Spatial transcriptomic reconstruction of the mouse olfactory glomerular map suggests principles of odor processing

Wang, I‐Hao; Murray, Evan; Andrews, Greg; Jiang, Hao-Ching; Park, Sung Jin; Donnard, Elisa; Durán-Laforet, Violeta; Bear, David M.; Faust, Travis E.; Garber, Manuel; Baer, Christina E.; Schafer, Dorothy P.; Weng, Zhiping; Chen, Fei; Macosko, Evan Z.; Greer, Paul L.

doi:10.1038/s41593-022-01030-8

Cited by 44 publications

(54 citation statements)

References 79 publications

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“…The Slide-seq technique was recently developed for high-resolution SRT using 10- μ m barcoded beads with an average of 1–3 cells per spot [9, 10], and Slide-seqV2 further improved the detection sensitivity. To show the scalability of PRECAST, we analyzed a mouse OB dataset generated using Slide-seqV2 technology [22]. In this dataset, spatial transcriptomic information was obtained from a total of 20 OB sections distributed evenly along the anterior–posterior axis.…”

Section: Resultsmentioning

confidence: 99%

“…Second, SRT profiles across multiple individuals, e.g., 12 tissue slides from three adult donors in human cortex [17], can be characterized, and the conclusions made based on transcriptomic differences among spatial cell/domain clusters across multiple individuals should be robust to chance variations occurring in only one replicate. Third, using multiple spatially slides along an axis from tissues/organs would allow the recovery of the organization of a tissue/organ map, e.g., multiple sections from a mouse olfactory bulb (OB) that are equally distributed along the anterior-posterior axis of the same mouse [18]. Moreover, when multiple SRT datasets from multiple clinical/biological conditions are available, integrative analysis to estimate shared embeddings representing variations among cell/domain clusters can provide the first step towards detecting genes that are differentially expressed between conditions [19].…”

Section: Introductionmentioning

confidence: 99%

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Probabilistic embedding, clustering, and alignment for integrating spatial transcriptomics data with PRECAST

Liu

Liao

Luo

et al. 2022

Preprint

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Spatially resolved transcriptomics are a set of emerging technologies that enable the transcriptomic profiling of tissues with the physical location of expressions. Although a variety of methods have been developed for data integration, most of them are for single-cell RNA-seq datasets without a consideration of spatial information. Thus, methods that are capable of integrating spatial transcriptomics data from multiple tissue slides, possibly from multiple individuals, are sincerely needed. Here, we present PRECAST, an efficient data integration method for multiple spatial transcriptomics data with non-cluster-relevant effects such as the complex batch effects. PRECAST unifies spatial factor analysis simultaneously with spatial clustering and embedding alignment, while requiring only partially shared cell/domain clusters across datasets. We showed that PRECAST effectively integrates multiple tissue slides with spots mixed across datasets and cell/domain clusters separated using both simulated datasets and four spatial transcriptomics datasets from either low- or near-single-cell-resolution sequencing. The method demonstrates improved cell/domain detection with outstanding visualization, and the estimated embeddings and cell/domain labels facilitate many downstream analyses. Using a hepatocellular carcinoma Visium dataset, PRECAST detected two cell lineages in tumor/normal epithelium via spatial RNA velocity analysis using its estimated embeddings and domain labels. We further showed the scalability of PRECAST with a mouse olfactory bulb Slide-seqV2 dataset for 16 slides with ∼600,000 spots.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Probabilistic embedding, clustering, and alignment for integrating spatial transcriptomics data with PRECAST

Liu

Liao

Luo

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Future studies should be able to probe response spectra for these glomeruli over even larger odorant panels, across concentrations, and chronically in the awake animal. Paired with platforms for linking functionally-identified glomeruli to their cognate ORs, such as retrograde labeling of OSNs (Oka et al, 2006; Shirasu et al, 2014; Saito et al, 2017) or spatial transcriptomics (Zhu et al, 2021; Wang et al, 2022), functionally identifying glomeruli could prove an efficient means of functionally deorphanizing and mapping mammalian ORs. Indeed, the number of functionally-identified glomeruli from our initial conservative survey exceeds the total number of OR-defined glomeruli whose functional properties have been characterized in prior studies to date (Peterlin et al, 2014; Shirasu et al, 2014; Saito et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

Mapping odorant sensitivities reveals a sparse but structured representation of olfactory chemical space by sensory input to the mouse olfactory bulb

Burton

Brown

Eiting

et al. 2022

Preprint

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SUMMARYIn olfactory systems, convergence of sensory neurons onto glomeruli generates a map of odorant receptor identity. How glomerular maps relate to sensory space remains unclear. We sought to better characterize this relationship in the mouse olfactory system by defining glomeruli in terms of the odorants to which they are most sensitive. Using high-throughput odorant delivery and ultrasensitive imaging of sensory inputs, we imaged responses to 185 odorants presented at concentrations determined to activate only one or a few glomeruli across the dorsal olfactory bulb. The resulting datasets defined the tuning properties of glomeruli - and, by inference, their cognate odorant receptors - in a low-concentration regime, and yielded consensus maps of glomerular sensitivity across a wide range of chemical space. Glomeruli were extremely narrowly tuned, with ∼25% responding to only one odorant, and extremely sensitive, responding to their effective odorants at sub-picomolar to nanomolar concentrations. Such narrow tuning in this concentration regime allowed for reliable functional identification of many glomeruli based on a single diagnostic odorant. At the same time, the response spectra of glomeruli responding to multiple odorants was best predicted by straightforward odorant structural features, and glomeruli sensitive to distinct odorants with common structural features were spatially clustered. These results define an underlying structure to the primary representation of sensory space by the mouse olfactory system.

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“…Multiplexed Error-Robust Fluorescent In Situ Hybridization (MERFISH): MERFISH is a single-cell transcriptome-scale RNA imaging method that uses error-robust barcodes to measure RNA transcripts in tissues. This is achieved by physically imprinting the barcodes on RNAs, and then measuring these barcodes through sequential rounds of imaging ( 111 , 112 ). Compared to the DSP, MERFISH offers single-cell and subcellular resolution in a whole tissue section.…”

Section: Comparison Of Dsp and Other Multiplex And High-plex Tissue-b...mentioning

confidence: 99%

Challenges and Opportunities for Immunoprofiling Using a Spatial High-Plex Technology: The NanoString GeoMx® Digital Spatial Profiler

et al. 2022

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Characterization of the tumor microenvironment through immunoprofiling has become an essential resource for the understanding of the complex immune cell interactions and the assessment of biomarkers for prognosis and prediction of immunotherapy response; however, these studies are often limited by tissue heterogeneity and sample size. The nanoString GeoMx® Digital Spatial Profiler (DSP) is a platform that allows high-plex profiling at the protein and RNA level, providing spatial and temporal assessment of tumors in frozen or formalin-fixed paraffin-embedded limited tissue sample. Recently, high-impact studies have shown the feasibility of using this technology to identify biomarkers in different settings, including predictive biomarkers for immunotherapy in different tumor types. These studies showed that compared to other multiplex and high-plex platforms, the DSP can interrogate a higher number of biomarkers with higher throughput; however, it does not provide single-cell resolution, including co-expression of biomarker or spatial information at the single-cell level. In this review, we will describe the technical overview of the platform, present current evidence of the advantages and limitations of the applications of this technology, and provide important considerations for the experimental design for translational immune-oncology research using this tissue-based high-plex profiling approach.

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Spatial transcriptomic reconstruction of the mouse olfactory glomerular map suggests principles of odor processing

Cited by 44 publications

References 79 publications

Probabilistic embedding, clustering, and alignment for integrating spatial transcriptomics data with PRECAST

Probabilistic embedding, clustering, and alignment for integrating spatial transcriptomics data with PRECAST

Mapping odorant sensitivities reveals a sparse but structured representation of olfactory chemical space by sensory input to the mouse olfactory bulb

Challenges and Opportunities for Immunoprofiling Using a Spatial High-Plex Technology: The NanoString GeoMx® Digital Spatial Profiler

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