Biology subjects

Lundeberg, J.

Publications and source records attributed to Lundeberg, J..

45 records · Page 3Linked to original sources

bioRxiv2019

Molecular Atlas Of The Adult Mouse Brain

Brain maps are essential for integrating information and interpreting the structure-function relationship of circuits and behavior. We aimed to generate a systematic classification of the adult mouse brain organization based on unbiased extraction of spatially-defining features. Applying whole-brain spatial transcriptomics, we captured the gene expression signatures to define the spatial organization of molecularly discrete subregions. We found that the molecular code contained sufficiently detailed information to directly deduce the complex spatial organization of the brain. This unsupervised molecular classification revealed new area- and layer-specific subregions, for example in isocortex and hippocampus, and a new division of striatum. The whole-brain molecular atlas further supports the identification of the spatial origin of single neurons using their gene expression profile, and forms the foundation to define a minimal gene set - a brain palette - that is sufficient to spatially annotate the adult brain. In summary, we have established a new molecular atlas to formally define the identity of brain regions, and a molecular code for mapping and targeting of discrete neuroanatomical domains.

neuroscience↗

bioRxiv2019

Splotch: Robust estimation of aligned spatial temporal gene expression data.

Spatial genomics technologies enable new approaches to study how cells interact and function in intact multicellular environments but present a host of technical and computational challenges. Here we describe Splotch, a novel computational framework for the analysis of spatially resolved transcriptomics data. Splotch aligns transcriptomics data from multiple tissue sections and timepoints to generate improved posterior estimates of gene expression. We demonstrate alignment of a large corpus of single-cell RNA-seq data into an automatically generated spatial-temporal coordinate and study optimal design for spatial transcriptomics experiments.

bioinformatics↗

bioRxiv2019

Spatial and temporal transcriptomics reveal microglia-astroglia crosstalk in the amyloid-β plaque cell niche of Alzheimer’s disease

The linear cause-consequence relationship linking amyloid-{beta} peptide (A{beta}) accumulation to neuronal dysfunction in Alzheimer disease (AD) is gradually replaced by the concept that A{beta} initiates complex inflammatory-like cellular alterations that progressively become A{beta} independent and lead to brain dyshomeostasis. Little is known about the pathophysiology of this cellular phase of AD. We use here two orthogonal technologies, Spatial Transcriptomics and in situ sequencing, to analyse the transcriptome changes in cells in the amyloid-{beta} plaque niche in a knock-in mouse model for AD. We identify a multicellular co-expressed gene network of 57 Plaque-Induced Genes (PIGs) that define a series of co-ordinated and spatially restricted microglia, astroglia and oligodendrocyte responses to progressing amyloid plaques encompassing complement, oxidative stress and inflammation. A separate oligodendrocyte network suggests abnormal myelination. Spatial Transcriptomics provides an unprecedented approach to untangle the dysregulated cellular network in the vicinity of pathogenic hallmarks of AD and other brain diseases.

neuroscience↗

bioRxiv2019

High-density spatial transcriptomics arrays for in situ tissue profiling

Tissue function relies on the precise spatial organization of cells characterized by distinct molecular profiles. Single-cell RNA-Seq captures molecular profiles but not spatial organization. Conversely, spatial profiling assays either lack global transcriptome information or are not at the single-cell level. Here, we develop High-Density Spatial Transcriptomics (HDST), a method for RNA-seq at high spatial resolution. Spatially barcoded reverse transcription oligonucleotides are coupled to beads that are then randomly deposited in individual wells on a slide. The barcoded beads are decoded and coupled to a specific spatial address. We then capture and spatially in situ label RNA from the same histological tissue sections placed on the bead array slide. HDST recovers hundreds of thousands of transcript-coupled barcodes per experiment at 2 m resolution. We demonstrate HDST in the mouse brain, use it to resolve spatial expression patterns and cell types, and show how to combine it with histological stains to relate expression patterns to tissue architecture and anatomy. HDST opens the way to 2D spatial analysis of tissues at high resolution.

genomics↗

bioRxiv2018

Transcriptional output, cell types densities and normalization in spatial transcriptomics

Spatial transcriptomics measures mRNA at hundreds of 100 micrometer-diameter spots evenly spread across 6.5x6.9 mm2 histological slices. Gene expression within each spot is commonly normalized by total read counts. However we show that the transcriptional output of individual spots reflects the number of cells they contain, hence total read counts per spot reflect relevant biology. Although per-spot read-count normalization reveals important enrichment trends, it may heavily distort cell-type-related absolute local expression and conceal important biological information.

genomics↗

bioRxiv2018

Spatiotemporal Dynamics of Molecular Pathology in Amyotrophic Lateral Sclerosis

Paralysis occurring in amyotrophic lateral sclerosis (ALS) results from denervation of skeletal muscle as a consequence of motor neuron degeneration. Interactions between motor neurons and glia contribute to motor neuron loss, but the spatiotemporal ordering of molecular events that drive these processes in intact spinal tissue remains poorly understood. Here, we use spatial transcriptomics to obtain gene expression measurements of mouse spinal cords over the course of disease, as well as of postmortem tissue from ALS patients, to characterize the underlying molecular mechanisms in ALS. We identify novel pathway dynamics, regional differences between microglia and astrocyte populations at early time-points, and discern perturbations in several transcriptional pathways shared between murine models of ALS and human postmortem spinal cords.\n\nOne Sentence SummaryAnalysis of the ALS spinal cord using Spatial Transcriptomics reveals spatiotemporal dynamics of disease driven gene regulation.

neuroscience↗

bioRxiv2018

Charting Tissue Expression Anatomy by Spatial Transcriptome Deconvolution

We create data-driven maps of transcriptomic anatomy with a probabilistic framework for unsupervised pattern discovery in spatial gene expression data. With convolved negative binomial regression we discover patterns which correspond to cell types, microenvironments, or tissue components, and that consist of gene expression profiles and spatial activity maps. Expression profiles quantify how strongly each gene is expressed in a given pattern, and spatial activity maps reflect where in space each pattern is active. Arbitrary covariates and prior hierarchies are supported to leverage complex experimental designs.\n\nWe demonstrate the method with Spatial Transcriptomics data of mouse brain and olfactory bulb. The discovered transcriptomic patterns correspond to neuroanatomically distinct cell layers. Moreover, batch effects are successfully addressed, leading to consistent pattern inference for multi-sample analyses. On this basis, we identify known and uncharacterized genes that are spatially differentially expressed in the hippocampal field between Ammons horn and the dentate gyrus.

bioinformatics↗

bioRxiv2018

Multidimensional transcriptomics provides detailed information about immune cell distribution and identity in HER2+ breast tumors

The comprehensive analysis of tumor tissue heterogeneity is crucial for determining specific disease states and establishing suitable treatment regimes. Here, we analyze tumor tissue sections from ten patients diagnosed with HER2+ breast cancer. We obtain and analyze multidimensional, genome-wide transcriptomics data to resolve spatial immune cell distribution and identity within the tissue sections. Furthermore, we determine the extent of immune cell infiltration in different regions of the tumor tissue, including invasive cancer regions. We combine cross-sectioning and computational alignment to build three-dimensional images of the transcriptional landscape of the tumor and its microenvironment. The three-dimensional data clearly demonstrates the heterogeneous nature of tumor-immune interactions and reveal interpatient differences in immune cell infiltration patterns. Our study shows the potential for an improved stratification and description of the tumor-immune interplay, which is likely to be essential in treatment decisions.

cancer biology↗

bioRxiv2017

The Human Cell Atlas

The recent advent of methods for high-throughput single-cell molecular profiling has catalyzed a growing sense in the scientific community that the time is ripe to complete the 150-year-old effort to identify all cell types in the human body, by undertaking a Human Cell Atlas Project as an international collaborative effort. The aim would be to define all human cell types in terms of distinctive molecular profiles (e.g., gene expression) and connect this information with classical cellular descriptions (e.g., location and morphology). A comprehensive reference map of the molecular state of cells in healthy human tissues would propel the systematic study of physiological states, developmental trajectories, regulatory circuitry and interactions of cells, as well as provide a framework for understanding cellular dysregulation in human disease. Here we describe the idea, its potential utility, early proofs-of-concept, and some design considerations for the Human Cell Atlas.

cell biology↗