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Fig. S14. Cell and layer segmentation in the visual cortex. (A) DAPI staining marks putative nuclei objects, which were merged together if the centroids of the objects were within a radius of 5 microns (pre-expansion, 100 px) (shown). (B) Reads within 12.5 microns (pre-expansion) of a centroid were ascribed to that cell; if two centroids were within 12.5 microns of a read (as shown), the reads were ascribed to their nearest neighbor. (C) Demonstration of the cell segmentation pipeline by coloring all reads assigned to an individual cell with a random color. (D) The segmentation of the visual cortex into layers. The excitatory neuron clusters from Fig. 4D-E are shown, alongside the layer segmentation in gradations of grey.
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Methods for highly multiplexed RNA imaging are limited in spatial resolution, and thus in their ability to localize transcripts to nanoscale and subcellular compartments. We adapt expansion microscopy, which physically expands biological specimens, for long-read untargeted and targeted in situ RNA sequencing. We applied untargeted expansion sequenc...
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The field of spatial transcriptomics is rapidly expanding, and with it the repertoire of available technologies. However, several of the transcriptome-wide spatial assays do not operate on a single cell level, but rather produce data comprised of contributions from a – potentially heterogeneous – mixture of cells. Still, these techniques are attrac...
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... High-resolution spatial transcriptomics can distinguish hundreds of different spots in the tissue (68); each containing tens of cells which expression profile can be deconvoluted with RNA sequencing. Coupled with the resolution of fluorescence in situ hybridization (FISH) and in situ sequencing (ISS), spatial transcriptome analysis of intact tissue sections can expose spatial nanoscale-resolution imaging (68,69). When combined with sc-RNAseq and spatial transcriptome positioning on breast cancer patients, this technique confirmed the aforementioned cell heterogeneity and explored the interaction within specific functional TME cells (Figure 2). ...
Breast cancer development and progression rely not only on the proliferation of neoplastic cells but also on the significant heterogeneity in the surrounding tumor microenvironment. Its unique microenvironment, including tumor-infiltrating lymphocytes, complex myeloid cells, lipid-associated macrophages, cancer-associated fibroblasts (CAFs), and other molecules that promote the growth and migration of tumor cells, has been shown to play a crucial role in the occurrence, growth, and metastasis of breast cancer. However, a detailed understanding of the complex microenvironment in breast cancer remains largely unknown. The unique pattern of breast cancer microenvironment cells has been poorly studied, and neither has the supportive role of these cells in pathogenesis been assessed. Single-cell multiomics biotechnology, especially single-cell RNA sequencing (scRNA-seq) reveals single-cell expression levels at much higher resolution, finely dissecting the molecular characteristics of tumor microenvironment. Here, we review the recent literature on breast cancer microenvironment, focusing on scRNA-seq studies and analyzing heterogeneity and spatial location of different cells, including T and B cells, macrophages/monocytes, neutrophils, and stromal cells. This review aims to provide a more comprehensive perception of breast cancer microenvironment and annotation for their clinical classification, diagnosis, and treatment. Furthermore, we discuss the impact of novel single-cell omics technologies, such as abundant omics exploration strategies, multiomics conjoint analysis mode, and deep learning network architecture, on the future research of breast cancer immune microenvironment.
... Using a gap-filling strategy to copy the endogenous sequences to the padlock probes, in situ sequencing also allows singlenucleotide variations in genes to be detected 20 . Recently, methods have been developed to improve the multiplexity or detection efficiency of in situ sequencing by eliminating the inefficient RNA-to-cDNA conversion step, improving sequencing accuracy, using hydrogelbase clearing or sample expansion, combining with FISH, or using more efficient gap-filling enzymes [21][22][23][24][25] . In particular, the STARmap method uses two-component padlock probes to allow direct binding to RNA and circularization, hydrogel chemistry to allow tissue clearing, and an improved sequencing method to reduce error, which together allowed imaging of 1,020 targeted genes with a detection efficiency that is comparable to that of single-cell RNA sequencing 21 . ...
... Reported estimates 21,23 of this detection efficiency range from 0.2% to <0.01%. Combination with expansion microscopy, as in expansion sequencing 24, helps mitigate the molecular crowding problem to some extent and increases the spatial resolution, but the untargeted expansion sequencing still has a much lower detection efficiency than targeted expansion sequencing of tens to hundreds of genes 24 . ...
... Spatial distributions of RNAs inside cells is an important mechanism of post-transcriptional regulation, which is vital for a variety of cellular processes, ranging from cell motility to embryo development to synaptic plasticity in neurons. The high spatial resolution of imaging-based single-cell transcriptomics allows the intracellular spatial organization of RNAs to be mapped at the genome scale (Fig. 2b), providing insights into distinct transcriptome compositions in different subcellular regions and compartments 5,6,14,24,34 , such as distinct transcriptome compositions and organizations in soma, axons and dendrites of neurons 24,34 . Despite its importance, intracellular organization of transcriptomes is underexplored for most cell types. ...
The recent advent of genome-scale imaging has enabled single-cell omics analysis in a spatially resolved manner in intact cells and tissues. These advances allow gene expression profiling of individual cells, and hence in situ identification and spatial mapping of cell types, in complex tissues. The high spatial resolution of these approaches further allows determination of the spatial organizations of the genome and transcriptome inside cells, both of which are key regulatory mechanisms for gene expression.
... Currently, alternative protocols that involve the direct probing of RNA with PLPs and RCA, such as SCRINSHOT (16) and targeted ExSeq (17) have also exhibited improved transcript detection efficiency. However, PBCV-1 DNA ligase used in these protocols have shown high tolerance for mismatches in ligation and extensive optimization would have to be undertaken for different tissue types to prevent off-target detection (13,16) . ...
Highly multiplexed spatial mapping of multiple transcripts within tissues allows for investigation of the transcriptomic and cellular diversity of mammalian organs previously unseen. Here we explore the possibilities of a direct RNA (dRNA) detection approach incorporating the use of padlock probes and rolling circle amplification in combination with hybridization-based in situ sequencing (HybISS) chemistry. We benchmark a dRNA targeting kit that circumvents the standard reverse transcription limiting, cDNA-based in situ sequencing (ISS). We found a five-fold increase in transcript detection efficiency when compared to cDNA-based ISS and also validated its multiplexing capability by targeting a curated panel of 50 genes from previous publications on mouse brain sections, leading to additional data interpretation such as de novo cell typing. With this increased efficiency, we maintain specificity, multiplexing capabilities and ease of implementation. Overall, the dRNA chemistry shows significant improvements in target detection efficiency, closing the gap between the gold standard of fluorescent in situ hybridization (FISH) based technologies and opens up possibilities to explore new biological questions previously not possible with cDNA-based ISS, nor with FISH.
... Next, we segmented our images to generate a custom mask for our images using the same color scheme adopted by the Allen ontology. For this, we applied semantic segmentation 18 , and segmented five classes in our histological image (Fig. 7a): background, cortex, cerebellum, white matter and other grey matter. As the training set is scarce, we adopted a combination of transfer learning and heavy augmentation during training (Fig. 7b) and validated it by inspecting predictions on test atlases (Fig. 7d). ...
... We used a semantic segmentation model from the Tensorflow Keras version of the segmentation_models library (https://github.com/qubvel/segmentation_models). Specifically, we chose a U-NET architecture 18 with a ResNet50 backbone 26 . All weights have been randomly initialized following the He scheme, with the exception of the ResNet50 encoder which was pretrained on ImageNet. ...
Charting a biological atlas of an organ, such as the brain, requires us to spatially-resolve whole transcriptomes of single cells, and to relate such cellular features to the histological and anatomical scales. Single-cell and single-nucleus RNA-Seq (sc/snRNA-seq) can map cells comprehensively, but relating those to their histological and anatomical positions in the context of an organ's common coordinate framework remains a major challenge and barrier to the construction of a cell atlas. Conversely, Spatial Transcriptomics allows for in-situ measurements at the histological level, but at lower spatial resolution and with limited sensitivity. Targeted in situ technologies solve both issues, but are limited in gene throughput which impedes profiling of the entire transcriptome. Finally, as samples are collected for profiling, their registration to anatomical atlases often require human supervision, which is a major obstacle to build pipelines at scale. Here, we demonstrate spatial mapping of cells, histology, and anatomy in the somatomotor area and the visual area of the healthy adult mouse brain. We devise Tangram, a method that aligns snRNA-seq data to various forms of spatial data collected from the same brain region, including MERFISH, STARmap, smFISH, and Spatial Transcriptomics (Visium), as well as histological images and public atlases. Tangram can map any type of sc/snRNA-seq data, including multi-modal data such as SHARE-seq data5, which we used to reveal spatial patterns of chromatin accessibility. We equipped Tangram with a deep learning computer vision pipeline, which allows for automatic identification of anatomical annotations on histological images of mouse brain. By doing so, Tangram reconstructs a genome-wide, anatomically-integrated, spatial map of the visual and somatomotor area with ~30,000 genes at single-cell resolution, revealing spatial gene expression and chromatin accessibility patterning beyond current limitation of in-situ technologies.
Spatially resolved proteomics is an emerging approach for mapping proteome heterogeneity of biological samples, however, it remains technically challenging due to the complexity of the tissue microsampling techniques and mass spectrometry analysis of nanoscale specimen volumes. Here, we describe a spatially resolved proteomics method based on the combination of tissue expansion with mass spectrometry-based proteomics, which we call Expansion Proteomics (ProteomEx). ProteomEx enables quantitative profiling of the spatial variability of the proteome in mammalian tissues at ~160 µm lateral resolution, equivalent to the tissue volume of 0.61 nL, using manual microsampling without the need for custom or special equipment. We validated and demonstrated the utility of ProteomEx for streamlined large-scale proteomics profiling of biological tissues including brain, liver, and breast cancer. We further applied ProteomEx for identifying proteins associated with Alzheimer’s disease in a mouse model by comparative proteomic analysis of brain subregions.
Since its introduction in 2015, expansion microscopy (ExM) allowed imaging a broad variety of biological structures in many models, at nanoscale resolution. Here, we describe in detail a protocol for application of ExM in whole-brains of zebrafish larvae and intact embryos, and discuss the considerations involved in the imaging of nonflat, whole-organ or organism samples, more broadly.
The function of many biological systems, such as embryos, liver lobules, intestinal villi, and tumors depends on the spatial organization of their cells. In the past decade high-throughput technologies have been developed to quantify gene expression in space, and computational methods have been developed that leverage spatial gene expression data to identify genes with spatial patterns and to delineate neighborhoods within tissues. To assess the ability and potential of spatial gene expression technologies to drive biological discovery, we present a curated database of literature on spatial transcriptomics dating back to 1987, along with a thorough analysis of trends in the field such as usage of experimental techniques, species, tissues studied and computational approaches used. Our analysis places current methods in historical context, and we derive insights about the field that can guide current research strategies. A companion supplement offers a more detailed look at the technologies and methods analyzed: https://pachterlab.github.io/LP_2021/.
