample screening process to increase the chance of obtaining high-quality data. Indeed, the upfront analysis of RNA quality can show considerable variability due to sample handling, storage, and/or intrinsic factors. We present RNA-Rescue Spatial Transcriptomics (RRST), a workflow designed to improve mRNA recovery from fresh frozen specimens with moderate to low RNA quality. First, we provide a benchmark of RRST against the standard Visium spatial gene expression protocol on high RNA quality samples represented by mouse brain and prostate cancer samples. Then, we test the RRST protocol on tissue sections collected from five challenging tissue types, including human lung, colon, small intestine, pediatric brain tumor, and mouse bone/cartilage. In total, we analyze 52 tissue sections and demonstrate that RRST is a versatile, powerful, and reproducible protocol for fresh frozen specimens of different qualities and origins.

B cell expansion hinders the stroma-epithelium regenerative cross talk during mucosal healing.

Therapeutic promotion of intestinal regeneration holds great promise, but defining the cellular mechanisms that influence tissue regeneration remains an unmet challenge. To gain insight into the process of mucosal healing, we longitudinally examined the immune cell composition during intestinal damage and regeneration. B cells were the dominant cell type in the healing colon, and single-cell RNA sequencing (scRNA-seq) revealed expansion of an IFN-induced B cell subset during experimental mucosal healing that predominantly located in damaged areas and associated with colitis severity. B cell depletion accelerated recovery upon injury, decreased epithelial ulceration, and enhanced gene expression programs associated with tissue remodeling. scRNA-seq from the epithelial and stromal compartments combined with spatial transcriptomics and multiplex immunostaining showed that B cells decreased interactions between stromal and epithelial cells during mucosal healing. Activated B cells disrupted the epithelial-stromal cross talk required for organoid survival. Thus, B cell expansion during injury impairs epithelial-stromal cell interactions required for mucosal healing, with implications for the treatment of IBD.

Spatio-temporal analysis of prostate tumors in situ suggests pre-existence of treatment-resistant clones.

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.

Spatially resolved clonal copy number alterations in benign and malignant tissue.

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.

SnapShot: Spatial transcriptomics.

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.