scholarly journals A spatial multi-omics atlas of the human lung reveals a novel immune cell survival niche

2021 ◽  
Author(s):  
Elo Madissoon ◽  
Amanda Jane Oliver ◽  
Vitalii Kleshchevnikov ◽  
Anna Wilbrey-Clark ◽  
Krzysztof Polanski ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Plasma Cells ◽  
Immune Cell ◽  
Human Lung ◽  
Interaction Analysis ◽  
Cell Types ◽  
Cell Interaction ◽  
Data Set ◽  
Duct Cells

Multiple distinct cell types of the human lung and airways have been defined by single cell RNA sequencing (scRNAseq). Here we present a multi-omics spatial lung atlas to define additional heterogeneity and novel cell types which we map back into the macro- and micro-anatomical tissue context to define functional tissue microenvironments. First, we have generated a single cell and nuclei RNA sequencing, VDJ-sequencing and Visium Spatial Transcriptomics data set from 5 different locations of the human lung and airways. Second, we define additional cell types/states, as well as spatially map novel and known human airway cell types, such as chondrocytes, submucosal gland (SMG) duct cells, distinct pericyte and smooth muscle subtypes, immune-recruiting fibroblasts, peribronchial and perichondrial fibroblasts, peripheral nerve associated fibroblasts and Schwann cells. Finally, we define a survival niche for IgA-secreting plasma cells at the SMG, comprising the newly defined epithelial SMG-Duct cells, and B and T lineage immune cells. Using our transcriptomic data for cell-cell interaction analysis, we propose a signalling circuit that establishes and supports this niche. Overall, we provide a transcriptional and spatial lung atlas with multiple novel cell types that allows for the study of specific tissue microenvironments such as the newly defined gland-associated lymphoid niche (GALN).

scholarly journals A molecular cell atlas of the human lung from single cell RNA sequencing

2019 ◽  
Author(s):  
Kyle J. Travaglini ◽  
Ahmad N. Nabhan ◽  
Lolita Penland ◽  
Rahul Sinha ◽  
Astrid Gillich ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Immune Cell ◽  
Human Lung ◽  
Expression Profiles ◽  
Cell Types ◽  
Specific Gene ◽  
Cell Trafficking ◽  
Single Cell Rna Sequencing

AbstractAlthough single cell RNA sequencing studies have begun providing compendia of cell expression profiles, it has proven more difficult to systematically identify and localize all molecular cell types in individual organs to create a full molecular cell atlas. Here we describe droplet- and plate-based single cell RNA sequencing applied to ∼75,000 human lung and blood cells, combined with a multi-pronged cell annotation approach, which have allowed us to define the gene expression profiles and anatomical locations of 58 cell populations in the human lung, including 41 of 45 previously known cell types or subtypes and 14 new ones. This comprehensive molecular atlas elucidates the biochemical functions of lung cell types and the cell-selective transcription factors and optimal markers for making and monitoring them; defines the cell targets of circulating hormones and predicts local signaling interactions including sources and targets of chemokines in immune cell trafficking and expression changes on lung homing; and identifies the cell types directly affected by lung disease genes and respiratory viruses. Comparison to mouse identified 17 molecular types that appear to have been gained or lost during lung evolution and others whose expression profiles have been substantially altered, revealing extensive plasticity of cell types and cell-type-specific gene expression during organ evolution including expression switches between cell types. This atlas provides the molecular foundation for investigating how lung cell identities, functions, and interactions are achieved in development and tissue engineering and altered in disease and evolution.

scholarly journals ACTINN: automated identification of cell types in single cell RNA sequencing

2019 ◽  
Author(s):  
Feiyang Ma ◽  
Matteo Pellegrini
Keyword(s):  
Neural Network ◽  
Single Cell ◽  
Rna Sequencing ◽  
Immune Cell ◽  
Cell Types ◽  
Mouse Cell ◽  
Supplementary Information ◽  
Cell Type ◽  
Human T Cell ◽  
Single Cell Rna Sequencing

Abstract Motivation Cell type identification is one of the major goals in single cell RNA sequencing (scRNA-seq). Current methods for assigning cell types typically involve the use of unsupervised clustering, the identification of signature genes in each cluster, followed by a manual lookup of these genes in the literature and databases to assign cell types. However, there are several limitations associated with these approaches, such as unwanted sources of variation that influence clustering and a lack of canonical markers for certain cell types. Here, we present ACTINN (Automated Cell Type Identification using Neural Networks), which employs a neural network with three hidden layers, trains on datasets with predefined cell types and predicts cell types for other datasets based on the trained parameters. Results We trained the neural network on a mouse cell type atlas (Tabula Muris Atlas) and a human immune cell dataset, and used it to predict cell types for mouse leukocytes, human PBMCs and human T cell sub types. The results showed that our neural network is fast and accurate, and should therefore be a useful tool to complement existing scRNA-seq pipelines. Availability and implementation The codes and datasets are available at https://figshare.com/articles/ACTINN/8967116. Tutorial is available at https://github.com/mafeiyang/ACTINN. All codes are implemented in python. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals A Comprehensive Multi-Center Cross-platform Benchmarking Study of Single-cell RNA Sequencing Using Reference Samples

2020 ◽  
Author(s):  
Wanqiu Chen ◽  
Yongmei Zhao ◽  
Xin Chen ◽  
Xiaojiang Xu ◽  
Zhaowei Yang ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Cell Types ◽  
Batch Effect ◽  
Batch Effects ◽  
Data Set ◽  
Batch Correction ◽  
Single Cell Rna Sequencing ◽  
Cross Platform ◽  
Reference Samples

AbstractSingle-cell RNA sequencing (scRNA-seq) has become a very powerful technology for biomedical research and is becoming much more affordable as methods continue to evolve, but it is unknown how reproducible different platforms are using different bioinformatics pipelines, particularly the recently developed scRNA-seq batch correction algorithms. We carried out a comprehensive multi-center cross-platform comparison on different scRNA-seq platforms using standard reference samples. We compared six pre-processing pipelines, seven bioinformatics normalization procedures, and seven batch effect correction methods including CCA, MNN, Scanorama, BBKNN, Harmony, limma and ComBat to evaluate the performance and reproducibility of 20 scRNA-seq data sets derived from four different platforms and centers. We benchmarked scRNA-seq performance across different platforms and testing sites using global gene expression profiles as well as some cell-type specific marker genes. We showed that there were large batch effects; and the reproducibility of scRNA-seq across platforms was dictated both by the expression level of genes selected and the batch correction methods used. We found that CCA, MNN, and BBKNN all corrected the batch variations fairly well for the scRNA-seq data derived from biologically similar samples across platforms/sites. However, for the scRNA-seq data derived from or consisting of biologically distinct samples, limma and ComBat failed to correct batch effects, whereas CCA over-corrected the batch effect and misclassified the cell types and samples. In contrast, MNN, Harmony and BBKNN separated biologically different samples/cell types into correspondingly distinct dimensional subspaces; however, consistent with this algorithm’s logic, MNN required that the samples evaluated each contain a shared portion of highly similar cells. In summary, we found a great cross-platform consistency in separating two distinct samples when an appropriate batch correction method was used. We hope this large cross-platform/site scRNA-seq data set will provide a valuable resource, and that our findings will offer useful advice for the single-cell sequencing community.

scholarly journals Application of single-cell RNA sequencing methodologies in understanding haematopoiesis and immunology

2019 ◽  
Vol 63 (2) ◽  
pp. 217-225 ◽  
Author(s):  
Anna M. Ranzoni ◽  
Paulina M. Strzelecka ◽  
Ana Cvejic
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Immune Cell ◽  
Cell Types ◽  
Tree Model ◽  
Future Directions ◽  
Single Cell Rna Sequencing ◽  
Lineage Tree ◽  
Cell Variation ◽  
Cellular Components

Abstract The blood and immune system are characterised by utmost diversity in its cellular components. This heterogeneity can solely be resolved with the application of single-cell technologies that enable precise examination of cell-to-cell variation. Single-cell transcriptomics is continuously pushing forward our understanding of processes driving haematopoiesis and immune responses in physiological settings as well as in disease. Remarkably, in the last five years, a number of studies involving single-cell RNA sequencing (scRNA-seq) allowed the discovery of new immune cell types and revealed that haematopoiesis is a continuous rather than a stepwise process, thus challenging the classical haematopoietic lineage tree model. This review summarises the most recent studies which applied scRNA-seq to answer outstanding questions in the fields of haematology and immunology and discusses the present challenges and future directions.

scholarly journals ADAPTS: Automated Deconvolution Augmentation of Profiles for Tissue Specific cells

2019 ◽  
Author(s):  
Samuel A Danziger ◽  
David L Gibbs ◽  
Ilya Shmulevich ◽  
Mark McConnell ◽  
Matthew WB Trotter ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Gene Expression Data ◽  
Immune Cell ◽  
De Novo ◽  
Cell Types ◽  
Expression Data ◽  
Cell Type ◽  
Data Set ◽  
Rnaseq Data

AbstractImmune cell infiltration of tumors can be an important component for determining patient outcomes, e.g. by inferring immune cell presence by deconvolving gene expression data drawn from a heterogenous mix of cell types. One particularly powerful family of deconvolution techniques uses signature matrices of genes that uniquely identify each cell type as determined from cell type purified gene expression data. Many methods of this type have been recently published, often including new signature matrices appropriate for a single purpose, such as investigating a specific type of tumor. The package ADAPTS helps users make the most of this expanding knowledge base by introducing a framework for cell type deconvolution. ADAPTS implements modular tools for customizing signature matrices for new tissue types by adding custom cell types or building new matrices de novo, including from single cell RNAseq data. It includes a common interface to several popular deconvolution algorithms that use a signature matrix to estimate the proportion of cell types present in heterogenous samples. ADAPTS also implements a novel method for clustering cell types into groups that are hard to distinguish by deconvolution and then re-splitting those clusters using hierarchical deconvolution. We demonstrate that the techniques implemented in ADAPTS improve the ability to reconstruct the cell types present in a single cell RNAseq data set in a blind predictive analysis. ADAPTS is currently available for use in R on CRAN and GitHub.

scholarly journals Case Report: Transformation From Cold to Hot Tumor in a Case of NSCLC Neoadjuvant Immunochemotherapy Pseudoprogression

2021 ◽  
Vol 12 ◽  
Author(s):  
Wenxiao Jia ◽  
Hui Zhu ◽  
Qianqian Gao ◽  
Jian Sun ◽  
Fujian Tan ◽  
...  
Keyword(s):  
Lymph Node ◽  
Single Cell ◽  
Rna Sequencing ◽  
Immune Cell ◽  
Cell Types ◽  
Surgical Pathology ◽  
Mass Cytometry ◽  
Single Cell Rna Sequencing ◽  
Squamous Cancer ◽  
Mediastinum Lymph Node

A 56-year-old male was diagnosed with right lung upper lobe squamous cancer with right hilar and mediastinum lymph node metastasis. After four cycles of neoadjuvant immunochemotherapy, reexamination by computed tomography showed progressive disease of the primary lesion. Then, the patient underwent a right lung upper lobectomy, and hilar and mediastinum lymph node dissection. Surgical pathology showed a partial response to immunochemotherapy. Single-cell RNA sequencing was used to characterize the infiltrating immune cell atlas after neoadjuvant immunochemotherapy; the most common infiltrating immune cell types were cytotoxic CD8+ T cells, monocyte-derived dendritic cells, and macrophages. Imaging mass cytometry revealed a transformation from cold to hot tumor after neoadjuvant immunochemotherapy. In this case study, we are the first to report a case of neoadjuvant immunochemotherapy pseudoprogression, proved by surgical pathology, single-cell RNA sequencing, and imaging mass cytometry. Both single-cell RNA sequencing and imaging mass cytometry revealed an activated immune microenvironment after neoadjuvant immunochemotherapy.

scholarly journals Automated identification of Cell Types in Single Cell RNA Sequencing

2019 ◽  
Author(s):  
Feiyang Ma ◽  
Matteo Pellegrini
Keyword(s):  
Neural Network ◽  
Single Cell ◽  
Rna Sequencing ◽  
Immune Cell ◽  
Cell Types ◽  
Marker Genes ◽  
Complex Data ◽  
Cell Type ◽  
Human T Cell ◽  
Single Cell Rna Sequencing

AbstractCell type identification is one of the major goals in single cell RNA sequencing (scRNA-seq). Current methods for assigning cell types typically involve the use of unsupervised clustering, the identification of signature genes in each cluster, followed by a manual lookup of these genes in the literature and databases to assign cell types. However, there are several limitations associated with these approaches, such as unwanted sources of variation that influence clustering and a lack of canonical markers for certain cell types. Here, we present ACTINN (Automated Cell Type Identification using Neural Networks), which employs a neural network with 3 hidden layers, trains on datasets with predefined cell types, and predicts cell types for other datasets based on the trained parameters. We trained the neural network on a mouse cell type atlas (Tabula Muris Atlas) and a human immune cell dataset, and used it to predict cell types for mouse leukocytes, human PBMCs and human T cell sub types. The results showed that our neural network is fast and accurate, and should therefore be a useful tool to complement existing scRNA-seq pipelines.Author SummarySingle cell RNA sequencing (scRNA-seq) provides high resolution profiling of the transcriptomes of individual cells, which inevitably results in high volumes of data that require complex data processing pipelines. Usually, one of the first steps in the analysis of scRNA-seq is to assign individual cells to known cell types. To accomplish this, traditional methods first group the cells into different clusters, then find marker genes, and finally use these to manually assign cell types for each cluster. Thus these methods require prior knowledge of cell type canonical markers, and some level of subjectivity to make the cell type assignments. As a result, the process is often laborious and requires domain specific expertise, which is a barrier for inexperienced users. By contrast, our neural network ACTINN automatically learns the features for each predefined cell type and uses these features to predict cell types for individual cells. This approach is computationally efficient and requires no domain expertise of the tissues being studied. We believe ACTINN allows users to rapidly identify cell types in their datasets, thus rendering the analysis of their scRNA-seq datasets more efficient.

scholarly journals Uncovering hypergraphs of cell-cell interaction from single cell RNA-sequencing data

2019 ◽  
Author(s):  
Koki Tsuyuzaki ◽  
Manabu Ishii ◽  
Itoshi Nikaido
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Cell Types ◽  
Cell Interaction ◽  
Receptor Expression ◽  
Sequencing Data ◽  
Sequencing Technologies ◽  
Single Cell Rna Sequencing ◽  
Ligand Expression ◽  
Cell Cell

AbstractComplex biological systems can be described as a multitude of cell-cell interactions (CCIs). Recent single-cell RNA-sequencing technologies have enabled the detection of CCIs and related ligand-receptor (L-R) gene expression simultaneously. However, previous data analysis methods have focused on only one-to-one CCIs between two cell types. To also detect many-to-many CCIs, we proposescTensor, a novel method for extracting representative triadic relationships (hypergraphs), which include (i) ligand-expression, (ii) receptor-expression, and (iii) L-R pairs. When applied to simulated and empirical datasets,scTensorwas able to detect some hypergraphs including paracrine/autocrine CCI patterns, which cannot be detected by previous methods.

scholarly journals A Single-cell Transcriptomic Atlas of the Developing Chicken Limb

2019 ◽  
Author(s):  
Christian Feregrino ◽  
Fabio Sacher ◽  
Oren Parnas ◽  
Patrick Tschopp
Keyword(s):  
Pattern Formation ◽  
Single Cell ◽  
Rna Sequencing ◽  
Cell Types ◽  
Cell Populations ◽  
Cell Type ◽  
Data Set ◽  
Cellular Resolution ◽  
Single Cell Rna Sequencing ◽  
Type Specification

AbstractBackgroundThrough precise implementation of distinct cell type specification programs, differentially regulated in both space and time, complex patterns emerge during organogenesis. Thanks to its easy experimental accessibility, the developing chicken limb has long served as a paradigm to study vertebrate pattern formation. Through decades’ worth of research, we now have a firm grasp on the molecular mechanisms driving limb formation at the tissue-level. However, to elucidate the dynamic interplay between transcriptional cell type specification programs and pattern formation at its relevant cellular scale, we lack appropriately resolved molecular data at the genome-wide level. Here, making use of droplet-based single-cell RNA-sequencing, we catalogue the developmental emergence of distinct tissue types and their transcriptome dynamics in the distal chicken limb, the so-called autopod, at cellular resolution.ResultsUsing single-cell RNA-sequencing technology, we sequenced a total of 17,628 cells coming from three key developmental stages of chicken autopod patterning. Overall, we identified 23 cell populations with distinct transcriptional profiles. Amongst them were small, albeit essential populations like the apical ectodermal ridge, demonstrating the ability to detect even rare cell types. Moreover, we uncovered the existence of molecularly distinct sub-populations within previously defined compartments of the developing limb, some of which have important signaling functions during autopod pattern formation. Finally, we inferred gene co-expression modules that coincide with distinct tissue types across developmental time, and used them to track patterning-relevant cell populations of the forming digits.ConclusionsWe provide a comprehensive functional genomics resource to study the molecular effectors of chicken limb patterning at cellular resolution. Our single-cell transcriptomic atlas captures all major cell populations of the developing autopod, and highlights the transcriptional complexity in many of its components. Finally, integrating our data-set with other single-cell transcriptomics resources will enable researchers to assess molecular similarities in orthologous cell types across the major tetrapod clades, and provide an extensive candidate gene list to functionally test cell-type-specific drivers of limb morphological diversification.

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