Biology subjects

Lundeberg, J.

Publications and source records attributed to Lundeberg, J..

At least 37 records · Page 2Linked to original sources

bioRxiv2022

Spatial transcriptomics of T and B cell receptors uncovers lymphocyte clonal dynamics in human tissue.

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.

immunology↗

bioRxiv2022

Expansion Spatial Transcriptomics

Capture array-based spatial transcriptomics methods have been widely used to resolve gene expression in diverse tissue contexts, however, their spatial resolution is limited by array density. We present Expansion Spatial Transcriptomics (Ex-ST) to overcome this limitation by clearing and expanding tissue prior to capturing the entire transcriptome. This approach allows us to achieve near cellular resolution and higher capture efficiency of lowly-expressed genes, which we demonstrate using mouse brain samples.

genomics↗

bioRxiv2022

Comprehensive cell atlas of the first-trimester developing human brain

The adult human brain likely comprises more than a thousand kinds of neurons, and an unknown number of glial cell types, but how cellular diversity arises during early brain development is not known. Here, in order to reveal the precise sequence of events during early brain development, we used single-cell RNA sequencing and spatial transcriptomics to uncover cell states and trajectories in human brains at 5 - 14 post-conceptional weeks (p.c.w.). We identified twelve major classes and over 600 distinct cell states, which mapped to precise spatial anatomical domains at 5 p.c.w. We uncovered detailed differentiation trajectories of the human forebrain, and a surprisingly large number of region-specific glioblasts maturing into distinct pre-astrocytes and pre-oligodendrocyte precursor cells (pre-OPCs). Our findings reveal the emergence of cell types during the critical first trimester of human brain development.

developmental biology↗

bioRxiv2022

Spatially resolved transcriptomic profiling of degraded and challenging fresh frozen samples

Spatially resolved transcriptomics (SRT) has enabled precise genome-wide mRNA expression profiling within tissue sections. The performance of unbiased SRT methods targeting the polyA tail of mRNA, relies on the availability of specimens with high RNA quality. Moreover, the high cost of currently available SRT 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), an SRT workflow designed to improve mRNA recovery from fresh frozen (FF) 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 demonstrate the RRST protocol on tissue sections collected from 5 challenging tissue types, including: human lung, colon, small intestine, pediatric brain tumor, and mouse bone/cartilage. In total, we analyzed 52 tissue sections and our results demonstrate that RRST is a versatile, powerful, and reproducible protocol for FF specimens of different qualities and origins.

genomics↗

bioRxiv2022

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

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.

developmental biology↗

bioRxiv2022

Integration of single-cell RNA and protein data identifies novel clinically-relevant lymphocyte phenotypes in breast cancers

Immune cells are critical determinants of solid tumour aetiology, but the diverse phenotypes of intra-tumoural immune cells remain incompletely characterised. We applied integrated single cell RNA sequencing (scRNA-Seq) and highly multiplexed protein epitope analysis to a cohort of breast cancer samples to resolve cell states within the tumour microenvironment. We reveal novel protein markers for resting and activated tumour infiltrating lymphocytes, and show that high expression of CD103 primarily marks exhausted CD8 rather than tissue resident CD8 T-cells in human breast cancers. We identify two distinct states of activated CD4+ T follicular helper (Tfh) cells. A population resembling conventional Tfh (cTfh) cells were localised primarily to lymphoid aggregates by spatial transcriptomics. In contrast, cancer associated Tfh (caTfh) cells expressing markers of tissue residency and exhaustion co-localized with cancer foci and signalled to macrophages. Importantly, increased caTfh : cTfh ratio associated with improved disease outcome and response to checkpoint immunotherapy.

immunology↗

bioRxiv2022

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

Cell types can be classified based on shared patterns of transcription. Variability in gene expression between individual cells of the same type has been ascribed to stochastic transcriptional bursting and transient cell states. We asked whether long-term, heritable differences in transcription can impart diversity within a cell type. Studying clonal human lymphocytes and mouse brain cells, we uncover a vast diversity of heritable transcriptional states among different clones of cells of the same type in vivo. In lymphocytes we show that this diversity is coupled to clone specific chromatin accessibility, resulting in distinct expression of genes by different clones. 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.

systems biology↗

bioRxiv2022

Developmental origins of cell heterogeneity in the human 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 first comprehensive topographic atlas of early human lung development. We report 83 cell states, several spatially-resolved developmental trajectories and predict cell interactions within defined tissue niches. We integrated scRNA-Seq and spatial transcriptomics into a web-based, open platform for interactive exploration. To illustrate the utility of our approach we show distinct states of secretory and neuroendocrine cells, largely overlapping with the programs activated either during lung fibrosis or small cell lung cancer progression. We define the origin of uncharacterized 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.

developmental biology↗

bioRxiv2021

A Landmark-based Common Coordinate Framework for Spatial Transcriptomics Data

1The increasing amount of spatial transcriptomics data prompts for means to amalgamate observations from distinct experiments, especially attractive is to cast quantities from different sources into a common coordinate frame-work (CCF) to relate signals across space. We here present a method that enables transfer of information from multiple samples to a reference representing a CCF, and show its utility by analyzing an assortment of real and synthetic data sets.

bioinformatics↗

bioRxiv2021

Cell types and clonal relations in the mouse brain revealed by single-cell and spatial transcriptomics

The mammalian brain contains a large number of specialized cells that develop from a thin sheet of neuroepithelial progenitor cells1,2. Recently, high throughput single-cell technologies have been used to define the molecular diversity of hundreds of cell types in the nervous system3,4. However, the lineage relationships between mature brain cells and progenitor cells are not well understood, because transcriptomic studies do not allow insights into clonal relationships and classical fate-mapping techniques are not scalable5,6. Here we show in vivo barcoding of early progenitor cells that enables simultaneous profiling of cell phenotypes and clonal relations in the mouse brain using single-cell and spatial transcriptomics. We reconstructed thousands of clones to uncover the existence of fate-restricted progenitor cells in the mouse hippocampal neuroepithelium and show that microglia are derived from few primitive myeloid precursors that massively expand to generate widely dispersed progeny. By coupling spatial transcriptomics with clonal barcoding, we disentangle migration patterns of clonally related cells in densely labelled tissue sections. Compared to classical fate mapping, our approach enables high-throughput dense reconstruction of cell phenotypes and clonal relations at the single-cell and tissue level in individual animals and provides an integrated approach for understanding tissue architecture.

neuroscience↗

bioRxiv2021

The spatial landscape of clonal somatic mutations in benign and malignant tissue

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 gain molecular insight into 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. 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 an unsupervised approach to capture the molecular and spatial continuums in a tissue context and challenge the rationale for treatment paradigms, including focal therapy.

genomics↗

bioRxiv2021

The spatial transcriptomic landscape of the healing intestine following damage

The intestinal barrier is composed of a complex cell network defining highly compartmentalized and specialized structures. Here, we use spatial transcriptomics (ST) to define how the transcriptomic landscape is spatially organized in the steady state and healing murine colon. At steady state conditions, we demonstrate a previously unappreciated molecular regionalization of the colon, which dramatically changes during mucosal healing. Here, we identified spatially-organized transcriptional programs defining compartmentalized mucosal healing, and regions with dominant wired pathways. Furthermore, we showed that decreased p53 activation defined areas with increased presence of proliferating epithelial stem cells. Finally, we used our resource to map transcriptomics modules associated with human diseases demonstrating that ST can be used to inform clinical practice. Overall, we provide a publicly available resource defining principles of transcriptomic regionalization of the colon during mucosal healing and a framework to develop and progress further hypotheses.

systems biology↗

bioRxiv2021

Spatial Transcriptomics to define transcriptional patterns of zonation and structural components in the liver

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.

genomics↗

bioRxiv2020

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 fulllength 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. 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 use of external single cell data allowed to nominate cell types expressing each isoform. Furthermore, SiT identifies differential isoform usage for several major genes implicated in brain function (Snap25, Bin1, Gnas) that we 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.

genomics↗

bioRxiv2020

Genome-wide Spatial Expression Profiling in FFPE Tissues

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 methods 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 methods potential to elucidate molecular mechanisms in heterogeneous clinical samples.

genetics↗

bioRxiv2020

Spatial Deconvolution of HER2-positive Breast Tumors Reveals Novel Intercellular Relationships

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.

cancer biology↗

bioRxiv2020

Super-resolved spatial transcriptomics by deep data fusion

In situ RNA capturing has made it possible to record histology and spatial gene expression from the same tissue section. Here, we introduce a method that combines data from both modalities to infer super-resolved full-transcriptome expression maps. Our method unravels transcriptional heterogeneity in micrometer-scale anatomical features and enables image-based in silico spatial transcriptomics without hybridization or sequencing.

bioinformatics↗

bioRxiv2019

Spatial mapping of cell types by integration of transcriptomics data

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.

bioinformatics↗