Background Glioblastoma is the deadliest brain cancer, characterized by large cellular diversity. Both neurodevelopment-like and mesenchymal-like cell states have been described, with the latter being strongly implicated in malignancy and disease progression. However, the spatial organization of these mesenchymal-like cell states has not been systematically described outside the tumor bulk. Methods We performed deep single-cell RNA sequencing of rare glioblastoma cases where tissue could be sampled from tumor core to macroscopically normal cortex and 888-plex enhanced electric single-molecule fluorescence in situ hybridization (EEL-FISH) spatial transcriptomics on a large cohort of standard resections. We also established four glioblastoma organoid lines to test in vitro inducibility of mesenchymal-like cell states under hypoxia and blood plasma exposure. Findings We discovered that previously defined mesenchymal-like tumor cell states were shared across both malignant and non-malignant cell types and spatially confined to the tumor bulk. Peripheral regions were instead dominated by neurodevelopment-like tumor states and endogenous microglia. In patient-derived organoids and non-malignant astrocytes, the mesenchymal transcriptional state could be reversibly induced in vitro by hypoxia and human plasma, indicative of a wound response. Multiplex single-molecule spatial transcriptomics revealed that the activation of mesenchymal-like states was associated with hypoxia and organized by distance to perivascular niches. Conclusions Our findings clarify the cellular landscape and biology of glioblastoma, wherein the mesenchymal state arises at least partly as a reactive tissue state shared by all cells in the tumor bulk. Funding This work was supported by Region Stockholm, Erling-Persson Family Foundation (Atlas of Childhood Disease), Hjärnfonden (FO2023-0309), Swedish Research Council (2022-01248), and Torsten Söderberg Foundation.

Glioblastoma is the deadliest brain cancer, characterized by great cellular diversity and unique histology. To understand the spatial organization of transcriptional cell states, we mapped the expression of 888 genes in centimeter-scaletissuesectionsfromalargepatientcohort.Wefoundahierarchyofcellularstatesakintonormal brain development, including proliferating and differentiated cells interacting with the stroma. We discovered that mesenchymal-like glioblastoma cells comprised a major glial-like wound-response component and a distinct gliosarcoma-specific malignant fibroblast type. Our analysis highlighted hypoxia, tissue damage, and woundhealingasmajorfactorsinglioblastomaspatialorganization.Tumormicroenvironment variedalongthe hypoxia gradient, inducing the recruitment of monocytes and pro-tumorigenic macrophages, and propagating wound response program activation in malignant glial cells. Our study reveals the dynamic progression of glioblastoma organization independent of its mutational profile, in response to tumor-induced injury.

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.

Raw RNA locations of the mouse atlas produced by EEL FISH for 168 genes.RNA files are in the .parquet format which can be opened with FISHscale (https://github.com/linnarsson-lab/FISHscale) or any other parquet file reader (https://arrow.apache.org/docs/index.html)RNA .parquet files Seven sagittal sections of the mouse brain with 168 detected genes, sampled at the medial-lateral positions of -140 µm, 600 µm, 1200 µm, 1810 µm, 2420 µm, 3000 µm and 3600 µm measured from the midline.Position and gene label for all RNA molecules."c_px_microscope_stitched" contains X coordinates. "r_px_microscope_stitched" contians Y coordinates. The unit are pixels with a size of 0.18 micrometer. Multiply by 0.18 to get um scale.Tissue polygons .csv files CSV files demarking the sample borders for the 7 mouse atlas sections. -140 µm, 600 µm, 1200 µm, 1810 µm, 2420 µm, 3000 µm, 3600 µm Some RNA molecules are detected outside the tissue area. To remove these you can open the dataset in FISHscale and give it the polygons in the .csv files. If you do not want to use FISHscale, you can use this code to find which points are inside the tissue: https://github.com/linnarsson-lab/FISHscale/blob/master/FISHscale/utils/inside_polygon.pyGene colors .pkl file Pickled Python dictionary with gene colors used in the paper for the mouse atlas.

Methods to spatially profile the transcriptome are dominated by a trade-off between resolution and throughput. Here we develop a method named Enhanced ELectric Fluorescence in situ Hybridization (EEL FISH) that can rapidly process large tissue samples without compromising spatial resolution. By electrophoretically transferring RNA from a tissue section onto a capture surface, EEL speeds up data acquisition by reducing the amount of imaging needed, while ensuring that RNA molecules move straight down toward the surface, preserving single-cell resolution. We apply EEL on eight entire sagittal sections of the mouse brain and measure the expression patterns of up to 440 genes to reveal complex tissue organization. Moreover, EEL can be used to study challenging human samples by removing autofluorescent lipofuscin, enabling the spatial transcriptome of the human visual cortex to be visualized. We provide full hardware specifications, all protocols and complete software for instrument control, image processing, data analysis and visualization.

Loom file containing the cell by gene table of the EEL mouse 440 gene experiment.Single cell data generated by EEL FISH on a saggital mouse brain section. .loom files can be opened with: http://loompy.org/Alternatively there is also a .tab file.Metadata Age - Age of animal. Codebook - Name of EEL codebook ColorDict - Cell cluster color dictionary. Cycles - Number of barcoding cycles.Expansion - Pixels the nuceli were expanded (pixelsize = 0.27um)Expansion_um - Expansion of nuclei in micrometer.Experiment - Experiment IDExperimentDate - Date of experimentFOVoverlapPercentage - Overlap between field of view.GenerationDate - Loom file generation date.LOOM_SPEC_VERSION - Loompy version.MaxHammingDist - maximum allowed Hamming distance.Operator - Experiment operator.Orientation - Cutting orientation.Probes - Probe sequences file.Protocol - Protocol used.Quality - Manual evaluation of quality.RNAfile - RNA file usedRemoval - Method of removal of overlapping RNA in overlapping fields of view.Sample - Sample IDSegmentation - Segmentation algorithm.Species - Species of sample.Stitching - Field of view stitching method.StitchingChannel - Between cycle alignment channel.Strain - Strain of animal.System - Microscope system used.Tissue - Tissue in experiment.TotalMolecules - Total number of molecules assigned to cells.Column metadata X - X coordinate in pixels of 0.27um (multiply by 0.27 to get micrometer) X_um - X coordinate in micrometer. Y - Y coordinate in pixels of 0.27um (multiply by 0.27 to get micrometer) Y_um - Y coordinate in micrometer. tSNE_X - tSNE component 1. tSNE_Y - tSNE component 2. Clusters - Cluster label of each cell. TotalMolecules - Total molecules per cell. Cluster colors are saved as individual RGB values: R - Red. G - Green. B - Blue.Row metadata Gene - Gene name GeneTotal - Total number of detected molecules per gene.

Several techniques are currently being developed for spatially resolved omics profiling, but each new method requires the setup of specific detection strategies or specialized instrumentation. Here we describe an imaging-free framework to localize high-throughput readouts within a tissue by cutting the sample into thin strips in a way that allows subsequent image reconstruction. We implemented this framework to transform a low-input RNA sequencing protocol into an imaging-free spatial transcriptomics technique (called STRP-seq) and validated it by profiling the spatial transcriptome of the mouse brain. We applied the technique to the brain of the Australian bearded dragon, Pogona vitticeps. Our results reveal the molecular anatomy of the telencephalon of this lizard, providing evidence for a marked regionalization of the reptilian pallium and subpallium. We expect that STRP-seq can be used to derive spatially resolved data from a range of other omics techniques. A refined spatial sampling technique transforms standard RNA sequencing into a spatial transcriptomics method.

Multiplexed fluorescence in situ hybridization techniques have enabled cell-type identification, linking transcriptional heterogeneity with spatial heterogeneity of cells. However, inaccurate cell segmentation reduces the efficacy of cell-type identification and tissue characterization. Here, we present a method called Spot-based Spatial cell-type Analysis by Multidimensional mRNA density estimation (SSAM), a robust cell segmentation-free computational framework for identifying cell-types and tissue domains in 2D and 3D. SSAM is applicable to a variety of in situ transcriptomics techniques and capable of integrating prior knowledge of cell types. We apply SSAM to three mouse brain tissue images: the somatosensory cortex imaged by osmFISH, the hypothalamic preoptic region by MERFISH, and the visual cortex by multiplexed smFISH. Here, we show that SSAM detects regions occupied by known cell types that were previously missed and discovers new cell types.

The ROBOFISH system is designed to do liquid handling, temperature control and imaging. We use it for cyclic RNA detection with Fluorescent in situ Hybridization but it can be used/adapted to perform any experiment that requires these 3 components. This guide contains all steps to build this machine and operate it.

Rheumatoid arthritis-associated joint pain is frequently observed independent of disease activity, suggesting unidentified pain mechanisms. We demonstrate that antibodies binding to cartilage, specific for collagen type II (CII) or cartilage oligomeric matrix protein (COMP), elicit mechanical hypersensitivity in mice, uncoupled from visual, histological and molecular indications of inflammation. Cartilage antibody-induced pain-like behavior does not depend on complement activation or joint inflammation, but instead on tissue antigen recognition and local immune complex (IC) formation. smFISH and IHC suggest that neuronal Fcgr1 and Fcgr2b mRNA are transported to peripheral ends of primary afferents. CII-ICs directly activate cultured WT but not FcRγ chain-deficient DRG neurons. In line with this observation, CII-IC does not induce mechanical hypersensitivity in FcRγ chain-deficient mice. Furthermore, injection of CII antibodies does not generate pain-like behavior in FcRγ chain-deficient mice or mice lacking activating FcγRs in neurons. In summary, this study defines functional coupling between autoantibodies and pain transmission that may facilitate the development of new disease-relevant pain therapeutics.

osmFISHisacyclicsinglemolecule fluorescentinsituhybridizationprotocolused toquantify theexpressionlevelof specific transcriptsintissuesections by direct labeling of individualRNAmolecules. The number of transcripts quantified in each round correspondthe numbers offluorophores available in the microscope setup. In order to quantify a large number of genes,osmFISH provides a method toremove theprobes/labelling (stripping) fromtheirtargets and get the tissue ready forthenextroundoflabeling-imaging-stripping. Even though theosmFISH protocol has been developed toperform multiple smFISH rounds, it can also be used asaquick and simple methodfor one-round smFISH. Furthermore, the protocol can be further extended toinclude more complex encoding/barcoding shemes that can be used to resolve a larger number of targets.

The global efforts towards the creation of a molecular census of the brain using single-cell transcriptomics is generating a large catalog of molecularly defined cell types lacking spatial information. Thus, new methods are needed to map a large number of cell-specific markers simultaneously on large tissue areas. Here, we developed a cyclic single molecule fluorescence in situ hybridization methodology and defined the cellular organization of the somatosensory cortex using markers identified by single-cell transcriptomics.

RNA abundance is a powerful indicator of the state of individual cells. Single-cell RNA sequencing can reveal RNA abundance with high quantitative accuracy, sensitivity and throughput1. However, this approach captures only a static snapshot at a point in time, posing a challenge for the analysis of time-resolved phenomena such as embryogenesis or tissue regeneration. Here we show that RNA velocity-the time derivative of the gene expression state-can be directly estimated by distinguishing between unspliced and spliced mRNAs in common single-cell RNA sequencing protocols. RNA velocity is a high-dimensional vector that predicts the future state of individual cells on a timescale of hours. We validate its accuracy in the neural crest lineage, demonstrate its use on multiple published datasets and technical platforms, reveal the branching lineage tree of the developing mouse hippocampus, and examine the kinetics of transcription in human embryonic brain. We expect RNA velocity to greatly aid the analysis of developmental lineages and cellular dynamics, particularly in humans.

Protocol to perform RNA transfer to a surface using the EEL method and detection with an automated fluidics and imaging machine called ROBOFISH. Paper title: Scalable in situ single-cell profiling by electrophoretic capture of mRNA Website: mousebrain.org Instructions to build the ROBOFISH system: Code for the ROBOFISH system

The mammalian nervous system executes complex behaviors controlled by specialized, precisely positioned, and interacting cell types. Here, we used RNA sequencing of half a million single cells to create a detailed census of cell types in the mouse nervous system. We mapped cell types spatially and derived a hierarchical, data-driven taxonomy. Neurons were the most diverse and were grouped by developmental anatomical units and by the expression of neurotransmitters and neuropeptides. Neuronal diversity was driven by genes encoding cell identity, synaptic connectivity, neurotransmission, and membrane conductance. We discovered seven distinct, regionally restricted astrocyte types that obeyed developmental boundaries and correlated with the spatial distribution of key glutamate and glycine neurotransmitters. In contrast, oligodendrocytes showed a loss of regional identity followed by a secondary diversification. The resource presented here lays a solid foundation for understanding the molecular architecture of the mammalian nervous system and enables genetic manipulation of specific cell types.

The stereotyped spatial architecture of the brain is both beautiful and fundamentally related to its function, extending from gross morphology to individual neuron types, where soma position, dendritic architecture, and axonal projections determine their roles in functional circuitry. Our understanding of the cell types that make up the brain is rapidly accelerating, driven in particular by recent advances in single-cell transcriptomics. However, understanding brain function, development, and disease will require linking molecular cell types to morphological, physiological, and behavioral correlates. Emerging spatially resolved transcriptomic methods promise to fill this gap by localizing molecularly defined cell types in tissues, with simultaneous detection of morphology, activity, or connectivity. Here, we review the requirements for spatial transcriptomic methods toward these goals, consider the challenges ahead, and describe promising applications.

Understanding human embryonic ventral midbrain is of major interest for Parkinson's disease. However, the cell types, their gene expression dynamics, and their relationship to commonly used rodent models remain to be defined. We performed single-cell RNA sequencing to examine ventral midbrain development in human and mouse. We found 25 molecularly defined human cell types, including five subtypes of radial glia-like cells and four progenitors. In the mouse, two mature fetal dopaminergic neuron subtypes diversified into five adult classes during postnatal development. Cell types and gene expression were generally conserved across species, but with clear differences in cell proliferation, developmental timing, and dopaminergic neuron development. Additionally, we developed a method to quantitatively assess the fidelity of dopaminergic neurons derived from human pluripotent stem cells, at a single-cell level. Thus, our study provides insight into the molecular programs controlling human midbrain development and provides a foundation for the development of cell replacement therapies.

Objective: Inhibition of the mammalian target of rapamycin (mTOR) pathway has been suggested as a possible antiepileptogenic strategy in temporal lobe epilepsy (TLE). Here we aim to elucidate whether mTOR inhibition has antiepileptogenic and/or antiseizure effects using different treatment strategies in the electrogenic poststatus epilepticus (SE) rat model. Methods: Effects of mTOR inhibitor rapamycin were tested using the following three treatment protocols: (1) "stop-treatment"-post-SE treatment (6 mg/kg/day) was discontinued after 3 weeks; rats were monitored for 5 more weeks thereafter, (2) "pretreatment"-rapamycin (3 mg/kg/day) was applied during 3 days preceding SE; and (3) "chronic phase-treatment"-5 days rapamycin treatment (3 mg/kg/day) in the chronic phase. We also tested curcumin, an alternative mTOR inhibitor with antiinflammatory and antioxidant effects, using chronic phase treatment. Seizures were continuously monitored using video-electroencephalography (EEG) recordings; mossy fiber sprouting, cell death, and inflammation were studied using immunohistochemistry. Blood was withdrawn regularly to assess rapamycin and curcumin levels with high performance liquid chromatography (HPLC). Results: Stop-treatment led to a strong reduction of seizures during the 3-week treatment and a gradual reappearance of seizures during the following 5 weeks. Three days pretreatment did not prevent seizure development, whereas 5-day rapamycin treatment in the chronic phase reduced seizure frequency. Washout of rapamycin was slow and associated with a gradual reappearance of seizures. Rapamycin treatment (both 3 and 6 mg/kg) led to body growth reduction. Curcumin treatment did not reduce seizure frequency or lead to a decrease in body weight. Significance: The present study indicates that rapamycin cannot prevent epilepsy in the electrical stimulation post-SE rat model but has seizure-suppressing properties as long as rapamycin blood levels are sufficiently high. Oral curcumin treatment had no effect on chronic seizures, possibly because it did not reach the brain at adequate levels.