Annotation of Spatially Resolved Single-cell Data with STELLAR

Spatial protein and RNA imaging technologies have been gaining rapid attention but current computational methods for annotating cells are based on techniques established for dissociated single-cell technologies and thus do not take spatial organization into account. Here we present STELLAR, a geometric deep learning method that utilizes spatial and molecular cell information to automatically assign cell types from an annotated reference set as well as discover new cell types and cell states. STELLAR transfers annotations across different dissection regions, tissues, and donors and detects higher-order tissue structures with dramatic time savings.

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Combined single-cell and spatial transcriptomics reveals the molecular, cellular and spatial bone marrow niche organization

10.1101/718395 ◽

2019 ◽

Cited By ~ 2

Author(s):

Chiara Baccin ◽

Jude Al-Sabah ◽

Lars Velten ◽

Patrick M. Helbling ◽

Florian Grünschläger ◽

...

Keyword(s):

Bone Marrow ◽

Single Cell ◽

Spatial Organization ◽

Cell Types ◽

Bone Marrow Niche ◽

Cellular Composition ◽

Spatially Resolved ◽

Resident Cell ◽

Cell Gene Expression ◽

Novel Strategy

SUMMARYThe bone marrow (BM) constitutes the primary site for life-long blood production and skeletal regeneration. However, its cellular composition and the spatial organization into distinct ‘niches’ remains controversial. Here, we combine single-cell and spatially resolved transcriptomics to systematically map the molecular and cellular composition of the endosteal, sinusoidal, and arteriolar BM niches. This allowed us to transcriptionally profile all major BM resident cell types, determine their localization, and clarify the cellular and spatial sources of key growth factors and cytokines. Our data demonstrate that previously unrecognized Cxcl12-abundant reticular (CAR) cell subsets (i.e. Adipo- and Osteo-CAR cells) differentially localize to sinusoidal or arteriolar surfaces, locally act as ‘professional cytokine secreting cells’, and thereby establish distinct peri-vascular micro-niches. Importantly, we also demonstrate that the 3-dimensional organization of the BM can be accurately inferred from single-cell gene expression data using the newly developed RNA-Magnet algorithm. Together, our study reveals the cellular and spatial organization of BM niches, and offers a novel strategy to dissect the complex organization of whole organs in a systematic manner.One Sentence SummaryIntegration of single-cell and spatial transcriptomics reveals the molecular, cellular and spatial organization of bone marrow niches

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The spatio-temporal landscape of lung pathology in SARS-CoV-2 infection

10.1101/2020.10.26.20219584 ◽

2020 ◽

Author(s):

André Figueiredo Rendeiro ◽

Hiranmayi Ravichandran ◽

Yaron Bram ◽

Steven Salvatore ◽

Alain Borczuk ◽

...

Keyword(s):

Single Cell ◽

Cell Types ◽

Alveolar Epithelial Cells ◽

Lung Damage ◽

Lung Pathology ◽

Alveolar Epithelial ◽

Spatially Resolved ◽

Injured Lung ◽

Spatio Temporal ◽

Cell Data

SummaryRecent studies have provided insights into the pathology and immune response to coronavirus disease 2019 (COVID-19)1–8. However thorough interrogation of the interplay between infected cells and the immune system at sites of infection is lacking. We use high parameter imaging mass cytometry9 targeting the expression of 36 proteins, to investigate at single cell resolution, the cellular composition and spatial architecture of human acute lung injury including SARS-CoV-2. This spatially resolved, single-cell data unravels the disordered structure of the infected and injured lung alongside the distribution of extensive immune infiltration. Neutrophil and macrophage infiltration are hallmarks of bacterial pneumonia and COVID-19, respectively. We provide evidence that SARS-CoV-2 infects predominantly alveolar epithelial cells and induces a localized hyper-inflammatory cell state associated with lung damage. By leveraging the temporal range of COVID-19 severe fatal disease in relation to the time of symptom onset, we observe increased macrophage extravasation, mesenchymal cells, and fibroblasts abundance concomitant with increased proximity between these cell types as the disease progresses, possibly as an attempt to repair the damaged lung tissue. This spatially resolved single-cell data allowed us to develop a biologically interpretable landscape of lung pathology from a structural, immunological and clinical standpoint. This spatial single-cell landscape enabled the pathophysiological characterization of the human lung from its macroscopic presentation to the single-cell, providing an important basis for the understanding of COVID-19, and lung pathology in general.

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XYZeq: Spatially resolved single-cell RNA sequencing reveals expression heterogeneity in the tumor microenvironment

Science Advances ◽

10.1126/sciadv.abg4755 ◽

2021 ◽

Vol 7 (17) ◽

pp. eabg4755

Author(s):

Youjin Lee ◽

Derek Bogdanoff ◽

Yutong Wang ◽

George C. Hartoularos ◽

Jonathan M. Woo ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Spatial Organization ◽

Cell Types ◽

Mouse Tumor ◽

Spatially Resolved ◽

Distinct Cell ◽

Single Cell Rna Sequencing ◽

A Cell ◽

Anatomical Space

Single-cell RNA sequencing (scRNA-seq) of tissues has revealed remarkable heterogeneity of cell types and states but does not provide information on the spatial organization of cells. To better understand how individual cells function within an anatomical space, we developed XYZeq, a workflow that encodes spatial metadata into scRNA-seq libraries. We used XYZeq to profile mouse tumor models to capture spatially barcoded transcriptomes from tens of thousands of cells. Analyses of these data revealed the spatial distribution of distinct cell types and a cell migration-associated transcriptomic program in tumor-associated mesenchymal stem cells (MSCs). Furthermore, we identify localized expression of tumor suppressor genes by MSCs that vary with proximity to the tumor core. We demonstrate that XYZeq can be used to map the transcriptome and spatial localization of individual cells in situ to reveal how cell composition and cell states can be affected by location within complex pathological tissue.

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Cell type prioritization in single-cell data

10.1101/2019.12.20.884916 ◽

2019 ◽

Cited By ~ 1

Author(s):

Michael A. Skinnider ◽

Jordan W. Squair ◽

Claudia Kathe ◽

Mark A. Anderson ◽

Matthieu Gautier ◽

...

Keyword(s):

Single Cell ◽

Neural Circuits ◽

Cell Types ◽

Chromatin Accessibility ◽

High Dimensional ◽

Machine Learning Method ◽

Learning Method ◽

Rna Seq ◽

Cell Type ◽

Cell Data

We present a machine-learning method to prioritize the cell types most responsive to biological perturbations within high-dimensional single-cell data. We validate our method, Augur (https://github.com/neurorestore/Augur), on a compendium of single-cell RNA-seq, chromatin accessibility, and imaging transcriptomics datasets. We apply Augur to expose the neural circuits that enable walking after paralysis in response to spinal cord neurostimulation.

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920 A single-cell spatially resolved MERFISH map of the colorectal tumor immune microenvironment

Journal for ImmunoTherapy of Cancer ◽

10.1136/jitc-2021-sitc2021.920 ◽

2021 ◽

Vol 9 (Suppl 3) ◽

pp. A965-A965

Author(s):

Colles Price ◽

Jonathan Chen ◽

Karin Pelka ◽

Sherry Chao ◽

Jiang He ◽

...

Keyword(s):

Colorectal Cancer ◽

Tumor Microenvironment ◽

Single Cell ◽

Spatial Organization ◽

Single Cells ◽

Cell Types ◽

List Type ◽

Cellular Composition ◽

Cell Type ◽

Spatially Resolved

BackgroundUnderstanding the tumor microenvironment (TIME) requires more than just a catalog of cell types and gene programs. It is critical to see the spatial organization of the cells are and where they form multicellular interaction networks. Here we present a single-cell spatially resolved transcriptomic analysis of human mismatch repair deficient (MMRd) and proficient (MMRp) colorectal cancer (CRC) specimens. High tumor mutational burden MMRd tumors are known to have an immune response characterized by higher cytolytic T cell infiltrates compared to MMRp tumors, making them an ideal system for spatial single-cell profiling and understanding how the immune-driven programs differ between these tumors.MethodsMERFISH is a massively multiplexed single molecule imaging technology which can simultaneously capture and measure the quantity and distribution of hundreds to thousands of RNA species within single cells across a tissue.1 We designed a MERFISH library of over 450 genes including genes important to proliferation, apoptosis, immune signaling, immune cell type pathways and other critical pathways in CRC. Patient samples were obtained commercially or through Massachusetts General Hospital. Samples were hybridized with the designed MERFISH library and stained with a cell boundary marker to delineate cells across the tissue. We performed unsupervised clustering to identify cell types and we explored calculated spatial statistics to characterize how the cell type distribution varied between MMRd and MMRp tumors. We identified the cellular composition of each tumor, including immune and stromal cells, and the spatial distribution of these cell types.ResultsUsing MERFISH, we were able to readily identify all cell types and states previously discovered by single-cell RNA sequencing2 in intact patient specimens, thus providing an accurate map of the cellular composition and spatial organization of these cells in the tumor microenvironment. Of note, previously predicted multicellular interaction networks2 appeared as spatially organized structures in the tissue and were distinct in MMRd versus MMRp tumor specimens. Our data provide a richness of concrete hypotheses about which cells are working together and how these cells function cooperatively, which will be critical in advancing immunotherapy in these immunologically distinct types of colorectal cancer.ConclusionsHere we present a single-cell resolved spatial map of the cell types and states in the tumor microenvironment of MMRd and MMRp cancer. This will aid the development of future immunotherapies for CRC patients.ReferencesChen KH, Boettiger AN, Moffitt JR, Wang S, Zhuang X. RNA imaging. Spatially resolved, highly multiplexed RNA profiling in single cells. Science 2015;348:AAA 6090.Pelka K, Hofree M, Chen J, Sarkizova S, Pirl JD, Jorgji V, et al. Multicellular immune hubs and their organization in MMRd and MMRp colorectal cancer. BioRxiv 2021;426796.Ethics ApprovalAll samples not commercially purchased were collected in accordance with IRB protocol DF/HCC IRB 02-240.

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Molecular, spatial, and functional single-cell profiling of the hypothalamic preoptic region

Science ◽

10.1126/science.aau5324 ◽

2018 ◽

Vol 362 (6416) ◽

pp. eaau5324 ◽

Cited By ~ 213

Author(s):

Jeffrey R. Moffitt ◽

Dhananjay Bambah-Mukku ◽

Stephen W. Eichhorn ◽

Eric Vaughn ◽

Karthik Shekhar ◽

...

Keyword(s):

Single Cell ◽

Spatial Organization ◽

Social Behaviors ◽

Cell Types ◽

Mapping Method ◽

Preoptic Region ◽

Neuronal Populations ◽

Spatially Resolved ◽

Mechanistic Investigation ◽

And Function

The hypothalamus controls essential social behaviors and homeostatic functions. However, the cellular architecture of hypothalamic nuclei—including the molecular identity, spatial organization, and function of distinct cell types—is poorly understood. Here, we developed an imaging-based in situ cell-type identification and mapping method and combined it with single-cell RNA-sequencing to create a molecularly annotated and spatially resolved cell atlas of the mouse hypothalamic preoptic region. We profiled ~1 million cells, identified ~70 neuronal populations characterized by distinct neuromodulatory signatures and spatial organizations, and defined specific neuronal populations activated during social behaviors in male and female mice, providing a high-resolution framework for mechanistic investigation of behavior circuits. The approach described opens a new avenue for the construction of cell atlases in diverse tissues and organisms.

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An unsupervised learning method for reconstructing cell spatial organization with application to the DREAM Single Cell Transcriptomics Challenge

F1000Research ◽

10.12688/f1000research.20446.1 ◽

2020 ◽

Vol 9 ◽

pp. 124

Author(s):

Yang Chen ◽

Disheng Mao ◽

Yuping Zhang ◽

Zhengqing Ouyang

Keyword(s):

Gene Expression ◽

Unsupervised Learning ◽

Single Cell ◽

Spatial Organization ◽

Single Cells ◽

Expression Patterns ◽

Cell Types ◽

Learning Method ◽

Driver Genes ◽

Clustering Approach

Single cell RNA sequencing (scRNA-seq) data analysis is important for building a global transcription landscape of all cell types in tissues, tracing cell lineages, and reconstructing cell spatial organizations. In this article, we propose an unsupervised learning method to predict spatial positions and gene expression of individual cells in Drosophila embryos using a small number of driver genes. Specifically, we develop a two-stage clustering approach, and compute a probability matrix of the spatial positions of single cells. This method is applied to dataset in the DREAM Single Cell Transcriptomics Challenge. The comparison with the “gold standard” suggests that our method is effective in reconstructing the cell positions and gene expression patterns in spatial tissues.

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Prioritization of cell types responsive to biological perturbations in single-cell data with Augur

Nature Protocols ◽

10.1038/s41596-021-00561-x ◽

2021 ◽

Author(s):

Jordan W. Squair ◽

Michael A. Skinnider ◽

Matthieu Gautier ◽

Leonard J. Foster ◽

Grégoire Courtine

Keyword(s):

Single Cell ◽

Cell Types ◽

Cell Data

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Identifying cell types from single-cell data based on similarities and dissimilarities between cells

BMC Bioinformatics ◽

10.1186/s12859-020-03873-z ◽

2021 ◽

Vol 22 (S3) ◽

Author(s):

Yuanyuan Li ◽

Ping Luo ◽

Yi Lu ◽

Fang-Xiang Wu

Keyword(s):

Gene Expression ◽

Single Cell ◽

Spectral Clustering ◽

Incidence Matrix ◽

Expression Patterns ◽

Cell Types ◽

Clustering Method ◽

Different Types ◽

Cell Data ◽

Spectral Clustering Method

Abstract Background With the development of the technology of single-cell sequence, revealing homogeneity and heterogeneity between cells has become a new area of computational systems biology research. However, the clustering of cell types becomes more complex with the mutual penetration between different types of cells and the instability of gene expression. One way of overcoming this problem is to group similar, related single cells together by the means of various clustering analysis methods. Although some methods such as spectral clustering can do well in the identification of cell types, they only consider the similarities between cells and ignore the influence of dissimilarities on clustering results. This methodology may limit the performance of most of the conventional clustering algorithms for the identification of clusters, it needs to develop special methods for high-dimensional sparse categorical data. Results Inspired by the phenomenon that same type cells have similar gene expression patterns, but different types of cells evoke dissimilar gene expression patterns, we improve the existing spectral clustering method for clustering single-cell data that is based on both similarities and dissimilarities between cells. The method first measures the similarity/dissimilarity among cells, then constructs the incidence matrix by fusing similarity matrix with dissimilarity matrix, and, finally, uses the eigenvalues of the incidence matrix to perform dimensionality reduction and employs the K-means algorithm in the low dimensional space to achieve clustering. The proposed improved spectral clustering method is compared with the conventional spectral clustering method in recognizing cell types on several real single-cell RNA-seq datasets. Conclusions In summary, we show that adding intercellular dissimilarity can effectively improve accuracy and achieve robustness and that improved spectral clustering method outperforms the traditional spectral clustering method in grouping cells.

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Single-cell transcriptomic analysis of mIHC images via antigen mapping

Science Advances ◽

10.1126/sciadv.abc5464 ◽

2021 ◽

Vol 7 (10) ◽

pp. eabc5464

Author(s):

Kiya W. Govek ◽

Emma C. Troisi ◽

Zhen Miao ◽

Rachael G. Aubin ◽

Steven Woodhouse ◽

...

Keyword(s):

Single Cell ◽

Spatial Patterns ◽

Cell Types ◽

Level Of Detail ◽

Cell Populations ◽

Sequencing Data ◽

Spatially Resolved ◽

Murine Spleen ◽

Single Cell Rna Sequencing ◽

Antibody Panel

Highly multiplexed immunohistochemistry (mIHC) enables the staining and quantification of dozens of antigens in a tissue section with single-cell resolution. However, annotating cell populations that differ little in the profiled antigens or for which the antibody panel does not include specific markers is challenging. To overcome this obstacle, we have developed an approach for enriching mIHC images with single-cell RNA sequencing data, building upon recent experimental procedures for augmenting single-cell transcriptomes with concurrent antigen measurements. Spatially-resolved Transcriptomics via Epitope Anchoring (STvEA) performs transcriptome-guided annotation of highly multiplexed cytometry datasets. It increases the level of detail in histological analyses by enabling the systematic annotation of nuanced cell populations, spatial patterns of transcription, and interactions between cell types. We demonstrate the utility of STvEA by uncovering the architecture of poorly characterized cell types in the murine spleen using published cytometry and mIHC data of this organ.

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