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Title: Three-dimensional intact-tissue sequencing of single-cell transcriptional states.
The mammalian brain consists of an intricate tapestry of cell types, with diversity crucial for function that arises from both differential gene expression and circuit-specific anatomy. Yet, retrieving high-content gene-expression information while retaining 3D positional anatomy at cellular resolution has been difficult, limiting integrative understanding of brain structure and function. Here we introduce and apply a technology for 3D intact-tissue RNA sequencing, termed STARmap (Spatially-resolved Transcript Amplicon Readout Mapping), which integrates highly-specific signal amplification, novel hydrogel-tissue chemistry, and an error-reduction sequencing process. The capabilities of STARmap were tested by mapping from 160 to 1,020 distinct genes simultaneously in sections of mouse brain at single-cell resolution with unprecedented efficiency, accuracy and reproducibility. These experiments led to the discovery of multiple new neocortical cell types, with gene markers and spatial patterns of organization not previously described, by comparison of the molecularly-defined architectures of sensory versus cognitive neocortex, and by quantification of expression of activity-regulated genes as a function of stimulation condition, spatial position, and cell typology. By adapting STARmap to thick tissue blocks, we observed and confirmed a novel molecularly-defined gradient distribution of excitatory neuron subtypes across cubic millimeter-scale volumes (>30,000 cells), and discovered a short-range 3D pattern of self-clustering shared by more » many inhibitory neuron subtypes that was accurately identifiable with a 3D STARmap approach. « less
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  1. Abstract

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  3. Abstract Motivation

    Single-cell RNA sequencing (scRNAseq) technologies allow for measurements of gene expression at a single-cell resolution. This provides researchers with a tremendous advantage for detecting heterogeneity, delineating cellular maps or identifying rare subpopulations. However, a critical complication remains: the low number of single-cell observations due to limitations by rarity of subpopulation, tissue degradation or cost. This absence of sufficient data may cause inaccuracy or irreproducibility of downstream analysis. In this work, we present Automated Cell-Type-informed Introspective Variational Autoencoder (ACTIVA): a novel framework for generating realistic synthetic data using a single-stream adversarial variational autoencoder conditioned with cell-type information. Within a single framework, ACTIVA can enlarge existing datasets and generate specific subpopulations on demand, as opposed to two separate models [such as single-cell GAN (scGAN) and conditional scGAN (cscGAN)]. Data generation and augmentation with ACTIVA can enhance scRNAseq pipelines and analysis, such as benchmarking new algorithms, studying the accuracy of classifiers and detecting marker genes. ACTIVA will facilitate analysis of smaller datasets, potentially reducing the number of patients and animals necessary in initial studies.


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    Availability and implementation

    The codes and datasets are hosted on Zenodo ( Tutorials are available at

    Supplementary information

    Supplementary data are available at Bioinformatics online.

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  4. Abstract

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