Phenotypically supervised single cell sequencing parses within-cell-type heterogeneity

Juvenile dermatomyositis (JDM) is a rare autoimmune condition with insufficient biomarkers and treatments, in part, due to incomplete knowledge of the cell types mediating disease. We investigated immunophenotypes and cell-specific genes associated with disease activity using multiplexed RNA and protein single-cell sequencing applied to PBMCs from 4 treatment-naive JDM (TN-JDM) subjects at baseline, 2, 4, and 6 months and 4 subjects with inactive disease. Analysis of 55,564 cells revealed separate clustering of TN-JDM cells within monocyte, NK, CD8+ effector T and naive B populations. The proportion of CD16+ monocytes was reduced in TN-JDM, and naive B cells were expanded. Cell-type differential gene expression analysis and hierarchical clustering identified a pan-cell-type IFN gene signature over-expressed in TN-JDM in all cell types and correlated with disease activity. TN-JDM monocytes displayed an inflammatory state: CD16+ monocytes expressed the highest IFN gene score and differential protein expression of adhesion molecules, CD49d and CD56, compared to CD14+ inflammatory monocytes. A transitional B cell population expressing higher CD24 and CD5 proteins and an IFN-hi naive B population were associated with TN-JDM and exhibited less CD39, an immunoregulatory protein. This data provides new insights into JDM immune dysregulation at cellular resolution and novel resource for myositis investigators.

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SCOPIT: sample size calculations for single-cell sequencing experiments

BMC Bioinformatics ◽

10.1186/s12859-019-3167-9 ◽

2019 ◽

Vol 20 (1) ◽

Cited By ~ 6

Author(s):

Alexander Davis ◽

Ruli Gao ◽

Nicholas E. Navin

Keyword(s):

Single Cell ◽

Web Application ◽

Multinomial Distribution ◽

R Package ◽

Cell Type ◽

Single Cell Sequencing ◽

Link Type ◽

Dna And Rna ◽

Sample Size Calculations ◽

Number Of Cells

Abstract Background In single cell DNA and RNA sequencing experiments, the number of cells to sequence must be decided before running an experiment, and afterwards, it is necessary to decide whether sufficient cells were sampled. These questions can be addressed by calculating the probability of sampling at least a defined number of cells from each subpopulation (cell type or cancer clone). Results We developed an interactive web application called SCOPIT (Single-Cell One-sided Probability Interactive Tool), which calculates the required probabilities using a multinomial distribution (www.navinlab.com/SCOPIT). In addition, we created an R package called pmultinom for scripting these calculations. Conclusions Our tool for fast multinomial calculations provide a simple and intuitive procedure for prospectively planning single-cell experiments or retrospectively evaluating if sufficient numbers of cells have been sequenced. The web application can be accessed at navinlab.com/SCOPIT.

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Sample demultiplexing, multiplet detection, experiment planning and novel cell type verification in single cell sequencing

10.1101/828483 ◽

2019 ◽

Cited By ~ 2

Author(s):

Hongyi Xin ◽

Qi Yan ◽

Yale Jiang ◽

Qiuyu Lian ◽

Jiadi Luo ◽

...

Keyword(s):

Single Cell ◽

State Of The Art ◽

Cell Types ◽

Gaussian Mixture ◽

Experiment Planning ◽

Cell Type ◽

Single Cell Sequencing ◽

Artificial Cell ◽

Real Cell ◽

Downstream Analysis

AbstractIdentifying and removing multiplets from downstream analysis is essential to improve the scalability and reliability of single cell RNA sequencing (scRNA-seq). High multiplet rates create artificial cell types in the dataset. Sample barcoding, including the cell hashing technology and the MULTI-seq technology, enables analytical identification of a fraction of multiplets in a scRNA-seq dataset.We propose a Gaussian-mixture-model-based multiplet identification method, GMM-Demux. GMM-Demux accurately identifies and removes the sample-barcoding-detectable multiplets and estimates the percentage of sample-barcoding-undetectable multiplets in the remaining dataset. GMM-Demux describes the droplet formation process with an augmented binomial probabilistic model, and uses the model to authenticate cell types discovered from a scRNA-seq dataset.We conducted two cell-hashing experiments, collected a public cell-hashing dataset, and generated a simulated cellhashing dataset. We compared the classification result of GMM-Demux against a state-of-the-art heuristic-based classifier. We show that GMM-Demux is more accurate, more stable, reduces the error rate by up to 69×, and is capable of reliably recognizing 9 multiplet-induced fake cell types and 8 real cell types in a PBMC scRNA-seq dataset.

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Schizophrenia is defined by cell-specific neuropathology and multiple neurodevelopmental mechanisms in patient-derived cerebral organoids

Molecular Psychiatry ◽

10.1038/s41380-021-01316-6 ◽

2021 ◽

Author(s):

Michael Notaras ◽

Aiman Lodhi ◽

Friederike Dündar ◽

Paul Collier ◽

Nicole M. Sayles ◽

...

Keyword(s):

Transcription Factor ◽

Growth Factor ◽

Single Cell ◽

Cortical Cell ◽

Peptide Fragments ◽

Cell Type ◽

Human Brain Tissue ◽

Cellular Survival ◽

Single Cell Sequencing ◽

Induced Pluripotent

AbstractDue to an inability to ethically access developing human brain tissue as well as identify prospective cases, early-arising neurodevelopmental and cell-specific signatures of Schizophrenia (Scz) have remained unknown and thus undefined. To overcome these challenges, we utilized patient-derived induced pluripotent stem cells (iPSCs) to generate 3D cerebral organoids to model neuropathology of Scz during this critical period. We discovered that Scz organoids exhibited ventricular neuropathology resulting in altered progenitor survival and disrupted neurogenesis. This ultimately yielded fewer neurons within developing cortical fields of Scz organoids. Single-cell sequencing revealed that Scz progenitors were specifically depleted of neuronal programming factors leading to a remodeling of cell-lineages, altered differentiation trajectories, and distorted cortical cell-type diversity. While Scz organoids were similar in their macromolecular diversity to organoids generated from healthy controls (Ctrls), four GWAS factors (PTN, COMT, PLCL1, and PODXL) and peptide fragments belonging to the POU-domain transcription factor family (e.g., POU3F2/BRN2) were altered. This revealed that Scz organoids principally differed not in their proteomic diversity, but specifically in their total quantity of disease and neurodevelopmental factors at the molecular level. Single-cell sequencing subsequently identified cell-type specific alterations in neuronal programming factors as well as a developmental switch in neurotrophic growth factor expression, indicating that Scz neuropathology can be encoded on a cell-type-by-cell-type basis. Furthermore, single-cell sequencing also specifically replicated the depletion of BRN2 (POU3F2) and PTN in both Scz progenitors and neurons. Subsequently, in two mechanistic rescue experiments we identified that the transcription factor BRN2 and growth factor PTN operate as mechanistic substrates of neurogenesis and cellular survival, respectively, in Scz organoids. Collectively, our work suggests that multiple mechanisms of Scz exist in patient-derived organoids, and that these disparate mechanisms converge upon primordial brain developmental pathways such as neuronal differentiation, survival, and growth factor support, which may amalgamate to elevate intrinsic risk of Scz.

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GMM-Demux: sample demultiplexing, multiplet detection, experiment planning, and novel cell-type verification in single cell sequencing

Genome Biology ◽

10.1186/s13059-020-02084-2 ◽

2020 ◽

Vol 21 (1) ◽

Cited By ~ 2

Author(s):

Hongyi Xin ◽

Qiuyu Lian ◽

Yale Jiang ◽

Jiadi Luo ◽

Xinjun Wang ◽

...

Keyword(s):

Single Cell ◽

Experiment Planning ◽

Cell Type ◽

Detection Experiment ◽

Single Cell Sequencing

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Cell-type composition analysis of scRNA-seq data with deep convolution neural network

10.21203/rs.3.rs-71522/v1 ◽

2020 ◽

Author(s):

He Ma ◽

Zhihao Fang ◽

Zongbin Liu ◽

Yan Chen

Keyword(s):

Single Cell ◽

Data Matrix ◽

Composition Analysis ◽

Cell Type ◽

Sequencing Data ◽

Gene Markers ◽

Continuous Increase ◽

Single Cell Sequencing ◽

Cell Type Composition ◽

Type Composition

Abstract BackgroundWith the rapid development of single-cell RNA sequencing (scRNA-seq), more large-scale single-cell sequencing data has been generated. Due to the continuous increase of single-cell sequencing data, the analysis of cell-type composition from single-cell transcriptomics has also to face huge challenges. Since the emergence of scRNA-seq technology, the size of sequencing datasets has grown more than 1 million times in just over a decade. Meanwhile, as more gene markers are discovered, the data dimension of single-cell sequencing becomes higher. All of these put forward more stringent requirements on data dimensionality reduction and clustering algorithms. Under the constraints of practical factors such as occurrence of noise and dropouts and the limitation of overhead, it is also required an effective and effcient method that can obtain accurate analysis results in a very short time, and has a competitive algorithm stability.ResultsWe present scCAE, an effective and effcient method based on convolution autoencoder that can accurately and rapidly analyze cell-type composition from single-cell transcriptomics datasets. Our method achieved the best results in the data sets that simulate the cell differentiation process among existing methods, which achieved the ARI of 69.64% and 68.83% at 10 and 25 clusters tasks. And, in the case of different dropouts, our method also works well. When the sparsity level of data metric is 71%, scCAE can achieved the ARI of 45.29%, which is the highest of the existing methods. In terms of algorithm overhead, our method has also achieved good results by comparing with several existing methods. It takes less time than most methods and takes up much less memory than other algorithms based neural networks.ConclusionsOur method, scCAE, has more accurate and reasonable results in the analysis of cell-types composition. And, because of the design of imputer, it can deal with a large number of dropouts in the data matrix. Because of the structure of convolution network, scCAE has less time and space overhead than other deep-learning-based methods. Thus, we demonstrate that scCAE is a competitive method for analysis of cell-type composition from scRNA-seq data. We expect that our study can be a stepping stone for further prosperity of single-cell transcriptomics analysis.

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Lineage recording of zebrafish embryogenesis reveals historical and ongoing lineage commitments

10.1101/2020.07.15.203760 ◽

2020 ◽

Author(s):

Zhuoxin Chen ◽

Chang Ye ◽

Zhan Liu ◽

Shanjun Deng ◽

Xionglei He ◽

...

Keyword(s):

Single Cell ◽

Cell Fate ◽

Cell Lineage ◽

Cell Types ◽

Lineage Tracing ◽

Cell Type ◽

Single Cell Sequencing ◽

Sequencing Technologies ◽

Development And Differentiation ◽

Zebrafish Embryogenesis

AbstractIt has been challenging to characterize the lineage relationships among cells in vertebrates, which comprise a great number of cells. Fortunately, recent progress has been made by combining the CRISPR barcoding system with single-cell sequencing technologies to provide an unprecedented opportunity to track lineage at single-cell resolution. However, due to errors and/or dropouts introduced by amplification and sequencing, reconstruction of accurate lineage relationships in complex organisms remains a challenge. Thus, improvements in both experimental design and computational analysis are necessary for lineage inference. In this study, we employed single-cell Lineage tracing On Endogenous Scarring Sites (scLOESS), a lineage recording strategy based on the CRISPR-Cas9 system, to trace cell fate commitments for zebrafish larvae. With rigorous quality control, we demonstrated that lineage commitments of complex organisms could be inferred from a limited number of barcoding sites. Together with cell-type characterization, our method could homogenously recover lineage information. In combination with the cell-type and lineage information, we depicted the development histories for germ layers as well as cell types. Furthermore, when combined with trajectory analysis, our methods could capture and resolve the ongoing lineage commitment events to gain further biological insights into later development and differentiation in complex organisms.

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Unveiling transposable element expression heterogeneity in cell fate regulation at the single-cell level

10.1101/2020.07.23.218800 ◽

2020 ◽

Cited By ~ 2

Author(s):

Jiangping He ◽

Isaac A. Babarinde ◽

Li Sun ◽

Shuyang Xu ◽

Ruhai Chen ◽

...

Keyword(s):

Single Cell ◽

Cell Fate ◽

Single Cells ◽

Embryonic Stem ◽

Cell Type ◽

Cell Level ◽

Single Cell Sequencing ◽

Processing Pipeline ◽

Sequencing Technologies ◽

Disease Specific

AbstractTransposable elements (TEs) make up a majority of a typical eukaryote’s genome, and contribute to cell heterogeneity and fate in unclear ways. Single cell-sequencing technologies are powerful tools to explore cells, however analysis is typically gene-centric and TE activity has not been addressed. Here, we developed a single-cell TE processing pipeline, scTE, and report the activity of TEs in single cells in a range of biological contexts. Specific TE types were expressed in subpopulations of embryonic stem cells and were dynamically regulated during pluripotency reprogramming, differentiation, and embryogenesis. Unexpectedly, TEs were expressed in somatic cells, including human disease-specific TEs that are undetectable in bulk analyses. Finally, we applied scTE to single cell ATAC-seq data, and demonstrate that scTE can discriminate cell type using chromatin accessibly of TEs alone. Overall, our results reveal the dynamic patterns of TEs in single cells and their contributions to cell fate and heterogeneity.

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BRN2 and PTN unveil multiple neurodevelopmental mechanisms in Schizophrenia patient-derived cerebral organoids

10.1101/2021.06.10.447949 ◽

2021 ◽

Author(s):

Michael Notaras ◽

Aiman Lodhi ◽

Friederike Dundar ◽

Paul Collier ◽

Nicole Sayles ◽

...

Keyword(s):

Transcription Factor ◽

Growth Factor ◽

Single Cell ◽

Cortical Cell ◽

Peptide Fragments ◽

Cell Type ◽

Human Brain Tissue ◽

Cellular Survival ◽

Single Cell Sequencing ◽

Pou Domain

Due to an inability to ethically access developing human brain tissue as well as identify prospective cases, early-arising neurodevelopmental and cell-specific signatures of Schizophrenia (Scz) have remained unknown and thus undefined. To overcome these challenges, we utilized Scz patient-derived stem cells to generate 3D cerebral organoids to model neuropathology of Scz during this critical period. We discovered that Scz organoids exhibited ventricular neuropathology resulting in altered progenitor survival and disrupted neurogenesis. This ultimately yielded fewer neurons within developing cortical fields of Scz organoids. Single-cell sequencing revealed that Scz progenitors were specifically depleted of neuronal programming factors leading to a remodeling of cell-lineages, altered differentiation trajectories, and distorted cortical cell-type diversity. While Scz organoids were 99.95% similar in their macromolecular diversity to Ctrls, four GWAS factors (PTN, COMT, PLCL1, and PODXL) and peptide fragments belonging to the POU-domain transcription factor family (e.g. POU3F2/BRN2) were altered. This revealed that Scz organoids principally differed not in their proteomic diversity, but specifically in their total quantity of disease and neurodevelopmental factors at the molecular level. Single-cell sequencing also subsequently identified cell-type specific alterations in neuronal programming factors and growth factors, and specifically replicated the depletion of POU3F2 (BRN2) and PTN in both Scz progenitors and neurons. Consequently, in two mechanistic rescue experiments we identified that the transcription factor POU3F2 (BRN2) and growth factor PTN operate as mechanistic substrates of neurogenesis and cellular survival, respectively, in Scz organoids. This suggests that multiple mechanisms of Scz exist in patient-derived organoids, and that these disparate mechanisms converge upon primordial brain developmental pathways such as neuronal differentiation, survival, and growth factor support, which may amalgamate to elevate intrinsic risk of Scz.

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Identifying transposable element expression dynamics and heterogeneity during development at the single-cell level with a processing pipeline scTE

Nature Communications ◽

10.1038/s41467-021-21808-x ◽

2021 ◽

Vol 12 (1) ◽

Cited By ~ 1

Author(s):

Jiangping He ◽

Isaac A. Babarinde ◽

Li Sun ◽

Shuyang Xu ◽

Ruhai Chen ◽

...

Keyword(s):

Single Cell ◽

Single Cells ◽

Embryonic Stem ◽

Cell Heterogeneity ◽

Cell Type ◽

Cell Level ◽

Single Cell Sequencing ◽

Processing Pipeline ◽

Sequencing Technologies ◽

Disease Specific

AbstractTransposable elements (TEs) make up a majority of a typical eukaryote’s genome, and contribute to cell heterogeneity in unclear ways. Single-cell sequencing technologies are powerful tools to explore cells, however analysis is typically gene-centric and TE expression has not been addressed. Here, we develop a single-cell TE processing pipeline, scTE, and report the expression of TEs in single cells in a range of biological contexts. Specific TE types are expressed in subpopulations of embryonic stem cells and are dynamically regulated during pluripotency reprogramming, differentiation, and embryogenesis. Unexpectedly, TEs are expressed in somatic cells, including human disease-specific TEs that are undetectable in bulk analyses. Finally, we apply scTE to single-cell ATAC-seq data, and demonstrate that scTE can discriminate cell type using chromatin accessibly of TEs alone. Overall, our results classify the dynamic patterns of TEs in single cells and their contributions to cell heterogeneity.

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