redPATH: Reconstructing the Pseudo Development Time of Cell Lineages in Single-Cell RNA-Seq Data and Applications in Cancer

AbstractRecent advancement of single-cell RNA-seq technology facilitates the study of cell lineages in developmental processes as well as cancer. In this manuscript, we developed a computational method, called redPATH, to reconstruct the pseudo developmental time of cell lineages using a consensus asymmetric Hamiltonian path algorithm. Besides, we implemented a novel approach to visualize the trajectory development of cells and visualization methods to provide biological insights. We validated the performance of redPATH by segmenting different stages of cell development on multiple neural stem cell and cancerous datasets, as well as other single-cell transcriptome data. In particular, we identified a subpopulation of malignant glioma cells, which are stem cell-like. These cells express known proliferative markers such as GFAP (also identified ATP1A2, IGFBPL1, ALDOC) and remain silenced in quiescent markers such as ID3. Furthermore, MCL1 is identified as a significant gene that regulates cell apoptosis, and CSF1R confirms previous studies for re-programming macrophages to control tumor growth. In conclusion, redPATH is a comprehensive tool for analyzing single-cell RNA-Seq datasets along a pseudo developmental time. The software is available via http://github.com/tinglab/redPATH.

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Single-cell transcriptome analysis of the zebrafish embryonic trunk

10.1101/2021.05.07.443129 ◽

2021 ◽

Author(s):

Sanjeeva S Metikala ◽

Satish Casie Chetty ◽

Saulius Sumanas

Keyword(s):

Single Cell ◽

Molecular Mechanisms ◽

Cell Lineage ◽

Cell Types ◽

Rna Seq ◽

Cell Lineages ◽

Distinct Cell ◽

Cell Transcriptome ◽

Single Cell Transcriptome ◽

Different Cell Types

During embryonic development, cells differentiate into a variety of distinct cell types and subtypes with diverse transcriptional profiles. To date, transcriptomic signatures of different cell lineages that arise during development have been only partially characterized. Here we used single-cell RNA-seq to perform transcriptomic analysis of over 20,000 cells disaggregated from the trunk region of zebrafish embryos at the 30 hpf stage. Transcriptional signatures of 27 different cell types and subtypes were identified and annotated during this analysis. This dataset will be a useful resource for many researchers in the fields of developmental and cellular biology and facilitate the understanding of molecular mechanisms that regulate cell lineage choices during development.

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Single-cell transcriptome analysis of the zebrafish embryonic trunk

PLoS ONE ◽

10.1371/journal.pone.0254024 ◽

2021 ◽

Vol 16 (7) ◽

pp. e0254024

Author(s):

Sanjeeva Metikala ◽

Satish Casie Chetty ◽

Saulius Sumanas

Keyword(s):

Single Cell ◽

Molecular Mechanisms ◽

Cell Lineage ◽

Cell Types ◽

Rna Seq ◽

Cell Lineages ◽

Distinct Cell ◽

Cell Transcriptome ◽

Single Cell Transcriptome ◽

Different Cell Types

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redPATH: Reconstructing the Pseudo Development Time of Cell Lineages in Single-cell RNA-seq Data and Applications in Cancer

Genomics Proteomics & Bioinformatics ◽

10.1016/j.gpb.2020.06.014 ◽

2021 ◽

Author(s):

Kaikun Xie ◽

Zehua Liu ◽

Ning Chen ◽

Ting Chen

Keyword(s):

Single Cell ◽

Development Time ◽

Rna Seq ◽

Cell Lineages

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Chromatin and Single-Cell RNA-Seq Profiling Reveal Dynamic Signaling and Metabolic Transitions during Human Spermatogonial Stem Cell Development

Cell Stem Cell ◽

10.1016/j.stem.2017.09.003 ◽

2017 ◽

Vol 21 (4) ◽

pp. 533-546.e6 ◽

Cited By ~ 86

Author(s):

Jingtao Guo ◽

Edward J. Grow ◽

Chongil Yi ◽

Hana Mlcochova ◽

Geoffrey J. Maher ◽

...

Keyword(s):

Stem Cell ◽

Single Cell ◽

Cell Development ◽

Spermatogonial Stem Cell ◽

Rna Seq

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Enhancing droplet-based single-nucleus RNA-seq resolution using the semi-supervised machine learning classifier DIEM

10.1101/786285 ◽

2019 ◽

Cited By ~ 4

Author(s):

Marcus Alvarez ◽

Elior Rahmani ◽

Brandon Jew ◽

Kristina M. Garske ◽

Zong Miao ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Cell Types ◽

Supervised Machine Learning ◽

Data Sets ◽

Rna Seq ◽

Novel Approach ◽

Single Nucleus ◽

Downstream Analysis

AbstractSingle-nucleus RNA sequencing (snRNA-seq) measures gene expression in individual nuclei instead of cells, allowing for unbiased cell type characterization in solid tissues. Contrary to single-cell RNA seq (scRNA-seq), we observe that snRNA-seq is commonly subject to contamination by high amounts of extranuclear background RNA, which can lead to identification of spurious cell types in downstream clustering analyses if overlooked. We present a novel approach to remove debris-contaminated droplets in snRNA-seq experiments, called Debris Identification using Expectation Maximization (DIEM). Our likelihood-based approach models the gene expression distribution of debris and cell types, which are estimated using EM. We evaluated DIEM using three snRNA-seq data sets: 1) human differentiating preadipocytes in vitro, 2) fresh mouse brain tissue, and 3) human frozen adipose tissue (AT) from six individuals. All three data sets showed various degrees of extranuclear RNA contamination. We observed that existing methods fail to account for contaminated droplets and led to spurious cell types. When compared to filtering using these state of the art methods, DIEM better removed droplets containing high levels of extranuclear RNA and led to higher quality clusters. Although DIEM was designed for snRNA-seq data, we also successfully applied DIEM to single-cell data. To conclude, our novel method DIEM removes debris-contaminated droplets from single-cell-based data fast and effectively, leading to cleaner downstream analysis. Our code is freely available for use at https://github.com/marcalva/diem.

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A computational method to aid in the design and analysis of single cell RNA-seq experiments

2017 IEEE 7th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS) ◽

10.1109/iccabs.2017.8114311 ◽

2017 ◽

Author(s):

Douglas Abrams ◽

Parveen Kumar ◽

Krishna Karuturi ◽

Joshy George

Keyword(s):

Single Cell ◽

Computational Method ◽

Rna Seq

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Abstract A42: Single-cell RNA-seq analysis of human pancreatic ductal adenocarcinoma identifies a novel cell type expressing the intestinal stem cell marker OLFM4

10.1158/1538-7445.panca19-a42 ◽

2019 ◽

Author(s):

Manisha Rao ◽

Alice Nemajerova ◽

Jinyu Li ◽

Mei Gao ◽

Ki Oh ◽

...

Keyword(s):

Stem Cell ◽

Single Cell ◽

Pancreatic Ductal Adenocarcinoma ◽

Stem Cell Marker ◽

Intestinal Stem Cell ◽

Ductal Adenocarcinoma ◽

Cell Marker ◽

Rna Seq ◽

Cell Type

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STACAS: Sub-Type Anchor Correction for Alignment in Seurat to integrate single-cell RNA-seq data

Bioinformatics ◽

10.1093/bioinformatics/btaa755 ◽

2020 ◽

Cited By ~ 1

Author(s):

Massimo Andreatta ◽

Santiago J Carmona

Keyword(s):

Single Cell ◽

Distance Measure ◽

Source Code ◽

Cell Types ◽

R Package ◽

Computational Method ◽

Biological Variability ◽

Rna Seq ◽

Batch Effects ◽

Guide Trees

Abstract Summary STACAS is a computational method for the identification of integration anchors in the Seurat environment, optimized for the integration of single-cell (sc) RNA-seq datasets that share only a subset of cell types. We demonstrate that by (i) correcting batch effects while preserving relevant biological variability across datasets, (ii) filtering aberrant integration anchors with a quantitative distance measure and (iii) constructing optimal guide trees for integration, STACAS can accurately align scRNA-seq datasets composed of only partially overlapping cell populations. Availability and implementation Source code and R package available at https://github.com/carmonalab/STACAS; Docker image available at https://hub.docker.com/repository/docker/mandrea1/stacas_demo.

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Single-cell transcriptome maps of myeloid blood cell lineages in Drosophila

Nature Communications ◽

10.1038/s41467-020-18135-y ◽

2020 ◽

Vol 11 (1) ◽

Cited By ~ 7

Author(s):

Bumsik Cho ◽

Sang-Ho Yoon ◽

Daewon Lee ◽

Ferdinand Koranteng ◽

Sudhir Gopal Tattikota ◽

...

Keyword(s):

Single Cell ◽

Lymph Gland ◽

Developmental Trajectory ◽

Hematopoietic Organ ◽

Cell Lineages ◽

Cellular Immune Responses ◽

Cell Transcriptome ◽

Cellular Immune ◽

Single Cell Transcriptome ◽

Crystal Cells

Abstract The Drosophila lymph gland, the larval hematopoietic organ comprised of prohemocytes and mature hemocytes, has been a valuable model for understanding mechanisms underlying hematopoiesis and immunity. Three types of mature hemocytes have been characterized in the lymph gland: plasmatocytes, lamellocytes, and crystal cells, which are analogous to vertebrate myeloid cells, yet molecular underpinnings of the lymph gland hemocytes have been less investigated. Here, we use single-cell RNA sequencing to comprehensively analyze heterogeneity of developing hemocytes in the lymph gland, and discover previously undescribed hemocyte types including adipohemocytes, stem-like prohemocytes, and intermediate prohemocytes. Additionally, we identify the developmental trajectory of hemocytes during normal development as well as the emergence of the lamellocyte lineage following active cellular immunity caused by wasp infestation. Finally, we establish similarities and differences between embryonically derived- and larval lymph gland hemocytes. Altogether, our study provides detailed insights into the hemocyte development and cellular immune responses at single-cell resolution.

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