Reference component analysis of single-cell transcriptomes elucidates cellular heterogeneity in human colorectal tumors

Li, Huipeng; Courtois, Elise T.; Sengupta, Debarka; Tan, Yuliana; Chen, Kok Hao; Goh, Jolene Jie Lin; Kong, Say Li; Chua, Clarinda; Hon, Lim Kiat; Tan, Wah Siew; Wong, Manzhi; Choi, Paul J.; Wee, Lionel; Hillmer, Axel M.; Tan, Iain Beehuat; Robson, Paul; Prabhakar, Shyam

doi:10.1038/ng.3818

Cited by 896 publications

(847 citation statements)

References 54 publications

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“…Among emerging technologies, scRNA-seq has facilitated the identification of developmental hierarchies, drug resistance programs, and patterns of immune infiltration relevant to tumor biology, diagnosis, and therapy (Kim et al, 2016; Li et al, 2017; Patel et al, 2014; Tirosh et al, 2016a; Tirosh et al, 2016b; Venteicher et al, 2017). Here, we applied the approach to characterize primary HNSCC tumors and matched LN metastases.…”

Section: Discussionmentioning

confidence: 99%

Single-Cell Transcriptomic Analysis of Primary and Metastatic Tumor Ecosystems in Head and Neck Cancer

Puram

Tirosh

Parikh

et al. 2017

Cell

1,769

147

2,350

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SUMMARY The diverse malignant, stromal, and immune cells in tumors affect growth, metastasis and response to therapy. We profiled transcriptomes of ~6,000 single cells from 18 head and neck squamous cell carcinoma (HNSCC) patients, including five matched pairs of primary tumors and lymph node metastases. Stromal and immune cells had consistent expression programs across patients. Conversely, malignant cells varied within and between tumors in their expression of signatures related to cell cycle, stress, hypoxia, epithelial differentiation, and partial epithelial-to-mesenchymal transition (p-EMT). Cells expressing the p-EMT program spatially localized to the leading edge of primary tumors. By integrating single-cell transcriptomes with bulk expression profiles for hundreds of tumors, we refined HNSCC subtypes by their malignant and stromal composition, and established p-EMT as an independent predictor of nodal metastasis, grade, and adverse pathologic features. Our results provide insight into the HNSCC ecosystem and define stromal interactions and a p-EMT program associated with metastasis.

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Section: Discussionmentioning

confidence: 99%

Single-Cell Transcriptomic Analysis of Primary and Metastatic Tumor Ecosystems in Head and Neck Cancer

Puram

Tirosh

Parikh

et al. 2017

Cell

1,769

147

2,350

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“…The performance of RaceID3 on this dataset was benchmarked against a number of alternative methods, i.e. Seurat 21,22 , SC3 23 , RCA 24 , ICGS 3 , based on the expression distribution of known lineage markers across clusters. An ideal clustering method is expected to maximize the fold enrichment of a marker gene in a particular cluster and minimize the spread of the expression domain across clusters and RaceID3 optimizes both metrics (see Fig.…”

Section: Introductionmentioning

confidence: 99%

FateID infers cell fate bias in multipotent progenitors from single-cell RNA-seq data

Herman

Sagar

Grün

2017

Preprint

159

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Differentiation of multipotent cells is a complex process governed by interactions of thousands of genes subject to substantial expression fluctuations. Resolving cell state heterogeneity arising during this process requires quantification of gene expression within individual cells. However, computational methods linking this heterogeneity to biases towards distinct cell fates are not well established. Here, we perform deep single-cell transcriptome sequencing of ~2,000 bone-marrow derived mouse hematopoietic progenitors enriched for lymphoid lineages. To resolve subtle transcriptome priming indicative of distinct lineage biases, we developed FateID, an iterative supervised learning algorithm for the probabilistic quantification of cell fate bias. FateID delineates domains of fate bias within progenitor populations and permits the derivation of high-resolution differentiation trajectories, revealing a common progenitor population of B cells and plasmacytoid dendritic cells, which we validated by in vitro differentiation assays. We expect that FateID will enhance our understanding of the process of cell fate choice in complex multi-lineage differentiation systems. INTRODUCTIONRecent studies utilizing scRNA-seq 1-5 and single-cell lineage tracing techniques 6-8 , call into question the traditional view of hematopoietic differentiation as a sequence of binary fate choices giving rise to a succession of increasingly fate restricted progenitor types 9 . Evidence from these studies rather suggests early cell fate priming starting at the level of multipotent progenitors (MPP) or even within the HSC pool. Pronounced heterogeneity of common myeloid progenitors (CMP) was elucidated with high resolution 1 , and an early fate bias emerging in human short term HSCs was suggested in a more recent study 2 . However, heterogeneity of lymphoid progenitors has not been well investigated with single-cell resolution. Since lymphoid progenitor heterogeneity was previously found by flow cytometry 10,11 , utilizing distinct sets of cell surface markers in combination with differentiation assays, we here perform an scRNA-seq analysis to comprehensively elucidate heterogeneity across lymphoid progenitors purified from the bone-marrow of adult mice. Although a number of methods for lineage reconstruction have been developed [12][13][14][15] , these algorithms are not specifically designed to uncover subtle transcriptome changes and uniquely assign cells to individual branches without accounting for multi-lineage bias. Weak transcriptome modulations also remain undiscovered by state-of-the-art clustering methods, which partition cells into groups without accounting for the co-existence of fate bias towards multiple lineages within individual cells. To elucidate the process of cell fate emergence, we introduce FateID, a computational method for the quantification of fate bias, manifested by subtle lineage specific transcriptome modulations within a multipotent progenitor . CC-BY-NC-ND 4.0 International license not peer-reviewed) is the aut...

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“…In particular, the ability to computationally cluster data from related cell populations could provide an in silico method to isolate data from rare exfoliated tumor cells (as compared to contaminant urothelial cells) [133][134][135] and could also indicate distinct subpopulations of cancerous cells that would likely benefit from treatment with a coordinated, concerted panel of drugs informed by knowledge of the characteristics of individual cells [136][137][138]. More broadly, application of single-cell transcriptomics to exfoliated bladder cancer cells from a large population of patients would provide a reference transcriptomic dataset of bladder cancer variants, useful for placing patients within a broader context of prior knowledge and for predicting efficacy of potential treatment paths based on historical data [139].…”

Section: Resultsmentioning

confidence: 99%

Microdevices for Non-Invasive Detection of Bladder Cancer

et al. 2017

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Bladder cancer holds the record for the highest lifetime cost on a per-patient basis. This is due to high recurrence rates, which necessitate invasive and costly long-term evaluation methods such as cystoscopy and imaging. Microfluidics is emerging as an important approach to contribute to initial diagnosis and follow-up, by enabling the precise manipulation of biological samples. Specifically, microdevices have been used for the isolation of cells or genetic material from blood samples, sparking significant interest as a versatile platform for non-invasive bladder cancer detection with voided urine. In this review, we revisit the methods of bladder cancer detection and describe various types of markers currently used for evaluation. We detail cutting-edge technologies and evaluate their merits in the detection, screening, and diagnosis of bladder cancer. Advantages of microscale devices over standard methods of detection, as well as their limitations, are provided. We conclude with a discussion of criteria for guiding microdevice development that could deepen our understanding of prognoses at the level of individual patients and the underlying biology of bladder cancer development. Collectively, the development and widespread application of improved microfluidic devices for bladder cancer could drive treatment breakthroughs and establish widespread, tangible outcomes on patients' long-term survival.

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Reference component analysis of single-cell transcriptomes elucidates cellular heterogeneity in human colorectal tumors

Cited by 896 publications

References 54 publications

Single-Cell Transcriptomic Analysis of Primary and Metastatic Tumor Ecosystems in Head and Neck Cancer

Single-Cell Transcriptomic Analysis of Primary and Metastatic Tumor Ecosystems in Head and Neck Cancer

FateID infers cell fate bias in multipotent progenitors from single-cell RNA-seq data

Microdevices for Non-Invasive Detection of Bladder Cancer

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