scholarly journals Multi-domain translation between single-cell imaging and sequencing data using autoencoders

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Karren Dai Yang ◽  
Anastasiya Belyaeva ◽  
Saradha Venkatachalapathy ◽  
Karthik Damodaran ◽  
Abigail Katcoff ◽  
...  
Keyword(s):  
T Cells ◽  
Single Cell ◽  
Cd4 T Cells ◽  
Cell Imaging ◽  
Cellular Heterogeneity ◽  
Rna Seq ◽  
Sequencing Data ◽  
Single Cell Imaging ◽  
Latent Space

AbstractThe development of single-cell methods for capturing different data modalities including imaging and sequencing has revolutionized our ability to identify heterogeneous cell states. Different data modalities provide different perspectives on a population of cells, and their integration is critical for studying cellular heterogeneity and its function. While various methods have been proposed to integrate different sequencing data modalities, coupling imaging and sequencing has been an open challenge. We here present an approach for integrating vastly different modalities by learning a probabilistic coupling between the different data modalities using autoencoders to map to a shared latent space. We validate this approach by integrating single-cell RNA-seq and chromatin images to identify distinct subpopulations of human naive CD4+ T-cells that are poised for activation. Collectively, our approach provides a framework to integrate and translate between data modalities that cannot yet be measured within the same cell for diverse applications in biomedical discovery.

scholarly journals Multi-Domain Translation between Single-Cell Imaging and Sequencing Data using Autoencoders

2019 ◽  
Author(s):  
Karren Dai Yang ◽  
Anastasiya Belyaeva ◽  
Saradha Venkatachalapathy ◽  
Karthik Damodaran ◽  
Adityanarayanan Radhakrishnan ◽  
...  
Keyword(s):  
T Cells ◽  
Single Cell ◽  
Cd4 T Cells ◽  
Cell Imaging ◽  
Rna Seq ◽  
Sequencing Data ◽  
Single Cell Imaging ◽  
Latent Space

The development of single-cell methods for capturing different data modalities including imaging and sequencing have revolutionized our ability to identify heterogeneous cell states. While various methods have been proposed to integrate different sequencing data modalities, coupling imaging and sequencing has been an open challenge. We here present an approach for integrating vastly different modalities by learning a probabilistic coupling between the different data modalities using autoencoders to map to a shared latent space. We validate this approach by integrating single-cell RNA-seq and chromatin images to identify distinct sub-populations of human naive CD4+ T-cells that are poised for activation. Collectively, our approach provides a framework to integrate and translate between data modalities that cannot yet be measured within the same cell for diverse applications in biomedical discovery.

scholarly journals Integrating single-cell RNA-seq and imaging with SCOPE-seq2

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Zhouzerui Liu ◽  
Jinzhou Yuan ◽  
Anna Lasorella ◽  
Antonio Iavarone ◽  
Jeffrey N. Bruce ◽  
...  
Keyword(s):  
Single Cell ◽  
Expression Profiling ◽  
Live Cell Imaging ◽  
Cell Imaging ◽  
Imaging Features ◽  
Primary Cells ◽  
Rna Seq ◽  
Single Cell Imaging ◽  
Cellular Identity ◽  
Surgical Sample

Abstract Live cell imaging allows direct observation and monitoring of phenotypes that are difficult to infer from transcriptomics. However, existing methods for linking microscopy and single-cell RNA-seq (scRNA-seq) have limited scalability. Here, we describe an upgraded version of Single Cell Optical Phenotyping and Expression (SCOPE-seq2) for combining single-cell imaging and expression profiling, with substantial improvements in throughput, molecular capture efficiency, linking accuracy, and compatibility with standard microscopy instrumentation. We introduce improved optically decodable mRNA capture beads and implement a more scalable and simplified optical decoding process. We demonstrate the utility of SCOPE-seq2 for fluorescence, morphological, and expression profiling of individual primary cells from a human glioblastoma (GBM) surgical sample, revealing relationships between simple imaging features and cellular identity, particularly among malignantly transformed tumor cells.

scholarly journals SMARTer single cell total RNA sequencing

2018 ◽  
Author(s):  
Verboom Karen ◽  
Everaert Celine ◽  
Bolduc Nathalie ◽  
Livak J. Kenneth ◽  
Yigit Nurten ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Expression Patterns ◽  
Transcript Level ◽  
Cellular Heterogeneity ◽  
Circular Rnas ◽  
Rna Seq ◽  
Sequencing Data ◽  
Total Rna ◽  
Sequencing Experiment

AbstractSingle cell RNA sequencing methods have been increasingly used to understand cellular heterogeneity. Nevertheless, most of these methods suffer from one or more limitations, such as focusing only on polyadenylated RNA, sequencing of only the 3’ end of the transcript, an exuberant fraction of reads mapping to ribosomal RNA, and the unstranded nature of the sequencing data. Here, we developed a novel single cell strand-specific total RNA library preparation method addressing all the aforementioned shortcomings. Our method was validated on a microfluidics system using three different cancer cell lines undergoing a chemical or genetic perturbation. We demonstrate that our total RNA-seq method detects an equal or higher number of genes compared to classic polyA[+] RNA-seq, including novel and non-polyadenylated genes. The obtained RNA expression patterns also recapitulate the expected biological signal. Inherent to total RNA-seq, our method is also able to detect circular RNAs. Taken together, SMARTer single cell total RNA sequencing is very well suited for any single cell sequencing experiment in which transcript level information is needed beyond polyadenylated genes.

scholarly journals CCSN: Single Cell RNA Sequencing Data Analysis by Conditional Cell-specific Network

2020 ◽  
Author(s):  
Lin Li ◽  
Hao Dai ◽  
Zhaoyuan Fang ◽  
Luonan Chen
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Network Flow ◽  
Single Cells ◽  
Cellular Heterogeneity ◽  
Rna Seq ◽  
Sequencing Data ◽  
Cell Clustering ◽  
A Cell

AbstractThe rapid advancement of single cell technologies has shed new light on the complex mechanisms of cellular heterogeneity. However, compared with bulk RNA sequencing (RNA-seq), single-cell RNA-seq (scRNA-seq) suffers from higher noise and lower coverage, which brings new computational difficulties. Based on statistical independence, cell-specific network (CSN) is able to quantify the overall associations between genes for each cell, yet suffering from a problem of overestimation related to indirect effects. To overcome this problem, we propose the “conditional cell-specific network” (CCSN) method, which can measure the direct associations between genes by eliminating the indirect associations. CCSN can be used for cell clustering and dimension reduction on a network basis of single cells. Intuitively, each CCSN can be viewed as the transformation from less “reliable” gene expression to more “reliable” gene-gene associations in a cell. Based on CCSN, we further design network flow entropy (NFE) to estimate the differentiation potency of a single cell. A number of scRNA-seq datasets were used to demonstrate the advantages of our approach: (1) one direct association network for one cell; (2) most existing scRNA-seq methods designed for gene expression matrices are also applicable to CCSN-transformed degree matrices; (3) CCSN-based NFE helps resolving the direction of differentiation trajectories by quantifying the potency of each cell. CCSN is publicly available at http://sysbio.sibcb.ac.cn/cb/chenlab/soft/CCSN.zip.

scholarly journals Single-cell lineage mapping of a diverse virus-specific naive CD4 T cell repertoire

2020 ◽  
Vol 218 (3) ◽  
Author(s):  
Achia Khatun ◽  
Moujtaba Y. Kasmani ◽  
Ryan Zander ◽  
David M. Schauder ◽  
Jeremy P. Snook ◽  
...  
Keyword(s):  
T Cells ◽  
Single Cell ◽  
Cell Fate ◽  
Cd4 T Cells ◽  
Cell Lineage ◽  
Clonal Diversity ◽  
Lymphocytic Choriomeningitis ◽  
Cellular Heterogeneity ◽  
Cell Repertoire ◽  
Lineage Mapping

Tracking how individual naive T cells from a natural TCR repertoire clonally expand, differentiate, and make lineage choices in response to an infection has not previously been possible. Here, using single-cell sequencing technology to identify clones by their unique TCR sequences, we were able to trace the clonal expansion, differentiation trajectory, and lineage commitment of individual virus-specific CD4 T cells during an acute lymphocytic choriomeningitis virus (LCMV) infection. Notably, we found previously unappreciated clonal diversity and cellular heterogeneity among virus-specific helper T cells. Interestingly, although most naive CD4 T cells gave rise to multiple lineages at the clonal level, ∼28% of naive cells exhibited a preferred lineage choice toward either Th1 or TFH cells. Mechanistically, we found that TCR structure, in particular the CDR3 motif of the TCR α chain, skewed lineage decisions toward the TFH cell fate.

scholarly journals Dissecting the landscape of activated CMV-stimulated CD4+ T cells in human by linking single-cell RNA-seq with T-cell receptor sequencing

2020 ◽  
Author(s):  
Menghua Lyu ◽  
Shiyu Wang ◽  
Kai Gao ◽  
Longlong Wang ◽  
Bin Li ◽  
...  
Keyword(s):  
T Cells ◽  
T Cell ◽  
Single Cell ◽  
T Cell Receptor ◽  
Cd4 T Cells ◽  
Cell Receptor ◽  
T Cell Subsets ◽  
Cd4 T Cell ◽  
Rna Seq ◽  
Cmv Infection

AbstractCD4 T cell is crucial in CMV infection, but its role is still unclear during this process. Here, we present a single-cell RNA-seq together with T cell receptor (TCR) sequencing to screen the heterogenicity and potential function of CMV pp65 reactivated CD4+ T cell subsets from human peripheral blood, and unveil their potential interactions. Notably, Treg composed the major part of these reactivated cells. Treg gene expression data revealed multiple transcripts of both inflammatory and inhibitory functions. Additionally, we describe the detailed phenotypes of CMV-reactivated effector-memory (Tem), cytotoxic T (CTL), and naïve T cells at the single-cell resolution, and implied the direct derivation of CTL from naïve CD4+ T cells. By analyzing the TCR repertoire, we identified a clonality in stimulated Tem and CTLs, and a tight relationship of Tem and CTL showing a large share in TCR. This study provides clues for understanding the function of CD4+ T cells subsets and unveils their interaction in CMV infection, and may promote the development of CMV immunotherapy.

scholarly journals Single-cell transcriptomics identifies an effectorness gradient shaping the response of CD4+ T cells to cytokines

2019 ◽  
Author(s):  
Eddie Cano-Gamez ◽  
Blagoje Soskic ◽  
Theodoros I. Roumeliotis ◽  
Ernest So ◽  
Deborah J. Smyth ◽  
...  
Keyword(s):  
T Cells ◽  
Single Cell ◽  
Cd4 T Cells ◽  
Memory T Cells ◽  
Effector Memory ◽  
Dependent Manner ◽  
Rna Seq ◽  
Cytokine Responses ◽  
Regulated Gene Expression ◽  
Memory Cd4 T Cells

AbstractNaïve CD4+ T cells coordinate the immune response by acquiring an effector phenotype in response to cytokines. However, the cytokine responses in memory T cells remain largely understudied. We used quantitative proteomics, bulk RNA-seq and single-cell RNA-seq of over 40,000 human naïve and memory CD4+ T cells to generate a detailed map of cytokine-regulated gene expression programs. We demonstrated that cytokine response differs substantially between naïve and memory T cells and showed that memory cells are unable to differentiate into the Th2 phenotype. Moreover, memory T cells acquire a Th17-like phenotype in response to iTreg polarization. At the single-cell level, we demonstrated that T cells form a continuum which progresses from naïve to effector memory T cells. This continuum is accompanied by a gradual increase in the expression levels of chemokines and cytokines and thus represents an effectorness gradient. Finally, we found that T cell cytokine responses are determined by where the cells lie in the effectorness gradient and identified genes whose expression is controlled by cytokines in an effectorness-dependent manner. Our results shed light on the heterogeneity of T cells and their responses to cytokines, provide insight into immune disease inflammation and could inform drug development.

scholarly journals Dissecting the Landscape of Activated CMV-Stimulated CD4+ T Cells in Humans by Linking Single-Cell RNA-Seq With T-Cell Receptor Sequencing

2021 ◽  
Vol 12 ◽  
Author(s):  
Menghua Lyu ◽  
Shiyu Wang ◽  
Kai Gao ◽  
Longlong Wang ◽  
Xijun Zhu ◽  
...  
Keyword(s):  
T Cells ◽  
T Cell ◽  
Single Cell ◽  
T Cell Receptor ◽  
Cd4 T Cells ◽  
Cell Receptor ◽  
T Cell Subsets ◽  
Cd4 T Cell ◽  
Rna Seq ◽  
Cmv Infection

CD4+ T cells are crucial in cytomegalovirus (CMV) infection, but their role in infection remains unclear. The heterogeneity and potential functions of CMVpp65-reactivated CD4+ T cell subsets isolated from human peripheral blood, as well as their potential interactions, were analyzed by single-cell RNA-seq and T cell receptor (TCR) sequencing. Tregs comprised the largest population of these reactivated cells, and analysis of Treg gene expression showed transcripts associated with both inflammatory and inhibitory functions. The detailed phenotypes of CMV-reactivated CD4+ cytotoxic T1 (CD4+ CTL1), CD4+ cytotoxic T2 (CD4+ CTL2), and recently activated CD4+ T (Tra) cells were analyzed in single cells. Assessment of the TCR repertoire of CMV-reactivated CD4+ T cells confirmed the clonal expansion of stimulated CD4+ CTL1 and CD4+ CTL2 cells, which share a large number of TCR repertoires. This study provides clues for resolving the functions of CD4+ T cell subsets and their interactions during CMV infection. The specific cell groups defined in this study can provide resources for understanding T cell responses to CMV infection.

scholarly journals Integrating single-cell RNA-seq and imaging with SCOPE-seq2

2020 ◽  
Author(s):  
Zhouzerui Liu ◽  
Jinzhou Yuan ◽  
Anna Lasorella ◽  
Antonio Iavarone ◽  
Jeffrey N. Bruce ◽  
...  
Keyword(s):  
Single Cell ◽  
Expression Profiling ◽  
Live Cell Imaging ◽  
Cell Imaging ◽  
Imaging Features ◽  
Primary Cells ◽  
Rna Seq ◽  
Single Cell Imaging ◽  
Cellular Identity ◽  
Surgical Sample

AbstractLive cell imaging allows direct observation and monitoring of phenotypes that are difficult to infer from transcriptomics. However, existing methods for linking microscopy and single-cell RNA-seq (scRNA-seq) have limited scalability. Here, we describe an upgraded version of Single Cell Optical Phenotyping and Expression (SCOPE-seq2) for combining single-cell imaging and expression profiling, with substantial improvements in throughput, molecular capture efficiency, linking accuracy, and compatibility with standard microscopy instrumentation. We introduce improved optically decodable mRNA capture beads and implement a more scalable and simplified optical decoding process. We demonstrate the utility of SCOPE-seq2 for fluorescence, morphological, and expression profiling of individual primary cells from a human glioblastoma (GBM) surgical sample, revealing relationships between simple imaging features and cellular identity, particularly among malignantly transformed tumor cells.

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